https://scientiairanica.sharif.edu/ Scientia Iranica en daily 1 Wed, 30 Aug 2017 00:00:00 +0430 Wed, 30 Aug 2017 00:00:00 +0430 https://scientiairanica.sharif.edu/article_4351.html In recent years, the binocular stereo vision has become more popular in many different areas because of the latest developments in three-dimensional (3-D) image processing technology that ensures rich information in comparison with other sensor types. This study presents a novel method based on the binocular stereo vision system to reduce the measurement error encountered frequently in the orthognathic surgery.  The main aim is to enhance the level of the accuracy of this sensitive operation. The developed system is not only useful for the preoperative assessment or the postoperative process but also can be utilized during the real-time operation. Additionally, this system provides a broader working field, more practical and healthier environment and less expensive setup. Therefore, the developed binocular stereo vision system may be acceptable for most surgeons. Experimental results show that the average error rate for all of X, Y and Z coordinates in the Cartesian system is 0.25 mm which is clinically acceptable (< 1.00 mm). The binocular stereo vision system would be a helpful throughout the orthognathic surgery to improve precision of the measurement and satisfy the healthy surgical operating environment https://scientiairanica.sharif.edu/article_21313.html Rotor blades are the most important part of wind turbine system, which are generally made up of polymer matrix based composites. The performance and lifetime of the wind turbine system often depend of the constituent of composite materials, properties of these materials, design of blades and manufacturing techniques. However the inspections after manufacturing of blades do reveal certain defects which need to addressed and fixed before it is sent for real time operations. Further offsetting usually occurs when closure of two blade halves lead to displacement of aerodynamic suction side from the aerodynamic pressure side. This work is concerned with the two main objectives: one is to repair leading edge cracks in the longitudinal direction, outside the area with existing external root over lamination, the second objective is to how offsetting is measured, evaluated and repaired especially in connection with: overbite and underbite. All these repair procedures were conducted on the glass fabric reinforced polyester blades manufactured by Hand lay-up technique. Until aforementioned repair procedures are not performed, the blades will not be sent to assembly stage. Ultrasonic inspection was conducted as per ASTM standards, ASTM E317 and ASTM E1316. https://scientiairanica.sharif.edu/article_22587.html Nowadays, the healthcare industry focuses on the COVID-19 more than any other issues. There are many close similarities between the symptoms of the coronavirus and the Influenza (flu) virus, which sometimes make it difficult to distinguish between them. So, it has prompted countries to start flu vaccination to prevent potential problems. To consider it, this study presents a multi-level supply chain for the flu vaccine during the COVID-19 pandemic. The problem pursues three main goals: cost minimization, maximizing demand allocation based on customer prioritization, and minimizing maximum lost customer demand. Due to the limited number of vaccines, a rate indicating the priority of each group of customers to receive the vaccine in the proposed model is considered. Customer prioritization can undermine justice because a flu patient is in critical condition but has low priority. Therefore, the third objective seeks to create justice and morality by minimizing the maximum lost demand. To evaluate the model, it is conducted based on a case study in Mazandaran province, Iran. The findings illuminate that 79 % of the demand will be met. Besides it shows that by increasing the capacity to 10%, the demand will be satisfied 9 percent more. https://scientiairanica.sharif.edu/article_22595.html Perishable product inventory management is a challenging issue because of its direct effect on companies' profits. The dependence of a product order cost on the order quantity is one of the practical but less examined assumptions in this problem literature. Hence, this paper considers the dependency between the order cost and order quantity as well as between the holding cost and the inventory level. This problem will have a non-convex object, and is not solvable through the usual mathematical methods. Thus, simulation-optimization approach is used to determine the perishable product inventory management policy with stochastic demand. The system dynamics approaches have been used to simulate the problem by minimizing the cost function. The casual diagram, inputs, output, and relation of the system are determined. A numerical example of a hypermarket is presented, and the optimal amount of the objective function is determined with optimization of the input variables via the experimental design’s method. Then, to rule out the effects of different errors, a robust optimization of the model is presented. The results show that the proposed replenishment policy could benefit the necessary decisions regarding inventory management and control of the perishable products which count in different errors. https://scientiairanica.sharif.edu/article_22648.html In recent years, sustainable development and environmental protection are getting more attention in construction projects. Hence, green road construction (GRC) supplier selection problem is the main key for organizations to grow their environmental and economical performances. Accordingly, a new hierarchical group decision fuzzy ranking framework is presented based on dynamic interval-valued hesitant fuzzy numbers (DIVHFN) and last aggregation approach to select the most appropriate GRC supplier. Thereby, DIVHFN theory and last aggregation concept could decrease the judgmental errors and data loss, respectively. Moreover, the weight of each criterion is obtained by proposing a new dynamic interval-valued hesitant fuzzy maximize deviation from ideal decision (DIVHF-MDfID) method. Furthermore, the experts' weight is determined by presenting a dynamic interval-valued hesitant fuzzy preference assessment (DIVHF-PA) method. Besides, to reach precise weights the opinions of experts are included in criteria/sub-criteria weights computations. Meanwhile, an actual case regarding GRC supplier evaluation and selection problem for a construction project is provided to detect the implementation process of the proposed approach. Finally, some comparative and sensitivity analysis are performed to confirm the validation and verification of the presented DIVHF-hierarchical group decision (DIVHF-HGD) approach. https://scientiairanica.sharif.edu/article_22676.html This study integrates production and maintenance planning with statistical process monitoring in the presence of dependent multiple assignable causes. To adapt the model to the reality, two assumptions are considered: (1) the assignable causes (ACs) are dependent, and (2) the occurrence of ACs can affect both process mean and variability. Given the second assumption, a non-central chi-square (NCS) chart is used to monitor the process. Since the occurrence rate of ACs increases over time, a non-uniform sampling scheme is presented to reduce the out-of-control time period. A sensitivity analysis is presented to explore how the number of AC types influences the cost terms. The results indicate that the more AC types, the higher quality loss and maintenance costs are imposed on the manufacturer. Moreover, three comparative studies are conducted for confirming the effectiveness of the model. The first comparative study shows that the total cost will be less than its real value when the interdependency among the ACs is ignored. The second comparison shows that the NCS chart outperforms the in detecting the process disturbances and leads to a less quality loss cost. Eventually, the last one represents that employing the non-uniform sampling strategy leads to a significant cost savings. https://scientiairanica.sharif.edu/article_22683.html Distribution management is an important part of the business cycle for distributors and wholesalers. It is also important for businesses to have successful distribution management systems in order to remain competitive and keep customers satisfied. In this research, we consider the competition between two multi-echelon supply chains under two different service systems where price and service level are considered as performance measures. For every supply chain, there are two different indirect channels for the sale of the products that have not been taken into account previously. Thus, each supply chain benefits from two rival sale channels with different structures. The different structures of these channels induce magnificent changes in the demand function. Hence, we develop two alternate scenarios which are based on the centralized and decentralized servicing conditions. In the centralized conditions, the manufacturer is authorized to service all the members of the chains. In the decentralized conditions, the sale channels are responsible for decision-making about service level for every member of the chains. Furthermore, the effects of competition and sensitivity analysis on the equilibrium of the scenarios and supply chain profit are discussed in a numerical example. https://scientiairanica.sharif.edu/article_22744.html In this paper, a new one-parameter distribution is proposed by unitizing the Lindley distribution through the hyperbolic tangent transformation. The goal is to map the functionality of the Lindley distribution on the unit interval, with the perspective of offering a new modeling option for treating unit data. In the first part, we provide the motivations and some mathematical properties of the new distribution. Two truncated moments and hazard rate functions are used to characterize the distribution. The emphasis is then switched to its statistical characteristics. Several methods are used to discuss the point estimation of the parameter. The related bias and mean squared error behavior is tested using Monte Carlo simulations for a range of sample sizes. To demonstrate the ability of the model to fit real data, distributional analyses are given. https://scientiairanica.sharif.edu/article_22745.html Time series forecasting is an important field of machine learning since many real-world events are related to time. Real-time data are commonly prone to errors due to irregular fluctuations, seasonal biases, and missing values in the data. The erroneous data causes inaccurate forecasting which leads to business loss. Moreover, the concept drift problem is a known problem in time series forecasting that also results in poor forecasting accuracy. This work presents an Adaptive Batched-Ranked Ensemble (ABRE) model that reduces the effect of fluctuation using the time-variant windowing technique. A data aggregation technique is developed and integrated with the offline training phase of the proposed model to tackle the concept drift problem. A meta-model is developed from the offline phase. This meta-model is exposed in the online forecasting phase which ensures faster execution for incoming data. The model is implemented for the medical workload prediction after testing and comparing with a few other heterogeneous ensemble models. The comparison results show in terms of the root mean squared error, the proposed model performs at least 65.7% better than the heterogeneous stacked ensemble models applied to the experimental dataset. Moreover, the ABRE model reduces the prediction error by approximately 73.6%. https://scientiairanica.sharif.edu/article_22772.html This paper investigates the main activities that lead to carbon emissions in a deteriorating items supply chain. This study applies a vendor-managed inventory (VMI) model for deteriorating items with a single-setup-single-delivery (SSSD) policy and a single-setup-multiple-delivery (SSMD) policy. We provide constructive managerial insights to both researchers and industries in their inventory management decisions in minimizing the total deteriorating items cost and the total carbon emissions. A numerical example is developed to optimize the delivery quantity and the number of deliveries per production cycle. The results enable companies to identify an effective inventory management critical to reducing carbon emissions, especially for deteriorating items. https://scientiairanica.sharif.edu/article_22790.html During the past few years, there have been some turbulent events in the world's economy, which have significantly influenced the performance of companies. Therefore, there is an urgent need to use a robust method to handle the existing uncertainties on the performance of the companies. This paper uses Evidence Theory (ET) to present an innovative and practical approach to consider the experts’ opinions which are based on the available evidence regarding the factors that influence the stock market. Subsequently, the study proposes a way to determine the changes in these factors from possible scenarios on historical data to find the return range of different investment alters to be used in robust optimization. Moreover, in a case study, the study examined the sensitivity of the Iranian capital market to exchange rate fluctuations under different scenarios which were due to the lack of a unified view of that rate’s value among experts as one of the mentioned factors. The preliminary results of a real-life case study reveal that the submitted approach is productive and practically useful. https://scientiairanica.sharif.edu/article_22816.html Development of technology and expansion of social media emerge group-buying mechanism as a popular strategy. In this paper, two competitive retailers are considered that sell the same product to customer in a market, Group buying and individual buying. Three Different competition scenarios are considered, in order to implement the first two strategies, the Stackelberg model is used where the group-buying retailer and the Individual retailer are assumed to be the leader and follower, respectively. For the third strategy, the Nash equilibrium is applied when they decide separately. We analysis the optimal and equilibrium strategy in each scenario. And also determine the conditions for each retailer to be present in the market in each scenarios. Finally, a numerical example is provided, in order to illustrate the effectiveness of the aforementioned three scenarios in models. https://scientiairanica.sharif.edu/article_22817.html The paper presents an extended VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method for solving group decision-making problems. The uncertainties given in the data are handled with the help of the intuitionistic fuzzy valued neutrosophic values (IFVNVs), which allow decision-makers to carry more detailed information while providing their preferences in the imprecise environment. The proposed VIKOR method utilized the features of IFVNVs and computed the distance measures between their pairs using $L^p$-metric and $L^\infty$-metric. The weights of the different criteria are computed by using the entropy-based measures for the families of IFVNVs. The presented method has been illustrated with a numerical example. A comparative interpretation and the sensitivity analysis of the parameter associated with the technique are achieved to reveal their influences. https://scientiairanica.sharif.edu/article_22818.html Flexible job shop scheduling under uncertainty plays an important role in real-world manufacturing systems. This paper deals with the flexible job shop scheduling problem to minimize the sum of jobs’ tardiness considering machines breakdown and order due date modification as two important disruptions in this production system. To this end, the problem is formulated as a mixed-integer linear programming model. In addition, two different strategies are proposed including allocating multiple machines to each job and selecting the best alternative process from other jobs to handle these disruptions. Since the problem is well-known strongly NP-hard, a hybrid metaheuristic algorithm based on the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) is proposed to solve the real-sized instances of these problems. Numerical experiments are used to evaluate the performance and effectiveness of the proposed hybrid algorithm. Obtained results for the small-sized instances show that the proposed algorithm provides proper solutions in terms of optimality and CPU Time. In addition, results for the medium- and large-sized scales validate the efficiency of the proposed algorithm and indicate that the proposed hybrid solution approach outperformed the classic GA in terms of the objective function value and the CPU time. https://scientiairanica.sharif.edu/article_22825.html Due to the rapid growth of COVID-19 data, this paper investigated an integrated approach for performance evaluation of countries at any time of the COVID-19 pandemic. First, the strategies implemented in countries were summarized in three systems: prevention, infection detection, and medical. Then, the input-output of the systems was identified. In Phase 1, after variable selection (tests, total cases, active cases, recovered cases, and deaths), data were collected for 100 countries with the highest infected cases by June 21, 2021. Then, mathematical modeling of two-stage data envelopment analysis with desirable-undesirable variables was performed using three basic ideas: independent, connected, and relational. By solving the relational model, the efficiency scores of the countries were obtained, and they were categorized into four classes based on these results. In Phase 2, 80% of the data were considered as training samples to generate a machine learning model via ensemble methods (i.e., Bag, Adaptive Boost, and Random Under-Sampling Boost). In Phase 3, the class of test samples was predicted using the optimal ensemble model. The results showed that in a small dataset, the Bag algorithm had 95% accuracy in predicting the class of test samples. https://scientiairanica.sharif.edu/article_22828.html In this paper, the regression-based approach was developed to monitor image data under two-scale analysis. In the first scale, wavelet transformation was used to extract the main features of geometric profile created from the images. The next scale was to monitor the small-scale components which could be expressed by correlation in error terms. To monitor correlation in error terms, one parametric and one non-parametric methods were developed. Parameters of the parametric model including spatial correlation coefficient and error term variance were estimated using Ordinary Least Squares (OLS) and Generalized Least Squares (GLS) estimators, respectively. In non-parametric method, no assumption was made about the structure of correlation in error terms. To extract useful information about the nature of correlation in error terms, Functional Principal Component Analysis (FPCA) was used. After extracting features for both scales, some appropriate test statistics were computed. Then, monitoring the process was performed by plotting these test statistics on corresponding control charts. Simulation and industrial case studies were also performed to evaluate the proposed method’s performance in detecting different shifts. The results indicated the proper performance of the proposed method in monitoring industrial processes to detect out-of-control conditions and identify the source of variability. https://scientiairanica.sharif.edu/article_22876.html The triple bottom line (TBL) strategy has provided the manufacturers with optimized production systems to achieve their economic, environmental, and social goals simultaneously. This paper presents a multi-objective mixed-integer programming model for the sustainable economic production quantity (S-EPQ) based on the TBL strategy. The model is to optimize the total profits, environmental emissions of manufacturing activities, and the turnover cost of the workers, which leads to keeping a sustainable number of workers as the social factor based on the working hours. According to numerous uncertain factors in the production process, demand uncertainty and the possibility of shortage have been considered including lost sales and full back-order. Due to the NP-hardness of the problem, Particle Swarm Optimization (PSO) algorithm is employed to find the optimal solutions and make the operational decisions of the production system. To prove the applicability of the proposed sustainable production system, a case study was conducted in the dairy industry of Iran. Moreover, an extensive analysis was done to evaluate the performance of the proposed multi-objective optimization model and heuristic solutions, and finally, some managerial insights were carried out for manufacturers of the dairy industry respecting the challenge of TBL. https://scientiairanica.sharif.edu/article_22887.html The Linear Fractional Programming (LFP) problem that optimizes the ratio of two linear objective functions under linear constraints has a wide range of application areas. Based on the traditional definition of continuity, we developed an exact iterative algorithm that does not depend on big-M coefficients. Removing the nonlinearity in the fractional objective function by converting the objective function into a linear form, an equivalent linear-iterative problem is obtained and a computationally efficient algorithm is proposed. We also analyze the unbounded and asymptotic solution case of the LFP. To demonstrate the efficiency of the proposed method, illustrative numerical examples are provided for all solution cases. Also, we analyze the validity of our algorithm \hl{and compare our results with the existing algorithm from the literature} by generating random large-scale test problems. https://scientiairanica.sharif.edu/article_22894.html In this paper, we design a resilient supply chain by determining risks, prioritizing resilient strategies, and also determining the relationships between them using the Quality Function Deployment (QFD) method during Covid-19. Moreover, those strategies with required further attention to minimize supply chain risks are determined by applying the Linear Physical Programming (LPP) approach, which is a flexible and easy approach in order to determine the accurate weights in the objective space. This research contributes to the growing literature on the resilient supply chain to demonstrate how to develop a mathematical model for designing a resilient supply chain using QFD and LPP methods during Covid-19. Based on the obtained results, three strategies play a crucial role in reducing the supply chain risks of a pharmaceutical company and also increasing its supply chain resilience based on the results: implementing appropriate and relevant policies in terms of the number and selection of suppliers, employing up-to-date procedures in pricing and market analysis, upgrading supply chain agility to cope with natural disasters. Hence, this study can bring important insights to managers and professionals involving with the supply chain area to use them in applying appropriate strategies while facing supply chain risks. https://scientiairanica.sharif.edu/article_22924.html Operating rooms serve as costly wards of hospitals, so any cost reduction affects the total costs. This study examined the supply chain of surgical supplies in operating rooms under uncertainty. Operating room consumable items are received from the supplier in sterile and nonsterile forms and then sent to the operating room before surgery. If the surgeon requests nonsterile items, these items are sent first to the sterile core, and then the sterilized items are transferred to the operating room. There may be requests during operation due to the patient's condition or other emergencies, like heavy bleeding and items breakdown. It is impossible to estimate statistical distribution for different situations in operations; hence, a robust approach was used for demand cases. Moreover, suppliers are different in terms of cost and quality which have a direct effect on the satisfaction of surgeons. Therefore, the present study must rank the suppliers as a problem. To do this, a model was used based on the structure of a hospital in Iran. First, the ARAS method was employed to rank suppliers, Then the augment ε-constraint was used to minimize costs and maximize surgeons’ satisfaction. Results indicated purchase cost and demand as the most effective parameters. https://scientiairanica.sharif.edu/article_22928.html The present paper acquaints some optimal class of estimators of population mean utilizing information on an auxiliary variable in ranked set sampling methodology. The effectiveness of acquainted estimators is studied regarding the estimators suggested by Samawi and Muttlak (1996), Yu and Lam (1997), Kadilar et al. (2009), Singh and Solanki (2013 a, b), Singh et al. (2014), Solanki and Singh (2016), Mehta and Mandowara (2016), Saini and Kumar (2016), Mehta et al. (2020) and Bhushan and Kumar (2022) which cover most of the familiar estimators. The optimality conditions have been established and followed up by a simulation study as well as a real data application and the results are constituted a rather satisfactory showing advancement over the all prominent estimators discussed in this article. https://scientiairanica.sharif.edu/article_22929.html One way to increase productivity is to increase throughputs without using more resources. In this paper, the issue of the optimal sequence of products in a job shop scheduling is raised, which has many uncertainties such as downtime, development time, etc. One of the key factors which affect operation time is the number of reworks. The number of reworks based on metallurgical parameters, the number of their operations according to defects count, and process time are quite probable. The innovation is in dealing with job-shop scheduling in which there are reworks in particular, and the addition of this parameter increases the complexity of JSSP. Therefore, this parameter is added to the mathematical model and with a combined method via the statistical method. The problem has been solved with simulation for meeting uncertain constraints and a heuristics approach for optimization. Implementing this model in a high-tech casting shop with a large number of different products reduces the Work in Process (WIP) and capital sleep, which reduces the number of parts in the queues. Also, decreasing the queue length in bottleneck has reduced the lead time and increased agility and, above all, increased the number of productions by about 3.3 percent. https://scientiairanica.sharif.edu/article_22984.html The rapid spread of Covid-19 since January 2020 has dramatically affected financial markets and economies worldwide, especially in the United States. This paper aims to utilize the regression model of D-Vine Copula introduced by Kraus to investigate the effects of each of three input variables (number of Corona cases, number of deaths, and news)on our three response variables which are three famous indices in the U.S(S&P 500, NASDAQ 100 and Dow Jones). Also, we examine the impact of the unemployment rate on financial markets and the economy using jobless claims reported by the department of labor during the first five months of the outbreak in the United States. At the end of the analysis, we use the C-Vine structure and the dependence coefficient of Kendall's tau to determine which news has the most effect on the three indices under our scrutiny in this study. Findings demonstrate that the fitted quantile curves of all input variables suggest that variable death has the most negative effect on S&P500 and Dow Jones and variable news has the most negative influence on NASDAQ100, and it can be concluded that variable D(GDP news)has the most effect on all mentioned indices. https://scientiairanica.sharif.edu/article_23009.html The recent pandemic of COVID-19 has had severe impacts on healthcare services especially the Food and Drug Administration for providing necessary medications for patients. The supplier selection problem in the COVID-19 pandemic is a crucial problem. This research aims to develop a data-driven model for sustainable and resilient supplier evaluation. At the outset, we identify the related criteria based on literature and experts and then calculate their weights using Fuzzy-Bests-Worst-Method (FBWM). Afterward, the Fuzzy Inference System (FIS) method is employed to evaluate the performance of the supplier. Finally, three different classification machine learning models are developed based on the supplier historical data in every criterion and also the FIS output as the target column. This study provides 22 criteria are identified and categorized into three-dimension (economic, social, environmental, and resilient). The results show that the case study managers pay more attention to ‘Responsiveness’ and ‘Ability’. The two-stage FIS results indicate that 35 records were evaluated as very poor, 70 ones as poor, 98 ones as moderate, 90 ones as good, and 57 as very good ones. Other companies could use the same model for their supplier selection decision-making to have a better decision based on historical data of their suppliers. https://scientiairanica.sharif.edu/article_23019.html The nature and high sensitivity of complex products and development projects lead to increased complexity at operational and organizational levels requiring particular types of organizing & management styles. Identifying the key competencies required for project managers, especially those managing complex research and development (R&D) projects in the oil and gas industry, can increase the chance of project success. The main objective of this research is to identify the key competencies required for managing complex products and systems (CoPS) in R&D projects carried out in Iran’s oil and gas industry. The Delphi- fuzzy approach is used to develop an effective measurement model based on group decision-making methods and fuzzy inference systems. The results and analyses of the model are used to determine the key competencies needed in the studied organization. The results demonstrate that risk management, system integration, and personal abilities with competency importance factors of 1.7, 1.6, and 1.6 respectively, are more important than other competencies while dealing with complexity in the projects studied. https://scientiairanica.sharif.edu/article_23020.html Precise and effective identification of modal parameters is important for a civil structure throughout the structural lifetime. A time-frequency method for modal parameter identification is developed based on a recently developed adaptive method for signal decomposition, i.e., empirical Fourier decomposition (EFD). The EFD is used to separate a multi-component free vibration response into the summation of several mono-component modal responses. The modal frequencies and modal damping ratios are calculated from the modal responses by using the empirical envelope method and a damping estimation method. Two numerical examples and one experimental example are provided to validate the EFD-based time-frequency method and highlight the improvements of the EFD-based method relative to the empirical mode decomposition (EMD)-based method. It is highlighted that the EFD-based method has a much higher frequency resolution and lower computational cost than the EMD-based method. Hence, the proposed EFD-based method is suitable for online modal parameter identifications of structures with closely spaced modes. In addition, the EFD-based method is useful for both linear and nonlinear systems. https://scientiairanica.sharif.edu/article_23026.html This paper studies the inventory of cash as a renewable resource in single machine scheduling problem. The status of cash is incorporated in to the objective function. Average and minimum available cash and maximum and average cash deficiency are contemplated subject to a worst-case scenario that for each job the cost [price] is paid [received] entirely at the start [end] of processing the job. For each objective, either proof of NP-Hardness or optimal scheduling rule are provided. Some scenario-based numerical experiments are also carried out which reveal and emphasize the effect of financial assumptions of this research in single machine scheduling. https://scientiairanica.sharif.edu/article_23035.html Online monitoring of high-dimensional processes variability in which the number of variables is larger than the sample size is a challenging issue for quality practitioners because the sample covariance matrix is not invariable. To deal with this challenge, a generalized multiple dependent state sampling (GMDS) chart based on ridge penalized likelihood ratio (RPLR) statistic is developed for Phase II monitoring of multivariate process variability under high-dimensional setting. The developed control chart benefits from three advantages: (1) departing from the conventional covariance matrix charts, it can be efficiently employed for both spars and non-spars covariance matrices; (2) it is able to detect spars shift patterns in which only a few covariance matrix elements are deviated from their nominal values; and (3) it outperforms the detectability of the RPLR chart in terms of average run length (ARL) and standard deviation of run length (SDRL). The performance of RPLR, MDS-RPLR, and GMDS-RPLR charts are compared using extensive simulation studies by considering different diagonal and/or off-diagonal covariance matrix disturbance. Moreover, sensitivity analysis are provided to analyze how the number of process variables and GMDS parameter affect the run length properties of the developed chart. https://scientiairanica.sharif.edu/article_23036.html The previous studies have shown that the application of option contracts affects the coordination of the supply chain. Though, based on authors’ research there appears to be no survey conducted to measure the effect of hedging on supply chain from quantitative viewpoint. Generally, it is assumed that the product price is held fixed in the hedging; however, the competitors or the partners might sell the product cheaper. This condition restricts the hedger's opportunity to benefit. In this study we examine if the application of hedging through option contracts improves the performance of the total supply chain. To illustrate the answer, the supply chain consisting of one supplier and two retailers are considered. Regarding hedging, eight scenarios are created. The results indicate that the total supply chain profit is at the maximum benefit- among all possible scenarios- when hedging is completed properly. The research provides new insights that how hedging can maximize total supply chain profit, although it is possible that each individual member’s profit may not be maximized. https://scientiairanica.sharif.edu/article_23039.html Natural and technology-induced disasters have posed significant threats to human life all around the world and caused many damages and losses so far. The current study addresses a location-routing problem to make an efficient and timely distribution plan in response to a possible earthquake. This problem considers uncertainty in such parameters as demand, access to routes, travel time, and the number of available vehicles. To deal with these uncertainties, stochastic programming (SP) is performed while the objective function is to minimize the time of carrying relief commodities (RCs) to affected areas. The problem is coded in the CPLEX solver to obtain optimal solutions to small-scale problems, and an adaptive large neighborhood search (ALNS) is proposed to solve mixed-integer linear formulas for large-scale problems. To validate the formulation and evaluate the performance of the proposed ALNS, several types of tests are devised. To shows the efficiency of the proposed ALNS, two other metaheuristic algorithms, the Genetic algorithm (GA) and simulated annealing algorithm (SA), are used as well. The results of the calculations suggest the satisfactory performance of the suggested algorithm and the effectiveness of the model for the desirable delivery of humanitarian aids to affected areas. https://scientiairanica.sharif.edu/article_23040.html In a decision-making problem, the uncertainty component of refusal and abstain along with the sub-parametrization features in the information are not catered by intuitionistic fuzzy/Pythagorean fuzzy sets. In the present communication, we first introduce the novel notion of picture fuzzy hypersoft matrix along with various important binary operations and properties. The inclusion of the novel notion of picture fuzzy hypersoft matrices allows the decision makers to define their preference in a more general sub-parameterized linguistic form for the evaluation of available alternatives. The proposition concentrates on presenting a robust decision-making framework for identifying the optimal and most suitable renewable energy source. In this regard, the revised definition of picture fuzzy hypersoft choice matrix/weighted choice matrix (PFHSCM/PFHSWCM), value matrix, and total score matrix have been presented. Further, two algorithms of decision-making for the selection of the best renewable energy sources have been provided along with appropriate illustrations and ranking descriptions. A numerical example has also been worked out for the sake of illustrating the proposed algorithms. Finally, for establishing the robustness of the MCDM algorithms, a necessary comparative analysis has been carried out successfully. https://scientiairanica.sharif.edu/article_23045.html Data envelopment analysis (DEA) has been recently employed for performance and efficiency evaluation of decision-making units (DMUs) with multiple inputs and outputs. Complex internal relations within a DMU required more accurate DEA models than existing classic DEA. In real world applications, shared resources, among stages of a supply chain, is of great interest for managers and decision makers. Unfair allotment of shared factors can render the assessment invalid. In this study, an innovative DEA model is formulated for efficiency evaluation of a supply chain with shared factors. In the first step, a linear DEA model is presented in the multiplier form, based on the slack-based measure for efficiency assessment, in order to obtain the optimal proportion of the shared resources and shared feedbacks which are dual-role factors are presented. Then, the aggregate efficiency is decomposed into its stage efficiencies. Next, the presented model is extended for deriving the Common Set of Weights (CSW) for efficiency evaluation of the entire chain and correspondence stages. The case of 20 sustainable supply chains in the oil industry is considered with the developed DEA approach. https://scientiairanica.sharif.edu/article_23067.html The present research investigates the competition between two products without packaging and with green packaging in a dual-channel supply chain with government intervention. The manufacturer supplies the product to the customers at the first level without packaging, and the packaging company supplies it at the second level with green packaging. The profits made by the supply chain members have been calculated under the two government policies for identification of the optimal decisions (the revenue-seeking policy along with environmental protection and the revenue-seeking policy alone). The objective of the government is to raise income in the first policy and to reduce the environmental effects as well in the second. The results indicate that the first government policy is to the manufacturer’s benefit, while the second is preferred by the government and the packaging company. Moreover, the low cost of green packaging is to the benefit of the packaging company in both government policies, which is the case for the manufacturer only in the second. It has also been demonstrated that the difference between the sales prices of the two products increases, and the competition between them decreases with an increase in the difference between the customers’ conceptions of their values. https://scientiairanica.sharif.edu/article_23075.html The outbreak of the COVID-19 pandemic has resulted in severe economic consequences across the globe. Businesses have adopted various approaches to handle the stress of drastic changes in how businesses and consumers behave altogether. The decisions regarding inventory policies are greatly affected by the production rates and volumes, hence during this pandemic, flexibility in production rates and volumes becomes a necessity to handle demand uncertainties. Another big challenge that comes upfront is about rebuilding trust cum awareness in consumers about safe production, services, and home deliveries through constant investment in advertisements. To achieve supreme standards of quality, the manufacturers cannot afford any chance to compromise the quality of the product being delivered. Hence, the paper employs two-stage rigorous inspection practices for the imperfect products that arise from the production process, the first inspection process being error-prone (delivering Type- I and Type-II errors) while the second one being perfect. The developed model maximizes the manufacturer’s total profit by optimizing the investment in service and advertisement with a flexible production rate. A numerical example and comprehensive sensitivity analysis are illustrated to support the pragmatism of the model. https://scientiairanica.sharif.edu/article_23078.html International electricity trade as a strategic commodity plays a prominent role in the foreign trade market of countries. Electricity export forecasting leads to better production planning, supply security, blackouts reduc-tion, and obligations fulfillment. This paper aimed to provide a model for electricity export forecasting. In this regard, electricity consumption in different consumer sectors, gas consumption, population, GDP, and electric-ity prices have been entered into the multiple regression model as predictor variables. Although R2 =.976, F=66.110, and SIG<.05 indicate the model appropriateness, the high correlation between the predictor variables created collinearity. In other words, Tolerance, VIF (variance inflation factor), Eigenvalue and the Condition Index are less than .2, more than 10, close to zero, and more than 15 respectively. To solve this problem, two hybrid methods of Multiple Regression-First Difference Function and Multiple Regression-PCA have been used. In the first hybrid method (R2 =.553) the Tolerance and VIF index still show the presence of collinearity. In the second hybrid method (R2 =.936, F=169.9, SIG<.05) due to all the mentioned indicators, the collinearity has been completely resolved. So, the MLR-PCA method is the most appropriate model for electricity export forecasting. The data collected from Iran have been used to illustrate the model. https://scientiairanica.sharif.edu/article_23081.html In some manufacturing or non-manufacturing systems, the process outcome is better characterized by a relationship between a main variable and some associated supporting variables. The monitoring of this functional relationship over time is termed as profile monitoring. This study aims to improve Phase II monitoring of simple linear profiles by using auxiliary information which is correlated with the main variable. To accomplish that, using a mean estimator, two auxiliary information-based (AIB) control charts namely AIB-MEWMA and AIB-DMEWMA charts are proposed for detecting different shifts in model parameters. Using two numerical examples based on simulation studies, the sensitivity of the proposed control charts is evaluated and compared with the existing MEWMA and DMEWMA charts in terms of the average run length (ARL) metric. The results of simulations reveal that the proposed charts perform better than the existing MEWMA and DMEWMA charts. The applicability of the proposed control charting methods is demonstrated using a real-life data example from the cylinder production process. https://scientiairanica.sharif.edu/article_23084.html Blockchain technology is a growing digital technology and provides competitive advantage for the food industry. The aim of this conceptual and empirical study is to identify the key indicators for creating a sustainable competitive advantage in the food industry based on Blockchain technology through investigating the literature. Then, using Delphi method, the importance of each indicator is determined based on the experts’ opinions. For this purpose, about 2346 online questionnaires were sent to university professors and experts in the field of blockchain. About 36 people responded to the online email and responded to the questionnaire completely. The research findings indicate that the indicators of permanent storage of information, supply chain coordination, improved performance appraisal, simplification of international transactions, traceability and fraud prevention are the top five indicators, respectively. This research helps the decision makers in the food industry to achieve a sustainable competitive advantage compared to their competitors in the market by using blockchain technology. https://scientiairanica.sharif.edu/article_23085.html Estimating the population mean is of prime concern in many studies, and calibrations are popular choices. A robust calibration estimator estimates the mean using the minimum covariance determinant (MCD) and the minimum volume ellipsoid (MVE) estimations under stratified random sampling. Efficiency comparisons have been made between the robust calibration estimator and classical calibration estimator. Simulations and empirical results show that the proposed robust calibration estimator has a lower mean square error than the calibration estimators. When the relative efficiency and computation times are considered together, it is seen that the proposed robust calibration estimators based on MCD estimates are more efficient. https://scientiairanica.sharif.edu/article_23088.html Banks have faced significant challenges due to national economic problems in recent years. There is now a greater need to assess the existing banking system and implement corrective action and strengthening measures. In this paper, financial data of 11 Iranian private banks are examined for 15 different periods and their performance is evaluated using the CAMELS indicator. Expert opinions were used to determine the weights of indicators and ratios, and their final weights were determined using the Best-Worst Method (BWM). Data Envelopment Analysis (DEA) was then used to calculate the efficiency score units (DMUs) and their ranking. Principal component analysis (PCA) was used to validate the DEA results. Several sensitive analyses were conducted on all private banks in Iran to verify their rankings. All 11 Iranian private banks were evaluated through a comprehensive sensitivity analysis, and they were evaluated independently based on the suggested indicators based on their respective performance. The intuitive results of sensitivity analysis were accurate through statistic test. Also, appropriate strategies for improving the performance of the banking system are presented using Strengths-Weaknesses-Opportunities-Threats (SWOT) matrix. Using the results, designers can build more intelligent system CAMELS perspective. https://scientiairanica.sharif.edu/article_23102.html Problems associated with soil-structure interaction have been determined and the Impendence Functions (IFs) evaluated. The aim of this paper is studying horizontal impedance function for surface footings by physical model tests. For this purpose, a cubic steel container was used as a testing environment for two different conditions, a rigid base, and a homogeneous half-space model. The effect of various parameters such as footing shape, embedment ratio, inertia, and dynamic force amplitude was studied in three shapes; rectangular, square, and circle footing. The results indicated that the massless impedance function theory was confirmed at the Dimensionless Frequency (DF) of less than 2.5. Also, in the rigid base model, soil response heavily depends on the vibration frequency, developed by boundary conditions and side walls, in contrast to the half-space model. The rigid base, distribution and reflection of waves in the soil, and dissipation of higher wave energy substantially influenced the dynamic response of soil–foundation system. Additionally, the embedment ratio significantly affected the impedance functions. https://scientiairanica.sharif.edu/article_23124.html In this study, the Modified Adolescent Identity Search Algorithm (MAISA) is proposed to optimize the weight of skeletal structures. The MAISA is a population-based method, similar to other metaheuristic methods. The most advantages of the proposed algorithm are its simplicity and having only one setting parameter. This research aims to increase the balance between exploration and exploitation, improve the convergence rate, and reduce the possibility of being trapped in local points. The applied changes extend the global search at the beginning of the optimization process, and as the number of iterations increases, the possibility of local search increases non-linearly. To evaluate the performance of the proposed method, several benchmark skeletal structure problems have been designed and optimized using the LRFD method under the requirements of the AISC design regulations. The objective function is to calculate the minimum weight of a structure by selecting appropriate discrete sections and considering the deformation and stress constraints. To demonstrate the superiority of the MAISA algorithm, its results have been compared with some popular metaheuristic algorithms. The results show that the proposed algorithm performs better than other metaheuristic methods. https://scientiairanica.sharif.edu/article_23126.html This paper aims to provide an equitable approach for the delocation via merging different bank branches. Due to the profit loss, some banks have resisted this change, so we developed a n/equity approach to modeling this issue to convince bank owners and employees. The proposed model is a mixed-integer programming model to have an equitable approach to fuzzy constraints based on the weighted sum of the remaining branches to the total number of branches of each type of bank. Moreover, this equitable approach was also used to avoid unemployment of the closed branches staff. Considering the harsh employment conditions and the turmoil of employees after the branched delocation, maximizing the retention of closed branch employees is considered the model's objective function. The result showed that using fuzzy constraints, equity can be well modeled. Moreover, increasing the equity coefficient reduces the number of facilities remaining in the system, and as a consequence, the desired efficiency (i.e., personnel retention) is reduced. So, we can reach the non-dominated answers. Finally, the results showed that reducing the minimum distance between facilities will allow more facilities to remain in the system and retain more staff. https://scientiairanica.sharif.edu/article_23127.html One of the patients' basic needs when referring to the hospital is to access doctors as soon as possible at a low cost. In this regard, many hospital managers aim to improve healthcare quality. They strive to plan and perform better patient flow in different parts of hospitals. With the widespread of Covid-19, the importance of this matter has become more apparent. Queueing systems are one of the methods that help recognize delays and help to identify bottlenecks. This paper has extended a queue theory model to measure the number of servers in each part of the hospital. The model aims to reduce the hospital's expected total cost, including the waiting time cost of the patients in queues, idle server cost, operating, and the marginal cost of the servers, in a covid-19 pandemic. The proposed model has been solved with Grasshopper Optimization Algorithm (GOA) for large-scale data. Then sensitivity analysis is presented to understand the model better and identify effective parameters. https://scientiairanica.sharif.edu/article_23128.html This study proposes six novel techniques on the customer’s priority while addressing the conventional hub location issue. Each strategy assigns a value to every customer based on distance and demand parameters, in which customers are prioritized based on this value. Then, the vehicle fleet is scheduled according to the customer’s priority. A new mixed-integer linear programming model is presented and applied for each strategy in a new hub location-scheduling problem solved by three approaches. Then, by using the CAB dataset, extensive experiments are designed to evaluate each strategy. The strategies are evaluated with statistical and non-statistical analyses and ranked accordingly. In each case, a comparison of the non-priority strategy with the best customer’s prioritization strategy shows that the non-priority strategy has an adverse effect on the delivery time (i.e., 129.7%, 171.68%, and 161.33% than the best strategy in the case of near, medium, and far nodes, respectively). In addition to the above tests, other tests are conducted to evaluate the optimum number of vehicles for different conditions. The results show that as the distance between customers and hubs increases, reducing the number of vehicles while increasing their capacity is preferable. Also, each strategy requires using a certain number of vehicles. https://scientiairanica.sharif.edu/article_23132.html In this study, two simulation models have been developed to predict the main stock price index of Borsa Istanbul (BIST100) with an artificial intelligence approach. In order to analyze the role of technical indicators in intraday predicting of stock markets, two different artificial neural network models have been developed in which different parameters are defined in the input layers. In the first model, 5 input parameters have been defined as open price (OP), highest price (HP), lowest price (LP), and two different moving averages (MA), 3 more parameters added as The Relative Strength Index (RSI), The Moving Average Convergence Divergence (MACD) and the moving average of MACD (TRIGGER). 70% of the data used in multi-layer network models developed with a total of 97 data sets have been used for training the model, 20% for validation and 10% for testing. The results show that both ANN models can predict BIST100 values with very low error rates. However, it is seen that the prediction performance of the first model, which has been developed by defining fewer input data, is higher than the second model. The results obtained support that the predictions made with intraday data are stronger between 13:00 and 16:30. https://scientiairanica.sharif.edu/article_23136.html Every firm must adjust to the needs of online trade to manage many consumers' contact channels, and using an Omnichannel is one such approach. The current study examines an omnichannel system, implementing three different scenarios and developing a system dynamics model. Simultaneously, the effects on cash flow, inventory, delivery time, and distribution costs are evaluated. As a result, the stock-flow curves and the causal model were created. The resulting concept was then tested and put into practice. In order to maximize the usage of the omnichannel system and expand the retailing capacity, three scenarios were created and put through simulation. Finally, the performance of the suggested model was evaluated by merging the first and second situations. According to the findings, a company's inventory is at its lowest point, and its cash flow is at its best when using the omnichannel system alone. The second scenario alone was not implemented since it did not result in what was desired. The third scenario provides the largest cash flow and delivery time values, according to a comparison of the results of the other scenarios. The first scenario is preferable if the company decides to lower the distribution costs at the cash flow expense. https://scientiairanica.sharif.edu/article_23147.html Many parameters affect the behavior of tall buildings under seismic loads, some of which are the main shock-after shock records and using some lateral load resistance systems in reinforced concrete tall buildings. End shear walls are a kind of shear walls, connecting their end in tall buildings. This study was conducted on two 30-story reinforced concrete structures, which were subjected to sequences of far fault records and analyzed by the nonlinear time history analysis. The results indicated a 51% decrease in maximum inter-story drift in 30 stories with end shear walls under sequence records. The normal Q-Q plots (quantile-quantile plot) presented approximately 20% reduction in the excepted normal domain in X and Y directions, respectively, in 30 stories with end shear walls. The kurtosis coefficient declined by 61 and 92% in the X and Y directions in 30-story structure end shear walls, respectively. Therefore, the end shear wall increased the confinement effects by decreasing the dispersion data of inter-story drift and improving seismic behavior. https://scientiairanica.sharif.edu/article_23157.html Various studies have shown that different banking business models are related to several variables that will change banks' strategies and impose various risks. This paper examines the data from 2006 to 2021 for 30 Iranian banks while identifying different variables affecting business models. K-Means, FCM, and PAM clustering approaches are used to cluster different Iranian banks. Also, by analyzing the liquidity risk, credit risk, and insolvency risk, the impact of the business model on various risks is examined. In the following, the changes in banks' business models are examined by carefully analyzing the state of different business models from 2006-2021. We found that banking business models shifted from SME-invested banks to SME-operating during shock periods, while the change is reversed during stable periods. Furthermore, large public banks have a small tendency to become large-funding banks in a period of economic stability. https://scientiairanica.sharif.edu/article_23158.html Uncertainties in wholesale market prices and consumer demand pose serious risks to electricity re-tailers. The penetration of distributed energy sources (DER) along with responsive demand has made it possible to exchange energy in distribution networks under a local market-based frame-work. These local markets provide a good opportunity for retailers to participate in meeting the needs of consumers, so that they can improve their economic situation by influencing local market prices through demand response (DR). Thus, the issue that need to be addressed is how the interac-tion of electricity retailer in the local market can affect its economic situation. This paper proposes a new framework to assist electricity retailers in optimizing the offering strategy in both wholesale and local electricity markets under uncertainty. The proposed framework is based on stochastic bi-level optimization model. At the upper level, with the aim of maximizing profit of retailer, optimal decisions are made on the power purchased from wholesale and local markets. At the lower level, the local market is ignored in which, with the aim of minimizing the operating cost. Wholesale mar-ket price uncertainties and the production of renewable resources are modeled using a set of sce-narios. https://scientiairanica.sharif.edu/article_23179.html This research aims to investigate the extent that an incorrect assumption for soil type may endanger the seismic safety of moment-resisting steel frame structures using probabilistic assessment. To this aim, first, a set of moment-resisting steel frame structures were designed for the site soil class C. The examined structures were 3-, 6-, and 9-storey moment-resisting steel frames designed according to ASCE7-16. Then, assuming that the actual soil type had been B, C, or D, seismic vulnerability assessments were performed using OpenSees software. For this purpose, a two-dimensional model of each structure was undergone the incremental nonlinear dynamic analysis (IDA) subjected to far-field, near-field (with pulse), and near-field (without pulse) ground motions of FEMA-P695. To take into account the effect of soil-structure interaction (SSI) on the exceedance probability of the models, the foundations were simulated as flexible elements. The fragility curves were developed for each model under each ground motion record type and accordingly the collapse margin ratio for each model was calculated. The results indicated that within the LS performance level at Sa(Design), site soil class B decreases the exceedance probability slightly but site soil class D tends to increase the exceedance probability significantly, especially as the height increases. https://scientiairanica.sharif.edu/article_23183.html In today’s dynamic and competitive climate, organizations need to have a coherent framework for performance management. The models and frameworks in the literature may not be consistent with businesses’ changing nature and peculiar goals. They mostly rely on results and are ambiguous about how to be translated into specific performance measures. In response to these issues, this study designs a four-layer organizational performance evaluation model. Each layer of this model is broken down hierarchically into the two levels of measure domains and measures. The effects of measure domains on their next layer are determined using a novel approach based on the hierarchy of the model. Subsequently, the measures are ranked using Additive Ratio Assessment (ARAS) method. Research validity is guaranteed via data triangulation and methods triangulation. Eventually, the insights obtained from analyzing the research results are offered to the target organization to improve its performance management. The principal scientific value added by this paper is to present a new organizational performance evaluation model that offers a complete classification of performance measures and captures a comprehensive picture of business components and their relationship with organizational results. Moreover, the proposed model can improve some drawbacks of current models and their implementation challenges. https://scientiairanica.sharif.edu/article_23184.html The municipal solid waste generation rate in Asian cities is dramatically increasing with rising urbanization and conventional interaction. However, as in many other cities in developing countries, the strategy for MSW disposal is neither environmentally sustainable nor appropriate for human health in Karachi. An enormous yield can be generated from Karachi’s solid waste, but it all depends on awareness and the implementation of appropriate technology. The selection of waste disposal technology is a sensitive issue in the research area of MSW management. This study considers the decision-making process as multi-attribute group decision-making problem that also handles uncertainty to select a sustainable waste disposal technology. For this purpose, 2-tuple linguistic q-rung orthopair fuzzy sets are used to permit decision-makers to make assessments in a broader space and to depict the unreliability and vague-ness in making real decisions using 2TL terms. 