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A model for simultaneous heat and mass transfer during the cooking process of frozen hamburger has been developed, using a modified form of Darcy's law to describe the capillary flow of moisture and Fourier's second law in cylindrical coordinates to describe the heat transfer. The effects of cooking time (0 to 12 min), cooking temperature (140 to 160 °C), and patty thickness (10 to 14 mm) during the hamburger cooking process on temperature profile, moisture content, and inactivation kinetics of Salmonella bacteria were investigated. The results showed that the predicted temperature and moisture values are in good agreement with the measured data. Due to the low convective heat transfer coefficient in the upper part of the sample at the beginning of the cooking process, non-uniformity in temperature was observed, which was resolved by flipping the hamburger and resulted in a reduction in cooking time. In addition, an increase in the heating temperature results in an increase in the rate of evaporation and moisture loss from the hamburger patty. The simulation results showed that at a cooking temperature of 140 °C and a patty thickness of 14 mm, all points of the hamburger will not achieve a 12D reduction of Salmonella and there is a possibility of salmonellosis under these conditions.

IntroductionDrying is one of the oldest methods of food preservation. To increase the efficiency of heat and mass transfer while maintaining product quality, the study of the drying process is crucial scientifically and meticulously. It is possible to conduct experimental tests, trial and error, in the drying process. However, this approach consumes time and cost, with a significant amount of energy resources. By harnessing available software and leveraging technological advancement to develop a general model for drying food under varying initial conditions, the drying process can be significantly optimized.Materials and MethodsThis study was conducted with the aim of simulating heat and mass transfer during Refractance window drying for aloe vera gel. Comsol Multiphysics version 5.6 is a three-dimensional model used to solve heat and mass transfer equations. For this purpose, the differential equations of heat and mass transfer were solved simultaneously and interdependently. The above model considered various initial conditions: water temperature of 60, 70, 80, and 90℃, and aloe vera gel thickness of 5 and 10 mm. The initial humidity and temperature of the aloe vera is uniform. The initial temperature is 4℃ and the initial humidity of the fresh aloe vera sample is 110 gwater/gdry matter. Heat is supplied only by hot water from the bottom surface of the product.Results and DiscussionThe drying time was needed to reduce the moisture content of aloe vera gel from 110 to 0.1 gwater/gdry matter during Refractance window drying. Aloe vera gel with a thickness of 5 mm dried in 120, 100, 70, and 50 minutes at water temperatures of 60, 70, 80, and 90℃, respectively. For a 10 mm thick layer of aloe vera gel, the drying time was 240, 190, 150, and 120 minutes, for water temperatures of 60 to 90℃, respectively. These results demonstrate the importance of both the water temperature and thickness on the drying time. Furthermore, the drying rate of aloe vera gel increased as the water temperature increased from 60 to 90℃, the drying rates were 0.915, 1.099, 1.57, and 2.198 gwater/min for 5 mm thickness and 0.457, 0.578, 0.732, and 0.915 gwater/min for 10 mm thick layer of aloe vera gel, respectively.ConclusionBased on the simulation results, the optimal model is with a water temperature of 90℃ and an aloe vera gel thickness of 5 mm. Overall, the modeling results are consistent with the results of experimental data.

Introduction Evaporation, the process by which water molecules escape a surface after absorbing sufficient energy to overcome vapor pressure, is a major contributor to water scarcity, especially in arid and semi-arid regions where heat readily facilitates this escape. Accurately estimating evaporation losses is crucial for effective water resource management, crop water demand prediction, and irrigation scheduling. Machine learning (ML) has emerged as a powerful tool for tackling the complex and stochastic nature of environmental problems. ML models excel at identifying relationships between predictor variables and outcomes (predictands), often surpassing traditional methods. However, their performance can vary depending on input factors and climatic conditions. Recently, hybrid techniques that combine multiple models have gained traction in climate and hydrology studies. These techniques leverage the strengths of different approaches within a single algorithm, potentially capturing more complex patterns in data series. This research will explore the potential of various individual ML models and propose a novel hybrid approach for estimating pan evaporation in Sistan and Baluchistan Province. Materials and Methods This study investigates pan evaporation simulation and prediction in Sistan and Baluchistan Province, Iran. Synoptic station data (1980-2019) served as model inputs, while pan evaporation measurements from these stations provided the observed values. In this research, in the approach of individual performance of data mining models, eight data mining models were used to simulate and predict evaporation from the pan. In addition to the individual performance approach, the combined VEDL approach was used to provide a hybrid model (a combination of the mentioned eight individual models of deep learning). In this hybrid approach to regression issues, the estimators of all models are averaged to obtain an estimate for a set called vote regressors (VRs). There are two approaches to awarding votes: average voting (AV) and weighted voting (WV). In the case of AV, the weights are equivalent and equal1. A disadvantage of AV is that all of the models in the ensemble are accepted as equally effective; however, this situation is very unlikely, especially if different machine learning algorithms are used. WV specifies a weight coefficient for each ensemble member. The weight can be a floating-point number between Zero and one, in which case the sum is equal to one, or an integer starting at one denoting the number of votes given to the corresponding ensemble member. the weight of each model was selected based on the accuracy of the model's performance using the evaluation criteria obtained from the training implementation section of individual models. the model’s performance was assessed using statistical measures, including R2, RMSE, MAE, and Taylor diagram. Results and Discussion The results showed that all the models had very good results in both the training and testing stages. All models exhibited excellent performance during training and testing. The Artificial Neural Network (ANN) achieved the highest accuracy in both phases at the Zahedan station (R² = 0.89, RMSE = 45.95 in training; R² = 0.96, RMSE = 44.18 in validation). It emerged as the best model for monthly pan evaporation prediction at this station. Other models also performed well, with the Support Vector Machine (SVM) and Random Forest (RF) models achieving R² values of 0.89 and 0.88 in training, respectively. Notably, the BART model ranked second in validation (R² = 0.96). The Tree Model (TM) had the lowest accuracy (R² = 0.84 and 0.93 in training and validation, respectively). Across all stations, ANN, SVM, and RF consistently delivered the best results in both training and testing. In the test phase, the SVM model outperformed others in Khash, Iranshahr, and Chabahar stations (R² = 0.94, 0.96, and 0.94, respectively). At the Saravan station, the RF model achieved the highest R² (0.94) during testing. To develop a hybrid data mining model, the Voting Ensemble for Deep Learning (VEDL) technique was employed with weighted voting in the training stage. The combined model significantly improved upon the best individual model. RMSE decreased from 45.95 to 33.1, R² increased from 0.89 to 0.94, and MAE improved from 32.92 to 23.9. Evaluation using the Taylor diagram further confirmed the superior performance of the VEDL model compared to the individual ANN model. Conclusion The results showed that among all the models, ANN, SVM, and RF models had the best performance in the two stages of training and verification. In the validation stage, the SVM model with R2 values equal to 0.94, 0.96, and 0.94 performed best in the Khash, Iranshahr, and Chabahar stations. At the Saravan station, in the Sensji validity stage, the RF model with an R2 value of 0.94 had the best performance among the models. The excellent performance of the models in the two stages of training and validation is another finding of the research, These results are consistent with the results of researchers who have expressed the appropriate efficiency of machine learning models in estimating evaporation/evaporation and transpiration in different climatic regions of Iran. The results of the combined model showed that the combined model improved the results compared to the best individual model so that the RMSE values increased from 45.95 to 33.1, the R2 values increased from 0.89 to 0.94, and the MAE value improved from 32.92 to 23.9. The use of the VEDL approach to estimate evaporation from the pan was a new approach that has not been used in past studies. Therefore, according to the results of this research, the proposed deep sensing model is proposed to estimate the evaporation of arid and semi-arid areas for water resources management and agricultural planning.

Stress concentration and nonlinear buckling caused by grain boundaries in two-dimensional structures and geometric defects are introduced as factors affecting the fracture behavior of two-dimensional materials. Since the mechanical behavior of polycrystalline nanostructures is not well known, in this article, the mechanical behavior of carbon nitride nanosheets was studied as a function of temperature and grain boundaries. The mechanical performance of polycrystalline carbon nitride was tested in the presence and absence of edge cracks and at temperatures from 100 K to 900 K. Molecular dynamics simulation was used as a cost-effective method for modeling and testing two-dimensional nanosheets by choosing the appropriate potential function and boundary conditions. The results showed that the mechanical properties of carbon nitride monocrystalline decrease with increasing temperature and the results were reported in zigzag direction higher than armchair. The same trend was observed for the polycrystalline structures. The increase in temperature caused the Young's modulus, failure stress and strain to decrease. Also, increasing the crack length from 5 to 25 angstroms caused a decrease in mechanical properties. On the other hand, increasing the number of regions for carbon nitride polycrystalline decreased the failure stress. In addition, the optimization was carried out by genetic algorithm and the results showed that the optimal value of Young's modulus for carbon nitride polycrystalline with 53 regions at a temperature of 586.95 K and a crack length of 6.52 angstroms is equivalent to 338.18 GPa. The results obtained from this study can be generalized to more complex cases to predict a deeper understanding of the next generations of two-dimensional structures. [ABSTRACT FROM AUTHOR]

This research models the desalination process using a direct contact membrane distillation method in a flat plate configuration with a PTFE membrane. The model employs computational fluid dynamics to solve the differential equations for momentum, heat, and mass transfer. Convective heat transfer in the membrane region is neglected, and mass transfer is described by empirical equations. The simulation results were then validated against available experimental data. Increasing the feed saltwater temperature from 42°C to 78°C led to a significant rise in permeate flux, from 3.59 L.m-2 .hr-1 to 18.64 L.m-2 .hr-1 . The results demonstrates that the distillation flux is directly proportional to the stream velocities and membrane porosity, but inversely proportional to the feed salt concentration, permeate inlet temperature, membrane thickness, pore tortuosity, and membrane conductive heat transfer coefficient. [ABSTRACT FROM AUTHOR]

Introduction Drying is one of the oldest methods of food preservation. To increase the efficiency of heat and mass transfer while maintaining product quality, the study of the drying process is crucial scientifically and meticulously. It is possible to conduct experimental tests, trial and error, in the drying process. However, this approach consumes time and cost, with a significant amount of energy resources. By harnessing available software and leveraging technological advancement to develop a general model for drying food under varying initial conditions, the drying process can be significantly optimized. Materials and Methods This study was conducted with the aim of simulating heat and mass transfer during Refractance window drying for aloe vera gel. Comsol Multiphysics version 5.6 is a three-dimensional model used to solve heat and mass transfer equations. For this purpose, the differential equations of heat and mass transfer were solved simultaneously and interdependently. The above model considered various initial conditions: water temperature of 60, 70, 80, and 90°C, and aloe vera gel thickness of 5 and 10 mm. The initial humidity and temperature of the aloe vera is uniform. The initial temperature is 4° and the initial humidity of the fresh aloe vera sample is 110 gwater/gdry matter. Heat is supplied only by hot water from the bottom surface of the product. Results and Discussion The drying time was needed to reduce the moisture content of aloe vera gel from 110 to 0.1 gwater/gdry matter during Refractance window drying. Aloe vera gel with a thickness of 5 mm dried in 120, 100, 70, and 50 minutes at water temperatures of 60, 70, 80, and 90°, respectively. For a 10 mm thick layer of aloe vera gel, the drying time was 240, 190, 150, and 120 minutes, for water temperatures of 60 to 90°, respectively. These results demonstrate the importance of both the water temperature and thickness on the drying time. Furthermore, the drying rate of aloe vera gel increased as the water temperature increased from 60 to 90°, the drying rates were 0.915, 1.099, 1.57, and 2.198 gwater/min for 5 mm thickness and 0.457, 0.578, 0.732, and 0.915 gwater/min for 10 mm thick layer of aloe vera gel, respectively. Conclusion Based on the simulation results, the optimal model is with a water temperature of 90° and an aloe vera gel thickness of 5 mm. Overall, the modeling results are consistent with the results of experimental data. [ABSTRACT FROM AUTHOR]

