https://modelling.semnan.ac.ir/ Journal of Modeling in Engineering en daily 1 Sat, 21 Mar 2026 00:00:00 +0330 Sat, 21 Mar 2026 00:00:00 +0330 https://modelling.semnan.ac.ir/article_9773.html The aim of this research is to present a model for designing tourism applications based on user experience. In this context, a qualitative analysis of the strengths and weaknesses of tourism applications and websites was conducted.Data were collected through semi-structured interviews with users of these platforms. To ensure the validity of the research tool, confirmation was obtained from experts in tourism and user experience design, and their feedback was used to refine and improve the survey instrument. Additionally, to ensure the reliability of the research, a transparent methodology and coding by multiple researchers were employed to guarantee the reproducibility of the results. Data analysis was conducted using Maxqda software. The findings indicate that key factors in user experience include service quality, access to accurate and complete information, and simplicity of the user interface. Users also emphasized features such as price comparison, fast loading speed, and trustworthiness. On the other hand, issues such as incomplete information, intrusive ads, and poor customer support were identified as the main weaknesses. The discussion and conclusion of this research suggest that improving service quality, reducing intrusive ads, and enhancing customer support can increase user satisfaction and assist developers of tourism applications and websites in delivering an optimized user experience. The results of this research indicate that improving service quality, reducing intrusive ads, and enhancing customer support can help increase user satisfaction and aid developers of tourism applications and websites in providing an optimized experience. https://modelling.semnan.ac.ir/article_10235.html The need for clean energy in today's world is increasing day by day due to the limited nature of fossil resources. Solar energy can be utilized in various domestic, agricultural, and industrial applications. Integrated solar storage collectors are extensively used for generating heat by absorbing solar energy and converting it into thermal energy. Therefore, there is a significant need to design and produce these collectors to achieve the highest performance and efficiency. In this research, considering a type of integrated solar storage collector and using the hybrid nanofluid MWCNT-Fe3O4, its effect at different Rayleigh numbers (103-106) and various nanofluid percentages (0-0.003) has been investigated. Velocity and temperature contours and streamlines in different positions of the hot absorber inside the collector have been examined. The local and average Nusselt numbers have also been studied. The results indicate an increase in heat transfer at the Rayleigh number and a high percentage of nanofluid. Additionally, the optimal position for placing the hot absorber is at the far right end of the collector. The results of this research are published for the first time and can be used for designing solar collectors. https://modelling.semnan.ac.ir/article_10022.html In recent years, with the expansion of online shopping and the importance of user feedback in improving the quality of products and services, sentiment analysis of user reviews has become one of the most important tools and opportunities for online businesses and services. In this research, various approaches based on Convolutional Neural Networks (CNN), BERT language model, and FastText language model have been examined for sentiment analysis of user reviews about mobile phones in aspects such as camera, battery, and price in an online marketplace. In this regard, the data were labeled based on aspects and categorized into three sentiments: positive, negative, and neutral. Additionally, to improve the performance of the proposed approaches and address data imbalance, data augmentation methods were utilized and their impact was analyzed. Finally, using the proposed models, very high accuracy in detecting positive, negative, and neutral reviews in each aspect was achieved. According to the results, the proposed CNN-based approach performed better than the other two proposed methods on the given data. https://modelling.semnan.ac.ir/article_10029.html The adverse effects of unwanted and disruptive noise in industrial and domestic applications are widely recognized. The significance of controlling such noise, particularly in household settings, has drawn the attention of many researchers to this field. This study aims to experimentally investigate the reduction of noise emitted by a vertical domestic washing machine. To this end, various acoustic absorbers were proposed and examined. A database was constructed using the results of these experiments, which includes recorded noise data from four directions around the washing machine at varying distances, in the presence of the proposed acoustic absorbers. In the proposed method, the effect of acoustic absorbers on noise intensity reduction is first evaluated. Subsequently, the impact of these absorbers on the recorded signals in the time-frequency domain is discussed. The findings indicate a direct influence of the acoustic absorbers on the recorded signals in all directions, with nearly equal intensity reductions. The reduction in sound intensity varied depending on the type of acoustic absorber used, with an average decrease of 6.35 dB. The highest and lowest performance were recorded for felt fiber and polystyrene foam, resulting in sound intensity reductions of 8 dB (10%) and 4.5 dB (4.5%), respectively. Additionally, the experiments show that the use of appropriate acoustic absorbers can significantly improve the quality of life in domestic environments by reducing unwanted and disruptive noise, thereby having a substantial positive effect on user well-being. https://modelling.semnan.ac.ir/article_10031.html One of the main challenges in electromagnetic design of synchronous reluctance motors (SynRMs) is to improve the quality of produced electromagnetic torque. In this paper, a new technique is presented for rotor optimization of SynRMs with the configuration of transversally laminated anisotropic (TLA) based on the response surface methodology (RSM) to reduce the torque ripple while maintaining the average value of produced electromagnetic torque. In this method, the radial position and thickness in the middle region of rotor flux barriers (FBs) with hyperbolic shape are optimized to achieve the minimum torque ripple and maximized average torque. To this end, two rotor topologies including 3 and 4 FBs per pole are considered for the analyzed SynRM. The optimization process is then started for each topology through designing the experiments in predefined space by using the RSM and conducting them by using the finite element method (FEM). The analysis of respone surface design is then done through constructing the polynomial regression for objective functions to study the effect of parameters on the torque ripple and the average torque. The optimal results which are very sensitive to geometry variations show a significant reduction in torque ripple without decreasing the average torque.      https://modelling.semnan.ac.ir/article_9701.html The addition of nanoparticles to the base fluid has adverse consequences, including a greater pressure drop than the base fluid, which leads to an increase in pump work, which is not economical. To improve the efficiency, we must look for a suitable range of particle volume fraction so that by adding this amount to the base fluid, the increase in heat transfer is greater than the increase in pressure drop. In this study, by introducing the criterion ratio of PEC performance evaluation, this suitable range of volume fraction for several types of individual particles and mixed particles in a part of an evaporator tube has been determined by numerical calculations and Eulerian-Lagrangian approach. The results showed that the use of mixed nanoparticles in the volume percentage range of less than 1% has better feedback than the nanofluid with single type particles. For water-ethylene glycol/Co nanofluid, this optimal volume percentage range is between 0.01 and 0.2 percent, and for water-ethylene glycol/CuO it is 0.01 to 0.14, and for the combined nanoparticle water-ethylene glycol/ Al2O3+TiO2 is equal to 0.01 to 0.04, for water-ethylene glycol/Al2O3 up to 0.03 percent by volume. Water-ethylene glycol/SiO2 nanofluid always has a PEC ratio of nanofluid and fluid less than 1, and from this point of view, its use in heat exchanger is not economical. https://modelling.semnan.ac.ir/article_9707.html Epilepsy is a type of brain disease that can be diagnosed by observing EEG signals. This disease often occurs in children, but some cases are also observed in adults. Early diagnosis of this disease is a challenging task for doctors. In this study, the authors have classified epileptic and normal EEG signals by adopting a deep learning approach. To obtain efficient features, the Dual-Tree Complex Wavelet Transform (DTCWT) is considered. Then, the wavelet coefficients are decomposed to extract nonlinear features. These features are used as input to the Radial Basis Function (RBF) hybrid base classifier. Using the proposed method, approximately 99% classification accuracy is achieved, which requires a significant improvement compared to previous proposed algorithms. This is the first time that nonlinear feature extraction from DT-CWT coefficients of an EEG signal is used for epilepsy diagnosis. https://modelling.semnan.ac.ir/article_10233.html This study investigates the premature failure of the sidewalls related to a sintering pallet car of pelletizing plant of Gol-e-Gohar Mining and Industrial Company, Sirjan, Iran. At first, the extent of failure and the distribution pattern of common defects of the sidewalls are investigated through visual inspections. In this regard, the quality of the walls is classified in five different levels, and more than half of the existing side walls are randomly examined at different time intervals. According to the amount of the damage, they are regarded into one of the defined levels. Then, by performing tensile and stiffness tests, the mechanical specifications of three specimens of the sidewalls are extracted and presented. Next, the chemical analyses of the three samples are determined via quant metric tests and the results along with the standard ranges of the relevant alloy are compared. Some thermal tests are implemented on the samples to investigate the temporal oxidation reactions of the regarded alloys. To study the microstructures of the samples, including the type of granulation and chemical composition of different phases, metallographic tests are employed, and the results are represented in the form of images. In addition, the effects of other factors such as fuel consumed by burners as well as operating conditions on the life of the part are discussed. Finally, the data obtained by the mentioned experiments are analyzed in details, and the effects of the different factors on the premature failure of the side walls of the pelletizing machines are discussed. https://modelling.semnan.ac.ir/article_10025.html The aim of this research is to investigate the effect of different parameters on thermal comfort of occupants in an office room equipped with single-sided natural ventilation. A three dimensional numerical model was implemented to predict temperature and velocity distribution in the space using Ansys Airpak software. Then, the temperature and velocity distribution obtained from the numerical solution is coupled with the standard thermal comfort indices (PMV, PPD) and local discomfort indices (DR and PD) in order to evaluate the thermal sensation of occupants. The results shows that the outside temperature between 18  and 20  is the best temperature range in establishing comfort conditions for all occupants and the PMV value is between -0.4 and 0.6, which is consistent with the determined criteria by ISO 7730. As the temperature increases, the mean age of air increases, but thremal discomfort due to draught (DR) decreases. By increasing the wind velocity and dimensions of the openings, the values ​​of PMV and PPD decrease and become closer to the permissible range of thermal comfort. The mean age of air decreases with the increase of the wind velocity and the dimensions of the openings and reaches its lowest value at a speed of 7 m/s, but DR remains constant with the change of wind velocity. According to the results, installing two openings has a better performance in establishing thermal comfort conditions than installing a single opening. https://modelling.semnan.ac.ir/article_9366.html of fossil fuels, as well as environmental restrictions, the demand for high-efficiency energy systems has risen. For this reason, there is a growing tendency to use combined cycle and cogeneration. In the current research, Brayton and reverse Brayton units have been employed as stimuli in the cogeneration system. In this study, a system for the simultaneous production of power and fresh water, operating on biogas fuel, is presented. The performance of the combined system has been evaluated in terms of the first and second laws of thermodynamics. Additionally, to examine the system's behavior with varying input parameters, a parametric study has been conducted. Also acc ording to the obtaine d results, the presented system has the a bility to produce 1031kW of power and 0.8498 m^3/h of fresh water. The energy and exergy efficiency of this system are calculated as 35.65% and 36.21%, , respectively.DOI: https: // doi.org / https://modelling.semnan.ac.ir/article_9696.html Single-phase power consists of two constant (DC) and fluctuating components in which fluctuating component will make the double frequency voltage ripple at DC side. This ripple causes several problems such as reduced efficiency, panel lifetime and increase of harmonic component at AC side. Different methods were proposed but these methods face different problems like dependency to the main converter, increased number of passive elements and unproper control system. The proposed structure acts as a DC parallel active filter. This structure acts as an independent current source controlled by voltage and its main task is to remove PV current ripple. Resonance-proportional controllers have the ability to minimize the steady state error at the resonance frequency but, on the other hand, this controller does not have the ability to provide a fast and appropriate dynamic