2TLq-ROFS provides a more effective strategy for handling imprecise and ambiguous information in the decision-making situation by using 2TL terms. The Maclaurin symmetric mean operator is a tool that can take into account interrelations between arguments.Furthermore, some relevant properties of these operators are mentioned. We develop an elegant MAGDM approach using the 2TLq-ROFWPMSM operator. https://scientiairanica.sharif.edu/article_23190.html Energy efficiency is one of the critical parameters affecting production in the natural stone sector, as it is in every industrial sector. High energy consumption negatively affects production costs, especially in stone cutting and surface treatments. Nowadays, it is crucial to predetermine energy consumption with reliable predictive techniques to produce with the lowest energy possible and sustain sectorial competition. This study conducted stone cutting tests with a computer-assisted circular cutting machine at different peripheral speeds (PSs) and advance rates (ARs). Unit energy (UE) consumptions were measured in stone cuttings. UE was evaluated regarding the circular stone cutting machine’s (CSCM's) operating parameters, some stone characteristics, vibration amplitude (VA), and sound level (SL) measured during cutting. Classical statistics (CS) and data mining (DM) techniques were used to predict UE. 287 and 24 cutting data sets were selected as training and testing data for CS and DM techniques, respectively. These techniques were also compared and provided more significant and reliable results of DM techniques than CS. DM techniques predicted the UE with high correlation coefficients obtained in the range of R2=0.963 and 0.973. DM models for UE prediction before stone cutting have been introduced for stone processing researchers and those interested. https://scientiairanica.sharif.edu/article_23194.html In the multivariate statistical process control literature, the profile is a function or curve representing the relation between two or more variables. Several types of complicated curves are conventionally applied to analyze the health of humans, such as Systolic and Diastolic blood pressure profiles. This paper proposes a new approach to interpreting the process capability indices of complex health profiles in the human body performance assessment. Using the popular multivariate statistical techniques of profile monitoring for complex health profiles is too complicated or impossible. Hence, the proposed method transforms the health profile into a univariate specification using dissimilarity indices. The applicability of the new approach is verified via a simulation study on an example of human blood pressure profiles. This application also represented the simplicity of the proposed method to conventional techniques and how the profile capability indices could be applied in the health evaluation. In addition, it provides valuable information to evaluate the heart performance in terms of blood pressure and make a judgment on the blood circulation system. https://scientiairanica.sharif.edu/article_23198.html In this paper, a constitutive model for the stress-strain response of municipal solid waste (MSW) is presented, considering the effects of temperature and aging. Our goal is to provide a model for predicting the behavior of these materials. The models used to predict the behavior of waste have used the theory of soils, and in this research, the theory of composite materials has been used for the first time. Cylindrical sample is modeled and loaded under triaxial loading conditions as observed in the experimental specimens. Fresh MSW, as a waste sample with a specific composition, was investigated. By using the optimization method, the constants of the given equation were obtained and the basic stress-strain model was presented based on the composite theory. The obtained results are compared with previous models' experimental data and results to verify the proposed model. At the strains of less than 30%, a good agreement can be reported between the numerical and experimental results. The root means square percentage of the relative error between the present model and the model of other researchers has shown acceptable results. It is also revealed that, as the temperature of the MSW increases, the stress on the waste is increased. https://scientiairanica.sharif.edu/article_23200.html This study presents a novel production control methodology using a multi-agent enterprise resource planning (MAERP) system that employs ERP modules as software agents for achieving enterprise-wide integration. Consequently, ERP is transformed from a decision-supporting system to a decision-making system. Based on a new data exchange framework developed in this study, five ERP modules, including health & safety and environment, human resource management, inventory control (WIP & BOM), quality control, and maintenance, are integrated as autonomous software agents. This method employs a wearable data monitoring device and proposes a new prototype of wireless health monitoring for production system operators and personnel, however, it can also be utilized for the entire enterprise. The proposed methodology involves monitoring, analyzing, and evaluating the performance of each work shift against real-time personnel health status data and their location on the production system’s shop floor. This paper describes how the presented methodology (termed MAERPPC) integrates commercially available enterprise resource planning systems using multi-agent production control systems and existing information technology. In addition to the benefits mentioned above, the proposed methodology considers the health status of personnel in different work shifts and its impact on the productivity and performance of the production system. https://scientiairanica.sharif.edu/article_23202.html The present study proposed a new modified group chain sampling plan for truncated life test when the lifetime of products follow a Kumaraswamy generalized power Weibull (KGPW) distribution. The results of optimal group size, mean ratio of true mean to the specified mean, operating characteristic values, minimum angles, acceptance quality level, lower quality level are obtained against the specified producer’s, consumer’s risk, test termination time, and mean ratio. The performance of the proposed chart is also monitored through a real life dataset of 63 single carbon fibers’ measurements with specified gauge length. Control limits are constructed to check the quality of strength of a single carbon fibers at gauge length of 20-mm. From the results, it is observed that when the test termination time increases the operating characteristic and mean ratio of proposed plan also increase disproportionately. https://scientiairanica.sharif.edu/article_23215.html Supplier selection and order allocation decisions are the main parties of a supply chain network which has a high impact on the economic performance of this network. This study using an Economic Order Quantity (EOQ) concept proposes an optimization model for the integrated supplier selection and order allocation problem where lot sizing, discounts, and disruptions are contributed among the first studies in this research area. To address the uncertainty, scenario-based stochastic programming is employed to consider both operational and disruption uncertainties. For solving the proposed model, not only the exact solver is employed but also an innovative algorithm based on a hybrid algorithm using Particle Swarm Optimization (PSO) and the Imperialist Competitive Algorithm (ICA) is utilized. To enhance the performance of our metaheuristic algorithm, the Taguchi experimental design method is employed. Some sensitivity analyses on the key parameters of our optimization model are done accordingly. The main findings are the performance of the proposed algorithm for solving large-scale tests and the practicality of the proposed model to address lot sizing, discounts, and disruptions. https://scientiairanica.sharif.edu/article_23223.html The operation and management of hydropower stations significantly influence the hydrodynamic and water quality conditions, which in turn exert large impacts on the environment and ecology. In this paper, a novel fuzzy logic-EWM (Entropy Weight Method) approach is proposed to perform comprehensive impact evaluations. Based on the natural and humanistic conditions of the Taizi River Basin, four criterion layers of economy, ecology, society, and management are determined. The fuzzy evaluation method is used to calculate the weight of the criterion layer, and each index under the criterion layer is determined. Further, the entropy weight model is used to calculate the weight of each index, which is compared with the weight of each indicator, as obtained by experts. The results showed that the subjective and objective results were consistent; the method accuracy was also acceptable. The study provided a new promising tool for fast comprehensive evaluations of the impacts of hydropower stations, and the evaluation case studies can provide experience for the comprehensive impact evaluation of hydropower stations in coastal regions. https://scientiairanica.sharif.edu/article_23225.html Confining structurally deficient concrete columns with externally bonded fiber-reinforced polymer (FRP) has been widely accepted as an effective technology for strengthening the ductility and strength of deficient concrete columns. However, prediction models for damaged and afterward repaired concrete based on soft computing methods are not available for the planning and maintenance of concrete structures. Therefore, this paper adopted two soft computing methods – artificial neural network (ANN) and Gaussian process regression (GPR) – to analyze observations obtained from 103 datasets of concentrically loaded FRP-confined predamaged concrete. The models only consider statistically significant variables with the ultimate strength of FRP-confined predamaged concrete. The statistically significant variables based on the multivariate regression analysis are corner radius ratio, FRP thickness, concrete strength, and damage degree. The coefficient of determination of the developed models is greater than 98% and there is a relatively low error between the measured and predicted values. The results of the current study highlight the merit of using soft computing methods in concrete technology given their extraordinary ability to comprehend multidimensional phenomena of concrete structures with ease and high predictivity over the existing empirical models. https://scientiairanica.sharif.edu/article_23226.html Despite prominent scholarly advancements in the field of operations research, limited literature has been reported on mathematical and heuristic approaches for schedule the low-volume low-variety production systems. This paper proposes a new approach for modeling and solving large-scale sequencing and scheduling problems in Low-Volume Low-variety production systems. The proposed non-linear mathematical programming models and genetic algorithms are subject to time and resource constraints, aimed at maximizing the number of activities completed in-station or intended to minimize the positive deviation to the aspiring time and resources budgets, in scenarios where the allocated work package must be completed in-station. The proposed algorithms are compatible with discrete and continuous-time scheduling problems and are found to be effective in modeling characteristics and constraints inherent in Low-Volume, Low-Variety production systems. To validate the proposed models, a real-world case study of a work center in the final assembly line of a private jet aircraft is conducted. https://scientiairanica.sharif.edu/article_23230.html In developed countries, the existence of credit scoring agencies helps to reduce the credit risk of banks across the globe by ensuring their good credit scores through a variety of techniques, including the use of machine learning and artificial intelligence. Nevertheless, the banking system in poor and developing countries is plagued by a lack of the reputable agencies for credit scoring of customers. As a result, their banks tend to internalize scoring according to Basel II & III and their Central Bank regulations. In this study, eight machine learning techniques were used to rank the credit scores of legal customers of an Iranian bank. The optimal probabilistic neural network (PNN) algorithm has been presented and we use the performance comparison between these 8 models to illustrate where financial-services customers fall into a category of good or bad based on the different techniques. Because of this combined technique, the banking system, especially the weak banking system, can categorize its customers into good and bad ones. In fact, the purpose of this paper is to create a hybrid approach for credit scoring Iranian banks' clients, thus obtaining the probability of default and credit risk models for the banking system. https://scientiairanica.sharif.edu/article_23233.html Sourcing resilience has become a primary concern in most closed-loop supply chains (CLSC). Companies face the option of sourcing their raw materials from suppliers or recycling centers though the latter can be disrupted sometimes. In this study, a multi-stage, stochastic programming (MSSP) model is developed to analyze how a company can proactively employ sourcing strategies along with pricing policies to enhance sourcing resilience in a CLSC network design problem, where the return of end-of-life (used) products into recycling centers is stochastic and sensitive to the purchasing price. The stochastic return is modelled using a scenario-tree-based approach. Since the sample average approximation algorithm (SAA) in scenario generation can lead to an increased number of scenarios and make the model hard to solve, a backward scenario reduction algorithm is employed to efficiently reduce the problem size. The developed model is implemented in the automotive battery industry. The findings indicate that an effective pricing policy can help determine the resilient sourcing strategy in the CLSC network design problem and, therefore, maximize the total profit and mitigate the disruption risks. Companies can invest in establishing recycling centers only in conditions of high return risk and formulation of a suitable pricing policy. https://scientiairanica.sharif.edu/article_23235.html With the growing use of flexible manufacturing systems (FMSs), it became more important to optimize them. Scheduling is one of the critical problems in FMS optimization. This study presents a new application of multi-criterion decision-making (MCDM) methods for scheduling problems of automated guided vehicles (AGVs). The problem addressed in this research refers to a situation, in which several orders from different job stations are submitted as a control system of AGVs. The research methodology consists of two steps. We first identify the criteria for finding the importance of jobs and then apply several MCDM methods (i.e., TOPSIS, VIKOR, and PROMRTHEE) to assign AGVs to the jobs. At last, we highlight the scheduling plan for AGVs using the mathematical formulation. This paper can bring some managerial insights not only for those who use an FMS but also for anyone who is working in the field of smart management. By scrutinizing the parameters and investigating the proposed methodology by a real-life case study, we highlight that the travel distance is significantly correlated with utilities. In other words, the more important are the jobs, the less distance is between jobs and AGVs. https://scientiairanica.sharif.edu/article_23239.html This study presents a four-objective model for a three-level hierarchical hub problem, in which nodes are connected to ground hubs in a star-like pattern at the lowest level of a hierarchy of demand, and ground hubs are connected to airport hubs at the next level up. This model finds the number of routes, network-related costs, and hub queue waiting times while raising the network’s route reliability. Due to the impact of service time on customer satisfaction, a time frame is considered, in which a penalty is assigned for the amount of delay. Both the airport and the ground are hub facilities in this regard. M/M/C/K queue systems are those found in airport and ground hubs. This problem aims to locate the predetermined hubs and assign nodes to the network. Due to the multi-objective model, LP-metric and goal attainment (GA) approaches are used to resolve it and verify multiple samples with varying weight values provided by the decision-maker. The results from the forgoing approaches are ranked using the simple additive weighting (SAW) method. A few parameters are treated as fuzzy numbers to make the model more realistic, and the Jimenez model and chance-constrained programming are used to present the findings. https://scientiairanica.sharif.edu/article_23259.html An integrated intelligent algorithm is proposed to optimize the reliability, maintainability, and total cost in the job shop production system. The algorithm consists of three basic modules of computer simulation. each comprising three phases of Algorithm, simulation, and Experiments/robustness validation. In the design phase, different scenarios are determined by changing parameters affecting the reliability, maintainability, and total cost. The job shop production system is simulated in the simulation phase. Then, a fuzzy simulation approach is implemented to run the simulation model for each scenario with ambiguous inputs. Accordingly, the investment cost, maintenance cost, mean time to repair (MTTR), and mean time to failure (MTTF) are obtained. Finally, the performance of different scenarios is assessed in the third module. ANN and DEA are separately used in this module and the preferred method is selected based on the robustness test and extensive sensitivity analysis. DEA and ANN are then employed to rank the design alternatives concerning the initial inputs and outputs. To show the applicability and superiority of the proposed integrated algorithm, it is applied to optimize the design of a fuzzy job shop production system consisting of five workstations. https://scientiairanica.sharif.edu/article_23260.html Firms outperforming competitors often get their success through innovation and new technological knowledge acquisition. This study offers a Three-Stage decision-making model for acquiring new technological knowledge and the optimal time to invest. In the first Stage, two competing firms decide to invest in a new technological knowledge without knowing its level. In the next stage, firms will develop and integrate it with their knowledge. Due to the uncertainty of new technological knowledge, a stochastic programming model is used to determine the optimal acquisition time. This model identifies the leader and follower by considering advantages such as branding and high market share as well as disadvantages such as high cost of uncertainty. Finally, we used Cournot and Stackelberg game to determine the winner in the market. The proposed model can be used as a decision-making tool to help organizations, in uncertainty, invest as leaders in acquiring new technological knowledge and entering the market, or wait until things are clear. The results of stochastic programing and game theory model show that the level of knowledge of firms at the time of production, knowledge absorption coefficient, and constant demand coefficient will have a special effect on determining the winner in the market. https://scientiairanica.sharif.edu/article_23261.html This study collects the anthropometric measurements and weights of 185 male individuals between 55 and 65 years old from Ankara city of Turkey. A total of 29 variables with three inputs and twenty-six outputs are collected. This paper aims to develop machine learning-based models to estimate anthropometric measurements from weight, stature, and eye height. These models are support vector regression (SVR) optimized with Bayesian based on quadratic kernel, Gaussian Process Regression (GPR) optimized with Bayesian based on matern5/2 kernel. This study contributes to SVR and GPR models by using Bayesian method to optimize the parameters as a difference from the literature. According to the literature review, applying these two models to anthropometric measurements for the first time is predicted. The estimation results are compared based on three metrics, namely Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE). GPR optimized with Bayesian model has better accuracy than SVR optimized with Bayesian for all combinations except interpupillary distance, according to the obtained results. The RMSE values of the best models selected for each combination varied between 0.255 and 0.319 during the testing phase. Especially the estimations made with GPR optimized with Bayesian have a shallow error rate. https://scientiairanica.sharif.edu/article_23268.html Agri-food supply chains include several processes, from cropping and harvesting to distribution. Integrating these processes to reduce costs and environmental impact and providing sufficient supply are the main goals of agri-food supply chain management. Rice, a so-called staple food, makes up a significant portion of the human diet worldwide. Due to the importance of rice, this paper proposes a mixed-integer linear programming model for rice supply chain design and planning that considers economic, environmental, and social dimensions. This model determines the optimal strategic and tactical decisions in the rice supply chain, including cropping pattern, supplier selection, and the location and capacity of new milling centers with parboiling technology. The model considers different rice varieties and irrigation water requirements of crops. Also, it investigates the benefits of renting as well as offering a partnership agreement to independent farmers. A case study of Iran farmlands is employed to show the applicability and advantages of the proposed model for the rice supply chain. To solve the proposed multi-objective model, the ɛ-constraint method is applied. The results indicate that opening milling centers with parboiling technology is profitable for the supply chain. Moreover, entering a partnership agreement is much more profitable than renting farmlands. https://scientiairanica.sharif.edu/article_23273.html This paper is aimed to examine the dynamics of cracked thin rectangular plates subjected to a moving non-stationary random load. A random load is considered with constant mean value, constant moving velocity along the plate, and five different covariance patterns,the white noise, constant, exponential, cosine wave, and exponential cosine covariance. Accordingly, an intact plate’s orthogonal polynomials in combination with the well-known corner functions are employed to explore the mechanical behavior of the cracked plate. As the non-dimensional deflection values, the functions of the squared mean values are obtained for the damped and undamped cracked plates at different points. An inclusive parametric study is performed to explore effects of the inclined crack angles and the crack lengths on the non-dimensional functions of squared mean values at middle point of the undamped and damped cracked plates. Based on the obtained results, it is concluded that there are nonlinear relations between increasing the incline crack angles as well as the crack lengths and the non-dimensional functions of squared mean values. Furthermore, it is found that in the exponential covariance cases, the effects of increasing crack angles and lengths on the non-dimensional squared mean values are profounder than the other four patterned covariance cases. https://scientiairanica.sharif.edu/article_23274.html Control charts are one of the critical tools for process monitoring. Test statistics are computed as a function of sample means in control charts for monitoring the process mean. In this study, these functions are modeled by filters. These filters have essential properties in both time and frequency domains. In previous studies, only their properties in the time domain have been considered. Thus, the resulting filters have sub-optimal performance. This study investigates the optimal design of these filters for monitoring the process mean. The behavior of these filters is analyzed not only in the time domain but also in the frequency domain. Properties such as stability, causality, minimum phase, and lowpass are considered in designing this filter. An optimization model is designed and solved using a Genetic Algorithm based on these properties to minimize the Average Run Length. The proposed optimal filter is compared with other control charts using simulation studies. Results showed the high speed of the proposed filter in detecting shifts in the process mean. The proposed optimal filter is also used to monitor the oil price of the OPEC basket. The results showed that shifts were detected at the right time using the proposed optimal filter. https://scientiairanica.sharif.edu/article_23294.html In this paper, the robust-fuzzy-probabilistic optimization method is used to control the multi-objective problem of a sustainable green integrated production system under uncertainty. The main objectives of the problem are to minimize the costs of the entire production system and the amount of greenhouse gas emissions due to the production of parts and reproduction of products in the system. Exact and metaheuristic methods have been used to solve the problem. The computational results show an increase in costs and emissions with increasing uncertainty rates. Managers should pay attention to the management of costs as economic aspects and the amount of greenhouse gas emissions as environmental aspects according to the Pareto aspect. The sample problems in larger sizes have been investigated by MOGWO and NSGA II algorithms. The calculations show the high efficiency of MOGWO algorithm in solving large size problems based on the comparison indices of meta-heuristic algorithms. https://scientiairanica.sharif.edu/article_23297.html Due to the imperfection of processes, the quality of some products may be unsatisfactory. Moreover, equipment failure can stop production for a while. Therefore, integrating the triple concepts of quality, maintenance, and inventory control has attracted attention. Triple concepts are the constituents of the pre-sale costs. Selling price and warranty are generally considered to maintain market share and maximize the producer's profit. Quality and maintenance in production should be considered to reduce the post-sale costs of the warranty. Despite interactions, integrating warranty with triple concepts has been neglected. We integrate the quadruple concepts in a bi-objective model to maximize the profit and minimize the pre-sale and post-sale costs under the free minimal repair warranty policy. A non-central chi-square (NCS) control chart monitors the mean and variance, simultaneously. The technology level is also considered for increasing product quality and reducing failures during the warranty period. Due to its high complexity, the model is solved by the particle swarm optimization algorithm. The proposed model is applied through a numerical example and three comparative studies. The results indicate the better performance of the NCS chart, the superiority of bi-objective optimization versus single-objective optimization, and the importance of integrating presale and postsale costs. https://scientiairanica.sharif.edu/article_23304.html Agricultural activities have adverse effects on the environment by emitting greenhouse gases and consuming great deals of freshwater. In addition, fruits constitute a substantial part of agricultural products used for balancing diets. In particular, pomegranate is one of the most used products by people of different cultures. In this study, a multi-objective mathematical model is developed to balance sustainability dimensions by focusing on selecting the optimal cultivation process and determining the optimal material flows between pomegranate supply chain facilities. The proposed model maximizes the total profit and the number of created job opportunities due to cultivation process selection and the establishment of plants. It also addresses the environmental impacts by minimizing fertilizer, pesticide, and water consumption in pomegranate cultivation. The model also considers the reverse flow of pomegranate peel and seeds to recapture the value of these products, commonly known as waste. A real case in the Mazandaran province of Iran is considered for validating the developed model. Finally, comprehensive sensitivity analyses are performed on the influential factors of the problem, and managerial implications are presented. https://scientiairanica.sharif.edu/article_23326.html Cost overrun and time delays in water industry construction projects persuade the authorities to improve the project management approach by adopting building information modeling (BIM). However, BIM is new in the water industry and, with the many aspects and extensive details involved, its successful implementation requires an effective strategic plan. To address this need, in this project a customized BIM strategic plan development method is proposed and followed for the water industry projects in Iran. As a result, two 5-year implementation phases are proposed and specifications of the required infrastructures are outlined. The first phase targets the implementation of BIM maturity level one in pilot projects and BIM maturity level two becomes mandatory at the end of phase two. To the best of the authors' knowledge, for the first time, a systematic method for the BIM strategic plan development in an industry, with multiple organizations involved, is proposed in this research. The two types of BIM committees introduced and utilized in this research can inspire other BIM strategic plan development efforts for large industries, e.g. transportation industry, the electric power industry, and the oil and gas industry, with multiple organizations in charge and different types of construction projects implemented. https://scientiairanica.sharif.edu/article_23327.html Abstract. Structural health monitoring of dams is a necessary issue in water supply network. Not identifying the local damages in these super structures and their extension could lead to abrupt and total collapse and impose major life or financial losses. This research investigates the effects of damage on the concrete arch dams and the methods for their detection. For this purpose the finite element model of a concrete arch dam was analyzed using the wavelet transform once by static frequency analysis and the second time when it is subjected to dynamic accelerograms. In the wavelet transform method using the static frequency analysis, cracks were formed on the dam body and the obtained results were compared to those obtained from Modal Assurance Criterion (MAC) method. In the wavelet transform method, by applying dynamic loads, different damage scenarios, including 16 damage locations and 3 damage levels (change in the materials properties) were compared to the healthy state of the dam. In order to process the wavelet transforms the displacements obtained from the modal and dynamic analyses were utilized. The results showed that employing wavelet transform method by applying dynamic loads could better detect the damages. https://scientiairanica.sharif.edu/article_23330.html Application of Kobayashi potentials (KP) is extended to the electromagnetic (EM) diffraction from the parallel-plate multiport. Standard integral identities are used for problem formulation, without the direct use of Weber-Schafheitlin (WS) integrals. The Fourier function space is exploited for the construction of the governing linear system of equations. A simple strategy is suggested for the evaluation of the required improper integrals. Two-dimensional T- and X-junctions are studied as special cases. Numerical results are validated through convergence test and asymptotic analysis. It is shown that whenever the wave number in the whole problem domain is positive, no real power transfers to the diffracted field in the horizontal section of the aforementioned structures. https://scientiairanica.sharif.edu/article_23334.html Present article proposes the neutrosophic moving average (NMA) control chart under neutrosophic statistics (NS) based on multiple dependent state (MDS), Repetitive and multiple dependent state repetitive (MDSR) sampling schemes. The neutrosophic moving average control chart is useful to monitor the process mean in the industries when the measurements expressed in terms of uncertainty or fuzzy or interval. In this circumstance, the existing monitoring designs could not be useful for the monitoring of mean accident or injury data. In the present investigation neutrosophic moving average control chart is developed under the NS. The chart coefficients of the proposed control chart are obtained using Monte Carlo simulation under NS. A comparative study between the three sampling schemes of neutrosophic moving average control chart under neutrosophic statistics (NS) is given. Two real examples from accident and injury data are taken to investigate the accomplishment of the proposed chart. Based on the simulation study and real data, the proposed chart is out performed over the existing control charts. https://scientiairanica.sharif.edu/article_23343.html The Master surgical scheduling (MSS) program is used at the tactical level of operating room scheduling, and its optimal creation can reduce the waiting queue of patients, as well as hospital costs. The patients’ length of stay (LOS) has a great impact on the downstream resources management. The uncertain nature of LOS and surgeries demand increases the challenges of MSS creation. The aim of the article is to determine the MSS program integrated with combination of surgical operations of each block of the operating rooms. For this purpose, a novel mathematical model was proposed for multi-objective MSS problems with a probabilistic LOS. Then, the chance-constrained programming method was employed to cope with the uncertain demands. The ε-constraint method was used for small-scale problems. Moreover, two metaheuristic algorithms including the multi-objective gray wolf optimizer (MOGWO) and the non-dominated sorting genetic algorithm-II (NSGAII) were designed to deal with large-scale problems. Based on the results, the MOGWO outperforms the NSGAII in terms of both the MID measure and the run time. The sensitivity analysis on the capacity of the wards parameter at different levels of demand uncertainty was performed to help managers to decide about the appropriate capacity of the wards. https://scientiairanica.sharif.edu/article_23344.html Portfolio optimization studies have traditionally assumed that portfolio managers manage only one portfolio. However, in reality, often manage multiple portfolios that can impact each other. This creates a need for fairness to all customers, which has led to the emergence of a new topic called "multiportfolio optimization". Previous studies have paid little attention to this issue, and the models used were not developed using real stock market data. These models were also limited to the selection phase and did not consider the ordering phase.This research provides a comprehensive process for addressing the multiportfolio problem, covering all sections from selection to ordering. It also implements the process using real stock market data. During this process, the market impact function is estimated using the I-STAR model for different stocks. The proposed model for market impact costs includes both permanent and temporary sections. The proposed models were tested using the Tehran Stock Exchange data in 2019.A comparison of the MPO model output with classical models indicates that the proposed model improves utility by an average of 15%. In the next phase, comparing the proposed ordering model with other models shows a reduction in market impact costs by an average of 26%. https://scientiairanica.sharif.edu/article_23353.html One of the most valuable components in power systems is power transformer whose failure may result in a significant power loss. Therefore, one of the critical issues in power transformer operation is its health monitoring. Moreover, it was shown that the aging rate of transformers is very sensitive to the hot spot temperature, and when this temperature exceeds a threshold value, the aging rate increases. Given the fact that using temperature sensors in prefabricated and built-in transformers is not practical, thermal models are used to estimate transformer hot spot temperature. Since the transformer hot spot temperature is a key factor in the condition monitoring of a transformer, and in case it will exceed a threshold value, preventive actions should be taken in the proposed algorithm, the sensitivity of this important parameter with respect to the load profile sampling time is investigated. This paper proposes a fast online algorithm for the estimation of power transformer hot spot temperature by reducing the number of calculations without sacrificing accuracy. The proposed algorithm is applied to a 250 MVA transformer using MATLAB software. The results were compared with the actual factory test results and the efficiency of the proposed algorithm was shown. https://scientiairanica.sharif.edu/article_23354.html In the previous paper, diverse data acquisition methods based on data types for condition monitoring wind turbines is explored. The present study investigates advanced signal processing techniques in the field of condition monitoring of wind turbines. Methods include synchronous sampling, signal decomposition, envelope analysis, statistical evaluation, model-based approaches, Bayesian methods, and artificial intelligence techniques. Comparison and analysis of these methods and their applications in wind turbine fault detection and diagnosis are presented in this coming study. Moreover, the survey encompasses innovative approaches using various data sources, addressing challenges in components like bearings, gearboxes, blades, and generators. Insights into the evolution of data-driven decision-making in the wind energy sector are provided, with a focus on strengths, limitations, and future directions. A summarized table offers an overview of studies, highlighting monitored components, data types, and methods. https://scientiairanica.sharif.edu/article_23355.html In recent high-risk and changing world, optimal resource allocation is significant, which in case of inappropriate resource allocation, will cause significant damage to organizations. In resource allocation and where there is a lack of resources, it is imperative to processes continue and the process's resilience and the risks posed by these lack and unsuitable allocations. If resource allocation is not done properly or is done in short supply, there will be consequences, e.g., processes do not continue properly or are not resilient, or they will be increase in the risks of the processes. So, addressing the three mentioned issues such as organizational resilience, business continuity, and risks of the operational processes is of great importance in the problem of resource allocation. Also, the lack of integrated attention to the mentioned issues in the modeling of resource allocation in order to process optimization will lead to a decrease in resources’ utilization. Therefore, in this paper, a novel integrated mathematical model has been developed for resource allocation with considering the process resilience and continuity. Thus, the objective functions of the model are defined according to the four measures of optimal resource allocation such as return, risk, resilience and process continuity. https://scientiairanica.sharif.edu/article_23356.html This paper presents a new spline adaptive filtering (SAF) algorithm based on signed regressor (SR) of input signal. The algorithm is called SR-SAF normalized least mean squares (SR-SAF-NLMS). The SR-SAF-NLMS is established through $L_{1}$-norm constraint to the proposed cost function. In this algorithm, the polarity of the input signal is used to adjust the weight coefficients and control point vectors. Therefore, the computational complexity, especially the number of multiplications, is significantly reduced. Furthermore, the performance of the SR-SAF-NLMS is close to the conventional SAF-NLMS. The good performance of the proposed algorithm is demonstrated through several simulation results in different scenarios. https://scientiairanica.sharif.edu/article_23357.html In this paper, a new chaotic system with three sinusoidal nonlinearitiesis reported. The basic behavior of the new chaotic system is analyzed bymeans of equilibrium points, stability and Lyapunov exponents. The newsystem has countably infinite number of equilibrium points, which is anovel feature of the system. The new system has multiple interestingfeatures such as topologically different attractors, coexisting attractors,offset boosted attractors and polarity reversed offset boosting attractors.These special features are analyzed and verified using classical tools suchas bifurcation diagrams, Lyapunov exponent plots and attractor diagrams.The bifurcation analysis and simulation results show that the proposedsystem has rich chaotic dynamics. Furthermore, the adaptive control andsynchronization of the new system are achieved using nonlinear feedbackcontrol methodology. MATLAB plots are shown to illustrate the controlresults for the new chaotic system with three sinusoidal nonlinearities https://scientiairanica.sharif.edu/article_23364.html To maintain a competitive advantage, manufacturers of household appliances should promote the product’s energy efficiency, considering the impact on customer purchasing behavior. Since the product’s energy efficiency and pricing policies influence customers’ purchasing decisions, manufacturers confront significant challenges in balancing costs and demand since they must consider their profit-maximizing objective and government regulations. The Stackelberg game framework represents the interactions between the government, the leader, and a manufacturer, the follower, incorporating the government’s involvement in environmentally dependent social welfare under a tax structure. This paper proposes closed-form equilibrium using a game theory approach and geometric programming (GP) to solve the government’s and manufacturers’ non-linear decision models. The analytical results offer insight into the management’s approach to the product’s energy efficiency. The findings demonstrate that when clients’ concerns about energy-saving grow, the net payoff to the total manufacturer revenue ratio continuously decreases. The outcomes imply that the manufacturer must allocate significant revenue to tax expenditures in markets with more price-sensitive clients. As a motivation for research, this paper explores the application of the proposed model by examining a numerical example of a real-world refrigerator manufacturer case to obtain further managerial insight. https://scientiairanica.sharif.edu/article_23366.html As a geocell reinforced structure can be subjected to earthquake and pullout loads during its life time, it is obligatory to assess the pullout capacity and soil-geosynthetic interaction of geocell under cyclic loads. This research investigated the cyclic and post-cyclic pullout behavior of geocell in cohesionless soil using a series of 24 multi-stage pullout tests. The results indicated that the ultimate post-cyclic pullout load was less than the monotonic pullout load. This was the result of a reciprocating motion from loading caused by the interlock between the geocell infill soil and the surrounding material, which weakened and broke during the cyclic phase. It was found that, as the grain size of the soil increased, the interlocking strength increased and consequently the ultimate post cyclic pullout load increased. The soil particle size had a significant effect on the cumulative displacement during the cyclic phase. Furthermore, the increases in the loading amplitude and the number of cycles decreased the interlocking resistance of the infill soil with the surrounding material, which decreased the ultimate post-cyclic pullout load. The effect of the loading frequency likely depended on the geocell infill soil density. https://scientiairanica.sharif.edu/article_23371.html The generation of electrical power through wind turbines has significantly increased nowadays. However, these systems are prone to faults that can disrupt the network and incur substantial costs for the generation units. Therefore, effective maintenance scheduling becomes crucial. A major challenge faced by wind turbines is their maintenance requirements, as any interruption in their operation and power generation can result in significant economic losses. Consequently, meticulous planning is indispensable to minimize such consequences. This paper that is the first part of the study conducts a survey of data acquisition methods in condition monitoring of wind turbines. In the second part, signal processing techniques for condition monitoring of wind turbines are presented. Furthermore, the paper examines a range of studies that have implemented practical condition monitoring methods in wind turbines, delving into the associated challenges and proposing potential solutions. Various methods such as vibration analysis, acoustic analysis, electrical parameter analysis, AI-based techniques, and fault-tolerant control have been employed for wind turbine maintenance. However, limitations exist in terms of data availability and computational burden. Future challenges include developing algorithms that require less data, reducing computational requirements, updating models with new conditions, enabling early detection and proactive maintenance, and reducing maintenance costs. https://scientiairanica.sharif.edu/article_23373.html This study intends to quantify, model and evaluate the impact of preventive maintenance (PM) on interruption duration (IntD) in electricity distribution systems, based on analyzing the data from the dashboard of an electricity distribution company and expert opinions. Following the data cleaning, the data are analyzed to identify the failure modes and their effects, to recognize the critical components (FMEA). Subsequently, the PM activities associated with them are investigated, to analyze the maintenance activities scheduling impact on IntD, employing expert judgment as a decision support. The data analysis reveals that the fuses and fuse holders experience the highest interruptions frequencies and durations, nominating them as critical components. Then, the impact of maintenance activity (inspection time) on the IntD percentage change is analyzed, leading to calculation of the sensitivity of IntD to maintenance activity. The quasi-linear shape of the IntD and ENS percentage decreases versus PM inspection time, i.e. the intended sensitivities is observed, thus two linear models are developed to represent this impact, suitable for maintenance optimization problems which need linear models convexity. Moreover, two indices of SIntDPM and SENSPM are introduced as maintenance KPIs representing the sensitivities, to prioritize PM actions versus their impact on IntD. https://scientiairanica.sharif.edu/article_23377.html We present an inventory model to determine the optimal selling price and cycle time for two mutually complementary commodities that are subject to deterioration. Each commodity's demand is influenced by its own selling price, the selling price of the complementary product, and the passage of time. Numerical examples and sensitivity analysis results are presented to demonstrate the usefulness of the inventory model. We conducted sensitivity analysis on the impacts of the changes in key parameters of the model on the decision variables and the objective (profitability) of the inventory system. We observed that as the deterioration rate of either item increases, the model proposes shorter replenishment cycle length, which reduces the profit. Our model’s novelty is the inclusion of mutual (two-way) complementarity in the Economic Order Quantity model, where both items are deteriorating and have time-dependent demands. https://scientiairanica.sharif.edu/article_23382.html Variable reluctance resolver is one of the popular sensors used in the industry. Because they can operate at high temperatures, withstand shock and less expensive and easier to manufacture. Despite the mentioned advantages, the variable reluctance resolver may suffer from faults that lead to inaccurate position. The paper aims to reduce position error under short circuit fault by changing parameters such as excitation frequency, number of signal and excitation poles, shape of the air gap, slot opening width and the number of teeth. The effect of each parameter on reducing the position error is examined to determine whether it has a positive, a negative or zero effect. Finally, the optimal case is introduced by selecting the best value for each parameter, which significantly reduces the position error. https://scientiairanica.sharif.edu/article_23384.html This study examines the behavior of Non-Newtonian Nano fluids in fluid flow and heat transfer over a permeable surface with injection and suction. The governing equations are converted into ODE using a similarity approach and solved numerically with the RKF45 method. Cu, CuO, TiO2, and Al2O3 nanoparticles are used in (CMC)/water as the base fluid to investigate the effects of power-law index, nanoparticle volume fraction, type, and permeability factor. The results indicate that Non-Newtonian nanofluids exhibit different behavior than Newtonian nanofluids in the presence of suction and injection. Non-Newtonian nanofluid performs better than Newtonian nanofluid in terms of heat transfer for injection and an impermeable plate, but changing the type of nanoparticles has a larger effect on heat transmission during suction. Additionally, using Non-Newtonian nanofluid in injection reduces heat transmission in all Three cases. The study's findings provide useful insights into the behavior of Non-Newtonian nanofluids for practical applications. Moreover, it is observed that the heat transfer rate is enhanced with increasing injection, whereas it is reduced with increasing suction. The results of the study could be useful for designing efficient heat transfer systems involving Non-Newtonian nanofluids over permeable surfaces with injection and suction. https://scientiairanica.sharif.edu/article_23391.html The cloud manufacturing system is a customer-oriented paradigm that benefits from centralized management of all available resources. This paper focuses on the integration of sub-task scheduling and logistics (ISSL) in the cloud manufacturing system with two main contributions: 1) using a combined transportation system which provides the advantage of transporting more than one sub-task by a vehicle at the same time, and 2) tasks can have different structure types including sequential or parallel. To get the model closer to reality, two factors are considered: 1) different task arrival times; and 2) The setup time/cost. The proposed model aims to optimize task completion time, cost, and average quality service concurrently. To solve the proposed model, GAMS software is utilized for small/medium-sized samples while a genetic algorithm is developed for larger-size samples. Three comparative studies are conducted; the findings show that employing combined logistics significantly impacts the cost imposed on cloud systems while the real task arrival time to the cloud platform and setup time/cost have a notable effect on the task completion time and cost, respectively. Eventually, a sensitivity analysis is undertaken to gain insight into the impact of execution time, service cost, and user preferences on the final solution. https://scientiairanica.sharif.edu/article_23392.html The amplitude-phase synthesis of an unequally spaced array is discussed in this study along with a new method that improves the amplitude dynamic range ratio with fewer elements. In this method, the Hankel matrix is established using the sampling points of a prescribed array factor. The array elements' locations are estimated using the eigenvalues of the Hankel matrix. The magnitude of the array currents is then calculated using the least-squares approach. By defining the reduction parameter, the amplitude dynamic range can be extremely reduced. Some theoretical and practical arrays are given to verify the performance of the proposed method. The obtained results are compared to those obtained by other methods, and simulation data. https://scientiairanica.sharif.edu/article_23400.html Adding steel fibers (SF) in SCC can change the cracking pattern and fracture performance. Hence, 75 notched SCC beams containing SF at volume percentages of 0.15, 0.3, 0.45, and 0.6% were made in this work and tested under the three-point bending load to investigate their brittleness and fracture behavior. To this end, work of fracture method WFM and size effect method SEM were used to analyze the fracture parameters. The results showed that increasing the steel fiber content from 0.15 to 0.6% increased fracture energy values obtained from WFM and SEM by 9.8 and 2.5 times, respectively, compared to SCC without fibers. Also, at a SF content of 0.6%, the characteristic length of concrete in WFM, and the fracture process zone and fracture toughness in SEM were 5.4, 3.3 and 1.7 times, respectively, those of SCC without fibers. The results of in WFM and in SEM showed that the fibrous SCC samples were more ductile. Eventually, obtained mechanical properties and test variables were used to develop multivariate prediction models for the fracture parameters of fibrous SCC. The prediction results of these models were compared the test data of the present study and other studies, and acceptable results were found. https://scientiairanica.sharif.edu/article_23401.html Designing a reliable telecommunication network for Advanced Metering Infrastructure (AMI) is crucial. This paper focuses on using LoRaWAN as the network and improving transmission reliability while maintaining fairness among smart meters. The Spreading Factor (SF) of LoRaWAN is used to control Packet Delivery Ratio (PDR) and measure reliability. Higher SFs increase redundancy and reliability, but collisions can occur between meters with the same SF. To optimize SF assignment, an SF assignment problem is solved to maximize the minimum achievable PDR, ensuring fairness. A novel solution called Reliable SF Assignment (RSFA) using a game of learning automata is proposed. Simulation results demonstrate that RSFA outperforms four conventional SF assignment approaches (ADR, AD MAIORA, CA-ADR, and EWS). RSFA improves packet collision rate, PDR, the number of served meters, and fairness index, albeit with a slight increase in transmission delay. https://scientiairanica.sharif.edu/article_23403.html The proper performance of the first-order reliability method (FORM) is main issue in structural reliability analysis that is dependent on the accuracy, efficiency, and robustness of the employed algorithm. In this paper, a new reliability analysis framework is presented to improve the performance of the first-order reliability method. The innovation of the proposed method, which is a development on the non-negative constraint method, accounts for the estimation of the step size to implement line search formulation. The non-negative constraint method is considered to generate a positive Lagrangian function, an unconstraint optimization problem, and a search direction vector. Then, the first-order Taylor approximation of the positive constraint is applied to find the trail design point. The next step is to consider this trial design point and Pade approximation of the non-negative limit state function (constraint) for appropriately computing the step size. The efficiency and robustness of the proposed algorithm shown in various benchmark numerical examples included a comparison with other first-order reliability methods. The numerical results indicate that the proposed method functions properly to pinpoint the reliability index by fast convergence rates compared to other methods. https://scientiairanica.sharif.edu/article_23404.html Natural gravel and Sand are growing more and more expensive due to its scarcity. Therefore, replacing natural aggregates with recycled materials has been a concern of researchers. In this paper, crumb rubber was utilized to supersede a few of the aggregate in percentages of 5, 10, and 15 aggregate volume. In concrete containing 15% rubber crumbs, 10%silica fume, and 10%zeolite were used along with crumb rubber. The findings of this investigation indicated that concrete containing 15% crumb rubber causes the greatest decline in compressive strength. Comparative to control concrete, compressive strength was decreased by 35% at 28 days and 36% at 7 days by substituting 15% of crumb rubber with aggregate volume. Additionally, it was discovered that the compressive strength of concrete containing 15% rubber crumbs rised by 23% and 33% at 28 days, and 21% and 34% at 7 days, respectively, when the mixture was containing 10% zeolite and 10% silica fume. Also, the greatest recline in flexural strength, tensile strength, and modulus of elasticity was related to concrete containing 15% crumb rubber, which was improved by adding 10% pozzolan. Finally, an elastic modulus prediction model for this type of concrete (recycled aggregate concrete) is presented. https://scientiairanica.sharif.edu/article_23406.html The problem of influence maximization is finding the best nodes at the beginning of the diffusion process to maximize the affected nodes at the end. Although there has been a great deal of modeling in this area, no studies have examined how network structure, size, and seed nodes affect these models. The present study has investigated this issue by designing and conducting an experiment. Erdos-Renyi, small-world ,and scale free networks with different sizes are examined in this work. Additionally, the variation between these structures and the number of seed nodes in the OAIM problem's output has been statistically examined for 1440 networks. As a result, while confirming the effect of network structure and size on the success of promoting an opinion in the network, recommendations have been provided for the message sent by the beneficiary in the OAIM problem. https://scientiairanica.sharif.edu/article_23411.html Investments in power capacity is widely investigated in the effects of the renewable energy support policy to promote capacity expansion. However, the existing studies have not considered the impact of investment behaviors on capacity expansion under the renewable portfolio standards (RPS) policy. To address this problem, we develop a two-stage decision model to assess investment behaviors with the RPS policy. The investment behaviors are divided into three scenarios: decentralized competition (DC), cooperated planning (CP), and centralized strategy (CS). We construct the Cournot game model for the DC scenario, the cooperative game model for the CP scenario, and the portfolio investment model for the CS scenario, respectively. The three models are introduced to the two-stage decision framework to capture the characteristics of investment behaviors of generators under the RPS policy. Compared to the DC scenario, the CP scenario has the most benefits and turns the green certificates market trade into an internal business; and the CS scenario could avoid the price risk from trading green certificates by considering renewable power quota during the process of investing in both technologies. https://scientiairanica.sharif.edu/article_23412.html Recently, sustainable development concept has attracted a great attention in many countries. Recycling plays a major role in sustainability by improving the waste management systems. In this study, pricing and ordering decisions of a recyclable waste are considered under a sustainable supply chain containing one collector, one separator, and one recycler. In this setting, the game-theoretic frameworks including Nash and Stackelberg models are developed to set the decisions. Finally, the obtained decisions are discussed and some results are provided. It is found that different interactions established among the members do not affect the collector’s decisions. From the recycler’s view, the Stackelberg game with lower prices is more preferable. It is more beneficial for the members to be established the Stackelberg game among them. The members achieve more profits by making strategies that reduce the self-price or enhance the cross-price sensitivities of the demands. Moreover, from the sustainable development perspective, the Stackelberg framework is better than the Nash structure by collecting and recycling more waste. https://scientiairanica.sharif.edu/article_23415.html A novel multi-band antenna with an improved higher-order mode radiation pattern based on characteristic mode analysis (CMA) is presented. The tri-band characteristic is achieved by exciting one higher-order mode and two orthogonal modes. The initial orthogonal mode is achieved by converting the antenna from a circular to an elliptical shape. Surface current reshaping transforms the higher-order conical radiation pattern mode into a broadside direction. The surface current nulls with out-of-phase are moved toward the patch corners using CMA. Characteristic modes excite with coaxial feed, which uses full-wave EM simulation. It excites two orthogonal modes at 1176 MHz, 1575 MHz for L5-band and L1-band applications respectively, and one higher-order mode at 2500 MHz for S-band applications. The proposed antenna has moderate gain and broadside radiation patterns across the operational frequency bands. The working of the antenna and validation are represented by equivalent circuit modeling. The experimental results present excellent agreement with EM simulated results. https://scientiairanica.sharif.edu/article_23420.html The application problems in the field of pattern recognition, clustering, and knowledge-based expert systems contain a lot of uncertainty in the form of imprecise, incomplete, and inexact information. These decision-making processes well utilize the notions of entropy, discriminant measure, and similarity measure which play a crucial role in the determination. In the present communication, a very recently proposed bi-parametric ($R$, $S$)-norm discriminant measure for picture fuzzy sets has been utilized and different important properties have been discussed. The bi-parametric discriminant measure would give diversification in handling the inexact/incomplete information in terms of obtaining the degree of association and closeness in the data of various applications. The monotonicity of the newly presented discriminant measure in relation to the involved parameters $R$ and $S$ has also been discussed in detail along with its empirical proof. Further, the bi-parametric measure under consideration has been successfully applied in the principle of minimum discriminant information with the help of some illustrative numerical applications in the field of pattern recognition/clustering, etc. Additionally, for the validity and efficacy of the presented approach, necessary and detailed comparison studies along with important findings, advantages, and limitations have been mentioned. https://scientiairanica.sharif.edu/article_23421.html Audiovisual integration (AVI) is a brain function that combines received information from visual and auditory sources. Delay in the development of multisensory integration functions makes functional problems in cases like autism spectrum disorder. However, the nature of the development of its network in the brain is poorly understood.We used resting-state functional magnetic resonance imaging data from ADHD 200 publicly available dataset. There are 192 records from typically developing children (92 Girls) in open eyes conditions which we used to make AVI networks based on functional connectivities. The minimum spanning tree tool was used to have comparable networks. We explored the measures of the extracted trees to discover changes in the developmental trajectory.The links of the AVI network undergo many changes before nine years and nine months, which form the main structure of the network. Most of these changes in links are related to the superior parietal gyrus area. The subsequent changes are related to setting the network performance, most of which are intra-regional links related to the superior temporal gyrus.Based on our results, it is recommended to perform localization tasks for rehabilitation or enhancement of AVI in the early stages of AVI development. https://scientiairanica.sharif.edu/article_23423.html Balancing of generation and demand is the most essential requirement for power system (PS) network. The frequency of generation varies for different kinds of sources incorporated in the PS network as well as for the variation of the loads. The power system integrated with different renewable energy (RE) sources needs to be controlled and stable with a small variation of loads. The automatic generation controller (AGC) is essential for achieving load frequency balance in the PS network. A mismatch of frequency between the supply and demand may lead to development of large system errors. In this paper, an intelligent and robust Fuzzy logic-based controller is proposed for AGC in power system incorporating different types of RE sources like solar, wind, and ocean-thermal. Controller parameters are optimally tuned using Firebug Swarm Optimization (FSO) algorithm. A 2-area-test system is considered as the test bench for the proposed controller. In a later stage a fuzzy hybrid 3DOF-PID controller is designed for controlling 3-area PS network containing RE sources . The designed controller is robust to load variation and the comparison of the various performance indices demonstrates the superiority of the proposed controller over other controllers . https://scientiairanica.sharif.edu/article_23441.html This paper proposes a high-order sliding mode control strategy to improve the performance of power decoupling control for double-fed induction generators (DFIG). Initially, the mathematical model of DFIGs is analyzed based on the principle of voltage-oriented vector control. DFIGs efficient operation is vital for grid stability and power quality. However, these generators are susceptible to disturbances and uncertainties, which can affect their performance. To address this issue and poor dynamic performance of traditional control methods in achieving power decoupling and the nonlinearity of the wind turbine model, the proposed control method utilizes high-order sliding mode controller using power function approaching law. Simulation results demonstrate that the proposed control approach outperforms traditional PI controllers in terms of accuracy, robustness, and dynamic and static performance. A series of experiments, including no-load grid connection and power decoupling control, were successfully conducted on this platform. Analysis of the experimental waveforms for independent adjustment of active and reactive power shows that when either the active or reactive power is independently changed, the other power remains relatively constant. Therefore, the experimental results verified that the grid-connected power control strategy of the system can achieve dynamic decoupling control between active and reactive power. https://scientiairanica.sharif.edu/article_23443.html Freight transport is a key enabler for the growth of the national industry and the opportunity to leverage Iran’s geographical position for freight transit. The main challenge facing Iran railways is prioritizing commodity groups. Given the dynamic conditions of rail transport in Iran, the use of dynamic MCDM models is inevitable. This study aims to develop a model that prioritizes alternatives based on the desirability they create over a finite future horizon. In all previous studies on dynamic multi-criteria decision-making, the behavior of alternatives with respect to criteria has been extracted periodically. The challenging subject for the implementation of these models is the correct choice of period length in which the information is extracted. Otherwise, this may be associated with the loss of information between periods. Therefore, we decided to develop models, which consider the behavioral changes of alternatives with respect to criteria, continuously. The models contain nine commodity groups as alternatives and "tonnage", "ton-kilometer" and "average revenue per ton-kilometer" as criteria. The findings derived from implementing the model reveal that minerals are poised to attain the highest rank in future. Furthermore, the subsequent ranks are anticipated to be occupied by Petroleum Products and industrial materials. https://scientiairanica.sharif.edu/article_23444.html As one or more poles are located on the right side of the s-plane, the unstable processes are challenging to control. The presence of dead time in such systems makes control much more difficult. This work focuses on the control of unstable processes with dead-time using sliding mode control in parallel control structure. Two controllers for set-point tracking and load-disturbance rejection are designed with PIDA based sliding surface. The parameters of continuous and discontinuous control law are obtained using particle swarm optimization technique. An objective function is constituted in terms of performance measure (integral absolute error). The proposed sliding mode controller design in parallel structure gives enhanced set-point tracking and load disturbance rejection. Illustrative examples demonstrate the superiority of the proposed controller over earlier reported work in this realm, especially in terms of load rejection. Furthermore, robustness of the proposed controller is also investigated by inclusion of perturbations in the parameters. The obtained results clearly show how well the suggested controller works. https://scientiairanica.sharif.edu/article_23447.html Landslides are one of the most dangerous natural phenomena. The occurrence of this phenomenon at low speeds and high rates causes financial and human losses without warning signs. Therefore, it is essential to study the geological and anthropogenic factors affecting the occurrence of this phenomenon and determine the potential landslide zones. This study aims to use a supervised convolutional artificial neural network to model landslide potential. For this, evidence maps of seven effective factors in landslide occurrence, including slope, slope direction, geology, precipitation, distance from the fault, height, and density of waterway, were prepared. Then the values in the maps were assigned by continuous fuzzy weights through a logistic function, without data classification to feed the convolution artificial neural network algorithm. For training the network and testing the results, 70% and 30% of training sites, in Oshvand basin, Hamedan province, Iran were used to generate landslide potential model. A prediction-area plot was used to evaluate and quantify the effectiveness of the models produced. The results showed that 70% of the landslides occurred in 30% of the area. https://scientiairanica.sharif.edu/article_23448.html This article proposes a hybrid unsupervised learning method as a combination of a novel two-level artificial neural network (TLANN) algorithm for data normalization regarding the removal of environmental variations and k-means clustering (KMC) for damage detection. In the proposed TLANN algorithm, feature samples are fed into the first neural network to generate its output and determine a residual matrix from the difference between the network input and output. In such a case, a new residual matrix from the difference between the input and output of the second network is extracted as the main feature for damage detection via the KMC, classical Silhouette value technique is applied to determine the number of clusters. The contribution of this article is to develop an innovative hybrid unsupervised learning method. The great advantages of this method consist in dealing with the negative effects of environmental variability and increasing the detectability of damage. The performance and reliability of the proposed method are validated by the well-known Z24 bridge along with several comparisons. Results show that the proposed hybrid method is highly capable of detecting damage and removing strong environmental variations. It is also observed that this method is superior to some classical and existing techniques. https://scientiairanica.sharif.edu/article_23450.html The numerical solution of the flow of non-Newtonian fluid induced by symmetrically stretching fractal sheet in the region of oblique stagnation point flow under the inducement of externally applied uniform magnetic field orthogonal to the flow is presented. The analysis is made under the assumption of a boundary layer which arrives at the system of partial differential equations which afterward is transformed into ordinary differential equations by using appropriate similarity transformations. The numerical solution of the modeled system of equation is obtained by parallel shooting technique and then presented for different variations of involved parameters. It is noted that enhancement in the magnetic field results in a decrease in horizontal velocity and the boundary layer becomes thinner. The behavior of streamlines shows that the symmetry of the flow is highly dependent on the obliqueness of the stagnation point flow. It is seen that when the ratio of a/c>1, the flow has a normal boundary layer structure but when a/c<1 then the structure shows inverted behavior. It is also seen that there exists no boundary layer when a/c=1. The obtained results are also compared with the available results in the literature and found in excellent agreement in the limiting cases https://scientiairanica.sharif.edu/article_23467.html This contribution introduces and investigates a small network of type-I, type-II, and type-III neurons. The considered network is made up of one Hindmarsh-Rose neuron (type-I), one FitzHugh-Nagumo neuron (type-II), and one Wilson neuron (type-III), all connected via a gap junction. The investigation of the stability in the presence of an external current revealed that the network is equilibrium-free; therefore, the network exhibits hidden collective behavior. The dynamical behavior of the model has been evaluated using the two-dimensional Largest Lyapunov Exponent (2D LLE), and it has been discovered that the network exhibits either regular or irregular firing patterns as the synaptic weights vary. It is also found that the network is able to exhibit the coexistence of firing activities involving coherent and incoherent spiking or coherent and incoherent bursting. Finally, the microcontroller integration of the set of considered neurons is presented, and the findings support those of the numerical simulations. https://scientiairanica.sharif.edu/article_23468.html Cord blood is the blood that obtains after the birth of a baby. Cord blood is rich in stem cells, which are used to treat a variety of diseases, including cancers and immune disorders. These treatments' effectiveness depends on the quantity of total nucleated cells (TNCs) in cord blood units (CBUs). Both public and private cord blood banks store these CBUs. Public banks rely on government funding for the cost of testing, storing, and maintaining CBUs. In addition, the quantity of TNCs in each CBU remains uncertain until the TNC test is conducted. This study aims to utilize ensemble learning algorithms to aid public banks in identifying and collecting potentially valuable CBUs prior to TNC testing in order to save the cost of TNC testing on CBUs that are not valuable. This study has three main contributions: Firstly, it demonstrates that the XGBoost and LightGBM algorithms can identify CBUs with TNC of more than 0.7×10^9,1×10^9, and 1.5×10^9; Secondly, the study combines the smote_NC method with Xgboost and LightGBM algorithms and evaluates each algorithm in identifying high TNC samples. Lastly, this article considers the effect of the phlebotomist experience on identifying high TNC samples, a variable overlooked in other studies. https://scientiairanica.sharif.edu/article_23471.html Nanofluids are strong candidates as heat carriers due to their excellent thermophysical properties. Among these thermophysical properties, viscosity is critical in heat transfer and pressure loss calculations. In this study, three different water-based nanofluids, Al2O3, TiO2, and ZnO, were prepared with volumetric concentrations ranging from 0.1% to 1%. The dynamic viscosities of these nanofluids were experimentally measured within a temperature range of 20 °C to 50 °C. Artificial neural networks (ANN) were employed to predict the results based on the experimental data. Two different approaches were applied in the implementation of the ANN method. The first approach involved creating three separate ANN models, each dedicated to predicting the viscosities of the three different nanofluids. The second approach used a single generalized ANN to predict the viscosities of all nanofluids. The results were evaluated using the criteria of R-squared (R2) and root mean square error (RMSE) values. In all models, R2 values exceeded 99%, while the RMSE values were calculated for the Al2O3, TiO2, and ZnO nanofluid ANN models and the generalized ANN model to be 0.40%, 0.30%, 0.04%, and 0.28% respectively. These results demonstrate that a nanofluid's viscosity can be effectively predicted individually and multiple nanofluids using an ANN model. https://scientiairanica.sharif.edu/article_23477.html A numerical study has been investigated for magnetohydrodynamics (MHD) pumping of viscoelastic nanofluid by means of heat transfer in a complex ciliated channel. The Jeffrey model is followed as a non-Newtonian fluid (blood) in current investigations because of its dual characteristics: one is viscosity effects and the second is elastic in nature. The fluid motion is parallel to the direction of metachronal waves. The metachronal waves are mobilized by the cilia transport. The magnetic force reflection with horizontal angle in inclined direction is implemented. The system identifying via distinct equations is expressed in wave frame which is further normalized the flow system by using scaling quantities. In the next step, the normalized form of rheological equations will be reduced by using lubrication approximations. The ND computational tool is implemented for simulation process. The fluid transportation is controlled by wave number, eccentric parameter of cilia, ciliated length, inclined magnetic force and non-Newtonian parameter. https://scientiairanica.sharif.edu/article_23480.html In this study, the investigation of various positions of receiver and transmitter coils in a wireless power transfer (WPT) system was conducted using magnetic resonance-based coupling theory. The aim was to determine the air gap limits for achieving high efficiency in analyzing different coil positions. Self-inductance, mutual inductance, and coupling coefficient were calculated for this purpose. The efficiency of the system was determined for different coil positions by calculating input and output powers. Additionally, an optimized wireless power transfer system was achieved using the developed Wound Healing Algorithm (WHA). The MATLAB Simulink was employed to obtain mutual inductance and coupling coefficient values based on the distance between the coils in the wireless power system. The efficiency of the system was then calculated. The results were compared with those obtained from ANSYS Maxwell for interpretation. The findings indicated efficient power transfer up to specific distances in various coil positions. The points where efficiency started to decrease provided insights into determining the air gap and angular limits of the designed WPT system. https://scientiairanica.sharif.edu/article_23486.html In measurement-based approaches, generators coherency can be determined on the basis of excited modes, which are obtained in the form of a vector of complex values. In order to find coherent generators in frequency domain, which is suitable for applications such as wide-area control of power systems, these complex vectors should be clustered on the basis of their similarities. To do that, in this paper a new density-based unsupervised learning approach is proposed for clustering complex vectors, which is crucial for finding coherent generators in online applications. The proposed coherency evaluation approach is very simple and more practical according to the reality of power systems since (i) eliminating the complexities in previous studies, it evaluates the coherency from the excited modes point of view using the complex correlation of frequency spectrums, (ii) it uses a new density-based learning approach with only one parameter setting making it more suitable for clustering generators. These features have been demonstrated on the well-known 16-machine, 68-bus system. https://scientiairanica.sharif.edu/article_23487.html In this work, the Cell Formation Problem (CFP) within manufacturing systems is evaluated, seeking to optimize production processes. Accordingly, the appropriateness of the existing evaluation measures for use in dynamic manufacturing scenarios is investigated with the view of enhancing their accuracy and efficacy. The obtained findings indicate the need to reevaluate the commonly adopted evaluation measures for CFP, potentially replacing them with data-driven and context-specific approaches. A quantitative methodology is successfully used to defines parameters that quantify the quality of evaluation measures, rendering such evaluation more robust and adaptable to specific contexts. While grouping efficacy is a commonly accepted measure in this research field, it was shown to exhibit drawbacks that do not justify its widespread popularity. In response to the identified research gaps, a refined objective function is proposed for the core CFP problem. This novel function is designed to enhance solution efficiency and accuracy, ultimately contributing to improved manufacturing processes. The aforementioned findings present a significant advancement in the understanding and application of evaluation measures in the CFP domain, offering a foundation for further research and potential enhancements in manufacturing optimization practices. https://scientiairanica.sharif.edu/article_23488.html In this research paper, a new chaotic jerk system is proposed, which is constructed using cubic and hyperbolic sine nonlinearities. A detailed dynamical analysis of the chaotic jerk system is presented with the bifurcation diagrams and Lyapunov exponent spectrums. The novelties of the proposed system are that it can exhibit bistability for two different initial conditions, amplitude control, and offset boosting control. We also carry out a detailed analysis of the amplitude control and offset boosting control for the proposed jerk system. Furthermore, a random number generator (RNG) is designed using the proposed chaotic jerk system. The study was developed in the Python-based Google Colaboratory environment. The obtained random numbers have successfully passed the NIST 800-22, FIPS140-1, and ENT statistical tests, and it has been shown that they can be used successfully in encryption areas. Biomedical image encryption application was carried out using the generated random numbers. Finally, the reliability of the encryption process has been proven by performing histogram, correlation, NPCR-UACI, and entropy analyses, key space analysis, key sensıtıvıty analysis, and robustness analyses. https://scientiairanica.sharif.edu/article_23491.html This paper addresses the multi-commodity maximum flow network interdiction problem (MC-MFNIP) which involves two opposite sides with conflicting objectives in the presence of uncertain arc capacities. In this problem, one party, follower tries to maximize the multi-commodity flow throughout the network, while the other party, leader, attempts to minimize the total flow quantity that the follower achieves using the limited interdiction resources. To tackle this challenge, this study presents an exact fuzzy-based optimization model that, for the first time, considers the prioritization of multiple-source and multiple-sink nodes for commodities while addressing the uncertainties inherent in arc capacities. The model also considers the feasibility degree that specify the level of risk the decision-maker is willing to accept for the MC-MFNIP, in which arc capacities are defined using triangular fuzzy numbers. The computational analyses are performed through a set of cases regarding the various sized networks and α-cut levels to test the model’s performance and track changes in flow quantities. It is worth noting that the model is efficient for devising fortification strategies against interdictions at an operational level. This is because the model quickly provides optimal information about the most critical arcs within seconds in all generated networks, ensuring tractability. https://scientiairanica.sharif.edu/article_23494.html Geofoam is widely used in civil engineering projects due to its low unit weight, insensitivity to moisture variations and high erosion resistance. In present study, the effect of expanded polystyrene (EPS) particulates on the shear strength and volumetric characteristics of sand has been investigated using direct shear test. Sand has been mixed with 0.1, 0.2 and 0.3% EPS as dry weight of soil and compacted to relative densities (Rd) of 60, 65, 70, 75 and 80% in a shear box 60×60×26 mm and subjected to normal pressures of 100, 200 and 300 kPa. Results showed that by the addition of geofoam particulates to sand, shear strength characteristics such as cohesion, angle of internal friction and dilation as well as the stiffness of the mixtures decreased resulting in overall reduction of the shear strength. Increasing the relative density of sand-geofoam particulate mixtures, reversed the changes in the preceding characteristics. Shear strength and stiffness of samples improved with increase in normal pressure whereas dilation angles decreased. Cohesions displayed by samples are apparent and attributed to the penetration of sand particles into geofoam particulates resulting in particle confinement and thus reduction of dilation. https://scientiairanica.sharif.edu/article_23495.html The aim of this research is to analyze the magneohydrodynamic heat transmission in anannular space partially porous between two coaxial cylinders with a permeable interface saturated by a hybrid nanofluid (water-Cu/Al2O3) and study the entropy generation to betterunderstand the heat transfer processes. The inner and outer cylinders are kept at a constanthot and cold temperature. The base walls are designed to be impermeable and insulated. Afinite difference-based vorticity-stream function is used to solve the nonlinear coupled conservation equations using Successive Over Relaxation approach. The obtained numerical outcomesin terms of streamlines, isotherms, Nusselt and Bejan numbers, and entropy generation are presented to demonstrate the effect of various control parameters. The findings of this numericalsimulation show that the increase in the Ra number improves the thermal energy transmissionacross the active wall. Further, a rise in nanoparticle concentration causes a rise in thermal conductivity, which contributes to enhancing the heat transfer rate. In addition, the mean entropygeneration elements rise with increasing Rayleigh number, Darcy number, and nanoparticleconcentration; however, with the exception of magnetic irreversibility, the reverse developmentis detected. Furthermore, the Bejan number is reduced in order to increase the Rayleigh andDarcy numbers. https://scientiairanica.sharif.edu/article_23496.html \textcolor{Red}{The Heisenberg ferromagnetic spin chain (HFSC) equation has substantial relevance in the fields of optics and electronics, particularly in the advancement of high-density electronic components and faster storage devices}. This integrable nonlinear Schrödinger equation characterizes the propagation of nonlinear waves in ferromagnetic spin chain systems. In a recent research paper, two powerful analytical methods, the $(\frac{\mathfrak{G}^{\prime}}{\mathfrak{K} \mathfrak{G}^{\prime}+\mathfrak{G}+\mathfrak{r}})$-expansion method and the extended hyperbolic function method (EHFM), were implemented to solve the (2+1)-dimensional HFSC equation. Most of the results obtained from the study are presented graphically, which can aid in the visualization and interpretation of the gained results. These findings will be useful in the development and optimization of spintronic devices and other electronic components that rely on the behavior of spin systems. \textcolor{Red}{The analytical solutions of the HFSC equation yield important insights that enhance our understanding and facilitate the application of magnetism, thermal properties, and topological phenomena across diverse fields such as materials science, condensed matter physics, and quantum technologies. These findings play a crucial role in advancing our knowledge and practical utilization of these phenomena in real-world applications.