Introduction Evaporation, the process by which water molecules escape a surface after absorbing sufficient energy to overcome vapor pressure, is a major contributor to water scarcity, especially in arid and semi-arid regions where heat readily facilitates this escape. Accurately estimating evaporation losses is crucial for effective water resource management, crop water demand prediction, and irrigation scheduling. Machine learning (ML) has emerged as a powerful tool for tackling the complex and stochastic nature of environmental problems. ML models excel at identifying relationships between predictor variables and outcomes (predictands), often surpassing traditional methods. However, their performance can vary depending on input factors and climatic conditions. Recently, hybrid techniques that combine multiple models have gained traction in climate and hydrology studies. These techniques leverage the strengths of different approaches within a single algorithm, potentially capturing more complex patterns in data series. This research will explore the potential of various individual ML models and propose a novel hybrid approach for estimating pan evaporation in Sistan and Baluchistan Province. Materials and Methods This study investigates pan evaporation simulation and prediction in Sistan and Baluchistan Province, Iran. Synoptic station data (1980-2019) served as model inputs, while pan evaporation measurements from these stations provided the observed values. In this research, in the approach of individual performance of data mining models, eight data mining models were used to simulate and predict evaporation from the pan. In addition to the individual performance approach, the combined VEDL approach was used to provide a hybrid model (a combination of the mentioned eight individual models of deep learning). In this hybrid approach to regression issues, the estimators of all models are averaged to obtain an estimate for a set called vote regressors (VRs). There are two approaches to awarding votes: average voting (AV) and weighted voting (WV). In the case of AV, the weights are equivalent and equal1. A disadvantage of AV is that all of the models in the ensemble are accepted as equally effective; however, this situation is very unlikely, especially if different machine learning algorithms are used. WV specifies a weight coefficient for each ensemble member. The weight can be a floatingpoint number between Zero and one, in which case the sum is equal to one, or an integer starting at one denoting the number of votes given to the corresponding ensemble member. the weight of each model was selected based on the accuracy of the model's performance using the evaluation criteria obtained from the training implementation section of individual models. Results and Discussion The results showed that all the models had very good results in both the training and testing stages. All models exhibited excellent performance during training and testing. The Artificial Neural Network (ANN) achieved the highest accuracy in both phases at the Zahedan station (R²= 0.89, RMSE = 45.95 in training; R² = 0.96, RMSE = 44.18 in validation). It emerged as the best model for monthly pan evaporation prediction at this station. Other models also performed well, with the Support Vector Machine (SVM) and Random Forest (RF) models achieving R² values of 0.89 and 0.88 in training, respectively. Notably, the BART model ranked second in validation (R² = 0.96). The Tree Model (TM) had the lowest accuracy (R² = 0.84 and 0.93 in training and validation, respectively). Across all stations, ANN, SVM, and RF consistently delivered the best results in both training and testing. In the test phase, the SVM model outperformed others in Khash, Iranshahr, and Chabahar stations (R² = 0.94, 0.96, and 0.94, respectively). At the Saravan station, the RF model achieved the highest R² (0.94) during testing. To develop a hybrid data mining model, the Voting Ensemble for Deep Learning (VEDL) technique was employed with weighted voting in the training stage. The combined model significantly improved upon the best individual model. RMSE decreased from 45.95 to 33.1, R² increased from 0.89 to 0.94, and MAE improved from 32.92 to 23.9. Evaluation using the Taylor diagram further confirmed the superior performance of the VEDL model compared to the individual ANN model. Conclusion The excellent performance of the models in the two stages of training and validation is another finding of the research, These results are consistent with the results of researchers who have expressed the appropriate efficiency of machine learning models in estimating evaporation/evaporation and transpiration in different climatic regions of Iran. The results of the combined model showed that the combined model improved the results compared to the best individual model so that the RMSE values increased from 45.95 to 33.1, the R² values increased from 0.89 to 0.94, and the MAE value improved from 32.92 to 23.9. The use of the VEDL approach to estimate evaporation from the pan was a new approach that has not been used in past studies. Therefore, according to the results of this research, the proposed deep sensing model is proposed to estimate the evaporation of arid and semi-arid areas for water resources management and agricultural planning. [ABSTRACT FROM AUTHOR]

Objective: In order to simulate yield, leaf area, and nitrogen content in rice plant by using the ORYZA2000 model under different nitrogen fertilizer amounts and various splitting approaches, a factorial experiment based on a randomized complete block design with three replications was carried out at a research farm located in the Mazandaran Province (Babol) during 2017-18. Methods: The first factor comprised four nitrogen fertilizer rates (50, 90, 130, and 170 Kg n ha-1, supplied as urea) and the second factor comprised various fertilizer splitting appraoches (T1: 70% basal+ 30% in tillering stage; T2: 33.33% basal+ 33.33% in tillering stage+ 33.33% in panicle initiation stage; T3: 25% basal+ 50% in tillering stage+ 25% in panicle initiation stage; T4: 25% basal+ 25% in tillering stage+ 50% in panicle initiation stage). Results: The results demonstrated that the model successfully simulated grain yield, biomass, grain nitrogen, and total nitrogen with RMSEn of four, four, nine, and six percent in calibration, and five, four, four, and eight percent in validation, respectively. The mean square error for the leaf area index was 0.5 and 0.4 in the model's calibration and validation, respectively. Conclusion: Based on the results obtained, the ORYZA2000 model can be used as a suitbale simulation model for selecting the best strategy for enhancing rice yield under different nitrogen fertilizer management in the rice-rich regions of northern Iran. [ABSTRACT FROM AUTHOR]

Genomic evaluation of crossbred progeny due to their limited pedigree and lack of genotyping and performance accessibility is inevitably based on the information from purebred parental populations to increase crossbred performance. Despite the fact that single-step genomic evaluation (ssGBLUP) method can use information from both genotyped and non-genotyped animals simultaneously, leading to more accurate genomic estimated breeding values, but due to the incompatibility of the genomic and pedigree relationship matrices, it might lead to dispersion (inflation/deflation) and bias in the estimated breeding values higher than that with the BLUP method. Therefore, in this study, the prediction ability of the genomic breeding value of crossbred progeny was investigated based on the ssGBLUP method and simulation data of the purebred parents and crossbred population, considering the different scaling factor of residual polygenic variance. Based on the results of this study, the use of different ratio of residual polygenic (β) variance to incorporate the genomic and pedigree relationship matrices did not considerably affect the accuracy, bias and dispersion of the predicted genomic breeding values in crossbred progeny. In addition, the effect of proportion of residual polygenic variance (β) in blending the genomic and pedigree relationship matrices does not significantly affect the convergence point. Therefore, to simplify the model, the default value of β (0.05) might be used in the inverse of the relationship matrix. [ABSTRACT FROM AUTHOR]

IntroductionClimate change is one of the most important issues in the world in the 21st century which affects various sectors of agriculture, forestry, water and financial markets, and has serious economic consequences (Reidsma et al., 2009). In recent years, the management of agricultural water consumption has always been considered as one of the important issues in water resources management. Koochaki and colleagues (Koochaki and Kamali, 2006) by evaluating the climatic indicators of Iran's agriculture showed that during the next 20 years, the average monthly temperature will increase in almost all regions of the country, and the increase in evaporation and transpiration is one of the most important consequences of this warming. Simulated climate parameters can be obtained through different general GCM atmospheric models. Due to the low spatial resolution of these models, its output should be downscaled using dynamic or statistical methods. Materials and MethodsThe LARS-WG model predicts meteorological variables for a period of time in the future by using a series of basic and fine-scale meteorological data, output of one of the GCM models. Research has shown that the LARS-WG model has the necessary accuracy for this task. Calculating the amount of evapotranspiration and yield of very complex plants are time-consuming and dependent on spending a lot of money and limited to the tests performed, the shortness of the test time and also the limitation in the number of scenarios that are checked by the test. Therefore, plant models are considered and evaluated by researchers. The AquaCrop model has demonstrated commendable accuracy in various regions of Iran and globally for forecasting plant growth, water consumption efficiency, and evapotranspiration requirements. These predictions hold significant potential for optimizing irrigation strategies across different agricultural settings. AquaCrop is one of the applied agricultural models that was obtained from the modification and revision of FAO publication No. 33 by prominent experts from all over the world. In this study, the values of green water footprint of winter wheat plant (Pishgam) were estimated in climatic conditions obtained from LARS-WG model and DKRZ database under scenarios 4.5 and 8.5 and at different planting dates (15 October, 1 November, 15 November, 30 November and 15 December), in the next 4 periods (2021-2040, 2041-2060, 2061-2080 and 2081-2100) and by Aquacrop model. Results and DiscussionThe results showed that if planting date is on October 15, in the climatic conditions obtained from the LARS-WG model and under scenarios 4.5 and 8.5, in all future periods, the footprint of green water will increase compared to its value in the base period, and if planting is the rest of the dates, in each of the next 4 periods, the average green water footprint will decrease compared to its value in the base period. The results obtained for the DKRZ database show that the green water footprint attained for the dates of cultivation and periods investigated in scenarios 4.5 and 8.5 does not have a particular trend. On the planting dates of October 15 and November 1 for the periods of 2061-2080 and 2081-2100, the green water footprint will decrease and on the other three dates (15 November, 30 November, and 1 November) for these periods, there will be an increasing trend. On 15 December, for the DKRZ database, in both scenarios defined for all periods, an increase in green water footprint compared to the base period is reported. However, in the period of 2081-2100 in scenario 8.5, a decrease compared to the base period will be observed. The highest amount of green water footprint in all these periods and models for the period 2041-2060 under the climatic conditions of the DKRZ database in scenario 4.5, if the planting date is 15 October, it is estimated that the amount of water consumed is equal to 4272 cubic meters per ton with a standard deviation of 5018 cubic meters per ton is predicted. The lowest footprint of green water for the period 2081-2100 under the climatic conditions obtained from the LARS-WG model in scenario 8.5, if the planting date is on 15 December, is reported to be 232 tons per hectare with a standard deviation of 52.3 tons per hectare.

Introduction: Despite numerous studies on supply chain cooperation, research shows that collaborations in chains with asymmetric power structures leading to R&D investment have received less attention. This study aims to develop a model for the supply chain of a complex product, which includes operational decisions for market supply and R&D investment. The objectives include determining the equilibrium point between R&D investment, product price, and production amount, and investigating the impact of R&D uncertainty, buyer fairness, and customer sensitivity to product technology level on supply chain performance. Methods: The model considers the risk of uncertainty in R&D output and a demand function dependent on product technology level. Developed under an asymmetric power structure, the model incorporates various cooperation contracts, including R&D cost sharing, production cost sharing, and revenue sharing. Each scenario is presented as a nonlinear two-level programming model, created using the Nash bargaining game approach and optimized through simulation. Results and discussion: The research indicates that uncertainty risk reduces supply chain profit, but cooperation contracts can improve performance compared to a decentralized structure. The revenue sharing contract generates higher profit for both the supply chain and the supplier. However, from the buyer’s perspective, when bargaining power is relatively low, R&D cost sharing and production cost sharing contracts are more beneficial. Increasing buyer fairness improves overall supply chain performance in revenue sharing and R&D cost sharing structures. Market sensitivity to product technology level enhances chain performance in production cost sharing and R&D cost sharing structures, but not all chain members benefit in revenue sharing structures. Market sensitivity to price and buyer fairness respectively decrease and increase supply chain performance. Conclusions: Considering the significant costs of R&D and production in complex products, addressing these costs in supply chain cooperation contracts and sharing them among influential factors can enhance supply chain performance. The bargaining power between buyers and sellers affects the type of contract. Given the unknown nature of information for supply chain parties, further models can be developed.