response. Dead Beat (DB) control method is a model-based digital control method with a fully digital nature that is capable of providing very fast dynamic response by placing the system poles at the center of the unit circle. The DB control method with a very simple structure is proposed to be used in parallel with the proportional-resonance controller as a hybrid control method. The combination of these two methods, while providing a very simple structure, has the ability to meet the control specifications appropriate to the proposed compensator. Several simulations show the effectiveness of the proposed structure and control method in which the current ripple is reduced from 10A to 0.5A. https://modelling.semnan.ac.ir/article_10008.html Model Predictive Control (MPC) is of great interest due to its ability to solve the constraints of nonlinear systems. In this article, the Model Predictive Direct Power Control (MPDPC) method is proposed for a two-level inverter grid-connected in a photovoltaic system. First, a predictive direct power model is determined, and then a cost function consisting of two parts of power quality regulation and switching frequency optimization by adjusting the system parameters, is used to select the optimal voltage vectors to improve the inverter performance. The simulation results in MATLAB software show the appropriate efficiency of the proposed control method in two modes of permanent and dynamic system operation, such that the harmonic reduction of the output currents from 8.98% to 1.11% is visible. The switching in the power electronic elements of the inverter was also optimized, and the switching frequency was reduced from 12650 Hz to 8700 Hz, which will reduce losses and also increase the life of the inverter. https://modelling.semnan.ac.ir/article_10018.html This paper investigates the optimal design of a solid-state fault current limiter (SSFCL) for controlling short-circuit currents in industrial power networks. The rapid increase of fault currents in power networks, due to the development of distributed generation sources and the growth of load demand, can cause severe damage to equipment. Therefore, the use of SSFCLs, which have a negligible impact under normal conditions and provide rapid limiting action during faults, has become increasingly important. In this study, a proposed topology based on the use of semiconductor switches (IGBT/SGTO) along with an intelligent control unit has been designed and simulated. Simulations were performed using PSCAD/EMTDC software under various scenarios (connection to the bus, after the transformer, and the presence of distributed generation sources), and the results demonstrate a significant improvement in network stability and a reduction in energy losses. The findings of this study can serve as a basis for improving protection systems in refineries and other energy industries. https://modelling.semnan.ac.ir/article_10019.html In this paper, the results of numerical modeling of water flow over a dam and the depth and length of the downstream scour hole are compared with the experimental results of Muhammad Abbas and Tanaka under different hydraulic conditions including two critical depths on the spillway crest (Dc) equal to 0.03 and 0.04 m and two downstream depths (Dp) of 0.07 and 0.09 m. For this purpose, the dimensionless parameters of the flow depths on the crest and downstream, namely Dc*=Dc/HE and Dp*=Dp/HE, have been used. Also, the dimensionless parameters Sd*=Sd/HT and Ls*=Ls/HT have been used for the scour depth and scour hole length. With increasing the dimensionless downstream depth (Dp*), it was observed that with increasing Dc* and correspondingly the critical flow depth on the spillway crest, the maximum depth and length of the scour hole increase, but with increasing the dimensionless critical depth Dc*, these values decrease. On average, the difference between the numerical and experimental results is less than 3/75%. It can be concluded that there is a good agreement between the results. Numerical simulation of physical phenomena allows for a more detailed study of the flow field, velocity vectors, and pressure contours, which is also discussed in this article. https://modelling.semnan.ac.ir/article_10020.html Power distribution companies with a wide network of energy distribution and control equipment, always seek to provide stable and safe electricity supply to customers. To do so, these organizations face challenges such as network wear and tear, load fluctuations, changing climates, rapid technology growth, unbalanced allocation of skilled manpower, and lack of financial resources. Qazvin Power Distribution Company, as an equipment-based organization, aims to establish a physical asset management system with the porpus of managing the cost, risk and performance of assets during their life cycle. The first step in implementing physical asset management is to determine and recognize the current conditions of the organization. This paper evaluates the management of physical assets using the Uptime model in Qazvin Power Distribution Company. The uptime model is one of the main and most practical models for evaluating physical assets. It evaluates the management condition of physical assets in the organization using its ten criteria. In this research, the evaluation steps are described and also the results of this evaluation are analyzed, then major strategies are defined in each aspect and suggestions for improvement measures are presented. These improvement measures and projects are ultimately prioritized using the TOPSIS approach. Furthermore, based on the identified priorities and prerequisite relationships between project implementation, a roadmap is presented to implement the physical asset management system in the company. Finaly, the actions and projects envisaged in this roadmap are scheduled as an implementation plan in three phases. https://modelling.semnan.ac.ir/article_10021.html The increase in the number and severity of natural disasters and their significant social and economic effects have made power system planners pay special attention to the security and resilience of power networks. For this purpose, this article tries to provide an efficient model for strengthening the resilience of distribution networks based on multi microgrids by optimally using energy storage systems and demand response programs. In the proposed method, a two-stage hierarchical approach has been developed in which the first stage of the incident is modeled and their impact on the distribution network is determined. Then, in the next stage, preventive and corrective measures are implemented to increase system readiness and reduce damages caused by severe accidents. At this stage, various tools such as energy storage, distributed and renewable production sources have been used in addition to responsive loads. In order to consider the uncertainty and the risk caused by it on the proper performance of the proposed design, the problem is done by robust optimization to obtain more realistic results than the deterministic state. Finally, in order to confirm the effectiveness of the proposed method in improving the resilience of distribution systems, a standard network of 33 buses has been used with different operating conditions, and the results obtained indicate its proper performance in the face of severe accidents and maintaining the resilience of the system. https://modelling.semnan.ac.ir/article_10024.html Increasing the heat transfer rate in various industries to improve the efficiency of equipment, prevent damage to parts and reduce costs is one of the essential discussions in the industry. In this research, an active heat sink and gallinsten liquid metal is considered as the working fluid. The effect of applying the magnetic field in the Y direction (perpendicular to the flow axis) to the thermal well has caused the creation of a force against the flow direction called the Lorentz force, which has caused the M-shaped velocity distribution. This type of velocity distribution has caused an increase in the flow velocity in the vicinity of the walls and a decrease in the flow velocity in the central line of the microchannel. By applying an alternating magnetic field in the X direction to the thermal well, the velocity distribution has become flat. The results showed that the effect of direct and alternating magnetic field has improved Nusselt number. By applying an alternating magnetic field in the Y direction, the Nusselt number is improved by 38% and by applying a magnetic field in the X direction, the Nusselt number is improved by 11.62%. The increase in the speed gradient resulting from the application of direct and alternating magnetic field has also increased the friction coefficient. In this research, by examining the phase difference of magnetic and electric fields from zero to 90 degrees, it was shown that the pressure drop and the friction coefficient resulting from the magnetic field have decreased. https://modelling.semnan.ac.ir/article_10026.html As a widely used technique in signal processing, Sparse representation has gained significant attention in various fields, including data compression, noise reduction in speech and image signals, pattern recognition, and other signal processing-related issues. In such representations, signals are linearly combined using a small number of dictionary atoms, leading to data dimensionality reduction and improved signal processing efficiency. To accurately represent speech data, an appropriate dictionary is required to effectively represent speech signals' characteristics. In this paper, dictionaries are trained using dictionary learning algorithms and sparse representations such as MOD, K-SVD, RAMC, UD4-MOD, and OMP, in the time, time-frequency, and wavelet transform domains. The performance of the obtained dictionaries is evaluated using various time-frequency metrics such as RE, MSE, fwSegSNR, SegSNR, PESQ, and STOI. The results demonstrate that employing the K-SVD dictionary learning algorithm in conjunction with the OMP sparse representation algorithm in the STFT domain achieves promising results for speech signal reconstruction. https://modelling.semnan.ac.ir/article_10027.html Skill during an earthquake and moving the injured to safe places is one of the treatment ways to prevent the earthquake disaster and save people's lives. Geographic information system (GIS) has a high power in earthquake risk zoning, so that the location of faults and distances and places prone to fire and explosion are already known. In these researches, by using geographic information system techniques in the ARCMAP software environment, analysis is done and finally, safe places extracted. In order to weight the layers, the linear programming model of the best-worst technique (BWM) is used, the data of which is created through the creation of a questionnaire that is scored by experts, and finally, the overlapping operation of the layers. Through the application of fuzzy functions, it is written to prepare the final map and select the safest places during an earthquake for the city of Tabriz. The innovation used in this research is the simultaneous use of GIS and BWM technique for the first time in the field of locating safe places to create earthquake shelters in Tabriz city. The main goal of this research is to help crisis management in urban planning and provide management suggestions for Tabriz. https://modelling.semnan.ac.ir/article_10028.html In this research, friction stir processing for producing Al/Al2O3 composite has been investigated. The mechanical (tensile strength) and wear properties of the reinforced composite are compared with processed and base materials. The response surface method has been used to determine the effect of the input parameters on the tensile strength and wear rate. In addition, by using analysis of variance, the optimal levels of the parameters have been specified to achieve maximum strength and minimum wear. The obtained results showed that adding the alumina powder significantly increases the strength and wear resistance, and can also make the microstructure uniform. In addition, by examining the optimal results, it was found that by selecting the rotational speed of 1200 rpm, the feed rate of 40 mm/min, and 0.6 g of alumina powder, the highest strength (384 MPa) and the lowest wear rate (7.4 mm3/min) can be achieved for the processed composite sample. https://modelling.semnan.ac.ir/article_10030.html This study applied machine learning algorithms, namely Support Vector Machine (SVM) and Random Forest (RF), to develop a mineral potential map for the Shadan region, situated within the Lut Block and Flysch-Ophiolite Belt of Eastern Iran. The research integrated multiple exploration datasets, including geological, geochemical, satellite imagery, and geomagnetic data, to identify promising areas for mineral exploration, specifically targeting porphyry copper and gold deposits. The performance of the models was evaluated using metrics like Accuracy, Sensitivity, ROC curves, and P-A plots. The SVM model demonstrated superior accuracy, successfully predicting 13% of the study area as high-potential zones for future drilling, which corresponded closely with existing drilling results. These identified target zones were predominantly located in regions with intense tectonic activity and were associated with rock units such as andesite, granite, and granodiorite. The study underscores the effectiveness of the SVM model in accurately delineating mineral-rich areas, providing a valuable basis for future exploration programs. https://modelling.semnan.ac.ir/article_10033.html Medical images are extensively used in medical science for diagnosis and treatment protocol design. Writing medical reports in text form can be error-prone for inexperienced physicians due to the deep understanding of the disease and its analysis. It is also time-consuming and laborious for experts due to the large number of patients they see in a day. Also, the existence of template reports for physicians can significantly increase their accuracy in diagnosing diseases and reduce errors caused by inattention to details. This research presents a deep learning-based model for the automatic generation of radiology reports. This model is based on a combination of a convolutional recurrent structure and an attention-based architecture called Res-LSTM-Attn. In this model, features are first extracted from medical images using a convolutional residual network, and based on a multi-label word model, a report is predicted. Then, using the LSTM recurrent neural network and multi-head