}\\ https://scientiairanica.sharif.edu/article_23497.html We investigated Plavix's continuous-flow synthesis through both homogeneous and heterogeneous catalysis utilizing a microreactor. The CuBr2 homogeneous catalyst was used in the former, whereas a Cu-based MOF (i.e., VNU-18 metal-organic framework) heterogeneous catalyst was first synthesized, then characterized, and ultimately utilized in the latter. Each catalytic system's performance was examined concerning factors including feed flowrate, reaction temperature, catalyst loading, residence time, and solvent. Plavix was produced with an optimum yield of 58.2% at 50 °C in 40 min when working under the continuous-flow homogeneous catalysis utilizing DMSO as solvent. Meanwhile, the performances of the batch- and microreactors were examined in the instance of heterogeneous catalysis. According to the findings, the reaction ceased utilizing the microreactor device after 25 min, yielding up to 42.7% product at room temperature using DMF as solvent. However, the product yield of 50.7% was attained in a batch system after 12 h. A comparison between the performance of the flow reactors with that of the batch system reveals that the flow systems are more promising to be the future trend of processing at the industrial scale for the Plavix production than that of the batch in terms of the comparable product yields and lowered reaction time. https://scientiairanica.sharif.edu/article_23501.html Nutritional profiles and food safety are being concerned with consumers in recent years because they are directly involved with human health. Researches into favorable nutritional profiles and heavy metal contamination of rice are a foundation for future food security. In this study, we studied and compared morphologies, chemical compositions, bioactive compounds, and heavy metal contamination of Paka-umpuel local rice (PLR) variety from Surin province, Thailand, which nine PLR samples were divided into two groups based on different cultivation processes: four samples for organic rice (OR) and five samples for non-organic rice (NR). The results showed that the two rice groups differed significantly in morphological parameters, except the L/W of the OR and NR kernels. The PLR exhibited the average contents of ash (1.34%), moisture (10.62%), protein (9.01%), fat (3.36%), and carbohydrate (75.37%). Besides, the OR group showed the highest total phenolic contents and antioxidant activities (344.06 mg GAE/100 g DW and 158.09 mg VCE/100 g DW, respectively). Interestingly, none of the PLR samples were contaminated with toxic heavy metals. This study provides a better understanding of rice cultivars that should be selected for consumers, and serves as an interesting breeding site in Thailand for supporting the one health approach. https://scientiairanica.sharif.edu/article_23503.html A high-quality health care system requires substantial financial resources. The question of how to efficiently use the health care system’s financial and medical resources has attracted the attention of researchers. The purpose of this study is to develop a network data envelopment analysis (DEA). Previous studies used the radial measure to assess efficiency in the network DEA model, but the radial measure might not satisfy the principles of unit invariance, translation invariance, and monotonicity. The developed model applied the non-radial measure to evaluate performance and suggested several modifications to the assessment of health care system efficiency. First, we redefine the relationships among financial resources, medical resources, medical care outcomes, and national health as a value-added process. Second, we assume disease prevention to be an individual division in the health care system. Third, we build an optimal degree measure for medical resources to investigate resource wastage and shortages. Fourth, we internalize variable transformation and external factors into a single DEA model. The empirical evaluation applies sample data from 21 regions to examine the proposed model, which results in several practical implications for Taiwan’s health care system. https://scientiairanica.sharif.edu/article_23505.html In nonlinear 4DOFs overhead crane systems, the trolley can move both longitudinally and transversely. To achieve precise control over the trolley's movement and minimize load fluctuations, suitable controllers are essential. This research focuses on developing and implementing two types of controllers: one based on pole-placement theory and another using a model predictive controller (MPC). Designing a pole-placement controller involves linearizing the system, and since the controllability reveals the system to be underactuated, two additional inputs are introduced. These additional inputs help determine alternate poles with desired conditions, and then the controller is designed by eliminating the extra inputs. For the MPC controller, various parameters, such as sample time and prediction horizon, are selected based on design criteria, making the controller functionally applicable. The pole-placement controller achieves rapid system response, exceeding expected input limits. Conversely, the system modeled with the MPC controller exhibits a slower tracking process with a consistent 1º vibration in the suspended load, but it stays within input limits. Furthermore, we compare the response of each controller to uncertainties. As a result, the pole-placement and MPC controllers are found to create a robust system for specific regions and a system with variable vibration frequencies and low amplitudes, respectively. https://scientiairanica.sharif.edu/article_23506.html Satisfying Design Limit Load for scarfed laminate and Design Ultimate Load for repaired laminate are required for certifying adhesively bonded repair. These regulations in association with contradictory influence of scarf angle on DLL and DUL makes the certification of a scarf repair a sophisticated procedure. Current study is dedicated to obtain ultimate strength of quasi-isotropic pristine laminates and their scarf joints with the aim of investigating the effect of scarf repair performance of a laminate on its design considerations using FEM. The results showed scarf joint strength is substantially affected by the way plies shuffle in quasi-isotropic laminates. Following the conventional design guideline to stack plies of composite laminate cannot favorably affect the strength of scarf joint of that laminate. Considering the scarf repair efficiency as one of the principles to design a laminate provides the opportunity for satisfying DUL and DLL, enhance the probability of approval of a scarf repair. https://scientiairanica.sharif.edu/article_23514.html In many real world applications, the performance of entities with undesirable outputs should be assessed while imprecise and vague information is presented. In this study, a fully fuzzy data envelopment analysis (FFDEA) approach is propounded to analyze the relative efficiency of decision making units (DMUs) in which the acceptance degree of decision maker that fuzzy constraints may be breached is incorporated. In order to achieve this purpose, the interval expectation of trapezoidal fuzzy numbers and the order relationship of trapezoidal fuzzy numbers are employed. Also, after converting the FFDEA model into an interval DEA model and then a bi-objective DEA model, parametric examination is applied to aggregate objectives. Therefore, input- and output-oriented FFDEA approaches with undesirable outputs are provided to estimate the relative efficiency of processes whilst the acceptance degree of the violated fuzzy constraints is considered. Datasets from the existing studies are used to clarify the introduced technique and to describe its applicability. https://scientiairanica.sharif.edu/article_23519.html Extensive exploration of ZnO luminous material for biomedical applications has been conducted in recent years due to its biocompatibility and optical characteristics that can be customized to meet specific needs. The ZnO was modified with carbon dots to produce a ZnO@COD composite with enhanced photoluminescence and to obtain ZnO luminescence to be used as a bioimaging material. The hydrothermal process was adopted to produce ZnO nanoparticles. The modification of ZnO with carbon quantum dots was performed via a simple method of stirring and sonication. The effects of variations in the amount of carbon dots on the optical properties of ZnO@CQD nanocomposites were investigated. The optical properties of ZnO@CQD were characterized using UV-Vis and photoluminescence spectroscopy. The UV-Vis spectra revealed a decrease in the gap energy of ZnO@CQD. Additionally, the photoluminescence spectroscopy showed a significant increase in photoluminescence intensity with the addition of 10 ml of carbon dots. There was also a redshift of the photoluminescence band to the long-wavelength region. The optical properties of the ZnO@CQD nanocomposites discovered in this study demonstrate their potential use as bioimaging materials in biomedical applications. https://scientiairanica.sharif.edu/article_23521.html Induction motors in electrical industries face stress and potential faults. Preventive maintenance, including fault detection, is vital for safety and energy conservation. Infrared imaging, though underutilized, can monitor machine conditions effectively. In response to this gap, this paper presents a novel motor fault identification method employing infrared thermography (IRT) in combination with image processing and machine learning techniques, with a particular focus on energy efficiency. IRT is harnessed for early fault detection to promote energy conservation. The approach involves the extraction of color and texture features from the motor's infrared images using the Gabor filter and GNS (global neighborhood structure) map. The proposed method integrates the faster R-CNN (Region-based Convolutional Neural Network) with the Speeded Up Robust Features (SURF) algorithm to enhance fault detection and classification accuracy. SURF serves as a feature descriptor for faster R-CNN, enabling object detection and fault classification based on the extracted features. Additionally, efficiency is assessed using the Finite Element Method (FEM) based on stator and rotor power, contributing to energy conservation through early fault detection in motors. Notably, the proposed motor fault classification is applicable under various loading conditions, consistently achieving accuracy rates exceeding 90%. https://scientiairanica.sharif.edu/article_23524.html The Strengthening asphalt and bitumen to increase their service life has received much attention in recent years. In this paper, the effect of nanoclay (NC) on the physical, thermal, and rheological properties of bitumen/waste polyethylene terephthalate (PET) composites is investigated. X-ray diffraction (XRD) and transmission electron microscopy (TEM) analyses showed the intercalated and partially exfoliated structure of NC layers. Fourier transform infrared (FTIR) spectra was conducted to evaluate the interactions between NC and bitumen. Morphological observation was performed with a field emission scanning electron microscope (FE-SEM). Thermogravimetric analysis (TGA) is used to measure thermal properties. The frequency sweep tests were done by using a dynamic shear rheometer to obtain storage modulus, phase angle, and rutting factor. Penetration, softening point and ductility tests performed to investigate the physical behavior of the samples. The morphological observations and XRD analyses indicated good dispersion of NC in the PET-modified bitumen matrix. Rheological analyses showed an increase in complex modulus of composites with adding of NC. TGA results showed that the presence of NC improved the thermal resistance of nanocomposites. The results showed that the addition of nanoclay and PET to bitumen binder can improve the performance of road pavements and help preserve the environment. https://scientiairanica.sharif.edu/article_23531.html The Commutation strategy of matrix converters for high-frequency unity power factor applications has been a challenge for years. The strategy should be simple, have a short duration, and possibly avoid current sensing. This paper proposes a new strategy for the bidirectional switch commutation of the three-phase to two-phase matrix converters supplying inductive power transfer (IPT) systems. This strategy's main merits are simplicity, the reduction of commutation time, and no need for detecting the load current sign, which is crucial for synthesizing the output voltage accurately. Pulse density modulation is used because of the high frequency output voltage and possibility of utilizing three phases in any particular output cycle leading sinusoidal shape of grid current. Presented simulation and experimental results verify the converter's appropriate function using the proposed strategy and a reduced switching state machine. https://scientiairanica.sharif.edu/article_23535.html Power system state estimation is vulnerable to stealthy false data injection attack (FDIA) that bypasses conventional bad data detectors. In this paper, an improved FDIA detection approach has been proposed using a phasor measurement unit (PMU) assisted linear power system state estimation scheme. The proposed detection approach tracks the changes of complex PMU measurements between the current time instant of the present-day and one step previous time instant of the previous day. This variation of complex PMU measurement is then compared with the variation of forecasted measurements. Manhattan distance has been applied to calculate the distance between the distribution of two different measurement variations. In the event of an FDIA, the Manhattan distance will increase significantly from normal conditions. The proposed approach has been validated on two IEEE benchmark test systems. The produced results clearly depict the efficacy of the proposed approach. https://scientiairanica.sharif.edu/article_23544.html The Endurance Time (ET) method is employed as a dynamic time history technique to analyze structures under artificially intensified acceleration time histories, known as Endurance Time Excitation Functions (ETEFs). Prior studies have shown that discrete wavelet transform (DWT) is an effective approach for generating ETEFs by representing signals as transform coefficients determined through optimization procedures. However, the impact of the chosen mother wavelet function on simulated ETEF accuracy remains unexplored. This study introduces a methodology to investigate the influence of mother wavelet functions on simulated ETEFs. Specifically, 31 mother wavelet function candidates from four families (Daubechies, Coiflet, Symlet, and Bio-Orthogonal) are examined. Results reveal that the choice of the mother wavelet function can lead to approximately 15% variation in simulated ETEFs' accuracy. The Daubechies wavelet family stands out as the preferred choice, exhibiting a diminished impact compared to alternative families. Remarkably, this wavelet family is associated with an importance factor of 5.5%, significantly lower than the 13% observed for the other families. Within the Daubechies family, db12 demonstrates optimal efficiency in generating linear response-based ETEFs. The research highlights the superiority of the Daubechies wavelet family, offering valuable insights to enhance ETEF simulation accuracy and reliability for effective ET method implementation. https://scientiairanica.sharif.edu/article_23545.html Today, renewable energy generation infrastructures are increasingly developed due to reduced fossil fuel resources and increased energy consumption. A biogas supply chain has a high potential to generate energy. This paper aims to design a bi-objective biogas supply chain network for power and fertilizer generation. A mixed-integer linear programming (MILP) model was developed for the multi-level biogas supply chain with biomass input under different parameter uncertainties. To cope with the intrinsic uncertainties of such value chains, a stochastic-robust programming approach was adopted. Realistic uncertainty modeling allowed for adjusting the conservatism level for a trade-off between performance and robustness. The adopted stochastic-robust programming pathway not only diminished the optimality fluctuations and provided a reasonable allocation space for uncertainties but also enhanced network flexibility and alleviated decision-making risks. Finally, the model was solved using the Benders decomposition(BD) algorithm. Drawing on previously generated solutions and Pareto optimal cuts, Benders cuts were enhanced, leading to more efficient and effective solutions. The implemented algorithm converged to the optimal solution at a reasonable rate. https://scientiairanica.sharif.edu/article_23546.html Delay-based optoelectronic oscillators (OEOs) use the large propagation delays of long optical fibers to produce ultra-low phase noise (PN) radio frequency (RF) oscillations. Most of the OEO-PN analysis approaches in the literature predict a singular value for the output variable’s PN-induced power spectral density (PSD) at zero offset frequency from the carrier. The current paper aims to resolve this issue. First the perturbation theory of classical oscillators is generalized to extract the stochastic delay differential equation (SDDE) governing the PN of OEOs. Then the well-known small delay approximation approach is used to derive a stochastic ordinary differential equation (SODE) governing the PN. Using the previously published solutions to this SODE, the phase noise PSD of the OEO is extracted in presence of both white and colored noise sources. As the small delay approximation is only valid for small frequency offsets, the obtained PSDs are only accurate at these offsets. This approach avoids the non-physically large values encountered in classical approaches. For larger frequency offsets one can use the classical noise analysis techniques in the literature. The validity of this approach is verified by comparing its results with those in the literature and direct Monte-Carlo simulations. https://scientiairanica.sharif.edu/article_23548.html This study addresses the formidable issue of corrosion faced by shipping companies, particularly in the Mediterranean Sea. Following the Spanish Legislation Royal Decree 1837/2000, a thorough visual inspection of various ship tanks was conducted, employing an inspection code for surface condition and repair prioritization. The predicament confronting shipping companies revolves around the detrimental impact of corrosion on vessels, leading to economic costs and safety concerns. Tanks such as the fore-peak, sanitary tank, center tank (1A), and double bottom tanks were scrutinized. The fore-peak exhibited significant corrosion (30%), necessitating an urgent epoxy coating. The sanitary tank, initially estimated at 6-25% corrosion, was revised to approximately 10%, requiring a high-pressure wash and epoxy coating. The center tank (1A) displayed localized corrosion (15%), emphasizing the need for prioritized repair with epoxy coating. Double bottom tanks 1 PT and 1 SD manifested corrosion (5%) and blisters (35%), necessitating repairs involving high-pressure washing and epoxy coating. Other tanks, such as freshwater tanks, demonstrated varying degrees of corrosion and required extensive repairs. The findings underscore the importance of customized maintenance strategies based on environmental factors. This study provides valuable insights for shipping companies navigating corrosive marine environments, underscoring the significance of timely detection and targeted repairs. https://scientiairanica.sharif.edu/article_23550.html Swimming microrobots with various applications in targeted drug delivery, diagnostics, and minimally invasive surgery, have attracted a lot of interest in recent years. They are usually steered by an external energy source such as a magnetic field. In this paper, we have proposed a novel low Reynolds number swimmer driven by a rotary magnetic field and discussed its design parameters. The microrobot consists of a central sphere and two arms, which create linear movement with its rotation. It is shown that the microrobot speed depends on its dimensions and the magnetic field angular velocity. Based on the of 140 micrometers per second. In addition, we have shown simultaneous control of two microrobots based on their different reactions to the same input. https://scientiairanica.sharif.edu/article_23552.html This paper presents the energy recovery efficiency of a Brushless Direct Current (BLDC) motor drive. The primary application of this system is to power an energy-efficient e-rickshaw during regenerative operation. Notably, this work eliminates the need for an additional converter to manage reverse power flow, a crucial step towards realizing a sustainable green transport model. A novel two-boost control method is implemented to three phase Voltage Source Inverter (VSI) for bidirectional energy flow connected between battery and the BLDC motor. This approach not only facilitates the transfer of energy from the BLDC motor to the battery but also serves as an effective means of generating braking force. By comparing the proposed two boost method with the conventional single boost method, it demonstrates that the energy regenerated is significantly higher in this novel approach. To validate the findings, a real time testing on OPAL-RT LAB experimental set up has been used, which validate the results obtained through MATLAB/SIMULINK simulations. This paper contributes to the advancement of energy-efficient transportation solutions, aligning with the growing emphasis on sustainable and environmentally friendly modes of travel. https://scientiairanica.sharif.edu/article_23553.html In this work, a unified approach for controller design has been presented which can be used in different control structures. To validate the proposal, output voltage regulation of the boost converter has been considered as control problem. The control structure considered are single feedback control structure, parallel control structure, two-degree of freedom internal model control structure and cascade control structure. In these control structures, the transfer function model obtained through simple closed-loop test has been used to derive the controller parameter. This modelling makes the control scheme independent of boost converter circuit parameter information as well as loads. The controller parameters in all these structures have been obtained through direct synthesis design in frequency domain. Experimental results for nominal and perturbed values are illustrated under changing set-point command and load-disturbance (variation in input voltage and duty cycle). Using small gain theorem, robustness of all the structures is evaluated where cascade control structure is found to have stability of 60% change in gain uncertainty. Performance of cascade structure is observed to be better in comparison with other structures. The control performance of the proposed work has also been compared with the recently reported works. https://scientiairanica.sharif.edu/article_23554.html Low-salinity waterflooding is an important method in enhanced oil recovery. Previous studies have extensively investigated the effects of salinity on asphaltene behavior at the water-oil contact surface. However, the influence of rocks has often been overlooked in these studies. This study investigates the impact of rock type (calcite/quartz) on these phenomena in the presence of various brines. UV-Vis spectroscopy was used to assess the asphaltene separation from fresh or aged bulk oil with brine. also, we utilized the recently developed “indirect method” by our group. Microscopic analysis of water droplet size was performed to evaluate emulsion stability. The results demonstrate that the UV-Vis absorbance for fresh oil is approximately 13.6 and that decreases to 11, 10.5, and 10.5 for the 2DSW/oil, calcite/SW/oil, and quartz/2DSW/oil emulsions, respectively. Additionally, the results show that for rock/FW/oil emulsion, compared to quartz, calcite presence increases asphaltene precipitation/deposition by about 38 wt%. Furthermore, the findings reveal that, across all salinities, the average size of water droplets is larger when calcite is present than quartz, suggesting greater instability in the calcite. These outcomes align with the results of IFT measurement, FTIR spectral analysis of oil, and zeta-potential determination for suspended calcite/quartz particles in different brine samples. https://scientiairanica.sharif.edu/article_23555.html This study investigates the magnetohydrodynamic flow and heat transfer between two parallel disks, considering suction and injection effects at the disks. The governing equations describing the flow and thermal transport are derived based on the principles of mass, momentum and energy conservation for an electrically conducting fluid. As the governing partial differential equations are highly nonlinear, similarity transformations are applied to transform them into coupled ordinary differential equations. Numerical techniques, namely the Hermite wavelet method and Differential transformation technique, are then employed to solve the transformed equations. Parametric effects of several influential parameters such as the Prandtl number, squeeze number, Hartmann number, suction/blowing parameter and thermophoresis parameter on the velocity and temperature profiles are systematically analyzed. Comparisons are made with previous findings in the literature. The results indicate significant dependence of flow behavior and heat transfer on the governing parameters. Velocity and temperature distributions in the boundary layer are presented and discussed in detail. The proposed mathematical model and numerical approach provide useful insights into heat transfer characteristics for parallel disk systems and similar engineering applications involving magnetohydrodynamic flows with suction or injection effects. https://scientiairanica.sharif.edu/article_23559.html The decline phase of a high-tech product's life cycle often causes variations in production schedules due to over or under production. To address this challenge, we propose a production-inventory model that considers decreasing demand and prices during pandemics. Our model aims to optimize replenishment, dispatching, and backordering policies, ultimately maximizing total profit for high-tech industries. Our proposed solution procedure derives optimal policies, taking into account the unique demands during the pandemics. We suggest adopting either a last-in-first-out (LIFO) or first-in-first-out (FIFO) backordering policy depending on whether profit or fairness is the primary concern. We have provided a numerical example and conducted a sensitivity analysis to demonstrate the practical application of the proposed model. By optimizing the replenishment, our model enables high-tech industries to maximize total profit, even in the face of declining demand and prices during the pandemics. Overall, our model represents a valuable tool for high-tech industries seeking to better manage production and inventory, and we believe it has the potential to increase product profitability during the declining phase of a product's life cycle. https://scientiairanica.sharif.edu/article_23560.html The effect of unsymmetrical thermal loading on the behavior of 2×2 pile groups is studied using 1g physical modeling. Three tests were conducted with 1, 2 and 3 energy piles in each pile group to apply the cyclic unsymmetrical thermal load. Model piles were closed-end aluminum pipes, and the model ground was fine-grained dry silty sand, placed in the container with dry tamping technique. 10 successive heating-cooling cycles with amplitude of ±6°C were applied to the energy piles. Displacements and rotations of the cap, axial forces and bending moments along the piles, changes in soil pressure under the pile tip and temperature distribution around the group are monitored and discussed in detail. A new parameter, named as “pile tip behavior index” (Ipt) is introduced to determine the elastic/plastic state of the soil under the pile tip during each test. Results suggest that build-up of plastic zones in the soil under the energy pile during first stages of the unsymmetrical thermal cycling along with redistribution of the mechanical surcharge among different piles of each group may contribute to cause unallowable rotations of the pile cap. https://scientiairanica.sharif.edu/article_23561.html In this paper, the traditional elasticity problem in which a homogenous isotropic half-space covered by an extensible thin membrane on the surface is subjected to an arbitrary load is treated. The strongly thin membrane with negligible flexural stiffness is attached perfectly to half-space such that bonding between surrounding media and continuity in elastic fields are strictly maintained. A newfound idea is developed to deal with thin film effects, the manner by which the thickness of film tends to zero but simultaneously its shear modulus tends to infinity and as a result, the value of those multiplications remains constant. Based on this idea, equivalent boundary conditions instead of coated thin film are proposed. By utilizing Hankel integral transform and Fourier expansion, Muki's potential functions are obtained in the transformed domain. Closed-form expressions in Hankel transformed domain are derived with general asymmetry. Derivation of thin film equation in the case of axial symmetry is presented along with limiting states of well-known Kelvin’s, Cerruti’s, and Mindlin’s problems, which are examined as specific cases. In addition, a numerical study has been carried out to present the proposed equations' results and explain the method’s efficacy. https://scientiairanica.sharif.edu/article_23572.html This study purposes to identify the key Design Requirements (DRs) for effective blockchain adoption in the service Supply Chain (SC). It focuses on the challenges as Customer Requirements (CRs), and the best practices as DRs, to overcome the challenges. first, the challenges and solutions were identified by review the literature. Then, the experts were asked to confirm those one that could be related to service SCs. The hybrid approach of Fuzzy Analytical Network Processing (FANP) and Quality Function Deployment (QFD) is applied to prioritize each challenge and solutions based on their relations. It shows that value chain cooperation is the most important requirements; and to achieve it, providing processes' details by all stockholders, short-value chain, and collaboration with value chain participants in a non-competitive initiative could be effective. The findings suggest the priorities of the all solutions vs. each challenge. The research has attempted to identify the non-technical challenges and their related solutions. So, the presented list of challenges might not contain all the relevant challenges. Future researches could focus on technical challenges, too. this study prepares an insight for service sector managers to identify and improve the key design requirements that their SC members have to meet. https://scientiairanica.sharif.edu/article_23577.html High-ground stress soft rock tunnels have experienced large deformations. Yielding and pressure relief fluid-filled lining support technology is an effective method for solving large soft rock tunnel deformation due to the deformation characteristics of high-ground stress soft rock tunnels. To give the surrounding rock a certain amount of deformation space, a layer of fluid filling is set up at the reserved deformation of the tunnel. By creating a force test model of the inflatable carcass and water-filled carcass, the straight section of the tunnel support structure is simulated. When supporting large deformation soft rock tunnels, inflatable and water-filled carcasses produce fluid homogenization load shedding and fluid drainage load shedding effects. Inflatable carcasses and water-filled carcasses without leakage reduce load by about 28% and 7%, respectively. The peak load reduction rate under liquid leakage can gradually reach 100 %. Inflatable carcasses can dissipate up to 90% of the work of external forces acting on the supporting structure. The load reduction rate of water-filled carcass is 1.2 times that of inflatable carcass. Fluid-filled materials can remove load from secondary lining support structures after yielding and pressure relief. Design and construction of high-ground stress soft rock tunnels can be influenced by research results. https://scientiairanica.sharif.edu/article_23579.html While selective attention is known to modulate speech encoding in cortical responses, it is unclear whether such modulation also occurs in brainstem responses. The present study investigates the impact of selective attention on brainstem responses to consonant-vowel stimuli. The study examined auditory brainstem responses to dichotically delivered /ba/ and /da/ in 15 normal hearing subjects (8 males). Subjects were asked to selectively attend to a stimulus, and their responses were evaluated using the short-time Fourier transform (STFT) and phase difference comparison. Our findings reveal significant changes in the mean phase values for brainstem responses during selective attention. The mean phase values in brainstem responses to da were consistently positive, whereas those in brainstem responses to ba were consistently negative compared to responses without attention. In the steady-state region, the mean difference of the brainstem responses varied in the high frequency (-0.022±0.008, 0.021±0.007) and middle frequency (-0.026±0.008, 0.024±0.007) ranges. Furthermore, the high frequency of the transitional part of the response changed (-0.024±0.01, 0.033±0.009) when attention was directed to /ba/ and /da/, respectively. Our findings suggest that selective attention can significantly alter brainstem responses during auditory processing, resulting in significant phase changes in both the middle and high-frequency ranges. https://scientiairanica.sharif.edu/article_23580.html Due to the special design of the segmental translator linear switched reluctance motor (STLSRM), it has a higher power density than conventional linear switched reluctance motor (LSRM). Having enough information about the parameters affecting the motor output can help to improve its performance. In this paper, a step-by-step and integrated method is presented for the standard design of STLSRM. After obtaining the design equations, the important and effective parameters on the STLSRM performance are determined and discussed. Using sensitivity analysis, design considerations and comprehensive instructions are provided to determine the STLSRM dimensions. In order to confirm the design process, a typical STLSRM is designed for the use of electric sliding doors, and with the goal of increasing the average instantaneous thrust force and reducing the force ripple, based on the design of experiments (DOE) approach and the response surface method (RSM), the optimization of STLSRM is done. In order to validate, using the finite element method (FEM), the performance characteristics of the optimized STLSRM including flux-linkage, co-energy, static force, instantaneous thrust force waveform and instantaneous current waveform are compared with those of derived from the initial design. https://scientiairanica.sharif.edu/article_23583.html The nature of routing and scheduling problems for providing required services to needed patients called home health care problems would include a remarkable level of uncertainty. These uncertainties may be due to the traffic congestion, the accessibility levels of staff members, and the service times of staff members to the patients. This paper presents a robust formulation aimed at the daily/weekly/monthly routing and scheduling of staff members under uncertainty for home health care services, which simultaneously optimize the cost factors and the service quality measures. Different requirements and preferences of patients, diverse vehicles, different skills for staff members, temporal inter-dependencies between services, continuity of care, and blood sampling requirements are considered to construct the Robust Optimization (RO) model. The robust solutions obtained through the mixed-integer linear programming model are compared to those obtained through the deterministic and Stochastic Optimization (SO) model using some randomly small- and medium-size generated test instances to evaluate the performance of the robust model. Finally, we present some efficient managerial insights to substantiate the importance of considering uncertainty in the optimization models ending up with proper routing and scheduling policies. https://scientiairanica.sharif.edu/article_23586.html The integration of renewable energy generation in grid results in reduced carbon emissions in the atmosphere. Despite technical advancements, critical issues oriented to protection strategies for renewable integration in microgrid structures still remain unresolved. Foremost aspects that need to be concentrated while securing the grid includes rapid fault interruption, auto reclosing prohibition and stress on the switching devices. This paper improves the fault isolation speed and voltage stress reduction by redesigning the inductance and capacitance value of Z-source DC circuit breaker. This strategical breaker is applied and tested for various types of faults in a 7-bus microgrid system, where its efficacy in interrupting the fault in existing with proposed proves less voltage stress and fast fault interruption. In addition, the proposed LC tuned breaker is tested with adaptive algorithm for auto tripping and fast reclosing in smart microgrid environment. The experimentation of the suggested work is carried out in Opal RT control hardware in loop (CHIL) testing. https://scientiairanica.sharif.edu/article_23588.html In this study, a generalized constitutive model, in which the yield and potential plastic surfaces do not need to be explicitly defined, is presented in the framework of the multilaminate method. In this framework, the constitutive relations are defined as the relationship between volumetric and deviatoric stresses and strains on several planes in different directions, called microplane. A new volumetric- deviatoric stress space is defined on the microplane, which has a volumetric component similar to the definition of mean stress. However, the deviatoric component is defined as the resultant of a normal deviatoric component and two shear components on the microplane. The use of the new innovative stress space facilitates the application of volumetric- deviatoric based constitutive models in the multilaminate framework. The results of the model for several types of clay and sand in drained and undrained conditions have been validated with experimental data and results presented by similar models and it was observed that the proposed model, in addition to the simplicity of the formulation, can correctly predict the behavior of sand and clay materials in different conditions. https://scientiairanica.sharif.edu/article_23591.html Fatigue crack growth and damage of adhesive layer in a composite-patched aluminum plate with three dimensional inclined crack was simulated using Extended Finite Element Method (XFEM) and Cohesive Zone Model (CZM). A Python script was developed to model fatigue crack growth using XFEM in ABAQUS environment. Three adhesive materials and five patch lay-up sequences were considered to investigate the size and shape of damaged (debonded) region in different configurations. The effect of including damage in adhesive layer on global response of the structure and 3D crack geometry in metallic structure was studied. The interaction between crack growth in bulk material and damage in adhesive layer was discussed. It was concluded that neglecting damage in adhesive layer results in 8.4 to 23.2 percent overestimated fatigue life for different sample configuration. Smoother crack geometry was obtained from damage-including models with respect to models that do not include damage. Crack front shape was also highly affected, despite the fact that the effect on crack trajectory was not significant. It was also observed that in specimens with lower final strength and high ductility adhesive, structural response is resulted from an interaction between damage in adhesive and crack growth in bulk material. https://scientiairanica.sharif.edu/article_23592.html During the friction stir welding (FSW) process, the influence of the tool tilt angle (TA) over mechanical characteristics and microstructure of wrought magnesium alloy was examined. FSW was performed using fixtures and a clamping system specially developed for the present work, by varying the TA from 0o-3o with an interval of 1o whilst the tool rotational and traverse speed were stable at 1400rpm and 112mm/min, distinctively. Microstructural characterization, hardness measurement, and tensile testing were executed to evaluate the FSW joint characteristics. When compared to other joints, an experimental analysis showed that the joint produced at 0o TA has better mechanical and metallurgical properties. The incorporated parameters 1400tool rpm, 112mm/min traversing speed produced defect-free welds and improved joint strength with the utmost tensile strength of 187MPa and micro-hardness of 75HV. Tensile strength decreased with increasing tool tilt angle, with the lowest joint strength of 161 MPa recorded at the maximum TA 3o. The coarse grains of the original alloy ~35 m altered to refined grains at the weld stirred zone, with an average grain size of 11 μm, 14 μm, 16 μm, and 18 μm, corresponding to 0o, 1o, 2o, and 3o TAs, respectively. https://scientiairanica.sharif.edu/article_23593.html Fault prediction and diagnosis play an essential role in safe and reliable operation of wind turbines (WT). An efficient fault prognosis method will help in the earlier identification of WT faults and failures, thereby reducing WT maintenance costs and improving their operating time. WT’s are often controlled by Supervisory Control and Data Acquisition (SCADA), which, apart from controlling, provides very rich data pertaining to working parameters of WT's. SCADA data, along with suitable algorithms, could be used for fault prediction and diagnosis of WT's. Most of WT prognosis systems make use of Support Vector Machines in conjunction with SCADA data to predetermine the faults that might occur in the turbines in near future. In these models, proper selection of SVM parameters is essential for precise fault classification. In this work, an optimised methodology using Artificial Bee Colony Optimisation (ABC) is proposed to find an optimal penalty factor and kernel function parameter that guarantees better classification accuracy for the SVM model. Based on the real time SCADA data availed from a wind farm, it is observed that the proposed ABC-SVM fault diagnosis model has a quick convergence rate and good accuracy compared to the other GA-SVM, PSO-SVM, and ACO-SVM based models. https://scientiairanica.sharif.edu/article_23597.html Nowadays, the participation of wind power plants in electricity markets has become a severe challenge due to their intermittent nature for decision makers of market. In the presence of uncertainties, some sellers and buyers experience a reduction in their satisfaction. This paper presents a new method for the participation of wind power plants and uncertain customers in a day-ahead electricity market based on the local marginal pricing mechanism to maximize the total profits of sellers and buyers considering their importance level through a two-level optimization problem. For this purpose, using the empirical cumulative distribution function and the Monte Carlo method, the uncertainties are modeled. Then, by defining some economic indices to evaluate participants' satisfaction and using the analytic hierarchy process, a new objective function is proposed to optimize the mentioned indices. Simulations are implemented on a realistic 8-bus sample system, and the results confirm the efficiency of the proposed method in significantly reducing the costs of producers and customers, and consequently their total profits. Based on the results obtained from the presented method, the expected ranges for total cost fall between 1,270.91$ and 1,719.50$, while the expected ranges for total payment range from 2,151.41$ to 2,192.58$. https://scientiairanica.sharif.edu/article_23599.html A novel cantilever piezoelectric energy harvester with a modified structure of tilting beam has been designed to harness energy from water waves. New suggestions are presented for analysis of the interaction of the nonlinear waves with the beam surface using a hybrid theoretical/experimental approach. The experimental tests are conducted to investigate the effect of different design parameters such as the longitudinal distance of the cantilever beam from the wave-maker, the shape of the oar-like tip of the beam, the angle of spatial orientation, the strength of the waves, the material of piezo-harvester, and the depth of the beam below the free-surface on the output voltage. Numerical simulations are conducted based on the beam deformation captured by a high-speed camera. It is found that adding the torsional moment led to the generation of about 13\% and 50\% higher rms and peak-to-peak voltages in comparison to the pure-bending case, respectively. It is seen that by increasing the indentation from 3 cm to 6 cm, about an 18\% increase can be captured in produced voltage. These results can be used to train a model for a control system to keep the optimum angle between the water waves and the beam. https://scientiairanica.sharif.edu/article_23601.html In this article, a novel iterative method is introduced to dynamic analysis of multi-degree-of-freedom systems in which all characteristics of the system could be simultaneously changed with respect to time. The proposed scheme is based on the differentiation of the original equation of motion, on the contrary to the conventional approaches which are usually based on the integration of the motion equation. The numerical investigation of the present method was carried out by analyzing the three systems with one, two and three degrees of freedom. Moreover, the obtained results were comprehensively verified by two distinct approaches: a system with invariant characteristics, and a system with varying mass and stiffness, with closed-form solution. It should be noted that some closed-form solutions of multi-degree-of-freedom (MDOF) time-varying systems were derived and presented for the first time in this article. https://scientiairanica.sharif.edu/article_23602.html Post-earthquake building safety assessment must rapidly evaluate structural integrity before imminent aftershocks to guide safe reoccupation decisions. While studies have quantified residual collapse capacity following seismic damage, computational demands of conventional Incremental Dynamic Analysis (IDA) methods pose challenges for urgent real-world evaluations. This research proposes an efficient framework using the Endurance Time (ET) method to estimate rapidly the residual collapse capacities needed for post-earthquake building safety assessment. The ET method leverages an incrementally scaled acceleration function in a single Nonlinear Response History Analysis (NRHA) to simulate demands across intensity levels up to complete collapse. We develop a procedure adapting ET for mainshock-aftershock damage assessment, introducing Representative Damage States (RDSs) as an alternative to IDA’s iterative analyses at fixed damage states. This paper demonstrates the framework’s steps on a steel moment frame benchmark modeled with OpenSees and subjected to earthquake scenarios emulating the 2015 Nepal event. The findings show the ET method can efficiently generate the complete residual collapse capacity diagram for a mainshock-damaged building, covering all potential damage states with minimal nonlinear analyses. This computational efficiency is advantageous for overcoming barriers in emergency assessments, enabling rapid evaluation of building collapse safety after significant seismic events. https://scientiairanica.sharif.edu/article_23611.html This study investigates the impact of activation energy on the flow behaviour of a Prandtl nanofluid that includes gyrotactic microorganisms over a stretching cylinder. This study's uniqueness is in examining Prandtl nanofluid using a non-Fourier heat and mass flow model incorporating thermal radiation. The fluid flow phenomena are defined by nonlinear differential equations incorporating two or more independent variables. The governing equations can be managed using an appropriate numerical technique such as bvp4c with the MATLAB solver. Based on the current investigation, the velocity profile reduces as the magnetic field values increase, while it increases concerning the curvature parameter for $\alpha =0$ and $\alpha ={\pi }/{2}$. The temperature $\theta \left( \eta \right)$increases as radiation values increase but decreases when the thermal relaxation parameter rises. Increased concentration, relaxation, and activation energy values lead to a higher local Sherwood number. The proposed model presents significant advantages with the potential to revolutionize a wide range of applications, including biodiesel production, hydrogen fuel, oil storage techniques, geothermal energy manufacturing, base liquid mechanics, oil emulsification processes, food production, sewage systems, and serving as a substantial source of renewable energy. https://scientiairanica.sharif.edu/article_23614.html In this paper the time-dependent sustainable vehicle routing problem is introduced as one of the less studied areas of VRP. Different challenges in city logistics including traffic congestion, path flexibility, and heterogeneous vehicles are also considered. The purpose of the problem is designing the service routes and determining optimal departure time from depot in a way that on the one hand, the amount of fuel consumed is minimized, and on the other hand, the workload of different drivers is balanced for more job satisfaction. Fuel consumption is a function of travelled distance, speed, load, and vehicle characteristics. The problem is formulated as a bi-objective MILP model and solved using augmented epsilon constraint method. To solve large-sized instances, two meta-heuristic algorithms based on genetic and fireworks algorithms are developed. In order to increase the efficiency of these algorithms, a clustering based heuristic method is proposed to generate good initial solutions. Numerical tests represent a better performance of the fireworks algorithm. Results show that striking workload balance among drivers has negligible effects on increment of fuel consumption. Also, optimization of departure time and stopping at the depot during rush hours has a considerable impact on the total fuel consumed. https://scientiairanica.sharif.edu/article_23615.html Purpose: The purpose of the current research is to design a holistic model for brand value-creation in sports clubs in Iran. The system dynamics approach has been chosen for the analysis of brand value creation due to the presence of various factors and complex interactions and feedback in time.Design: An integrated dynamic model for brand value-creation of volleyball clubs is developed which incorporates brand equity, financial, physical, social, and human capital, competition environment, laws, social responsibility, brand communications, and club stakeholders including the federation, sponsors, shareholders, and fans variables. Determination of the system’s bounds, identification of variables, and evaluation of behavior and their inter-relationships variables were done based on the literature review and experts’ opinions. The proposed dynamic model was validated by using a set of strategies. The outcomes of ranking strategies are presented based on simulations and using hybrid multi-criteria decision-making analysis.Findings: The obtained results indicate that human resources especially star players and active participation in online brand communities have had the most effect on the value-creation of the club’s brand.Originality: This study is one of the first research that investigated the complex relationships between sports club brand value creation variables by using the system dynamics. https://scientiairanica.sharif.edu/article_23616.html Inventory management for growing items is crucial in many industries, including agriculture, aquaculture, and animal husbandry. This paper develops a new mathematical model for the inventory management of growing products in a multi-vendor, multi-livestock, multi-rancher supply chain. The possibility of partial backorder shortages is considered, and both backorder and lost sale shortages are possible. To address environmental concerns, the carbon emissions of the system are limited by a direct cap policy. The main objective is to determine the optimal ordering and shortage quantity for each livestock type for each rancher. We incorporate the Hill coordination strategy into our proposed model to provide a centralized decision-making framework. Given the nonlinearity and dimensionality of the model, we propose metaheuristic algorithms as the solution approach. To this end, genetic algorithms, differential evolution, and particle swarm optimization algorithms are designed and implemented for the problem. The input parameters of all algorithms are tuned using Taguchi's design of experiments. We evaluate the performance of these algorithms by solving several numerical instances in small, medium, and large size categories. The experimental results show that the genetic algorithm outperforms the other metaheuristics regarding the quality of solutions. Finally, some suggestions for extending the current study are discussed https://scientiairanica.sharif.edu/article_23617.html This study addresses the rock-fluid interactions in the carbonate rock acidizing from molecular and macroscopic insights. After treating the aged rock slices, the desorbed hydrocarbon chemistry, wettability alteration, and rock dissolution were studied by using the ATR–FTIR technique, contact angle measurement, and weight loss calculation. To investigate the effect of companying ions on the acidizing, the acid solutions were prepared with various dissolving salts (NaCl, Na2SO4, MgCl2, and CaCl2 ) and HCl concentrations. The acid solutions (concentrations of 0.05% and 0.2%) with a base of distilled water showed a higher rock dissolution respect to the ones with a brine water base. The weakest dissolution was observed in the solution with the formation water base, showing the inhibition effect of formation water on dissolution. The acidizing of the aged rock slice increased the rock hydrophilicity. Based on the contact angles, the Na2SO4 and KCl are prohibiting agents for hydrocarbon desorption. According to ATR-FTIR characteristic indexes, aromatic hydrocarbons have a greater desorption respect to the aliphatic ones. The asphaltenes have a higher potential to be desorbed from the rock surface in the acidizing. It was shown that the NaCl and CaCl2 were more feasible for desorbing the aromatic hydrocarbons. https://scientiairanica.sharif.edu/article_23618.html This work presents a small reconfigurable UWB (Ultra-Wide-Band) antenna with quad-band notches that may be used for multi-notching bands and anti-interference. It consists of a semi-circle engraved stepped-cut monopole antenna with two bevels. The slotted ground is also provided with two bevels to get enhanced bandwidth, from 3.1 to 11.67 GHz (116%). The two bevels also aid in achieving impedance matching, particularly over 9.9 GHz. To get notch characteristics covering the 5G band in 3.40-3.70 GHz (n78) and 4.84-5.70 GHz (n46), the patch is provided with L-shaped and Ladder-shaped stubs of quarter wavelength. A band-notch function is obtained at 3.88-4.32 GHz (n77, C-band), 7.10-7.61 GHz (X-band satellite downlink), and 8.04-8.68 GHz (ITU-8 GHz) by incorporating a half wavelength C-shaped slot, an I-shaped stub on the patch, and two RSRRs close to microstrip feed-line. The suggested antenna has eight PIN diodes that regulate several operating modes, allowing it to accomplish single/double/triple/quadruple band-notch characteristics. The size of the patch is 0.25λ x 0.37λ mm2 and printed on Rogers RT/duroid-5880 substrate. The simulated, measured, and equivalent circuit results match well with each other. It offers a stable gain and acceptable radiation pattern throughout the UWB band except for notch bands. https://scientiairanica.sharif.edu/article_23620.html The pseudopotential method has grown as a powerful tool for multiphase fluid flow simulations within the lattice Boltzmann method framework. We consider that due to its simplicity and computational efficiency, the pseudopotential method could be explored also inside the framework of more traditional Computational Fluid Dynamic methods such as Finite Difference, Finite Volume or Finite Element methods. Following this idea, in this work we start from the macroscopic equations resulting from the pseudopotential lattice Boltzmann method and discretize it by a simple Finite Difference scheme. This pseudopotential based finite difference method is then tested in different benchmark problems such as a planar interface, a smooth droplet oscillation, and a single droplet evaporation. Excellent results were obtained in all tests. One of the advantages of the proposed method is that mesh refinement is straightforward, and converged solutions can be used as a tool of validation for the lattice Boltzmann method. Results indicate that the pseudopotential method is suitable to be used with standard discretization methods such as Finite Difference and that in future works more robust discretizations can be used to further enhance the pseudopotential method application. https://scientiairanica.sharif.edu/article_23621.html With the development of artificial intelligence, automatic garbage classification technology gradually replaces the traditional manual sorting method. Deep neural networks are popular in the field of artificial intelligence, however, it faces the problems of a number of layers, millions of parameters, the heavy computation and storage, which inevitably limits its application for garbage classification in practice. In order to improve the efficiency of waste classification, a garbage image classification model based on double branches binary neural network (DBBNN) is proposed in this paper. In DBBNN, an improved network architecture with an extra compensation module is designed to offset the information loss. Based on hinge loss function, an improved network loss function named HP-loss is proposed. Combined with the exponential decreasing learning rate, the DBBNN model is trained to meet the requirements of garbage classification task. In order to illustrate the performance of the proposed model, comparative experiments on CIFAR-10 and GIGO public datasets have been done for seven different models. Then, DBBNN is applied for automatic garbage classification on our dataset of garbage objects. The experimental results illustrate that the proposed DBBNN exceeds other four compared models in terms of classification accuracy. https://scientiairanica.sharif.edu/article_23622.html This paper introduces a fractional-order meminductor emulator (FOMI) with both grounded and floating configurations, showcasing its applications in a chaotic oscillator and a circuit designed for adaptive learning. It employs one voltage differencing inverted buffered amplifier (VDIBA), a current follower (CF), a fractional-capacitor, and a conventional capacitor. The transient analysis, pinched hysteresis loops, and non-volatile characteristics obtained for the suggested FOMI serve as clear indicators of the circuit's effective operation. The variations in pinched hysteresis loops with changes in the fractional-order (α) and frequency also support the theoretical concepts. The circuit's robust performance has been assessed by analyzing simulation results under varying conditions of temperature and supply voltage. The analysis also involves examining the impact of varying capacitor values on pinched hysteresis loops. Monte Carlo and corner analyses support the robust behaviour of the circuit. The suggested FOMI's potential uses have been demonstrated using the chaotic oscillator and adaptive learning circuits. https://scientiairanica.sharif.edu/article_23625.html Using a consequent pole structure has been common in radial flux machines, and no axial flux machine with a consequent pole structure has been produced so far. This article aims to design an example of an axial flux generator with a simple structure that will reduce magnet consumption, cogging torque, and manufacturing cost without changing the nominal specifications. The proposed generator has a double-sided structure with a sector coil and poles. N identical poles (with only N sequence and iron poles between them) are installed on the rotor and the stator is placed between the rotors. The stator consists of coils that are wrapped concentrically around the teeth. The three-dimensional finite-element numerical methods have been used to evaluate the proposed generator's characteristics. The simulation results of the proposed generator show that the intended goals have been well achieved. https://scientiairanica.sharif.edu/article_23626.html Communication networks can be represented as graphs, where vertices representnetwork nodes and edges represent connections between them. Various graphtheory parameters, such as connectivity, toughness, tenacity, binding number,scattering number, and integrity, were presented to assess the vulnerability ofnetworks. Calculating the values of these vulnerability parameters can be challenging,particularly for certain classes of graphs, such as generalized Petersengraphs, due to their diverse structures. This paper establishes upper and lowerbounds for the tenacity of generalized Petersen graphs. We demonstrate a lowerbound of 1 for the tenacity, T (GPG(n, k)), across all values of n and k. Additionally,we explore the tenacity values of generalized Petersen graphs and presenta general upper bound for the tenacity value in this graph type. By using therelationship between the tenacity parameter and the connectivity (κ) and toughness(t) parameters, we also update some theorems related to the connectivityand toughness of generalized Petersen graphs. https://scientiairanica.sharif.edu/article_23627.html This communication presents the novel triple-band monopole antenna in the shape of a traditional patch antenna, which is made up of a rectangular radiator with a slot and double stub loaded defected-ground structure. The antenna works on the triple bands of 2.4, 3.5, and 5.8 GHz, respectively, and it is made on the standard FR-4 dielectric material. The volume covered by the antenna is relatively small, which is 20 x 20 x 1.6 mm3 or 0.16 × 0.16 x 0.0128 λ30 (λ0 is the wavelength in free space at 2.4 GHz). The data obtained from the measurements prove that the antenna has bandwidths of 100 MHz at 2.4 GHz, 100 MHz at 3.5 GHz, and 300 MHz at 5.8 GHz bands, respectively. It has maximum gain values of 2.91, 0.5, and 5.5 dBi, and the antenna works efficiently on the proposed WLAN and WiMAX frequency bands. It can be posited that the suggested antenna system is a suitable option for WLAN and WiMAX band wireless applications. https://scientiairanica.sharif.edu/article_23634.html In transit systems,automatic fare collection systems(AFCs)are widely used.Passengers are often required to use their smart cards only when entering stops,so their destination is unknown.Most of the methods proposed for addressing this problem, require network-level AFC data.Data regarding AutomatedVehicleLocation(AVL)may also be required.However,the problem remains unresolved when only one line's AFC data is available.The purpose of this paper is to solve this issue for specific applications,such as crowding analysis or crowding-related problems such as calculating perceived travel times.In our proposed method,rather than minimizing errors,the model is constructed so that desirable errors are produced to counter undesirable errors.The task is accomplished by employing an imbalanced binary class classification based on thresholding solutions for each stop.A classification indicates whether a passenger is alighting or has already alighted at the study or previous stops.Although the model may produce incorrect predictions for a particular stop,it will be adjusted to make a deliberate error:for every incorrect prediction of alighting, there will be a few incorrect predictions of not alighting.As a result of this technique,we are able to estimate how many passengers are on board the bus.In fact,the proposed model of this paper has the functionality of an Automatic Passenger Counting(APC)system when the line does not have one. https://scientiairanica.sharif.edu/article_23635.html Dependence on private cars has led to numerous problems, where AVs could be a potential solution. Pooled service, however, could be a much bigger step towards sustainable transportation. This paper presents a modal shift analysis emphasizing socio-economic, travel characteristics and their interaction for a sample of private car users in Tehran. A stated preference survey was designed in 2021 exclusively for the research purposes, and 491 valid questionnaires were gathered. One of the main contributions is considering the impact of the number of persons in shared autonomous vehicle with pooled service (SAVWPS). Estimation results of discrete choice model reveal that, high-income respondents, owning a personal car and being a man decrease the likelihood of modal shift to SAVWPS. A negative impact is also observed for travel time, travel cost, waiting time and number of persons in SAVWPS. A significant systematic heterogeneity is observed in the interaction effect of travel time and dissatisfied respondents with internet taxis due to the pandemic. Considering this taste variation, a lower travel time in SAVs could increase the modal shift likelihood among these travelers. The findings could help transportation decision-makers identify the factors affecting modal shift to SAVWPS to achieve a more sustainable transportation system. https://scientiairanica.sharif.edu/article_23636.html In this study, calendering process of an Oldroyd 4-constant model with the non-linear slip condition is presented. The fundamental laws are used to formulate the flow equations and then are simplified under lubrication approximation theory. We introduced the stream function to eradicate the pressure gradient and then numerically solved the final equations using the "bvp4c method" to determine the stream function and velocity profiles. The pressure gradient, pressure, and mechanical quantities of calendering operations are computed using the Runge-Kutta 4th-order approach. Using a variety of graphs, it is discussed how the slip, Hartmann number, and material parameters of an Oldroyd 4-constant fluid affect the velocity, pressure gradient, and other associated characteristics of calendering. The results reveal that on comparing to the no-slip situation, the pressure distribution inside the calender and the length of contact decreases with increasing slip parameter values. On the other hand, the Hartmann number is responsible to enhance pressure. Furthermore, a reduction is observed in final sheet thickness with increases the values of the slip parameter (Kn). The force, and power are the decreasing function of α1, conversely, these quantities increase with enhancing the values of exit points. https://scientiairanica.sharif.edu/article_23637.html The analysis of surface waves in an initially stressed homogeneous magneto-elastic material with voids source, corrugated and impedance boundary conditions influenced by an applied mechanical force on the surface of the material is the hallmark of this investigation. The framework of the study also encompasses the