Introduction Agriculture is a cornerstone of many developing economies, providing food, income, and employment for millions of people. It is also projected to play a vital role in feeding a global population of 9.1 billion people by 2050. However, there are growing concerns about the environmental impact of agriculture, particularly in arid and semi-arid regions like Iran. Managing water and fertilizer usage in agriculture is crucial to ensuring food security and sustainability. However, conducting field experiments to assess the interaction of all factors involved is expensive and time-consuming. This research focuses on optimizing maize production in Kerman province, a region where maize is a major crop. The research is motivated by the need to improve resource management in Iran, where water and fertilizer resources are limited. The APSIM model is used to determine the best management scenario for maize production in Kerman province. APSIM is a crop growth simulation model that can be used to predict the impact of different management practices on crop yield, water use efficiency, and nutrient use efficiency. The use of APSIM in this research provides a cost-effective and time-efficient alternative to conducting extensive field experiments. The results of this research will contribute to the development of sustainable and efficient agricultural practices in Kerman province and similar regions. These regions are characterized by resource constraints, such as limited water and fertilizer availability. The research aimed to simulate the effect of management parameters (planting date and irrigation) on Crop yield and subsequently achieve the optimal management scenario.Materials and MethodsThe APSIM model was used for simulation in three regions of Bardsir (temperate to cold climate), Jiroft (hot and humid climate), and Orzuye (hot and dry climate). The model requires four series of data: climate, soil, management, and crop data. The required climate data (from 1998 to 2018) including daily maximum and minimum temperatures, length of sunny hours, and daily precipitation were collected and prepared from the synoptic weather stations of the three mentioned regions.The management data set for each of the study regions was prepared in the form of questionnaires and field research from experts of the Agricultural Jihad Organization, the Agricultural Research Center Organization, and prominent farmers in those regions. The crop data includes the plant genetic coefficients of the maize single cross hybrid 704, which were obtained from the calibration of the APSIM model. To optimize planting date and irrigation management in the studied areas, different planting and irrigation date treatments were investigated. In this research, planting date treatments included the conventional planting date of the region, 20 days before the conventional planting date (as early planting date), and 20 days after the conventional planting date (as late planting date). Irrigation treatments included the usual number of irrigations in the region (13 irrigations), less irrigation (11 irrigations), and more irrigation (15 irrigations).Results and DiscussionOur results showed that the model successfully simulated maize phenology, especially maturity date, with high accuracy for all fertilizer amounts tested. The model performance in predicting biomass under different nitrogen treatments was also satisfactory, with a minimal difference between observed data and model results. The nRMSE of grain yield in the calibration stage was 11.2% and in the validation stage was 9%. The nRMSE for calibration of the biological yield of SC 704 was 14.8% and for validation was 13.9%. Also, the model was able to simulate phenology with very high accuracy (especially the days to maturity). Overall, the nRMSE of days to flowering was less than 10% and for the days to maturity was less than 5%. Late planting dates consistently showed better performance across regions and irrigation treatments, resulting in significantly increased grain yield compared to conventional and early planting dates. The highest seed yield was obtained with 15 times of irrigation, among the various irrigation treatments. Late planting combined with 15 times of irrigation yielded the best results in Kerman province, particularly in Bardsir, with a yield of 9300 kg ha-1. Optimal moisture and air conditions, along with the cultivation of a late-maturing variety, contributed to the higher seed yield. These findings are consistent with previous research that has confirmed the positive impact of late planting and extended ripening periods on maize yield.ConclusionOur results showed that the model simulates the growth and yield of single cross 704 corn in Kerman province well, even after 20 days of late planting. Long-term simulation experiments showed that maize grain yield varied depending on the region, with the highest yield in Bardsir (8317 kg ha-1) and the lowest yield in Jiroft (4735 kg ha-1). The optimum maize grain yield (8872.8 kg ha-1) was obtained by the interaction effect of late planting date and 15 times of irrigation.

This research aims to optimize the mixing process in gas-lift anaerobic digesters of municipal sewage sludge since mixing and maintaining uniform contact between methanogenic bacteria and nutrients is essential. Wastewater municipal sludge sampling was performed at the Ahvaz West treatment plant (Chonibeh, Iran) during the summer of 2022. A Computational Fluid Dynamics (CFD) model was implemented to simulate, optimize, and confirm the simulation process using ANSYS Fluent software 19.0. The velocity of the inlet-gas into the digester was determined and a draft tube and a conical hanging baffle were added to the digester design. Different inlet-gas velocities were investigated to optimize the mixing in the digester. Furthermore, turbulence kinetic energy and other evaluation indexes related to the sludge particles such as their velocity, velocity gradient, and eddy viscosity were studied. The optimal inlet-gas velocity was determined to be 0.3 ms-1. The simulation results were validated using the Particle Image Velocimetry (PIV) method and the correlation between CFD and PIV contours was statistically sufficient (98.8% at the bottom corner of the digester’s wall). The results showed that the model used for simulating, optimizing, and verifying the simulation process is valid. It can be recommended for gas-lift anaerobic digesters with the following specifications: cylindrical tank with a height-to-diameter ratio of 1.5, draft tube-to-digester diameter ratio of 0.2, draft tube-to-fluid height ratio of 0.75, the conical hanging baffle distance from the fluid level equal to 0.125 of the fluid height, and its outer diameter-to-digester diameter of 2/3.

Purpose: There is a direct relationship between the number of machinery in any industrial problem, their costs of purchase, and the space they occupy. Accordingly, due considerations about the constraints are required in space and budget. Workshop production problems under uncertainty conditions including more than two pieces of machinery are regarded as NP-hard. Therefore, using common research in operation techniques, one cannot embark upon modelling and finding the optimal answer for such problems. Hence, this study aims to propose a conceptual mathematical model for the problem of industrial production. The research problem involves cost limitations while working out the solution through simulation studies. Design/methodology/approach: First a mathematical model has been proposed for workshop production with cost and space constraints. Then by enlisting the help of Arena software, 14 simulation models have been designed for machine ‘m’ and work ‘n’ of different production sequences. Finally, by solving a numerical model example, the problem of nearing the optimal condition of the problem, considering minimizing the objective function of the tardiness and earliness costs, has been resolved. This measure has been adopted to determine the optimal number of machinery, considering work prioritization laws in work cells by executing the model and scatter search algorithm and designing relevant experiments. Findings: Solving a numerical example considering the default number of machinery and the LPT optimization method resulted in the best answer when the total tardiness and earliness costs were intended. In contrast, in the case of increasing the number of machineries, both the LPT (longest process time) and CR (critical ratio) methods led to the best answers with the help of dispersed search algorithms in simulation studies. The results included the cost and space constraints. Research implications: One of the prominent features of simulation is that with the minimum distance between the problem and the real world (not removing the limitations and complexities for solving the problem, which is usually done by removing the facts in other problem-solving methods, to reduce the complexity and the possibility of solving the problem) problem-solving and decision- making scenarios are applied. This helps managers in decision-making. Most of the time, the physical study of systems is very difficult and costly, or even impossible; therefore, an alternative model should be used to study the system. Originality/value: A careful investigation of previous research and relevant literature reveals that none of the studies in the literature have simultaneously dealt with the problem of the number of machinery in conditions of cost and space limitations. Therefore, the present study incorporated the mentioned constraints in the scheduling problems and adopted simulation problem-solving methods. It resulted in delving and enriching the subject further. [ABSTRACT FROM AUTHOR]

The aim of this research is to optimize the combined system consisting of solar photovoltaic panels and a diesel generator as two independent decision variables and five responses or the optimization objective function, including system electricity consumption, system gas consumption, diesel fuel consumption, the amount of bio-pollutants reduction. The environment and the return on investment are dependent variables of this research; an optimization method is used to achieve the best possible design in Transis software, and the response level method is used to find the best combination of selected factors in the system. The main purpose of the response surface is to estimate and predict the effect of independent variables on the dependent variable after implementing the system in Transis software and optimizing by the response surface method. The results showed that the area of photovoltaic panels in the optimal state is equal to 11770 square meters and the optimal diesel generator power is equal to 984 kW. Also, in optimal conditions, the system has the best performance, and the combined utility is equal to 0.740, which indicates that the performance of the optimal system is close to the ideal state, i.e., one. In terms of energy consumption, the optimal system achieves a total electricity consumption of 1026860 kilowatts, a total gas consumption of 205182 cubic meters, a total diesel fuel consumption of 1338030 liters, an amount of environmental pollutants of 3693.23 kilograms, and an investment return period of 1.679 years. Also, strategy one, which includes the direct purchase of the total electricity demand from the grid and the direct sale of the total electricity produced by the system to the grid, seems to be economically more economical. The results of the simulation showed that the investigated combined system is a suitable solution to simultaneous energy production. It is electric and thermal, and it is capable of producing electric and thermal energy throughout the year. [ABSTRACT FROM AUTHOR]

Accurate simulation of geometrical properties of fractures is an important goal in rock engineering. One of the most capable methods for simulating the random nature of geometrical properties of fractures is Discrete Fracture Network (DFN) random modelling, which presents the heterogeneous nature of fractured rock mass with statistically defined geometrical properties. Up to now, all properties of fractures such as location, shape, orientation, size (persistence), spacing, and opening of joints have been simulated and applied in 3D DFN modelling. In this research, a statistical solution based on Kernel’s non-parametric distribution is used for simulating roughness. Through this method, even those geometric properties of fractures which do not have their own specific distribution functions can be simulated. After simulating the roughness value, the roughness geometry should also be simulated in a way that evokes the roughness value. Therefore, in order to simulate the surface of fractures in this research, the DRS method is applied in 2D and then, developed into 3D. At the end, simulation of discontinuity’s roughness is added as a separate package to DFN-FRAC3D computer program. DFN-FRAC3D computer program, as one of the most capable tools in this field, is able to develop a 3D fracture network block model by using the surveyed data and then simulating geometrical properties of the fracture; thus, by applying the results of this research in this compute software, all geometrical properties of fractures can be simulated. Finally, in order to explain the results of this research, outcomes of DFN-FRAC3D computer program for both with and without applying the roughness property on DFN are compared. [ABSTRACT FROM AUTHOR]

Introduction Climate change is one of the most important issues in the world in the 21st century which affects various sectors of agriculture, forestry, water and financial markets, and has serious economic consequences (Reidsma et al., 2009). In recent years, the management of agricultural water consumption has always been considered as one of the important issues in water resources management. Koochaki and colleagues (Koochaki and Kamali, 2006) by evaluating the climatic indicators of Iran's agriculture showed that during the next 20 years, the average monthly temperature will increase in almost all regions of the country, and the increase in evaporation and transpiration is one of the most important consequences of this warming. Simulated climate parameters can be obtained through different general GCM atmospheric models. Due to the low spatial resolution of these models, its output should be downscaled using dynamic or statistical methods. Materials and Methods The LARS-WG model predicts meteorological variables for a period of time in the future by using a series of basic and fine-scale meteorological data, output of one of the GCM models. Research has shown that the LARS-WG model has the necessary accuracy for this task. Calculating the amount of evapotranspiration and yield of very complex plants are time-consuming and dependent on spending a lot of money and limited to the tests performed, the shortness of the test time and also the limitation in the number of scenarios that are checked by the test. Therefore, plant models are considered and evaluated by researchers. The AquaCrop model has demonstrated commendable accuracy in various regions of Iran and globally for forecasting plant growth, water consumption efficiency, and evapotranspiration requirements. These predictions hold significant potential for optimizing irrigation strategies across different agricultural settings. AquaCrop is one of the applied agricultural models that was obtained from the modification and revision of FAO publication No. 33 by prominent experts from all over the world. In this study, the values of green water footprint of winter wheat plant (Pishgam) were estimated in climatic conditions obtained from LARS-WG model and DKRZ database under scenarios 4.5 and 8.5 and at different planting dates (15 October, 1 November, 15 November, 30 November and 15 December), in the next 4 periods (2021-2040, 2041-2060, 2061-2080 and 2081-2100) and by Aquacrop model. Results and Discussion The results showed that if planting date is on October 15, in the climatic conditions obtained from the LARSWG model and under scenarios 4.5 and 8.5, in all future periods, the footprint of green water will increase compared to its value in the base period, and if planting is the rest of the dates, in each of the next 4 periods, the average green water footprint will decrease compared to its value in the base period. The results obtained for the DKRZ database show that the green water footprint attained for the dates of cultivation and periods investigated in scenarios 4.5 and 8.5 does not have a particular trend. On the planting dates of October 15 and November 1 for the periods of 2061- 2080 and 2081-2100, the green water footprint will decrease and on the other three dates (15 November, 30 November, and 1 November) for these periods, there will be an increasing trend. On 15 December, for the DKRZ database, in both scenarios defined for all periods, an increase in green water footprint compared to the base period is reported. However, in the period of 2081-2100 in scenario 8.5, a decrease compared to the base period will be observed. The highest amount of green water footprint in all these periods and models for the period 2041-2060 under the climatic conditions of the DKRZ database in scenario 4.5, if the planting date is 15 October, it is estimated that the amount of water consumed is equal to 4272 cubic meters per ton with a standard deviation of 5018 cubic meters per ton is predicted. The lowest footprint of green water for the period 2081-2100 under the climatic conditions obtained from the LARS-WG model in scenario 8.5, if the planting date is on 15 December, is reported to be 232 tons per hectare with a standard deviation of 52.3 tons per hectare. [ABSTRACT FROM AUTHOR]

AbstractIntroduction: An artificial neural network (ANN) is a powerful data-driven tool capable of learning the linear and nonlinear relationships governing different systems. However, determining the best-performing algorithm in terms of convergence speed and accuracy for a particular problem is still a fundamental challenge for users of artificial neural networks.Methods: We investigated the most effective algorithm among widely used processes to simulate and estimate nonlinear water quality parameters. For this purpose, we constructed 42 models combining artificial neural network topology (single or multilayer) and training processes. The quality parameters’ data acquired at 107 wells throughout the aquifer of Qorveh-Dehgolan plain were used for training (data from 1996 to 2013) and to test (data from 2014 to 2016) each model.Findings: The results showed that artificial neural networks with a hidden layer that benefits from the optimal number of neurons could simulate the aquifer behavior with high accuracy and in less time. Also, increasing the number of hidden layers while increasing the response accuracy increases the number of optimal network neurons and the duration of the problem analysis. Finally, artificial neural networks based on the Broyden-Fletcher-Goldfarb (BFG) method had the highest efficiency in simulating aquifer behavior, although the performance of the Levenberg Marquart (LM) method is very close to BFG. BFG is more efficient than LM due to its lower Mean Square Error and Standard Deviation (3.46 and 3.09, respectively).