attention layers, the final report is generated. The performance of the proposed models was evaluated based on the BLEU 1-4, ROUGE-L, and CIDEr-D criteria. The results showed that the proposed model outperformed previous studies in generating long reports in terms of CIDEr-D and ROUGE-L metrics, with improvements of 7.2% and 3.2%, respectively. https://modelling.semnan.ac.ir/article_10034.html This study explores the integration of solar tower technology and thermal energy storage (TES) within the framework of a supercritical carbon dioxide (S-CO2) Brayton cycle, a copper-chlorine (Cu-Cl) hydrogen production cycle, and a heat recovery steam generator (HRSG) for the generation of superheated steam. The combination of these subsystems enhances overall energy efficiency, ensures uninterrupted operation, and minimizes exergy loss. Comprehensive energy and exergy analyses were conducted to evaluate the performance and requirements of each subsystem, highlighting the thermodynamic advantages of this integrated approach. The Engineering Equation Solver (EES) software was used to model the system and calculate thermodynamic properties from the EES library. The results show that, in the basic design configuration, exergy destruction amounts to 9930 kW in the solar tower, 7111 kW in the S-CO2 Brayton cycle, and 9735 kW in the Cu-Cl cycle. The system is capable of producing 4226 kW of power, 2697 kW of heat, and 0.04971 kg/s of hydrogen. The overall energy and exergy efficiencies of the plant are 17.48% and 18.72%, respectively. These findings demonstrate that this integrated system effectively addresses existing gaps in the literature by presenting a novel combination of solar-powered Cu-Cl hydrogen production and superheated steam generation. The proposed system contributes to the advancement of renewable, efficient, and continuous energy production https://modelling.semnan.ac.ir/article_10036.html In recent years, numerical simulations of blood flow in arteries have effectively contributed to understanding the mechanisms of some cardiovascular diseases and selecting the appropriate treatment scenario. In this study, blood flow in a real geometry of the thoracic aortic artery of a patient with aneurysm was numerically modeled. The effect of vessel surface roughness along with flow regime and various turbulence models on wall shear stress (WSS) and hemodynamic parameters such as time average wall shear stress (TAWSS), relative residence time (RRT), and oscillatory shear index (OSI) were investigated. The simulation results show that the presence of roughness increases the area of areas with high RRT values. Also, increasing roughness reduces the area of regions with high TAWSS values. Increasing surface roughness increase the average pressure inside the vessel. In all the regimes and models studied in this article, the general pattern of TAWSS distribution is almost the same. TAWSS has low values in the aneurysm sac region. High values of OSI are observed around the aortic arch region. Considering the distribution of hemodynamic parameters, the end regions of the aneurysm sac and the inner aortic arch in the studied patient are susceptible to atherosclerosis. https://modelling.semnan.ac.ir/article_10037.html The rheological properties of drilling fluids are essential parameters in optimizing drilling operations and reducing the total cost of drilling. In this research, in the first stage, the effect of adding herbal polymers of pulverized and unpulverized rice husk on the amount of shear stress of water-based drilling mud (a mixture of water and bentonite) at different shear rates has been investigated and compared. After determining the plastic viscosity (PV) and yield point (YP) of the samples based on the Bingham model, no uniform trend was observed in the changes in the rheological properties of the drilling mud with the increase in the mass of each additive to the base fluid. In the next step of the research, a model based on a two-layer feedforward artificial neural network is designed to predict the shear stress of the studied drilling muds for the input of the arbitrary mass of the additive polymer and arbitrary shear rate of the mud sample, and the network was trained for each set of data corresponding to each of the additives, which resulted in accurate and favorable estimation results. The percentage of average and maximum error obtained for the output values corresponding to the network test data is smaller compared to the results of applying the widely used Herschel-Bulkley model. Moreover, we found through sensitivity analysis that the importance and degree of influence of the shear rate on changes in shear stress are higher compared to the additive mass. https://modelling.semnan.ac.ir/article_10038.html Nowadays, the development of energy systems based on efficient renewable energy has been the focus of researchers to overcome environmental issues. This study presents a multi-generation system with solid oxide fuel cell, Brayton modular helium, reverse osmosis desalination, Stirling engine and cascaded absorption-condensation refrigeration. In this way, the system's functioning was examined from the perspective of the first law of thermodynamics. Then the second law of thermodynamics was used to determine the exergy efficiency of each subsystem and the amount of exergy destruction. The proposed cycle was subjected to an exergoeconomic analysis. At the end, in order to understand the behavior of the performance criteria of the system with the design parameters, a comprehensive parametric study has been conducted. The results show that the proposed cogeneration system can produce 9.705 MW of net power, 8.45 kg/s of fresh water and 68.79kW of cooling. Also, the energy and exergy efficiency of the whole production system at the same time have been calculated as 55.02 and 49.82%, respectively. Also, the product's CO2 emission rate and component's investment cost rate are 17.59 kg/MWh and 105.7 $/kWh, respectively https://modelling.semnan.ac.ir/article_10039.html The presence of cracks in a beam changes the dynamic characteristics of the beam. Therefore, to assess the condition of the beam, its natural frequencies must be examined. In this study, using a numerical solution based on the Rayleigh method, the natural frequencies of a beam with two cracks are calculated based on the depth and location of the cracks. Next, using the Python programming language, the aforementioned mathematical relationship is entered into this program to solve this relationship sequentially for different inputs by creating iterative loops. The goal of this is to produce a dataset that can be used to train machine learning algorithms such as random forest regression, gradient boosting regression, multilayer perceptron, and decision tree regression to predict the natural frequency. The key innovation in this study is the use of a network search method to determine the optimal amount of data for each algorithm, which increases accuracy and introduces a new criterion for comparison called "required data volume". The study found that increasing the size of the dataset generally increases the prediction accuracy of the algorithms. In addition, algorithms that predict a single output have higher accuracy compared to those that predict multiple outputs. The study demonstrates the effective use of machine learning algorithms for predicting natural frequencies. The gradient boosting regression algorithm with an accuracy of 84.10% and the random forest regression algorithm with an accuracy of 83.73% emerged as the superior methods for predicting beam frequencies. https://modelling.semnan.ac.ir/article_10041.html Thermal fins, as one of the key components in enhancing the performance of heat transfer systems, play a vital role in various industries. This study investigates the effects of key dimensionless, temperature-dependent parameters, including the thermo-geometric parameter 𝑀, which represents the ratio of convective to conductive heat transfer, as well as the heat transfer coefficient exponent 𝑛, and the thermal conductivity exponent 𝛽 on the thermal efficiency of fins. These parameters were defined to nondimensionalize the available analytical solutions in the literature. To achieve this, Response Surface Methodology (RSM) was employed as an advanced statistical and modeling tool. A full factorial design with six levels for each parameter was used. The proposed model demonstrated excellent predictive accuracy, with an R² value of 0.990 and a p-value < 0.0001. Results revealed that increasing the parameter 𝑀 decreases thermal efficiency due to enhanced convective losses and localized temperature gradients. Similarly, higher values of 𝑛 lead to efficiency reduction by concentrating heat transfer in hotter regions of the fin. Conversely, variations in 𝛽 showed a smaller effect on overall performance but contributed to a more uniform temperature distribution. The use of the response surface method not only reduces the computational cost, but also allows for rapid and accurate analysis of complex conditions. The results of this research can be used as an effective guide for the optimal design of fins in the heat transfer industries. https://modelling.semnan.ac.ir/article_10042.html The presence of non-uniform pollution deposits on the surface of insulators, along with mechanical defects, significantly affects the performance of overhead line insulators. However until now, no comprehensive studies have been conducted on the simultaneous effect of different types of non-uniform pollution and mechanical defects on insulator performance. In this paper the effects of uniform and non-uniform pollution, including the ring-shaped (RNU) and ring-longitudinal (RLNU) types, along with mechanical defects such as a damaged shed and a defective core, on the potential distribution and electric field of a 20 kV polymeric insulator have been investigated. The simulations were performed using COMSOL Multiphysics and the results were analyzed. In the case of longitudinal non-uniform pollution, the pollution severity was assumed to be heavy in the regions near the electrodes and light in the middle region. Additionally for the ring-shaped non-uniform pollution the inner and outer areas were assumed to be equal with pollution applied to the inner region of the insulator. The results indicate that the maximum electric field in the uniform pollution condition is higher than in other pollution patterns. Moreover the presence of mechanical defects, such as damage to the shed and core, has a significant impact on the potential distribution and the maximum electric field. Specifically compared to a healthy insulator the presence of these defects leads to an increase in the maximum electric field in three regions: the high-voltage region the middle region, and the low-voltage region. https://modelling.semnan.ac.ir/article_10043.html In this paper, the existence of a stable limit cycle in a diaphragm thermos-acoustic oscillator is analyzed using the Lyapunov stability theorem of perturbed dynamic systems. In this regard, first the dynamic differential equations of the thermos-acoustic oscillator are written as the state equations. Then, in order to system have a limit cycle, the error equations are extracted using the system equations and the desired states of the limit cycle. Next, a new Lyapunov function is introduced to asymptotic stability analysis of the error dynamics. Three conditions are examine, first condition of stability related to the positive definiteness of the Lyapunov function and the second condition related to the negativity of the derivative of this function. Third condition also guarantees that the error state is located within a certain bound to ensure that an asymptotic stable limit cycle occurs under these conditions. Besides, the upper and lower bounds of the error are obtained. Also, the effect of some important physical parameters of the system on the obtained error's bounds has been analyzed. The results obtained showed that the presented method has been able to successfully solve the most important challenge of this type of thermal oscillator, namely ensuring selecting the proper parameters for a thermo-acoustic oscillations https://modelling.semnan.ac.ir/article_10044.html Fault location in transmission lines is one of the fundamental pillars of transmission line protection. Before implementing fault location algorithms in protective relays, they are implemented in the form of simulation in various software. MATLAB software is a suitable candidate for this problem because it has a programming platform and Simulink environment for power system simulation. Also, because some algorithms use traveling waves to estimate the short-circuit distance, their implementation in the software platform requires transmission lines with a frequency-dependent parameter model. In general, in the MATLAB software library until 2020, there were two types of line models with fixed parameters, namely Three Phase PI Section Line and Distributed Parameters Line. The former is a compact model and the latter is a distributed model of the transmission line, whose performance was flawed in implementing fault location algorithms based on traveling waves. Since 2020, with the addition of another line model to the library of this software, the necessary conditions have been provided for the implementation of fault location algorithms based on the traveling wave method. Therefore, in this article, the complete steps of implementing the fault location algorithm in transmission lines with distributed parameters (Distributed Parameters Line and Frequency Depended) in the form of Simulink and MATLAB software programming have been presented. The results of implementing the fault location algorithm in the form of software simulation with frequency-dependent transmission lines confirm the successful performance of the mentioned model compared to the other two models. https://modelling.semnan.ac.ir/article_10045.html This paper proposes a noise variance estimation method using the Empirical Mode Decomposition (EMD) of signals and the characteristics of the decomposed layers.The main idea is based on the fact that the layers obtained from the EMD decomposition of the noisy signal will contain several layers with some pure signal components, several layers in the form of pure noise, and layers in the form of a combination of signal and noise. According to the definition of signal and noise in this study, the highest signal power is in the final layers, and the highest noise power is in the initial layers of the decomposition. Based on this