use of normal mode solution approach, non-dimensionalization of the resulting equations of motion and grooved boundary conditions occasioned by the modeled problem. The distribution of the displacement components, normal and shear stresses, volume fraction fields were analytically and graphically presented using Mathematica Software for a particular chosen material which hitherto demonstrates the effects of the contributing physical quantities on the material. The initial stress, voids source, and Mechanical force have remarkable effects to the behavior of the distribution profiles on the material. Increased influences of the magnetic fields decrease the amplitude of the distribution functions whereas impedance parameter induced a mechanical like resistance to the distributions. Thus, this work should prove useful in understanding studies involving seismology. Also, researchers in the fields of Geophysics, Mathematics of waves, Material Sciences, amongst others should be able to find the work helpful. https://scientiairanica.sharif.edu/article_23651.html Laser cutting is a widely used process in industry for cutting metals, plastics, and other materials. However, it can result in residual stresses and surface roughness, which can affect the quality and performance of the final product. Thus, minimizing residual stress and surface roughness in laser cutting is an important research topic in the field of part manufacturing to enhance reliability and performance of machined parts. A virtual machining system for predicting and minimizing residual stress and surface roughness in laser cutting operations is developed using simulation and optimization techniques. The Stainless Steel Johnson Cook models are used to calculate the cutting temperature throughout laser cutting operations. The residual stress during laser cutting process is then calculated using the finite element approach. To minimize the Residual Stress and Surface Roughness in the operations, the Taguchi optimization technique is utilized to obtain the optimum cutting speed, laser power and beam size during cutting operations. Thus, using the optimized machining parameters, the residual stresses and surface roughness of the sample machined parts are decreased by 23.3% and 25.8% respectively. Therefore, the developed virtual machining procedures can be an effective tool in enhancing reliability and performance of machined parts using laser cutting operations. https://scientiairanica.sharif.edu/article_23652.html In a high-temperature smelting furnace, the bank layer acts as a barrier to protect the brick wall against the highly corrosive liquid slag. The present contribution proposes an inverse heat conduction method as a simultaneous parameter and function estimation approach to precisely predict the time-varying bank thickness from temperature measurements inside the refractory brick wall. The crucial parameters that affect the bank formation include the thermal conductivity of both slag and refractory brick wall, and the heat transfer coefficient between the external wall of the furnace and the surrounding environment. These parameters, as well as the time-varying heat load of the furnace, constitute the unknowns of the inverse solution. The enthalpy method is adopted to simulate the phase change process. A combination of the finite difference approximation and the adjoint problem is employed to compute the gradient vector. The sensitivity and adjoint equations for a furnace with non-constant density Phase Change Material are derived for the first time in the present study. The verification of the proposed hybrid method has been performed via several simulated experiments. The results for the case with errorless measurement showed that the error of the solid front is within the range of approximately ±2%. https://scientiairanica.sharif.edu/article_23655.html Solar power prediction holds a significant impact for future renewable energy scenario. To achieve a more accurate predicted output a novel prediction technique has been included in this paper for short and medium term solar power prediction. Initially the original solar power is decomposed into a set of subseries using VMD based decomposition technique. Data augmentation technique is applied for generating more training data thus avoiding the problem of over fitting. Then a novel prediction model based on LSTM (long short term memory) MKRVFLN (multi kernel based random vector functional link network) is proposed for point prediction of short term and medium term solar power. A fuzzy entropy based strategy is implemented for partitioning the subseries and assigning it to LSTM and MKRVFLN. For further improvement in prediction accuracy WCO technique is used for obtaining optimised kernel parameters. The performance of the proposed technique is compared with seven other prediction techniques. Solar power data from two different data sites are considered for comparison purpose. The experiment is performed on two aspects: short term and medium term point prediction, the result analysis shows that the proposed solar power prediction model shows excellent result as compared to other traditional methods. https://scientiairanica.sharif.edu/article_23656.html Ergonomic risk assessment tools are commonly used to evaluate the risk of musculoskeletal injuries during manual material handling (MMH) activities. This study aimed to compare and evaluate the performance of ten widely-used ergonomic risk assessment tools (REBA, RULA, QEC, NIOSH, WISHA, ManTRA, MAC, Washington state, ACGIH-TLV, and Snook’s tables) in assessing risk of injury to workers during various lifting, lowering, pulling, pushing, carrying, and prolonged static activities. Twenty-one different MMH activities including one- and two-handed, stoop/squat, symmetric/asymmetric tasks with various hand-load horizontal and vertical positions, weights, vibration, and task frequencies were assessed using the foregoing ten ergonomic risk assessment tools. A unique risk level classification was introduced to compare the outcomes of these tools. For a given MMH activity, the estimated levels of risk by different tools were found to be more consistent between the tools for high- and low-demanding tasks, and less consistent and in some cases contradictory for moderately-demanding tasks. RULA, ACGIH TLV, REBA, and QEC were the most and MAC and WISHA were the least conservative tools in their assessments. Comparison of these risk assessment tools revealed their similarities/dissimilarities and strengths/limitations thereby providing users with a comprehensive guideline for proper selection of these tools in practical applications. https://scientiairanica.sharif.edu/article_23657.html This paper presents a new 3D analytical technique for electromagnetic modeling and analysis the slotless axial flux permanent magnet (AFPM) machines. In the proposed technique, Laplace/Poisson equation is solved in different regions while considering the magnetic scalar potential as the main variable. To consider the curvature effect and radial dependency of permanent magnets (PMs), the 3D distribution of PM magnetization is introduced based on Fourier-Bessel series. The boundary conditions are then used to determine the unknown constants of Fourier-Bessel series in air-gap region. The influence of skewed PMs on the flux-linkage of stator phases is studied by using the proposed model under no-load condition while constant keeping the volume of PMs. The proposed 3D model is also extended to calculate the air-gap magnetic field due to stator currents and to extract the inductance matrix. The dynamic performance of studied slotless AFPM motor is then analyzed under voltage-input condition. In final, the accuracy of proposed 3D technique is verified by comparing the corresponding results obtained through proposed analytical model and 3D finite element method (FEM). https://scientiairanica.sharif.edu/article_23664.html The Vehicle-to-Grid (V2G) network enables electric vehicles (EV) to connect and exchange both energy and data with the Smart Grid (SG), thus ensuring bidirectional communication and contributing to environmental protection. However, the V2G network faces various security challenges, including data integrity, the security of electrical systems, physical protection of charging systems, data confidentiality, and system interoperability. Therefore, it is crucial to implement appropriate security mechanisms.This paper proposes a bio-inspired intrusion detection system (IDS) based on machine learning to predict and mitigate attacks on V2G network. The objective of this work is to enhance the security of V2G networks by providing solutions against Man-in-the-Middle (MitM) and Denial of Service (DoS) attacks. Simulations conducted using the MiniV2G simulator show that the proposed IDS achieves a detection accuracy of 98.93%, thereby improving the reliability of the V2G network for users and offering better protection for electric vehicle charging stations against DoS and MitM attacks. https://scientiairanica.sharif.edu/article_23667.html Pavement plays a crucial role in transportation because it is a permanent surface for use in road networks. The health of the pavement ensures the safety and convenience of drivers and passengers. In the past few decades, pavement management systems have encountered challenges which often have produced solutions with excessive demand for resources, but low-accuracy results. New approaches must be developed in order to quickly and economically identify pavement failure, especially cracks. This paper proposes a fast and accurate method for segmentation of all types of cracks in asphalt pavement images based on generative adversarial networks (GANs). The proposed model learns the mapping between two domains of pavement images and images of segmented cracks. This approach does not necessitate any preprocessing or post-processing tasks, and the model generates new images without the need to classify each pixel. It detects cracks with high accuracy using a conditional image-to-image translation. In this study, the model took an average of 0.29 s to identify the cracks in each image. This outstanding crack identification had a precision of 85.76%, a recall of 89.81% and an F1-score of 87.72%. https://scientiairanica.sharif.edu/article_23668.html Non-Newtonian fluids play a crucial role in a wide range of applications involving the exchange of heat and mass. Nanoparticles are one of the key strategies for improving the performance of non-Newtonian fluids in terms of heat and mass transport. Nanoparticles, such as aluminum oxideand titanium dioxide, have exceptional thermal properties due to their high thermal conductivity. To thoroughly understand and optimize the behavior of non-Newtonian nanofluids over a cone surface, we employ numerical techniques. we also investigate the impacts of magnetohydrodynamics, thermal radiation (0.5 ≤ Rd ≤ 1.5), and dual diffusion (0.4 ≤ Nb ≤ 0.8 and 0.3 ≤ Nt ≤ 0.7). Additionally, we examine the impact of a fluid containing microorganisms on mass transmission and heat transfer. In order to convert the interconnected, non-linear governing partial differential equations into non-linear ODE's. Then transform this into a set of first-order ODEs. Subsequently, we utilize the Keller Box finite difference approach to obtain a solution for the non-linear ODE. The results of our study indicate that incorporating thermal radiation and MHD (magnetohydrodynamics) leads to increased rates of heat and mass transfer by enhancing the diffusion of microorganisms. We validate our observations by comparing them to prior research. https://scientiairanica.sharif.edu/article_23669.html The irradiation pattern of high-intensity focused ultrasound (HIFU) has a significant impact on the therapeutic effect of tumor tissue. In this paper, based on the spheroidal beam equation (SBE) nonlinear acoustics equation and the Pennes bio-heat transfer equation, a thermal lesion prediction model of acoustic-thermal coupling is constructed. The effects of different irradiation patterns, such as HIFU irradiation power and pulse number combination, fractionated heating and sustained heating, and changes in the half-opening angle of a concave spherical ultrasonic transducer on thermal lesion in porcine liver tissue were studied. The research results indicated that the difference in thermal lesion area between the “low power × long pulse” and “high power × short pulse” irradiation pattern was relatively small, and their length and width were almost the same. The area of thermal lesion produced by sustained irradiation of porcine liver tissue by HIFU was significantly larger than the area of thermal lesion produced by fractionated heating. The larger the half-opening angle of the transducer, the larger the thermal lesion area generated by irradiating porcine liver tissue. By selecting appropriate irradiation patterns during HIFU treatment, the optimal thermal lesion area can be obtained in biological tissue. https://scientiairanica.sharif.edu/article_23670.html To empower precise manipulation of flexible nanoscale objects, the Hybrid Reluctance Actuators (HRA) prove to be the most effective type of actuator, capable of applying forces with a very high resolution to the dynamic system. In this paper, the circuit model of the HRA is formulated by incorporating equations governing structural uncertainties, extracting nonlinear state-space equations. Stability control laws are considered for position and velocity control modes, ensuring stable and accurate performance. The actuator system imposes constraints leading to saturation limitations on the electromagnetic force. By using a mapping of the actuator constraints to the desired trajectory, the system will track a modified desired trajectory instead of the primary one. Due to the complex direct dynamic relationships of the actuator, explicit relationships for its inverse dynamics cannot be derived. Therefore, an adaptive lookup table is employed to address this issue, updated at each time step, determining the relationship between the control input and the electromagnetic force. System dynamics equations along with the controller are simulated in the MATLAB environment. Simulation results in two different tracking scenarios demonstrate the accuracy of the designed control system with a 2 nm precision, while adhering to the saturation range of input voltage. https://scientiairanica.sharif.edu/article_23672.html In today's competitive business environment, companies need to manage their limited resources. The Multi-Floor Facility Layout Problem (MFLP) is an approach to manage limited space and budgets. The goal of MFLP is to determine the placement of facilities in a multi-floor building without any overlapping with the aim of minimizing costs. In this study, a Multi-floor Multi-row Facility Layout Problem (MFMRFLP) model is proposed. The proposed model presented an MFLP with a multi-row layout on each floor. Besides the layout of the facilities, the model also determines the elevator location based on both horizontal and vertical movements. Since the problem is NP-hard, a genetic algorithm (GA) was also employed to solve the problem. The proposed GA is compared against an exact method to evaluate their performances. The results demonstrate the GA's efficiency in solving the MFMRFLP within a reasonable timeframe, outperforming the exact method, particularly in large-scale instances. Specifically, the GA achieved optimal or near-optimal solutions, showing its superior performance in solving complex, real-world facility layout optimization problems. https://scientiairanica.sharif.edu/article_23673.html The DC Circuit Breaker (DCCB) is the first protective component in the DC network. On the other hand, the converters used in all DC networks are sensitive to the excess current and voltage caused by the fault. The HDCCB based on the current commutation drive circuit (CCDC) is one of the structures that provide a speedy interruption of the fault current as well as reduced losses in the pre-fault circuit while maintaining a simple structure. This structure can only be successfully interrupted at a specific rate of fault current and a specific peak of fault current, while changing the location of the fault causes a change in the peak and rate of increase of the fault current, which disrupts the interruption performance of this HDCCB. This study aims to evaluate and analyze the various structures of DCCB particularly the HDCCB based on CCDC, which is considered superior to other DCCBs. Furthermore, it emphasizes the limitations of interrupting the various peaks of fault currents using HDCCB based on CCDC, with simulated verification by Finite Element (FE). Finally, multiple proposals for further research are made to improve the performance of the CCDC structure as an ideal structure. https://scientiairanica.sharif.edu/article_23674.html Cardiogenic shock, resulting from cardiac dysfunction, poses a dire threat during cardiac emergencies, necessitating prompt inpatient transfers to intensive care units and aggressive interventions for blood pressure management and adjunctive therapies. Hence, developing an optimal non-invasive decision support system for clinicians is paramount for prognostication and efficient patient transfers to specialized care units. This study aims to enhance the medical referral process for cardiogenic shock patients through Machine Learning (ML) algorithms. Analyzing data from 201 heart patients admitted to emergency wards in 2020, the study employs an Artificial Intelligence (AI)-based model with feature selection and decision phases. The feature selection phase entails analyzing 34 parameters related to the patient's health status, while the decision phase determines treatment outcomes using ensemble-based ML algorithms. Results reveal a mean patient age of 69.44 years, with 57.2% being male, and a concerning 47.7% succumbing within 30 days. Notably, the model's decision phase demonstrates an impressive predictive accuracy of 86% in determining treatment efficacy. Thus, the imperative for an optimal non-invasive decision support system for clinicians is emphasized, enabling proactive prognostication and informed patient transfers to specialized care facilities. https://scientiairanica.sharif.edu/article_23675.html One of the common types of failures in asphalt pavements is cracking. Considering the environmental importance of material recycling, in this research, calcium lignosulfonate (CLS) as a waste product of cellulose production, was used as a bitumen additive, and the cracking resistance of semi-circular bending (SCB) samples of asphalt mixtures containing bitumen modified with 5% to 20% CLS was investigated at low and medium temperatures. To measure the performance of asphalt mixtures, the parameters of fracture energy (Gf), fracture toughness (K1c), flexibility index (FI) and cracking resistance index (CRI) were used. Also, rotational viscosity (RV) test was performed to determine the pumpability of bitumens. The results showed that CLS particles can increase the stiffness and viscosity of bitumen in high-temperature conditions. However, it was lower than the maximum allowed amount. At low temperature, with the addition of CLS, K1c and Gf indices were increased, and their highest values were related to bitumen with a 15% additive. At intermediate temperature, Gf index was increased until before the critical load, and FI and CRI were increased up to 15% and then decreased slightly. But Gf index was increased with a decreasing trend after the critical load until the end. https://scientiairanica.sharif.edu/article_23676.html The goal of the current study is to examine how the magnetic dipole effects on nanofluid flow over an extended surface. Based on a constant, non-porous material with a velocity slip condition, this investigation has been reported. The effects of the variable viscosity and thermal conductivity are explored for two different types of nanofluids. The experiment's findings involved dispersing in water and ethylene glycol base solutions. For both types of nanofluids, the fundamental governing equations are converted into nonlinear ordinary differential equations using the suitable transformation and solved using the bvp4c technique. The viscosity and porosity parameters decay the velocity field. Furthermore, the transport of heat is decaying function of viscous dissipation factor, growing function of Prandtl factor. https://scientiairanica.sharif.edu/article_23679.html The study focuses on controlling the angle and position of a high-friction Inverted Pendulum on ‎a moving Cart (IPC) system. An experimental setup with a low-quality, high-friction gearbox is ‎built to make the problem more challenging. The friction force is measured and found to be ‎dependent on the Cart position. A simple position-dependent friction curve is fitted to the ‎experimental measurement and added to the dynamic model of the plant for simulation ‎purposes. Since the IPC dynamic equation is not input-output linearizable, an approximate ‎feedback linearization method is employed, followed by a Sliding Mode Control (SMC) ‎approach. A Direct-Adaptive Fuzzy Sliding Mode Control (AFSMC) approach is then tailored ‎to mimic the feedback linearization part using an adaptive fuzzy engine, reducing the model-‎based part of the control. The uncertainty bound is estimated online and used in the switching ‎part of the controller to reduce control input chatter. Both SMC and the less model-dependent ‎AFSMC are implemented in simulations and practical implementations. While both methods ‎perform well in the nominal case, the superior performance of AFSMC is revealed when ‎intentionally induced uncertainty and noise are applied to the model and to the Cart position ‎sensor, respectively.‎ https://scientiairanica.sharif.edu/article_23680.html To extend the application of FLe in computing the degree of causality, we employ granular fuzzy causality tactics to determine the direction of causality among related variables when there is only imperfect information. Our approach involves a hierarchy of nested inferences of interval Type-2 fuzzy sets to achieve more approximate reasoning for more workable solutions, in the sense of f-valid philosophy. To deal with intrinsic hard uncertainty in the problem architecture, we leverage expert knowledge about the problem structure. Our method involves three key steps: encoding reasons into interval Type-2 fuzzy, utilizing the interaction of concepts in forward reasoning through qualitative descriptions and allowing a certain level of uncertainty, and determining the direction of valid results based on extended fuzzy logic. Our simulation results demonstrate the reliability of our proposed method when compared with traditional paradigms of precise reasoning. Overall, this work highlights the potential of FLe in causality problems and provides a basic framework for handling causality in uncertain domains. https://scientiairanica.sharif.edu/article_23681.html This study aimed to predict antenatal depression in erythroid women during their 3rd trimester. We investigated prediction using four feature selection methods: Extra-tree classifier, Fisher score, and PCA. We also merged the common features of the Extra-tree classifier and Fisher score and applied them to predicting antenatal depression in the 3rd trimester of pregnancy. We gathered data from 62 women and their corresponding 18 attributes and evaluated them using the Hamilton depression rating scale (HAM-D). Seven ML models were implemented to predict antenatal depression, including k-nearest neighbors, Support vector machine, random forest, decision tree, bagging classifier, multi-layer perception, and naïve Bayes. Therefore, the trained models were evaluated using various metrics, including accuracy, sensitivity, specificity, precision, F1 score, FNR, FPR, and area under the receiver operating characteristic curve. Ultimately all models were prioritized using TOPSIS with different feature selection methods, and the best model was found to be DT without implementing any feature selection. The results of this study show the most important factors in predicting depression in the 3rd trimester of pregnancy. https://scientiairanica.sharif.edu/article_23684.html Increasing the train speed in railway networks is the most important factors in improving the fields of attracting more passengers and cargo than other transportation modes. The construction of new railway structures with the aim of increasing the speed of trains requires a lot of money. Therefore, the purpose of this study is to investigate the possibility of increasing the speed train using a low-cost method compared to the construction of new structures, such as changing the conditions of bridge bearings in existing concrete slab deck bridges. First, a 3D finite element model of the bridge and the train was created by considering the interaction of the track and the train, and it was validated based on the results of a valid field test. Then, the effect of increasing the speed of the train on the results of the vertical displacement and vertical acceleration of the bridge deck and the vertical acceleration of the train car body under different bridge bearing conditions is investigated. The obtained results show that by reducing the stiffness of the bridge bearing, the maximum values of vertical displacement and vertical acceleration of the bridge deck increase, but the vertical acceleration of the train carbody decreases. https://scientiairanica.sharif.edu/article_23685.html The developments in ocean energy have prompted researchers to investigate the floating offshore wind turbines (FOWTs). Therefore, the need to stabilize this structure is a crucial aspect in control engineering. The presence of disturbances and noise highlights the importance of implementing an intelligent control approach. This paper focuses on the nonlinear FOWT with an online feedback control system utilizing deep reinforcement learning (DRL) algorithms. The inherent characteristics of DRL allow the FOWT to adapt to changing environments, employing two parallel networks known as online-target. An observer system is integrated with direct gain based on the measured outputs from available sensors, demonstrating global asymptotic stability through a Lyapunov function. Furthermore, an agent trained using DQN in the adapted environment requires minimal instances to determine the optimal control policy. Simulation tests conducted in MATLAB exhibit the superior performance of the proposed observer-controller compared to the LQR approach in terms of FOWT stabilization. Additionally, it is shown that the Luenberger observer doesn’t perform as effectively as the newly developed observer in presence of uncertainty, unknown disturbances. Finally, the outcomes are compared with the gain scheduling PI control method recommended by Jonkman as a well-known benchmark to validate the accuracy of the simulation results. https://scientiairanica.sharif.edu/article_23686.html The multi-unit system forms the foundation for various efficiency assessments in operational machining management, necessitating the evaluation of critical parameters. This research article analyzes a Markovian machine repair problem employing a controllable strategy and imperfect repair. Imperfect repair denotes that the repairer services failed machines, but the service may not be successful. The chosen controllable threshold-based strategy addresses the challenge of admitting failed units when the number in the reparable queue reaches the system's capacity to prevent significant expected waiting times. Repairers cease admitting new failed units until the queue size decreases to a predetermined level. Unadmitted failed units may undergo repair at an external facility, incurring additional costs. The number of failed units in the waiting line and the expected number of service-gained units are pivotal in a well-designed control policy. Utilizing the Laplace transform method, we derive the transient-state distribution of failed units in the system, establish various performance measures, and calculate the reliability function and mean time to the first failure of the system. Extensive numerical experiments and sensitivity analyses are also conducted to provide a comprehensive understanding of the studied system. https://scientiairanica.sharif.edu/article_23687.html The gear and rack jacking system is a crucial load transmission device in the Gear and Rack Drilling Rig (GRDR). Vibration induced by stiffness excitation during meshing between the gear and rack is a significant factor that affects the transmission performance. This paper focuses on the jacking mechanism of the GRDR and proposes a gear position design method based on time-varying meshing stiffness. A time-varying mesh stiffness model is established, considering tooth profile by the slice-iteration method. With the White Shark Optimizer (WSO) global search optimization algorithm, the gear position conditions are explored based on the fluctuation of stiffness. The load-bearing performance and dynamic characteristics of the mechanism are effectively improved. Dynamic analysis is conducted before and after the optimization of the scheme, and the improvements of the gear position optimization to the displacement of the transmission mechanism are verified through the meshing process of the rack and gear. The results show that the proposed optimization design may reduce the fluctuation by 89.64% and the maximum displacement by 9% compared to before. The proposed design method can effectively improve the motion performance, which is significant for optimizing the jacking system of the GRDR. https://scientiairanica.sharif.edu/article_23688.html Semi-supervised clustering, a technique that combines semi-supervised learning and clustering, is widely employed in the field of machine learning. However, clustering itself poses challenges as it is an NP-hard and multi-objective problem. Consequently, meta-heuristic and multi-objective algorithms have shown greater success in addressing this problem. Nonetheless, these algorithms often encounter issues such as being trapped in local optima and requiring manual parameter adjustments. This research paper introduces an algorithm that tackles the problem of semi-supervised clustering by creating convex hulls of the initial labeled data within each cluster. It also incorporates the labeling of data enclosed within these convex hulls and the adaptive adjustment of parameters using a multi-objective cuckoo algorithm. To enhance the results, labeled data is utilized in the initialization and learning phases of the algorithm. The proposed approach is evaluated using 11 UCI datasets and five synthetic datasets in various experiments. The statistical and numerical analysis demonstrates that the proposed method outperforms the other six algorithms used for comparison. The experiments employ four evaluation criteria, namely ARI, Accuracy, NMI, and F-measure. The results show the superiority of the proposed method across the majority of the datasets. https://scientiairanica.sharif.edu/article_23689.html Biomass sources are receiving increasing attention in the field of academic research and manufacturing as a suitable alternative to fossil fuels due to their renewable capability and economic advantage. This study addresses a multi-echelon and multi-product biomass supply chain network considering input material diversity. The first layer of the considered supply chain consists of five supply centers of Jatropha, Norouzak‎‎‎, Oleander‎‎, microalgae, and waste cooking of oil. The second layer dedicated to oil extraction and pre-refining of waste cooking of oil. Biorefineries are considered in the third layer and finally, production centers of the final products including drug, biodiesel, and cosmetics are located in the fourth layer. A mixed-integer bi-objective mathematical programming model is proposed to minimize the total expected cost as well as the environmental impact simultaneously. Besides of solving the problem using data of a case study, sensitivity analysis is conducted to investigate the effect of variations in the capacity of centers as well as demand on two objective functions and the final values of decision variables. The result shows efficiency of the proposed model in solving the problem at hand and providing proper alternatives for managers in different various situations. https://scientiairanica.sharif.edu/article_23691.html Abstract: In this study, energy, exergy and exergoeconomic analysis of a solar-geothermal hybrid Organic Rankine Cycle System with nominal 50 [kW] turbine capacity was conducted at a range of 50-90 [°C] geothermal source temperatures in Konya, Turkey. A new generation R1234yf was assumed as the working fluid of the system. An average of 336 [MWh/year] annual electricity generation was obtained for all source temperatures, 8.1% of which was with solar energy effect. Exergy efficiencies were approximately 6.2% for all geothermal temperatures, while the energy efficiencies increased at a range of 4.3%-10.0% with the increase of geothermal source temperatures. Exergoeconomic analysis was performed with the SPECO method. According the exergoeconomic analysis results, electricity generation costs of the system were determined as 32.2, 38.8, 36.1, 36.5 and 37.8 [USD/MWh] with the increase of geothermal source temperature. https://scientiairanica.sharif.edu/article_23692.html Indoor thermal comfort has met with continuous and rising interest due to its impact on human health and work productivity. Indeed, various factors can influence both airflow characteristics and thermal comfort in indoor environments. This study comprehensively investigates the impact of input velocity on indoor airflow characteristics and thermal comfort. A numerical model was developed, and an experimental setup was implemented, with the numerical results verified through a meticulous comparison with test data—specifically, air velocity and datas obtained from a cabin test occupied by a human body. To ensure the precision of the simulations, turbulence and grid independence analyses were consistently integrated into the numerical model optimization process. Additionally, numerous numerical simulations were conducted to scrutinize the effects of inlet velocity. The analysis reveals that airflow characteristics within the cabin test are predominantly influenced by the input velocity. Furthermore, comparative analysis demonstrates the input velocity direct impact on thermal comfort index. Specifically, the maximum expected PD% value for V=1 m.s-1 increases significantly, by 1.6, 2.2, and 2.63 times, respectively, compared to cases V=0.5, 0.33 and 0.25 m.s-1. In summary, this study illuminates the substantial inlet velocity effects emphasizing critical importance of precise modeling and control for shaping optimal indoor environment. https://scientiairanica.sharif.edu/article_23693.html The Ebola virus is a highly infectious disease that can propagate throughout apopulation depending on how people interact in society. This research presents a modifiedmathematical model of the Ebola Virus Disease, using effective control strategies such asQuarantine, Self-Isolated and Hospitalized Individuals. These Compartments have playeda key role in understanding the transmission of the Ebola Virus Disease in the society. Byusing a conformable derivative, a system of equations has been developed for the EbolaVirus Disease model. Basic reproduction number R0 has been determined using the Nextgenerationmatrix method. To understand the impact of parameter variations on Ebolavirus disease, sensitivity analysis of R0 has been observed. Stability analysis has been calculatedat both the disease-free equilibrium point and the disease-present equilibrium pointto assess the behaviour of virus. The conformable derivative facilitates a smooth transitionfrom fractional order to classical models as the parameter (c) approaches to 1. Additionally,implementation of quarantine, self-isolation, and hospitalization emerges as a highly effectivestrategy, significantly reduced Ebola virus disease in society. These findings enhanceour understanding of Ebola dynamics and offer critical implications for effective outbreakcontrol strategies. https://scientiairanica.sharif.edu/article_23695.html One of the crucial issues in supply chains is the selection of an appropriate transportation mode for logistics operations. Various criteria and factors influence this decision, and it can vary across different supply chains. The objective of this paper is to evaluate transportation modes for the medical equipment supply chain based on sustainability, agility, and resilience criteria under uncertain conditions. Considering the significant impact of uncertainty in different scenarios, the evaluation of transportation modes needs to be conducted accordingly. Therefore, the Stochastic Fuzzy Best Worth Method (SFBWM) is utilized for this purpose and the indicators are assessed. The findings indicate that cost, speed, carrying capacity, flexibility, and national economy are the most important indicators. The main contribution of this study is the presentation of a developed approach using the Fuzzy Inference System (FIS). In this approach, the evaluation indicators are hierarchical and weighted, and the Hierarchy Weighted FIS (HWFIS) method is proposed. Based on the achieved results, agility was selected as the most important one, and sustainability and resiliency have the same rank with the same weights. Also, outputs demonstrate that the airplane is the most favorable option. https://scientiairanica.sharif.edu/article_23696.html The optimal daily operation of multi-carrier energy hub systems is a tremendous challenge for operators due to the mutual impact of different energies, non-constant efficiency and partial loads of several internal equipment, various energy purchasing prices, and different energy demands at the hub output. In addition, the uncertainties of various renewable energy sources, energy purchasing prices, and energy demands may create serious risks for the hub’s operating costs. This paper presents a comprehensive risk-based decision-making framework for multi-carrier energy hub systems to address the mentioned challenges. In the proposed framework, the effects of the responsibility of cooling, thermal and electrical energy demands, and different kinds of energy storage, particularly ice storage, as well as the integration of several renewable energy sources, are investigated. The well-known CVaR method and the 2m+1 Point Estimate Method (PEM), which is a fast uncertainty analysis method based on the Taylor series, are employed to evaluate the risks associated with the system uncertainties. Moreover, to solve the complex non-linear problem of risk-based daily scheduling of an integrated energy hub, a new self-adaptive optimization method based on the Wavelet theory named Self-adaptive Modified Slime Mould Algorithm (SMSMA) is introduced to ensure moving toward the global optimum. https://scientiairanica.sharif.edu/article_23697.html We analyze stagnation point flow of Casson fluid along an unsteady stretch sheet through a porous medium with impacts of inclined magnetic filed, heat source/sink, natural convection, variable heat flux, and velocity slip. The governing partial differential equations (PDEs) representing formulated fluid flow model are converted to non-linear dimensionless ordinary differential equations (ODEs) by applying similarity transformations. The ODEs solutions are evaluated numerically and the results are presented graphically showing influences of the governing parameters on velocity and temperature profiles. Enhancing behavior of the velocity profile is observed by augmenting the Grashof number and velocity ratio parameter while it is declining for magnetic parameter, unsteadiness parameter, porosity parameter, inclined angle, Casson parameter, and velocity slip parameter. The temperature profile increases by raising the magnetic parameter, inclined angle, unsteadiness parameter, porosity parameter, heat source/sink parameter, and velocity slip parameter, while it is reducing for raising values of velocity ratio parameter and Grashof number. We also showed accuracy of present results as compared with previous numerical solutions for skin friction values against unsteadiness parameter. https://scientiairanica.sharif.edu/article_23698.html In this paper, the coastal vulnerability of Lake Issyk-Kul has been defined through the Integrated Coastal Vulnerability Index (ICVI) using the Coastal Vulnerability Index (CVI) and the Socio-Economic Vulnerability Index (SVI). Lake Issyk-Kul is an important object of this kind of research due to the presence of both pristine, little modified by man, and significantly transformed coastlines allows to evaluate the different degrees of vulnerability of coastal ecosystems. The results of the study emphasize the importance of reassessing the vulnerability of coastal ecosystems depending on the specific natural, climatic, and socio-economic conditions of each area using the ICVI index. The Integrated Coastal Vulnerability Index provides an integrated assessment and state of coastal ecosystems and can be used to assess such fragile ecosystems that are affected by the entire watershed. The identification of vulnerability using this index allows for proactive planning adapted by the relevant authorities and management, which can be scaled up to increase the resilience of coastal zones to changing conditions. https://scientiairanica.sharif.edu/article_23700.html Mutation dynamics of the cancer is modelled here analytically considering the chemotherapy injection as its related controlling input. Controlling the metastasis of cancer cells without considering the mutation challenge, results in drug resistance and failure of the treatment. In order to implement the required corrections on the injection dosage of the input, the model of the closed loop system of the cancer is required considering the mutation phenomenon. Thus the analytic model of the cancer mutation for which the chemotherapy can be employed as its corresponding controlling input is extracted in this paper. Considering the fact that the model of a biological system is always an approximate of the real system, robust sliding mode controller is employed. It is shown that by the aid of the proposed model and controlling strategy, not only the cancer cells can be converted to zero, but also its probable mutation risk will be blocked and the treatment process consequently will be accomplished in a stable mode. Verification of the developed model is performed by comparing the results with previous studies and the efficiency of the designed robust controller is evaluated by simulating the system and conducting some comparative simulation scenarios in MATLAB. https://scientiairanica.sharif.edu/article_23701.html This work concerns improving thermal efficiency by investigating the experimental and numerical investigation of thermal performance of different types of balls with high and low thermal conductivity (glass and stainless steel balls) as packed beds in the second channel of the collector. The porous media is attached in the lower channel in order to enhance the heat transfer characteristics of the flow. Value of porosity, solar radiation, mass flow and conductivity are studied. Turbulent flow is implied for continuity, momentum and energy equations of the flow through solar air collectors. Novel CFD application using FORTRAN language was applied, the cells blocked as solid with porosity value. The results show that the thermal efficiency increases with increase in mass flow rate, while the exit temperature decreases for high mass flow rate. The maximum thermal efficiency was achieved with higher thermal conductivity (stainless steel balls) which reached 85.9% at porosity 33.5% while; it reached 76.02% with glass balls at porosity 42.8%. The decrease in porosity increased efficiency. The efficiency with stainless steel balls and glass balls as porous media reached 62 and 45% higher than without porous media. https://scientiairanica.sharif.edu/article_23702.html Designing and developing smart antennas with adaptive radiation characteristics is an integral part for present-day communication systems. The versatile capabilities of Time-modulated fourth-dimensional (4D) antenna arrays can provide that crucial adaptability if properly designed. This work discusses an effective analysis of 4D antenna arrays to achieve less-attenuating radiation patterns with simultaneously suppressed sidelobe and sidebands. The 4D arrays offer an additional benefit over standard arrays in the sense that the requisite amplitude tapering to lower the undesired radiations can be accomplished by controlling only the switch ON times of the radiating elements instead of using attenuators. The idea of splitting pulses by keeping the total switch ON durations constant, is exploited here as an additional degree of freedom for beamforming of all the radiation patterns. The unwanted radiations in terms of sidelobes as well as sideband radiations at the fundamental and harmonic frequencies, respectively are simultaneously minimized to improve the radiation efficiencies of the 4D array. To address the conflicting aims for the synthesis of radiation patterns, a wavelet-mutation based heuristic method is also proposed. The multi-objective problem in hand is modulated in to a single objective cost function as minimization problem. The proposed outcomes are compared with other works. https://scientiairanica.sharif.edu/article_23703.html Recently the work on nanofluids are aimed by ultrafine elements (≤100 nm) that are adjourned in a diversity of conventional liquids, for instance, lubricate, ethylene glycol, remedial liquefied emollient, etc. The straightforward liquids' heat transport possessions improved by consuming nano-particles. Often exploited nano-particles, for instance non-metallic and metallic significantly progress the transporter liquids' thermal features. Furthermore, nanofluids can be developed in security structures, control fusion plants, reheating astral, chilling, etc. The objective of the current effort is to explore the thermal aspects of radiation in mixed convection flow of magneto Prandtl nanofluids in a stagnation point frame. The concept of boundary layer approaches a mathematical formulation is performed. Additionally, the similarity variables approach has been exploited to attain ODEs and then computed by operating a finite element strategy. Furthermore, researchers are observing small nano-particles as they have remarkable possessions, for instance outstanding thermal transfer, which is required in progressive nanotechnology; materials work, heat exchangers, and machineries. The worth of this broad study is to develop the heat changes. The numerous features of the aggregation factors on nanofluid velocity, temperature and concentration fields, as well as the skin friction, local Nusselt and Sherwood number are examined and demonstrated graphically. https://scientiairanica.sharif.edu/article_23704.html Estimating the origin-destination (OD) matrix based on traffic counts in a transportation network has received substantial attention in recent decades. Several studies have attempted to incorporate a stochastic user equilibrium (SUE) constraint into the OD estimation framework to deal with uncertainties. These studies have mainly adopted a multinomial logit (MNL) route choice model which has a restrictive assumption that does not allow for overlapping paths. This paper addresses the path overlapping problem by employing corrected logit route choice models, namely C-logit and path-size logit (PS-logit), that partially capture similarity/correlation among paths by a correction term in the MNL structure. A gradient algorithm (developed by Spiess) is also utilized to solve the SUE-based OD matrix estimation problem. Numerical experiments on the well-known Winnipeg network show that considering correlated/overlapping paths in the OD estimation process using C-logit or PS-logit route choice models results in more accurate OD matrices than the MNL-based procedure. https://scientiairanica.sharif.edu/article_23705.html This paper presents a method for designing single-sided linear synchronous motor (SSLSM). In the proposed method, electrical circuit equations of the linear synchronous motor (LSM) at steady-state are used. In addition, a new method is proposed to model the “end effect” phenomenon by deriving an effective field current. To do this, induced currents in rail-way windings due to DC-excitation and the effect of these currents on induced voltage in rail-way windings are considered. So, in addition to the proposed design method, the main contribution of the manuscript is the modeling of the end effect in LSMs. Due to its simplicity, the proposed design method can be easily used in optimization of LSM that requires iterative algorithms. Therefore, by choosing appropriate design variables, an optimization is done to maximize efficiency and power factor of the motor as well as to minimize mover and rail weights. To validate the optimized design, 3-D Finite Element Method (FEM) is used. The comparison of the results of the FEM and the proposed method confirms the accuracy and effectiveness of the latter. https://scientiairanica.sharif.edu/article_23706.html The Masjed-e-Soleyman dam, situated in southern Iran, is a rock-fill dam with a clay core, reaching a height of 178 meters. During the construction and impounding phases, notable pore water pressure was developed within the core. The dissipation rate of this pressure is considerably slow, persisting long after impounding. Nonetheless, progressive deformations and irregularities have been observed and documented on the surface of the dam's body, with no significant decrease in the rate of deformation. These deformations have raised concerns regarding the safety and stability of the superstructure. This study aims to investigate all factors influencing such deformation behavior by analyzing instrumental data and employing a mechanical-fluid three-dimensional numerical model. A modified softening-hardening constitutive model is utilized to simulate the phenomena of rock-fill particle crushing and saturated collapse within the upstream rock-fill shell materials. Additionally, a viscoelastic creep model is employed to simulate creep deformations. Subsequently, a robust hypothesis concerning the long-term mechanism of dam deformation behavior is formulated. According to this hypothesis, the main contributors to the complex behavior of this dam are the creep deformations of the rock-fill shell and the clay core's deformation under constant volume conditions. https://scientiairanica.sharif.edu/article_23707.html In this paper, we introduce estimators for the population coefficient of variation within a two-occasion, stratified, successive sampling framework, aiming to mitigate the impact of non-response and measurement errors. We derive calibrated weights for the strata and thoroughly examine the properties of the proposed estimator through comprehensive numerical and simulation studies. Furthermore, we provide valuable recommendations for survey statisticians, guiding them on effective applications in real-world survey scenarios. By addressing the challenges of non-response and measurement errors within a stratified sampling approach, our proposed estimators aim to enhance the accuracy and precision of coefficient of variation estimates, ensuring more precise and accurate results. https://scientiairanica.sharif.edu/article_23708.html The pharmaceutical sector's pricing impacts healthcare costs and services significantly. Improper medicine pricing profoundly affects public health and healthcare services. Pricing is a key factor in the profitability of the pharmaceutical supply chain. The optimal supply chain maximizes satisfaction and aligns with economic and social goals. The main challenge for the pharmaceutical supply chain is balancing pricing with drug quality and innovation. Stakeholder satisfaction in pricing negotiations has declined in recent years. Pharmaceutical manufacturers face pressure due to inappropriate pricing, competition, and legal restrictions. Unilateral stress on one component affects the entire chain's performance and industry development.IRIran's pharmaceutical industry has embraced branding to support manufacturers and industry growth. This paper provides a model of Iran's pharmaceutical supply chain, focusing on domestic brand generics. Using game theory, optimal wholesale and retail prices were calculated in a competitive market setting to achieve maximum profit under three scenarios. The optimal price of the brand-generic product is about 300% higher than the price of the generic product. The positive impact of brand-generic supply chain coordination on customer surplus and social welfare is 10% and 45%. Subsidies impact the optimal prices by 3-5% reduction, customer surplus improves 3%, and social welfare significantly increases by 43%. https://scientiairanica.sharif.edu/article_23709.html Individuals who rarely engage in manual lifting and lowering operations in their daily lives (novices) are more likely to be injured by these actions than experts. While there are many studies in the literature on manual material handling in industrial applications, studies evaluating the risks for novice individuals are limited. This study aims to determine the reasons that cause the preference of load-lifting and load-lowering behavior that changes depending on sex-related differences in people who are novices in lifting. In this context, the subjects were asked to perform squatting manual material lifting, stooping manual material lifting, symmetrical manual material lowering, and asymmetric manual material lowering actions. Kinematic calculations were made by the Denavit-Hartenberg method using the 3D human skeleton model. The iterative Newton-Euler method was used to calculate the net reaction moments at the L5/S1 joint, which has the highest risk of injury. As a result, it was observed that females behaved differently from males during the act of manual material lifting but exhibited similar behavior during the act of manual material lowering. https://scientiairanica.sharif.edu/article_23710.html During the design process, it is vital to make sure that an automobile's structure can absorb an impact load and minimize stress levels in order to protect the driver or the body of the vehicle. The car collision, whose body is made from the new Enset fiber-reinforced composite material and the existing glass fiber-reinforced composite material is investigated and compared using an explicit dynamics setup on the ANSYS Workbench. Using a car body composed of Enset fiber-reinforced composite material and the current glass fiber-reinforced composite material, which are modeled on ANSYS Composite Prepost (ACP), the equivalent stress and deformation on impact with a static steel wall and a moving car with speeds of 120 m/s and 200 m/s are investigated. For both materials, the deformation and stress produced as a result of the crash analysis are displayed and elaborated. For 120 m/s of car speed, the maximum deformation and stress that can result from a collision are 94.5 mm and 6.707 GPa for Enset fiber-reinforced composite material and 84 mm and 19.866 GPa for glass-reinforced composite material, respectively. Enset fiber-reinforced composite material has an outstanding energy absorption characteristic; the vibration of the vehicle body built from this material is reduced. https://scientiairanica.sharif.edu/article_23711.html The aim of the current study is to investigate the numerical and theoretical analysis of hybrid nanofluid (NF)-suspended Cu and Al2O3 nanoparticles in water. The basic flow equations contain the influence of thermal radiation, magnetic field, temperature-dependent viscosity, cross-diffusion, and heat source. The basic flow equations described by Navier-Stokes have been altered to self-similar equations via transformations of variables. The transformed system is then solved numerically via the BVP4C approach. For stability exploration, the stability analysis is performed via mathematically and graphically. The impact of emerging factors on flow characteristics is elaborated through graphs. The present numerical results are correlated to the published work, and excellent agreement has been established. It is investigated that the velocity curves show decreasing phenomena due to the augmented values of variable viscosity and magnetic field. Opposite behaviour is reported for the permeability factor, Grashop, and modified Grashop numbers. The fluid energy and concentration are increasing functions of the Dafour number, Eckert number, and Soret number. https://scientiairanica.sharif.edu/article_23713.html Before implementing a mechanical capturing/coupling process between structures, collision occurs inevitably and the motion characteristics of a system may be significantly affected. The short time duration and the large force generated by impact leads to make the motion instability as well as to keep some probable embedded subsystems unprotected. To do this, it is aimed to propose an effective probe-cone buffering mechanism with capacity of impact energy dissipation in this article. To reduce the impact force and to provide the required protection of systems, probe including spherical tip mass is mounted on the active structure by the proposed buffer and cone on the passive one, as well. The kinetic effect of the tip mass is also investigated as an effective parameter on the proposed mechanism. By an example problem, the theoretical impact model is verified by data reported in the literature. Then, a virtual model is built by a commercial multibody dynamics analysis software to verify theoretical results and develop the proposed buffer. It will be shown herein that the proposed mechanism takes some advantages such as providing a remarkable decreasing effect on impact compared to the traditional rigid probe and performing the successful capture process in different coupling scenarios https://scientiairanica.sharif.edu/article_23714.html Abstract—Automatic speech recognition (ASR) plays a vital role in various domains, improving search engines, aiding healthcare with medical reporting and diagnosis, enhancing service delivery, and facilitating effective communication in service providers. This paper introduces FNNRA (Flexible Neural Network with Recursive Architecture), a novel method aimed at addressing overfitting issues in environments with limited training datasets in the field of automatic speech recognition (ASR). FNNRA utilizes a sophisticated architecture to extract and analyze important data features while maintaining data integrity through deep network layers. Theoretical and practical evaluations demonstrate FNNRA's ability to handle speaker variations, effectively train with limited datasets, and extend its applicability beyond speech recognition. The method is evaluated on established datasets like CallHome, TIMIT, and FarsDAT, showcasing its adaptability and efficacy across different data contexts. Comparative analysis with leading speech recognition methods reveals FNNRA's superior performance, achieving significant reductions in phoneme recognition errors by approximately 7.88%. This research sets a strong foundation for future advancements in the field and underscores FNNRA's potential in enhancing recognition systems, warranting further investigation. https://scientiairanica.sharif.edu/article_23715.html Solid state transformers (SST) are one of the newest and fastest growing components in the modern power systems. The extended SST has ac/dc, dc/dc and dc/ac stages for full network maneuvering and control. SST modeling is utilized in the analysis and simulation of applications with ac or dc input/output such as smart grids, dc micro grids, renewable energy applications and electrical transportation systems. Modeling, analyzing, designing, simulating and applying the SST is challenging and complex due to the large number of semiconductor switches. As a solution, the averaged models for any stages were presented but these models have some limitations which are number of modules in stages and dynamic and integrated modeling and those are dissolved in this paper. The proposed models are presented in two different forms which are mathematical differential equations; furthermore, equivalent electrical circuits and these models can be used for transient and steady-state analysis of each SST stages separately and interactively. The closed loop control structure has been built for all SST stages. The averaged differential equations are simulated in SIMULINK/MATLAB software as the simulation results verified the proposed model. https://scientiairanica.sharif.edu/article_23721.html Technology adoption in agriculture has creatively solved and eased many farming problems. Home gardeners grow various plants throughout the year. Many of them show interest in growing roses at their houses. In a survey conducted with home gardeners 70% of them have roses in their garden and 80% of them added that their plants get infected often. Early and accurate diagnosis of the diseases may reduce the likelihood that the plant will suffer further harm and spread. The substantial advancements in deep learning have created the opportunity to improve the coordination and accuracy of the system for identifying plant diseases. An improvised ResNet architecture-RhodaNet is proposed to identify the rose disease at an early stage. RhodaNet architecture uses concatenation in stacked layers preserving both spatial and channel information even where input and output have different channel sizes or feature representations. RoseNet dataset from Mendeley data with 2993 images was considered for the study with 5 diseases Black spot, Downy Mildew Powdery Mildew. Mosaic, Botrytis Blight. Our proposed model gives an accuracy of 96% whereas ResNet and DenseNet gives 91.56% and 93.50% respectively. RhodaAPP predicts the type of disease and its remedy serves as an appropriate solution for home gardeners. https://scientiairanica.sharif.edu/article_23722.html This work aims to investigate an energy-efficient no-wait flexible flow shop problem considering deteriorating and learning effects under uncertainty. To do this, a data-driven decision-making framework is developed in this research. At the outset, a multi-objective mathematical model is proposed for the research problem that minimizes the makespan, total tardiness, and total energy consumption. Then, to tackle uncertainty, a data-driven approach based on the fuzzy robust optimization, Seasonal Autoregressive Integrated Moving Average and Support Vector Regression methods is developed. Afterwards, to solve the proposed model, a hybrid approach based on the LP-Metric method and metaheuristic algorithms is proposed. The achieved outputs confirm the appropriate performance of the developed data-driven approach. Based on the obtained results, the developed hybrid metaheuristic algorithm shows an appropriate performance in both computational time and solution quality metrics. Also, the outputs indicate that the objective functions of the proposed model have increased when the due date parameter increases. Additionally, results show that with the increase in the absolute value of the learning coefficient, the first, second, and third objective functions of the model have decreased. https://scientiairanica.sharif.edu/article_23723.html Integrating both loss and minimum angle method (MAM) as objective functions in the economic-statistical modeling of variable acceptance sampling plans (VASPs) can yield the most cost-effective plan with the ideal operating characteristic (OC) curve. Nevertheless, occurring crises can disrupt organizational input parameters, causing inefficiencies in providing solutions. This study develops the first robust designs of VASPs, accounting for the uncertainty of input parameters. Unlike previous studies that assume fixed inputs, this research considers deviations from nominal values to address parameter uncertainty. In this way, the challenge of parameter uncertainty's impact on the effectiveness of designs is investigated. We propose a solution procedure based on Particle swarm optimization (PSO). Findings from case studies reveal that (1) a marginal cost increase in the Cost-MAM model significantly reduces overall risks, (2) the repetitive group sampling plan yields lower costs and risks, and (3) tolerating increased costs is imperative to manage potential uncertainty. https://scientiairanica.sharif.edu/article_23724.html Highly modified asphalt binders have become the preferred choice for high-grade flexible pavements; however, current research lacks clarity on the mechanisms of short-term oxidation and degradation among different modifiers. In response, this study investigates the short-term aging of various modified asphalt binders, including SBS, crumb rubber, PPA, and gilsonite. The study utilizes a PG 58-22 neat asphalt and eight modified binders, examining their aging behavior at temperatures of 163°C, 178°C, and 193°C. The study utilizes FTIR, oscillation tests, Multiple Stress Creep Recovery (MSCR), and master curve techniques to analyze the binders post-aging. Results show that PPA reduces aging by preventing asphaltenes micelle agglomeration, whereas other modifiers show increased aging and carbonyl formation. Gilsonite-modified binders exhibit the least aging resistance, while CR and SBS display softening effects with stable modulus post-aging. Higher modifier dosages (20% CR, 24% Gilso, 7.5% SBS) reduce aging severity by increasing viscosity, which limits the flow and reduces oxidation and volatilization within the RTFOT. https://scientiairanica.sharif.edu/article_23725.html Zinc leaching residue, a bulk solid waste from non-ferrous industries, is produced in excess of 6 million tons annually in China. Exploring effective treatment and resource utilization technologies for zinc leaching residue has become an important topic in the field of environmental engineering. In this study, zinc leaching residue was first desulfurized to improve its suitability as a cement admixture. Subsequently, the desulfurization products were reduced, and the effects of reduction conditions on the reduction rates of zinc, iron and lead were systematically studied. Finally, the properties of cement prepared using zinc leaching residue reduction slag as cement admixture were analyzed. Research has found that a desulfurization rate of more than 95% can be achieved at 1300 °C and a holding time of 10 min. Under the conditions of 1350 °C, 40 min of holding time and a calcium-to-silicon ratio of 0.8, the reduction rates of iron, zinc and lead all reached over 97%. Using zinc leaching residue reduction slag as cement admixture, with appropriate addition amount, the prepared cement meets industry standards. This article provides insights into the co-production of cement from bulk solid waste in the non-ferrous metal industry. https://scientiairanica.sharif.edu/article_23735.html During earthquakes, a pivotal process, known as 'fault rupture propagation' unfolds, involving the fracture of rock on the fault plane, advancing toward the ground surface. This phenomenon significantly affects nearby infrastructure upon contact with the ground. Shallow foundations, vital structures, that fall within their impact radius and their behavior while interacting with a fault should be studied. This study employs a 2D discrete element model, exploring reverse fault rupture-soil shallow foundation interaction in granular soils of varying densities. The research highlights the foundation's location as the most influential parameter affecting the characteristics of the formed shear band. Regardless of other factors, the shallow foundation consistently diverts fracture paths. As the footing's weight increases, this diversion intensifies. Regarding foundation rotation during faulting, increased weight and reduced distance from the fault's location generally mitigate rotation. Soil density's impact on rotation varies, causing a decrease in some cases and an increase in others. Also, by utilizing a proposed criterion, the safety of the foundation in interaction with reverse fault is evaluated and several tables have been made to predict the safety of the foundation under different conditions. https://scientiairanica.sharif.edu/article_23738.html In this paper, we introduce a novel multi-objective mixed integer non-linear model as an optimization multi-objective of the reliability-redundancy allocation problem (RRAP) in a series-parallel system to maximize system reliability and minimize total cost. Most studies on RRAP assume the components are homogeneous, the reliability of the components is predefined, and redundancy strategies in each subsystem are considered cold-standby or active. Each of the above assumptions serves as a constraint that doesn’t broaden solution regions. In the proposed multi-objective model, the components are heterogeneous. In addition, mixed strategies (cold-standby and active redundancies) can be used in each subsystem. The reliability of subsystem components is uncertain and is considered a decision variable. Since the proposed model is a multi-objective model, a multiple evolutionary algorithm called NSGA-II will be used to solve the proposed multi-objective mixed-integer non-linear (MOMINL) with non-homogeneous and cold standby components by problem, and the performance of the proposed mathematical model will be assessed by a well-known problem-testing method. The optimization result leads to a higher reliability value and a minimum total cost compared to the previous studies, which shows the effectiveness of the proposed model and proves that the proposed method outperforms the previous ones. https://scientiairanica.sharif.edu/article_23742.html Adsorption cooling is a tried-and-true efficient heat conversion technology that can substantially reduce pollution while increasing energy efficiency. The conventional single-bed adsorption system for cooling purposes has a low efficiency because it is prone to intermittent cooling. Moreover, the adsorbent-adsorbate pair of typical adsorption system for cooling contributes significantly to its inefficiency. This study suggests a composite adsorbent that is synthesized by activated carbon as the parent element, with expanded natural graphite powder and metal-organic framework as secondary elements. In the composite, polyvinylpyrrolidone is the binder. The design, development, and performance study of the cooling system operated with activated carbon-ethanol as well as the composite adsorbent-ethanol has been examined. For experimental purposes, a two-bed adsorption system has been designed, operating with solar energy and a chilling capacity of 600 W. The performance of the system using the suggested working pair has been experimentally evaluated. It is found that coefficient of performance of the system is 0.54 at a desorption temperature 88°C. It has been observed that the COP of the system increases by 20.69% compared to the adsorption system that utilizes activated carbon-ethanol. The introduction of the composite adsorbent-adsorbate operating pair could greatly enhance the performance of adsorption cooling systems. https://scientiairanica.sharif.edu/article_23743.html The current work is dedicated to simulating the natural convection of Al2O3-Cu/Water hybrid nanofluid within the tilted quarter-elliptical chamber being affected by a magnetic field at different angles. The ellipse equation is considered for modeling the chamber in such a way that the ratio of height to length takes a constant value of 2. The temperature inside the chamber's curving wall has been kept low or cold, while the small smooth wall of the chamber is presumed adiabatic. The chamber’s larger wall is subjected to three different types of heating. All chamber walls have no velocity. The effect of the Hartmann number (Ha=0, 15, 30, 45), angle of exerted magnetic field (λ=0, 45◦, 90◦), chamber wall heating type (sinusoidal heating, linear heating, and constant temperature heating), chamber inclination angle (Г=-90◦, 0, +90◦), and the Rayleigh number (Ra=103, 104, 105) on the values related to the properties of heat and flow transport have undergone extensive study. The correctness of the results was guaranteed by comparing the findings of prior investigations with the current work. https://scientiairanica.sharif.edu/article_23744.html To ensure the smooth and reliable operation of high performance drives it is essential to diminish the parasitic effects such as pulsating torques produced by the traction motor. Inherent large torque ripple is accordingly the well-known challenge of Synchronous Reluctance Motors (SyRMs) for such a high-performance application. This paper aims to investigate the opportunity of SyRM torque profile smoothening via inserting the Permanent Magnet (PM) materials into the rotor structure. For this purpose, the Conventional PM assisted SyRM (CPMaSyRM) rotor and a recently introduced axially segmented rotor configuration, i.e. the axial integration of a PM rotor and a SyRM rotor, will be studied. It will be shown that although the torque ripple alleviation in the CPMaSyRM design via PMs parameters optimization is too restricted, the axially segmented design is significantly capable of pulsating torque compensation. The PM material utilization, power factor, and efficiency will also be evaluated for two motors. Furthermore, a computationally efficient algorithm will be proposed for the axially segmented rotor torque profile optimization, and the effect of axial field coupling will be discussed. https://scientiairanica.sharif.edu/article_23745.html