Adequate comprehension of the challenges and influential elements of the religious tourism sector is necessary for its positive development. In this regard, the present research was conducted to present a model that can simulate the development of religious tourism in Yazd province by considering the influential factors. A mixed method (qualitative and quantitative) was carried out in this study, and the research method was applied in terms of purpose and descriptive-analytical in terms of nature. The statistical population of this research included 12 theoretical and experimental experts. Questionnaires and interviews were used to collect data with non-random and purposeful sampling. In the qualitative section, the main and sub-categories of religious tourism development in Yazd province were identified using the thematic analysis method. Then, in the quantitative part, the identified influential factors were prioritized using the interpretive structural modeling (ISM) method. Finally, the criteria obtained from the qualitative research analysis were simulated using the agent-based modeling (ABM) method. Based on the research results, 35 initial codes, 12 sub-themes, and six main themes (religious tourism policies, economic, infrastructure, environmental empowerment, marketing, and cultural) were identified. Religious tourism policies were recognized as the most influential factor. Also, the results showed that at the end of the simulation period, the rate of religious tourism development in Yazd Province had reached 70%. Based on the findings of this study, it is recommended that tourism managers give particular attention to the influential factors that have been identified as having a significant impact on the development of religious tourism.IntroductionTourism is one of the most significant social and economic phenomena of the modern era, a valuable instrument for achieving development. Religious tourism is among the oldest forms of tourism and the largest market segment for travel. Today, this type of tourism has been able to place itself in the context of global tourism due to its unique structural and functional features, so its sphere of influence covers the whole world (Ghaffari et al., 2022). The vast country of Iran also has blessed places, shrines, and many holy places, which has made it favorable for the development of religious tourism. For this reason, knowing the capacities of the country's provinces is one of the most important influencing factors, among which is Yazd Province, which also has excellent potential for developing religious tourism. Therefore, according to the explanations presented above, this research aims to present a model that can simulate the development of religious tourism in Yazd Province by considering critical factors with a future research approach so that it can play a positive role in helping managers develop religious tourism.Literature ReviewOne of the main types of tourism created to travel to religious locations and fulfill religious obligations is religious tourism (Jamshidi et al., 2019). Religious tourism can be considered the most sustainable type of tourism because pilgrimage and religious tourism have their roots in religious beliefs, and in its specialized sense and beyond dependence on time and leisure, it is an essential factor of human geography in shaping travel, creating concentration and numerous cultural perspectives (Sharma et al., 2023).MethodologyThe current applied study employed a mixed methodology, combining qualitative and quantitative methods with a descriptive-analytical focus regarding the nature of the research. Managers of religious tourism and university faculty members made up the statistical population for this study. Due to the high number of experts in this field and the possibility of not knowing all theoretical experts (university faculty members) and practical experts (tourism managers) by the researcher, sampling by snowball method was continued until reaching theoretical saturation (N=12). Interviews and questionnaires were used to collect research data. The steps of this research are as follows:In the first step, the thematic analysis method, one of the qualitative methods, was used to identify the factors affecting the development of religious tourism in Yazd Province. In the second step, the ISM method was used to prioritize (level) the factors. In the third step, the development of religious tourism in Yazd Province was simulated.In the qualitative part of the research, the validity of the interview using the Q-sort method and its reliability using Cohen’s Kappa index were checked and confirmed. In the quantitative part of the research, the validity of the questionnaires using the relative content validity method and its reliability based on the test-retest method was evaluated and confirmed. Also, experts’ opinions were obtained during face-to-face interviews to check the validity of the Agent-Based Model.ResultsBased on the research results, 35 initial codes, 12 sub-themes, and six main themes were identified. Due to having the highest driving power and the lowest dependence power, the factor of religious tourism policies was placed in the fifth level of the ISM. It was recognized as the most influential factor. Also, the results showed that at the end of the simulation period, the rate of religious tourism development in Yazd Province was 70%.DiscussionAlthough with different degrees, all six factors are essential for the development of religious tourism, and their prioritization was only due to greater understanding and recognition of more influential factors. The results showed that at the end of the simulation period, the rate of religious tourism development in Yazd Province was 70%. Therefore, applying the discussed factors can influence the growth of tourism development and be used in the policies and actions of the relevant managers. In line with the results of the religious tourism development simulation, as a general policy, the managers of tourism organizations in Yazd Province should implement the current research model. In this way, the research model is implemented in a specific period, and its ability to improve the effectiveness of religious tourism development is measured if, in practice, the research model leads to development in future periods.ConclusionThe results of this study indicate that managers of religious tourism should incorporate the factors identified into their development plans and plan the long-term horizons of religious tourism around these factors. It is essential to remember that merely incorporating these elements into the plans for development is unlikely to result in complete development; as such, it is imperative to address this problem from a fundamental standpoint and consider other significant influencing factors that are occasionally external to the current system.

AquaCrop model was developed to simulate crop response to water consumption and irrigation management. The model is easy to use, works with limited input, and has acceptable accuracy. On the other hand, there are different methods for estimating evapotranspiration, whose performance is different in various climatic conditions. The purpose of this research is to investigate the effect of different methods to estimate evapotranspiration of the reference plant in various climates of Iran on estimating the yield of maize and wheat using AquaCrop. To fulfil the experiment, 40-year meteorological data (1980-2020) of five cities of the country (Urmia, Mashhad, Rasht, Qazvin, and Yazd) were used. First, evapotranspiration was estimated using the FAO-56 and five temperature and radiation methods daily. Then, the yield value of these two plants was simulated by AquaCrop and compared with the FAO-56 by error statistical criteria determination coefficient (R2), normal root means square error (NRMSE) and Nash-Sutcliffe index (NS). According to the results, among the two temperature methods Blaney-Criddle method with the NRMSE is in the range of 0-20%, R2 and Nash-Sutcliffe are, close to the optimal value of one for maize and wheat in parameter simulation are acceptable. About radiation methods, the Priestley-Taylor and the Turc methods in simulation of maize yield. Also about radiation methods for wheat, the Turc and the Makkink method for simulation of yield are desirable.

Background and Aim: Operating room students have experienced anxiety for various reasons that affect their educational process such as stitching. Due to the effective role of simulation in improving the education of students, this study was conducted with the aim of the effect of animal skin suturing simulation on the skill level and anxiety level of operating room students. Materials and Methods: In this study, 30 people (75%) of the samples were aged 18-25 and 10 people (25%) of them were 26-35. 10 people (25%) were men and 30 people (75%) were women. Also, in terms of marriage, 7 people (17.5%) were married and 33 people (82.5%) were single. The present quasi-experimental study was performed with a two-group plan before and after with 40 operating room students of Shahrekord University of Medical Sciences (Shahrekord, Broujen) in 2020. The samples were randomly assigned to two test and control groups using the method based on the purpose of selection. Data were collected before and after the intervention by Spielberger Anxiety Tool and Suturing Skills Questionnaire and analyzed by SPSS software and descriptive and analytical statistical tests (Chi-square test, t-test, paired t-test). Results: The t-test test showed that the mean of obvious anxiety in the intervention group (36.35±10.22) and the control group (41.15±7.92) after the intervention was not significant (P=0.346). Also, the mean hidden anxiety in the intervention group (36.65±10.47) and the control group (38.65±6.13) had no significant difference (P=0.089). The t-test test showed that there was a significant difference in sewing skills after the intervention in the two intervention groups (28.2±58.22) and the control group (23.42±3.12) (P=0.04). Conclusion: The texture of suture mannequins is very different from human skin in terms of consistency, and it does not convey the same feeling of sutures on natural skin to students. On the other hand, it is easier to enter and exit and move the needle and thread in the sheepskin, and this provides the students with the possibility of stitching more easily. Therefore, it is suggested to buy and prepare sheep skin for training students and teach them stitches on it.

One of the widely used methods to remove acid gas is surface adsorption using solid molecular sieves. The advantages of H2S adsorption with molecular sieves can be seen in the reversibility of the process, chemical and thermal stability, no secondary product, and the high selectivity of H2S in these adsorbents. Therefore, in this study, Aspen-Adsim software was used to investigate the effect of various parameters on adsorption of H2S on 4A molecular sieve adsorbent. The desired bed is considered vertical and one-dimensional. The results related to the breakthrough curves and saturation time showed that the maximum amount of adsorption occurred in the early times. As the adsorption time increases, the rate of H2S adsorption decreases. The results of investigating the effect of operating parameters showed that increasing the tower pressure from 90 to 104 bar reduces the output H2S content by 21 ppm. Also, reducing the feed temperature showed significant results on the amount of H2S absorption and the reduction of the output H2S content, so that for every one degree of temperature reduction in the feed gas temperature, the dew point temperature of the output was reduced by 1.6°C. Due to the high amount of absorbent and its heat capacity compared to the amount of H2S in the gas phase, a slight temperature increase occurs along the tower so that the difference between the inlet and outlet temperatures does not exceed 1˚C. According to the breakthrough curve, it was also determined that after 7920 seconds, the entire bed of the tower is saturated with H2S molecules. [ABSTRACT FROM AUTHOR]

Introduction and Objective: Excessive exploitation of groundwater in Damghan plain has caused many problems such as land subsidence in the study area, along with the reduction of the groundwater level and the loss of a part of the aquifer and the reduction of the specific yield of aquifer. Therefore, in order to control the withdrawal from the aquifer and as a result to reduce the drop in the groundwater level in that plain, it was investigated. For this purpose, in this research, the mathematical modeling of Damghan Plain was done for the optimal management of exploitation and providing the optimal cultivation pattern in order to save water consumption with the focus on controlling the groundwater level and limiting land subsidence. Material and Methods: In this research, Damghan plain aquifer was first simulated using MODFLOW mathematical model. Then, taking into account different scenarios of aquifer exploitation, using the NSGAII genetic algorithm, optimization of multi-objective exploitation of water resources and optimal management of water supply and demand in the agricultural sector was done. The results of the model were evaluated based on statistical error parameters to predict the ground water level. Results: The results of this research showed that the MODFLOW numerical model was able to simulate the groundwater level of the studied plain well in steady and unsteady conditions in the calibration and validation stages. Also, the results of the simulation-optimization algorithm showed that by changing the cultivation pattern and also by changing the type of irrigation system from traditional to modern irrigation and as a result of increasing the irrigation efficiency (90 percent), the average drop of the groundwater level of the plain can be reduced. It reduced the groundwater level of the study area from 0.49 m in existing conditions to 0.07 m in optimal conditions. This will compensate the deficit of the aquifer from 31.90 to 5.1 million cubic meters per year. Conclusion: Using management strategies such as changing the cultivation pattern, increasing irrigation efficiency, and also by controlling surface water and injecting it into groundwater, the process of reducing the groundwater level in the studied plain can be completely controlled and provided 100% of the water needs of the plain. [ABSTRACT FROM AUTHOR]

Introduction and Objective: In recent years, the use of SWAT model has been used as a common tool for simulating flow rate, sediment production and evaluating different scenarios to reduce sediment production and runoff. The current research also aims to evaluate the changes in runoff and sediment under the influence of irrigation water reduction, using the SWAT model in the irrigation and drainage network of Dez River. Material and Methods: Recalibration and validation of the model for simulating runoff (Shoshtar, Arab Asad, Shavor Bridge, Harmeleh and Bamdej stations) and sedimentation (Shoshtar, Shavor Bridge, Harmaleh and Bamdezh stations) using statistical data from 1995 to 2012 and 2013 It was done until 2017. The simulation results were also evaluated using R2 and NSE coefficients. Then the scenarios of 10, 20 and 30% reduction of irrigation water were introduced to the model to evaluate their effect on runoff and sedimentation of the study area. Results: The results of model evaluation using R2 and NSE coefficients indicate the appropriate performance of the model in simulating the mentioned parameters. The results of the evaluation of scenarios of 10, 20 and 30 percent reduction of irrigation water show that applying the 10 percent scenario had the least impact on the amount of sedimentation. On the other hand, applying the scenario of 30% reduction of irrigation water will increase the runoff (30-60%) due to the reduction of the collection of runoffs for irrigation purposes and consequently the increase of the amount of sediment (20-50%) in the irrigation and drainage network of Dez River. The obtained results indicate that the increase in the amount of runoff in the basin, especially in the rainy season, has caused an increase in erosion and consequently an increase in sediment in the basin. Therefore, it can be stated that the time of applying the scenario and the characteristics of the soil in the area are the most effective components on the amount of runoff and sediment in the basin. Conclusion: According to the results obtained, according to the effect of runoff on erosion and the amount of sediment in a watershed, it is necessary to use methods to control and reduce runoff, such as watershed operations, structures to contain and store runoff, and use modern irrigation methods to prevent soil erosion. Because traditional irrigation methods, in addition to water losses, cause a decrease in soil fertility and an increase in sediment in the drains of agricultural lands. In lands that are irrigated by traditional methods, sometimes the amount of water used is more than the infiltration capacity of the soil. This causes erosion of the surface layer of the soil by the runoff from water accumulation. Therefore, agricultural management approaches in basins prone to soil erosion should be focused on modern irrigation methods with minimum runoff and drainage output. [ABSTRACT FROM AUTHOR]