principle, the noise variance was estimated by calculating the noise energy and the correlation of the noise with different sub-signals from each layer. Based on the simulation results, it is observed that the proposed method in estimating the noise variance has an error reduction of about 1.5, 3, 5 and 1.7 percent, respectively, compared to the Maciej, Elvander, Cai and Wang methods. https://modelling.semnan.ac.ir/article_10046.html Due to economic and technical constraints, hybrid lines (over head transmission line – power cables) remain necessary in suburban and urban areas. A critical challenge in studying these hybrid lines is their performance under transient lightning and switching surges. These transient overvoltages risk a significant threat to the insulation of power grid equipment, with cables being particularly vulnerable. This article analyzes the critical parameters influencing the magnitude of transient overvoltages in hybrid lines and explores various methods for mitigating these surges. Utilizing EMTP-RV software for modeling and simulation, the article meticulously investigates the maximum stresses imposed on cable insulation by overvoltages. Additionally, it presents solutions and recommendations for reducing transient overvoltages to acceptable levels. The results of the studies showed that by using the proposed solutions, the range of transient stresses caused by lightning and switching in the combined lines can be sufficiently reduced to prevent the destruction of equipment insulation (especially cable insulation). https://modelling.semnan.ac.ir/article_10047.html Improving the environmental behavior of manufacturers plays a crucial role in mitigating harmful environmental impacts and enhancing social welfare. This study models a closed-loop supply chain of the online-to-offline type, consisting of a manufacturer and a retailer, where decisions are made within the framework of a Stackelberg game. The manufacturer is responsible for setting the level of product greenness and investing in corporate social responsibility, while the retailer handles product pricing and the collection of used products at the end of their life cycle. Market demand is considered a function of product price, greenness, and the extent of the firm's social responsibility. The model is examined across four scenarios both with and without government intervention, and under centralized and decentralized decision-making structures. The findings reveal that centralized supply chain structures lead to higher levels of environmental and CSR performance, thereby contributing more effectively to social welfare compared to decentralized systems. Moreover, government intervention through minimum thresholds for product greenness and CSR enhances environmentally conscious decision-making and plays a significant role in aligning corporate economic goals with broader social objectives.The results indicate that in the presence of effective regulatory policies, firms are more inclined to invest in sustainability and responsible practices. From a managerial perspective, companies that adopt centralized decision-making and collaborate with regulatory bodies are more likely to achieve sustainable and accountable economic performance. For policymakers, the insights from this study can inform the development of more effective regulatory and incentive-based frameworks to promote CSR and sustainability throughout the CLSC. https://modelling.semnan.ac.ir/article_10048.html This paper proposes dead beat current control (DBCC) based on predictive model control for six-phase asymmetric permanent magnet synchronous machine (PMSM). First, the DBCC solution is adopted to obtain the expected reference voltage vector (RVV); Then two groups of virtual vectors, a total of 24 vectors with different values will be defined for it. Due to the reduction of current harmonics, two in-phase virtual vectors that are closer to RVV are subsequently selected as predictor vectors. The next step is to define a cost function consisting of the error between the RVV and the available prediction vectors, Then, the two selected virtual vectors are evaluated and the function that minimizes the cost function is evaluated at the next moment, only two predictor vectors are needed, which greatly reduces the computational burden. Meanwhile, the weighting coefficient of torque prediction control is avoided. Moreover, to achieve easy implementation with standard PWM switching sequence, 18 virtual vectors are replaced by them. Finally, the proposed method has been studied comparatively with other methods. The results and validation of the article have been done using MATLAB software. https://modelling.semnan.ac.ir/article_10049.html Today, the demand for natural gas (NG) as a low-emission energy carrier is on the rise. Simultaneously, the share of liquefied natural gas (LNG) as a method of gas transportation is also increasing. The high-pressure gas pipelines for urban gas supply, along with pressure reduction stations (PRSs), have expanded significantly in Iran over the past few years. In the PRS, the pressure energy of NG is lost. Therefore, LNG production using the self-cooling method in PRS (LPRS) is one solution to recover this energy and produce LNG simultaneously. This method requires attention due to very low electricity consumption and simplicity compared to other liquefaction methods. Replacing LPRS instead of PRS, in addition to creating a product sales market, can decrease energy waste in PRS. The feasibility of the plant construction requires both technical and economic evaluations. In this research, we have designed an LPRS process specifically for the Neka power plant. After identifying suitable thermodynamic points, an economic evaluation that considered the entire LNG production chain is conducted. In this study, through a parametric analysis, the liquefaction rate has achieved 27.6%. The coefficient of performance, specific power consumption, and exergy efficiency are 0.68, 160.0, and 61.8% at the maximum liquefaction point, respectively. Furthermore, the results of the economic study show that the highest IRR and the lowest break-even point are calculated as 38% and 2.4 years, which are realized at the maximum liquefaction point. https://modelling.semnan.ac.ir/article_10050.html This research aims to evaluate the life cycle of the impact of using bioethanol in gasoline engines and the emission of greenhouse gases in each of the above stages. The production of bioethanol was obtained from the used biomass in a retention time of five days. In this research, the engine test was done with a two-stroke, single-cylinder engine. Performance parameters (braking power, brake-specific fuel consumption, and engine braking efficiency) and emission parameters (carbon dioxide emission, carbon monoxide emission, and nitrogen oxide emission) were recorded. Then, the list of power production cycles (to produce 1 kW of power) was prepared. Next, by applying the ISO14040 standard and the IMPACT2002+ assessment method, the life cycle assessment was performed with SIMAPRO software. According to the results obtained from the engine's performance test stage, increasing the bioethanol percentage in gasoline fuel reduced the amount of power production. Also, increasing the rate of bioethanol in gasoline fuel increased the consumption of special brake fuel. Further, with the increase in the percentage of bioethanol in gasoline fuel, from zero to five percent, the braking thermal efficiency of the engine decreased and increased from five to seven percent. According to the results obtained from the engine emission study, increasing the percentage of bioethanol from zero to two percent increased the emission of carbon monoxide, and from 2 to seven percent decreased the emission of carbon monoxide. https://modelling.semnan.ac.ir/article_10051.html The methods of increasing heat transfer are current topics in the field of heat transfer in heat exchangers. The most important methods used to improve heat transfer are the use of nanofluids, changing the shape of the channel body, using porous materials, and using baffles in the flow path. The change in the shape of the channel body from a flat wall to a wavy wall causes heat transfer in heat exchangers. Therefore, the innovation of this research is the use of a baffle in the middle of the channel with a wavy wall to change the shape of the flow lines and fluid turbulence. The aim of this research is to simultaneously investigate the effect of using nanofluids, wavy walls, and baffles on heat transfer using computational fluid dynamics. For this purpose, the effect of Reynolds number, wall wavelength, and water-titanium oxide nanofluid concentration on heat transfer was investigated, assuming laminar, steady, and continuous flow. The channel simulation was performed in two dimensions using Ansys CFX2023R1 software. The results showed that in a channel with corrugated and baffled walls, using a nanofluid with a lower concentration (φ=0), higher flow velocity (Re=500), and a wall with a shorter wavelength (λ=12mm) resulted in a larger Nu number. https://modelling.semnan.ac.ir/article_10114.html The packaging industry has a direct relationship with the company's branding by protecting the goods during transportation. An issue that will have a direct effect on other companies in the supply chain of the necessities industry. Therefore, by knowing the importance of this supply chain, the existing conditions can be reviewed and presented to experts. From another point of view, the economic, social and environmental issues of this industry are also very important. In this regard, in the article, the elements that can be interrupted in a network including the supply of essentials of the packaging industry with dysfunction, 12 main and 172 sub-indices are identified, and in the second step, each characteristic is prioritized and the evaluations are specified. Potentially, a questionnaire will be published among experts to identify important risks. Then, after collecting the questionnaires, the inconsistency rate index of each questionnaire is calculated and after the final approval of each questionnaire with the fuzzy hierarchical method (FAHP). In the third stage, after identifying the potential questions, a letter will be presented to the supply chain experts in order to provide a suitable solution to the potential risks. In the fourth stage, to recognize resolutions, experts are divided into three decision categories, which will include risk-taking decision makers, neutral decision makers, and risk-averse decision makers. https://modelling.semnan.ac.ir/article_10115.html This study investigated the effect of temperature and solid volume fraction factors on the relative thermal conductivity of three-component nanofluid MWCNTs(20%)-MgO(70%)-Al2O3(10%)/Water. The experiment was conducted in the temperature range of 30-50℃, with eight different solid volume fractions ranging from 0.035% to 2.25%. The Hot Wire Transient Method using a KD2-Pro device was employed for measurement purposes. Results showed a noticeable increase in the thermal conductivity with increasing temperature and solid volume fraction in the maximum condition (50°C and φ=2.25). The measured value indicated a 31.7% enhancement compared to the base fluid. The experimental data was also analyzed using the Response Surface Method, which confirmed the accuracy of the experimental results and provided a comprehensive third-degree correlation. Furthermore, sensitivity analysis and examination of the margin of error of the created thermophysical model demonstrated its validity, as the margin of error was reported to be within the range of -1.65<MOD<0.98. Lastly sensitivity analysis showed that the minimum effect is in low volume fractions, and with the increase of the volume fraction, the sensitivity has an increasing trend At the end of the research process, in a new work, using the Nonaka science creation method in the science of knowledge management under the four sections of socialization, externalization, combination and internalization of knowledge, the process of producing and documenting the obtained scientific findings was examined and reported. https://modelling.semnan.ac.ir/article_10116.html Frequency instability is one of the fundamental problems in power systems that occurs when a disturbance, increase in demand, or change in system structure causes a progressive and uncontrollable drop in frequency. Research has shown that the use of simple-structured controllers with fewer degrees of freedom, like proportional-integral control (PI) and proportional-integral-derivative control (PID), cannot achieve the desired characteristics of the system, such as the speed of response to disturbance and robustness to system changes. This paper presents a new two-degree-of-freedom proportional-integral-derivative-accelerative (2DOF-PIDA) control method for the load frequency control problem of the power systems to enhance the dynamic response and robustness in the presence of the generation rate constraint and the governor dead-band. The control parameters are adjusted using an improved particle swarm optimization (IPSO) algorithm. In addition, to further improve the control performance, a modified objective function including Integral Time multiplied Absolute Error (ITAE), the settling time, and the maximum overshoot of the frequency and tie-line power deviations with appropriate weighting coefficients was used. The performance of the proposed controller was studied using simulation on an interconnected two-area power network and compared with that of PI and PID controls. The results show that the proposed 2DOF-PIDA method performs better in comparison with PI and PID controllers in terms of the dynamic response and maximum response deviation, as well as robustness to parameter changes. https://modelling.semnan.ac.ir/article_10117.html Nowadays, motorcycles and bicycles are considered one of the main forms of vehicles in many countries. Considering the process of self-driving vehicles and especially cars, the need to investigate intelligent systems is felt more. This process of self-driving vehicles is mostly seen on everyday four-wheeled vehicles because these vehicles do not need separate systems to stand up and maintain balance. Regarding the self-driving of other vehicles such as motorcycles or bicycles, the first challenge to be solved is maintaining the balance of these vehicles. In general, there are two main methods of maintaining balance or balancing systems based on two wheels. The first method of dynamic balance is based on the displacement of the