The efficiency of portrait image selection and analysis systems is completely dependent on the quality of the face image, which depends on various factors. Since real-time manual selection of high-quality portrait photos from a sequence of different frames or images is usually impossible, using automatic methods can be useful in selecting photos, especially in large collections. Existing automatic methods may not be able to perform like humans in portrait classification. These methods may consider only special factors like emotional state or gaze direction to select an image. In this work, a large collection of facial images was collected, and under a subjective quality assessment study, they were judged by more than 80 people. A deep classifier network using transfer learning and the fine-tuning approach is proposed, which is learned end-to-end having the subjective labels to select good portrait images objectively. Quantitative and qualitative results show that this model performs better than state-of-the-art image classification networks. In addition, our qualitative evaluation showed that our model can separate good portrait images in the way that humans do. Therefore, this model can be reliably used in mobile phones, digital Cameras, and other imaging systems. https://scientiairanica.sharif.edu/article_23746.html In this study, lateral and longitudinal buckling behaviors of perforated composite plates are investigated numerically. The composite plates are assumed to consist of two types of fabric, unidirectional and woven fabrics. The effects of changing the location, diameter and number of holes in the plate for unidirectional and woven fabric types on the lateral and longitudinal buckling strength are compared between the unperforated plate and each other. In addition, the effects of changing the fiber orientation angle on the lateral and longitudinal buckling load of the unidirectional fabric are also examined. Finally, the changes in the mode shape in two planes in lateral buckling and in a single plane in longitudinal buckling are shown, and the normalized amplitude change in the upper point of the free end of the plate is analyzed for different hole cases. In this study, the SolidWorks program is used for model design and the Simulation module of the SolidWorks program is used for numerical analysis. https://scientiairanica.sharif.edu/article_23747.html Due to conflictions between different purposes and objectives of the industrial robots, a multi-objective optimization is required to find the non-dominant answers to choose from. In this article, we have introduced a new optimization objective which involves the robot’s high acceleration, actuator effort reduction; while avoiding the singularities which is applicable on any robot with an approximate known inverse dynamic. The cost function is defined by designing a computed torque control on robot. Our objectives are defined as: 1. minimizing the root mean square (RMS) power needed in motors for movements with the maximum acceleration of the end effector in different directions (cost/effort reduction), 2. Minimizing the mean Jacobian condition number in a predefined workspace (kinematic performance improvement) and 3. Minimizing the mass matrix condition number in a predefined workspace (dynamic performance improvement). The multi-objective problem is numerically solved with Genetic algorithm SPEA-II and non-dominant solutions known as Pareto fronts are resulted. Geometrical design variables are chosen from Pareto fronts by three different MCDM methods. Eventually, cost functions of the designed robot are compared with a Fanuc M-2iA/3S model. https://scientiairanica.sharif.edu/article_23752.html This study develops a quantitative approach to evaluate the resilience of stock markets of oil-supplying countries. To this point, the data from the stock market of seven Middle East countries are used, the shock periods are identified, an initial form of resilience is developed, and the performance of stock markets based on the oil price systemic risk is modified. Since during the disaster period, the pattern of changes in the financial markets is very important, a new approach to evaluate the amount of performance reduction after the disaster and recovering to the pre-disaster point is proposed. The proper performance of the proposed approach in showing the resilience of stock markets in different time steps has been evaluated quantitatively and qualitatively and the main policy of countries are reviewed. Our results indicate a positive and significant impact of the oil price shock on all stock markets, while the resilience of the best stock market is 20% higher than the worst market. Also, our introduced correction factor for the resilience measure has been able to provide a more realistic view of the resilience, as shown in the comparison of the resilience of countries and their economic indicators. https://scientiairanica.sharif.edu/article_23755.html Metal matrix composites (MMCs) are a type of sophisticated materials including a metal or alloy matrix blended with a reinforcing phase, which may exist as particles-whiskers, . Hybrid metal matrix composites (HMMCs) are sophisticated types of materials that utilize various reinforcing agents to improve the characteristics of the underlying metal matrix. Hybrid aluminum metal matrix composites (HAMMCs) are a specific category of metal matrix composites that utilize aluminum or its alloys as the primary material and include two or more distinct forms of reinforcements. The primary objective of this work is to investigate the mechanical and microstructural characteristics of hybrid composites consisting of Al6061, GNPs, and CeO2. The Al-6061 alloy is used as the matrix material, while graphene nanoplatelets (GNPs) and cerium oxide (CeO2) are used as reinforcing agents. The hybrid composites were produced by employing a 2-step stir-casting technique with proportions of 94:1:3, 96:3:3, and 94:3:1 of Al-6061: GNPs: CeO2. The 96:3:3 proportion was found to be the best which has a 24.47% increment in hardness value having the lowest grain size of 6.36nm. The maximum mechanical properties such as tensile strength (148.239 MPa), flexural strength (265 MPa), and impact strength (13.062 Joules) in the same proportion HAMMCs specimen. https://scientiairanica.sharif.edu/article_23756.html Blockchain technology makes agricultural supply chain management absolutely transparent, immutable, decentralized, and secured. The activities are stored in a disseminated shared record with connections towards a decentralized file system, which makes the supply chain tamper proof and traceable from farmhouse to table, which ensures that the food crops sold are safe. Farmers are granted with agri-insurance to buy seeds and during natural calamities. Successful agreements may guarantee cryptocurrency evidence of distribution when products are delivered with automated bitcoin payments to all parties. Farmers are given sound advice by a government panel, which allows to boost crop productivity and an aid to make more informed decisions in farming. The end user can outlook the complete information from the origin of the food crop to the finished goods journey. The proposed system that deals with agricultural food crops uses blockchain technology which provides safety, agreement, dispersed ledger, speedy payout, and decentralization, by accomplishing profit to each and every registered stakeholder and increasing confidence among farmers since the transactions are transparent in blockchain system. Moreover, the recommended system has a user-friendly interface to view and perform all the transaction activities. https://scientiairanica.sharif.edu/article_23757.html In this article, a new reliability analysis algorithm is proposed to calculate the probability density function of the bearing capacity of the foundation resting on rock mass. Despite common approaches used by other investigators, four parameters with uncertainties have been adopted in this study as random variables, including GSI index, strength of intact rock (ci), intact rock constant (mi), and rock mass disturbance factor (D). In the extended Subset Simulation (SS) proposed in this study, the samples at the first stage are produced using the Monte Carlo Simulation (MCS), while at the next levels, a Markov chain based on the Metropolis-Hastings algorithm is applied to each subset. Finally, statistical parameters of the PDF of bearing capacity are discussed. The results obtained showed that (A) The SS method converges with a much smaller number of samples than those given by the MCS method; (B) Parameters UCS and GSI have the greatest effect on the bearing capacity; (C) As the coefficient of variation of the input variables increases, the value of the reliability index decreases and therefore the probability of system failure increases.; (D) When the negative coefficient of correlation is used, a decrease in the variation of bearing capacity is observed. https://scientiairanica.sharif.edu/article_23758.html Using green corrosion inhibitors, which lower corrosion rates without endangering the environment, is one of the most important strategies for reducing corrosion. An extract from the leaf of the Mukia maderaspatana plant was used in this investigation as a green corrosion inhibitor for mild steel in hydrochloric acid. The anti-corrosion properties of plant leaf extract were verified by a number of experimental procedures, such as weight loss analysis, electrochemical spectroscopy, gravimetric measurements, surface analysis using FTIR, and SEM, at various temperatures. A plant extract added at 1000 ppm significantly increases the inhibitory effectiveness up to a maximum of 95%. To confirm that the mild steel surface in an HCl solution had a protective layer, surface analysis was done. An investigate using a scanning electron microscope showed that the protective layer formed by the green inhibitor. According to the study, the extract from Mukia maderaspatana is a strong, environmentally friendly inhibitor that efficiently stops mild steel from corroding in extremely acidic situations. https://scientiairanica.sharif.edu/article_23759.html The degradation of pollutants in a planar continuous flow photocatalytic microreactor was simulated using COMSOL Multiphysics. The Langmuir-Hinshelwood model was proposed to describe the available experimental data for the reaction kinetics of cephalexin (CEX) degradation from aqueous solution using the Bi2WO6/CNT/TiO2 photocatalyst, and the kinetic constants of this model were evaluated. The predicted results for the breakthrough curves of this pollutant at the microreactor outlet were compared with experimental data. The mean absolute error between the model and experimental values at inlet CEX concentrations of 30, 40, 50, and 60 mg/L was 3.3%, indicating a good model prediction. The parametric study results indicate that increasing the length of the microreactor from 50 to 100 mm enhances the removal efficiency from 82 to 97%. Additionally, reducing the microreactor depth from 300 to 100 µm increases the removal efficiency from 82 to 92%. The calculated Damköhler number under the optimal experimental conditions was 0.42, indicating that the photocatalytic process is primarily controlled by reaction kinetics rather than mass transfer limitations. https://scientiairanica.sharif.edu/article_23760.html Originating from the Japanese electronics assembly industry, the seru production mode offers high efficiency, flexibility, and rapid responsiveness in manufacturing. This paper addresses the unspecified dynamic seru scheduling problem with resource constraints (UDSS-R), where resource usage must not exceed the available total at any given time. The UDSS-R problem is formulated as a mixed-integer linear programming (MILP) model aimed at minimizing the makespan. A two-stage heuristic algorithm is proposed subsequently: the first stage addresses the regular seru scheduling problem (without resource constraints) by assigning jobs to serus, and the second stage uses a dynamic programming algorithm based on the 0-1 knapsack problem to finalize the schedule. Computational experiments demonstrate the practicality and effectiveness of the proposed MILP model and the two-stage heuristic algorithm in solving the UDSS-R problem https://scientiairanica.sharif.edu/article_23761.html Linear resolver is a good candidate for position estimation in severe environments. Performance evaluation of these electromagnetic sensors is a crucial job in the designing process. Due to its 3-D structure, 3-D time-stepping finite element method (FEM) is required for evaluating its performance. However, the simulation time of 3-D FEM is too much, making it unsuitable for design and optimization goals where lots of simulations are needed. Moreover, the quality of the response is affected by the simulation's mesh quality and step time. Therefore, in this paper, the Field Reconstruction Method (FRM) is developed for the evaluation of the proposed sensor. The accuracy and speed of the proposed analytical model are evaluated by 3-D FEM simulations in both AC and DC excitation voltage. Finally, to verify the success of the proposed design and the analytical calculations, the introduced resolver is prototyped and tested. The results show good agreement among the analytical, FEM, and practical assessments https://scientiairanica.sharif.edu/article_23763.html This study uses an elastic-perfectly plastic single degree of freedom (SDOF) system to investigate reaction of machinery-supporting foundations to harmonic and pulse loads. An exponentially decaying pulse is used and applied to an already operating machine foundation which may experience an emergency shutdown due to extreme loading. Based on response at end of positive phase of pulse, two cases are analyzed. The governing equations are derived and solved numerically. Random response of SDOF system, before vibrations die out, can be observed due to the interaction of the two different loadings. Overestimation of the response for zero decay coefficient, b representing a triangular pulse, is noted. The absolute maximum displacement, influenced by the negative phase, changes by approximately 10-11% as the mass increases from 180 tons to 220 tons. Stiffness of the system and damping ratio affected the response to maximum extent. For various input parameters considered, displacement reduces by an order of 73% with increase in stiffness and by about 65% when damping ratio increased. The results indicate that negative phase of pulse loading has significant impact, particularly on less stiff and less damped systems. https://scientiairanica.sharif.edu/article_23765.html Ear canal deformation caused by jaw joint movement is simulated in this paper and introduced as a candidate source of power to supply hearing aids devices power. The ear mold is prepared and the CT-Scan is performed using this mold in both open and close positions of the mouth. The ear canal model is created with Mimix, 3matic and Catia softwares. The mandible motion in cases of discussion, text reading, poem reading, greeting and speaking loudly is captured by opti-track camera and processed in Matlab software. Ear canal model and mandible path are imported to Abaqus finite element analysis software. Using the finite element model the output voltage of piezoelectric for each case of talking is calculated. Required electrical circuit to convert generated voltage from AC to DC is modeled in Matlab simulink and output power is determined. The results show that this module could supply a low power hearing aid for about 14 hours with considering individual talking activity per day. Also, with increasing the human age, the amount of output voltage from the piezoelectric harvesting device increases. https://scientiairanica.sharif.edu/article_23766.html Implementing a high-layer thickness additive manufacturing (AM) can significantly reduce the manufacturing time. With this, the cracking phenomenon and microstructure of the additively manufactured parts should be controlled. This paper investigates the surface micro-cracks and microstructures of Ti6Al4V alloy manufactured by high layer thickness laser-based directed energy position additive manufacturing process. No cracks were visibly present under the naked eye. Solidification cracking within a disposition boundary was present on some parts of the as-printed Ti6Al4V surface. Trans-deposition boundary cracks were visible under the optical microscope as liquation cracking. After polishing, the cracks were almost eliminated, with small isolated cracks on the polished surface. These cracks and concentrated C depositions confirmed with SEM-EDX can act as stress concentration points and crack initiation sites. SEM images showed α-lath structures with Widmanstätten pattern, and α+β Ti grains were observed. Post-processing methods such as removing the topmost crack surfaces, shot peening, laser shock peening and heat treatment can be adopted to reduce the cracks and enhance the performance of the as-deposited parts. https://scientiairanica.sharif.edu/article_23769.html As the number of distributed generation (DG)systems connected to the utility grid is increasing the issueof synchronization between the DGs and the grid isbecoming more profound. This paper presents a simplephase-locked loop (PLL) for the proper operation of theinverters even when the utility grid voltage is subjected toconsiderable abnormal conditions, such as changes infrequency and voltage amplitude or the presence of gridvoltage harmonics. The proposed PLL is based on zero crossing points, which makes it robust and compatible invarious grid voltage frequencies and amplitudes. Since inthe proposed PLL the grid voltage phase and amplitude areextracted without Park matrix and notch filter, it demandslower number of clocks for the calculations with respect to theexisting methods. This feature is practically desired sincethe proposed PLL can be executed on a simple and low-costmicrocontroller. The performance of the proposed PLL hasbeen evaluated by simulation and experimental tests. Theresults demonstrate the functionality of the proposed PLLon a 1.6 kW single-phase inverter. https://scientiairanica.sharif.edu/article_23770.html Desalination is an appropriate response to climate change, and increasing population, industrial activities, and drought. This study introduced a freshwater generation system using solar distillation via evacuated tube collectors (ETCs). Two geometries of evacuated tubes (Designs I and II) were used and the performance of the proposed system was evaluated regarding freshwater productivity, gained output ratio (GOR), and cost per liter (CPL). The difference between these tubes was the volume of the water circulating within them due to different internal geometries. The system benefitted from zero liquid discharge technology. Maximum freshwater productivity values of 1145 and 1325 mL.h-1.m-2 were obtained for Designs (I) and (II), respectively. It occurred in June 2023 under a maximum solar radiation intensity of 1010 W.m-2. A maximum GOR of 0.71 and 0.82 was calculated at this peak solar radiation intensity for Designs (I) and (II), respectively. The quality of produced water met the standards of drinking water. The cost analysis led to the CPL of 0.0137 and 0.0132 US $ for Designs (I) and (II), respectively. The performance comparison of the proposed designs with other direct desalination units confirmed the superiority of these designs based on freshwater productivity, GOR, and CPL. https://scientiairanica.sharif.edu/article_23777.html A new structural design known as “fillet” on the cavity’s edges is incorporated to enhance the computational and physical domain. For momentum, concentration, and temperature distribution, a Galerkin finite element discretization with quadratic interpolation functions is utilized, and for pressure distribution, we use linear interpolation functions. Elements in the shape of triangles and rectangles are used to discretize the domain. The PARADISO matrix factorized-based nonlinear solver and the Newton method are used to solve nonlinear discretized problems. The analysis of several factors such as Hartmann number (0-50), Rayleigh number (10^4-10^6 ), Lewis number (0.1-10), and inclination angle (0°-90°) is conducted to investigate the impact of flow on streamlines, isotherms and isoconcentration patterns and graphical and tabular representations are used to show the local heat transfer, kinetic energy, and mass fluxes. It is found that heat and mass transfer rate decreases for the variation in Hartman number, and has opposite trend for the variation in Lewis number. Our findings shed light on how DD processes behave in cavities that are exposed to magnetic fields, and they may be useful in optimizing and designing MHD devices for commercial use. https://scientiairanica.sharif.edu/article_23787.html This study aims to generate electrical energy using a wind turbine mounted in the front of a vehicle. In this study, the impact of the wind turbine on vehicle aerodynamics is evaluated, particularly in terms of aerodynamic drag, pressure, and flow velocity, while highlighting the potential benefits of energy production. The results showed that the aerodynamic drag coefficient of the vehicle increased by approximately 0.38% due to the wind turbine. However, the net energy gain produced by the wind turbine mounted on the vehicle while the vehicle is moving at a speed of 27 m/s was observed to be approximately 6.51% of the aerodynamic energy loss of the master model vehicle. In conclusion, this type of wind turbine system has the potential to enhance fuel efficiency and reduce environmental pollution. This study demonstrates that the use of wind turbines in electric vehicles can have positive effects on energy management. https://scientiairanica.sharif.edu/article_23788.html Motor current signature analysis is cited in many articles to detect the rotor bar fracture. Because of the small amplitude at the fault frequency relative to the main frequency amplitude, the former cannot be easily distinguished from the latter. As a solution, a method based on the algebraic identity of trinomial expansion of the stator current is proposed which enables us to display the difference between the frequency of the fault signature and the base frequency. In addition, the components at unwanted frequencies are attenuated using low-pass-filter. After that, the frequency weighting techniques are used to magnify the component at the target frequency compared to the lower frequencies than it. Indeed, a weighting method based on differentiation is used and another method based on convolution is proposed as tools to enhance the clarity of the frequency spectrum display for a broken rotor bar. To validate the proposed methods, they are examined on the laboratory data obtained from three different operating conditions including the direct online start, the direct torque control, and the scalar control and the results show the ability of the proposed methods in fault detection of an induction motor. https://scientiairanica.sharif.edu/article_23789.html High efficiency PM machines are usually built with high number of poles to meet high power density, and low torque ripple requirements. Their electronic commutation relies on the absolute rotor position data. Therefore, the resolver's pole numbers must equal the PM's pole numbers. Achieving multi-speed performance using traditional winding configurations needs high number of slots. For the limited outer diameter and the size of the sensor, it is limited by the teeth’s mechanical strength. Therefore, Fractional-Slot Concentrated Winding is proposed for multi-speed resolvers. However, FSCWs suffer from high sub-harmonics which cause high position-error of the sensor. To suppress the undesirable harmonics, usually multi-layer winding configuration is employed. Increasing the number of winding layers leads to increase the complexity of the mass production, as well as increasing the possibility of windings’ fault. Hence, in this paper a new technique based on using flux barriers in the stator core is proposed. Three different shapes are presented for the flux barriers. Then, the sensor’s performance equipped by 2- and 4-layer FSCWs, with and without different flux barriers in the stator core, are evaluated using finite-element method. Finally, three prototypes are built and tested. The measured results verify the success of the proposed techniques. https://scientiairanica.sharif.edu/article_23790.html The increase in the application of unconventional resources and the presence of heterogeneity in such reservoirs have increased the need to consider multi-scale models. Some of the recent studies indicate that sub-resolution porosity (SRP) has a profound influence on the flow characteristics of porous. Thus, the objective of this work is to present a technique to restore lost details from images of porous media. Interpolation and filtering are some of the conventional techniques that have long been used to improve image resolution. However, this field has changed with the current development of artificial intelligence, especially generative adversarial networks (GAN). GANs consist of two neural networks: A generator and a discriminator, which are trained in an adversarial manner to produce realistic images. When applied to petroleum engineering, GANs are employed in super-resolution tasks, in which the GANs learn to reconstruct high-resolution images from low-resolution inputs. In particular, a synthetic dataset of low-resolution images is generated from an existing dataset of a sandstone sample. In this research, we employ RealESRGAN which is more effective than previous models such as SRGAN. Finally, we constructed a pore network based on generated images and the model's results were almost identical to the actual model. https://scientiairanica.sharif.edu/article_23791.html The defense and aerospace industries make use of the third generation aluminium-lithium alloy. This specific research aims to enhance the friction stir welding (FSW) process parameters by using Taguchi-grey relational analysis (GRA) and principal component analysis (PCA). The weld quality depends upon various factors like traverse speed, rotational speed, tilt angle, shoulder diameter, and tool pin shape. Combining PCA with the deep-rooted Taguchi-GRA approach accurately determines the weights of the responses. Using a Taguchi L16 orthogonal array, 16 tests were conducted. The resulting weld joints underwent a series of different tests, such as tensile strength, yield strength, percentage elongation, weld zone hardness, heat affected zone hardness, bending load, and heat affected zone breadth. Based on the results obtained from Taguchi-GRA-PCA, the optimal process parameters were identified as a traverse speed of 160 mm/min, rotational speed of 900 rpm, tilt angle of 20, shoulder diameter of 16 mm, and a straight square tool pin profile. The analysis of variance (ANOVA) revealed the significance of all five characteristics, with rotational speed having the most influence, accounting for 43.56% of the overall result. and a confirmation experiment under ideal conditions was conducted, it showed 19.06% of improvement in the total weld quality. https://scientiairanica.sharif.edu/article_23792.html In this research paper, a novel real-time experimental reliability model is proposed, and system vibration signals are utilized to investigate and mitigate the impact of misalignment on components in rotor systems. To quantify reliability and comprehend the intricate relationship, we introduce modified design equations and employ a simulation-based methodology in the stress-strength interference approach. Additionally, we propose a framework for establishing safe and critical thresholds for parallel misalignment and rotational speed, aiming to meet specified reliability targets for the flexible coupling hub. The vibration analysis and subsequent illustration of the model in this research work shows that the reliability of the coupling is start deteriorating (at 95 Hz) much before the first critical speed (150 Hz) of the test-setup. Thus, this model proves effective in industrial rotor systems for preemptive maintenance tasks and manage misalignment levels among components designed for specific rotational speeds. https://scientiairanica.sharif.edu/article_23793.html This article presents a compact circular-shaped patch Ultra-Wide Band (UWB) antenna for the Internet of Things (IoT) and wearable applications. The proposed antenna is excited by a Coplanar Waveguide (CPW) feed line with circular and dual I-shaped slots in the ground to improve the impedance bandwidth. The antenna presents maximum peak gain of 7.8 dB. The antenna has compact size of 25x24 mm2, it is simulated and fabricated on the Cordura textile substrate with 0.5 mm thickness. The antenna performance in free space and on the human body phantom is presented in terms of impedance bandwidth, Specific Absorption Rate (SAR), and radiation characteristics. The SAR is calculated on a hypothetical body phantom at three different spots to validate the specified limits by international organizations for human safety (IEEE Standards). The maximum SAR values observed for 10 g average body mass are 0.258, 0.232, and 0.367 W/Kg, at the back side of the body, arm, and leg, respectively. The investigated results of the antenna in simulation and measurement are confirm its candidacy for the IoT applications and body-worn devices. https://scientiairanica.sharif.edu/article_23794.html In this study, we investigated the PEH performance in a wind tunnel experiment with a 250 mm × 250 mm cross-section. The PEH comprised a piezoelectric bimorph and a rectangular substrate. Wind flow induced by a blower within the tunnel achieves 6, 7, and 8 m/s speeds. A rectifier directed the flow toward the windmill blade for optimal energy generation. Three variation slopes of the flow rectifier were analyzed in terms of output voltage and deflection. The results show that the PEH performance improves as windspeed and the slope angle increase. At an angle of 40° and a speed of 6 m/s, PEH generates a voltage of 5.77 V and increases by 1.2 times at a windspeed of 8 m/s and an angle of 60° (12.67 V). Similar conditions occur in effective voltage (Vrms), where the value increases 1.6 times from 2.02 V to 5.33 V. Also, he deflection of the PEH increased by 37% from 1.49 cm to 2.05 cm. An Angle of 60° has a shorter narrowed area, thus changing the velocity vector to become faster. Wind speed increases encourage the windmill to rotate faster, which leads to the deflection of PEH, therefore, generates higher electricity. https://scientiairanica.sharif.edu/article_23795.html We focus on a chaotic differential system in 3-dimension, including an absolute term and a line of equilibrium points. Which describes as 𝑥⁄ = 𝑦 , 𝑦⁄ = −𝑎𝑥 + 𝑦𝑧 , 𝑧⁄ = 𝑏|𝑦| −𝑐𝑥𝑦 −𝑥2 . This system has an implementation by using electronic components. The first purpose of this paper is to provide sufficient conditions for the existence of a limit cycle bifurcating from the zero-Hopf equilibrium point located at the origin of the coordinates. The second aim is to study the integrability of each differential system, one defined in half-space 𝑦 ≥ 0 and the other in half-space 𝑦 < 0. We prove that these two systems have no polynomial, rational, or Darboux first integrals for any value of 𝑎, 𝑏, and 𝑐.Furthermore, we provide a formal series and an analytic first integral of these systems. We alsofind Darboux polynomials and exponential factors. https://scientiairanica.sharif.edu/article_23796.html Forecasting maximum daily electricity price (MDEP) is important due to its benefits for not only hydropower operators but also transmission system and distribution network operators. Based on the proven applicability of artificial intelligence tools for energy price prediction, this article aims to quantify the uncertainty of Artificial Neural Networks (ANNs) and hybrid convolution Neural Networks-Long Short-Term Memory (CNN-LSTM) models for MDEP forecasting using Monte Carlo simulation. The uncertainties were analyzed by calculating P-factor and d-factor indices and analyzing outliers and distribution of errors in different seasons. The results demonstrate the higher performance of the CNN-LSTM model compared to the ANN model. The CNN-LSTM has a d-factor less than 0.961 while the ANN model has a d-factor of 1.21 in spring season. The prediction performance of the CNN-LSTM model is strongly higher than that of the ANN model in summer when EPs touch their peak values. Analysis of prediction errors and outliers also confirms the higher accuracy and lower uncertainty of CNN-LSTM model compared to ANN. Consequently, from the viewpoint of hydropower producers, the CNN-LSTM model is much more reliable, especially during periods of peak energy consumption with high energy prices in summer which is a challenging time to forecast MDEP. https://scientiairanica.sharif.edu/article_23797.html To achieve comprehensive energy utilization and economical operation of the system, an optimization operation strategy for a small-scale distillation plant integrated energy system (IES) is proposed in this paper. The system integrates liquid air energy storage sub-system (LAES), thermal energy storage sub-system (TES), and PV modules for the steel mill. The innovation of this work lies in the application of Information Gap Decision Theory (IGDT) to manage uncertainties in waste heat availability, enabling robust energy scheduling across risk-neutral, risk-averse, and risk-seeking scenarios. The paper investigated the impacts of robustness and opportunity radius on the system operating cost and schedule of LAES. The IGDT-based strategy proved most cost-effective, lowering total operating costs to $29,295, outperforming MOGWOA and POA, which yielded $31,748 and $31,657, respectively. The analysis also highlighted that risk awareness critically influences system costs. Specifically, in the risk-averse scenario, increasing the robustness radius from 0.0416 to 0.3279 raised costs from $30,090 to $39,534, while the risk-seeking approach offered a slight reduction in opportunity cost. These results confirm the effectiveness of the IGDT method and underscore its potential in optimizing IES operations under uncertainty. https://scientiairanica.sharif.edu/article_23798.html This study extensively investigated the fabrication and wear properties of aluminum titanate (Al₂TiO₅) and mullite (3Al₂O₃-2SiO₂) doped porcelain ceramic-composites produced by the powder metallurgy method. The porcelain ceramics were prepared by powder metallurgy and the wear resistance and other mechanical properties were evaluated based on the data obtained. The experimental wear results were modeled and analyzed using ensemble learning(EL) methods and Artificial Neural Networks (ANN). Boosting and random forest(RF) algorithms were employed among the ensemble methods. Basic statistical measures such as R², RMSE, MAE, and MAPE were utilized to evaluate model performance. Boosting and RF methods also produced successful results, but ANN was found to be superior in terms of accuracy and overall performance. In the study, pure porcelain (P), mullite doped porcelain (PM), aluminum titanate doped porcelain (PAT) and aluminum titanate-mullite doped (PMAT) porcelain models were investigated and compared separately. The findings provide valuable contributions to the development of high-performance ceramic-composites in materials engineering and optimization of the wear behavior of these materials. In this context, the applicability of advanced machine learning methods in materials science and the advantages of these methods are discussed in detail. https://scientiairanica.sharif.edu/article_23802.html This study aims to develop a dynamic portfolio trading system for high-risk profiles of cryptocurrencies in two phases: 1) portfolio selection and 2) portfolio construction. In the first phase, we propose a novel algorithmic trading model applying a Convolutional Neural Network (CNN) using a 2-D convolution layer with eight kernels of 3×3 sizes based on the prediction of selected technical indicators to predict buy/sell trading signals. To effectively increase the accuracy of the CNN model, first, the H-step ahead predictions of the selected technical indicators based on Long-short-term-memory (LSTM) along with the indicators themselves have been used to construct input matrices of the CNN model. A new price labeling approach was proposed to determine buying or selling points using the zigzag indicator (ZZ) in our CNN model. Assets with buy signals have been selected to construct the proposed portfolio. In the second phase, we propose a novel robust approach based on Holt-Winters-Multiplicative (HWM) to determine the realized crypto portfolio weights robustly by considering the seasonal effects. The experimental results show that our developed system outperforms the competing models for 30 cryptocurrencies with a high-risk profile in the two phases. https://scientiairanica.sharif.edu/article_23803.html The present article investigates the coupled nonlinear dynamics of the offshore wind turbine with the floating tension leg platform through numerical methods. The NREL 5MW wind turbine installed on MIT/NREL TLP is chosen as the floating offshore wind turbine. The offshore wind turbine equation of motion is derived using Hamilton's Principle, considering the floating platform surge, heave, and wind turbine's tower transverse motions. The wind turbine tower is modeled using the Euler-Bernoulli beam theory, and the effects of the platform surge and heave motions have been considered. Then, the Galerkin method is applied to the derived partial differential equations of motion of the tower to reduce them to a set of nonlinear ordinary differential equations. Afterward, by utilizing direct time integration, the effects of the frequency and amplitude of wave forcing and the length of the mooring lines on the floating wind turbine's global dynamics are studied. Finally, results are depicted as the frequency response curves and the Poincare maps' bifurcation diagrams. The phase-plane portraits, Poincare maps, and fast Fourier Transforms (FFTs) highlight points of interest in the parameter space. https://scientiairanica.sharif.edu/article_23804.html In this study, machine learning (ML) models are developed to predict the value of interfacial region thickness (IRT) and ultimate tensile strength (UTS) of diffusion-bonded Al-Ni based on molecular dynamics simulation data. Molecular dynamics simulations are performed to simulate the diffusion bonding of Al-Ni with three parameters with three to four level for each parameter. The results of the simulations that are used to generate the ML models are the value of IRT and UTS. The temperature have influenced the performance of ML models with significant impact, indicated by the value of MSE and R2 that used temperature as the input parameters with an excellent performance. However, the combination of the three parameters as the input shows the best performance, indicated by the MSE value of 0 and the R2 value of 1, showing that the ML models performance will increase with the increase of the input data. Furthermore, the models with the highest performance throughout the test are the NN models, followed by the kNN, whereas the other three models showing average performance. This study has successfully developed ML models to predict the IRT and UTS from the molecular dynamics simulation data of diffusion bonding of Al-Ni. https://scientiairanica.sharif.edu/article_23805.html The presence of joints and defects around openings contributes to the instability of these flaws. In nature, rocks and concrete materials are often layered, anisotropic, and contain numerous joints. This article discusses laboratory preparation and testing of layered gypsum-concrete specimens with varying geometries. Specifically, single joints above the openings were created at different angles and subsequently tested. The study focused on the instability and failure of the specimens in relation to the angularity of joints, anisotropy, and the geometry of the openings. Additionally, numerical modeling of the tested specimens was conducted using ABAQUS software to analyze the behavior of the openings when interacting with joints or faults. To ensure accuracy, ABAQUS was calibrated using data from uniaxial compression tests. The modeled specimens were then examined under uniaxial compression conditions, incorporating various opening configurations with joints. The findings indicated that tensile cracks were the most prevalent mode of failure in these modeled specimens. Notably, as the distance from the tunnel roof to the joint increased, its impact on tunnel instability diminished due to an increase in failure stress. https://scientiairanica.sharif.edu/article_23808.html Objectives: In rehabilitation, hand robotics, and kinesiology, a crucial challenge is establishing a connection between surface electromyogram (sEMG) signals and the kinematics of joints and upper limbs.Methods: The present research introduces a deep recurrent neural network regressor that uses LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) cells, leveraging sEMG signals to estimate finger joint angles. This investigation used the DB2 series of the Ninapro dataset. Results: Remarkably, many joint angles can be estimated with up to 97% accuracy when based on the correlation coefficient criterion. This indicates a strong positive correlation between the estimated values and the actual values of these joints. However, it is significant to note that there is a trade-off between accuracy, learning time, and the number of parameters used, which varies based on the type of cells employed in the network. Conclusions: Using deep LSTM neural networks, it is possible to estimate the interphalangeal joint angles of the fingers with an accuracy of 97%. LSTM cells offer more precise predictions but require more learning time and parameters than GRU cells. The study also explores the significance of efficient muscles in executing movements, potentially influencing the varying estimation accuracy among different joints. https://scientiairanica.sharif.edu/article_23810.html Forecasting the long-term electrical yearly peak load is pivotal in power system expansion planning. The accuracy of the forecasting method holds immense significance in preempting economic losses and budgetary issues arising from unwarranted or inadequate investments. Although conventional techniques like time-series methodologies such as Auto-Regressive Integrated Moving Average (ARIMA) are extensively employed for long-term electrical peak load and energy demand forecasts, their limitations in dealing with inefficiencies, nonlinearity, and seasonality trends present considerable challenges. This paper proposes a novel approach that leverages the ARIMA method, incorporating Support Vector Regression (SVR) and the Genetic Algorithm (GA) technique. This approach aims to forecast the long-term yearly peak load of the Tehran Regional Electric Company (TREC), Iran’s largest regional electric company. The SVR algorithm parameters are fine-tuned to minimize forecasting errors using a combination of GA and the ARIMA method. The resulting optimized forecasting approach, ARIMA-GA-SVR, is applied in a real-life case study network within TREC. Comparative analysis with existing forecasting methods is conducted. The ARIMA-GA-SVR approach is a reliable and accurate forecasting solution based on established error criteria and simulation outcomes. https://scientiairanica.sharif.edu/article_23811.html A post-fault model of six-phase PMSMs with an open-circuit fault is presented. First, the two key properties of the well-known vector space decomposition transformation for the healthy machine, namely decoupling and orthogonality, are discussed. Then a dedicated transformation with similar properties for a faulty machine with one open-circuit fault is derived. It maps the phasedomain variables on a new five-dimensional space; the current which builds the magnetomotive force vector in the airgap is mapped on a separate sub-plane, and the currents which do not affect the magnetomotive force vector on the three remaining dimensions called zero-sequence axes. The proposed transformation is generic and can be applied to different star connections, i.e., grounded, connected, and isolated star points. Then, the model of the machine linked with equivalent circuit diagrams is developed. Simulation results are provided to illustrate the properties of the transformation and show the validity of the proposed model. https://scientiairanica.sharif.edu/article_23812.html A Sixth-Order Generalized Integrator (SiOGI) with a Frequency-Locked Loop (FLL) control system is simulated in this paper. The proposed SiOGI-FLL addresses Power Quality (PQ) challenges by integrating nonlinear loads to remove power quality issues under Direct Current (DC) offset, grid harmonics, and unbalanced grid conditions. The developed SiOGI-FLL tackles power quality problems associated with renewable energy integration with the grid. The SiOGI-FLL system offers enhanced frequency tracking and precise phase detection, which helps maintain accurate synchronization with the grid. The proposed system can efficiently improve PQ issues and ensure seamless integration of Renewable Energy Sources (RESs) by utilising advanced control techniques. To evaluate the performance of the proposed SiOGI-FLL, extensive simulation studies are conducted under various operating conditions, such as DC offset, unbalanced grid, and grid harmonic scenarios with Linear Load (LL), Non-Linear Load (NLL), and UnBalanced Loads (UBL). The simulation results demonstrate the superior capabilities of the SiOGI-FLL control scheme. https://scientiairanica.sharif.edu/article_23813.html This study investigated the combined effects of loading rate and applied strain on the behavior of auxetic materials and developed a user-defined material model to capture these effects during loading. First, test specimens were made of conventional polyurethane foams according to ASTM D-3574; then, with a thermo-mechanical process, the conventional foam was converted to auxetic polyurethane foam. Tensile experiments were conducted by applying different strains (i.e., 10, 40, and 80%) and loading rates (i.e., 0.01, 1, and 5 s-1). Displacements were tracked during the tests by digital image correlation. Statistical analysis using analysis of variance (ANOVA) showed that both strain and loading rate significantly affect Poisson’s ratio (p˂0.0001). It was also found that transient strain auxeticity (TSA) depends on the applied loading rate. The user-defined material was developed from the experiments and used in a finite element model (FEM) to capture Poisson's ratio variations because of changing loading circumstances. The model's predictive ability (i.e., the model's validity) was examined by performing different experiments (i.e., different from those used for developing the model), showing a maximum difference of 10% compared to measured Poisson’s ratio-strain curves from experiments. https://scientiairanica.sharif.edu/article_23814.html This manuscript analyses a Coplanar Waveguide (CPW) fed ground-slotted antenna for dual-frequency operations. The proposed design comprises a ground circular slot and a modified angular ring with two added strips to obtain dual operating bands and polarization characteristics. The proposed structure is printed on an FR-4 epoxy substrate with a thickness (h) of 1.6 mm and  = 4.4. The overall antenna size is 32×32 mm2. The measured reflection coefficient, axial ratio, gain, and radiation pattern are in good agreement with simulated results. The lower band operated at 2450 MHz frequency with impedance bandwidths of 275 MHz (from 2350 MHz–2625 MHz) and linearly polarized (LP), and the upper band operated at 5200 MHz with 500 MHz (from 4950 MHz–5450 MHz) operation band coverage and circularly polarized (CP). The upper band is circularly polarized with a 3 dB axial bandwidth from 4850 to 5400 MHz. The designed structure is suitable for on and off-body applications in lower and upper bands, respectively. The proposed antenna characteristics have also been examined on the human arm, and analysis of the Specific Absorption Rate (SAR) to ensure human tissue safety. https://scientiairanica.sharif.edu/article_23819.html This study presents a mathematical model for the fractional Oldroyd-B fluid flow through a ciliated tube. The proposed model simulates the movement of mucus (fractional Oldroyd-B fluid) within the respiratory tract, where symplectic and antiplectic metachronal wave patterns are generated by ciliary motion. The model, though intricate, is simplified using the lubrication approach, and the resulting partial differential Equations (pdes) are solved using the fractional Adomian Decomposition Method (ADM). For the analysis of fluid flow, mathematical expressions for the pressure gradient, pressure rise, frictional force, and streamlines are derived, plotted, and discussed. The graphical results demonstrate that the symplectic wave pattern is more effective than the antiplectic wave pattern in transporting mucus through the respiratory tract. https://scientiairanica.sharif.edu/article_23820.html A new power management model is presented here to prevent excessive frequency deviations by the more commitment of higher inertia power plants and more contribution of renewable resources or energy storage systems fast inertia response. To have a mixed-integer linear programming model, the primary frequency response constraints are linearized. Using distributionally robust optimization to model uncertainty of renewable sources in the primary frequency response strategy, considering technical limitation of network, and considering suitable case studies to investigate the capability of the proposed scheme are contributions of this paper. Model is examined on a real isolated microgrid. Results show that by activation of distributed energy resources the power management can be done with lower cost. Energy not supplied of microgrid can be reduced when energy storage systems are utilized as energy buffers in network. Comparing robust model with deterministic method shows the more expensive management procedure, however, a more frequency stability is obtained in the contingency condition. The proposed plan with the presence of only frequency control leads to a 361% increase in planning cost. However, if renewable resources are added to this scheme, a 62% reduction in planning cost occurs. https://scientiairanica.sharif.edu/article_23821.html Automatic License Plate Recognition (ALPR) is crucial in Intelligent Transportation System but faces challenges like weather and light conditions, camera angles, and license plate distortion. With advances in deep learning, as well as computing platforms, particularly GPUs, these algorithms have found major applications. The task becomes even more complex with Iranian license plates due to the strong similarity of some Persian characters, and the need for real-time processing is often overlooked. Consequently, this work proposes a two-stage deep learning-based algorithm for ALPR, with impressive precision and real-time applications. The methodology involves License Plate Detection (LPD) and Character Recognition (CR) using separate fine-tuned YOLOv5 networks, extracting characters in two sequential steps. The model shows robustness under challenging scenarios such as uneven lighting, low-quality images, and noise. Experimental results show an end-to-end mean Average Precision (mAP) of 95.5% and an inference speed of 23 Frames Per Second (FPS), meeting real-time requirements. Specifically, a mAP of 98.2% is achieved in the CR stage, effectively addressing character similarity issues. The developed model is implemented on the Jetson Nano, an embedded device, using DeepStream and demonstrates strong performance. For real-time detection, the TensorRT-based model deployed on the Jetson Nano achieved 6 FPS inference speed. https://scientiairanica.sharif.edu/article_23822.html In this work, we examine the Heimburg model, which describes how electromechanical pulses are transmitted through nerves by using the generalizing Riccati equation mapping method. This approach is regarded as one of the most recent efficient analytical approaches for nonlinear evolution equations, yielding numerous different types of solutions for the model under consideration. We get novel analytic exact solitary wave solutions, including exponential, hyperbolic, and trigonometric functions. These solutions comprises solitary wave, kink, singular kink, periodic, singular soliton, combined dark bright soliton, and breather soliton. To understand the physical principles and significance of the technique the well-furnished results are ultimately displayed in a variety of 2D, 3D, and contour profiles. Furthermore, this system's linearized stability is examined. The results of this work shed light on the importance of studying various nonlinear wave phenomena in nonlinear optics and physics by showing how important it is to understand the behaviour and physical meaning of the studied model. The employed methodology possesses sufficient capability, efficacy, and brevity to enable further research. https://scientiairanica.sharif.edu/article_23823.html This article provides the design of a dual frequency, dual-polarized microstrip patch antenna using a Single Rectangular Loop (SRL) element. The proposed antenna has a physical footprint of 50 x 40 x 1.2 mm3 and a dielectric constant of 4.2. A single rectangular loop is placed with truncated corners on the inner patch to excite the right-hand circular polarization. The prototype is designed to operate in two modes with frequency and polarization re-configurability. The re-configurable mechanism is controlled by a pair of PIN diodes (BAR64-02V), which connect the inner patch and outer loop. In the ON state, the antenna exhibits right-hand circular polarization (RHCP) as a narrow-band antenna. It is capable of operating at frequencies between 5 GHz and 5.43 GHz. Furthermore, this device provides linear polarization (LP) in the frequency range of 3.75 GHz to 3.86 GHz. It exhibits 110 MHz of bandwidth in 3.8 GHz (S-band) and provides 430 MHz bandwidth in 5.2 GHz WLAN operating frequencies. The suggested antenna provides a maximum gain of 8.56 dBi. The antenna is fabricated and tested to prove its performance parameters. All the simulations are performed using high Frequency Structure(HFSS) software and results are presented with discussions. https://scientiairanica.sharif.edu/article_23824.html Over the last 28 years, numerous investigations have been conducted to enhance common fluids like water by dispersing nanoparticles such as Fe3O4. To examine the time-dependent natural convection of such nanofluid under the influence of exothermic reaction in tandem with other governing parameters, the Finite Element technique is applied in the current work. Controlling factors such as Ra (103 to105), Ha (0 to 60), Fk (0 to 4), enclosure wavelength (single wave, double wave, and three waves), magnetic field inclination angle (0o to 90o), and volume fraction of nanoparticles (0 to 0.08). The vertical wavy walls are considered at Th while the middle flat vertical walls are kept at Tc leaving all other surfaces at adiabatic condition. Results revealed that raising Ra to 105, when applying double waves at the active walls, was shown to have the most impact on the mean Nu, increasing by 142.85% for Ha=12, φ=0.02, Fk=1 at 5 sec. Additionally, Nuavg was found to increase significantly when Fk is raised from 0 to 4, increasing by 76.12% in the absence of magnetic effects for Ra=105, and φ =0.04. Also, an impressive impact of nanoparticles was achieved by increasing the volume percentage of nanoparticles in the base https://scientiairanica.sharif.edu/article_23825.html In this paper, the development of a three-phase AC regenerative programmable electronic load is presented. The AC load consists of two parts, a three-phase AC to DC current-controlled PWM converter as the programmable electronic load and a DC to AC grid-connected PWM voltage source inverter as the regeneration system. DC to AC converter can receive current in constant current, constant power, and constant impedance modes to emulate various types of load profiles. In the constant current mode, the system can emulate current harmonics with programmed amplitude and phase to emulate nonlinear load profiles. Due to the harmonic current control capability, proportional-resonant current controller with harmonic controllers is used in AC to DC converter. On the other-side, the DC to AC converter regulates the DC-link voltage and regenerates the power received by electronic load by injecting it to the grid. To increase the robustness of the DC-link voltage control system against power oscillations caused by electronic load in constant current mode with harmonic current, the DC-link voltage controller is modified with adding a multi frequency notch filter and its performance is verified. Simulation and experimental results verify the performance of the proposed electronic load and its control system. https://scientiairanica.sharif.edu/article_23827.html This research presents a novel quadruple boost 9-level single-phase switched-capacitor (SC) inverter. The proposed topology (PT) comprises eight semiconductor switches, two capacitors, one power diode, and a single supply source unit that can synthesize 9 levels of voltage at the output terminal. A brief description of structural design, operating principle, modulation strategy, and determination of optimum values of the capacitances are presented in this work. Simulation and experimental studies have been carried out under different loading conditions to validate the effectiveness of the PT. Further, a fair comparative study with state-of-the-art SC topologies regarding component count per level, gain, total standing voltage, and cost function proves the merits of the suggested novel work. https://scientiairanica.sharif.edu/article_23828.html Federated Machine Learning offers an exciting pathway for collaborative model training, enabling numerous users to contribute without disclosing their raw data. Yet, maintaining the security and privacy of both data and model within distributed settings continues to pose significant challenges. Identifying outliers plays a vital role in pinpointing abnormal behaviors that have the potential to weaken prediction accuracy or compromise the integrity of the model. The paper introduces a system that harnesses autoencoders in conjunction with anomaly scoring techniques and thresholding mechanisms to preemptively detect anomalies within the dataset prior to model training. The objective of the system is to optimize preprocessing stages by proactively filtering out potential breaches before data is introduced into the distributed environment. In the context of FML, where the model is trained across a distributed network, vulnerabilities arise as model parameters are exposed to evasion attempts. These attempts aim to undermine model integrity by manipulating the aggregation process. A protocol termed incentivized Probabilistic Byzantine Fault Tolerance is developed to ensure the integrity of the model during its training process in a distributed environment. The proposed framework offers a holistic solution to enhance security and integrity in distributed machine learning environment without compromising the system performance. https://scientiairanica.sharif.edu/article_23829.html Insufficient training samples is one of the major challenges in deep learning, and one promising solution is data augmentation. Most existing methods for text data augmentation use a fixed strategy, in which some simple operations such as word replacement, insertion, deletion, and shuffling are selected randomly and applied to the text words that are also randomly sampled with equal probability. In this paper, a task-independent text augmentation approach is proposed, which, by weighting data augmentation operations using genetic algorithm, intelligently chooses the appropriate type and position of these operations for each sentences in the dataset. To evaluate the effectiveness of the proposed method, extensive experiments were conducted on several sentiment analysis datasets. In comparison with the baseline method (without data augmentation), EDA (a well-known task-independent method for text augmentation) and TTA (a state-of-the-art text augmentation method for sentiment analysis), the proposed method improves the average accuracy by 9.19%, 3.63%, and 1.04% on datasets of size 100, and by 5.27%, 3.18%, and 1.18% on datasets of size 500, respectively. https://scientiairanica.sharif.edu/article_23862.html Hydrophobic deep eutectic solvents (HDESs), due to many advances, have found profound applications in many fields including the extraction process. This study delves into the drops behavior of a green HDES, synthesized from dodecanoic acid and octanoic acid precursors (1:3 molar ratio) in separation of phenol from aqueous phase. The solvent has the desired properties of low interfacial tension with water and low viscosity. Based on various relevant criteria, the generated drops in a pilot column were in circulating mode and the terminal velocities were close to the Klee-Treybal model. In mass transfer study, as the key factor in evaluating the HDES performance, extraction fractions were found within (0.13−0.19) and the overall mass transfer coefficient within (12.48−24.09) μm/s, comparable with imidazolium-based ionic liquids as alternatives of DESs. For the aim of modelling, the mass transfer coefficient data were precisely reproduced according to the modified Newman’s equation, taking into account the local continuous phase mass transfer resistance and an effective molecular diffusivity. A maximum deviation of 9.3% was relevant to the experimental data compared with the model predicted values. The results highlight industrial scale application of HDESs for extracting pollutants. https://scientiairanica.sharif.edu/article_23863.html Harmful algal blooms (HABs) pose significant threats to public health, tourism, fisheries, and ecosystems. This study investigates the use of rice husk ash silica/chitosan composite films reinforced with (3-glycidyloxypropyl)triethoxysilane (CHT/SiO2/GPTEOS) and glycerol (CHT/SiO2/Gly)  for the controlled removal of toxic HABs cells, Alexandrium minutum. IR spectral results confirm that crosslinking within the films occurs through condensation reactions and hydrogen bonding between silanol (Si-OH), hydroxyl (-OH), and amine (-NH2) groups. The algal removal efficiency (RE;%) of CHT/SiO2/Gly was 26.5±10.81%, while CHT/SiO2/GPTEOS achieved a markedly higher RE of 50.06 ± 11.90%. The lower RE of CHT/SiO2/Gly was attributed to the film’s swelling, which allowed trapped algae cells to escape, and reduced electrostatic interactions between the negatively charged algae cells and the film surface. Digital microscopy analysis revealed that the algae cells attached to the CHT/SiO2/Gly ruptured due to the stress exerted by the amine groups. Meanwhile, the structure of the algae cells remained intact on CHT/SiO2/GPTEOS. The films were easily separated from the algae culture and exhibited excellent biodegradability, degrading completely within 30 days of burial in soil. These findings demonstrate the potential of CHT/SiO2/GPTEOS as an environmentally sustainable material for recovery and mitigating effects of HABs. https://scientiairanica.sharif.edu/article_23864.html Chalcocite ore, which has high silica but low iron contents, is uneconomical to smelt using conventional two stage smelting processes. In this paper, thermodynamic analysis and experimental verification of the direct-to-blister smelting of chalcocite ore were studied. The equilibrium compositions, slag phase diagrams and slag viscosities indicated that the direct-to-blister smelting of chalcocite ore is unreasonable, and the thermodynamic adjustment scheme shows that adding pyrite and calcium oxide to the chalcocite ore can make the slag type reasonable. The feasibility control plan of the slag type was verified by experiments, and the experimental data were analyzed by chemical composition analysis, XRD, EPMA and viscosity measuring apparatus. The experimental results are in good agreement with the theoretical calculations. The copper recovery is between 80% and 91%, while the content of copper in the slag is in the range of 8% to 15%, indicating that direct-to-blister smelting can be realized on a lab scale. https://scientiairanica.sharif.edu/article_23865.html Solvent based paint sludge contains volatile organic compounds (VOCs) that negatively impact both ecosystems and human health. These VOCs contribute to ground-level ozone formation and are key precursors in the creation of secondary organic pollutants in the atmosphere. There are limited studies that quantify the gaseous compounds released from solvent based paint sludge. In this context, we conducted our experimental study on detection of gaseous emission from paint sludge samples taken from an Algerian paint industry, in both short and long period of storage. Results revealed that paint sludge samples emitted a wide range of VOCs; over 36 compounds were identified, quantified and classified into different chemical groups. Among the compounds identified, aliphatic hydrocarbons, aromatic hydrocarbons, esters, ketones and alkanes. The collected sample from the waste storage area(PS2)contains a higher concentration of oxygenated compounds than those collected in the regeneration station (PS1) and in the waste storage chamber (PS3). Therefore, the gas emitted from paint sludge increased depending on the type and duration of storage. Given that many of the identified VOCs are carcinogenic, toxic, or harmful by inhalation, it is essential to regulate gas emissions during waste management and treatment. https://scientiairanica.sharif.edu/article_23866.html The aim to current investigation is to explore the thermal analysis for hybrid nanofluid with help of fractional model. The properties of hybrid nanofluid have been observed with interaction of titanium dioxide  and molybdenum disulfide  nanoparticles. Water and blood are used to characterize the properties of base liquid. The flow pattern is based on natural convective flow due to inclined surface. Two fractional algorithms namely Caputo-Fabrizio (CF) and Atangana-Baleanu (AB) are used to perform the analytical simulations. A comparative analysis between both AB and CF operators is presented to justify the accuracy of these fractional techniques. The flow model contains comparative impact of water based hybrid nanofluid  and blood based hybrid nanofluid . The numerical values of skin friction and Nusselt number are also calculated. Thermal observations are presented for both nanoparticles and base fluids. The computations reveal that heat transfer declined for hybrid nanofluid when fractional parameters have been considered. The velocity profile declined due to inclination angle and fractional parameters. Furthermore, Nusselt number enhances over time due to fractional effects. The claimed findings conveying applications in the thermal management systems, energy efficient systems, MHD technologies, industrial heat transfer, solar thermal collectors, heat transfer devices etc. https://scientiairanica.sharif.edu/article_23867.html This paper proposes a novel method based on histogram shifting using signed digits for data hiding. Our proposed method takes the prediction errors obtained from the original image using a 4×4 block-wise prediction. Then, we embed the information in the prediction errors of the image using the histogram shifting technique. A crucial point regarding the embeddable data in this method is that we divide the binary stream into equal parts of two, three, or four bits. For each two, three, or four-bit digit, we consider a numerical equivalent using the approach described in this paper. Subsequently, based on each of the signed digits, assigned floating numbers are used to represent the embeddable information instead of the binary stream. Experimental results for a sample image, "Airplane", with four-bit data segmentation demonstrate an outstanding embedding capacity of 825,080 bits and a PSNR of 33.87 dB, indicating that our proposed scheme achieves a remarkably high embedding capacity while maintaining an acceptable visual quality. https://scientiairanica.sharif.edu/article_23868.html In recent years, Parkinson's disease (PD) has become a global health problem. Early diagnosis of the disease has a high impact on the quality of treatment. Various machine learning methods and classification algorithms have been proposed to enhance the accuracy in PD detection. Accordingly, this paper proposes a hybrid intelligent system, which involves preprocessing using normalization, feature selection using an Improved Binary Whale Optimization Algorithm (IBWOA), and classification using a New Ensemble Learning Strategy (NELS). In this paper, IBWOA was used to choose the optimal subset of features for prediction, while NELS was employed to handle the learning process. The PD dataset required for the purposes of this research was extracted from the UCI machine learning database. The experimental results showed that the combination of preprocessing, feature selection, and ensemble learning gave a classification accuracy of 96.9231% for the PD dataset. The results also showed that both the main phases of feature selection and ensemble learning are effective in improving system performance. The detection accuracy of the proposed system improved by 0.9231% compared to the best model in the current literature.  