Due to the upcoming climate threats, challenge of water shortage and its impact on the food security of the growing population in Iran, the optimal use of soil and water resources is very important. With the development of remote sensing technologies, free access to a variety of field data has become widely available, which can be used to reduce the uncertainty of simulation models. The aim of this study was to simulate actual evapotranspiration (ETa) and rice crop coefficients (Kc) during its growth stages using the SWAP model updated with satellite data and evaluate the accuracy of the results with/without updating. This research was conducted at the National Rice Research Institute of Iran in Rasht in the year of 2017. Based on the obtained results, total ETa measured by lysimeter and simulated by SWAP model with and without updating were 395.4, 373.2 and 363.6 mm, respectively. The average crop coefficients during the growth stages of vegetative, reproductive and ripening were estimated as 1.13, 1.49, 1.21, respectively. The crop coefficients for the proposed stages estimated by SWAP model without using satellite data were 1.02, 1.39, 1.04, respectively. After updating with satellite data, the crop coefficients were modified as 1.05, 1.43 and 1.07, respectively. Finally, the statistical analysis indicated that the SWAP model has a reasonable performance in estimation of ETa (RMSE=0.89; EF=0.98; R2=0.74) and rice crop coefficients (RMSE=0.53; EF=0.96; R2=0.63). The results indicate that the SWAP model combined with satellite data improved the accuracy of ETa estimation (RMSE=0.75; EF=0.99; R2=0.86) and rice crop coefficient (RMSE=0.40; EF=0.99; R2=0.74) at field scale. [ABSTRACT FROM AUTHOR]

AbstractIntroduction: Managing and monitoring the quantity of groundwater resources, along with assessing the intensity and direction of their flow, constitute fundamental principles for sustainable utilization of such resources. This study aims to utilize GMS software to simulate and analyze groundwater resources in Nobandegan plain, Fars, Iran. The objectives include deriving long-term variations in these resources, evaluating the water balance, and ascertaining the direction and intensity of groundwater flow.Methods: Data related to pumping wells, observation wells, topographic features of the plain, and rainfall, among other pertinent information, were gathered using reports from Fars Regional Water Company. Subsequently, a comprehensive hydraulic model was developed to simulate, calibrate, and validate the groundwater flow within the plain. Following this, the overall trajectory and flow intensity within the plain aquifer were simulated.Findings:The hydrograph pertaining to groundwater within the plain reveals a ~50 meter water table drop over the course of the investigation period. The predominant groundwater flow within this plain is directed from the northern mountainous regions toward the southwestern expanses. The maximum hydraulic head is recorded at approximately 1283 meters, while the minimum stands at 1175 meters above mean sea level, indicating a 108-m drop in the aquifer water table elevation. The simulation results of the groundwater flow within the plain reveal an increased velocity in the northern regions and, to some extent, in the southern reaches of the plain. The findings of this study may be employed for groundwater quality studies on this plain.

IntroductionAs a result of droughts and human interventions, the Urmia Lake basin has faced increasing unallowable withdrawal of water resources and environmental requirements share. Over the past two decades, a series of natural and human factors have gradually changed the water balance of Lake Urmia. In addition, decreases in precipitation have been the main reason for the gap between water resources and increasing water consumption in the basin, especially in the agricultural sector. In order to deal with the Urmia Lake catastrophe, a working group was formed for the Urmia Lake restoration. The working group planned and proposed solutions to reach the ecological level of the lake in three stages within 10 years through in-basin water supply and inter-basin water transfer programs. The present research, while investigating the solutions for providing the environmental water requirement of Lake Urmia using the Analytic Hierarchy Process (AHP) method, changes in the water volume of Lake Urmia in the scenarios of water transfer from the surface flow of each entering river in the lake in the years of high intensity severe to mild drought, the implementation of the approvals of the national working group to restore Urmia Lake, including a 40% reduction in agricultural consumption from the sources of dams and other rivers without dams, releasing water from dams in non-agricultural seasons, transferring water from urban wastewater and finally transferring water between the Zab basin has quantitatively been evaluated using the MODSIM simulation model. This research will help to predict the amount of transferable water in drought, normal, and drought conditions and to plan the water accounting of the Urmia catchment. Materials and MethodsThe watershed of Lake Urmia, located in the northwest of Iran, with an area of 51,876 square kilometers, is one of the six main watersheds of the country, which is located between the provinces of West Azerbaijan, East Azerbaijan, and Kurdistan. In terms of its territorial location, the catchment area of the Zab Kochak river is in the western part of the international border with Iraq and in the catchment area of the Western Border River basin. A significant part of the Zab watershed (source of water transfer) is located outside the country and in Iraq. The route of water transfer between the basins of the Zab basin is from the place of two dams, Silweh and Kaniseeb, which after entering the Godarchai river will eventually enter the Urmia lake water body. The different solutions from the working group’s approval have been selected for developing six scenarios. Then, the Scenarios have been evaluated by the MODSIM model and AHP analysis method. The scenarios are including reducing agricultural expenses from dams in operation, saving by reducing agricultural expenses from other rivers, transferring water between basins, transferring effluent to Lake Urmia, releasing water from dams in operation to Lake Urmia. A hierarchical structure was developed with the aim of evaluating solutions to save Lake Urmia through internal sources and transfer water sources. In this structure, there are four criteria of climate, consumption, economic-social, and environmental status. Sub-criteria of drought, surface water extraction from rivers, extraction from dam sources, extraction from groundwater resources, development of agricultural lands, and ensuring the sustainability of river flow. and providing solutions for inter-basin water transfer, a 40% reduction of agricultural water consumption from surface water sources of rivers and dams, wastewater transfer, and a 40% reduction of agricultural water consumption from underground water sources in Expert Choice software, were analyzed. Results and DiscussionIn order to evaluate the effectiveness of the defined scenarios, the simulation model was implemented for future conditions during the next 10 or 15 water years for different scenarios. The results show that the implementation of scenarios 4 and 6, will bring the largest increase in the volume of lake water, of course, with the occurrence of precipitation and proper input. Regarding the results of scenario 1 and the sameness of its results in two periods of 10 and 15 years, it can be stated because in the definition of the scenario, it is a dry year and the amount of evaporation of the lake water is more than the amount of input into the lake. There has been no increase in the lake's water volume for several consecutive years. Also, the results show that in wet and dry years, if the maximum transfer flow scenario is implemented, we will not have a flow from the Zab river to the downstream side, and it will not be accepted from the hydrodiplomacy point of view for the neighboring country in terms of influencing the use of the Dukan dam. Therefore, water diplomacy solutions are needed to reduce environmental threats. Therefore, the issue of transferring water from the Zab basin to Lake Urmia cannot be considered guaranteed in the long term due to the impossibility of reducing the outflow of water from the country to zero. ConclusionIn conclusion, in order to evaluate all scenarios to satisfy the environmental needs of Lake Urmia shows that the protection of the lake requires correcting the mistakes of the path traveled in the current and past years, and preferably with a 40% reduction in agricultural consumption from dams and other rivers, groundwater in the basin is the main supplier of Urmia Lake. Inter-basin water transfer is the next priority because the source catchment is able to transfer water in the face of droughts due to the reduction of runoff and related challenges as well as its negative economic and social effects. It will not be as much as predicted and included in the program (600 million cubic meters per year). In addition to that, water diplomacy is necessary in terms of impacting downstream transboundary uses and reducing environmental threats. The results of this research and the high costs of water supply requirements, in parallel with the investigation of controversial options and costs of inter-basin water transfer projects, macro-management of water resources towards demand and consumption management and water saving programs and the modification of water consumption patterns and the legality of the behavior of water users from within the catchment area of Lake Urmia should be low-cost, sustainable and reliable solutions.

Background and purpose: The emergency department of a hospital is akin to its heart, where smooth operations can save many lives. This research aims to reduce patient waiting time in the emergency department using simulation techniques. Methods: This descriptive-analytical study was conducted cross-sectionally in 2019 at the Qaim Hospital in Mashhad. The study focused on accurately modeling the patient flow process in the emergency department using simulation techniques and Arena software. Key performance indicators such as patient waiting time, number of discharged patients (system output), length of stay, resource efficiency, and improvements in the emergency department were evaluated. Results: The simulation model's results indicated that the laboratory, specialist doctor examination, and pharmacy departments had the longest waiting times in this department, respectively. Conclusion: To reduce patient waiting times and improve conditions, the most effective and cost-efficient solution is to add a laboratory technician to the laboratory department during the [15-23] shift at Qaim Hospital in Mashhad, which is projected to decrease patient waiting times by 66 minutes.

Awareness of water resources status is essential for the proper management of resources and planning for the future due to the occurrence of climate change in most parts of the world and its impact on different parts of the water cycle. Hence, many studies have been carried out in different regions to analyze the effects of climate change on the hydrological process in the coming periods. The present study examined the effects of climate change on surface runoff using the Atmosphere-Ocean General Circulation Model (AOGCM) in Khomeini Shahr City. The maximum and minimum temperatures and precipitation of the upcoming period (2020-2049) were simulated using a weighted average of three models for each of the minimum and maximum temperatures and precipitation parameters based on the scenario A2 and B1 (pessimistic and optimistic states, respectively) of the AOGCM-AR4 models. The LARS-WG model was also used to measure the downscaling. The HEC-HMS was used to predict runoff. The effects of climate change in the coming period (2020-2049) compared with the observation period (1971-2000), in the A2 scenario, the minimum and maximum temperatures would increase by 1.1 and 1.6 Degrees Celsius, respectively, and the precipitation would decrease 17.8 percent. In the B1 scenario, the minimum and maximum temperatures would increase by 1.1 and 1.4 degrees Celsius, respectively, and the precipitation would decrease by 13 percent. The results of runoff were different in the six scenarios in the way the most runoff reduction is related to the scenario of fixed land use and scenario A2 (22.2% reduction), and the most increase is related to the scenario of 45% urban growth and scenario B1 (5.8% increase). So, according to increase urban texture in the future and consequently enhance the volume of runoff, this volume of runoff can be used to feed groundwater, irrigate gardens, and green space in the city.

In a polywell reactor, it is critical to have a stable and energetic virtual cathode that is necessary to accelerate ions and create fusion interactions. Increasing the confinement time of virtual cathode electrons is of critical importance in the performance of polywell reactors. In this paper, COMSOL Multiphysics software was used to perform a three-dimensional numerical simulation in order to investigate the impacts of effective variables of a polywell on the virtual cathode. The findings indicated that the confinement time depends on the distance between the coils, coil radius, coil current, and the kinetic energy of the injected electrons. In addition, by using the simulation results, the dependence of the mentioned parameters on the confinement time is obtained, and then a mathematical model was developed.

