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Simulation of runoff and hydrograph production is widely used in analyzing the behavior of basin against precipitation, calculating the volume and peak of floods, the amount of losses and the possibility of designing the dimensions of structures. HEC-HMS is one of the most common simulator models In this study, with the aim of analyzing the sensitivity of flood hydrograph routing parameters in the Merk River basin, first, the physiographic characteristics of the basin were constructed with the HEC-GeoHMS plugin in the Arc GIS environment and the DEM map of the area and they were called in the HEC-HMS model. then by entering the three flood event data and its corresponding precipitation, computational hydrograph is created. For calibration, at first by trial and error, the model was placed in a suitable range which can be optimized using differential evolution algorithm. In optimizing the target function to make the average at least of the sum of squares of error, the K and X Muskingum parameters, which are particularly sensitive to hydrograph production, placed the model optimization and its performance in the excellent category so that for the event of November 2015 floods and a stop of 50 iteration (NSE=0.871, PBias=25.52, RMSE=0.4, NRMSE=2.63), for the event of March 2016 floods (NSE=0.731, PBias=28.82, RMSE=0.5, NRMSE=1.01) and in February 2020 (NSE=0.834, PBias=7.96, RMSE=0.4, NRMSE=0.95) which indicate an excellent performance of the model after optimization of the Muskingum coefficients by differential evolution algorithm (DE). [ABSTRACT FROM AUTHOR]

Introduction: The prevalence of COVID-19 has significantly impacted work environments and the workforce. Therefore, identifying the most important preventive and control strategies, as well as assessing their effectiveness, is of paramount importance. Various studies have shown that machine learning algorithms can be used to predict complex and nonlinear issues, including predicting the behavior of various diseases such as COVID-19 and the parameters affecting it, and can be beneficial. The purpose of this study has been to examine the importance of preventive measures and hygiene behaviors in preventing COVID-19 in the oil refining industry using various machine learning models. Material and Methods: For this purpose, demographic information and health behaviors of individuals were collected. Subsequently, a multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) models were compared to enhance the analysis of the effects of preventive measures on COVID-19 infection. Finally, the most influential factors affecting the likelihood of COVID-19 infection were determined using sensitivity analysis. Results: The results showed that the accuracies achieved in predicting the impact of preventive measures and health behaviors on COVID-19 in occupational settings were 78.1%, 81.2%, and 78.1% by MLP, RBF, and SVM respectively. The RBF model was identified as the most accurate model for predicting the impact of health behaviors on COVID-19 disease Additionally, the level of social distancing with customers, handwashing frequency and disinfection, the availability of cleansing and disinfecting agents for hands and surfaces in the workplace, and gatherings for eating meals and snacks were identified as the most significant health behaviors influencing the prevalence of COVID-19 in the workplace. Conclusion: Studies of this nature can underscore the importance of attention to preventive measures and health behaviors in unprecedented circumstances. Furthermore, the utilization of artificial intelligence models and tools such as DSS (Decision Support Systems) can serve as powerful tools for optimizing control measures in work environments.

Due to the existence of various uncertainties in the materials and modeling of these structures, the quantification of these uncertainties requires sensitivity analysis and reducing the number of variable parameters to reduce cost and time in modeling. In this research, numerical analysis of Maku dam located in West Azerbaijan province which is considered as a case study is performed using FLAC2D software and the static response is obtained. The effect of construction layers and the amount and shape of contours of stress and displacement were compared and validated with previous researches. The 49 random variables (RVs) (of materials properties in the zones of the core, shell, filter, drainage, alluvium and bedrock) was considered. By performing sensitivity analysis and using the Tornado diagram, the number of variable parameters is reduced and 18 important RVs were identified for the modeling and analysis of the earth dam. The results showed that altering the dry density (γd ) of the shell causes a change of about 8% and altering the Poisson's ratio (ν) and also the modulus of elasticity (E) of the core leads to a change of about 7% in earth dams’ response. Finally, parameters of dry density (γd ), Poisson's ratio (ν), modulus of elasticity (E) and internal friction angle (ϕ) were identified as more sensitive parameters that have the greatest impact on the response of earth dams. [ABSTRACT FROM AUTHOR]