center of mass of the robot and the other method that is examined in this article is based on the induction of separate torque on the robot by different operators (such as the reaction wheel or the gyroscopic torque control system (CMG). ) is. In this article, the modeling of the CMG system installed on a bicycle is first discussed, and then the possibility of balancing the bicycle assembly and the gyroscopic torque control system at rest using a PID controller is measured. The obtained results show the relative success of the system in stabilizing the robot, and in the next steps of the research, the possibility of laboratory implementation of the design will be evaluated. https://modelling.semnan.ac.ir/article_10118.html Faults and failures in wide electrical power distribution systems can occur for various reasons and have destructive effects on the power quality and also suitable system service continuity if the failure is not identified and resolved in the shortest possible time. Hence, in order to improve the reliability and enhance the network adequacy, fast and accurate fault location is an important concern in the system protection area. The use of Artificial Intelligence (AI) techniques in the field of fault location has been introduced as a practical and useful method from view point of researchers in recent years. In AI-based methods, the estimator can be trained by offline methods to be able to provide fast online estimation of the fault location or fault segment. These methods require a significant amount of training data, which can be based on historical records or generated in a simulation process. Research shows that AI-based methods are less sensitive to noise in the input data and significantly more accurate than serious competitors including impedance-based methods. This paper in fact provides a comprehensive and conceptual review on artificial intelligence-based methods with the aim of fault location in the electrical energy distribution networks. For this purpose, the advantages and disadvantages of the proposed methods presented in the published papers mainly since 2014 are categorized, compared and concluded. https://modelling.semnan.ac.ir/article_10119.html Stimulation of a thin copper wire with a high current and a short rise time is a well-known phenomenon that can lead to the wire explosion and consequently create plasma similar to arc discharge. In this paper, the behavior of an exploding wire is investigated using the 4th-5th order Runge-Kutta numerical method in MATLAB to calculate electron density. The proposed model includes a capacitor bank, a stray resistor and an inductor in the circuit, and a variable resistor that represents the exploding wire. The results show that the behavior of the exploding wire can be dependent on various factors such as the current applied to the wire, the current rise time, and the wire diameter. Finally, a two-stage structure consisting of two pulsed power circuits is presented, the output of which will be a toroidal plasmoid. This plasmoid is created by applying a strong current pulse to the primary plasma. Numerical calculations indicate a plasma electron density in the range of 1018-1019/cm⁻³, which agrees with experimentally reported values in previous studies. Plasmoid is actually a coherent combination of plasma and magnetic fields that has many applications, such as being used as defense cannon against missile attacks and as an ignition source for turbines, combustion engines, and rocket engines. https://modelling.semnan.ac.ir/article_10120.html Asthma is a complex disease characterized by chronic airway inflammation and immune system dysfunction. In recent decades, researchers have conducted extensive studies on plants with anti-allergic and anti-asthmatic effects. In this study, an agent-based model is presented to simulate the interactions between immune cells and lung tissue during asthmatic inflammation. For the first time, herbal drug treatments for asthma have been incorporated into the model based on experimental data and previous modeling studies. The model parameters were determined using experimental data from laboratory studies on asthma treatment with herbal compounds. The model's outputs align well with experimental data and effectively replicate them. Using this model, we have investigated several phenomena related to asthma treatment: (1) Determining the optimal day to initiate treatment, (2) Evaluating the contribution of each inflammatory pathway to lung health, (3) Assessing the effect of anti-inflammatory pathways on lung tissue health, (4) Performing global sensitivity analysis and identifying key parameters in the model, (5) Examining how removing drug effects on pro-inflammatory and anti-inflammatory pathways influences the model's output. The findings of this study validate existing experimental research and provide valuable insights into understanding asthma pathogenesis and potential therapeutic approaches using herbal medicines. https://modelling.semnan.ac.ir/article_10121.html In this paper, a new structure for a multilevel inverter based on the switched capacitor is presented. The proposed sub-module with one source and one capacitor is able to generate 5 voltage levels at the output, in addition to this, the proposed cascade structure with two isolated DC voltage sources along with two capacitors is able to generate 25 voltage levels at the output. Also, this structure is able to increase the input voltage using a capacitor. The proposed structure is able to charge the capacitor without using a sensor, in other words, the proposed structure is self-charging. In addition to working separately from the grid, the proposed structure can also be used connected to the grid for photovoltaic applications. To confirm the proposed structure, a quantitative comparison with similar structures has been done. In addition, a heat loss simulation has been done for the proposed structure. The simulation results confirm the functionality of the proposed structure. https://modelling.semnan.ac.ir/article_10122.html Mathematical modeling offers a powerful framework for understanding complex problems and developing practical solutions. This paper investigates target detection in Coherent Frequency Diverse Array (C-FDA) radar systems without relying on training data. The target detection problem is formulated as a binary composite hypothesis testing-problem. Without applying any simplifications to the received signal model, we employ the full general model to derive new detectors based on the Generalized Likelihood Ratio Test (GLRT), Rao, and Wald test principles. The resulting detectors differ structurally and exhibit varying performance depending on the signaling scheme and detection scenarios. Extensive simulation results demonstrate that the proposed Wald and GLRT-based detectors consistently outperform the Rao detector in terms of detection probability. Furthermore, a comparison with an existing method for C-FDA systems shows that the proposed detectors achieve superior performance, offering approximately 3 dB improvement in signal-to-disturbance ratio. Furthermore, in multi-target scenarios, the proposed detectors exhibit significantly enhanced resolution and target separation in the range–angle domain. https://modelling.semnan.ac.ir/article_10123.html Data mining is a process to discover patterns from a dataset and it can play an important role in understanding the multifaceted aspects of development on a global scale. Policymakers and researchers can formulate more effective strategies to promote sustainable development by identifying key indexes of prosperity, their interrelationships, and the impact of each index on other indexes. In this paper, first, 12 indexes of prosperity provided by Legatum institute are introduced. Then the values of these indexes are collected for 167 countries and statistical analyzes are presented. In the following, a heat map of the mutual influence of indexes is presented, which shows the influence of each index on other indexes. Finally, prediction models are presented for each index using regression and neural network methods, and they will be evaluated based on different measures. The values of the evaluation measures prove that the results from the prediction models were up to 98% similar to the actual results in the best conditions. Therefore, prediction models can determine how much other indexes will change with the improvement of one index. Based on this, policymakers and researchers can plan and prioritize to improve the status of indexes. https://modelling.semnan.ac.ir/article_10124.html The broadcast nature of wireless communication poses a risk in terms of security. In this article, we use physical layer security as a security method in wireless networks. We consider the case where the adversary is able to eavesdrop on the secret message or send a jamming signal to the main receiver. To create a safe margin, we assume the best situation for the enemy, and we provide many facilities to him, and with such an assumption that creates a safe margin for us, we try to establish security in the system. Since the entire channel state information is available to the adversary, he will be able to intelligently choose between the two modes of eavesdropping and sending a jamming signal, which will result in a lower final secure rate. Obviously, in practice, the adversary hides his chosen state from the authorized sender and receiver, so detecting the state of the enemy, allocating resources and finally establishing security based on the diagnosis that may be wrong is a practical challenge in such systems. In this article, a probability-based solution to deal with this challenge is proposed and evaluated. In this solution, the probability of changing the enemy's state is taken into account, and power allocation is done by choosing the appropriate criteria for changing the enemy's state. We examine the probability-based solution in systems with channel uncertainty in the form of a pillar. Also, the dual method is used to solve the problem of power allocation. https://modelling.semnan.ac.ir/article_10125.html Alzheimer's disease, as one of the major and progressive challenges in public health, requires innovative solutions for early and accurate diagnosis. This research presents a hybrid deep learning model for the early detection of Alzheimer's using MRI images. Unlike conventional approaches that rely solely on pre-trained models, the proposed model combines the VGG16 architecture with an auxiliary CNN path that includes Dense layers and dropout. This auxiliary path, as a key branch, plays a significant role in enhancing the extraction of complex features and reducing the risk of overfitting. To address the severe class imbalance in the MRI dataset (such as only 64 samples in the moderate symptom class), a combination of the complementary SMOTE and data augmentation methods was used, which together improved the model's generalization in classifying rare classes. The dataset used was extracted from the public Kaggle datasets. Furthermore, precise experimental analyses were conducted on key parameters such as learning rate, image dimensions, batch size, dropout rate, and optimizer type, leading to the selection of the optimal model configuration. The final model achieved an accuracy and F1-score of 99.5%, demonstrating excellent performance in diagnosing Alzheimer's patients. The results of this research indicate that the intelligent use of advanced deep learning architectures, combined with data engineering solutions, can effectively contribute to the development of intelligent medical diagnostic systems and more precise management of neurological diseases. https://modelling.semnan.ac.ir/article_10126.html When lightning strikes the guard wires or the top of a transmission tower, the lightning current is discharged into the ground through the tower structure. This causes an increase in the tower's potential, resulting in an overvoltage across the insulator string. If the voltage across the insulator exceeds its dielectric withstand level, a back-flashover (BFO) may occur. Overhead transmission lines (OHLs) passing through rocky mountainous regions are particularly vulnerable to lightning, due to shorter striking distances and high soil resistivity. Therefore, accurately estimating the maximum overvoltage caused by lightning in mountainous regions is an important issue. This paper presents an analysis of BFO patterns to investigate the impact of various OHL design parameters in rocky mountainous areas particularly the design of an effective tower footing grounding system, with the aim of improving lightning performance. The simulation was conducted on a 132 kV double-circuit OHL using EMTP-RV software. The obtained results were validated with the presented results in reference and demonstrated good agreement. https://modelling.semnan.ac.ir/article_10127.html The behavior of the solid waste gasification system, methanol production technology, and internal combustion engine fueled by syngas has been investigated. The purpose of this study is to investigate the probability distribution of data related to waste, syngas, methanol production, and power generation using the Monte Carlo method. In fact, in this paper, the purpose of this study is to obtain the probability distribution of synthesis gas from gasification technology, the probability distribution of methanol production from methanol production from synthesis gas, and the probability distribution of power generation from internal combustion engine technology. Also, this study presents a method for estimating the capacity factor of a waste-to-energy power plant. Monte Carlo simulation was performed using MATLAB R2019a. The results showed that the system has a syngas flow rate of 33.53 Nm3/h, a methanol production amount of 16.49 kW, a production power of 10.35 kW, and a power factor of 0.985.By examining the research related to waste gasification and power generation with an engine, it is observed that all of them have considered a constant analysis for waste, while in reality, the analysis of waste in waste gasification plants is variable and has a statistical form. After obtaining the probability distribution of outputs, two scenarios of electricity sales and methanol sales from synthesis gas were examined economically. The results show that the scenario of electricity production from synthesis gas is more economical overall. Also, the economic factors are suitable for the methanol production scenario and both scenarios are economically justifiable. https://modelling.semnan.ac.ir/article_10128.html Since the