IntroductionWheat (Triticum aestivum L.) has become very important as a valuable strategic product with high energy level. The importance of investigating environmental stresses and their role in predicting and evaluating the growth and crops yield is essential. A wide range of plant response to stress is extended to morphological, physiological and biochemical responses. Considering the rapid advancement in computer model development, plant growth models have emerged as a valuable tool to predict changes in production yield. These growth simulation models effectively incorporate the intricate influences of various factors, such as climate, soil characteristics, and management practices on crop yield. By doing so, they offer a cost-effective and time-efficient alternative to traditional field research methods. Material and MethodsThis research was conducted in the research farm of Varamin province, which has a silty loam soil texture. The latitude and longitude of the region are 35º 32ʹ N and 51º 64ʹ E, respectively. Its height above sea level is 21 meters. According to Demarten classification, Varamin has a temperate humid climate. The long-term mean temperature of Varamin is 11.18 ° C and the total long-term rainfall is 780 mm. In this study, in order to simulate irrigated wheat cv. Mehregan growth under drought stress, an experimental based on completely randomized blocks (CRBD) including: non-stress as control (NS), water stress at booting stage (WSB), water stress at flowering stage (WSF), water stress at milking stage (WSM) and water stress at doughing stage (WSD) with three replications during growth season 2019-2020 was carried out in Varamin, Iran. Crop growth simulation was done using SSM-wheat model. This model simulates growth and yield on a daily basis as a function of weather conditions, soil characteristics and crop management (cultivar, planting date, plant density, irrigation regime). Results and DiscussionBased on the results, the simulation of the phenological stages of irrigated wheat cv. Mehregan under water stress condition using SSM-wheat model showed that there was no difference between observed and simulated values. Summary, the values of day to termination of seed growth (TSG) were observed under non- stress, stress in the booting stage, flowering, milking and doughing of the grains, 222, 219, 219, 221, 221 days, respectively andsimulation values with 224, 221, 220, 221, respectively. However, with their simulation values, there were slight differences with 224, 221, 220, 221, respectively. Acceptable values of RMSE (11.7 g.m-2) and CV (3.5) indexes showed the high ability of the SSM model in simulating the grain yield of irrigated wheat cv. Mehregan under water stress conditions. Grain yield values were observed in non-stress conditions of 5783, water stress in booting, flowering, milking and doughing of the grain stages in 5423, 5160, 5006 and 5100 kg. h-1, respectively. While the simulated values were 5630, 5220, 4920, 4680 and 4880 kg. h-1, respectively. Based on the findings, observed and simulated values of leaf area index (LAI) were observed under water stress condition in the booting, flowering, milking and doughing of the grain stages (4.3 and 4.47), (4.33) and 4.46), (4.4 and 4.57) and (4.4 and 4.58) cm-2, respectively. Evaluation of the 1000-grain weight of irrigated wheat cv. Mehregan under the water stress showed that the SSM model was highly accurate. RMSE (4.6 g.m-2) and CV (1.8) values indicate the ability of the SSM model to simulate the 1000-grain weight of irrigated wheat cv. Mehregan. Also, the simulated values of the harvest index were 34.7 % in non-stress conditions, which decreased by 6 % compared to the observed value. Harvest index values were observed under water stress conditions in the in the booting, flowering, milking and doughing of the grain stages in 30.2, 29.3, 29.9 and 29.5 %, respectively. Compared to its observed values, it was reduced by 3, 3.5, 5, and 5.5 %, respectively. ConclusionBased on the findings, the slight difference between the observed and simulated values demonstrates the SSM model's capability to accurately capture water stress impacts on the phenological stages, grain yield, and yield components of irrigated wheat cv. Mehregan during critical growth stages, including booting, flowering, milking, and doughing. The results indicate that the SSM model is effective in simulating wheat growth under water stress conditions, showcasing its potential as a valuable tool for modeling irrigated wheat growth. The model's ability to account for water stress and its effects on various growth parameters makes it a reliable and efficient tool for predicting crop performance in water-limited environments.

The main goal of this research is management and comprehensive planning to optimally use the available water resources of Karde dam, using the WEAP model, and supply the demand in the agriculture and drinking sectors, considering the growth of their needs in the future. For this purpose, Karde Dam was first simulated in the environment of WEAP model and the model was implemented for basic conditions and seven different scenarios of development plans. According to the results obtained for the reference scenario, this dam alone does not respond to all the needs defined completely in the horizon of the project, except by applying management measures in the form of scenarios, which will reduce water consumption in different sectors of demand. Among these scenarios are demand management, increasing irrigation efficiency, using both at the same time in one scenario, changing or reducing the cultivation pattern, etc. As a result, by applying the low irrigation scenario and increasing the efficiency at the same time, it is possible to reduce the lack of water demand by 37% compared to the reference scenario, and the reservoir storage volume in this scenario increases by 25% compared to the reservoir storage volume in the reference scenario.

There is good reason to expect crude oil prices to follow nonlinear models. However, previous research has considered the linear assumption to investigate the existence of a unit root. Unit root linear tests such as ADF, PP, and KPSS are provided for linear models. These tests are not suitable for nonlinear time series. Because the model deviation from the linear state may be considered as a random permanent deviation. The purpose of this article is to test the nonlinear unit root of crude oil prices, specifically Brent and WTI oil in the period 2019-2020 daily. For several decades now, various classes of nonlinear models have been introduced. These models introduce a wider range of dynamics than linear models in time series. A special type of these models that economists pay attention to are TAR models. In these models, as in linear models, valid statistical analysis requires distinguishing between the deterministic trend and the stochastic trend. In this study, the Bayesian unit root test for the general SETAR (1) model has been used with respect to the necessary and sufficient conditions for the maintenance of SETAR processes based on the article by Petrocyl and Wolford (1984). A nonlinear unit root test was performed using Bayesian validity interval. The results show that Brent crude oil prices in both regimes contain a unit root that is consistent with similar findings for the production or consumption of crude oil..

In this paper, we study a production system that is subject to network failures and produces perishable goods. We assume that the system has preventive and corrective maintenance activities and can return defective items for rework. Our objective is to find the optimal production rate that minimizes the total cost of production, inventory, spoilage, and maintenance over a long planning horizon. We consider the uncertainty of machine failures and use discrete event simulation and ARENA.14 software to estimate the performance measures of the system. We also use data envelopment analysis to evaluate the efficiency of the system and identify the best scenario. The results show the effectiveness of our proposed model.

Due to the lack of fresh water, production of potable water is one of the important issues for mankind. Capacitive deionization is one of the methods that has recently attracted the attention of researchers due to its simplicity, low price and low energy consumption. The main challenge of this method is high energy consumption at high water concentrations. Therefore, this paper aims to investigate the effect of different effective parameters to improve the system performance. These parameters include feeding voltage, process time, electrode surface area and its capacitance value, overall transfer coefficient, volumetric flow rate and concentration of the feed water, and micropores’ volume, whose effects on energy consumption and number of cycles required to produce potable water are investigated. Results showed that the electrode capacitance and micropores’ volume decreased the necessary process cycles (reducing desalination process time) to produce potable water without significant changes in the energy consumption. The feeding voltage, volumetric flow rate and concentration of the feed water significantly affected the process time and energy consumption. For feed water concentration between 5 and 25 mM, results showed that the minimum values for the desalination process time, electrode surface area, and overall transfer coefficient, are 400 s, 50 cm2 and 0.9 µm/s, respectively. To improve the performance of desalination process in the capacitive deionization cell, development on the physical properties (increasing micropores) and the electrical properties (increasing capacitance value) of the electrodes, as the most important parameters, is suggested.

Introduction Groundwater is an essential natural resource that is widely used to meet domestic, industrial, and agricultural needs. In recent years, the amount of withdrawal from groundwater has been more than the amount of its recharging leading to going out of balance. Since groundwater is in the ground and it is not possible to observe directly, identifying its properties is time-consuming and expensive. On the other hand, problems such as inconsistent and incomplete input information, heterogeneous aquifers, etc., have made groundwater study difficult. In many watersheds, groundwater resources are the strategic and primary source of water supply for different users. However, groundwater extraction has exceeded the amount rate of recharge in many regions around the world, resulting in harmful ecological and environmental problems, such as water level decline, water quality degradation, drying up of wells, increased pumping costs, and land subsidence. Assessing groundwater resources for their available water volume and obtaining an accurate prediction of groundwater levels (GWL) is central to sustainable management (i.e., balancing between demand and supply) of groundwater and surface water resources in a watershed. Therefore, tools such as groundwater modeling are used to solve this problem. Simulation of groundwater flow with mathematical models is an indirect approach to solving problems with lower costs than direct methods. In fact, the use of mathematical modeling is to simulate the natural conditions of the water surface with mathematical relationships. Groundwater modeling is done using differential equations, and one of the most widely used methods in solving groundwater problems is the use of finite differences and finite elements. Accordingly, the groundwater modeling system (GMS) model and the MODFLOW code were used in this research to study the Mahabad aquifer. Next, the trend of changes in the groundwater level of the range was analyzed by non-parametric tests. Accordingly, the groundwater modeling system (GMS) model and the MODFLOW code were used in this research to study the Mahabad aquifer. Materials and Methods The study area of Mahabad is located in West Azarbaijan province. GMS software and MODFLOW code were used for groundwater simulation. Using the information of 22 observation wells, exploitation wells information, river information, recharge, and withdrawal from groundwater, the desired model was built. . The model was run in September 2015 for the steady-state and October 2010- September 2011 for the transient state with a monthly time step. The values for hydraulic conductivity and storage coefficient were calibrated for the steady and transient states, respectively. Aquifer thickness varied from 60 to 200 m, and the cell size was considered 200 × 200 m. Rainfall infiltration, return flow, and input flows feed the aquifer. Seventeen percent of the monthly rainfall was considered rainfall infiltration that fed the aquifer. Moreover, based on the wells' primary use, return water from the wells was considered about70, 75, and 20% for drinking water, industrial and agricultural wells, respectively. The GWL is higher in the South part of the aquifer compared to other parts and, as we move from the South part of the aquifer towards its central and southern regions, the GWL declines. In conclusion, the groundwater flows from the upper South part of the aquifer towards its lower part. More exploitation wells are in the aquifer's central section, and most of their extracted water is used for urban and agricultural purposes. It was then implemented in two stable and unstable modes and its performance was evaluated with root mean square (RMSE), mean absolute error (MAE), and coefficient of determination (R2) criteria. Various statistical methods have been provided to analyze the trend of time series. Among them, non-parametric methods are more useful in the time series of hydrological variables. These methods are suitable for time series that have elongation or skewness and are independent of the statistical distribution of the time series. In the following, the Mann-Kendall method and Sen’s slope were used to determine the trend of the groundwater level at significant levels of 90, 95, 99, and 99.9%. Results and Discussion The simulation results showed that there is a very good agreement between the simulation and observational data. The model evaluation criteria including RMSE, MAE, and R2 for two stable and unstable modes were calculated as 0.84, 0.63, and 0.99, as well as 0.88, 0.72, and 0.98 m, respectively. These values showed the appropriate efficiency of the model. Based on the results, the highest level of groundwater was in the south of the Mahabad aquifer and the lowest level was in the north of the aquifer. The optimized values ​​of hydraulic conductivity, special yield, aquifer thicknesses, values ​​of exploitation wells, and aquifer transmissivity were determined from the groundwater simulation results. The results of the Mann-Kendall test showed that Haji Khosh, Gapis, and Gorg Tapeh stations had the highest downward trend. So, in these stations, the downward trend was more significant at the level of 0.99%. The Mann-Kendall Z-parameter values were positive for the Qom Qala station, which indicated the rising trend of the underground water level in this area. The results of Sen’s slope test also confirmed the results of the Mann-Kendall test. It was so that the Sen’s slope test showed that the downward slope of the three stations Haji Khosh, Gapis, and Gorg Tapeh occurs more strongly. Conclusion The results of this research showed that GMS and MODFLOW codes are suitable tools for simulating groundwater and the condition of the aquifer with proper accuracy. Also, the results of Mann-Kendall and Sen’s slope tests showed that out of 19 wells, almost 18 had a downward trend, which shows that the Mahabad aquifer is not in a favorable condition and with the increase in harvesting and decrease in rainfall, especially in recent years, its situation will worsen. The Mann-Kendall test showed that the Mahabad aquifer is in poor condition so out of the 19 investigated wells, approximately 18 wells had a downward trend in the groundwater level. The age slope estimator method also confirmed the Mann-Kendall results. Examining the obtained results exhibits that the use of new approaches for simulation provides the opportunity to manage and balance the allocation of groundwater resources effectively. Further, the use of new tools can be considered for implementing balancing scenarios related to groundwater resources.

The Maisotsenko cycle is a novel thermodynamic concept which captures the internal energy in an air cooling process by using the latent heat of evaporation of water. This concept is a regenerative indirect evaporative cooling cycle (RIEC) which utilizes a new arrangement of airflow streams to achieve better performance. In the field of air conditioning, this cycle is known as a high efficiency cooling cycle due to the cooling of the air-water vapor flow temperature to its dew point temperature. Most commonly known variant of this concept is a crossflow cooler which will be examined in this paper. In the present study, the governing equations of a heat and mass exchanger based on the Maisotsenko cycle has been extracted. This heat and mass exchanger has been analyzed and simulated in the month of July, which is the hottest month of the year, in the climatic conditions of Tehran. Temperature analysis utilizing the finite difference method has shown that the heat and mass exchanger based on the Maisotsenko cycle has superior performance than existing devices in the cooling air process in the field of air conditioning technology. the air temperature in Tehran decreases from 40.2o C to 22.2o C when passing through the device. The possibility of employing a Maisotsenko cycle air conditioner in different climates of Iran has been investigated in this study. The results revealed that the cooler based on the Maisotsenko cycle performs satisfactorily for more than 85 percent of the population and more than 86 percent of the regions of Iran. Also, according to the acquired data, the air conditioner based on the Maisotsenko cycle operates with an average coefficient of performance of 31 in various regions of Iran. The airflow passing through this heat and mass exchanger is cooled to 90% of it’s inlet wet bubble temperature.