https://scientiairanica.sharif.edu/article_23869.html The study develops a machine learning technique to predict structural deformation in corroded reinforced concrete (RC) beams, enabling a more accurate assessment of building structural health and potential long-term degradation risks. The method combines least squares support vector regression (LSSVR) with an innovative opposition sea-horse optimizer (OSH) to handle the nonlinear and multivariable aspects of deflection prediction. The OSH optimizer enhances the LSSVR's performance by fine-tuning its learning process. The research validated the predictive approach by analyzing 150 samples from deteriorating residential structures in southern Vietnam, employing cross-validation techniques to verify the model's precision and reliability. The OSH-LSSVR approach significantly surpasses traditional predictive models, including artificial neural networks, multivariate adaptive regression splines, and support vector regression. Empirical evaluation reveals exceptional performance metrics, with a root-mean-square error of 1.896 mm, mean absolute error of 1.198 mm, and a coefficient of determination of 0.891, underscoring its advanced predictive capabilities. The developed model provides civil engineers with an advanced tool for predicting RC beam deflection, opening new avenues for research in structural optimization, early warning systems, and proactive safety strategies. https://scientiairanica.sharif.edu/article_23870.html This paper proposes an accurate magnetic equivalent circuit (MEC) model for synchronous reluctance motors (SynRMs) which is including the limited number of permeances in air-gap, and iron parts of stator and rotor. In proposed approach, the complex geometry of SynRM is divided into some parts, and the conformal maps are then used to calculate the permeance of relevant part in different paths. The conformal maps such as Schwartz-Christoffel (S-C) mapping is used to transform the complex geometry of different parts of SynRM into the canonical geometry in rectangle form. The permeance in relavant direction is then easily calculated in canonical geometry. It should be noted that the permeance is maintained while mapping the geometry from the physical domain into the canonical domain. After calculating the linear and non-linear permeances, the non-linear system of equations is created through writing the magnetic equation in different nodes of motor geometry. An iterative method is then used to solve the system of non-linear equations, to determine the scalar magnetic potential in all magnetic nodes, and to calculate the motor performance. In final, the accuracy of proposed MEC model is verified through comparing with corresponding results obtained from finite element method (FEM) and experiment set-up. https://scientiairanica.sharif.edu/article_23871.html The permanent magnet brushless DC (PMBLDC) motor has proven enhanced performance over new classes of motors. The vector-controlled PMBLDC motor is mainly required for speed control drives such as electrical vehicles, robotics, and automotive industries. These motor drive systems typically incorporate a DC-link capacitor and position sensors. In PMBLDC motors, torque is significantly affected by the commutation pattern and phase current. The commutation process involves using the DC-link capacitor to reverse the current direction, resulting in considerable fluctuations in the torque profile. However, the conventional space vector pulse width modulation (SVPWM) technique falls short in effectively minimizing torque ripple, especially in automotive applications. To address this challenge, a modified field-oriented control (FOC) based SVPWM technique is proposed. This method prevents the reversal of DC-link current, thereby reducing torque ripple during commutation. The various operating modes of the proposed technique are comprehensively explained. By eliminating DC-link current reversal, the FOC-based SVPWM technique achieves a notable reduction in torque ripple. The proposed approach has been validated through simulations conducted using the Matlab Simulink toolbox, and experimental results obtained using an FPGA controller further confirm the effectiveness of the simulation findings. https://scientiairanica.sharif.edu/article_23872.html This paper introduces a novel approach for predicting magnetorheological (MR) valve pressure drop using deep neural networks. Rather than regressing the pressure drop directly, the proposed methodology predicts magnetic flux densities across different zones of the valve and then uses these predictions to compute the MR fluid’s yield stress and the valve’s pressure drop. The proposed approach can be further deployed for optimization purposes and provides insight into the magnetic field distribution within the MR valve as a function of design parameters. The approach leverages finite element simulations encompassing 125 variations of geometric parameters (gap sizes) and control parameters (electrical currents). A multilayer neural network architecture is tuned by testing 72 configurations of activation functions, numbers of hidden nodes, and layers, with model selection based on mean squared error and R^2. The final model demonstrates high fidelity with R^2 more than 0.98 for both training and testing. By capturing how magnetic flux density varies as a function of design and control parameters, the proposed framework facilitates efficient optimization and design of MR valves without exhaustive simulation. These results underscore the method’s ability to capture the complex dynamics governing MR valve pressure drop and provide valuable insights for valve sizing and performance prediction. https://scientiairanica.sharif.edu/article_23873.html Graphs are versatile mathematical models used to solve a wide range of practical and mathematical problems. However, meeting the specific requirements of applications, such as wireless sensor network key pre-distribution, small-world complex network modeling, and Ramsey theorems, necessitates graph designs with tailored characteristics and a combination of desirable properties. These properties include minimum vulnerability, minimum diameter, regularity, Hamiltonian connectivity, and minimal complete subgraph representation. Incorporating multiple such features into a single graph presents a challenge due to conflicting properties and the exponential difficulty of achieving a desired combination. To address this challenge and provide more realistic representations, we propose an algorithm for generating a novel class of graphs called Tight graphs. We conduct a thorough investigation into the properties of these graphs and demonstrate their potential applicability in diverse domains, including wireless sensor network key pre-distribution, small-world complex network models, and improved lower bounds for Ramsey numbers. Our algorithm enables the design of graphs that strike a balance between desirable properties, catering to the specific requirements of various applications. https://scientiairanica.sharif.edu/article_23874.html Nanoscience enables the manipulation of matter at the nanoscale, with biologically synthesized nanoparticles offering applications in medicine, optics, and agriculture. In this study, the antibacterial (phytopathogenic) effect of silver nano particles (AgNPs) produced using the aqueous extract of the aerial parts (flowers and leaves) of the Lonicera caprifolium was investigated using the Agar well diffusion method. UV-Vis spectroscopy (300–700 nm) confirmed AgNP synthesis, while SEM/TEM revealed spherical nanoparticles sized 10–50 nm (flower extract) and 10–80 nm (leaf extract). FTIR identified organic compounds involved in reduction. Based on the results of this research, the nanoparticles produced by the aqueous extract of the Lonicera caprifolium have effective antimicrobial activity against Pectobactrrium carotovorum subsp carotovorum (Pcc). In this way, the aerial parts (flowers and leaves) of Lonicera caprifolium can be used as a useful biological resource for the synthesis of AgNPs on an industrial scale at a very low cost. This research aims to contribute valuable insights into the potential use of plant-mediated silver nanoparticles as effective antibacterial agents for combating phytopathogens and addressing challenges in agricultural disease management. https://scientiairanica.sharif.edu/article_23875.html In this study, seeds of C.sativum purchased from the market were extracted using Soxhlet extraction in vitro, from which 38 active components were confirmed by GC – MS and FT – IR analysis. All 38 ligands were docked using AutoDoc Vina with the four proteins (PDB ID: 4F6X, 3ACX, 2ZCQ, 2ZCS) obtained from RCSB PDB. Out of 38, two ligands from plant extract (2,4-DTBP and 3-ethyl-5-isopropyl-2-methyl phenol) showed good docking scores of more than -7.5 kcal / mol. Among those four proteins, PDB ID 4F6X has the highest docking affinity of -7.9 kcal / mol when compared to the other compounds. Well diffusion method confirmed the antimicrobial activity. In silico analysis using ADMET studies showed that the compounds have high log Kp values, high GI, BBB permeability, high HIA, and low toxicity levels. Non-violation of Lipinski’s rule of five was found in both the ligands. Also, the values of hydrogen Bond, RMSD, RMSF, SASA and Rg fall within a good range. In post-MD, MM–PBSA studies, the results showed that the total binding free energies are more contributing in 3ACX and 2ZCS complexes than in 4F6X and 2ZCQ complexes. Hence, both the in vitro and in silico studies provide the evidence that the two active components have good interaction with the four protein targets. https://scientiairanica.sharif.edu/article_23876.html Background: Musculoskeletal injuries are common diseases among workers causing a substantial economic burden on society. Therefore, assessment of musculoskeletal injury risks during occupational activities is essential for the design of subsequent effective interventions and management programs. Methods: Risk of three occupational activities performed by one worker in a shot-peening station of a car manufacturing company has been investigated using several biomechanical (i.e., musculoskeletal models such as HCBCF, Regression models, 3DSSPP and AnyBody) and ergonomics (e.g., Washington State tables, WISHA, NIOSH, MAC, Snook’s Table, ManTRA, QEC, OWAS, REBA, and RULA) risk assessment tools. The worker’s activities involved manual material handling of gearbox shafts and pushing/pulling of a carrier containing these shafts. Results: Our findings indicated a high risk of musculoskeletal injuries during all activities. Therefore, engineering and administrative interventions were provided. After the interventions, injury risk during pushing and pulling activities was fully managed to a safe zone by using overhead cranes. The lifting task was also rendered within a safe zone through the application of administrative interventions and using an appropriate work table. Conclusion: Comprehensive risk assessments by biomechanical and ergonomic tools, managed risks to safe levels, and load dynamics effect in risk assessments were considered. https://scientiairanica.sharif.edu/article_23877.html This manuscript unveils a novel approach to crafting a reconfigurable circularly polarized patch antenna, facilitating its operation across various frequency bands (ITS/WLAN/INSAT). The technique involves using a circular patch with defected ground having slotted symmetrical slits and two p-i-n diodes, which can switch between ON and OFF states to achieve far-field polarization reconfigurability among linear polarization (LP), right-hand circular polarization (RHCP), and left-hand circular polarization (LHCP). The antenna has been fabricated and shows good matching with the simulated results, indicating its potential for use in various wireless communication applications. The measured bandwidth (|S11|<-10dB) of the proposed antenna is 19.81% and 10% for the two LP modes, while the 3dB axial ratio bandwidth spans 4.56% and 8.33% for the two CP modes. Within these two bands, circularly polarized radiation with opposite polarization states is achieved, aligning with the impedance matching band (|S11|<-10dB). The designed antenna exhibits a peak gain of approximately 3.5 dBi and 2.9 dBi within the 3 dB axial ratio bandwidths in the lower and upper bands, respectively. https://scientiairanica.sharif.edu/article_23878.html In this article, a bi-objective location-routing mathematical model has been developed for the distribution of the Last Mile Delivery post parcels. The objectives of the model include minimization cost and tardiness penalty entered into the system due to the waiting of customers.  In order to solving the purposed mathematical model, a multi-objective Whale Optimization Algorithm (WOA) based on the Pareto archive and a Non-Dominated Sorting Genetic Algorithm (NSGA-II) have been used. The mathematical model solved for a sample problem by each of the algorithms, and the results have been compared according to the multi-objective evaluation criteria such as Quality Metric (QM), Diversity Metric (DM), Spacing Metric (SM), Number of Solutions (NOS), Mean Ideal Distance (MID), and computational time. The reviews of results indicate that the proposed multi-objective WOA has a higher capability in achieving accurate, diverse, and high-quality solutions rather than NSGA-II. In other words, the proposed multi-objective WOA act more efficiently to explore the feasible solution area of the problem by spending more computational time to achieve optimal solutions. https://scientiairanica.sharif.edu/article_23885.html Chromium (Cr) exists in various forms in surface water and groundwater. The high solubility and mobility of Cr(VI) make it a very hazardous material that pollutes water and soil ecosystems. In this study, Fe(II)-g-C3N4 was synthesized using melamine and FeCl3.6H2O via the thermal calcination method. Fe(II)-g-C3N4 was analyzed by XRD, FTIR, SEM, and DLS. It was shown that the g-C3N4 structure is a suitable distributor and stabilizer for iron particles. g-C3N4 reduced the accumulation of iron particles, increasing the efficiency of the redox reaction between Fe(II) and Cr(VI). The removal efficiency of Cr(VI) was influenced by pH, temperature, and the concentrations of Fe(II)-g-C3N4 and Cr(VI). Lower pH values, higher Fe(II)-g-C3N4 concentrations, and elevated temperature improved the kobs and Cr(VI) elimination from water. Cr(VI) adsorption efficiency in the 30, 50, and 70 ppm solutions by Fe(II)-g-C3N4 nanoparticles was 99%, 93.9%, and 80.7%, respectively. Furthermore, the highest kobs (0.0835 min-1) was observed at a 30 ppm Cr(VI) concentration. https://scientiairanica.sharif.edu/article_23887.html A highly accurate numerical algorithm has been preferred and used toget numerical solutions of solitary wave, boundary-forcing and waveundulation solutions of the nonlinear Equal Width Wave (EW) equation. Sincethe boundary-forcing solutions of the EW equation do not exist in theliterature it's firstly obtained successfully and introduced in this study.Wave generation with different values of the impulse, which is related to the forced-boundary in the EW equation, is investigated.Using low-order modified B-spline and less number of nodal points are twoadvantages of the present algorithm. Choosing modified cubic B-splinesprevents the appearance of the dummy points. To see the difference betweenthe present technique with other methods four applications existing in theliterature with many different values of parameters are investigated andcomparisons with nearly forty different techniques are reported. For all ofthe comparisons, undoubtedly present algorithm produces better resultsexcept only one method using more than three times nodal points. Theproduced invariants are also in good agreement with the exact values. Ratesof the convergence are computed. https://scientiairanica.sharif.edu/article_23889.html Music plays a major role in daily life and serves as a key means for expressing human emotions. Automatic classification of Iranian traditional music tracks is a fascinating yet challenging subject, particularly for those interested in Iranian music dastgahs (subgenres).This paper proposes a novel method for Iranian traditional music genre recognition using Persian music tracks. Six Iranian music genres, namely Shour, Nava, Mahour, Segah, Chahargah, and Homayoun, are considered. To accurately detect genres, convolutional neural networks (CNNs), one-dimensional convolutional neural networks (1DCNNs), and long short-term memories (LSTMs) are employed.All models are fed extracted pitch features, with the music pitch converted into a sequential note vector and a visual representation in the form of a graph illustrating the musical structure. Finally, an ensemble model combines the predictions from all models.The proposed approach is evaluated using the "Arg" database, which includes solo melodic instrument tracks with no limitations on playing style, instrument, tempo, or techniques. The proposed method achieved a recognition accuracy of 77.35%, which improved to 80.44% with the use of data augmentation techniques.The experimental results, including accuracy, F1 score, and standard deviation (STD), demonstrate the effectiveness of the approach, showing better performance compared to other methods for Iranian genre recognition. https://scientiairanica.sharif.edu/article_23890.html This paper presents a novel tri-band antenna achieved through Characteristic Mode Analysis (CMA). The antenna's tri-band characteristics are realized by exciting two orthogonal modes and one higher-order mode at 2400 MHz, 3500 MHz, and 5200 MHz for Wi-Fi, 5G Sub-6, and WiMAX applications, respectively, with a broadside radiation pattern. The proposed antenna design involves modifying a circular patch antenna by inserting three equilateral triangle slots. The surface current distribution is reshaped during the antenna modification to achieve the desired frequencies with broadside radiation patterns. The addition of equilateral triangle slots increases the surface current path, resulting in a miniaturized antenna structure with size 0.32x0.32x0.01 in which is wavelength of lover band 2400MHz. All required modes are stimulated using a 50Ω coaxial feed line in full-wave electromagnetic simulation after the optimization of the proposed antenna using CMA. The proposed antenna's prototype is made of low-cost FR4 material and is validated by experimentation. The measured operating resonance frequencies, with impedance bandwidths at S11 ≤ −10 dB, are found to be 2395 MHz (2380-2420MHz), 3500 MHz (3450-3550MHz), and 5283 MHz (5200-5348). Corresponding broadside gains are measured at 5.6 dBi, 6.3 dBi, and 6.9 dBi, respectively. https://scientiairanica.sharif.edu/article_23891.html Wind energy plays a pivotal role in the transition to sustainable energy sources. This paper presents a robust control strategy, the Variable Gain Super Twisting Sliding Mode Control (VGSTA-SMC), applied at the maximum power point tracking (MPPT) level. VGSTA-SMC is designed to precisely locate the MPPT, maximizing wind energy extraction while enhancing tracking performance and system stability. The primary objective is to improve the performance of Doubly Fed Induction Generator (DFIG) wind turbines under sudden wind speed variations by reducing the chattering phenomenon, managing external disturbances, and addressing slow response issues. The effectiveness of the proposed control strategy is verified by comparing it with Third-Order Sliding Mode Control (TO-SMC) and Proportional-Integral (PI) control. Simulation results using MATLAB/Simulink demonstrate that the proposed method enhances resilience to wind speed fluctuations and system uncertainties, ensuring smooth and efficient operation. By overcoming the limitations of conventional control methods, this strategy supports the broader adoption of wind energy systems. The study underscores the potential of advanced control techniques in optimizing renewable energy systems. https://scientiairanica.sharif.edu/article_23892.html Abstract: In modern engineering, optimizing energy hub-based microgrids that incorporate renewable energy resources to meet both electrical and thermal demands presents a significant challenge. This study focuses on the stochastic optimization of a multi-carrier energy microgrid, integrating renewable energy sources to enhance efficiency and reduce operational costs. A demand response program is employed to optimize the allocation of costs and improve the load profiles for both electricity and thermal energy. To address the uncertainty of renewable resources, a scenario-based planning approach is implemented to reduce the impact of variability. The model schedules energy production and consumption for a 24-hour period, with objective functions targeting energy purchase costs, fuel costs, profits from energy sales, and greenhouse gas emission reduction. The proposed methodology is tested on a sample microgrid system using Python solvers for optimization. Results, analyzed under various scenarios, show a significant reduction in costs when compared to conventional systems. Specifically, the total cost for meeting electrical and thermal demands through the traditional electricity and gas network is 279,910 cents, while the optimized system reduces the cost to 164,682 cents, yielding a savings of approximately 41%. These findings highlight the effectiveness of the proposed optimization model in reducing both costs and environmental impact. https://scientiairanica.sharif.edu/article_23893.html Global warming, fueled by rising greenhouse gas concentrations in the atmosphere, stands as a significant environmental challenge in the 21st century. The mounting concerns have sparked a surge in research efforts aimed at harnessing CO2 conversion into valuable compounds. This investigation delves into the synthesis and characterization MI-101(Fe)-NH2-GO-Fe3O4 photocatalyst, particularly focusing on the impact of NH2 surface modification on MIL-101(Fe) in methanol production enhancement. The results notably indicate an enhancement in the performance of the photocatalyst following the surface modification of MIL-101(Fe). Notably, MIL-101(Fe)-NH2-GO with 10% Fe3O4 photocatalyst exhibits the narrowest band gap at 1.68 eV, indicating superiority in catalytic activity. The study further establishes a p-n heterojunction in MIL-101(Fe)-NH2-GO with 10 Wt.% Fe3O4 photocatalyst, signifying advanced catalytic properties. An experimental framework utilizing design of experiment was implemented to investigate the influence of various factors, including time, catalyst dosage, weight percentage of Fe3O4, and pH, on operational parameters, with the aim of optimizing these variables. The study achieved methanol production of 2978.25 mg/L under optimal conditions, using 0.46 g of MIL-101(Fe)-NH2-GO with 7.82% Fe3O4 photocatalyst in 107 min at pH 5. Further examination of the process through GC-MS identified methanol, formamide, and formaldehyde as the main products. https://scientiairanica.sharif.edu/article_23895.html Manual material lifting is a common activity in daily life and industrial work environments, placing significant stress on the L5/S1 joint in the lower back. This study aims to compare the biomechanical impacts of squat and stoop lifting techniques on the L5/S1 joint to assess load distribution and potential injury risks in manual handling tasks. In contrast to previous studies using boxes with handles, our experiments involve lifting cargo boxes from the bottom without handles, providing a more realistic simulation for cargo handling workers. Five healthy male participants performed squat and stoop lifts with weights of 4, 8, 12, and 16 kg. Markerless motion capture was conducted using the Kinect v2 sensor, and kinematic and kinetic data were analyzed with the OpenSim biomechanical modeling software. Results indicated that squat lifting reduced compression forces by approximately 9% and shear forces by 25% at the L5/S1 joint compared to stoop lifting for heavier loads. These findings align with previous literature, demonstrating that squat lifting may better distribute loads across the lumbar spine, suggesting it as a potentially safer method for handling heavier loads. https://scientiairanica.sharif.edu/article_23896.html According to the advantages of consequent pole machines, this paper presents a fast analytical model for estimating the components of the PM flux density distribution in the air gap for an axial flux permanent magnet consequent pole generator (AFPM-CP). The sample generator has a double-sided structure with a sector coil and poles. N identical poles (with only identical N poles in sequence and iron poles between them) are installed on the rotor, and the stator is placed between the rotors. The stator consists of coils that are wrapped concentrically around the teeth. A one-dimensional analytical solution based on the magnetic equivalent circuit method and a two-dimensional analytical solution based on the finite element method were presented to predict the performance characteristics of a three-phase AFPM-CP under no-load operating conditions. The effects of the stator slots have been studied using the air gap and flux path function methods. For verification purposes, the magnetic equivalent circuit (MEC) analytical results are compared with those of the finite element method (FEM). To further validate the MEC analytical and FEM results, 3D-FEM analysis has also been performed. This means that the proposed MEC method is effective for the AFPM-CP machine. https://scientiairanica.sharif.edu/article_23897.html Production of LiMn2O4 from aqueous solutions containing LiNO3 and Mn(NO3)2•4H2O in a premixed flame-assisted spray pyrolysis reactor was investigated. The effect of air to fuel flow ratio and residence time on flame length, flame temperature, product crystallinity, mean particle diameter, and yield were investigated. The effect of fuel flow and the concentration of the reactant solution on the average particle size and yield were studied. According to the thermal gravimetry test, LiNO3 decomposed to Li2O at 700°C and Mn(NO3)2 decomposed to manganese oxides between 180°C to 500°C. Also, the mixture of reactants converted to LiMn2O4 at about 480°C. The XRD patterns revealed Mn2O3 as the main impurity. The average size of the crystals was in the range of 20 to 50 nm. The dynamic light scattering test showed that the maximum and the minimum mean particle sizes were 436 and 210 nm, respectively. The highest product separation efficiency was 30%. Further investigation is needed to identify and quantify loss mechanisms such as wall deposition and gas-phase nucleation leading to filter bypass. Future improvements could include optimizing reactor geometry, using cyclone separators more effectively, and modifying the flame configuration to reduce particle loss. https://scientiairanica.sharif.edu/article_23898.html Among enhanced oil recovery (EOR) methods, low-salinity waterflooding (LSWF) stands out as a practical and environmentally friendly approach. However, oil-brine incompatibility can cause asphaltene instability, precipitation, and likely formation damage. Due to the lack of published studies at the pore-scale, this work uniquely visualizes asphaltene precipitation during LSWF using a microfluidic technique, allowing real-time observation of oil-brine interactions and damage assessment. Transparent glass micromodels are utilized to simulate oil displacement near the wellbore. Real crude oil samples and various synthetic brines are tested to analyze the effects of asphaltene content and brine salinity on precipitation and deposition pattern. The findings reveal a direct correlation between crude oil asphaltene content and deposition. Lower salinity brines formed stable emulsions, increasing asphaltene deposition in swept zones. In contrast, unswept regions, such as dead-ends, experience lower deposition. This behavior can be attributed to the limited fluid dynamics in unswept regions, where low shear and restricted oil–water contact reduce the formation of emulsions and consequently asphaltene destabilization. Notably, two-times diluted Persian Gulf water (2DSW, ~23,800 ppm) led to 6.8% volumetric precipitation, compared to 5.1% for high-salinity formation brine (FW, ~189,000 ppm). Despite slightly higher deposition with low-salinity brines, the associated salinity change risks—such as plugging, flow impairment, and formation damage due to asphaltene deposition—are negligible under the studied conditions. This is a favorable outcome for deployment of LSWF. Moreover, the study highlights the critical role of brine composition and oil characteristics in LSWF, emphasizing the need for oil-brine compatibility assessment to mitigate any asphaltene-related damage and ensure effective recovery. These insights are essential for optimizing LSWF and minimizing formation damage risks in practical applications. https://scientiairanica.sharif.edu/article_23899.html The present study focuses on the investigation of fatigue characteristics of fibrous composite materials with mixed mode delamination. An experimental approach was adopted, wherein a newly designed testing device was utilized to conduct fatigue tests under constant loading conditions at ambient temperatures. The tests were carried out at constant speed at 10 Hz using a variable speed drive to control the frequency. The influence of initial crack size on different forces was examined by testing rectangular specimens with fiber orientations of [0, 90º]16s. The specimens were fabricated using woven roving E-glass fibers and polyester resin. The test results revealed that the delamination propagation was highest in the upper quarter of the specimens' cross section under various applied forces. Additionally, the cracks in the middle and lower quarters of the cross section exhibited similar propagation behavior. The study also discussed the relationship between cycles to failure, crack length, and the impact of delamination on the fatigue life of the specimens. https://scientiairanica.sharif.edu/article_23900.html The influence of MHD upon the Marangoni boundary layer of hybrid nanofluid flow along an extended surface is mathematically analysed and presented in this work. Similarity transformations reduce the controlling equations to dimensionless form. The reduced simultaneous equations can be analytically resolved. The semi-analytical expressions for the dimensionless velocity as well as temperature are attained by utilizing Ananthaswamy Sivasankari technique (ASM) and Modified q-Homotopy analysis approach (Mq-HAM) respectively. The results of the dimensionless quantities for the amounts of physical components including dimensionless skin friction coefficient and non-dimensional Nusselt number are depicted in tabular and graphical forms to interpret significant consequences. The physical parameters involved in the model such as the Marangoni parameter, Magnetic field factor, Prandtl number, volume fraction of nanoparticles and constant mass flux velocity are depicted graphically to show their effects on the velocity and temperature respectively. The applied magnetic field parameter's direction and strength have a significant impact on the fluid flow. The skin friction factor and local Nusselt number are important parameters in MHD Marangoni boundary layers. Our results on these parameters are used in fields where precise control of heat transfer and fluid flow is essential, such as crystal growth, microfluidics, welding processes, and the manufacturing of electronic components. The proposed technique shall be extended to address non-linear challenges in physical science especially MHD flow issues. https://scientiairanica.sharif.edu/article_23901.html Gas injection is a widely applied enhanced oil recovery (EOR) technique, but its efficiency is often limited by gas channeling and gravity override in high-permeability zones. To address these issues, this study explores the use of natural polymer lignin as a novel foam stabilizer in gas-injected EOR systems. Lignin’s amphiphilic nature enables it to improve foam stability under harsh conditions, including high salinity and oil contamination. Laboratory experiments were conducted using two surfactants (CTAB and SDS), three oil types (heptane, toluene, heptol), and aqueous systems with varying salinity (distilled water and synthetic seawater). Surface tension measurements, static foam generation, and foam stability assessments were performed. Results showed that lignin significantly enhances foam stability, particularly when combined with CTAB. In distilled water, increasing lignin concentration from 100 to 2000 ppm led to a more than threefold increase in foam half-life, from 241 to 801 minutes. Moreover, CTAB–lignin systems showed superior performance compared to SDS or lignin alone, especially under saline and oily conditions. These findings suggest that lignin-enhanced foams are promising, cost-effective, and environmentally friendly candidates for improving gas mobility control in EOR operations, particularly in complex reservoirs. https://scientiairanica.sharif.edu/article_23902.html In this study, we proposed a dynamic heat transfer model for the thermal simulation of single-phase flow in heated pipes, which are widely used in industrial applications such as power plants, renewable energy systems, and other forced convection systems. At present, the proposed model achieves real-time calculations relied on lumped parameter models (LPM). While distributed parameter models (DPM) offered a better computational accuracy. Unlike traditional LPMs, which often oversimplify transient dynamics, our model incorporates a dynamic heat transfer equation with explicit variable representation, allowing for explicit time-marching calculations. Comparative analyses with DPMs and LPMs demonstrate that our model achieves higher accuracy than LPMs while maintaining computational efficiency suitable for real-time applications. This advancement addresses the limitations of existing methods, providing a cost-effective and precise solution for simulating heated pipe dynamics under transient conditions. Engineering applications could benefit from this study by incorporating the model as part of simulators for real-time optimization of thermal systems, such as single-phase heat transfer sections in power plant boilers and industrial systems. Future prospects include extending the model to account for segmented calculation factors such as water and superheated steam segments in two-phase flow, further improving its computational efficiency and applicability in complex industrial scenarios through moving boundary modeling. https://scientiairanica.sharif.edu/article_23903.html Fused deposition modeling (FDM) is a rapidly growing 3D printing technology. The continuously developing 3D printing technology allows parts with different structures to coexist in one part. In the study, the effects of using 0.1, 0.2, and 0.3 mm layer thicknesses determined within the same structure on tensile strength and build time were experimentally investigated. The tensile test sample thickness (3.6 mm) was divided into three sheets with different layer thicknesses, and the effect of the ratio of the inner sheet thickness (1.2 mm, 1.8 mm, and 2.4 mm) to the sample thickness (33.33%, 50%, and 66.66%) was also evaluated. The experimental results were also analyzed using the Pareto front multi-objective optimization method. While the lowest tensile strengths were obtained in samples where 0.2 mm and 0.3 mm layer thicknesses were used together, the highest tensile strengths were obtained in samples where 0.1 mm layer thickness was used together with 0.2 mm and 0.3 mm layers. Considering the build times of samples with fixed and variable layer thicknesses without compromising on strength, approximately 20% savings were achieved in the build times of samples with variable layer thicknesses. However, when tensile strength and production time are optimized, optimum results for the most balanced solution were obtained using 0.3 mm outer sheet layer thicknesses and 0.1 mm inner sheet layer thicknesses together. This situation also directly affects production costs. As a result of the study, it was determined that variable layer thickness is significant in terms of both strength and build time. https://scientiairanica.sharif.edu/article_23905.html The effect of production inputs on product properties in the production of polyethylene terephthalate (PET) material by additive manufacturing method was investigated and optimum production parameters were tried to be determined. The degradation of PET material in different filling patterns, mechanical and physical properties of the samples produced at different print speeds, layer thicknesses and filling patterns were investigated. In the study, it was determined that the most effective input on the mechanical properties of the samples was the filling pattern, followed by the print speed and the least effective input was the layer thickness. However, it was determined that the most effective inputs on the surface roughness and dimensional accuracy of the produced samples were layer thickness and filling pattern, respectively. Since the degree of influence of the filling pattern on mechanical and physical properties is higher than other production inputs, hydrolytic degradation properties of 9 different filling patterns were investigated. https://scientiairanica.sharif.edu/article_23906.html Laminar non-transfer arc of laminar plasma arc has the characteristics of long jet length, low temperature gradient, and good controllability, and is often used for cutting, spraying, and surface treatment, but rarely for welding due to its lower arc heat. The combined laminar plasma arc, composed of the laminar non-transfer arc and the transfer arc, has better welding penetration, but research on its welding characteristics is still lacking. The arc behavior is studied in this paper to expand its application range. The experimental results show that the laminar plasma arc can be obtained with the proper shielding gas and plasma gas flow. The maximum arc ignition height is mainly determined by non-transfer arc current, while arc shape and arc pressure mainly depend on transfer arc current. The arc pressure sharply decreases with the increase of arc ignition height below 20 mm. Furthermore, potential welding applications and their prospects have been discussed. It was found that the transfer arc plays a crucial role in welding formation, while the non-transfer arc can enhance welding adaptability due to the control of arc ignition height. This heat source is highly suitable for thick plate narrow gap welding and shows promising prospects. https://scientiairanica.sharif.edu/article_23908.html Thrombolytic therapy is an effective method for dissolving blood clots that block cerebral arteries and cause strokes. Through this procedure, plasminogen activators are used to recanalize vessels and restore blood flow. This study investigates the dissolution of clots with different structures using three plasminogen activator drugs. Fibrin clots with coarse and fine fibrin fibers, as well as retracted clots representing aged clots with reduced serum, are analyzed. The dissolution model includes the dynamics of flow within the vessel and clot, the elasticity of the vessel wall, and its interaction with the fluid. Drug transport into the clot is modeled by convection and diffusion. The results indicate that treatment regimens with alteplase, reteplase, and tenecteplase are safe and effective in dissolving clots across all structures considered. However, lysis activation time and vessel recanalization time are significantly shorter with tenecteplase and alteplase compared to reteplase. Additionally, coarse clots with larger fibrin fibers dissolve faster than fine clots, and retracted clots require nearly twice as much time to dissolve. https://scientiairanica.sharif.edu/article_23910.html This study investigates and quantifies the bullwhip effect (BWE) as a coordination tool in the supply chain under the revenue sharing contract (RSC). The network considered consists of one or more main and rival suppliers in one layer, and multiple retailers in another layer. RSC contracts are being signed between suppliers and retailers, and retailers adopt the substitution of similar commodities in a competitive environment. This study examines different demand forecast methods, analyzes the largest lower bound for the variance ratio of demand estimation, and presents a new model to reduce BWE and determine the optimal contract parameters. Results show that the variance ratio index of the total demand of suppliers to the variance of the total demand of retailers outperforms other indicators. In the presence of RSC, this ratio is lower than in the case without a contract, there by preventing BWE amplification. Based on the results of the numerical analysis, the variance ratio of demands under the revenue-sharing contract was lower than in the scenario without a contract. The largest lower bound with a contract was 0.026, while it was 0.1 without a contract, indicating that the Bullwhip Effect (BWE) is amplified in the absence of a contract. The analysis of variance changes in retailers' demand estimates by suppliers, based on contract profit percentages, revealed that the BWE decreases when suppliers increase the contract profit percentage for retailers within the intervals of 0.4-0.5 and 0.6-0.8. https://scientiairanica.sharif.edu/article_23912.html In the context of intelligent transportation systems real-time vehicle detection and tracking on highways present significant challenges due to the complexity of high-resolution imagery, varying lighting conditions and occlusions. Existing systems often struggle to balance computational efficiency with the ability to detect fine-grained vehicle attributes. This paper proposes FYNet, a novel architecture based on YOLOv5, designed for real-time vehicle localization and attribute identification. FYNet introduces a novel Path Aggregation Network to enhance multi-scale feature extraction, reduce computational overhead, and improve detection accuracy for objects of varying sizes, from license plates to long vehicles. With five outputs at different resolutions, FYNet achieves a robust detection across all object sizes, an inference speed of 16.3ms per 4K image (60 FPS) , and reduces computations to 0.6 GFLOPs. The StrongSORT method, which is based on DeepSORT, is used to track the vehicles and assign a single ID for each one. A simple yet effective strategy of separating front and rear views of vehicles into distinct classes also improved the model's mean average precision (mAP) by 1.8%. It also helped the model to recognize the characteristics of the vehicles with fewer parameters and higher accuracy. To evaluate the model, a private dataset, called IRVA, with more than 300K labels from 11 classes, is prepared. It has several new features compared to the existing industrial datasets in ITS. The FYNet outperforms the standard YOLOv5 in both inference speed and accuracy, achieving a 0.6% improvement in object recognition accuracy while maintaining real-time performance on 4K images. https://scientiairanica.sharif.edu/article_23913.html In recent decades, multi-domain (air-water) optical wireless communications have garnered significant attention from both scientific and civil communities due to their diverse impacts and applications. However, in addition to security challenges, these types of communications face limitations regarding direct cross-medium communication, transmission capacity, transmission range, and energy consumption due to water properties and reflections occurring at the interface between the two mediums. While various solutions have been proposed to address these challenges, the majority of them are either not energy-efficient or fail to guarantee communication security in specific applications. Therefore, in this research, we propose a secure covert communication protocol with energy efficiency for multi-domain communication applications to address the aforementioned challenges. In this protocol, to enhance security and reduce bandwidth consumption, data is sampled based on its entropy and then simultaneously compressed and encrypted according to its sparsity level. Next, the resulting output is modulated onto amplified spontaneous emission (ASE) noise, hidden, and spread out over time using a chirped fiber Bragg grating (CFBG). The signal is then transmitted through a wide-field optical system. Here, we utilize an array of ultrasonic sensors and a prediction algorithm to calculate the optimal water surface impact point. We also, implement laser diode switching to optimize energy efficiency and enhance data transmission capacity, while utilizing On-Off Keying (OOK) pulse-based modulation to minimize implementation complexity. https://scientiairanica.sharif.edu/article_23914.html This study examine and analyze the amount of infrared radiations from top of the atmosphere using the NASA CERES Ordering tool data and other techniques. These radiations are responsible for the excessive heat waves in the upper atmosphere. Collected the data from the website with the help of NASA satellite named EBAF 4.2. The data barred into clear and all-sky monthly data. Used panoply software first to convert the NC file to CSV which is readable for excel. Then mark the maximum values and average of months and do the graphical analysis after the daily data conversion which is provide the information of the graph starting and ending and the difference between the values of all-sky and clear sky. In the end, concluded that the hotter years have higher IR flux values at that upper atmosphere just like 2014 and some other years, and these affected due to some major factors that discussed, such as cloud cover, surface temperature, greenhouse gas variations etc. Clouds that are present in the higher altitude known as the cirrus clouds absorb incoming radiation. The earth varying temperature controlled by incoming and outgoing radiations and another factor is greenhouse gas variation in the surface temperature. https://scientiairanica.sharif.edu/article_23915.html Wide use of radioelectronic systems requires more complex antennas. Determining the location of the excitation source (feed) in a complex antenna structure plays an important role and depends on many factors. Currently, this choice is often made based on the designer experience and requires careful modeling and analysis. One of the popular methods used in such programs is the characteristic mode analysis (CMA). However, previous CMA-based studies that determine the source location often considered the excitation of low-order modes. In this paper, we present an algorithm to determine the optimal source location in a wire grid antenna that excites all modes reasonably well. This algorithm is based on the product of characteristic current and modal significance to obtain the maximum possible current distribution along the an-tenna wires and radiation field. The proposed algorithm is tested here on the example of dipole and conical spiral structures in the frequency ranges of 0.1–1 GHz and 9–11 GHz, respectively, and verified by the method of moments. https://scientiairanica.sharif.edu/article_23917.html A study on agave fibers shows a strong correlation between fiber diameter and the mechanical behavior of gypsum/agave biocomposites (GAB). The finest fibers (0.03 mm) demonstrate very high tensile strength (2853.03 MPa) and a significant elastic modulus (225.83 GPa), giving the composite great stiffness but also increased brittleness. When mixed into a gypsum matrix, agave fibers generally improve the material’s performance. However, under compression, adding fibers reduces strength (from 7.96 MPa to 1.69 MPa at 3 wt%) due to increased porosity and weak bonding between fiber and matrix. Using a moderate fiber content (1 wt%) enhances ductility and flexural strength (3.35 MPa), while also increasing flexibility (flexural strain of 3.98 × 10⁻² %) without harming cohesion. On the other hand, fiber contents of 2 wt% or more reduce stiffness (down to 168.48 MPa at 3 wt%) because of weaker adhesion and higher porosity, which leads to structural weakness. Thermal conductivity also varies with fiber content. Adding 1 wt% slightly raises it (0.4445 W/m•K), while 3 wt% significantly lowers it (0.3075 W/m•K), improving insulation through better fiber dispersion and porosity control. Overall, agave fibers present strong potential for sustainable biocomposites, offering a promising balance between mechanical strength and thermal performance. https://scientiairanica.sharif.edu/article_23918.html This study proposes a hybrid deep learning approach combining U-Net and Generative Adversarial Network (GAN) architectures for the segmentation and texture-based reconstruction of 3D multi-mineral porous media images. The dataset consists of high-resolution 3D Leopard sandstone images, segmented into four key mineral classes: macro-pores, clay, quartz, and high-density minerals. Our approach leverages the feature extraction capabilities of a ResNet-18 backbone within U-Net, pre-trained specifically for multi-mineral segmentation, which then feeds these detailed features into a GAN framework for image reconstruction. The model effectively bridges segmentation and reconstruction, achieving superior image quality and structural fidelity compared to standalone GAN models by preserving intricate textures and maintaining macroscopic rock structures. Quantitative assessments reveal that the hybrid model yields porosity and absolute permeability values with minimal discrepancies (2.25% and 1.54% error, respectively) compared to actual data. These findings highlight the model's ability to replicate critical geophysical metrics and generate accurate 3D representations. Unlike traditional methods that either focused solely on segmentation or reconstruction, our model uniquely integrates segmentation-driven texture data for image reconstruction, offering a novel solution for geoscientific applications in hydrogeology, petroleum engineering, and environmental science https://scientiairanica.sharif.edu/article_23923.html This paper presents a comprehensive vulnerability analysis of LoRaWAN networks, focusing on the effects of electromagnetic interference (EMI) and power supply disturbance (PSD) faults on network performance. The study begins by outlining the fundamental principles of LoRaWAN, including its architecture, key management, and security features. It then delves into the potential vulnerabilities introduced by EMI and PSD, cause data corruption, and lead to security breaches. Through a series of physical experiments, a developed framework evaluates the impact of these factors on LoRaWAN networks under various conditions. The results reveal that EMI and PSD can significantly degrade the performance of LoRaWAN networks, leading to packet loss, increased latency, and compromised data integrity. Additionally, these vulnerabilities can be exploited by attackers to launch sophisticated attacks. The study also highlights the importance of continuous monitoring and adaptive security measures to ensure the resilience of LoRaWAN networks against EMI and PSD. Finally , this comprehensive vulnerability analysis underscores the need for robust security and reliability measures in LoRaWAN networks to safeguard against the adverse effects of EMI and PSD. The findings contribute to the ongoing efforts to enhance the security and performance of IoT networks, ensuring their reliable operation in diverse and challenging environments. https://scientiairanica.sharif.edu/article_23924.html Nowadays, many researchers focus on the use of clean energy instead of fossil fuels. The utilization of electric vehicles (EVs), in addition to reducing fuel consumption and associated costs and minimizing environmental pollution, allows for the recovery of a significant amount of energy during the braking mode. Another crucial advantage is the reduction of noise emissions when using EVs. The selection of an efficient electric motor with high power plays a vital role in EV applications. Also, the modeling process is growing more complicated as the need for greater efficiency, higher power density, and lower costs drives motors toward higher speeds and more compact designs. Consequently, interactions across multiple physical domains (such as electromagnetic, thermal, and structural) must now be considered, even during the early stages of design. In this paper, a design algorithm is introduced for the delta-shaped interior permanent magnet synchronous motor (IPMSM), facilitating the completion of the motor's initial design. The design optimization of this type of IPMSM for EV application is also considered. The motor's performance, demagnetization, mechanical stress, and thermal performance are evaluated using the finite element method (FEM). Based on the optimized design criteria, a suitable design can be chosen from the best set of designs obtained. https://scientiairanica.sharif.edu/article_23928.html Effective thermal management is crucial for ensuring the safety, durability, and efficiency of battery systems, particularly under high-load conditions. This research investigates the impact of various fan arrangements on the temperature characteristics of a lithium-ion (LiB) battery module. Three different battery thermal management systems (BTMS) were set and stated as BTMS I, BTMS II, and BTMS III. In BTMS I, one fan was located at the inlet, and a hole was left at the outlet. In BTMS II, two fans were located side by side at the inlet, with the outlet configured the same as in BTMS I. Lastly, in BTMS III, one fan was placed at the inlet, and another at the outlet. Reductions of up to 27.8% in maximum cell temperature (Tmax) and up to 57.2% in maximum cell-to-cell temperature difference (TD) were achieved with BTMS I and BTMS II, respectively. https://scientiairanica.sharif.edu/article_23929.html Accurate estimation of joint angles during limb movements plays a crucial role in the rehabilitation and diagnosis of neuromuscular and rheumatic disorders. This study aims to predict finger joint kinematics from surface electromyography (sEMG) signals using Long Short-Term Memory (LSTM) networks, which are well-suited for modeling temporal dependencies in physiological data. To enhance the model's generalization and reduce overfitting, four regularization strategies—LASSO, ridge, elastic net, and dropout—were systematically evaluated. Among these, LASSO and ridge regularization showed optimal performance when their coefficients were set to 0.0005, effectively balancing model complexity and prediction accuracy. While dropout was also beneficial, its performance declined at higher rates, with 0.2 identified as the most effective setting. The inclusion of appropriate regularization techniques led to a significant improvement in model accuracy, up to 20%, demonstrating their critical role in refining EMG-based kinematic estimation. The proposed LSTM model achieved a maximum prediction accuracy of 98% and an average of 96%, evaluated using the Pearson correlation coefficient. The results highlight the importance of selecting the appropriate regularization parameters to optimize both prediction accuracy and training speed in deep learning tasks designed to estimate joint angles. https://scientiairanica.sharif.edu/article_23930.html Online education has become increasingly popular in academic and scientific communities, and the COVID-19 pandemic has accelerated its adoption worldwide, particularly in developing countries. However, many instructors were unable to effectively utilize the available learning management system (LMS) educational tools due to their lack of experience, leading to less effective online education compared to traditional face-to-face teaching. To address these challenges, we propose an effective model for optimizing the usage of LMS educational tools by instructors. The proposed model is developed using Fuzzy logic and the Genetic optimization algorithm, and a flowchart is designed to facilitate its implementation. The model is applied to an undergraduate Physics course, and the output results are analyzed using statistical methods. Our findings demonstrate the positive impact of the proposed model on students' performance. The outcomes of this study can aid in the optimal design of online-course plans based on the utilized LMS educational tools while simultaneously improving students' learning. The proposed model has the potential to be adapted for use in various theoretical and, with slight modifications, for practical courses across different disciplines, contributing to enhancing the effectiveness of online education in the post-pandemic era. https://scientiairanica.sharif.edu/article_23933.html The present work aims at developing an Aluminium metal Matrix Composite (AMC) with an industrial waste Lime Stone Powder (LSP) as reinforcement, which is available in plenty at no cost. The main objective of the present study is to investigate the effect of four control parameters viz.: load (L), percentage of LSP reinforcement (R), sliding distance (D), sliding velocity (V) on two tribological properties wear rate (WR) and coefficient of friction (CF) of AMC reinforced with LSP. The composites are manufactured using the stir casting process with varying LSP percentages (0%, 4%, 8%, 12%, 16%). The wear test is conducted using Pin-on-disc apparatus. Taguchi L25 orthogonal array design is employed to investigate the effect of the above control parameters on the tribological responses. Analysis of Variance (ANOVA) employment indicates that wear rate depends on sliding distance and load, while the coefficient of friction depends on load and wt.% of LSP. The minimum wear rate of Al-LSP is found to be 0.1952, while the minimum coefficient of friction of Al-LSP determined is 0.0905. To minimize wear rate and coefficient of friction simultaneously, a composite objective function is created using Grey Relational Analysis (GRA). https://scientiairanica.sharif.edu/article_23939.html Time Series Alignment is a crucial task in signal processing with wide-ranging applications. Real-world signals often suffer from temporal shifts and scaling, leading to errors in raw data classification. This paper presents a novel Deep Learning-based approach for Multiple Time Series Alignment (MTSA). While existing methods mainly focus on Multiple Sequence Alignment (MSA) for biological sequences, there is a notable lack of alignment techniques for numerical time series. Traditional methods also typically address pairwise alignment, whereas our approach aligns all signals simultaneously, improving both alignment efficiency and computational speed. By decomposing to piece-wise linear sections, we introduce varying complexity into the warping function while ensuring compliance with three key constraints: boundary, monotonicity, and continuity conditions. We propose a deep convolutional network with a novel loss function that addresses key limitations of Dynamic Time Warping (DTW). Experiments on the UCR Archive 2018, involving 129 time series datasets, show that our method significantly enhances classification accuracy, warping average, and runtime efficiency across most datasets. https://scientiairanica.sharif.edu/article_23940.html This study investigates the multiphase leakage dynamics of high-pressure CO2 pipelines under near-field conditions, focusing on safety risks posed by phase transitions and Joule-Thomson cooling during accidental releases. A novel CFD-based real gas model is developed, integrating the Span-Wagner (S-W) and Peng-Robinson (P-R) equations of state to accurately capture liquid-vapor phase behavior. The Eulerian mixing model with user-defined functions (UDFs) is implemented to simulate gas-liquid phase transitions through controlled mass/energy source terms. Comparative analysis using k-ε and k-ω turbulence models reveals distinct jet structures under different leakage scenarios. Key findings include: (1) leakage aperture significantly impacts Mach disk formation and velocity decay; (2) initial pipeline pressure strongly influences temperature drop amplitude; (3) environmental conditions dominate low-temperature diffusion patterns. Turbulence model comparisons show k-ε performs better for large-aperture leaks (≥10 mm), while k-ω yields superior results for small-aperture cases (<5 mm). The validated model (R²>0.95 with experimental data) provides critical insights into velocity field evolution, cryogenic zone propagation, and shock wave formation. This work contributes to safer CCS system design by quantifying hazard zones and informing emergency response planning, with implications for pipeline integrity management and risk assessment frameworks. https://scientiairanica.sharif.edu/article_23942.html This paper presents an advanced hybrid control approach combining Adaptive Neuro-Fuzzy Inference System (ANFIS) and Finite Control Set Model Predictive Current Control (FCS-MPCC) for a T-type multilevel inverter (T-MLI)-driven Permanent Magnet Synchronous Motor (PMSM) for Electric Vehicle (EV) application. The proposed Hybrid ANFIS-FCS-MPCC technique, alongside the T-MLI, is designed to enhance the power quality and improve the motor response by reducing the torque ripples, noise-vibration harshness (NVH), and improving the transient and steady-state response. The proposed technique resulted in a notable reduction, including a 66% drop in torque oscillations, a 50% reduction in speed overshoots, and a 33% minimization of current harmonic distortion, in comparison to the conventional MPCC methodology. MATLAB/SIMULINK platform is used to simulate the proposed technique and a prototype is developed in the laboratory to validate the efficacy of the proposed technique, Further, a comparative analysis is performed with the conventional techniques to show the effectiveness of the proposed technique in terms of motor speed, torque ripples, and stator current response. https://scientiairanica.sharif.edu/article_23943.html In naturally fractured gas reservoirs with highly heterogeneous fracture distributions, high-velocity non-Darcy (HVND) flow tends to occur near the wellbore, especially in fracture-intensive zones. As flow velocity increases, inertial and nonlinear effects become significant, causing deviation from linear Darcy behavior. The conventional Darcy flow equation neglects these nonlinear factors and thus fails to represent the actual flow conditions accurately. To address this, a dual-porosity, dual-permeability two-region composite model is developed, applying Izbash’s equation to the inner HVND zone and Darcy flow to the outer zone. The model incorporates porosity and permeability contrasts between matrix and fractures, along with wellbore storage and skin effects. A semi-analytical solution is obtained using line source function, linearization, Laplace transform, and Stehfest inversion. Bottomhole pressure and derivative curves reveal seven flow stages, including non-Darcy crossflow, transition flow, and Darcy crossflow. The non-Darcy index quantifies HVND intensity; lower values indicate stronger nonlinearity. More intensive fractures enhance interregional transmissibility and storage, amplifying transition-stage responses. The proposed model effectively characterizes HVND behavior in gas reservoirs with spatially heterogeneous fractures, providing a theoretical basis for analyzing complex well test responses. https://scientiairanica.sharif.edu/article_23944.html A comparative study was conducted on Moringa, Watermelon, and Sunflower seed oils using conventional (n-hexane) and supercritical CO₂ (SC-CO₂) extraction to evaluate their potential for edible and industrial applications. Physico-chemical analysis revealed that SC-CO₂-extracted oils had slightly lower refractive indices and specific gravities, aligning with previously reported trends. Moringa oil extracted via SC-CO₂ demonstrated superior thermal stability, with a flash point of 288.50 °C and fire point of 296.40 °C, and lower peroxide (1.47–1.95 meq O₂/kg) and acid values (0.74–0.92%) compared to hexane-extracted oil (1.89 meq O₂/kg, 0.85%). Watermelon oil also exhibited improved thermal properties, with flash and fire points reaching 292.4 °C and 305.3 °C, respectively, and slightly reduced peroxide (1.8–2.9 meq O₂/kg) and acid values (2.24–2.93 mg KOH/g) than hexane-extracted oil (2.9 meq O₂/kg, 2.97 mg KOH/g). Similarly, Sunflower oil showed fire points of 339–342 °C, flash points of 314–316 °C, and peroxide and acid values of 6.15–6.93 meq O₂/kg and 3.13–3.86 mg KOH/g, slightly outperforming hexane oil (6.72 meq O₂/kg, 3.53 mg KOH/g). Fatty acid analysis confirmed high unsaturation: 66.04–79.98% (Moringa), 74.23% (Watermelon), and 88.13–91.14% (Sunflower), mainly cis-oleic and linoleic acids. SC-CO₂ extraction offers thermally stable, oxidation-resistant, and solvent-free oils. https://scientiairanica.sharif.edu/article_23957.html There have been some advances in multivariate control charts with the ability to monitor both the mean and variability of processes. However, due to the complexity of production processes, the assumption of single assignable causes is not close to the real-life conditions. As a novel contribution, this article proposes a new control chart for monitoring the mean and variability of a multivariate normal process which is under effects of multiple assignable causes. We develop a Markov chain model to compute the average run length and average time to signal (ATS) values. We also make it fully adaptive by varying all control chart parameters. The presented model involves complex non-linear models with a mix of continuous and discrete decision variables, and discontinuous, non-convex solution spaces. Therefore, one of the most suitable metaheuristic search approaches, Genetic algorithm is implemented. Numerical examples based on the Taguchi method are presented and sensitivity analyses are conducted to measure the performance of the proposed chart. https://scientiairanica.sharif.edu/article_23958.html Kiwifruit is widely consumed all over the world due to its nutritional compounds. It is recommended to prevent many types of diseases. The thermal concentration requires high temperatures, which destroy heat-sensitive compounds. Membrane processes are a suitable alternative to thermal processes due to the absence of high temperatures, simplicity, and lower cost. In this research, the juice of four varieties of kiwifruit, including Hayward, Abbott, Monty, and Allison, was clarified by ultrafiltration, and the permeate flux was measured at different pressures and flow rate. The results showed that the highest permeate flux was obtained at 2 bar and 20 mL/s. Also, Abbott and Allison had the highest and lowest product yield, respectively. The ultrafiltration permeate was treated by nanofiltration and the concentration value was investigated at different volume concentration factors (VCF). The results showed that by increasing the VCF to 4, the concentration of the target nutrient compounds begins in most varieties. Polyphenolic compounds, flavonoids, and antioxidant properties especially in Abbott and Hayward has increased about seven times. Hayward was seen to have the highest increase in polyphenolic compounds. The Hermia model showed that the cake formation mechanism was the dominant fouling mechanism in most of the processes. https://scientiairanica.sharif.edu/article_23959.html In the present study, microorganisms whose stimulus is influenced by gravity and viscosity is taken for consideration in Casson fluid saturated porous medium. The behaviour of microorganisms in Casson fluid due to gravitational modulation and internal heat generated in the system is studied analytically. Darcy-Brinkman model is implemented to analyze thermo bioconvection in horizontal flow of the fluid. Stationary mode of convection produces both linear and nonlinear stability. The threshold Rayleigh number, which addresses the initiation of bioconvection, is determined by linear stability analysis. This comprises parameters for which marginal stability curves are plotted to understand the stability and onset of convection of the system against wavenumber . The equation for Nusselt number is also used to describe nonlinear stability and to graphically illustrate how various parameters affect heat transfer in the system. In heat transfer analysis, the Ginzburg-Landau equation which characterizes the modulation amplitude is significant. Here, the behaviour of gyrotactic microorganisms in Casson fluid due to various parameters under the influence of gravity modulation and internal heat is picturised. From this study, we find that the Casson parameter decreases the critical Rayleigh number leads to the advancement of onset of convection and reduces heat transfer in the system. https://scientiairanica.sharif.edu/article_23961.html This study explored the double-stratified micropolar nanofluid stagnation point flow enclosed by porous medium with activation energy and chemical reaction. The phenomena of hydrodynamic magnetic field acting normal direction to stretching surface also incorporated here. System of governing equations are owned by key parameters and converted into ODEs with help of similarity transformation. Numerical solutions are encountered with the aid of Runge-Kutta-Fehlberg 5th order method, accompanied by shooting technique. Comparison between skin friction coefficient and local couple stress in micropolar constant and porous parameter with well-known research findings is also reported. Numerical formulation and investigation are made on local skin friction, local couple stress, heat and mass transfer rate in various parameters. A pictorial representation of abundant parameterson linear and angular velocity, temperature and concentration field is analyzed. The gradual gain of the micropolar constant enhances the fluid’s velocity and angular velocity profile. Solutal and chemical reaction rate constant are diminish the mass transfer of fluid. This study can be used in many eye-catching applications, such as biomedical engineering, lap-on-a-chip devices, cooling agent in electrical devices, automobiles and oil recovery processes. https://scientiairanica.sharif.edu/article_23962.html In this study, we numerically investigate two significant medical models, Ebola Viral Disease (EVD) and Blood Ethanol Concentration (BEC) models-both formulated using Caputo-fractional derivatives. We develop and apply the Modified Fractional Euler Method (MFEM) for their solution, with a specific focus on error analysis. Comparative studies with the classical Runge-Kutta fourth-order method (RK4M) demonstrate that MFEM provides a computationally efficient and accurate alternative for solving such systems. The major features of the given procedure are its ease of application to this type of problem and other systems in various fields, in addition to the absence of numerical errors accumulating. Finally, we can control the increase in the convergence rate and the stability of the simulation process. The convergence examination and error estimation for the suggested scheme are also included. The importance of this study also lies in its contribution to our understanding of the dynamics of these two models in their fractional form. In addition, those numerical investigations demonstrate how control parameters affect specific components within these models. https://scientiairanica.sharif.edu/article_23963.html This paper briefly introduces a novel blade motion simulator with a modular and scalable design that has the ability to simulate a combination of both pitching and plunging motions together with the possibility of adding bending or torsional compliances. The device can setup motions while adjusting amplitude, frequency, and time-lag independently. This option makes it possible to simulate both energy-harvesting and propulsive flapping motions. Also, the installation of different torsional and bending compliances with different spring stiffness and orientations can simulate different material properties in order to take aeroelastic effects into account during the motion. Moreover, some results of wind tunnel tests are also presented. These tests demonstrate that the oscillatory motion generated by the simulator largely conforms to the desired motion programmed into the device. Also, the results of measuring the lift coefficient have been compared with Theodorsen's theory, and a good accuracy has been observed. Also, in the following, as a practical example of the performance of the device for a specific case, the hysteresis loop of forces and torque of a blade has been analyzed and investigated. https://scientiairanica.sharif.edu/article_23964.html In this paper, a new structure for an axial flux consequent pole resolver (AFCPR) with concentrated windings on the stator is presented and analyzed. In this proposed design, the magnetic poles of the rotor are created by symmetrical saliences in the ferromagnetic core of the rotor instead of using permanent magnets, arranged in an alternating pattern with the same magnetic direction. By applying current to the concentrated windings of the stator along the axial direction, a suitable flux density is generated to produce iron poles. The main innovation of this structure lies in the elimination of permanent magnets and the simple, lightweight, and robust design of the rotor, which enhances reliability in position and speed estimation. The performance of the proposed resolver has been examined using two different winding patterns to generate sinusoidal and cosine signals, in order to analyze their effects on improving functional harmonics and reducing position error. Three-dimensional simulations using the 3D Finite Element Analysis (3D-FEA) method demonstrate that this resolver has adequate accuracy in position estimation and that the selection of the appropriate winding pattern can significantly improve positioning accuracy. https://scientiairanica.sharif.edu/article_23965.html Recently, the growing demand for customized products and advances in smart technologies have accelerated the shift toward cloud manufacturing (CMg). Although CMg offers high flexibility, its dynamic nature introduces major scheduling challenges, such as new task arrivals and strict delivery constraints, which are often overlooked in existing models. To address these limitations, this study formulates a dynamic scheduling problem in CMg (DSPCMg) that integrates new task arrivals with the objective of minimizing delivery time deviations. Given the NP-hardness of the problem, five well-established metaheuristic algorithms are implemented, and six hybrid algorithms are developed to achieve a better balance between global exploration and local exploitation. In addition to modeling dynamic task arrivals, the proposed framework incorporates sequence-dependent setup times, delivery time windows, and logistics considerations within a unified formulation. The performance of the proposed algorithms is evaluated using test problems and 30 benchmark instances for both the scheduling and rescheduling stages. Computational experiments show that the hybrid KA-TS algorithm achieves the best performance in the scheduling stage, whereas GA-TS performs best in rescheduling scenarios. Moreover, the proposed rescheduling approach reduces delivery deviation by up to 45% and machine idle time by up to 32% compared with fixed initial schedules. Finally, sensitivity analysis further highlights that increases in logistics times and the number of new tasks significantly raise delivery time deviations. https://scientiairanica.sharif.edu/article_23966.html Epilepsy is a critical neurological disorder affecting millions worldwide, requiring accurate and timely detection to prevent life-threatening complications. Clinical devices achieve high accuracy in seizure detection, ensuring reliable medical monitoring. However, the demand for wearable devices necessitates lightweight, low-power, real-time solutions. Wearable EEG-based seizure detection requires efficient signal encoding to optimize power consumption while maintaining classification accuracy and computational efficiency. In this study, we hypothesize that applying Linear Predictive Coding over long EEG segments provides a computationally efficient approach suitable for wearable applications. To evaluate this, EEG signals were analyzed using Linear Predictive Coding, Discrete Wavelet Transform, and Power Spectral Density-based features, and classified using Multilayer Perceptron, Random Forest, and Support Vector Machines. Among the tested combinations, the Linear Predictive Coding and Random Forest model achieved the best energy efficiency with an average consumption of 2.73 microjoules per percent and classification accuracy of 93.18%. One-way analysis of variance showed no significant accuracy difference among feature extraction methods (p = 0.856) but revealed a significant difference in energy efficiency (p = 1.93 × 10⁻⁷⁵). These findings demonstrate that Linear Predictive Coding is a promising technique for wearable seizure detection, offering a balance between accuracy and energy efficiency for next-generation medical applications. https://scientiairanica.sharif.edu/article_23967.html Due to technological advances, efficient structural systems are increasingly required in modern industries. Many industrial buildings, such as warehouses, hangars, fire stations, and sports halls, are designed with large spans and non-flat roofs, often sloped or curved. This paper presents a comparative optimal design of steel truss sheds with two roof types (sloping and curved) and two column types (rectangular and circular). The Enhanced Colliding Body Optimization (ECBO) algorithm is employed, offering a balanced mechanism of exploration and exploitation, which improves efficiency compared to algorithms like PSO and GA. The design process follows AISC specifications, considering stress, displacement, and slenderness limitations. Structural members are modeled with discrete cross-sectional variables, and the trusses are analyzed under dead, live, snow, wind, and earthquake loads. Results indicate that the truss with a sloping roof and rectangular columns achieved the minimum weight while maintaining uniform stress distribution. This configuration demonstrated superior structural performance regarding strength and serviceability, highlighting its suitability for industrial applications. Overall, the findings emphasize the role of optimization methods in enhancing structural efficiency and provide guidance for the practical design of lightweight and resilient steel sheds. https://scientiairanica.sharif.edu/article_23968.html Microservice architectures are preferred for their scalability and flexibility, however, managing distributed transactions in these systems poses significant challenges, especially in terms of consistency and fault tolerance.To address these issues, this study evaluates two distinct approaches: Paxos, a consensus algorithm that ensures agreement among distributed nodes, and the enhanced Saga pattern, a transaction coordination framework that manages local transactions with compensating actions. We implemented both methods in a microservice-based application deployed across distributed nodes and assessed their performance under various load conditions and failure scenarios. The results show that integrating Paxos significantly improves throughput and reduces latency, offering strong consistency and robust fault tolerance. In contrast, the enhanced Saga pattern, while effective in managing compensating transactions and maintaining eventual consistency, demonstrated lower performance in high-load environments. These findings highlight the trade-offs between consensus-based and coordination-based transaction management in microservice architectures and provide practical insights for system designers seeking scalable and reliable solutions.  