Introduction: The study of erosion and sedimentation is a crucial aspect and an integral part of watershed studies in a basin. The methods employed to prevent erosion and sedimentation play a significant role in basin management planning. However, due to the lack of accurate statistics on erosion and sediment amounts, it becomes necessary to utilize estimation models for assessing erosion and sedimentation. These models are often region-specific and may have a wide range of uncertainty in different areas. One notable benefit of modeling, regardless of its type, is its ability to analyze natural processes within the basin. On the other hand, understanding erosion and sedimentation processes in a region can be an effective step in managing, planning, and prioritizing available resources. In this research, the aim was to determine the sensitivity of the MPSIAC model to the relationships among its parameters. Additionally, the goal was to assess the model's sensitivity to changes in these parameters and their impact on sedimentation. By identifying the influential ground and atmospheric parameters, it becomes possible to prioritize their effect on water sedimentation in the region. Materials and methods: Through the sensitivity analysis of the model, it is possible to identify the flexibility of the model to the changes of its various parameters. It is also possible to determine the relationships between model variables and prioritize the parameters affecting the model output. Sensitivity analysis can be used in the calibration stage, in such a way that results are accurate, and time and cost are saved. In this study, to analyze the sensitivity of the MPSIAC model, first, the nine parameters of the model were examined. Then, according to the arrangement of different parameters in the structure of this model, the parameters were numbered from X1 to X9. Then, the score range of each parameter was entered in its calculation table according to the modified model of PSIAC. In this step, the average of each parameter was calculated according to its upper and lower limits. To achieve this, the nine parameters of the model were standardized, and the sedimentation rate was estimated by varying each parameter within its lower and upper limits while keeping the other parameter(s) fixed at their averages. With each change, the sediment discharge was calculated. Results and Discussion: In the analysis of the results, the slope of the curves of the standardized parameters relative to the dependent variable (specific sediment) is considered. So that, any part of the curve that has more slope changes means that the model is more sensitive to minor changes in that parameter in that interval. In this study, the results of this sensitivity analysis reveal that the slope factor and runoff volume have a considerable influence on sediment yield. For slopes up to approximately 15%, the slope parameter has a relatively smaller impact compared to other parameters. However, as the slope value increases, its effect becomes more pronounced, indicating that high slopes (greater than 15%) have the greatest impact on sedimentation. The runoff factor exhibits a sudden increase in sensitivity at higher flow rates. Conclusion: In examining the effect of different parameters on the model performance, the parameters that had the largest slope of changes can be considered as the most effective parameters in sedimentation of the basin. In other words, the model demonstrates high sensitivity to slight changes in specific peak discharge values above 20 m³/s/km². Therefore, implementing integrated (biomechanical) management measures to control slope steepness and reduce runoff volume can be an effective strategy for mitigating sedimentation. Therefore, based on the results, the role of land factors is very important, such as slope and then runoff, in the erodibility and sediment production in basins. Of course, it should be noted that climatic parameters directly and indirectly affect the amount of peak flood discharge and should not be neglected for their importance and influence in flooding and subsequently the erodibility of the watershed. [ABSTRACT FROM AUTHOR]