coronavirus was recognized as a pandemic infectious disease in 2019, most people were forced to stay at home during the pandemic. Given that social networks are a popular medium among people and during the pandemic, the analysis of user-generated social content can provide new insights and be effective for tracking the occurrence of the pandemic over time. This study aimed to provide a model to predict the incidence rate of COVID-19 in the first wave of the pandemic in Iran, through the analysis of Persian Instagram posts. Using the synergetic technique, three features of semantic similarity, fear feeling, and hope feeling were extracted from Instagram posts. For this purpose, word embedding techniques (Word2Vec, Glove, FastText) were used to calculate semantic similarity, and a BERT-based classifier model was used to identify fear and hope feelings. To improve performance, the SBERT model was also used instead of classical embedding methods. Then, a support vector regression (SVR) model was trained using statistical indices based on these features to predict the daily incidence rate of COVID-19. The results showed that the synergy of semantic similarity and fear sentiment features using SBERT in the SVM model provided the highest performance with a coefficient of determination (R²) of 0.52, which showed a significant improvement over the baseline methods. These findings indicate that the automatic combination of semantic and sentiment features can be an effective indicator for monitoring epidemics through social networks. https://modelling.semnan.ac.ir/article_10129.html In this paper, the results of scour depth (Ds) and scour pit length (Ld) are compared with the experimental results conducted by Elnikhely and Fathy at 4 different discharges, 0.007, 0.008, 0.009 and 0.010 m3/s. The dimensionless parameters are the ratio of scour depth to the depth of the outfall (Yt), i.e. Ds/Yt, and the ratio of scour pit length to the depth of the outfall, i.e. Ld/Yt. It is observed that with increasing discharge, the maximum values of the dimensionless ratios Ds/Yt and Ld/Yt have a decreasing trend. This is because with increasing discharge, the depth of the outfall increases. This research showed that the numerical model has a good ability to simulate the sediment transport and scour process downstream of the triangular convoluted spillway. The numerical results are in good agreement with the experimental results and the rate of change of the scour pit at the bottom of the spillway is close to zero. With increasing discharge, the maximum values of the depth and length of the scour pit have decreased. On average, the difference between the numerical and experimental results is less than 5%. It can be concluded that there is a good agreement between the results. Numerical simulation of physical phenomena allows for a more detailed study of the flow field, velocity vectors and pressure contours, which is also discussed in this article. https://modelling.semnan.ac.ir/article_10130.html A photovoltaic cell converts solar radiation energy into electrical energy. Radiation on the photovoltaic surface causes an increase in the temperature of the photovoltaic cell, which leads to a decrease in the efficiency of the photovoltaic system. In this paper, a new structure of a thermal collector for absorbing energy from the operation of the photovoltaic cell was examined using Computational Fluid Dynamics (CFD) simulations. The main geometry is a flat-plate photovoltaic collector with a spiral cooling channel beneath the plate, using water as the working fluid within the laminar flow Reynolds number range. In this paper, the effect of the presence of turbulators with different angles of 15°, 30°, 45°, 60°, 75° and 90° was simulated. Then, the presence of nanofluid with aluminum oxide nanoparticles in different volume fractions of 1 to 4% was investigated. Also, the effect of a combined system of turbulators and nanofluid with nanoparticles on the increase in heat transfer was investigated. The CFD simulation results were evaluated using two performance criteria: the ratio of total heat transfer rate during cooling to the power required for flow circulation and the standard deviation criterion (evaluating the temperature distribution uniformity on the photovoltaic surface). The results showed that the system with a 15° turbulator provided better hydraulic and thermal performance compared to the baseline system, the nanofluid system, and the combined system. https://modelling.semnan.ac.ir/article_10131.html The identification of electroencephalography (EEG) signals related to motor imagery plays a key role in the analysis and evaluation of neural functions in brain-computer interface (BCI) systems. However, considerable individual differences in EEG patterns pose a significant challenge for designing accurate and generalizable models. Moreover, the ability to successfully recognize motor imagery from shorter signal durations has a direct impact on improving the efficiency and practical usability of these technologies. In this study, an innovative hybrid framework is proposed for classifying motor imagery EEG signals, introducing a two-stage architecture based on the combination of an enhanced Informer and the EEGNet model. In this architecture, the EEG signals, after initial frequency feature extraction, are first fed into the enhanced Informer module. This module, leveraging sparse attention mechanisms and adaptive frequency filters (FAA), effectively captures long-term temporal dependencies within the EEG data. The output of the Informer is then passed to the EEGNet model, which, through its specialized convolutional layers (spatial convolution, depthwise convolution, and separable temporal convolution), purposefully extracts spatial-temporal features from the EEG signals and generates a compact and discriminative representation for final classification. Experimental results demonstrate that the proposed model achieves 85.20% accuracy in cross-subject evaluation on the standard PhysioNet dataset with short 2-second trial durations. Comparative analyses with state-of-the-art models indicate that the proposed approach offers competitive and improved performance, particularly in handling shorter signal durations and participant diversity. https://modelling.semnan.ac.ir/article_10132.html Rotor eccentricity (RE) is one of the common faults in induction motors (IMs). We will calculate the rotor bar current (RBC) in this condition. This helps to the precision modeling of motors under fault conditions. Besides, the dynamic behavior of the IM under dynamic eccentricity (DE) and static eccentricity (SE) will be analyzed. To monitor the condition of electric machines to prevent unexpected failure, we need accurate modeling of motor behavior and performance in different operating conditions. The multiple coupled circuit (MCC) model is one of the powerful methods for modeling and analyzing electric machines in healthy and faulty conditions, including RE faults, which have been widely used in recent years. This paper calculates the RBC under RE conditions on a 2-pole squirrel cage induction motor (SCIM) with a nominal specification of 1.1 kW, 220/380 V, and 50 Hz, using an MCC model based on the winding function theory (WFT) and the stator data. Also, the behavior of other motor characteristics is evaluated. This paper presents a new method for calculating the RBC of an IM. This method is analytical and can be used to model the IM precisely. The results enable us to calculate and compare the primary parameters of IMs for both healthy and faulty conditions, including losses, efficiency, and torque ripple. https://modelling.semnan.ac.ir/article_10216.html This paper aims to investigate the behavior of concrete and composite columns subjected to blast loading through finite element analysis, validated against experiments adopted from the literature. To this end, after validating the finite element model, the behavior of two concrete columns with square and circular sections, and a composite column with a circular section with the same mechanical characteristics was investigated under the effect of blast loading. The results indicate that the greatest damage typically occurs near the columns' supports, with square sections showing higher vulnerability compared to circular sections. Introducing composite column can reduce this damage by approximately 59%, while also effectively decreasing maximum displacement and bending moment by up to 55%. Additionally, a parametric analysis was conducted to identify influential parameters on composite column behavior. The analysis suggests that selecting concrete with a compressive strength of 40 MPa represents an optimal choice for minimizing damage in the investigated columns, as further increases in concrete strength yield marginal benefits in terms of column damage and displacement. https://modelling.semnan.ac.ir/article_10217.html Quantum-dot Cellular Automata (QCA) technology, due to its unique structure, ultra-low power consumption, and high operating speed, has emerged as a promising alternative to semiconductor transistors. This research aims to design and implement a reversible 4×4 Wallace tree multiplier with optimized propagation delay, speed, and hardware resource usage. In this regard, a reversible 4:2 compressor along with reversible half and full adders within the QCA technology were employed. The proposed design was simulated using the QCA Designer tool, demonstrating that the multiplier consists of approximately 50 quantum cells, with a latency of four clock phases and an approximate area of 0.15 µm². Compared to previous designs, it achieves nearly 20% improvement in speed and approximately 30% reduction in power consumption. The key innovation lies in the use of reversible logic and a 4:2 compressor to reduce delay and energy consumption while maintaining high multiplier performance in QCA technology, thereby achieving an optimal balance among speed, power consumption, and circuit area. https://modelling.semnan.ac.ir/article_10218.html In this study, the solidification behavior of four commercial steel grades was numerically investigated and compared. Due to the different compositions of these steel grades, their properties varied significantly. The full Navier-Stokes equations were used to simulate turbulent flow. A new solidification source term was added to the governing equations, to model the phase change.The numerical method and developed model were based on the actual dimensions of the continuous casting machine at Mobarakeh Steel Complax. Appropriate boundary conditions were applied in each region. The thickness of the solid shell at the mold exit and during the casting process was calculated, and the metallurgical length was determined. Finally, the effect of liquidus and solidus temperatures on the metallurgical length for the four steel grades was determined. The effects of liquidus and solidus temperatures on the solidification front of four steel grades were investigated. Results showed that the third Steel Grade exhibited the thickest solid shell, with a thickness of 38 mm at the mold exit, among the four grades examined. Conversely, the forth Steel Grade essentially formed no solid shell. Additionally, under the same conditions, the metallurgical length was shortest in the third Steel Grade and longest in the first Steel Grade.The forth Steel Grade does not solidify completely within the casting machine length due to its low solidification temperature. https://modelling.semnan.ac.ir/article_10219.html In this paper, an 1×8 linear array of printed circuit board antennas with a Chebyshev aperture distribution is designed and simulated for fifth-generation (5G) applications. The primary goal of this design is to reduce mutual coupling between array elements using neutralization lines. The design process is carried out in two stages. In the first stage, the antenna array is designed and simulated with specifications including a central frequency of 28 GHz, a sidelobe level of -20 dB, and dimensions of 45×10.5×1.55 mm. In the second stage, after reducing the spacing between elements to half (1 mm) to compact the structure, neutralization lines are proposed to significantly reduce mutual coupling between the elements. These neutralization lines, placed between each element, substantially suppress unwanted surface currents responsible for mutual coupling. The dimensions of these lines are derived based on analytical equations. To validate the proposed design, simulations are conducted in two antenna analysis software environments, with the results confirming each other. The simulation results indicate that mutual coupling between adjacent elements is reduced to -29.76 dB. The bandwidth of the proposed structure is 3.09 GHz, ranging from 25.84 to 28.95 GHz, and the minimum mutual coupling between adjacent elements within this bandwidth is -32.84 dB. Additionally, the maximum gain of the antenna array is 8.92 dB. Finally, the efficiency of the proposed method is compared with other published research. https://modelling.semnan.ac.ir/article_10220.html In this paper, a proposal is outlined for a single-narrow band metamaterial absorber designed to operate within the THz field. The structure comprises a combination of graphene and silver placed on top of SiO_2 in the subsequent layer, and silver at the bottom. Absorption is at its best at a frequency of 9.38, with 99.7% absorbance and a quality factor of 17.67. The symmetrical nature of the structure results in identical frequency and absorption percentages for both transverse electric (TE) and transverse magnetic (TM) polarizations. The frequency of the resonance is influenced by the refractive index of the medium around the absorber, indicating that the presented structure has the potential to serve as a highly sensitive sensor for refractive index measurements. We have effectively analyzed the sensor's performance and obtained a precise estimation of its maximum sensitivity by determining the slope of the resonance frequency change line resulting from variations in the refractive index. The suggestion sensor demonstrates heightened sensitivity and an admirable figure of merit (FOM) which is highly applicable in medical contexts. The refractive index typically ranges from 1.34 to 1.39 for the majority of biological samples. The potential uses of the absorber encompass high-resolution imaging and precise biomedical detecting and sensing, etc. https://modelling.semnan.ac.ir/article_10221.html In this paper, a novel method based on the Adaptive Short-Time Fourier Transform (ASTFT) is proposed for reliable