Nowadays, by expanding the application of thin layers in industry and medical sciences, their fabrication methods have also received attention. One of those methods is the vaporizing material method with the help of an electron gun. The most important part in the electron gun is the electron optic, which is responsible for producing and accelerating electrons, so that it becomes possible to vaporize refractory materials in a shorter period of time by better modifying and controlling the electron beam (the shape and diameter of the electron beam) at the target location. The reduction and control of the beam diameter in this evaporation source depends on various parameters such as device geometry, magnetic field intensity, electric power, etc. Therefore, in this research, the effect of those parameters was investigated by conducting experiments and using finite element and modeling software. The simulation results revealed that the effect of the effective parameters on the beam diameter can be predicted to a good extent, so that the diameter of the electron beam decreases by changing the geometrical shape, size and displacement of the output beam components. Then, the new electron gun, compared to the existing prototype, is optimized by applying these changes in the construction of the device and conducting experiments, and its beam diameter is reduced by 40% to be more focused.

The increasing growth of vehicles and the decrease in road safety in developing countries have led to an increase in urban accident statistics. Urban management policymakers have aimed to implement effective solutions to improve the level of safety, reduce economic costs caused by accidents and environmental consequences of fossil fuel consumption. This is possible by implementing traffic calming measures, which change the behavior of drivers according to the environment by limiting the speed of vehicles. This study has tried to evaluate traffic calming measures by presenting 4 proposed scenarios, such as changing the speed reduction facilities, building a signalized intersection, and reducing the road width to calm the traffic flow on one of the high-traffic and accident-prone roads in Qom using Aimsun simulator software. The evaluation of accidents showed that the construction of new signalized intersections has reduced the average speed of vehicles by 35.5%, and the total number of accidents has increased from 474 in 2018 to 217. But this has led to an increase in the delay time and more time has been wasted. Further, the results of the investigation showed that by removing the speed reduction facilities and increasing the width of the road, the travel time was reduced by 19.9 and 9.3 percent, respectively, compared to the existing situation, and by constructing the signalized intersection, the speed of vehicles decreased by 12.9 percent.

Background and Aim: Operating room students have experienced anxiety for various reasons that affect their educational process such as stitching. Due to the effective role of simulation in improving the education of students, this study was conducted with the aim of the effect of animal skin suturing simulation on the skill level and anxiety level of operating room students. Materials and Methods: In this study, 30 people (75%) of the samples were aged 18-25 and 10 people (25%) of them were 26-35. 10 people (25%) were men and 30 people (75%) were women. Also, in terms of marriage, 7 people (17.5%) were married and 33 people (82.5%) were single. The present quasi-experimental study was performed with a two-group plan before and after with 40 operating room students of Shahrekord University of Medical Sciences (Shahrekord, Broujen) in 2020. The samples were randomly assigned to two test and control groups using the method based on the purpose of selection. Data were collected before and after the intervention by Spielberger Anxiety Tool and Suturing Skills Questionnaire and analyzed by SPSS software and descriptive and analytical statistical tests (Chi-square test, t-test, paired t-test). Results: The t-test test showed that the mean of obvious anxiety in the intervention group (36.35±10.22) and the control group (41.15±7.92) after the intervention was not significant (P=0.346). Also, the mean hidden anxiety in the intervention group (36.65±10.47) and the control group (38.65±6.13) had no significant difference (P=0.089). The t-test test showed that there was a significant difference in sewing skills after the intervention in the two intervention groups (28.2±58.22) and the control group (23.42±3.12) (P=0.04). Conclusion: The texture of suture mannequins is very different from human skin in terms of consistency, and it does not convey the same feeling of sutures on natural skin to students. On the other hand, it is easier to enter and exit and move the needle and thread in the sheepskin, and this provides the students with the possibility of stitching more easily. Therefore, it is suggested to buy and prepare sheep skin for training students and teach them stitches on it. [ABSTRACT FROM AUTHOR]

During the last two decades, some international treaties have played a decisive role in facilitating scientific collaboration between member countries. For this reason, this study aims to predict the effect of Iran’s membership in these treaties on three things: first, the amount of scientific collaboration between Iran and the members of each treaty; second, the total amount of Iran’s international scientific collaboration; third, the extent of Iran’s dependence on scientific collaboration with the USA as the most key actor in the international scientific collaboration network. According to the background, the treaties that have a significant impact on international scientific collaboration are G7, G20, EU, OECD, OPEC, APEC, and BRICS. This study is based on the simulation method (with an agent-based modeling approach). This method was used to simulate the network of scientific collaboration between independent countries with more than one million people in the period of 2023-2042. The secondary data required for the simulation were collected from the Web of Science database and the United Nations database. Hypotheses related to possible scenarios were tested based on difference-in-difference statistical model. All calculations, including network simulation and hypothesis testing, were performed in the R software environment. The findings showed that Iran’s joining G20, OECD, and APEC will significantly increase Iran’s scientific collaboration with the members of each treaty. However, Iran’s membership in the BRICS will not have a significant effect on its collaboration with the BRICS members. Also, Iran’s joining each of these four treaties will significantly increase the total amount of Iran’s international scientific collaboration. However, the increase in the G20 scenario is higher, and in the BRICS scenario is lower than other treaties. At the same time, Iran’s membership in G20 and BRICS will lead to a significant increase in Iran’s dependence on collaboration with the USA. In return, Iran’s membership in OECD and APEC will play a significant role in reducing Iran’s dependence on collaboration with the USA. Another advantage of Iran’s membership in the OECD is strengthening its collaboration with two of the key players in Europe (France and Italy). One of the side benefits of Iran’s joining APEC is the significant increase in its collaboration with the key player in East Asia (China). The results indicated that Iran’s joining OECD and APEC will help to increase Iran’s visibility in the international scientific collaboration network and reduce Iran’s dependence on collaboration with the USA. [ABSTRACT FROM AUTHOR]

The critical situation of water in the country and the increasing demand for water in different sectors, today requires the necessity of proper planning for the use of different water resources. Evaluating the impact of different exploitation scenarios using simulation methods and comprehensive planning of water resources plays a very important role in the management of these resources. The models that have been used are 1) WRM model (water resources planning) 2) WEAP model(Water Evaluation And Planning System) due to their comprehensiveness in considering the combination of physical, hydrological, and management processes in the simulation, the ability to evaluate scenarios and water allocation, learning its use in Different parts of the world and free access are among the most used models. Evaluating the impact of different exploitation scenarios using simulation methods and comprehensive planning of water resources plays a very important role in the management of these resources. The models that have been used 1) WRM model (water resources planning) 2) WEAP model due to its comprehensiveness in considering the combination of physical, hydrological, and management processes in the simulation, the ability to evaluate scenarios and water allocation, learning its use in Different parts of the world and free access are among the most used models. The main goal of this research is management and comprehensive planning to optimally use the available water resources of Karde dam, using the WEAP model, and supply the demand in the agriculture and drinking sectors, considering the growth of their needs in the future. For this purpose, Karde Dam was first simulated in the environment of WEAP model and the model was implemented for basic conditions and seven different scenarios of development plans. According to the results obtained for the reference scenario, this dam alone does not respond to all the needs defined completely in the horizon of the project, except by applying management measures in the form of scenarios, which will reduce water consumption in different sectors of demand. Among these scenarios are demand management, increasing irrigation efficiency, using both at the same time in one scenario, changing or reducing the cultivation pattern, etc. As a result, by applying the low irrigation scenario and increasing the efficiency at the same time, it is possible to reduce the lack of water demand by 37% compared to the reference scenario, and the reservoir storage volume in this scenario increases by 25% compared to the reservoir storage volume in the reference scenario. [ABSTRACT FROM AUTHOR]

Nowdays, supply chain specialists are looking for the integrated development of the supply chain model in order to increase the competitiveness, effectiveness and reduce the problems in the supply chain. They always seek to identify and develop this process so that they can cover more aspects of the chain. Adding effectiveness indicators along with LARG indicators and using the basics of the dynamic system to improve the efficiency of the supply chain is one of the innovations of this study. At first, by using research literature and studies, 12 headings of indicators were selected as LARG-E indicators. Then, with the Fuzzy Delphi method, the relationships and importance of each of these components were determined, and more important variables were modeled for further investigation. With using the concepts of dynamic systems, causal loops were drawn. Then, to check the function of the model, dynamic hypotheses were developed with the opinion of experts. In the next step, the flow diagram of the model and also the validation tests of the proposed model were done. Finally, by examining the outputs obtained from the proposed scenarios, it was found that most of variables have better behavior in LARG-E approach.IntroductionIn recent years, with the addition of various competitions in the world markets, many researches have been conducted to use new technologies and researches in order to improve the production process and increase the effectiveness of these competitions as much as possible (Mohghar et al., 2017). All the goals that work in this field increase the competitiveness of the organization. This competition is by reducing costs, being present in the market and satisfying the customer. To increase profits, protect the environment, keep the markets stable and meet the expectations of customers, organizations should be provided using the existing environments in a set of customers (Pisha et al., 2016). Use chain management requires the use of new facilities and improvements to previous findings such as lean, agile, resilience and green to increase speed and competitiveness, selection and decision-making to achieve the organization and maximum effectiveness.Today, supply chain specialists are looking for the integrated development of the supply chain model to increase the effectiveness and efficiency of the supply chain in order to increase competitiveness and reduce supply chain problems. In this case, there is a consensus among experts that there is no comprehensive model. All the mentioned cases make it inevitable to design a comprehensive and effective model for the supply chain. The verifiable issue is the conflicts and the non-alignment of all the indicators of the paradigms with each other. LARG paradigms, without considering the spirit of effectiveness in each supply chain, cannot fully protect it against continuous changes in the competitive market arena. A comprehensive model that pays particular attention to effectiveness while implementing LARG paradigms has not been examined in the literature review and the consensus of experts. Therefore, in this research, we are looking to design a comprehensive model in a LARG-effective manner so that the effect of various LARG-effective indicators on the performance of the supply chain can be investigated. The integration of LARG paradigms has been studied a lot so far, but its development is based on the concepts of innovation effectiveness of this research, and in this way, the dynamic system approach was used.Materials and MethodsTo formulate a LARG supply chain, first the framework, indicators and variables of each LARG paradigm were extracted from the research literature, then in order to develop them with effective concepts, the effective supply chain was studied. In order to implement the fuzzy Delphi approach, based on the effectiveness indicators extracted from the subject literature and LARG supply chain approaches, operational indicators were provided to the experts participating in the research in the form of a questionnaire via email and after initial coordination. After collecting the completed questionnaires, fuzzification operations, fuzzy averaging and then de-fuzzification were performed. The results were brought to the attention of the participants and they were asked to apply their desired changes according to the obtained results. This approach reached the saturation stage in the third round and there was no change in the opinions of the participants and the consensus of the panel experts was the final and trusted output of the Delphi method. Finally, according to these weights, 9 quantitative variables had the highest importance and were used for dynamic modeling. The simulation stage is done with the help of software and Nasim. According to the features of modeling based on system dynamics, this approach was chosen as the main research tool in this study because there are linear relationships between the variables and there are nested feedbacks between the variables of the subsystems, the importance of simultaneously improving the performance in different layers of the producer, supplier and distributor. Which is one of the goals of this research, with this approach, it can be a very suitable tool for decision-making by the senior managers of the organization.Discussion and ResultsOrganizations are trying to improve their competitiveness by adopting Lean, Resilient, Green and Agile strategies; But as it was said, the implementation of these paradigms, which sometimes have conflicting results, requires a new integration and index to align the goals. So far, many researches have been done by merging two or more paradigms, the combination of all 4 paradigms called LARG has greatly helped to improve the performance of supply chains, but in this research, in order to improve the conflicts between paradigms, a new concept of spiritual effectiveness was given to the supply chain. Understanding the dynamics of applying the above four strategies and their effectiveness was done using the dynamic systems approach. In this research, the indicators of the LARG supply chain were defined based on theoretical foundations and interviews with experts; then the effectiveness indicators were placed next to them. These indicators were implemented in the printing and ink industry. In this way, an effective LARG integrated system was defined; then, using a dynamic model, dynamic hypotheses were first defined and state and flow diagrams were drawn. After correctness of the model and validation of the model, two scenarios were examined for 8 important variables. After applying the scenarios, the performance of LARG and effective LARG was compared. By applying each scenario in the designed model, it was possible to check the effect of new indicators on the variables and their behavior.ConclusionsAs a result, if the components of the effective supply chain are properly integrated with the LARG concepts, they integrate the conflict that may exist between the LARG paradigms and play the role of synchronization and improvement as a ruler and standard. The effective management of the LARG supply chain may not be defined as an independent variable, but it is a result of variables and indicators that improved performance in most cases.