The drying process is one of the methods of processing vegetables and fruits, helping to reduce the volume of the product, facilitate transportation, increase preservation ability, and reduce microbial activities. As a fast and effective heat source with thermal and non-thermal effects, microwaves can directly affect food and thus accelerate physicochemical reactions and drying rates, and produce high quality dried products. The purpose of this research is to use microwave pretreatment to increase the mass transfer rate in the drying process of carrot slices and to model the process using the genetic algorithm-artificial neural network method. In this study, the effects of microwave treatment time at five levels of 0, 15, 30, 45, and 60 seconds on the drying time and moisture content of carrot slices were investigated in three replications. This process was modeled using the genetic algorithm-artificial neural network method with 2 inputs (microwave processing time and drying process duration) and 1 output (moisture percentage). The results showed that by increasing the microwave treatment time, the rate of moisture removal from the samples increased and thus the drying time decreased. Different training algorithms were evaluated and the Levenberg– Marquardt algorithm was chosen as the best algorithm. Based on modeling data analysis, the Perceptron artificial neural network with 2-5-1 structure is the most suitable network to predict the moisture content of microwaves-treated carrot slices. In this study, the values of mean squared error (MSE), normalized mean squared error (NMSE), mean absolute error (MAE), and correlation coefficient (r) for predicting the moisture content of carrot slices during drying process were equal to 5.298, 0.006, 1.650, and 0.997, respectively. The results of the optimal neural network sensitivity analysis showed that the drying process duration was the most effective factor in predicting the moisture content of carrot slices. [ABSTRACT FROM AUTHOR]

Background and Objectives: scouring is a phenomenon that occurs due to the passage of water flow at the boundaries of contact with other objects in hydraulic structures. The basis of this phenomenon is the creation of a vacuum at the contact boundaries of two environments, due to the water velocity gradient. In general, scouring occurs in the vicinity of various structures such as bridge foundations, submerged spillways, downstream of spillways. With the increase in the dimensions of the scour hole, there is a possibility of overturning and destroying these structures. So that every year significant costs are spent on the repair and reconstruction of various structures, including bridge foundations that have been destroyed due to erosion. Estimation and prediction of scouring around the piers play a significant role to design these structures since with increasing dimensions of scour hole, stability of the pier is threatened; as a result, the structure may be destructed. In this study, scour hole in the vicinity of twin and three piers was estimated by using fuzzy c-means clustering of ANFIS (ANFIS-FCM) network technique. Methodology: Firstly, the parameters affecting scour hole around twin and three piers including Froude number (Fr), the ratio of the pier diameter to the flow depth (D/h), and the ratio of the distance between the piers to the flow depth (d/h) were detected. Subsequently, seven ANFIS-FCM models were defined by means of these dimensional input parameters. It should be stated that 70% of the experimental data were utilized to training the models and 30% of the rest were applied to testing. Then, the superior ANFIS-FCM model and the most important input parameter were introduced by implementing a sensitivity analysis. The premium model as a function of all input parameters simulated the scour values with a reasonable accuracy. Findings: For instance, the correlation coefficient (R), the scatter index (SI), and the Nash-Sutcliff efficiency coefficient (NSC) are respectively computed to be 0.988, 0.106, and 0.976. Furthermore, the Froude number was considered as the most important input parameter. Finally, a computer code was introduced to simulate the scour hole around the twin and three piers. Conclusion: In this study, a neuro-fuzzy technique of the new artificial intelligence method called ANFIS Network Fuzzy C-Mean Classification (ANFIS-FCM) was used to simulate the scour depth in the vicinity of paired and triple bridge foundations. To validate the results of the simulations, 70% of the observed values were used to train the artificial intelligence model and the remaining 30% were used to test it. Then, using the input parameters, seven ANFIS-FCM models were defined, and by analyzing the results Modeling, the best model and the most effective input parameters were introduced. The superior model (ANFIS-FCM 1) simulated scour values with acceptable accuracy. This model estimated scour values according to all input parameters and Fr and D/h parameters were identified as the most effective input parameters. For example, the values of RMSE, MAE and VAF were calculated as 0.025, 0.019 and 97.507 respectively for the test conditions of ANFIS-FCM 1 model (superior model). It should be mentioned that in order to estimate scour depth in the vicinity of double and triple bridge foundations, a computer code was proposed for use in practical work for engineers. One of scour hole, stability of the pier is threatened; as a result, the structure may be destructed. In this study, scour hole in the vicinity of twin and three piers was estimated by using fuzzy c-means clustering of ANFIS (ANFIS-FCM) network technique. Firstly, the parameters affecting scour hole around twin and three piers including Froude number (Fr), the ratio of the pier diameter to the flow depth (D/h), and the ratio of the distance between the piers to the flow depth (d/h) were detected. Subsequently, seven ANFIS-FCM models were defined by means of these dimensional input parameters. It should be stated that 70% of the experimental data were utilized to training the models and 30% of the rest were applied to testing. Then, the superior ANFISFCM model and the most important input parameter were introduced by implementing a sensitivity analysis. The premium model as a function of all input parameters simulated the scour values with a reasonable accuracy. For instance, the correlation coefficient (R), the scatter index (SI), and the Nash-Sutcliff efficiency coefficient (NSC) are respectively computed to be 0.988, 0.106, and 0.976. Furthermore, the Froude number was considered as the most important input parameter. Finally, a computer code was introduced to simulate the scour hole around the twin and three piers. [ABSTRACT FROM AUTHOR]