https://scientiairanica.sharif.edu/article_23969.html As autonomous vehicles continue to gain traction, the need for highly accurate and energy-efficient systems to enhance safety and performance becomes increasingly critical. Effectively managing the tradeoff between energy consumption and reliability in these systems requires the ability to predict various operational conditions. With the rapid advancements in Large Language Models and models like ChatGPT, new opportunities for improving predictions in autonomous vehicle operations have emerged. This paper proposes RevEAL, which utilizes Large Language Models as map reader co-drivers to predict essential parameters for optimizing the energy-reliability balance during AV operations. Experimental results demonstrate that RevEAL achieved up to 67% driving accuracy and a 53.4% reduction in total energy consumption, depending on the operating scenario. Additionally, RevEAL reduced power consumption by 33% compared to selected baseline configurations, highlighting its strength in maintaining a practical balance between navigation performance and energy efficiency. These findings underscore the potential of RevEAL to contribute to the development of more adaptive and resource-aware autonomous driving systems. https://scientiairanica.sharif.edu/article_23970.html This paper presents a comprehensive analysis of the impact of early cache eviction on the aging of cache cells. It highlights that, in addition to the previously identified factors contributing to static noise margin (SNM) degradation in cache cells, the state of cached data plays a critical role in this process. The analysis reveals that the uneven distribution of clean and dirty data blocks across the lines of a cache set can also be a significant factor in SNM degradation. To address this issue, this study proposes an early cache eviction strategy aimed at balancing the distribution of dirty and clean data blocks over cache lines, thereby mitigating SNM degradation. To achieve this, the decision tree of the Pseudo-LRU replacement policy is redesigned to incorporate cache line states and address conflict miss types. Experimental results demonstrate that the enhanced cache improves the hold and read SNM by approximately 10% and 12%, respectively, while incurring negligible cache hit reduction and minimal area and energy overheads. https://scientiairanica.sharif.edu/article_23971.html In this study, a design for a horizontal-axis wind turbine was created to recover some of the energy lost by moving vehicles and turn it into electrical energy, and its effectiveness was examined using computer simulations. The geometry of the wind turbine system was modeled using SolidWorks software. The CFD simulation study was carried out using the SST k-ω turbulence model. ANSYS Fluent commercial software was used in the simulations. The initial inlet velocity was taken at three different values of 10 m/s, 15 m/s and 27 m/s, respectively. The results obtained indicate that the designed wind turbine has the potential to generate power for an electric vehicle. In the designed vehicle-type wind turbine system, it has been determined that an efficient blade design and consequent improvement of airflow characteristics will increase the generated electrical power and reduce the additional impact on vehicle aerodynamics. Furthermore, the position of the designed wind turbine on the vehicle significantly influences the vehicle's aerodynamics. It has been determined that positioning the wind turbine in a way that increases the vehicle's projection area may lead to an increase in the aerodynamic losses of the vehicle. https://scientiairanica.sharif.edu/article_23972.html In this paper, a negative capacitance gate-stack field effect diode (FED) with reduced subthreshold slope, increased on-state current (ION) and decreased off-state current (IOFF) has been proposed. By using the HfO2 ferroelectric layer following by the SiO2 dielectric layer under the two gates of the device, a negative capacitance structure is created in the gate-stack. Therefore, the gate control over the channel is strengthened which improves the short channel effects and digital performance of the proposed device. Comparing to the conventional FED, the negative capacitance structure in the gate-stack of the proposed FED amplifies the voltage amplification in the channel which causes larger induced carrier concentration for the same applied gate voltages. As the result, smaller IOFF, higher ION, higher ION/IOFF ratio and smaller subthreshold slope are achieved in the proposed FED. In addition, the proposed device exhibits smaller gate capacitance, smaller gate delay time and smaller energy delay product which provide better switching performance with less energy consumption.  https://scientiairanica.sharif.edu/article_23974.html Resource restrictions in healthcare IoT devices necessitates cloud services for data processing, which is prone to single point of failure, delays, and security risks. Meanwhile, exploiting blockchain can enhance security, and data ownership while providing decentralization. Optimal solutions must guarantee confidentiality, data integrity, and access control while also being cost-effective, scalable, and compatible with existing systems. Therefore, we present EdgeLinker, a blockchain-based IoT framework that uses Proof-of-Authority consensus, integrates smart contracts for access control, and employs advanced cryptographic algorithms for secure data communication between edge and fog devices. While implementing a real-world fog testbed for performance evaluations, this paper conducts a scrutinized security analysis. This analysis includes but is not limited to Sybil attacks, consensus manipulation, replay attacks, 51% attacks, traffic analysis, message spoofing, unauthorized access, Distributed Denial of Service (DDoS), and transaction malleability. EdgeLinker has shown enhanced security and privacy at reasonable costs, making it a cost-effective and practical solution for healthcare fog applications. Compared to the state-of-the-art, EdgeLinker achieves a 35% improvement in data read time without significant changes in blockchain write-time and provides better throughput in both reading and writing transactions. Furthermore, it shows energy and resource consumption improvements and channel latency in secure and non-secure modes. https://scientiairanica.sharif.edu/article_23975.html Recent advancements in smart grid technology and the increasing adoption of renewable energy sources highlight the need for an alternative to conventional transformers. Traditional transformers suffer from issues such as lack of controllability, large size, and weight, making them less suitable for modern grid applications. Solid-State Transformers (SSTs) have emerged as an attractive solution due to their controllability, compact size, and power density. In this paper, a novel SST structure is proposed with a new modulation technique, which reduces one of the conventional energy conversion stages by directly converting DC voltage into a medium-frequency waveform with a controlled amplitude. The proposed structure simplifies the control system and enhances power density compared to traditional SSTs. The effectiveness of the proposed structure is validated through both simulation and experimental results, which demonstrate improved performance in terms of efficiency, harmonic reduction, and reduced system cost. https://scientiairanica.sharif.edu/article_23976.html Switched Reluctance Motors (SRMs) are classified into two types, rotary and linear, based on the type of movement. The linear switched reluctance motors (LSRMs) have the same structure as rotary types and therefore they have all the advantages of SRMs. In this paper, for the LSRM, two analytical models based on field equations in electromagnetics and Magnetic Equivalent Circuit (MEC) method are presented. The static characteristic of the flux linked with a phase is predicted in the fully aligned and fully unaligned as well as intermediate positions of the mover. Then, based on the phase voltage equation, the dynamic analysis of the motor is done and the instantaneous phase current and instantaneous thrust force are determined. By applying the developed analytical models to a three-phase LSRM, the simulation results are presented and compared with those of the Finite Element Method (FEM) for validation. The comparisons clearly show that the presented models have appropriate calculation speed and accuracy. https://scientiairanica.sharif.edu/article_23987.html The short circuit fault (SCF) is a dangerous condition threatening the health of insulated gate bipolar transistors (IGBTs). Thus, SCF can be considered a critical issue for the reliability of power electronic converters. The SCF behavior of IGBTs and their failure factors when operating in an arm configuration are the focus of this paper. This paper considers the non-idealities of the circuit and differences in IGBT parameters and describes the SCF fault. It was found that there is a significant voltage imbalance among the IGBTs during SCF, which leads to a notable energy imbalance between the devices. The voltage and energy imbalances depend significantly on the inevitable discrepancies in the circuitry and internal parameters of the IGBTs. To verify these findings, a detailed PSPICE simulation is conducted, and experimental results are also reported for different scenarios in SCFs. The results confirm that short-circuit energy among the devices can differ significantly. Moreover, the voltage distribution across IGBTs strongly depends on both their intrinsic parameter mismatches and the operating conditions. https://scientiairanica.sharif.edu/article_23988.html Permanent Magnet Synchronous Motors (PMSMs) are extensively employed in various applications, including electric vehicles, industrial machinery, and precision motion control systems, owing to their high efficiency, power density, and excellent controllability. The performance of these motors is strongly influenced by several design parameters, notably the slots-per-pole-per-phase ratio, stator winding configuration, permanent magnet arrangement, and slot opening width. This study investigates the impact of each of these parameters on key motor performance metrics, such as output torque and torque ripple. In this study, the effects of each parameter on output torque and torque ripple are examined individually and in combination. Simulation results demonstrate that appropriate optimization of these factors can significantly enhance the motor's electromagnetic performance. Based on these findings, an improved motor structure is proposed, and its performance is compared to that of the initial design. https://scientiairanica.sharif.edu/article_23993.html This paper presents an efficient method for frequency-constrained optimization of large-scale cyclically symmetric domes. The approach integrates the improved hybrid growth optimizer (IHGO) algorithm with an eigenvalue decomposition method. IHGO incorporates the exploration mechanism of the improved arithmetic optimization algorithm (IAOA) into its learning phase, along with algorithm-specific modifications. While these modifications are general and problem-independent, their effectiveness in broader structural optimization tasks remains unexplored. To enhance computational efficiency, a decomposition-based method performs free vibration analysis. This method partitions the eigenvalue problem into smaller, decoupled sub-eigenproblems through block-diagonalization of structural matrices, significantly reducing CPU time and memory requirements compared to the standard method (which solves the full eigenvalue problem without decomposition). The performance of IHGO is demonstrated via optimization of two large-scale domes, comparing results against the original growth optimizer (GO) and literature-best solutions. These comparisons highlight the outstanding computational efficiency and accuracy of IHGO. The results confirm the robustness and computational advantages of IHGO, establishing it as a powerful tool for large-scale structural optimization under natural frequency constraints. https://scientiairanica.sharif.edu/article_23998.html Brain-Computer Interfaces (BCIs) help individuals with severe disabilities communicate using brain activity. Most existing systems are designed for Latin alphabets and overlook the challenges of non-Latin and right-to-left (RTL) scripts (such as connected letters). To address this issue, a hybrid BCI system has been developed using Steady-State Visual Evoked Potential (SSVEP) and Rapid Serial Visual Presentation (RSVP) paradigms. In this method, 36 characters are divided into 3 groups of 12, each further split into 4 subgroups of 3. SSVEP is used to identify the target group, and Triple RSVP is employed to detect the subgroup. The final character is determined using single-frequency SSVEP. Signal processing is performed using Power Spectral Density Analysis (PSDA), wavelet transform, and Support Vector Machine (SVM). Test results on 7 healthy individuals showed a system accuracy of 91.2±3.4% and an Information Transfer Rate (ITR) of 21.5±1.64 bits/min. When SSVEP stimulation time was reduced by 1 second, accuracy remained at 90.5%, while ITR increased to 25.37 bits/min. Unlike Latin-based systems, this one is optimized for complex and right-to-left scripts and performs better than single-modality methods. This advancement marks an important step in developing inclusive BCI technology for non-Latin users. https://scientiairanica.sharif.edu/article_23999.html Implantable cardioverter-defibrillators (ICDs) are among the most frequently used internal battery-powered implantable systems, allowing for the permanent monitoring of heart signals and inducing cardiac shocks in the event of heart arrhythmias. Evidently, a considerable portion of the system's energy is lost when an electric shock occurs, or the system is prepared to induce an electric shock (by charging the battery), shortening the ICD service life. Then, the system should be surgically replaced in a short time. Contrary to the previous works, this study introduces a novel technique to manage and compensate for the internal energy of ICDs and develop complementary energy channels through optimized inductive links. Different inductive drivers are analyzed and compared based on the physical ICD conditions to design an efficient auxiliary inductive wireless energy transfer system. A comparison of wireless power transfer scenarios indicated that power transfer using electromagnetic field induction and coupling coils was optimal. This study also aims to simultaneously enhance the coupling factor and quality factor of the coils by employing geometric optimization in coil width, spacing, and number of turns. The analytical and simulation models were validated, and the experimental results demonstrated a maximum WPT efficiency of 51%, which outperforms similar works reported in recent literature. https://scientiairanica.sharif.edu/article_24000.html This study investigates the catalytic mechanisms underlying the chemical vapor deposition (CVD) of iron (Fe) and cobalt (Co) on palladium-decorated reduced graphene oxide (Pd/rGO) compared to bare rGO. Pd nanoparticles (~9.37 nm) on rGO significantly enhance precursor decomposition and catalyst formation. Temperature-resolved gas-phase Fourier-transform infrared (FTIR) spectroscopy tracked ferrocene and cobaltocene decomposition, revealing distinct optimal temperature windows—termed Frontier Thermal Bulwarks (FTBs)—for Fe (100–270 °C) and Co (40–140 °C) deposition. Pd/rGO exhibited earlier onset of gas-phase precursor decomposition (~100 °C for Fe and ~40 °C for Co) and higher C–H stretching band intensities than rGO, highlighting Pd’s role in efficient H₂ dissociation and ligand activation. Unique spectral features, including 1037/1094 cm⁻¹ peaks for cobaltocene, correspond to specific intermediate species arising from its 19-electron instability. The selective CVD on Pd/rGO promotes intimate Fe/Co-Pd interactions, which may enhance catalytic performance while minimizing precious metal usage. These findings provide key insights for optimizing CVD conditions in the development of advanced nanostructured catalysts for energy and environmental applications. https://scientiairanica.sharif.edu/article_24001.html This research addresses the challenges of heat transfer optimization in non-Newtonian Boger dusty nanofluid flow across a stretching sheet by investigating the combined effects of viscoelasticity, reversible reactions, magnetohydrodynamics, and buoyancy forces. This study is highly relevant to various engineering problems, particularly in solar energy systems. In such applications, stretched sheet models simulate solar absorber surfaces; dusty nanofluids enhance thermal absorption and conductivity; nanoparticle aggregation affects thermal efficiency and flow stability under solar exposure; and magnetic fields regulate the fluid motion in magnetized environments. The effective thermophysical properties of nanofluids, incorporating aggregation and homogeneous models, are analyzed using the Tiwari-Das model. The non-dimensionalization of the governing partial differential equations is carried out via the similarity technique, and the transformed ordinary differential equations are solved using the shooting method and the Runge-Kutta-Fehlberg (RKF45) technique. The consistency of numerical results is verified through comparison with previously published research for limiting cases. The influence of underlying factors on flow and thermal characteristics is systematically explored through plots. It is observed that aggregated nanoparticles constrict streamlines, diminishing flow speed while improving heat transfer by augmenting temperature and Nusselt number. Dusty nanofluid moves more slowly than pure nanofluid due to higher drag induced by suspended dust particles. The equilibrium constant positively impacts the temperature profile, whereas the velocity and concentration profiles are negatively affected. https://scientiairanica.sharif.edu/article_24002.html This paper presents hardened standard cells against transient faults using device-level 3D-TCAD simulation insights. First, at the device level vulnerable zones in common standard cells—including NAND, NOR, and INV cells—are precisely identified under different strike energy, angle, and location scenarios. Then, transistor-level hardened NAND, NOR, and INV cell designs are proposed based on the vulnerable zones from device-level investigations, achieving high resilience to faults. Finally, the proposed hardened designs are reevaluated extensively using both device-level and circuit-level simulations, demonstrating full immunity against SETs and high immunity against single-event multiple transients on adjacent cells. Compared to related hardening methods, the designs achieve a significantly lower probability of failure up to 94.8%, while incurring moderate overheads in terms of area and power consumption, and achieving improved delay. Our device-level analysis reveals that NAND cells exhibit the highest vulnerability to particle strikes, while among adjacent-cell combinations, NAND-INV pairs show the highest vulnerability to multiple transients. In contrast, NOR-INV combinations demonstrate the lowest vulnerability. These findings underscore the effectiveness of leveraging device-level insights to develop highly reliable designs for soft error mitigation in safety-critical applications. https://scientiairanica.sharif.edu/article_24003.html This paper investigates the heat and mass transfer features in a time-dependent bioconvective flow of a Williamson nanofluid containing gyrotactic microorganisms over a radially stretching sheet. Bioconvection arises from the collective motion of motile microorganisms, such as algae or bacteria, which generates a density gradient and induces fluid motion. These microorganisms enhance the mixing and stability of nanofluids, making them highly relevant for microscale thermal systems, biomedical devices, and environmental applications. The Buongiorno nanofluid model is employed to describe nanoparticle transport driven by Brownian motion and thermophoresis. The energy and concentration equations are further modified using refined forms of Fourier’s and Fick’s laws to incorporate nonlinear thermal radiation, Joule heating, Newtonian heating, and a first-order chemical reaction. The resulting system of nonlinear partial differential equations is transformed into a set of ordinary differential equations using similarity transformations and solved numerically via the shooting method. The numerical results are validated through a comparison table with previously published data and show excellent agreement. Graphical key findings indicate that microorganism concentration decreases with increasing bioconvective Schmidt number, microorganism difference parameter, and Peclet number. This study presents a novel integration of multiple transport mechanisms and contributes to the design and optimization of nanofluid-based thermal systems. https://scientiairanica.sharif.edu/article_24009.html The increasing complexity of modern vehicles and the widespread adoption of the Controller Area Network (CAN) Bus have amplified the need for efficient, low-cost security protocols to protect in-vehicle networks (IVNs) from cyberattacks. In this paper, we introduce the Sporadic Authenticated Encryption (SAE) method, a novel approach to securing CAN-Bus communication against replay, sniffing, and spoofing attacks. Unlike traditional methods which authenticate all messages or rely on fixed authentication intervals, SAE dynamically adjusts the interval of message authentications based on the Lyapunov stability, ensuring the system stability even in adverse conditions. Through extensive simulations, we demonstrated significant improvements of SAE over existing methods, achieving a 19% reduction in high-priority message delays and up to 23% improvement in low-priority message delays in comparison to the periodic authentication schemes. By leveraging Lyapunov theory, SAE optimally schedules authenticated messages while maintaining the system stability and reducing the communication overhead. Moreover, SAE ensures schedulability under worst-case conditions, guaranteeing the timely delivery of all messages. These results position SAE as a robust and practical solution for enhancing security and stability in resource-constrained automotive networks. https://scientiairanica.sharif.edu/article_24010.html Transformerless multilevel inverters (TMLIs) are emerging as a highly attractive solution for grid-connected solar photovoltaic (PV) systems. However, in TMLIs, leakage current will flow due to the absence of galvanic isolation between the PV DC side and the grid AC side. This article proposes a nine-level transformerless direct ground connection type inverter employing three switched capacitors (SCs) to generate a multilevel output waveform. This single-stage topology delivers a quadratic boost and a multilevel output suitable for grid connection while maintaining a common ground connection to suppress leakage current. Due to the self-balancing nature of the capacitor voltages, the proposed topology does not require sensor monitoring or dedicated control circuits. The voltage stress on utilised semiconductors and SCs is within the output voltage, which reduces the inverter’s size and cost. In addition, the lower total standing voltage per unit and capacitor voltage diversity factor are also less than those of the other recent topologies. Further, a detailed comparison is needed to show the merits of the proposed inverter over other recent alternatives, including its leakage current removal and boosting factor. Simulations were conducted at an 895 W output power level using the MATLAB/PLECS tools to assess the practical applicability of the proposed inverter. These simulations were further validated through a laboratory experimental setup and presented with the corresponding results. https://scientiairanica.sharif.edu/article_24011.html In the analysis of nonlinear dynamical systems, developing an accurate understanding of simple mechanical models—such as the pendulum—is of fundamental importance in both engineering and physics. Although the simple pendulum is often introduced in its linearized form for small oscillations, its true behavior becomes highly nonlinear at larger amplitudes. The nonlinear pendulum, therefore, serves as a classical yet powerful example for exploring the rich dynamics that emerge in real-world systems where linear approximations fail. In this study, the non-linear dynamic analysis of a simple pendulum is revisited. Two new formulas for the period and frequency are proposed based on the Harmonic Balance Method and the Akbari-Ganji Method. Furthermore, to obtain more accurate results, improvements are made to the formulas of the harmonic balance method and the Akbari-Ganji method. These improvements provide more reliable outcomes, especially in systems requiring high accuracy. Two of the most prominent formulas in the literature are derived using the Akbari-Ganji Method. As a result of this, the frequencies obtained by the present method and the other methods are compared. The obtained results emphasize the accuracy and efficiency of the proposed approaches. Consequently, this study encourages the use of alternative methods in the analysis of non-linear dynamic systems. https://scientiairanica.sharif.edu/article_24012.html In this study, ZnO nanoparticles (NPs) and RGO@ZnO nanocomposites (NCs) were integrated as electron transport layers (ETLs) using spin-coating-free deposition methods and cost-effective and scalable fabrication of planar perovskite solar cells was achieved. ZnO NPs and RGO@ZnO NCs were synthesized via a rapid hydrothermal process and characterized using XRD, SEM, UV-Vis, and FTIR techniques. Structural and optical analyses confirmed the formation of highly crystalline and uniform nanostructures, with RGO integration enhancing light absorption and narrowing the band gap. The films were deposited using the doctor blade technique, and the MAPbI₃ perovskite layer was formed by a simple drop-casting method. PCBM was used as the hole transport layer, and a silver (Ag) paste was applied as the top electrode. Photovoltaic performances of two device structures, ITO/ZnO/MAPbI₃/PCBM/Ag and ITO/RGO@ZnO/MAPbI₃/PCBM/Ag, were compared. Devices incorporating RGO@ZnO exhibited significantly improved efficiency up to threefold under standard solar illumination due to reduced charge recombination, improved energy level alignment, and enhanced carrier mobility. Moreover, these devices maintained high performance under indoor LED lighting, demonstrating superior low-light adaptability. https://scientiairanica.sharif.edu/article_24026.html An increase in temperature during a fire in concrete members causes physical and chemical changes and reduces compressive strength. Therefore, even if the structure remains stable after the fire, evaluating its seismic behavior is of great importance. Accordingly, this study aims to investigate the seismic fragility of reinforced concrete moment frames that have been damaged by fire and have not been repaired or strengthened, using probabilistic and sensitivity analysis methods. For this purpose, a seven-story reinforced concrete frame was first designed, and its thermo-mechanical properties were modeled in OpenSees software. Three fire scenarios lasting one, two, and three hours were applied to the structure. The heat transfer analysis of the beam and column sections damaged during the fire was carried out using Abaqus software. The incremental dynamic analysis and seismic fragility curves of the structure were obtained and compared using both deterministic and probabilistic approaches, considering uncertainties in material properties, gravity load, seismic load, and geometry. The results of these curves show that the probability that the structure will exceed the limit states (IO, LS, and CP) increases as the duration of thermal loading increases. Specifically, the probability of exceedance from the LS limit state at a spectral acceleration of Sa= 0.9g for the no-fire case and for frames exposed to one-, two-, and three-hour fires is 71%, 86%, 90%, and 97%, respectively. The results of the seismic sensitivity analysis indicate that reinforcement yield stress, concrete strength, and reinforcement cover are more seismically sensitive compared to other random variables. https://scientiairanica.sharif.edu/article_24027.html This paper presents the development of a novel low profile dual-band co-planar waveguide (CPW)-fed split ring-shaped antenna with improved radiation performance in the broadside direction using a frequency selective surface (FSS)-based artificial magnetic conductor (AMC). The CPW-fed split ring antenna is developed to operate at the two operating bands with the center frequencies of 2.4 GHz and 5.8 GHz, having bi-directional radiation characteristics. The FSS-based AMC is designed with the reflection phase close to 0° for the corresponding resonating frequencies of both the working bands of the antenna. The proposed AMC is introduced as a substrate at a relatively lower spacing of λ/12, which is much less than the quarter-wavelength spacing as used in the conventional approach for enhancing antenna gain at the broadside direction. By embedding the FSS-based AMC layer, the antenna exhibits the highest gain of 8.5 dBi and 8.92 dBi in the lower and upper bands, respectively. https://scientiairanica.sharif.edu/article_24028.html This research paper focuses on designing and analysing a tri-band antenna for RF energy harvesting within the sub-6 GHz spectrum, specifically targeting 2.1 GHz (3G), 2.3 GHz (4G), and 3.5 GHz (5G). The antenna structure incorporates three dipole elements that resonate at distinct frequencies while maintaining a compact footprint. Characteristic Mode Analysis (CMA) is applied to refine the antenna design, improving its radiation efficiency and ensuring enhanced broadside performance. The antenna's performance is evaluated through simulated and measured S₁₁ parameters and radiation patterns. A tri-band bandpass filter is integrated into the system to enhance impedance matching across the 2.1 GHz, 2.3 GHz, and 3.5 GHz bands, ensuring efficient signal transfer at each operational frequency. Additionally, an RF-to-DC rectification circuit is designed using a Schottky diode alongside a low-pass filter and load resistor, enabling effective conversion of ambient RF signals into usable DC power for energy harvesting applications. The results obtained from this study validate the tri-band antenna’s capability to efficiently harvest RF energy within the 3G, 4G, and 5G frequency ranges. By leveraging Characteristic Mode Analysis (CMA), the proposed design optimizes radiation efficiency, while the rectifier circuit exhibits stable RF-to-DC conversion across different power levels. https://scientiairanica.sharif.edu/article_24040.html This study aims to comprehensively analyze the performance of solid oxide fuel cells (SOFCs) operating at high temperatures and converting chemical energy directly into electrical energy, considering the effects of multiple parameters. The cell performance was evaluated in the analysis performed on a cell with an active surface area of ​​0.01 m², a temperature range of 573–1673 K and pore diameters of 3–15 µm. The performance evaluation involved a meticulous examination of activation, ohmic, and concentration losses, along with the determination of cell potential, power density, and thermal efficiency through theoretical analyses. The findings showed that the temperature increase positively affected the cell efficiency up to a certain threshold; it was determined that the thermal efficiency reached its peak especially in the temperature range of 1073 - 1273 K. A 20% efficiency increase was achieved under the conditions of a temperature increase of 210 K, a current density of 10000 A/m² and a pore diameter of 8 µm. While 33.04% and 21.41% efficiency values ​​were obtained with 5 µm and 10 µm pore diameters, respectively, at a constant temperature of 873 K, a 60% increase in pore diameter provided only an 8% efficiency increase, which revealed that the increase in pore diameter had limited and negative effects on efficiency. Unlike the generally single parameter focused cases in the literature, this research demonstrates conclusively that the length of the width diameter will lead to a noticeable decrease in thermal efficiency by providing a joint analysis of multiple variables in the cell. https://scientiairanica.sharif.edu/article_24041.html In this study, energy analysis, engine performance, and noise emission tests of Euro diesel and safflower methyl ester fuels were conducted on a diesel engine. The experiments were conducted independently for each fuel type across engine speeds ranging from 1000 to 2400 rpm, and the physicochemical properties of the fuels were characterized and evaluated through engine testing. Noise emission values were recorded from four different points around the engine at a distance of one meter and were compared with those of reference diesel fuel. According to the test results, the most suitable fuel type was determined based on engine performance. noise emission and energy analysis. In this study, modeling was performed using artificial neural networks (ANNs) based on experimentally obtained data, and the noise emission characteristics of B100 and D100 fuels were analyzed. Both raw and normalized datasets were evaluated to assess the predictive accuracy of the models. It was concluded that the predictive success was closely associated with the choice of training algorithms and transfer functions utilized. The findings highlight that selecting suitable models and algorithms tailored to the structure of the dataset plays a critical role in enhancing prediction accuracy. https://scientiairanica.sharif.edu/article_24042.html This research article introduces a novel chaotic hyperjerk system incorporating two hyperbolic sinusoidal functions and explores its bifurcation analysis in both integer and fractional orders. The bifurcation analysis, Lyapunov spectrum analysis and phase portraits show that the proposed system exhibits wide range of complex phenomena such as chaos and multistability. The multistability phenomenon is analysed in detail with the fractional-order modelling, where the system’s fractional dynamics are captured using Garappa method. The development of fractional order chaotic system results in more complex dynamical response and the presence of hyperchaos over a broad range of system parameter. Additionally, the proposed integer order hyperjerk system is implemented on a DE10-Standard digital board, which comprises a Cyclone V SE 5CSXFC6D6F31C6N FPGA to realize its chaotic behaviour for various real-time applications. The results suggest that the proposed system could serve as a promising candidate in various domains where multistability and fractional-order systems are of interest. https://scientiairanica.sharif.edu/article_24043.html Growing nonlinear loads in modern grids cause higher harmonics, prompting new grid codes that require inverter-based sources to participate in harmonic mitigation. This paper presents a novel approach to mitigate harmonics in microgrids by utilizing the available capacity of Photovoltaic (PV) systems, merging clean energy production with enhanced power quality. The proposed control strategy employs Extremum Seeking Control (ESC) due to its maturity and ease of implementation. The ESC minimizes the Point of Common Coupling (PCC) voltage Total Harmonic Distortion (THD) when load and grid impedances are unknown. Controller parameter tuning depends on available inverter capacity and desired control performance. To mitigate PCC harmonic voltage, the PV injects a suitable harmonic current, in phase with the load harmonic current, ensuring maximum cancellation. The injected harmonic current magnitude remains lower than the load current magnitude, considering the available inverter capacity. Simulations in PSCAD validate this technique, demonstrating significant PCC voltage THD reduction without prior knowledge of the load current harmonic. Specifically, the voltage THD and the current THD with the ESC, are reduced to 0.29% and 2.40%. Performance comparisons with an Active Power Filter (APF) exhibit the ESC has superior performance (APF reduced voltage THD to 0.69% and current THD to 5.61%). These results demonstrate the ESC's effectiveness and potential as a viable solution for harmonic mitigation in microgrids, offering a significant improvement in power quality and supporting the increasing integration of renewable energy sources. https://scientiairanica.sharif.edu/article_24046.html The present mathematical framework theoretically investigates the impact of the fractional model on heat transfer advancement in mixed convection magnetohydrodynamics Maxwell hybrid nanofluid flow through a bi-directional stretching sheet. A Caputo-time derivative model is adopted in the work to inspect the behavior of fractional parameters on flow and heat transfer properties. Nanoparticles like copper and titanium dioxide, and base fluid water are considered for forming a hybrid nanofluid. Also, magnetic, buoyancy, and heating effects are considered. A system of non-linear coupled governing equations with the model of Caputo-time fractional derivative is subjected to non-dimensional forms by inserting appropriate non-dimensional quantities. Numerical results for the developing non-linear problem are acquired using a finite difference approximation technique together with the L1 algorithm. The impact of the involved flow influential elements on heat transfer and flow characteristics is analyzed and portrayed graphically. From the study, it is identified that the strengthening of fluid flow of hybrid nanofluid is directly correlated with the order of fractional derivatives, and the reverse trend is observed in thermal distribution. https://scientiairanica.sharif.edu/article_24048.html Multi-phase PMSMs are considered to increase the reliability of the high-power propulsion systems. This paper presents a model-free current and speed predictive control (MFPCSC) method based on ultra-local model for an asymmetric six-phase PMSM with non-sinusoidal back-EMFs. The proposed MFPCSC method is robust to model uncertainties and disturbances. To maximize the reliability, each phase has two windings and each winding has an open-end connection, in which the drive topology is fully modular. Due to the modular structure, there are limitations in the modeling and control of the motor, in which the conventional methods in dq reference frames cannot be employed. Also, due to torque ripples caused from non-sinusoidal back-EMFs, the current shaping method has been used in the six-axis stationary reference frame. To make the control method robust to changes in motor model parameters and reduce sensitivity to current controllers, six extended state observers (ESOs) based on an ultra-local model are used to estimate current and total disturbances. The superiority of the proposed control method over conventional control methods based on hysteresis, PI and proportional-resonant (PR) current regulators is verified by simulation in Simulink, and some experimental test results are presented to validate the proposed theories. https://scientiairanica.sharif.edu/article_24049.html Alignment is a critical pre-processing step before initiating inertial navigation operations. The extended Kalman filter (EKF), commonly employed for aligning strap-down inertial navigation systems (SINS), faces challenges due to linearization limitations. In response to some of these challenges, the invariant extended Kalman filter (IEKF) was developed. A key requirement for utilizing IEKF is that the system dynamics must exhibit a group affine property. However, considering inertial measurement unit (IMU) errors as state variables violates this condition. This paper introduces an improved version of IEKF, named the proportional-integral invariant Extended Kalman Filter (PI-IEKF). This approach initially ignores IMU bias and drift to preserve the group affine property. To compensate for these uncertainties, the Kalman filter's innovation term is reformulated into a proportional-integral (PI) structure. The PI gains are tuned based on Lyapunov stability criteria by solving a Linear Matrix Inequality (LMI). The PI-IEKF not only addresses the uncertainties but also enhances alignment accuracy. The proposed approach was evaluated through simulations and field tests. Simulations addressed the nonlinear alignment problem in marine environment with typical wave disturbances, while field tests used an open-source dataset from vehicle-mounted sensors. Comparative results with conventional IEKF demonstrated significant improvements in convergence speed and robustness. https://scientiairanica.sharif.edu/article_24050.html A two-way relay network refers to a communication scenario in which two nodes exchange information with each other, but they are out of transmission range of each other. In this scenario, a relay node can facilitate the information exchange between the two communication nodes. The relay node receives signals from both nodes, processes them, and then forwards the processed signals to the nodes. Interference is the fundamental challenge of two-way relay channels which reduces the received signal quality, where beamforming reduces the interference. Further, when the number of pairs of users increases, the spectral efficiency (SE) decreases dramatically. The purpose of this study is to spectrally efficient user association in two-way network-coded device to device (D2D) communication with multiple antennas relay. We optimize the beamforming vectors at the relay to maximize the achievable sum-rate. In the proposed method, D2D users are selected from the pairs that have the highest correlation channel gains. However, to increase the efficiency of transmission, those pairs whose average data rate does not reach the threshold rate, are removed and other pairs are replaced. The results show that the proposed method can improve the outage probability and spectral efficiency in two-way communication in comparison with other methods. https://scientiairanica.sharif.edu/article_24051.html Tiled-based streaming is a promising method for 360-degree video streaming that reduces transmission bandwidth while enhancing the quality of experience (QoE). However, the tiling layout and the bitrate of the tiles significantly affect the QoE. This paper proposes FlexiTile, a novel and flexible method that maximizes QoE in tiled-based 360-degree video streaming. FlexiTile achieves this through joint optimization of the tiling layout and bandwidth allocation for each tile. This optimization considers the rate-distortion characteristics of the video content and user viewing direction preferences. FlexiTile leverages a formulation based on the Set Cover problem and an efficient and accurate piece-wise linear estimation of the rate-distortion model for candidate tiles. FlexiTile is evaluated under various transmission constraints and a subjective quality test. Compared to the baseline Notile tiling scheme, FlexiTile achieves a 1.9 dB average improvement in viewport PSNR using the BD metric. Additionally, it outperforms other state-ofthe-art methods in terms of viewing quality. Furthermore, FlexiTile demonstrates improvement over existing tiled-based streaming methods by optimizing tile bandwidth allocation. Experiments show a 1.3 dB average improvement in Uniform tiling (4×2) when the bandwidth allocation is based on the FlexiTile optimization method. FlexiTile surpasses existing methodologies by providing adaptable tiling layout and bitrate allocation strategies, resulting in a notable enhancement of QoE. https://scientiairanica.sharif.edu/article_24059.html In this paper, a single row machine layout problem is considered with zoning constraints and mutual clearances under an enhanced objective of minimizing material flow cost and machine installation cost. The problem is restricted by positive and negative zoning constraints to represent real-life problems. Moreover, the clearances needed between machine pairs are divided into two types, which are must and extra clearances. Extra clearances are reduced through mutual use between adjacent machines to decrease material flow costs. Objective function also considers fixed costs of locating machines which usually neglected in machine layout problems in literature but a necessity in real-life problems. Two mathematical models, namely nonlinear and linear mixed integer programs, are formulated to solve the problem optimally and to compare the effect of linearity and nonlinearity in mathematical programming formulations in terms of solution quality and time. The mathematical models are not effective in terms of time for large problem instances; therefore, a genetic algorithm is proposed to generate high-quality solutions within a reasonable time. It is shown that the genetic algorithm outperforms both the nonlinear and linear mathematical models with lower cost and shorter time. https://scientiairanica.sharif.edu/article_24060.html In this research, a bidding problem for a wind-power plant participating in a day-ahead power market with uncertain correlated market prices is studied. A new robust optimization approach considering correlation among uncertainty on the hourly prices in a day-ahead market is developed. This results in solutions with lower level of over-conservatism. For this purpose a new correlated polyhedral uncertainty set is introduced. To consider the uncertainty of market clearing prices and the value of power produced by wind power producer a bidding algorithm is developed. Results of the study using a robust modelling the bidding problem reveal that the appliance of the proposed model on the bidding problem for a price-taker wind power plant in a day-ahead market with uncertain correlated data leads to solutions with superior performance than that of the conventional polyhedral uncertainty sets. https://scientiairanica.sharif.edu/article_24062.html Statistical process control provides various types of control charts for monitoring of mean and variance shifts in the industrial production process individually as well as jointly to improve and maintain the quality products. The authors proposed these control charts based on sample values selected to calculate the desired statistics assuming that these values are measured correctly. But in a real life situation, measurements of the values may suffer from errors ultimately affecting the efficiency of the control charts. A few of the researchers in the field of control charts also discussed the problem of measurement error during process monitoring and proposed solutions to avoid losses of producers. We also present a hybrid exponentially weighted moving average control chart for joint monitoring of mean as well as variance and study the effect of measurement error on the efficiency of this control chart and name it as Max-HEWMAME control chart. The impact of measurement error has been shown in the calculations and presented in the shape of average run lengths ( ) and standard deviations of run lengths ( ) using the Monte Carlo simulation method. A real life example is also included to support the simulation results. https://scientiairanica.sharif.edu/article_24073.html Nanoparticles have become significant in biomedical fields due to its angiogenesis-inhibiting, antimicrobial, antiviral and anti-inflammatory properties. This study involved producing a straightforward, eco-friendly reliable and cost-efficient technique for producing silver nanoparticles (Ag-NPs) by leaf extracts of Terminalia bellerica. AgNO₃ solution and an aqueous extract of Terminalia bellerica were used in a reduction process to make Ag-NPs. The characterization was performed through visual inspection, UV–Vis spectroscopy, and Fourier-transform infrared spectroscopy. Experimental outcome revealed that Terminalia bellerica leaf extract mediated Ag-NPs exhibited a yellowish to brown color with maximum absorption peak of at 460 nm, confirming the successful bio-fabrication of Ag-NPs. Furthermore Ag-NPs remained stable at 5°C for up to 15 days. According to optimization results, the production of Ag-NPs is maximum at a pH of 11, with incubation temperature of 80°C, and a concentration is 1 mM AgNO3, which ensures stability and prevents aggregation. These conditions promote the reduction of Ag+ ions for effective synthesis of Ag-NPs, resulting in a high yield and reliable quality for practical applications. These Ag-NPs demonstrated strong antimicrobial activity against Staphylococcus aureus (MTCC-96), Pseudomonas aeruginosa (MTCC-1688), Escherichia coli (MTCC-1302), Bacillus cereus (MTCC-1307) and Salmonella Typhi (MTCC-98), found minimum inhibitory concentration lies between from 0.5 μg/ml to 5 μg/ml. This study came to the conclusion that leaf extract of Terminalia bellerica is a useful reducing agent for creating stable silver nanoparticles with strong antioxidant and antibacterial properties. https://scientiairanica.sharif.edu/article_24080.html In this article the complex dynamics of a laser model, which externally injected class 𝐵 which is described by a system of three nonlinear ordinary differential equations with two parameters for field intensity phase and population inversion, are studied. In particular, we investigate the integrability and nonintegrabilty of laser system in three dimension. We prove that system is complete integrable only when the parameters are zero. Particularly, we study polynomial, rational, Darboux and analytic first integrals of the mentioned system. Moreover, we compute all the invariant algebraic surfaces and exponential factors of this system. We find sufficient conditions for the existence of periodic orbits emanating from an equilibrium point origin of a laser differential system with a first integral. https://scientiairanica.sharif.edu/article_24082.html The coronavirus spread rapidly in the world and caused the disease of Covid-19. The proposed research was conducted with the aim of classifying people with Covid-19 from other people. Among all the imaging modalities, lung ultrasound images were used to diagnose covid-19. The open source Point-Care-of-Ultrasound (POCUS) database was collected, which contained 59 Lung Ultrasound (LUS) images. In this research, KNN classifier with K-fold cross validation was used to classify the feature matrix obtained from Fractional B-spline Wavelet Transform (FBSWT). In the proposed method, Block-Matching and 3D filtering (BM3D) filter was used in some methods, which had acceptable results. The proposed method was used to classify healthy people from patients with Covid-19. The results show that when the features based on the wavelet transform (WT) are used, the proposed method can be achieved 90.90% sensitivity, 92.30% specificity, and 91.42% accuracy. While the features extracted from FBSWT show that the proposed method can be achieved 95.45% sensitivity, 93.30% specificity, and 94.28% accuracy. Fractional transforms, especially the FBSWT, can be a useful tool for image processing. They can be used for various purposes such as detection and classification. By using FBSWT, it is possible to accurately diagnose the disease of covid-19. https://scientiairanica.sharif.edu/article_24094.html This study investigates successfully producing a Mn0.3Fe2.7O4 nanofluid-based magnetic field sensor from iron sand. The characterization exhibited that Mn0.3Fe2.7O4 nanoparticles covered by polyvinyl alcohol (PVA) surfactant were fabricated, as demonstrated by the presence of Fe–O and Mn–O from Mn0.3Fe2.7O4, and C=C, CH2, C–H, C–C from PVA. A single-phase spinel cubic structure originating from Mn0.3Fe2.7O4 nanoparticles was formed with a crystallite size of 10.5–13.2 nm. Furthermore, based on the scanning electron microscopy image, the nanoparticles had a spherical shape with a particle size distribution of 23.9–33.3 nm. The band gap value increased with PVA surfactant addition from 3.085 eV to 3.504 eV. The Mn0.3Fe2.7O4 nanoparticles with a PVA surfactant had superparamagnetic properties with a saturation magnetization value of 46.45–36.54 emu/g. Furthermore, as the refractive index value of the filler decreased, it affected the application of an optical-based sensor. The light intensity increases close to the magnetic field since only nanofluids near the external source is exposed to the magnetic field. Interestingly, the light intensity after adding PVA was greater than that of the nanofluids without PVA, i.e., up to 2.1 lux. Thus, Mn0.3Fe2.7O4 nanofluids can be potentially applied as a magnetic field sensor. https://scientiairanica.sharif.edu/article_24095.html Vegetable oil (rice bran oil; RBO) as an economic, non-toxic green diluent has been used for phenol removal through green emulsion liquid membrane (GELM) process with an aim to reduce environmental glitches triggered by traditional petroleum-based solvents used in ELM processes. GELM is naturally benign and cost-effective as compared to other existing membranes. During this study, novel GELM was composed of a surfactant (Span 80) as stabilizing agent, NaOH as driving force agent, and green solvent (RBO), a diluent as liquid membrane. The study was performed to visualize and analyse the effect of process factors (feed phase pH, treat ratio, phase ratio, stirring time, stirring speed, and NaOH concentration) on both phenol extraction and GELM stability. Extraction efficiency (90 %) and stability of 135  min without employing carrier agent have been achieved at optimized experimental conditions viz 450 rpm of agitation speed, pH 0.45 of feed solution, 20 min of extraction time, 0.166 [M] of NaOH concentration, 2:1 (v/v) of treat ratio, 0.45 (v/v) of phase ratio, and 30 ± 1  of extraction temperature. This study has confirmed that the RBO-based GELM separation technique has the ability to cut the noxious diluents requirement for ELM formulation.  https://scientiairanica.sharif.edu/article_24097.html The main aim of this current research work is to examine about the “Emulsion Liquid Membrane” (ELM) stability via membrane breakage (%) for lactic acid (LA) extraction from the feed phase. This research article mainly discusses about the detailed experimental study of the various process parameters affecting the membrane breakage and its performance. The ELM formulation is done by using organic phase constituents containing extractant (tri-octylamine (TOA)), diluents i.e., hexane and surfactant Span 80), and internal phase (0.1 M sodium carbonate solution). The optimal numbers of several process variables for gaining a stable ELM are as follows: emulsification time: 20 min, emulsification speed: 2000 rpm, span 80 concentrations: 4% (v/v), internal phase concentration: 0.1 [M], extractant (TOA) concentration: 10%, phase ratio: 1.0 (v/v), treat ratio: 2 (v/v), and stirring speed: 200 rpm. The percentage (%) lactic acid efficiency stripped into the ELM with the lowest membrane breakage of 4.5 % was found 95 %. Overall, the findings of this research related to ELM formulation having good stability suggest that this ELM based technology for lactic acid extraction from aqueous feed phase has great potential. https://scientiairanica.sharif.edu/article_24098.html Crambe tataria and Crambe orientalis are non-cultivated plant species in Türkiye that can survive under harsh natural conditions. Due to their erucic acid content, their oils are not considered edible. Biodiesel derived from non-edible oils offers a significant renewable fuel surrogate for internal combustion engine applications, providing favorable environmental and economic outcomes. The aim of the current experimental study is to explore and measure the effects of petroleum-based diesel fuel and fuel mixtures of Crambe tataria and Crambe orientalis oil methyl esters on the performance and emission characteristics of a single-cylinder, four-stroke, naturally aspirated, direct injection compression ignition (CI) engine. The arranged test fuel samples consisted of six different mixtures by volume: COB20, COB40, COB60, and CTB20, CTB40, CTB60. The standard diesel fuel was denoted as D0. Tests on the engine were carried out at load levels of 15 N, 30 N, 45 N, and 60 N, with the engine speed stabilized at 2200 rpm. Pursuant to the experimental results, the brake specific fuel consumption (BSFC), NOx emission values, and combustion duration of the biodiesel mixture fuels were come by greater. HC, CO, and soot emissions, heat dissipation rates, in-cylinder temperature, cumulative heat release rate, indicated mean effective pressure (IMEP), and rate of pressure rise (RI) values were detected in lesser match against to diesel fuel. As a result, the findings indicate that biodiesels from Crambe tataria and Crambe orientalis have the capability to replace petroleum-based diesel fuel in CI engine applications. https://scientiairanica.sharif.edu/article_24099.html The high penetration of renewable Distributed Generators (DGs) in the Active Distribution Network (ADN) in addition to its advantages brings great challenges for the ADN, due to their intermittent and uncertain generations. Increasing network flexibility using Soft Open Points (SOPs) is an effective solution to overcome these challenges. However, an SOP-based ADN may contain various renewable or Controllable DGs (CDGs), and autonomous interconnected Microgrids (MGs). Accordingly, the uncertainty of load and renewable generation makes its scheduling more complex. In this paper, a novel optimal scenario-based framework is proposed to schedule an SOP-included ADN with multi-interconnected microgrids, based on the forecasted scenarios of demand and renewable DGs generation. In the proposed framework, all technical constraints, such as AC load flow equations, SOP's operational limitations, and DG's production range, are modeled in a Second-Order Cone (SOC) programming format. The energy transaction between the ADN and the other agents, i.e., MGs, and Upstream Network (UN) is also considered. This model can be optimally solved in an acceptable time. To show the effectiveness of the proposed method, it is implemented on the IEEE 33-bus distribution network. The simulation results confirm its high accuracy and speed. https://scientiairanica.sharif.edu/article_24100.html A novel model-based approach for closed-loop control and maximum power point tracking(MPPT) of thermoelectric generators (TEG) has been presented using the nonlinear Lyapunov-based approach. As the TEG power derivative is always zero at maximum power point (MPP), the TEG power derivative can be employed as a feedback signal for the controller. Hence, the reference value of controller will always be zero which simplifies the controller structure significantly. Since the reference calculation block can be removed, there is no need for a cascade multi-loop controller and for this reason, the controller dynamic response can be improved. Due to the elimination of reference calculation unit, the proposed controller enjoys a superior performance, e.g., during temperature and load changes. The asymptotic stability of proposed controller has been proved. To evaluate the accuracy and efficiency of the proposed approach, it is simulated by using MATLAB software. Moreover, the experimental responses are provided by employing the TMS320F28335 digital signal processor from Texas Instruments. According to the simulation and experimental results and despite temperature and load values changes in a wide range, it is shown that the proposed closed-loop system enjoys a stable and robust performance as well as fast dynamic response and zero steady-state error. https://scientiairanica.sharif.edu/article_24106.html The automated guided vehicle path planning performance is highly dependent on the collected LiDAR data. This research centered on studying the LiDAR specifications for automated guided vehicle path planning problem using deep reinforcement learning. Three data collection approaches with differences in the data sample size and scanning range were considered to obtain 15 LiDAR specifications, and the training quality and the path planning performance were evaluated in order to find the optimal LiDAR specification. The most effective results were achieved using the approach of collecting the "distance and angle" pairs of the nearest obstacles. This method consistently reached all the defined targets in both seen and unseen static environments, with no collisions. Additionally, only limited collisions happened in an unseen dynamic environment containing moving obstacles. Notably, increasing the number of data samples can extend the training time, and the excessive data may also complicate decision-making with no significant benefits in terms of the path planning performance. https://scientiairanica.sharif.edu/article_24107.html Vibrating feeders play a crucial role in various industries by ensuring the efficient transfer of materials and ensuring optimum material flow rates through effective operation. This study investigates the stress variations in a specific vibrating feeder during static and dynamic phases. It determines the cause of damage to a feeder that has previously been damaged and proposes solutions to prevent future damage through minor design modifications. The loads on the feeder were analyzed in detail using ANSYS software, and the finite element method was used to identify the fatigue zones. The results of the static, modal, transient structural, and fatigue analyses indicate that fatigue-related stresses and incorrect operating frequencies may lead to the feeder's fracture. This analysis provides valuable information to strengthen the design and prevent future failures. This study uniquely integrates multi-body dynamics analysis to examine the dynamic interactions between feeder components, offering enhanced predictive capabilities for operational performance. The findings emphasize the importance of iterative design processes and advanced analytical techniques in developing robust and durable mechanical systems. Future research directions include exploring alternative materials, optimizing vibro motor configurations, investigating resonance control strategies, and incorporating energy-efficient designs and real-time monitoring to further enhance feeder performance in diverse industrial applications. https://scientiairanica.sharif.edu/article_24108.html This paper presents an adaptive methodology for optimizing dwell time allocation in group target tracking. The approach formulates dwell time allocation as an optimization problem characterized by a cost function that integrates resource utilization and tracking accuracy. Enhancing the quality of observations is essential for improving tracking accuracy in the geometry and kinematics of group targets. This quality is influenced by the covariance of group target observations and the number of detected targets. This research clarifies the relationship between the covariance of group target observations and the number of detected targets concerning dwell time. Additionally, an analytical relationship is established between the Bayesian Cramér-Rao lower bound (BCRLB) and dwell time for evaluating accuracy in the geometry and kinematics of group target tracking. The proposed adaptive strategy is recursively optimized throughout the tracking cycle, demonstrating significant improvements over conventional methods. Simulation results indicate that optimal dwell time allocation can achieve a fourfold reduction in error compared to a constant dwell time approach, as well as a fivefold decrease in resource consumption relative to conventional methods that either detect all group members or employ a fixed dwell time. https://scientiairanica.sharif.edu/article_24109.html In this paper, a compact dual-band eight-mode substrate-integrated waveguide (EMSIW) MIMO (Multiple-Input Multiple-Output) antenna is presented for S and C band applications. By utilizing the EMSIW approach, the electrical size of the antenna is reduced to 1/8th of a conventional square SIW cavity resonator. The eight-mode configuration preserves the dominant mode at the same frequency, while significantly reducing the antenna size. The dominant mode resonates at 2.5 GHz, and a higher-order mode operates at 5.8 GHz by incorporating a slot in the design. To improve the radiation pattern at 5.8 GHz, the surface current nulls near the center of the EMSIW patch are reshaped, enhancing broadside radiation. The 2x2 MIMO antenna is designed to minimize mutual coupling by maintaining specific electric and magnetic wall configurations, ensuring minimal electric field interaction between the antenna ports. Fabricated on a low-cost FR4 substrate, the proposed MIMO antenna demonstrates excellent agreement between simulated and measured results. This compact design is a promising solution for satellite S-band and C-band applications, offering enhanced higher-order mode radiation performance and minimal mutual coupling. https://scientiairanica.sharif.edu/article_24110.html This paper introduces a framework for the optimal allocation of protection and control devices including reclosers, sectionalizers, fuses, remote-controlled switches, and manual switches in power distribution systems. Central to this study is the development of the GRIDMAP model, a scalable, matrix-based representation of the distribution network topology. GRIDMAP enables detailed modeling of complex grid structures, supports both permanent and temporary fault analysis, and integrates real-world operational constraints such as device relocation and load growth. Building upon this modeling foundation, a Modified Particle Swarm Optimization (MIPSO) algorithm is implemented to solve the allocation problem. The algorithm leverages GRIDMAP’s structure to evaluate candidate locations efficiently, apply relocation strategies for existing devices, and minimize both installation and customer interruption costs. The MIPSO model outperforms ACO and ICA in cost and speed, shows stable results over 10 runs, and sensitivity analysis highlights the trade-off between equipment quality, total cost, and computation time. Notably, the model allows the relocation of existing equipment within the optimization process, and can be extended to other metaheuristic algorithms with ease. The proposed method is validated through four distinct scenarios on a modified IEEE 69-bus distribution system. Overall, GRIDMAP proves to be a robust and adaptable tool for advanced distribution grid planning and reliability enhancement. https://scientiairanica.sharif.edu/article_24113.html A high-end autonomous vehicle is expected to have at least a hundred different electronic subsystems. Each one of them takes power from the battery through Power Management Unit (PMU). Having an efficient PMU is crucial and is expected to supply the required level of uninterrupted power. PMU consists of several buck converters which translate a higher voltage level to required lower voltage levels. A PMU is more reliable if it consists efficient and well-structured voltage converters. In this paper, a fault-tolerant buck converter is designed which outputs 3.3 volts. A simple yet effective technique is proposed to design a fault-tolerant buck DC-DC converter by bypassing the faulty converter leg. The proposed system utilizes a signal processing-based method for fault detection. The secondary converter is activated only upon the confirmed prognosis of a faulty primary converter. Ripple content in the output Aluminum Electrolytic Capacitor (AEC) voltage is monitored and used as a primary health indicator for the converter. An experimental setup is built and tested in the laboratory. Experimental results indicate a smooth transition from the primary converter to the secondary demonstrating an uninterrupted power supply along with the simplicity and effectiveness of the proposed solution. https://scientiairanica.sharif.edu/article_24125.html Using quantitative analysis of intracellular metabolites is a valuable method for understanding biochemical processes in cellular systems. This study aimed to objectively evaluate various quenching–extraction techniques for intracellular metabolite profiling of Bacillus subtilis expressing serine alkaline protease (SAP) and to link metabolomics data with fermentation optimization. Among the tested methods (cold methanol, cold ethanol, and a mixture of acetonitrile and chloroform combined with fast filtration), cold methanol yielded the most efficient and reproducible extraction. Metabolomic analysis identified glucose 6-phosphate (G6P), fructose 6-phosphate (F6P), serine, and glycine as the most influential intracellular metabolites. Using the Box–Behnken design, different concentrations of the identified metabolites were systematically varied, and the model predicted optimal levels (G6P 0.52 mM, F6P 0.054 mM, serine 1.72 mM, glycine 0.07 mM) that enhanced SAP activity by nearly 300%, reaching 1530.33 U/mL compared to the control. These results establish a validated workflow that couples metabolomics with statistical methods to achieve enhanced protease production, offering both methodological and industrial relevance. https://scientiairanica.sharif.edu/article_24127.html This investigation presents the heat and mass transfer phenomenon for the chemically reactive flow of second grade fluid subject to the homogeneous and heterogeneous (HH) chemical reactions. The viscosity of fluid is assumed to be temperature dependent instead of constant. The motivations for considering the viscosity as a function of temperature is justified with applications of metallurgical process, crude oil extraction, geothermal systems and machinery lubrication. Additionally, viscous dissipation and temperature dependent heat generation and absorption effects are also introduced to improve the thermal transportation phenomenon. The interaction of different new variables facilitates the problem into dimensionless form. The numerical achievements are predicted with implementing the Runge Kutta (RK4) method. The physical onset behind the parameters have been reported. The tabular quantitative analysis is performed for different physical quantities. https://scientiairanica.sharif.edu/article_24128.html This article focuses on the influence of the shape factor of entropy generation on the MHD flow of an Eyring–Powell hybrid nanofluid past a permeable over a curved stretched sheet with Cattaneo–Christov heat flux. Using Homotopy Perturbation Method (HPM) and the shooting method, the governing nonlinear coupled PDEs are converted into ODEs with similarity variables and solved (R-K 4th order). Magnetic field, mixed convection, Eyring-Powell fluid, thermal relaxation, curvature, and thermal radiation are studied and represented in terms of velocity, temperature, entropy production, Bejan number, heat transfer, and coefficients of skin friction. To compare outcomes, we employ the HPM. The Homotopy Perturbation Method produces more precise and reliable results than the numerical method. When a magnetic field affected the hybrid nanofluid as it increased over a curved stretching sheet, the velocity profile decreased. In actuality, the Lorentz force increases as the magnetic field result increases, acting against the flow of the liquid to slow down the system. In the presence of a curved stretching sheet, the velocity profile also decreases as a result of increased magnetic