detection of faults under power swing conditions. The core of this approach lies in analyzing the frequency content of current signals and calculating their spectral energy. The innovation of the proposed algorithm is its dynamic adaptation of the time-frequency analysis window width: under normal and power swing conditions, a wider window is used to achieve high frequency resolution, while at the moment of fault occurrence, a narrower window is employed to enhance time resolution and enable faster detection. This adaptive mechanism is governed by estimating the instantaneous frequency of the signal and calculating its local stationarity length. To evaluate the proposed method, simulations were conducted in PSCAD/EMTDC and MATLAB environments on a standard power system network. Results demonstrate that ASTFT can detect symmetrical and asymmetric faults during power swings regardless of swing frequency (ranging from 0.5 to 5 Hz), fault location, fault resistance, or fault initiation time. Notably, the detection time of ASTFT is on average 15% faster than the classical STFT method (less than 20 milliseconds), and it maintains robust performance even in the presence of disturbances. Due to its high speed, accuracy, and insensitivity to varying network conditions, ASTFT offers a practical and effective solution for enhancing the reliability of distance relays in modern power systems. https://modelling.semnan.ac.ir/article_10256.html MIMO-OFDM system uses the merits of both OFDM and MIMO systems and facilitates the high-rate data transmission capability by increasing the reliability of the communication systems. The MIMO system uses multiple antennas in the transmitter and receiver to create spatial diversity and reduce the destructive effects of fading in wireless communication channels. Besides these advantages, the MIMO-OFDM system faces a high PAPR problem like the OFDM system. One of the well-known methods for PAPR reduction in MIMO-OFDM systems is the partial transmit sequence (PTS). The PTS method searches all possible combinations of phase factors to find the minimum PAPR, which increases the computational complexity. To solve this challenge, we propose the IFF-PTS algorithm, which combines the PTS algorithm and an improved version of the firefly algorithm (IFF). The proposed IFF-PTS algorithm overcomes the computational complexity of the exhaustive search to find the optimal phase factors in the PTS and significantly reduces the PAPR metric. The proposed IFF-PTS algorithm is evaluated in different scenarios and compared with the state-of-the-art algorithms. The results of the simulations show the superiority of the proposed algorithm compared with the counterpart algorithms. When the number of subblocks is 8, the IFF-PTS algorithm has reduced the PAPR value to 6.8dB. The computational complexity of the proposed method is 2.4% of the complexity of OPTS and has significantly reduced the PAPR value. For the number of subblocks of 16, the IFF-PTS method has only 1.2% of the complexity of OPTS but has reduced the PAPR value to 6.4dB. https://modelling.semnan.ac.ir/article_10257.html Controlling the temperature of the car catalyst is one of the effective factors in improving the efficiency of this part. The internal structure of the catalyst must be placed under certain thermal condition for optimally converting of exhaust gases. The theoretical and experimental results show that the internal structure of the catalyst in the temperature range of 300 °C to 600 °C has the greatest effect on greenhouse gases conversion. In this research, a theoretical solution has been proposed to prevent the deviation of the catalyst temperature from the defined limits. In order to prevent the raisin up the temperature of the catalyst, a double-walled converter with an asymmetric flow has been used. This part is installed before the car catalyst. The exhaust gases pass through it and then enter the catalyst. The cooler designed with four different lengths of 0.5 m, 1 m, 1.5 m and 2m. the performance of the design is investigated numerically using a commercial finite volume code. The state of heat transfer and its effect on the gaeses temperature reduction has been evaluated. According to the results, the 0.5 m in length cooler reduced the temperature to 291.6 °C. By selecting the temperature reducing converter and automatically activating the fluid circulation operation at the critical temperature, the performance of the electromechanical system derived from sensors and actuators have been investigated. https://modelling.semnan.ac.ir/article_10258.html Providing services for real-time, latency-sensitive IoT applications is a major challenge. . Fog environments can significantly reduce the latency of services because they move resources to the nearest edge of the network. On the other hand, fog nodes are not able to provide all the required resources at a large scale due to energy and processing power limitations. For this reason, the problem of optimizing service requests and energy consumption is raised as a major challenge. The nature of this problem is NP-hard, and therefore, exact optimization solutions are insufficient and impractical for large-scale problems. In this regard, a new approach based on the plant defense algorithm (Plant Defense Optimization Algorithm) is proposed as a novel solution to this challenge. This algorithm is inspired by the genetic defense behavior of plants. In the proposed algorithm, genes are first classified using the K-means clustering method. Then, the plant defense optimization algorithm operators are applied specifically to each solution. These operators are designed to avoid local optimality. In this method, the cost function is calculated as a combination of two factors: time delay and energy consumption. The plant defense optimization algorithm is tested in a simulated environment in which environmental dynamics and changes are carefully considered. Finally, the performance of the plant defense optimization algorithm is evaluated and compared with other methods. The experimental results show that the overall delay in the proposed approach is improved between 23.84% and 48.51% compared to other algorithms. https://modelling.semnan.ac.ir/article_10275.html One of the promising approaches to reducing pollutant emissions in diesel engines is the application of dual-fuel combustion using compressed natural gas (CNG) alongside diesel fuel. In this study, a conventional compression ignition (CI) engine (MT440C model) was structurally modified to operate under a dual-fuel mode without the need for a spark-ignition system. The primary objective is to investigate the feasibility of using CNG in CI engines and to compare key operational and emission characteristics—including engine power output and exhaust emissions—under different engine speeds (1200, 1400, 1600, 1800, and 2000 rpm). To enable accurate and real-time prediction of nitrogen oxides (NOx) emissions, a novel deep convolutional neural network (DCNN) architecture was proposed. The model is designed to extract high-dimensional temporal-spatial features from the multi-variable time-series dataset and model complex nonlinear dependencies in dual-fuel combustion. Experimental results demonstrate superior predictive performance, achieving a root mean square error (RMSE) of 21.70 and a coefficient of determination (R²) of 0.997, significantly outperforming existing baseline models in the literature. The outstanding accuracy and robustness of the proposed DCNN model underscore its applicability for integration into real-time smart engine control systems aimed at optimizing emissions in hybrid combustion platforms. https://modelling.semnan.ac.ir/article_10285.html Cerebral aneurysms are dangerous bulges in the walls of brain arteries that can lead to life-threatening hemorrhagic stroke if ruptured. Predicting their behavior and identifying high-risk regions through hemodynamic analysis is crucial in early diagnosis and therapeutic planning. In this study, blood flow within a saccular aneurysm located on the basilar artery of the Circle of Willis is numerically investigated using computational fluid dynamics (CFD). The geometry is modeled based on standard anatomical data from previous studies, and blood is considered as an incompressible, non-Newtonian, transient, and laminar fluid. Inlet boundary conditions are defined according to a realistic cardiac cycle, discretized into five key systole phases. The results present detailed distributions of velocity, pressure, and wall shear stress (WSS) fields within the aneurysm and parent artery throughout the cardiac cycle. Findings indicate that during systole, strong vortex formation occurs inside the aneurysm sac, leading to blood stasis. Approximately 16% of the incoming flow remains trapped within the aneurysm by the end of the third cycle. Moreover, wall shear stress significantly decreases in specific regions—particularly near the aneurysm dome—creating conditions conducive to thrombus formation and endothelial dysfunction, which may accelerate wall degeneration. Pressure fluctuations inside the aneurysm are also considerable, especially during peak systole, increasing the risk of rupture. These results highlight the critical influence of hemodynamic factors on aneurysm stability and rupture potential. https://modelling.semnan.ac.ir/article_10316.html Industrial X-ray computed tomography (CT) systems have become indispensable in non-destructive testing, quality control, and material characterization. The high-energy ionizing radiation emitted by their X ray generators require robust shielding solutions to protect operators and the surrounding environment while complying with international safety standards, such as NCRP guidelines. This study presents the design, Monte Carlo modeling, construction, and performance evaluation of a custom shielding chamber developed for an industrial CT X ray generator. Using the MCNPX 2.7.0 simulation code, the radiation field around the generator was modeled under realistic operating conditions to optimize the shielding geometry and material configuration. The designed chamber, fabricated from 4 mm thick stainless steel with an additional 5 cm lead block positioned in the beam’s forward direction, was constructed in the laboratory and experimentally validated. Comparisons between simulated and measured dose rates at multiple locations around the chamber (measured using a calibrated GRAETZ DoseDG dosimeter) showed excellent agreement, with all measurements remaining below the regulatory limit of 10 µSv/h in controlled areas. The validated model can serve as a predictive tool for designing shielding enclosures for a wide range of photon energies and generator configurations, reducing prototyping time and ensuring compliance with occupational radiation protection standards. https://modelling.semnan.ac.ir/article_10317.html In recent years, emotion recognition as a new method for natural human-computer interaction has attracted the attention of many researchers. Because the automatic recognition of emotion from speech or facial expressions alone has uncertainties, it is expected that emotion recognition based on the fusion of audio-visual information can be done with better accuracy. The purpose of this article is to present an effective method for emotion recognition from emotional speech and images of visible facial expressions and infrared images, based on a hybrid model. For this purpose, in the proposed model, the deep learning model is used to represent the visual-auditory features and the brain emotional learning (BEL) model, inspired by the limbic system of the brain, is used for the fusion of three-modality information. In the proposed model, the existing audio-visual database in the field of multimodal emotion recognition, Enterface'05, has been used for various experiments. The recognition accuracy of the presented model in the best case for this database is 94.20%, which has the highest efficiency compared to other fusion methods. https://modelling.semnan.ac.ir/article_10327.html Following occurrence of transient single-phase fault on transmission line, a primary arc is typically initiated. For fault clearing, the protection system temporarily disconnects the affected phase. After this operation, the primary arc is extinguished; but, due to the electric field surrounding the healthy phases and capacitance between the conductors, a large voltage is induced on the opened phase. So, due to the weakness of the insulation properties at the fault location, this voltage can lead to a reestablishing the arc at fault location, known as secondary arc. A secondary arc continues until the insulation properties of the air around the fault return to normal, so the reclosing operation will fail if it is reconnected prior to the secondary arc being extinguished. Therefore, in recent years, some studies have been conducted on identifying the secondary arc extinction time and the proper procedure for reclosing. This article aims to evaluate the insulation conditions at the fault location after the secondary arc has been extinguished to determine the appropriate time for reclosing. For this purpose, it is necessary to estimate the maximum voltage at the moment of closing immediately following the extinction of the secondary arc. Following this, the reignition voltage at the fault location is continuously calculated and by comparing its magnitude with the maximum voltage estimated from the moment of closing, a reclosing command is issued. In order to conduct this study, a 400kV line is simulated under different fault conditions and the proposed method is implemented, analyzed, verified, and evaluated. https://modelling.semnan.ac.ir/article_10328.html fuels, enhancing energy security, and reducing emissions. Renewables modernize power grids and drive the shift toward low-carbon networks. Peer-to-peer (P2P) local energy trading, as an innovative approach, enables direct transactions between consumers and small producers, supporting renewable integration and optimal use of distributed energy resources (DERs). This decentralized model enhances flexibility, resilience, and efficiency by reducing losses and managing the intermittency of renewable generation. This paper proposes a two-stage, two-layer decentralized local electricity market for smart microgrids. In the first stage, a bargaining game model unit commitment (BGMU) schedules distributed units and determines optimal energy plans of prosumers. These plans guide DERs to initiate packet-based trading in the second stage. The second stage introduces a two-layer P2P market. In the first layer, prosumers