This paper investigates unreliable multi-product assembly lines with mixed (serial-parallel) layout model in which machines failures and repairing probabilities are considered. The aim of this study is to develop a multi-objective mathematical model consisting the maximization of the throughput rate of the system and the minimization of the total cost of reducing mean processing times and the total buffer capacities with respect to the optimal values of the mean processing time of each product in each workstation and the buffer capacity between workstations. For this purpose, in order to configure the structure of the mathematical model, Simulation, Design of Experiments and Response Surface Methodology are used and to solve it, the meta-heuristic algorithms including Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Non-Dominated Ranked Genetic Algorithm (NRGA) are implemented. The validity of the multi-objective mathematical model and the application of the proposed methodology for solving the model is examined on a case study. Finally, the performance of the algorithms used in this study is evaluated. The results show that the proposed multi-objective mathematical model is valid for optimizing unreliable production lines and has the ability to achieve optimal (near optimal) solutions in other similar problems with larger scale and more complexity.IntroductionA production line consists of a sequence of workstations, in each of which parts are processed by machines. In this setup, each workstation includes a number of similar or dissimilar parallel machines, and a buffer is placed between any two consecutive workstations. In production lines, the buffer capacity and processing time of machinery have a significant impact on the system's performance. The presence of buffers helps the system to maintain production despite possible conditions or accidents, such as machinery failure or changes in processing time. Previous research has investigated production lines without any possibility of machinery failure, referred to as "safe production lines." However, in real production lines, machinery failure is inevitable. Therefore, several studies have focused on "uncertain production lines,"assuming the existence of a probability of failure in a deterministic or exponential distribution. This research examines uncertain production lines with a combined layout, resulting from the combination of parallel deployment of machines within each workstation, if necessary, and serial deployment of workstations. The objective of this research is to determine the optimal values (or values close to optimal) of the average processing time of each product in each workstation, as well as the volume of buffers, as decision variables. The approach aims to maximize the system's output while minimizing the costs associated with reducing the processing time of workstations and minimizing the total volume of buffers between stations. Moreover, simulation can be applied without interrupting the production line or consuming significant resources. In this research, due to the high cost and time involved, implementing the proposed changes on the system is not cost-effective for investigating the changes in the production system's output rate. Therefore, the simulation technique has been utilized to optimize the production line.Research methodThe present study aims to develop a multi-objective mathematical model, based on simulation, to optimize multi-product production lines. In the first step, the structure of the multi-objective mathematical model is defined, along with the basic assumptions. To adopt a realistic approach in the model structure, the simulation technique has been employed to address the first objective function, which is maximizing the output rate of the production line. To achieve this, the desired production system is simulated. The design of experiments is used to generate scenarios for implementation in the simulated model, and the response surface methodology is utilized to analyze the relationship between the input variables (such as the average processing time of each product type in each workstation and the buffer volume between stations) and the response variable (production rate).ResultsTo implement the proposed methodology based on the designed multi-objective programming model, a case study of a three-product production line with 9 workstations and 8 buffers was conducted. Subsequently, to compare the performance of the optimization algorithms, five indicators were used: distance from the ideal solution, maximum dispersion, access rate, spacing, and time. For this purpose, 30 random problems, similar to the mathematical model of the case study, were generated and solved. Based on the results obtained, both algorithms exhibited similar performance in all indices, except for the maximum dispersion index.ConclusionsIn this article, the structure of a multi-objective mathematical model was sought in uncertain multi-product production lines with the combined arrangement of machines in series-parallel (parallel installation of machines in workstations if needed and installation of workstations in series). The objective was to determine the optimal values of the average processing time of each type of product in each workstation and the buffer volume of each station, with the goals of maximizing the production rate, minimizing the costs resulting from reducing the processing time, and the total volume of inter-station buffers simultaneously. To investigate the changes in the output rate of the production system, due to the high cost and time, it was deemed not cost-effective to implement the proposed changes on the system. Therefore, the combination of simulation techniques, design of experiments, and response surface methodology was used to fit the relevant metamodel. In the proposed approach of this research, taking a realistic view of production line modeling, the probability of machinery failure, as well as the possibility of repairability and return to the system, were considered in the form of statistical distribution functions. Additionally, all time parameters, including the arrival time between the parts, the start-up time of all the machines, the processing time, the time between two failures, and the repair time of the machines, were non-deterministic and subject to statistical distributions. Finally, to solve the structured mathematical model, two meta-heuristic algorithms (NSGA-II) and (NRGA) were considered.

One of the driving factors for creating axial flow inside the gas centrifuge rotor to increase the separation performance is the scoop. Due to the exposure of the scoop to the high Mach gas flow, the flow will be shocked, and strong gradients will occur in the flow. In this research, the gas flow around the scoop in a two-dimensional state (r-θ) is simulated by the Direct Simulation Monte Carlo method (DSMC) using the dsmcFoam solver at different distances of the scoop from the rotor wall. The results show that increasing the distance of the scoop from the rotor wall and decreasing the contact angle of the gas flow with the scoop reduces the maximum temperature and drag force. For instance, increasing the distance of the scoop from the wall by 31% (from 8 to 10.5 mm) in 3800 Pa wall pressure and 85° contact angle, causing a maximum temperature decrease of 1.3% (from 596 to 588 K) and also the drag force is reduced by 49.4% (2412 to 1221 dyn). Furthermore, reducing the angle of the gas flow with the scoop by 11.8% (from 85 ° to 75 °) in 3800 Pa wall pressure and at the distance of the scoop from the wall equal to 10.5 mm causes a maximum temperature decrease of 6.8% (from 588 To 548 K) and the drag force is reduced by 50.3% (1552 to 771 dyn).

In this study, the accuracy of the Adaptive Network-Based Fuzzy Inference System (ANFIS) in integrating with the Gray Wolf Algorithm (ANFIS-GWO) in predicting groundwater level was evaluated for the first time using unpublished observational data from 1998 to 2018 in the Zarandieh aquifer, central Iran. Three observational wells were randomly selected for analysis. Assessment of evaluation criteria demonstrated that among the proposed scenarios using the hybrid model, the D scenario was selected as the optimal scenario with input data including the previous month's groundwater level, precipitation, temperature, and groundwater extraction. In the D scenario, parameters including MAPE, RMSE, and NASH were 0.29 m, 0.47 m, and 0.99, respectively for the first observational well. Also, C scenario with input data including the previous month's groundwater level, precipitation, and groundwater extraction for the second observational well, for the same parameters mentioned above equal to 0.20 m, 0.26 m, and 0.99. As well for the third observational well, the A scenario with input data including the previous month's groundwater level for the same parameters equal to 0.29 m, 0.41 m, and 0.99 as the optimal scenarios were selected using the ANFIS-GWO model. Based on the results, the Gray wolf algorithm in training the ANFIS model was able to reduce the average forecast error by equal to 0.03 (RMSE) and 0.02 (MAPE) meter and increased the average NASH value equal to 0.01 and increased the accuracy of predictions.

The purpose of this article is to model variables and scenarios affecting emergency evacuation during crises such as war and earthquake. The statistical population includes 160 specialists, academic staff and graduate students. The research method is descriptive-analytical. Random sampling method and Cochran's formula was used. Analysis has been done based on Wizard, Any Logic and Anp software. 8 scenarios with a maximum of two incompatible factors are considered for modeling in Any Logic. The more critical scenario obtained from the simulation was selected and the solutions were checked in the Anp software. Previous researches have not explained the simulated scenarios with the scientific method. We have used decision-making techniques and combined them with simulation to select solutions and scenarios. The evacuation time in the fifth scenario is longer than the other scenarios and we considered this scenario more critical. The results of Anp show that the criteria of adding doors and removing obstacles with weights of 0.287 and 0.262 have been ranked the highest. According to the normalized weight in the sub-criteria, in the criterion of time, the escape time in the corridors and stairs (with a weight of 0.65), in the cost, executive dimension (with a weight of 0.608), in the ability to execute, the ability to direct the operation. (with a weight of 0.8), in environmental effects, impact on the public (with a weight of 0.705) and in guarantee of traffic, the status of traffic management (with a weight of 0.756) have been ranked highest.

In this work, the process of biogas production from palm oil factory effluent was simulated and then the produced biosynthetic gas was sweetened. For this purpose, the biogas production process from wastewater treatment was simulated using SuperPro Designer v9.0 software. Then, the resulting biogas entered the chemical absorption and reforming sections for sweetening and conversion to syngas, respectively, and these steps were simulated with Aspen HYSYS v11.0 software. The simulation results of the first stage showed that the effluent feed of this factory with a flow rate of 42000 kg/h and COD of 62000 mg/L leads to the production of 1786 kg/h biogas containing various compounds such as methane, carbon dioxide, hydrogen sulfide and water with the molar fraction of 0.446, 0.245, 0.178 and 0.040, respectively. In the chemical absorption section, MEA solvent 10 %wt. and solvent-to-gas molar ratio of 13.51 were used, which led to the efficient removal of CO2 and H2S up to 1 ppm and 99.99%, respectively. The examination of temperature changes in the absorption tower also showed that the temperature increases along the absorption tower. In the methane steam-reforming unit, two different strategies were used: 1) plug flow reactor (with fluid package of Peng-Robinson-Stryjek–Vera) and 2) conversion and equilibrium reactors (with fluid package of Peng-Robinson). The results showed that the purity of hydrogen in the biogas produced in the second strategy (conversion and equilibrium reactors) was higher than the first one (plug flow reactor), and on the other hand, the purity of CO2 was zero in the second strategy.

The aims of this study is to provide a conceptual model of hospital accreditation in Iran through qualitative research. The grounded theory model was compiled using the results of the analysis of the interviews. Then, with the help of the grounded theory model and the results of qualitative analysis, a state diagram was obtained. Using the state diagram, initial sampling, systematic review of sources and results of interviews, 9 conceptual agents "governance organizations, management and leadership, clinical personnel, support personnel, hospital infrastructure, assessor, standards, assessment method and service recipient" were identified. Finally, the conceptual model of agent-based, environment, behavioral rules of agents and their input and output interactions was presented. In future researches, reinforcement learning models can be designed according to the conceptual model of this study, so that by using it, software developers can develop a suitable framework for solving complex problems in the field of hospital accreditation. Because the field of hospital management systems is one of the desirable types of socio-technical systems that have high capacities.

Background and Objectives: Agriculture is one of the most important economic activities highly dependent on climatic conditions more than any other factors. Climate change significantly affects crop production. Due to the limitation in production factors, inputs, climatic and geographic properties of different provinces, identification of the comparative advantage of crops is essential. Lack of knowledge about comparative advantage would lead to improper and non-optimal use of resources. Therefore, knowledge about comparative advantage in different regions is useful to plan and exploit the resources. The present study was conducted to identify the comparative advantage of some important rainfed winter crops. Materials and Methods: In the agronomic section of this study, the yield of rainfed crops including wheat, barley, chickpea and lentil in Ardabil, Fars and Khorasan Razavi provinces were simulated using the SSM-iCrop2 model. Long-term meteorological data were used to decrease climatic fluctuations and more precise estimation. Economic evaluations were performed using policy analysis matrix (PAM), domestic resource cost (DRC) and social cost-benefit index (SCB) methods. Also, nominal protection coefficient (NPC), effective protection of crops (EPC) and nominal protection input (NPI) were measured. In the agronomic section of this study, the yield of each region was simulated according to the climatic conditions and using the SSM-iCrop2 model (to determine the production potential of each region). For this purpose, long-term weather data were used and simulation was done for a 30-year period from 1986 to 2015. Under the moderate production conditions (moderate agronomic management or unsuitable climatic conditions), 50 percent of the yield potential, and under the optimum production conditions (optimum agronomic management or good climatic conditions), 70 percent of the potential yield is attainable. Therefore, 50 and 70 percent of yield potential were simulated using the SSM-iCrop2 model and their effect on comparative advantage and social profit were investigated. Results: The results indicate that according to the DRC index, Ardabil in wheat, chickpea and lentil production, Khorasan Razavi in chickpea and lentil and Fars in chickpea production have a comparative advantage. Based on the SCB index, the production of wheat in Ardabil, chickpea in all three provinces and lentil in Ardabil and Khorasan Razavi provinces have social profit. According to the results, increase yield leads to improved comparative advantage and social profit so that all three provinces will have social profit in crop production (rainfed, wheat, barley, chickpea and lentil) provided that the farmers attain at least 50 percent of the yield potential (exploitable yield). According to the results of the simulation, Fars and Ardabil had the highest exploitable yield in wheat production, and regarding barley, chickpea and lentil, the highest exploitable yield was related to Ardabil province. Based on the actual yields, only Ardabil province had high yields and was superior to other provinces in terms of the economic indices. Conclusion: Wheat, barley, chickpea and lentil crops had higher yield in Ardabil province due to the suitable climatic conditions of this region. One of the reasons for the advantage of crop production in Ardabil is its higher yield which has increased the income of growers compared with the other two provinces, and production of rainfed crops in this province has led to benefits. [ABSTRACT FROM AUTHOR]