parameters. In the three shapes, the temperature profile rises with increasing thermal radiation and Brinkman number values (sphere, platelet, and blade). https://scientiairanica.sharif.edu/article_24150.html This paper reveals the low-frequency noise of an Indium Arsenide Antimonide (InAsSb) channel dielectric modulated trench gate junctionless Dual-Gate FET (DG FET) and demonstrates that low-frequency noise in a DG FET can be suppressed by changing the mole fraction of arsenic in the Indium Arsenide Antimonide (InAsSb) channel dielectric modulated trench gate junctionless Dual-Gate FET. The effects of the Arsenic composition on the Junctionless InAsSb DG FET device's sensitivity and performance against low-frequency noise are investigated. The results ensure that composition fraction plays a very important role in the Streptavidin, APTES and Ferro-cytochrome biomolecules detection and reduction of  the low-frequency noise. According to the current investigation, a significant quantity of low-frequency noise can be produced by either a low or high content of Arsenic. For neutral and charged biomolecules at 1 Hz frequency, an intermediate molar percentage of Arsenic, ideally between 45% and 65%, can efficiently suppress the noise and produce a signal-to-noise ratio of greater than 104 in the Dielectric-Modulated Trench Junctionless Indium Arsenide Antimonide (InAsSb) Dual-Gate(DG) FET. https://scientiairanica.sharif.edu/article_24151.html Although Data Envelopment Analysis (DEA) assumes deterministic data, a great volume of data might be stochastic. The Global Malmquist Productivity Index (GMPI) is a highly effective instrument for productivity analysis in DEA. This paper extends GMPI in the presence of stochastic data. Our new stochastic DEA model is a Chance-Constrained Programming (CCP) model, which is converted to a deterministic programming problem with a linear objective function and quadratic constraints. For efficiency evaluation purposes, in this paper, the weak disposability principle is used to model Russell’s measure in the presence of undesirable outputs. The main contribution of this paper is to develop a global Russell model with stochastic data. A case study is presented to illustrate the applicability of the proposed models. https://scientiairanica.sharif.edu/article_24152.html This study presents the synthesis of CuO-TiO₂-ZnO (CuTiZn) ternary nanocomposite via the use of Kenger (Gundelia tournefortii) gum extract as a biogenic reducing and stabilizing agent. The synthesized CuTiZn nanocomposite was characterized via X-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR). XRD analysis confirmed the formation of a CuO-TiO₂-ZnO nanocomposite with a monoclinic crystal structure. SEM images revealed spherical, well-dispersed particles, whereas FTIR spectra indicated the presence of functional groups from the Kenger gum extract involved in the reduction process. Biological applications, including antibacterial activity against both gram-positive and gram-negative bacteria, were evaluated, yielding promising results. The antimicrobial activity of the biosynthesized ternary nanocomposite was evidenced by the formation of inhibition zones ranging from 13.5 to 18 mm in diameter against both gram-positive and gram-negative bacteria. The antioxidant properties were assessed via DPPH radical scavenging assays, which revealed an inhibition rate of approximately 34% at a concentration of 12.5 mg/mL. In anticancer activity assays, the CuTiZn nanocomposite exhibited an IC₅₀ value of 201.1 µg/mL against the MCF-7 human breast cancer cell line. These findings suggest that the biosynthesized CuTiZn nanocomposite possesses promising antimicrobial, antioxidant, and anticancer properties, highlighting their potential for biomedical applications. This study, for the first time, uses the green synthesis of CuTiZn ternary nanocomposite using Kenger (Gundelia tournefortii) gum agent. This eco-friendly approach not only minimizes toxic reagents but also enables the production of multifunctional nanocomposite with superior antibacterial and cytotoxic performance due to the synergistic effects of the three metal oxides. https://scientiairanica.sharif.edu/article_24155.html Rockfill materials experience gradual deformation over time, which can cause serious problems in rockfill structures, especially in dams. This deformation may lead to reduced confinement of the dam core by the shells, resulting in large deformations within the dam body, as well as cracking in filters and core, a loss of freeboard, and damage to associated facilities. The creep behavior of rockfill materials is influenced by various factors, such as stress level, material composition, aggregate shape, particle size, and moisture content. This study presents the application of the Soft Soil Creep model to simulate both volumetric and shear creep in rockfill materials. The model parameters were determined from large-scale oedometer tests using an integrated procedure that combines experimental data with numerical back-analysis, ensuring a physically consistent parameter set. Furthermore, the study analyzes the influence of these parameters on rockfill behavior, highlighting their role in controlling the creep deformations. As a case study, the creep behavior of the rockfill shells in the 177-m-high Masjed Soleyman Dam is investigated. The study confirms the model reliability in predicting dam settlement patterns through comparison with measured data and observations. Localized computed shear strain in upper level of the dam exceeded 50 % aligning with what accrued in the prototype. https://scientiairanica.sharif.edu/article_24164.html This study aims to provide an integrated decision-making approach in maritime transportation problems. The duty of hatch cover is to prevent the ingress of water into the cargo hold and protect the goods from being damped and damaged. Hence, it has the considerable role in productivity of maritime transportation systems. Since each hatch cover has distinguished properties with respect to criteria than the others, the hatch cover selection problem (HCSP) can be considered as a multi-criteria decision-making (MCDM) problem. In this paper, interval type-2 fuzzy sets (IT2FSs) are first used to weight criteria and evaluations of hatch covers with respect to criteria. In addition, an integrated group Shannon entropy- based weighted aggregated sum product assessment (WASPAS) approach is applied to solve the HCSP using the limit distance mean (LDM) in which the interval type-2 fuzzy Shannon entropy approach is used to determine the objective weights and then they are integrated with the subjective weights. On the other hand, in order to demonstrate the effectiveness and practicability of the proposed method, it is fulfilled in a real case study and the ranked results are compared with the others. https://scientiairanica.sharif.edu/article_24165.html In this article, for achieving effective and environmentally friendly production scheduling, we investigate a Multi-objective Low-carbon Hybrid Flow-shop Scheduling Problem (MLHFSP) with the consideration of machines with varied energy usage ratios. The problem is formulated by a multi-objective mathematical model with two optimization objectives, i.e., minimizing Total Carbon Emission (TCE) and makespan (Cmax). We primarily analyse the formation of TCE and derive its mathematical expression. MLHFSP is Non-deterministic Polynomial-hard (NP-hard), therefore, to tackle the model, an Improved multi-objective Teaching-Learning-Based Optimization (ITLBO) algorithm is proposed. The ITLBO algorithm mainly contains global search-based teaching phase and local search-based learning phase. In ITLBO, a solution is represented by two vectors, i.e., job sequence vector and machine assignment vector. Sigma method is utilized to quantify each individual, and to avoid local optimum, Sequential Neighbourhood Search (SNS) method is also adopted. Experimental results validate the feasibility and effectiveness of the proposed ITLBO in addressing MLHFSP. The research findings help manufacturing engineers to seek a sophisticated balance between carbon emission reduction and makespan reduction. https://scientiairanica.sharif.edu/article_24168.html In this study, different Ranked-Based Sampling (RBS) schemes are used to design a sensitive control chart to monitor the small or moderate shifts in the process mean, and are named combined Exponentially Weighted Moving Average (EWMA) Moving Average (MA) RBS (CEM-RBS) control charts. The Average Run-Length (ARL) and the Standard Deviation of the Run-Length (SDRL) are computed through Monte Carlo (MC) simulation runs to evaluate the performance of the proposed charts in comparison with the existing charts such as MA, EWMA, EWMA-MA, and the EWMA under RBS control charts. It is proved through a comparative study that the proposed CEM–RBS charts indicate a significant improvement in the performance of the EWMA-MA chart by using the RBS concept. A real dataset-based example is also included to explain the concept in detail. https://scientiairanica.sharif.edu/article_24169.html In Reliability-Redundancy Allocation Problem (RRAP), the reliability and redundancy of components in a given system configuration are determined while considering some problem-specific constraints. RRAP can be applied in various industries. Moreover, queueing systems are among the most common systems in the manufacturing and service industries. Failure in queueing systems can result in unwanted severe damages. Reliability analysis of queueing systems should be conducted concerning their performance measures. Therefore, a RRAP of a queueing system considering queueing costs is studied in this article. The proposed cost function includes queueing, repair, and energy consumption costs. A Memetic Algorithm (MA) is used to obtain optimal redundancy and failure rates of components and the system’s service rate, which affects the energy consumption level. Extensive numerical experiments and sensitivity analyses are performed to present the problem’s applicability and the proposed algorithm. https://scientiairanica.sharif.edu/article_24171.html In this study, slight hydroforming is introduced as a localized, low-pressure deformation technique for joining double-walled tubes, where controlled radial deformation rather than full tube forming produces a mechanically interlocked ST52/GGG70 interface. A Box–Behnken response surface methodology (RSM) was employed to evaluate the effects of applied forming load (expressed as equivalent internal pressure), forming temperature (25–75 °C), and Nano-Al₂O₃ concentration (0–10 wt%) on the greatest diameter reduction (D1). The analysis revealed that the applied forming load contributed 62.4% of the total effect on D1, followed by temperature (23.1%) and nanofluid content (14.5%). The developed quadratic model exhibited excellent predictive capability (R² = 0.9874), and the optimal parameters (≈520 MPa, 73 °C, and 5.6 wt% Nano-Al₂O₃) resulted in a maximum diameter reduction of approximately 14.2%, consistent with the model prediction. Microscopy of specimens produced under optimized conditions revealed a wavy interfacial morphology with an average waviness amplitude of 18–25 µm, accompanied by a thin and discontinuous 3–6 µm intermetallic layer. This interfacial configuration effectively disrupts crack propagation paths and enhances joint integrity. Overall, the results demonstrate that optimized slight hydroforming enables the formation of a crack-resistant, mechanically interlocked interface, confirming its potential as an efficient and controllable joining technique for multi-material tubular structures. https://scientiairanica.sharif.edu/article_24173.html Time series analysis and accurate forecasting of energy prices are critical for both policymakers and market participants. In the practical analysis of price time series, the coefficients play vital roles; however, their accurate estimation is a challenging issue as they are affected by external factors. This study proposes a new modelling approach for Artificial Neural Networks (ANNs) models based on fuzzy logic. For this purpose, we reformulated an ANN model as a fuzzy nonlinear regression model to capture the advantages of both fuzzy regression and ANN methodologies. This clear-box model can be applied to not only uncertain, ambiguous, and complex environments, but it is also capable of modelling nonlinear patterns. To illustrate the capability of the proposed approach, we report a case study of liquified natural gas (LNG) prices in Japan’s market (the world’s largest natural gas importer). The results support that the performance of the proposed approach is acceptable; moreover, it can deal with uncertain and complex environments as a clear-box model. https://scientiairanica.sharif.edu/article_24174.html Understanding centrifugal pump local flow characteristics under different piping systems flow conditions is important. Single and two-phase flows applications via centrifugal pump are widely used in many industries. In the present work, the behavior of emulsion (oil-in-water) and air-water mixture flows through a single stage radial flow type centrifugal pump at three different pump rotation speeds has been studied experimentally and analytically. Effects of oil type, oil concentrations and air injected into the pump suction side on the pump performance under different operating conditions were considered. For each operating condition, pump discharge, generated head, efficiency and the power required to drive the pump were mainly affected by the type of oil, oil concentrations, the amount of air bubbles flowing through the pump impeller and the air injection position in the suction pipe. The results obtained from this study are valuable for chemical and petroleum industries. https://scientiairanica.sharif.edu/article_24175.html In this paper, four Simulation Optimization (SO) models are developed by combining simulation and Genetic Algorithm (GA). In the proposed models, optimal values of inventory control parameters and the number of facilities to be opened are determined simultaneously for periodic review and continuous review systems, respectively. Furthermore, single-product and multi-components of Closed-Loop Supply Chain (CLSC) network are created considering two different objective functions of review systems to gain a sustainable competitive advantage for companies. We seek to offer valuable insights for creating robust and user-friendly CLSC network where the forward network includes suppliers, plants, retailers, and customers, and reverse network includes collection centers, disassembly centers, refurbishing centers, and disposal center. The results of this study demonstrated that four SO models have a significant potential to satisfy the customer’s needs since the average service level of the models is at least 81.8%. The total supply chain cost can be decreased at least 3% and at most 22% on average with the proposed continuous review model whose objective is the minimization of differences between the total overordering cost and the total underordering cost (C-D). Furthermore, the total lost sales cost can be improved at least 15% and at most 89% on average with the C-D model. https://scientiairanica.sharif.edu/article_24176.html This paper presented investigates nanofluid heat transfer inside the channel. The considered fluid was a nanofluid consisting of water and aluminum oxide nanoparticles. Nanofluid volume fraction from 0 to 4% and Reynolds numbers from 5 to 700 have been investigated. Numerous numerical results have been obtained, including: velocity profile, temperature, pressure and Nusselt number. Pressure, velocity and temperature contours were also presented and streamlines were drawn at different Reynolds numbers. The geometry of this research, decreases the pressure drop. For example, at a Reynolds number of 200, the fluid temperature increase in the geometry of this paper was %181 higher than that of a simple tube (with similar conditions), and the pressure drop was 19% lower than the pressure drop of a simple tube. Also a stagnation point was formed in the flow, where the highest pressure in the problem occurred at that point. https://scientiairanica.sharif.edu/article_24180.html In this paper, the quantitative flexibility of EH is evaluated considering a flexibility index which is based upon available maximum capacity as well as the response time of generating units. Here, the impact of simultaneous EH operation and construction are investigated on quantitative flexibility considering both uncertain supply and demand side resources. Hence, a new structure so-called multi-objective simultaneous operation/construction optimization of multi-carrier EH is presented which consists of a decrease in operation and construction costs as well increase in power system flexibility. The demand side uncertainties, including thermal/electrical demand, are implemented by the Gaussian distribution function, and uncertainty on the supply side, including gas pressure uncertainty (GPU), is modeled by the probabilistic–possibilistic Z-number method. Also, in multifarious cases, the performance of the proposed index is evaluated. It is shown how flexible resources like electrical storage systems (ESS), thermal storage systems (TSS), electrical demand response programs (EDRP), and thermal demand response programs (TDRP) can increase the flexibility of the EH. It is also conducted that how the flexibility enhancement can increase construction costs. https://scientiairanica.sharif.edu/article_24181.html This paper proposes a hybrid method for order reduction of the bilinear system model using Balanced Truncation (BT) and Bilinear Iterative Rational Krylov Algorithm (BIRKA). Bilinear BT (BBT) has low accuracy but guarantees stability, while BIRKA convergence suffers from sensitivity to initial choice of reduced-order system. The proposed method first determines the order of the reduced bilinear model by minimizing the index of Integral Square Error (ISE). Then, the initial guess of reduced-order system is provided via two approaches, BBT and Linear BT (LBT), to guarantee the convergence of BIRKA. The result of BBT is a good stable initial guess for BIRKA, but it is very computationally expensive to solve the generalized Lyapunov equations to find the solution. LBT decreases the computational complexity by providing the initial guess via solving the Lyapunov equations. To further decrease the complexity, the condition number is substituted in place of the eigenvalues in BIRKA. Three bilinear test systems are considered to show the efficiency of proposed method. Finally, the performance of the proposed method is compared with some classical methods. The results show that the convergence probability of BIRKA increases. Also, the time for the determining the model order reduction decreases. https://scientiairanica.sharif.edu/article_24182.html The specification of stretching and folding properties, particularly in time series, is of substantial interest. This study is sought to perceive the relationship between stretching and folding and irregular discontinuities in hand motion trajectories during target tracking tasks. In this regard, a new method is proposed based on compiling physiological characteristics and hand motion dynamics’ geometrical traits. Thus, five tracking conditions are designed in which participants are instructed to track different target motion patterns. In these experiments, sinusoidal and trapezoidal target movements with frequencies of 0.1 and 0.3 Hz, as well as pseudo-periodic target motion created by summing two sinusoids with frequencies of 0.117 and 0.278 Hz, are used as visual targets. The results illustrate that nonuniform discontinuities are noticeable properties of the hand motion trajectory. Also, the largest Lyapunov exponent, correlation dimension, and fractal dimension corroborate that the tracking attractor is low-dimensional and chaotic. Moreover, the results are compared with the curvature-based method, and its modified version is presented by taking advantage of the proposed method. As a result, through the suggested method, stretching and folding points are well discerned regardless of discontinuities. This method can deal with the systems with intermittency in both times- and state space. https://scientiairanica.sharif.edu/article_24183.html This paper proposes a systematic approach to the design of a supervisory control scheme using a new method of the Hybrid Automata in Power Quality (HAPQ) improvement. In order to access a better power quality, a model is introduced for optimal controller uses and the general behavior of the power system is presented with Hybrid Automata (HA). The formalism of a hybrid system contains continuous dynamic and discrete switching behavior in a control and modeling framework. Hence, it can facilitate achieving a detailed description of the system while highlighting safety issues in the criteria of system design. This study uses a power system HA model as a discrete event system (DES) plant and controller. Discrete Event Systems (DESs) are utilized to examine HA models along with the presence and absence of disturbances and control features such as non-linear load, Static Compensator (STATCOM) and Filter. In order to examine the power quality and voltage stability, power quality indicators such as total harmonic distortion of voltage (THDv), eigenvalues, bus voltage and stability theory of switched systems are evaluated. The simulation results reveal the effective performance of the proposed supervisory controller HAPQ model to enhance power quality and stability in power systems. https://scientiairanica.sharif.edu/article_24184.html Medical image segmentation is an essential step in various diagnostic and treatment procedures. This study aimed to conduct a systematic review of state-of-the-art segmentation methods based on the target. The target complexity is a considerable challenge in medical image segmentation and the first issue that experts confront in diagnosing or treating patients. Additionally, each group of targets has similar characteristics, motivating to provide a target-based review to compare the deep-learning (DL)-based studies. This is the first time that a target-based review of medical image segmentation has provided a focus on recent DL developments. This study categorized publications into three targets: tumors, vessels, and pathological. Using a PRISMA strategy while considering the inclusion and exclusion criteria, 118 articles were identified on Google Scholar and PubMed from 2015 to 2023 in the fields of brain, liver, and lung tumors, blood vessels, and pathology image segmentation. This review could assist researchers in selecting the proper network and being aware of possible challenges. We also concluded that medical image segmentation using DL as a cross-disciplinary field is involved with both complex medical data and technical issues. Consequently, new interpretable approaches may be able to bridge the gap between medical specialists and artificial intelligence researchers. https://scientiairanica.sharif.edu/article_24185.html Offshore floating wind turbines (FWT) decrease adverse climate change effects without occupying significant land and harvesting fields. Owing to the earth planet unexpected climate, online adaptive feedback control of FWTs will be effective in the sense of optimal and uniform energy capture. In this paper, a deep reinforcement learning (DRL)-based control system is proposed to offset both the disturbance and noise effects. Large variations of wind and water waves generate enormous information give rise to convergent learning of deep neural networks model of the wind turbine. As a result of the disturbance and wind abrupt changes, an adaptive inverse control equipped with DRL could easily cope with the inherent drawback of DRL i.e., tracking error. Furthermore, received rewards in the DRL algorithm are passed through the newly designed training algorithm to predict control actions such that the loss function is decreased. The attenuation of disturbance and noise on the tracking performance of closed-loop FWT is clarified through software implementation tests while the weight’s convergency and update rules are proved by the direct Lyapunov theorem. https://scientiairanica.sharif.edu/article_24186.html Chronic Obstructive Pulmonary Disease (COPD) is a common respiratory disease characterized by chronic inflammation of the lung airways and destruction of lung tissue that leads to air restriction. Asthma and Chronic Obstructive Pulmonary Disease are the most common respiratory diseases that cause the death of about 180,000 people worldwide every year, and the death rate of COPD is eight times higher than the death rate of asthma. It is the third leading cause of death worldwide. Time-frequency transformation has been used to diagnose and evaluate the severity of this disease using recorded signals, which are dynamic and non-static. In this research, the S transform is used as a tool to extract features from the lung signal. S transform has a higher frequency resolution than the wavelet transforms at low frequencies, and at high frequencies, it has a lower frequency resolution but a higher time resolution. After feature extraction using S transformation, mathematical statistics were applied to reduce feature dimensions. The results indicate that with K-fold validation for KNN classification, the accuracy, precision, and sensitivity values are 98.39%, 97.45%, and 93.88%, respectively, and for SVM, the results are 95.23%, 92.59%, and 83.33%. https://scientiairanica.sharif.edu/article_24187.html In the present time, mobile communication is essential for the daily life of human beings and for that modulation schemes are very important for the quality and speed of transmission. Orthogonal frequency division multiplexing (OFDM) is one of the best schemes for mobile communication. In this study, we focused to reduce the PAPR using a novel algorithm using the OFDM system. To reduce the PAPR, the Partial transmit sequence (PTS) has been proposed in this study and simulated with different modulation techniques. The main parameter for OFDM is peak-to-average power ratio (PAPR) because it is high which is not desirable for the transmission system. This parameter has been further minimized in low complexity conditions in PTS that include the binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), and quadrature amplitude modulation (QAM) modulation scheme. The obtained simulated results show clearly that among various modulation techniques, the BPSK is the optimum technique for proposed algorithm to reduce the PAPR. https://scientiairanica.sharif.edu/article_24188.html This research work investigates the mechanical and dry friction wear behavior of the AA5022 matrix reinforced with nano-scaled particles. In this work, nano-scaled vanadium and tin were blended with an aluminum matrix, and the aluminum composite samples with different weight percentages of nano-scaled vanadium and tin at 0–10 wt.% range were fabricated through a muffle furnace and stir-casting techniques. The obtained results reveal the tensile strength and microhardness progressively increase with nano-scaled vanadium and tin addition in the aluminum matrix to 6 wt% of each. The wear loss and wear rate gradually decrease in the same samples. The internal structure of nano-scaled vanadium and tin reinforced hybrid aluminum composite samples was examined by scanning electron microscopy, which reveals that, sample C (6wt% of nano-scaled vanadium and tin particles) microstructure appeared uniform distribution and dispersed intensively in the aluminum matrix. The pure AA5022 was also studied to compare the mechanical and tribological properties. The sample C improved by 39% in tensile strength and improved by 22% in micro hardness. Moreover, hybrid aluminum composite samples showed improved wear-resistant characteristics, which can be used for various tribological applications, especially in electric automotive and modern aerospace. https://scientiairanica.sharif.edu/article_24189.html Thermo-hydraulic analysis of nano-fluid flow phenomena through rectangular channels has currently created much interest among researchers, and is being used in a variety of engineering applications. Hydrothermal properties of oil-MWCNT nano-fluid flow in a rectangular channel embedded with obstacles under uniform heat flux have been studied numerically with the variations of the values of Re and different forms of obstacle. The governing equations have been solved using the finite volume approach, and fluent software has been used to visualize the simulation results. The impact of various forms of obstacle (plane, trapezoidal, elliptical, and triangular), the volume fraction of nano-particle (D%), and Re on the different thermo-hydraulic fluid flow phenomena have been investigated numerically. It has been discovered that, in terms of the various characteristics of hydrothermal flow phenomena, plane obstacles are more pronounced, whereas elliptical obstacles are less pronounced. Moreover, it has been found that the vortex length reduces as the number of barriers put on the higher walls increases. The current study's findings will be extremely beneficial in a variety of engineering applications, including glass blowing, and continuous metal casting, especially in a variety of manufacturing processes like transpiration cooling, and laser pulse heating. https://scientiairanica.sharif.edu/article_24190.html This article uses the model test to investigate the effect of static trim angle on the hydrodynamic performance of a semi-planing catamaran named AUT-SEM00. First, the resistance test results in the calm water at different speeds for static trim angles of 0.00 to 4.00 degrees are presented. The best static trim angle hence the lowest resistance is 2.0o at a speed of 3.78 m/s which leads to the dynamic trim of 2.5o. Then, the model test in three regular waves in a range of static trim angles is conducted, and the results are analyzed. It has been seen that the best static trim angle in comparison to the other rim angle depicts up to 9% less resistance. Additionally, it is shown that the static trim angle does not significantly affect pitch motion and vertical accelerations. As far as the regular wave length is concerned, the pitch and accelerations on CG and FP rapidly increase as wave length increases. There is a strong correlation between vertical acceleration and pitch motion where both have the same tendency concerning the wave length. https://scientiairanica.sharif.edu/article_24191.html The addition of different nanoparticles in conventional fluid with proper quantity gives the hybrid fluids which have higher thermo-physical properties. The geometry of the hybrid nanoparticles has substantial impacts in numerous engineering and bio-medical applications. Blasius and Sakiadis flows are considered under under the impact of viscous dissipation phenomenon. Both flows are exemplified at the surface of laminar boundary layer conditions in water-based hybrid nanofluid comprising copper and Titania with Prandtl number 6.2. The heat transport is executed by the implication of intelligent computing paradigm through process of Artificial Levenberg Marquardt back propagated neural networks. The nonlinear PDE’s which governs the fluid flow are organized into set of nonlinear ODE’s by using the similarity functions. Runge-Kutta-Fehlberg’s fourth-fifth order (RKF-45) based shooting scheme is utilized to solve the reduced ODE’s. The larger buoyancy parameter values enhanced the velocity over stationary surface with constant free-stream velocity for Blasius flow and vertical movement of planer surface is moved in stagnant free-stream for Sakiadis flow. The thermal and concentration profiles are reduced against higher buoyancy values for both the cases. https://scientiairanica.sharif.edu/article_24192.html This study explores the sensitivity of tetra-hybrid microfluidic flow to localized magnetic fields, focusing on their impact on flow dynamics, stress distribution, and thermal behavior. A rectangular cavity (aspect ratio 4:1) filled with a tetra-hybrid nanofluid is analyzed, with the top and bottom walls moving in the same direction. A confined magnetic field, structured in horizontal and vertical strips, is introduced to assess its influence. An alternating-direction implicit method has been used to enhance numerical stability and efficiently solve the discretized governing equations, and the single-phase model has been used to model the fluid. Furthermore, custom MATLAB codes, employing the Stream-Vorticity formulation and a finite difference method, are used to solve the governing equations. The findings demonstrate that in-creasing the magnetic field strength up to 500 enhances heat transfer by 65%. Among nanostructures, a 20% silver concentration yields the highest improvement, increasing the Nusselt number by 313%, fol-lowed by SWCNT (54%), TiO₂ (43%), and Cu (31%). Regarding skin friction, silver and TiO₂ reduce it by 65%, while Cu lowers it by 52%. However, SWCNT exhibits an opposite effect, increasing skin friction by 138% due to its elongated structure, which enhances flow resistance. These findings highlight the poten-tial for controlled magnetic field applications to optimize nanofluid performance in advanced thermal and biomedical systems. https://scientiairanica.sharif.edu/article_24193.html The peristaltic flow of nanofluids is a topic of growing interest in fluid dynamics. This study investigates the effect of temperature-dependent viscosity and electric conductivity on the peristaltic flow of nanofluids. The mathematical model of the peristaltic flow is developed using the governing equations of continuity, momentum, and energy for a Newtonian fluid. Large wavelength and small Reynolds number assumptions are used to study peristaltic flow to simplify the equations of continuity, momentum, and energy. In this article, the nanofluids are assumed to be electrically conducting and temperature dependent, and the effects of Hartman number and Eckert number is studied. The resulting equations are solved using the Shooting Method. The results show that the temperature-dependent viscosity and electric conductivity significantly affect the peristaltic flow of nanofluids. The flow rate and pressure gradient decrease with increasing viscosity and conductivity while the temperature and heat transfer rate increase. Moreover, the nanofluid concentration and particle size significantly impact the flow characteristics. In conclusion, this study comprehensively analyses the peristaltic flow of nanofluids with temperature-dependent viscosity and electric conductivity. The results can be useful for understanding the behaviour of nanofluids in various applications, such as drug delivery systems, microfluidics, and thermal management. https://scientiairanica.sharif.edu/article_24194.html The control of crystallite size, shape, and synthetic conditions of nanomaterials is considerable due to control the nanomaterials outstanding features. Accordingly, in this study, copper (Cu) nanoparticles were synthesized using the chemical reduction method which utilized copper nitrate and copper sulfate as the Cu precursors and L-ascorbic acid as the reducing agent without any capping agent in various reduction times (4, 8, 12, 16, 20, and 24 hours).XRD, TEM, FESEM, and EDS were used to evaluate the produced Cu nanoparticles. The results showed that the crystallinity of the synthesized Cu nanoparticles rose with increasing reduction time. Moreover, copper nitrate-derived copper nanoparticles showed higher crystallinity than copper sulfate. After a 24-hour reduction period, the Cu nanoparticles' crystallite size increased to 45 ± 11 nm, however it stayed mostly stable from 4 to 20 hours. Furthermore, the crystallite size of Cu nanoparticles produced by copper nitrate was significantly larger than those made with copper sulfate. The primary outcome was the production of high-quality Cu nanoparticles by chemically reducing copper nitrate with L-ascorbic acid for 20 hours, yielding fine and pure Cu crystallites. The findings of this study provide new insights into the effects of reduction time and precursor selection on the synthesis of Cu nanoparticles, which could help optimize nanoparticle size and crystallinity and have implications for a wide range of applications in electronics, catalysis, and materials science. https://scientiairanica.sharif.edu/article_24195.html Here the concept of heat transport mechanisms and stagnation point of the MHD Williamson nanofluid have been elaborated with Brownian motion and thermophoresis diffusion past a permeable stretching/shrinking cylinder. Both the conditions of velocity slip and heat source/sink effects are considered. The shooting algorithm with Runge-Kutta-Fehlberg method has been exploited for solutions of ODEs. The effect on drag coefficient, heat and mass transport rates as well as the dimensionless velocity, temperature and concentration fields of the physical boundaries objectives of the study are graphically delineated and thoroughly discussed. As nanoparticle concentration increases at the outer surface of the boundary layer, the rising patterns of Nusselt number as well as skin friction are observed. Dual solutions with the critical value of the mass transfer parameter (0<s_c<s)and the shrinking parameter (χ_c<χ) are obtained for different related parameters in some domains and shrinking parameter. It's worth mentioning that just for the contracting scenario; there are a range of solutions. The slip factor increases the thermal transport amount at the surface of the shrinking cylinder. Here the outcomes noted the average increase of skin friction with respect to γ (curvature) factor to 24.8% with brilliant results compared with former prose. https://scientiairanica.sharif.edu/article_24196.html The current study scrutinizes the influence of heat energy and slip effect using hybrid (HN) suspended in the Ethylene–Glycol (EG) as a based liquid over the curved surface impinging Modified Fourier Law (MFL) surrounding dust nanoparticles. The modeled mathematical equations in term of PDEs are transformed to convectional differential equations and are computed numerically via Finite Element Method (FEM). The flow characteristics are examined by assign numerical values to the physical parameters. The novelty of the problem is to examine the stability of dusty-hybrid nanofluid with slip effect. The HN effectiveness is significantly higher compared to that exhibited by the traditional NF (nanofluid). The consequences of the first-order slip variable, the curved variable, and the pulling force contribution on the velocity field, DPV (dust phase velocity), temperature field, and DPT (dust phase temperature) all increase with time. When porous factor is increased, it is seen that the DPV is enhanced. For dissimilar magnitudes of nanoparticles solid volume fraction, opposite behavior is observed for velocity field and DPV. The heat of the fluid drops in relation to the thermal relaxation coefficient. For the endorsement of the mathematical flow system error approximations has been computed. https://scientiairanica.sharif.edu/article_24197.html The fabrication of Fe3O4@SiO2 core-shell was prepared from natural iron sand as Fe3O4 core resource and in situ SiO2-coating method. The Fe3O4@SiO2 was synthesized via an ultrasonic route with various ratios of tetraethyl orthosilicate (TEOS) to evaluate the core-shell's morphological, pore structure, and magnetic properties. XRD and FTIR were used to characterize the prepared Fe3O4 and Fe3O4@SiO2. SEM analyzed the effect of the TEOS on the particle size, and TEM observed the morphology of core-shell and shell thickness. The BET data revealed that Fe3O4@SiO2(65) exhibited a larger diameter pore size of 88.17 nm and eight times higher BET surface area (80.23 m2/g) than Fe3O4@SiO2(45) (27.69 nm; 10.5 m2/g). The VSM data indicated that the more TEOS addition on Fe3O4@SiO2 caused the decrease of magnetization value but still gave good magnetic properties from 95.32 emu/g for Fe3O4@SiO2(45) to 17.02 emu/g for Fe3O4@SiO2(65). The study found that the higher content of SiO2 reduced the agglomeration of the Fe3O4 core indicated by no hysteresis loop on the N2 adsorption-desorption curve of Fe3O4@SiO2(65), resulting in core-shell material with better properties in higher specific surface area, average pore size and volume for further application. https://scientiairanica.sharif.edu/article_24198.html This study investigates the magnetohydrodynamics and fluctuating mixing convection of hybrid nanofluid flows containing dusty fluids in a permeable vertical wedge. A mathematical model based on the concept of fluid flow was developed. The model uses the boundary layer approximation to simplify the partial differential equations. Using appropriate transformations, this set of differential equations is converted into a general system of differential equations. We used the method of obstacles and the bvp4c approach to solve the obtained nonlinear dimensionless differential equations. We worked out to gain results of all values of non dimensional space variable ξ and small values of ξ. The effects of involving physical significant parameters are highlighted by graphs. As the Richardson number increases, both the friction coefficient and the Nusselt number increase. This behavior demonstrates the complex interplay between buoyancy and momentum/heat transfer in the flow. Skin friction and Nusselt number declined due to increasing values of thermal slip (thermal slip). In summary, increased thermal slip affects fluid–solid surface interactions, resulting in lower skin friction (less resistance to fluid flow) and lower Nüssel number (reduced convective heat transfer). https://scientiairanica.sharif.edu/article_24199.html Nanotechnology took interest of research community for the reason that of its vast range of uses and applications like killing cancerous cell, preparation of medicines, nano-robot technology, manufacturing of modern aircraft, distillation process of water, biomedical engineering, thermal storage as well as transfer system, cooling of electronic devices, aircraft engines, power plants, coolants in nuclear reactors, construction industry etc. While considering these outstanding applications, a major determination of contemporary research work is to explore the role of radiation phenomena in ferrofluid having magnetic effects too configured by impervious surfaces. Concept of microorganism is utilized to make suspension more stable, here nano sized particles are used with the effect of bio-convection to solve the simultaneous equations having magnetic effects. Research revealed that study of bio-convection is an interesting field because of its use in biotechnology as well as bioengineering procedures; most recently numerous models of bio convection were explored to produce more microbes for phenomena of microorganisms. Major purpose behind this investigation is exploration of two dimensional Cross fluid flow of ferrofluid with magnetic dipole effects. https://scientiairanica.sharif.edu/article_24200.html Abstract: The objective of current work is to characterizes the thermal impact of carbon nanotubes due to rotating disk with applications of thermal radiation, heat source and slip effects. The single walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) with suspension of ethylene glycol (EG) has been used to analyze the problem. The base fluid consequences are justified by using Casson fluid. The flow model is justified with interaction of velocity and thermal slip effects. Thermal characteristics of SWCNT and MWCNTs along ethylene glycol (EG) base materials has been presented. The heat transfer investigation is based on implementation of Cattaneo-Christov approach. Numerical computations are performed with help of shooting scheme. Comparative thermal analysis is performed for traditional nanofluid (SWCNT/EG) hybrid nanofluid (SWCNTs-MWCNTs/EG). Physical visualization of results for enhancement of heat transfer phenomenon is performed. It has been observed that heat transfer phenomenon is more exclusive for hybrid nanofluid (SWCNTs-MWCNTs/EG) as compared to nanofluid (SWCNT/EG. The thermal profile enhances for Casson fluid parameter. https://scientiairanica.sharif.edu/article_24201.html The current explores the consequences of radiative heat and the variable porosity on the steady flow of an incompressible hybridised nanomaterial comprising hybrid alloy nanoparticles (AA7072, AA7075) along with water (H2O) as the conventional fluid. The proposed investigation is performed within the framework of the Riga plate. Because of the complimentary benefits of nanoparticles, hybrid nanofluid is used to improve the efficacy of heat transfer fluids. The flow scenario is characterized by a system of dimensional, nonlinear differential equations renewed to a set of nonlinear dimensionless ordinary differential equations using appropriate similarity substitution. The Keller Box Method (KBM) then solves these transformed equations. Graphs have been used to inspect the effects of changing physical factors on fluid flow, temperature, and other important measurements. This study evaluates the dependability of the results by comparing them to previous research. The results reveal the significant impact of flow medium inverse porosity influences both the boundary layers to the flow resistance and heat transference characteristics. The Riga plate significantly affects the fluid flow and heat transmission, leading to variations in velocity profiles and thermal gradients due to the generated electromagnetic field. Radiant heat influences become more pronounced at higher temperatures, growing heat transport for high-temperature applications. The combined effects of hybrid nanomaterials, radiative heat transfer, electromagnetic field result in affecting skin friction and the heat transfer coefficient. Finally, the hybridization of AA7072 and AA7075 aluminum alloys in the nanoliquid significantly augments thermal conductivity, resulting in enhanced heat transportation rates compared to conventional fluids. https://scientiairanica.sharif.edu/article_24202.html Small and Medium-sized Enterprises (SMEs) can enhance their performance and increase competitiveness through the utilization of smart technologies. However, selecting appropriate smart technologies is a problematic decision due to conflicting demands (such as interoperability versus cost) and inherent uncertainties. The current study develops a comprehensive decision support framework for the selection optimal smart technologies in SMEs. The suggested model is a tripartite model involving: (1) recognition and validation of key indicators, (2) analysis of causal relationship and weighting through Fuzzy Interpretive Structural Modeling (FISM) and Fuzzy DEMATEL (FDEMATEL), and (3) technology ranking through an Adaptive Neuro-Fuzzy Inference System (ANFIS) integrated with a hybrid optimization algorithm and Type-2 Fuzzy Analytic Hierarchy Process (Type-2 Fuzzy AHP). Data were gathered by interviewing 15 SMEs and surveying 10 experts using questionnaires. Results indicated that among 12 indicators, "compatibility with existing processes," "return on investment," and "information security had the most significant impact. The Type-2 Fuzzy AHP and hybrid ANFIS model gave the best performance with accuracy of R2=0.956 in nonlinear relationship capturing and ranking alternatives under conditions of uncertainty. Framework robustness was confirmed by scenario analysis (optimistic, probable, and pessimistic), and technology rankings were stable in all scenarios. Results indicated that Internet of Things (IoT), blockchain, and artificial intelligence were found to be the most appropriate alternatives for the SMEs under investigation. https://scientiairanica.sharif.edu/article_24203.html Nitrogen rejection from high-nitrogen natural gas (NG) streams is energy-intensive and reduces the economic viability of such resources. Various nitrogen rejection technologies have been developed to address this issue. This study presents a comprehensive economic evaluation of two cryogenic nitrogen rejection unit (NRU) configurations: single-column and double-column. The research introduces an economic enhancement by incorporating the rejected nitrogen into the value chain, proposing its use in enhanced oil recovery (EOR) instead of atmospheric venting. For this purpose, a compression-injection unit is integrated into the process. The evaluation considers four scenarios (two NRU configurations and two nitrogen utilization options) and uses net present value (NPV) as the optimization objective. Simulation and optimization results show that the double-column configuration is the most profitable when nitrogen is vented, with NPVs of $894 million and $1,073 million for single- and double-column configurations, respectively. However, when nitrogen is utilized for EOR, NPVs increase significantly to $4,787 million and $4,861 million, respectively. The economic advantage of the double-column configuration diminishes in the EOR case due to higher compression costs, highlighting that the optimal choice is context-dependent, as the single-column configuration becomes more profitable at increased EOR throughputs. https://scientiairanica.sharif.edu/article_24204.html A coplanar waveguide (CPW) antenna for 5G internet of things (IoT) communication is designed and experimentally verified in this article. The proposed antenna's radiator comprises of a simple microstrip patch with a single slot, along with cylindrical cut, and a 50 Ω microstrip feed line. This design structure with a slot allows it to resonate in upper frequency ranges and enhance the bandwidth. The construction uses the single layered RT5880 substrate, with two coplanar grounded planes positioned on either side. The measured wide impedance bandwidth of the proposed antenna is less than -10 dB of 133.7% (3 GHz to 15.1 GHz). The parametric evaluation of the proposed antenna design is performed utilizing the length and width of the slots Ls, Ws, and radius R for minimal return loss and consistent performance over entire operating frequency band. The proposed antenna produces omnidirectional radiation patterns across the entire working band, with a peak gain of 6.5 dBi. The suggested antenna has been fabricated and measured, where both the simulated and measured results closely match with one another. The physical dimensions of the proposed antenna are 30 × 24 × 0.5 mm3. Due to its low cost, wide impedance bandwidth, and seamless integration, this antenna is well-suited for 5G mobile Internet of Things (IoT) applications https://scientiairanica.sharif.edu/article_24208.html This paper describes the three-dimensional Casson nanofluid's rotating flow with bioconvection Phenomenon containing microorganisms, thermal radiation, and magnetic effects. This research has real-world applications in a variety of processes, including oceanography, crystal growth, computer storage devices, lubrication, and rotating machinery. The present model incorporates the Buongiorno nanofuid model, which describes the Brownian and thermophersis motion of nanoparticles. The boundaries layer approximation and non-Newtonian three-dimensional Casson fluid model are encompassed in the modelling of the nonlinear partial differential system. Finding an analytical clarification to the 3D Casson nanofluid flow past a rotating frame involving bio-convection phenomenon nonlinear differential equation is the goal of this task. Graphical results are obtained using the OHAM. The effects of different numbers are examined with respect to velocities, thermal field, magnetic field, nanoparticle concentration, and microorganism field. The temperature distribution is lessened with a greater Prandtl number. The temperature and solute field of a species increases with an upsurge in the estimation of the thermophoresis parameter. The microorganism’s field decreases when the Peclet parameter, bioconvection Lewis number, and Microorganisms difference number increase. https://scientiairanica.sharif.edu/article_24209.html The primary goal of this study is to investigate the shear-thinning ternary hybrid magnetohydrodynamics nanofluid flow through an exponentially stretched cylinder incorporating Arrhenius energy and varying thermal conductivity. Darcy-forchheimer impact and a magnetic field are also employed in this flow model. The Casson ternary hybrid nanofluid mechanism is utilized in conjunction with Molybdenum disulfide, Silver, and Copper nanoparticles. The assortment of partial differential equations (PDEs) in the mathematical framework is simplified to ordinary differential equations (ODEs) by implementing the similarity transformation. MATLAB computing approach Bvp4c is used to achieve the numerical solutions for regulating ODEs and portray the graphs for numerous emerging variables. The core findings indicate that the non-Newtonian ternary hybrid nanofluid highlights a more noticeable thermal and mass transport enrichment than the hybrid nanofluid. The thermal transmission rate for polymer-based trihybrid nanofluid is almost 3% superior in contrast to hybrid nanofluid. Also, the absolute shear rate for ternary hybrid nanofluid is nearly 5.02% better than the hybrid nanofluid. Additionally, the research significantly advances the forecasting of the significance of shear-thinning ternary hybrid nanofluid in the thermal transport processes. The findings reflect strong consistency with earlier released studies. https://scientiairanica.sharif.edu/article_24211.html Technological development has led to a diversification of loads in transmission and distribution systems. The rise of non-linear loads in the system is one of the biggest effects of this variation as semiconductor technology develops. Nonlinear loads are characterized by current and voltage characteristics that are not purely sinusoidal, also known as harmonics. Harmonics cause the system insulation to degrade and increase energy loss. Therefore, it's crucial to get rid of harmonics before they occur. This study intends to lower the risk of distribution system damage by employing complex harmonic forecasting methods. An RNN-based forecasting algorithm has been created by using actual system power quality data obtained from the Organized Industrial Zone in Bandırma, Turkey. Parameters that are most likely to be neglected in simulation studies are also taken into account in the calculation by using actual data. Active power data, current harmonic data and calendar data were used together to design harmonic forecasting model. Graphs and calculations were used to discuss the results. The obtained minimum values of the RMSE, MAE, and MAPE are 2,116, 0,666 and 11,619, respectively. The convergence as a result of these calculations has allowed high forecasting performance of power quality distortions. https://scientiairanica.sharif.edu/article_24213.html This paper introduces a newly designed four-dimensional memristive chaotic system. The novel oscillator is chaotic regarding the findings that the system’s dynamic has one positive Lyapunov exponent. Also, due to the results of the equilibrium points analysis, it is shown that the oscillator has a line of equilibria, so the attractors of this system are hidden. Moreover, the study of energy dissipation of this system, power spectrum, and Poincaré sections are conducted to investigate the system's dynamics. The complex features of this system are investigated with the aid of bifurcation diagrams, Lyapunov exponents spectra, approximate entropy, and basin of attraction. https://scientiairanica.sharif.edu/article_24214.html The time-varying equivalent roll damping coefficient and natural frequency in a nonlinear state-space model of a floating body are discussed in this article using an augmented extended Kalman filtering (EKF) method. In this paper, we present an estimation technique that can identify changes in the damping properties of the system considered by a single parameter based on roll angle data. The model used an augmented EKF to overcome parameter variability and noisy measurement input. The calculated error was compared with the covariance matrix's theoretical restrictions to determine whether the filtering was effective. It is found that the equivalent damping coefficient and natural frequency obtained from the EKF method is a more accurate depiction of the roll dynamics from the general estimation procedure given by the literature. The suggested technique has the ability to eliminate random noise from the measured signal. The effect of measurement noise levels on identification accuracy was investigated and discussed. https://scientiairanica.sharif.edu/article_24215.html Heart failure (HF) is a cardiac dysfunction disease with a high mortality rate that is mostly calculated via registry data. The objective of this work was to predict in-hospital mortality in patients hospitalized with HF utilizing their before-hospitalization registry data. The data include 3968 HF records extracted from Persian Registry Of cardio Vascular diseasE (PROVE)/HF registry.We proposed a method that contains an imbalanced ensemble probabilistic model which using registry data predicts HF patients who die during hospitalization from those who survive. The suggested ensemble model uses machine learning models that several ones, namely Decision Tree, Random Forest, LDA, Logistic Regression, SVM, KNN, and XGBoost were evaluated. We also used feature importance analysis to find the important ones and reduce the complexity.The results illustrated the proposed method can predict in-hospital mortality of HF patients using XGBoost that outperformed all others. Feature importance ranking obtained by XGBoost demonstrated that the proposed method can achieve an acceptable performance with the first 18 important features and XGBoost (accuracy: 76.4%±1.6%, sensitivity: 76.8%±6.9%, specificity: 76.4%±1.8%). Moreover, statistical analysis presented significant predictors of in-hospital mortality (P-value<0.01).In conclusion the proposed method can effectively predict in-hospital mortality of HF patients using the imbalanced data. https://scientiairanica.sharif.edu/article_24217.html Due to their recurrence and complex biology, brain tumors remain among the most challenging cancers and significantly contribute to global cancer mortality. With continuous development in precision oncology, accurately predicting patient-specific drug responses is essential for effective treatment and drug design. In this study, we propose a novel multimodal framework utilizing a Light Gradient Boosting Machine (LightGBM) regressor for brain tumors drug response prediction. The model integrates both biological and chemical data features using three modality-specific Variational Autoencoders (VAEs) to encode gene expression, gene mutation, and drug molecular fingerprint features respectively. The integrated feature representations are used by the LightGBM regressor to predict the half-maximal inhibitory concentration IC50 of drugs. Reliable results are obtained using subject-level stratified nested cross-validation. Our model has yielded improved RMSE and correlation R² values 1.12 and 0.76, respectively. These results are statistically significant (p<0.05) as compared to several existing models. Furthermore, using proposed model five FDA-approved drugs with the most accurately predicted IC50 values were identified. Using the statistical analysis of Glioblastoma (GBM) cell lines, we explored several over-expressed genes: EGFR, MKI67, BIRC5, TOP2A, AURKA and under-expressed genes: GFAP, MBP, NEFL, SLC1A2, PLP1., highlighting their biological roles in tumor progression and suppression. For clinical perspective, we have carried out the Survival analysis that showed that highly expressed tumor genes did not significantly affect normal patient survival (p > 0.05). It is anticipated that this study would be useful in precision oncology for anticancer drug development. https://scientiairanica.sharif.edu/article_24218.html The global pursuit of sustainable and non-toxic materials for optoelectronic applications has directed increasing attention toward lead-free halide perovskites. In this study, the structural, electronic, optical, and mechanical properties of Ca3PBr3 were systematically investigated using first-principles calculations based on density functional theory (DFT). Both the generalized gradient approximation (GGA-PBE) and the hybrid HSE06 exchange-correlation functional were employed, with spin–orbit coupling (SOC) incorporated to capture relativistic effects. The results reveal that Ca3PBr3 possesses a direct bandgap of 1.64 eV (GGA) and 2.47 eV (HSE06), which can be efficiently tuned within a range of 0.38 eV under biaxial strain from -4% to +4%. The projected density of states (PDOS) indicates that the valence band is primarily derived from Br-4p and P-3p orbitals, while Ca-3d states dominate the conduction band. Optical analyses show strong light absorption in the visible range, with strain-dependent modulation of the absorption edge, refractive index, and dielectric function. Mechanical stability tests confirm that Ca3PBr3 satisfies the Born criteria under all applied strain conditions, maintaining robust structural integrity. These findings identify Ca3PBr3 as a mechanically stable, environmentally benign, and strain-tunable semiconductor suitable for next-generation flexible optoelectronic, piezoelectric, and spintronic devices. https://scientiairanica.sharif.edu/article_24221.html Attention to a food Supply Chain (SC) has increased recently due to population growth and increased demand for food. Aquaculture development is advantageous as fish is a crucial constituent of the food basket of households. This study first presents a new bi-objective and multi-period mathematical model of a fish Closed-Loop Supply Chain (CLSC). The model is addressed by utilizing the Multi-Objective Keshtel Algorithm (MOKA), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Simulated Annealing (MOSA). The Taguchi method is employed to tune these metaheuristics to attain superior performance, and the ε-constraint method is used in solving small-sized problems to validate them. The results show that the exact method cannot solve large-sized problems.The solutions are compared in terms of different performance metrics. Using the ‘Filtering/Displaced Ideal Solution’ (F/DIS) method, NSGA-II and MOKA with a direct distance of 0.4228 and 0.8976 have the first and second performance ranks, respectively. Also, a case study including a trout CLSC in the north of Iran is investigated. The results and the case study show that the developed model can be applied to the proposed solution approach. https://scientiairanica.sharif.edu/article_24224.html Traditional citrus fruit classification relied on weight-based sorting and manual inspection, often leading to inconsistencies in quality assessment. This research presents an optimized deep learning approach for classifying ripe, semi-ripe, and unripe tangerines using an enhanced YOLOv7-tiny convolutional object detection model. We integrate a custom-designed convolutional block attention module (CBAM) to improve feature extraction and classification performance. This proposed CBAM-enhanced model serves as the teacher in a knowledge distillation framework, guiding a compact student model to achieve high accuracy with significantly reduced computational demands. The distillation process employs a composite loss function that includes Kullback–Leibler (KL) divergence to align soft probability distributions, mean squared error (MSE) to match intermediate feature maps, and cross-entropy for hard-label supervision. To further optimize the model, structured pruning removes redundant filters, and post-training quantization (PTQ) converts weights and activations from 32-bit floating-point to 8-bit integers, enabling deployment on resource-limited microcontrollers. The final student model (with CBAM) achieves a classification accuracy of 95.97%, with only a 0.93% drop in accuracy compared to the original YOLOv7- tiny model with accuracy (96.9%). Moreover, the model size is reduced by 31 (from 11,945 KB to 385 KB), and inference latency decreases by 5.25 (from 27.3 ms to 5.2 ms), making it well-suitable for real-time embedded inference. The resulting student model (without CBAM) is trained on a tangerine orchard dataset and deployed on an STM32H743 ARM Cortex-M7 microcontroller, equipped with 2 MB flash memory, a 480 MHz processor, and a DCMI interface for real-time image processing. https://scientiairanica.sharif.edu/article_24225.html Administrative corruption poses a significant challenge within public and organizational operations. This paper proposes a simple decision support system to estimate the probability of individual corruption and the level of societal corruption, and to use these estimates for monitoring and detection over time. Each period (e.g., monthly), the organization updates the corruption probability of every person using two inputs: (a) relatively stable personal features from the World Values Survey (WVS), and (b) live organizational signals from the internal whistleblowing system, third-party sources, observed suspicious actions, and proximity to confirmed cases. To reflect the wider environment, we also include a public context signal: a society-level corruption-risk forecast derived from countries’ Sustainable Development Goals (SDGs) indicators. We analyze three types of data from international databases using standard data analysis and machine-learning methods and report model performance. To examine effectiveness, we simulate a small, hypothetical organization using profiles derived from the WVS dataset and evaluate the approach under controlled conditions. The proposed approach flags anomalous individuals and detects system-level abnormalities within a reasonable timeframe, while keeping the process transparent, periodic, and feasible under realistic inspection constraints. https://scientiairanica.sharif.edu/article_24229.html Aiming at the non-stationary characteristics of oil pressure vibration signals containing particulate, a method for predicting particulate content in oil was proposed based on vibration characteristic frequency extraction by vibrational mode decomposition (VMD), variable selection using successive projections algorithm (SPA) and T_S fuzzy identification combined. Firstly, the pressure vibration signal was decomposed by VMD and a series of narrow-band characteristic frequency matrices were obtained. Then, variables were selected using SPA to construct the feature vector matrix. Finally, the feature vector matrix was used as the input of T_S fuzzy identification to identify the content of particulate in oil. The results showed that the VMD reconstruction of the original oil sample pressure signals could well characterize the main variation of the original signal; the 19 variables were selected from the characteristic frequency of the vibration signal from the oil pressure using SPA, the 19 pressure vibration characteristic frequency of 11 sample sets SPA selected was taken as the input variable of T_S identification model; for each set of sample, the predicted output of the content of particulate in oil was obtained, model prediction decision coefficient is 0.8637, the root mean square error is 0.1979, a reasonable prediction effect was obtained. https://scientiairanica.sharif.edu/article_24233.html This paper presents an antenna operating from 3.44 to 6.45 GHz, integrated with a frequency-selective surface (FSS) to enhance gain. The physical size of the antenna is 22 × 29 × 0.8 mm³, and the electrical size is 0.249λ × 0.329λ × 0.009λ at 3.4GHz. The antenna geometry consists of a modified elliptical patch with a ‘Ғ’ slot and a stepped slotted partial ground plane, which helps achieve a wideband response. The maximum gain of the antenna is 4 dBi. Furthermore, to enhance the antenna's gain and improve the bandwidth, a modified cross-shaped frequency-selective surface structure with a 4×4 array is designed and fabricated. The total dimensions of the unit cell and 4×4 array are 18 mm × 18 mm and 80 mm × 80 mm, respectively. This FSS is positioned at an optimized distance of H = 22.2 mm from the antenna to achieve high gain and maintain a wideband response. Using FSS, an improvement in gain is observed, with a maximum gain of 8.62 dBi achieved. Additionally, an improvement in the bandwidth is observed. The FSS extended the bandwidth from 3.18 GHz to 6.40 GHz. The prototype of the suggested antenna and FSS has been fabricated and tested, and the results indicate that the simulated and tested values match very well, except for minor deviations due to fabrication and testing tolerances. https://scientiairanica.sharif.edu/article_24234.html Despite all success of recommender systems, these systems face natural problems such as cold start and data sparseness. To face these issues, in this work a novel hybrid recommender is proposed that leverages the strengths of both collaborative and content-based methods together with sentiment analysis. The proposed system improves recommendation accuracy, alleviates cold start and sparsity issues, predicts future interactions, and adds a new context layer to recommendations, by effectively integrating demographic data, user clustering, Machine Learning (ML) models, sentiment analysis, and improved link prediction strategy. Proposed system was evaluated using the popular MovieLens dataset, and test results indicate the system significantly improves handling new users. The proposed system achieved a decline of 5.2% in Mean Absolute Error (MAE) and 6.8% in Root Mean Square Error (RMSE), compared to baseline methods. Moreover, results indicate that the proposed recommender performs significantly better under user and item cold start conditions, with a reduction of 29.6% and 27.4% in MAE, respectively. Lastly, integrating sentiment analysis and an improved link prediction approach provides a boost of 7% in Precision and 10.8% in Recall. Therefore, such hybrid approaches can be successful in alleviating cold start and sparsity problems, and enhancing overall system performance. https://scientiairanica.sharif.edu/article_24235.html Human leukocyte antigen (HLA) compatibility is critical for the success of hematopoietic stem cell transplantation, particularly for patients without matched related donors. As cord blood units (CBUs) play a growing role in bridging donor gaps, cord blood banks must optimize donor selection and CBUs’ storage to maximize HLA diversity. A prenatal, pre-collection prediction framework is developed to estimate whether a CBU’s future HLA profile will be rare or common within a large banking registry. Rarity was defined as a binary label based on observed 6/6 low-resolution matches at the HLA-A, HLA-B, and HLA-DRB1 loci (0 matches = rare; ≥1 = common). Demographic, medical, and obstetric features were preprocessed via one-hot encoding, degree-2 polynomial expansion, and Principal Component Analysis (PCA); feature selection used Random Forest (RF) importance within a leakage-controlled pipeline. Five machine learning models, including K-Nearest Neighbors (KNN), RF, XGBoost, and a deep multilayer perceptron (MLP), are used, with each evaluated with and without Genetic Algorithm (GA) hyperparameter tuning on identical cross-validation folds, and a TOPSIS multi-criteria ranking was performed to rank algorithms. The GA-tuned MLP performs best (Accuracy 0.74, ROC-AUC 0.84) and ranks first by Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), with GA-tuned XGBoost close behind. These results demonstrate that prenatal, pre-collection prediction of HLA rarity is feasible and can support prioritization for confirmatory genotyping to enhance diversity in cord-blood inventories. https://scientiairanica.sharif.edu/article_24236.html Accurate change detection in remote sensing imagery requires sophisticated multi-scale temporal feature integration and attention mechanisms. Current methods suffer from suboptimal multi-scale information utilization due to uniform attention deployment and insufficient feature discriminability from deterministic processing. We propose a novel framework addressing these limitations through two key innovations. First, an Adaptive Scale-Context Attention module with resolution-aware orchestration strategically applies spatial attention at higher scales for precise boundary delineation and channel attention at lower scales for semantic feature selection. Second, and most importantly, we introduce a stochastic attention refinement mechanism that revolutionizes attention-based change detection by learning adaptive modulation patterns through contextual pattern embedding. This stochastic framework employs posterior and prior distributions to model context-dependent enhancement patterns, applying learned contextual representations to dynamically calibrate attention scores and significantly improve feature discriminability beyond deterministic approaches. Our method processes bi-temporal images through dual-stream encoders, applies Adaptive Scale-Context Attention modules with stochastic enhancement across multiple scales, and reconstructs change maps through semantically-aware upsampling. Extensive experiments on four benchmark datasets demonstrate superior performance: we achieve 93.82% F1-score on DSIFN-CD, 94.37% F1-score on WHU building dataset, 91.85% F1-score on LEVIR-CD, and 76.19% F1-score on MSRS-CD, while maintaining computational efficiency with only 6.8M parameters. Comprehensive ablation studies validate the effectiveness of both resolution-aware attention orchestration and stochastic enhancement, establishing a new paradigm for efficient and accurate remote sensing change detection.