exchange energy packets via a decentralized mechanism, but some bids remain unmatched due to limited competitiveness. To resolve this, the second layer trades remaining packets through a market-based mechanism, ensuring full utilization of resources. Simulation results show that the proposed BGMU achieves optimal scheduling of DERs while considering distribution constraints. The first-layer P2P market reduces total operation cost by 57% compared to wholesale trading, and adding the second layer lowers costs by an additional 9%. Overall, the proposed framework improves efficiency, reduces costs, and enhances the role of renewables in smart microgrids. https://modelling.semnan.ac.ir/article_10339.html In recent years, with the advancement of technology, the design of low-power circuits with minimal area has gained significant attention. Researchers have been seeking structures that enable the design and implementation of low-power electronic systems. Reversible logic has emerged as a promising and practical technology, particularly applicable in low-power CMOS systems and quantum computing. One of the most critical aspects of electronic circuit design is power consumption. Reversible logic, due to its ability to preserve and recover input data, has been proposed as a solution to mitigate energy dissipation. Moreover, it offers additional advantages such as increased circuit speed, reduced quantum cost, decreased garbage outputs, reduced circuit depth, and extended hardware lifespan. In this paper, a reversible Carry Look-Ahead Adder is proposed, employing reversible gates such as NOT, TR, Feynman, and Peres to achieve an optimized design. Comparative analysis shows that the proposed 4-bit adder outperforms existing works in reducing the number of constant inputs. Overall, with an equal quantum cost to comparable designs, the proposed approach achieves a 46% reduction in constant inputs. It is worth noting that the proposed design has also been implemented on FPGA using the VHDL hardware description language and the Xilinx Vivado toolchain. https://modelling.semnan.ac.ir/article_10340.html This paper addresses the analysis and solution of the problem of creating covering arrays (CA) in the context of combinatorial testing. Combinatorial testing, as a fundamental approach in software quality evaluation and testing, helps identify errors and weaknesses in software by combining various variables and conditions. The main goal of this study is to design and create a covering array capable of covering all possible subsets of variables and conditions. To achieve this, new and advanced metaheuristic algorithms are utilized, which have not been previously applied in combinatorial testing. These algorithms leverage evolution-based optimizations and algorithms inspired by natural mechanisms to solve the covering array problem. Specifically, the combination of the Biogeography-Based Optimization (BBO) algorithm with the ROBDD algorithm is introduced as an innovative approach to generate minimal test sequences in covering arrays. These metaheuristic algorithms are recognized for their high capability in solving optimization problems and finding optimal solutions. In this study, these methods are used to generate covering arrays that cover all combinatorial test conditions and provide the best possible results. https://modelling.semnan.ac.ir/article_10365.html In this paper, first, analytical relationships of flat plate collector absorption rate and solar-to-thermal energy efficiency are presented. For the considered collector, without the use of cooling water, the collector stagnation temperature is 132.5 degrees Celsius (absorber plate temperature) and by entering the cooling water of one liter per minute, the absorber plate temperature decreases to 33 degrees Celsius and the collector efficiency reaches to 77%. To predict the collector efficiency, three machine learning models were used: linear, random forest, and decision tree. Seven parameters of solar radiation intensity, collector tilt angle, wind speed, pipe diameter, number of pipes, ambient temperature, and cooling water flow rate, were selected as input parameters. Comparison of the predicted efficiency with actual values showed that the linear model has a weaker evaluation than the other two models. The random forest and decision tree models perform prediction with almost equal ability and high accuracy (the random forest model predicts negligibly better than the decision tree model). In addition, among the input parameters, changes in collector tilt angle, solar radiation and wind speed insignificantly affects the efficiency. The cooling water flow rate has the greatest effect. The pipe diameter, ambient temperature and the number of the tubes, have a moderate effect. https://modelling.semnan.ac.ir/article_10370.html In this study, the free vibration analysis of a cylindrical panel and a sandwich plate with carbon–epoxy facesheets reinforced by shape memory alloy (SMA) wires and a polyurethane foam core reinforced with alumina nanoparticles was conducted in a thermal environment. The displacement field was formulated based on the third-order shear deformation theory (TSDT), and Hamilton’s principle was employed to derive the governing equations. The one-dimensional Brinson model was used to characterize the behavior of the SMA, and the problem was solved using the finite element method consistent with the third-order shear deformation theory. The effects of various boundary conditions, the volume fraction and prestrain of SMA wires, the nanoparticle volume fraction, fiber orientation, SMA wire temperature, and the geometric parameters of the panel on the natural frequencies were investigated. The results indicated that increasing the panel’s opening angle leads to an increase in the natural frequency of the structure, and the panel having a 15-degree opening angle exhibiting the highest natural frequency. Increasing the prestrain and volume fraction of the shape memory alloy wires postpones the onset of thermal increasing-induced degradation, thereby preserving the structural stability and integrity up to considerably higher temperatures. Different fiber orientation configurations, under identical structural conditions, enable access to a range of natural frequencies. Furthermore, the results demonstrated that by controlling the design parameters, the natural frequencies of the structure can be optimally tuned for specific applications. https://modelling.semnan.ac.ir/article_10418.html Monkeypox has recently emerged as a significant public health challenge, necessitating efficient diagnostic methods for timely detection. This study addresses the critical need for rapid and accurate diagnosis of monkeypox by leveraging artificial intelligence and transfer learning techniques, aiming to overcome limitations in current diagnostic methods. A publicly available dataset from Kaggle, comprising 228 original images labeled as monkeypox and other skin conditions, was augmented to 3192 images to address data scarcity and enhance model robustness. Pre-trained deep learning models, VGG19 and EfficientNetB4, were employed to extract image features, which were then classified using the XGBoost algorithm, known for its effectiveness in structured data classification. The proposed approach achieved high accuracy rates of 100% and 97.02% for the original and augmented datasets, respectively. Additionally, 5-fold cross-validation results demonstrated accuracies of 85.98% for the original dataset and 93.42% for the augmented set, highlighting the model's strong generalization capabilities. The proposed approach combines transfer learning with ensemble classification, providing a scalable solution that shows improved performance over several existing diagnostic methods in terms of accuracy and computational efficiency. The findings underscore the transformative potential of AI-driven diagnostic tools in public health, paving the way for more rapid, accessible, and accurate detection strategies for monkeypox and other emerging infectious diseases. https://modelling.semnan.ac.ir/article_10419.html Black powder, as one of the most critical solid contaminants in natural gas transmission and distribution pipelines, can significantly affect the accuracy of metering equipment. In this study, the influence of black powder particles on the performance of a 2-inch turbine gas meter was investigated through numerical modeling of gas–particle two-phase flow. Simulations were conducted for three types of gas measurement and pressure reduction stations (CGS, TBS, and HM) under summer and winter conditions, and the developed model was validated against laboratory data. The results indicate that, within the typical mass loading range observed in transmission pipelines (10-7 to 10-5), the presence of black powder has a negligible impact on meter accuracy. However, the sensitivity analysis revealed that at mass loading values above approximately m ≈ 1.25, the velocity profile deviates by more than 5%, leading to a noticeable deterioration in meter performance. These findings provide practical guidance on setting contamination limits as well as planning maintenance and monitoring strategies for turbine gas meters. https://modelling.semnan.ac.ir/article_10429.html This paper proposes a novel framework for the optimal bidding strategy of a flexible virtual power plant (VPP) in the day-ahead market while considering the participation of responsive loads in local energy markets. In the proposed structure, a co-opimization model for energy and reserve scheduling is developed to analyze the impact of inter-day trading of responsive loads on the VPP’s profit, taking into account the operators’ risk-aversion indices. Moreover, the effect of VPP participation in the local market on its operational behavior in other markets is examined. The results demonstrate how such participation can compensate for the imbalances caused by forecasting errors and significantly reduce the VPP’s transactions in the real-time market. The proposed model is formulated as a three-stage stochastic bi-level optimization problem and solved using a stochastic dual dynamic programming approach. Simulation results reveal that participation in the inter-day market can increase the VPP’s profit by up to 2.5%, while notably decreasing its real-time market transactions—sometimes nearly to zero. https://modelling.semnan.ac.ir/article_10431.html Storing and processing information obtained from sensors and sensors, as well as using wireless and real-time prediction systems and data analysis, creates added value. Therefore, evaluating the performance of sensors in computer networks is important. The purpose of this research is to present a model for evaluating the performance of sensors using mathematical models and optimizing the results using simulation tools. The research data is related to a computer network simulated in the OPNET environment. This computer network is designed based on sensors and sensors that are placed in the gas pipeline in order to identify and diagnose critical defects and maintenance program data. In the present research, the problem is modeled using the mathematical model BCC and SBM of data envelopment analysis hyper-efficiency, and after identifying efficient units, the performance efficiency values are optimized with the MLP neural network and its combination with the cuckoo algorithm. To measure the accuracy of the model performance, the parameters of mean square error, correlation coefficient, standard deviation and mean absolute error have been evaluated. The results of the research indicate that these values are 0.2605, 0.123666, 0.66 and 0.89853 in the neural network, and 0.1037, 0.03462222, 0.43 and 0.94829 in the hybrid algorithm, respectively. Accordingly, the use of the hybrid algorithm improves the data learning process in the network and increases the accuracy in the final outputs of the research model, https://modelling.semnan.ac.ir/article_10435.html Cellular solids especially metallic foams have unique characteristics especially in energy absorption applications and good strength to weight ratio. So the uniaxial crushing tests are essential for characterization purposes. The main problem in the mechanical behavior investigations is impossibility to use strain gages and other contact methods for displacement and strain measurement purpose because of their porosity on the faces. In the present research we manipulate the digital image correlation or simply DIC technique for deformation and strain field measurement in quasi-static uniaxial compression test. We use GOM inspect and NCORR software for DIC implementation. Using the image processing method, the displacement and strain field are obtained continuously. For verification purpose, we conduct comprehensive finite element simulation using ABAQUS 2019 commercial non-linear code. Due to the importance of micro-structural modeling in mechanical behavior prediction of cellular solids, we use geometric reconstruction technique based on computed tomography or CT scan images. Also geometric modifications are performed using digital optical microscope. Comparison of DIC results and FEA simulation outputs shows good accordance. https://modelling.semnan.ac.ir/article_10557.html Graph Neural Networks (GNNs) excel at learning from graph-structured data but suffer significant performance degradation under distribution shifts between training and test environments. This paper proposes a Siamese-based contrastive learning framework for improving out-of-distribution (OOD) generalization in node classification tasks. Our approach generates positive samples through feature matrix perturbation without requiring negative samples, thereby reducing computational complexity. The model employs dual GCN encoders and MLP classifiers with shared weights, optimized using a three-component loss function that maximizes representation similarity, prediction consistency, and classification accuracy. Experimental evaluation on GOOD benchmark datasets across both covariate and concept shift scenarios demonstrates that our method outperforms baseline approaches. This work demonstrates that contrastive learning with Siamese architecture offers a computationally efficient and effective solution for enhancing GNN robustness under distribution shifts, with promising implications for real-world applications requiring reliable model performance in dynamic environments. The proposed method on average and in the GAP metric, has reduced the performance gap between IID and OOD scenarios by 19.75%, while also achieving an average OOD accuracy of 55.04%.