The most sustainable and cost-effective approach to enhance river water quality is by actively managing its self-purification. This study's aim is to explore potential management scenarios for enhancing the river's self-purification capacity. The QUAL2Kw model was used to simulate the water quality and self-purification capacity in Dez River in Iran. The model was calibrated and validated using recorded data of three monitoring stations along the river. Five parameters, namely DO, BOD, COD, NO3-N, and NH4-N were calculated and compared with field data. The Margin of Safety was presented and added to the value of each parameter for better water management and protection. Sensitivity analysis was conducted to identify the most influential parameters in water quality simulation for Dez River. The study presented and compared the self-purification capacity across six proposed scenarios for managing water quality. The results showed that the oxidation rate, nitrification rate, and denitrification rate were the most influential parameters in simulating water quality using QUAL2Kw. Among the scenarios considered, the fourth scenario, which included urban and industrial sewage point sources as diffuse sources, exhibited the highest level of self-purification, estimated at 2,246,170.01 kg/day. In all scenarios, the self-purification capacity for COD exceeded that of other parameters along the river, with the highest COD self-purification reaching approximately 167,034.9 kg/day.

Soil conservation requires a suitable model and framework to assess soil erosion and sediment yield based on land use scenarios. Therefore, the present study was conducted with the aim of investigating the sensitivity of the G2 model to the soil erodibility factor in the Kasilian representative watershed in the period of 2001-2021. Points such as the use of global soilgrid database and comparison with soil sampling data, removal of soil erodibility in rocky areas, and removal of outlier data created by systematic error were included in the current research. The results showed that qualitatively, the rate of soil erosion estimated based on each of the mentioned points was completely in the minimum category. The initial results of the soil erosion rate obtained from the global soilgrid database and the IDW method were 4.31 and 4.33 t ha-1 y-1, respectively, which were not significantly different based on the independent t test. But the effect of other changes, including removal of soil erodibility in rocky areas and removal of outlier data on the change of soil erosion estimates using one-way ANOVA and Duncan's tests were significant (P

Soil conservation requires a suitable model and framework to assess soil erosion and sediment yield based on land use scenarios. Therefore, the present study was conducted with the aim of investigating the sensitivity of the G2 model to the soil erodibility factor in the Kasilian representative watershed in the period of 2001-2021. Points such as the use of global soilgrid database and comparison with soil sampling data, removal of soil erodibility in rocky areas, and removal of outlier data created by systematic error were included in the current research. The results showed that qualitatively, the rate of soil erosion estimated based on each of the mentioned points was completely in the minimum category. The initial results of the soil erosion rate obtained from the global soilgrid database and the IDW method were 4.31 and 4.33 t ha-1 y-1, respectively, which were not significantly different based on the independent t test. But the effect of other changes, including removal of soil erodibility in rocky areas and removal of outlier data on the change of soil erosion estimates using one-way ANOVA and Duncan's tests were significant (P<0.01). The estimated average rate of soil erosion in the watershed after following the mentioned points was reduced by more than 65% compared to the initial results and became closer to the data of erosion plot. [ABSTRACT FROM AUTHOR]

This study used the benefit-cost analysis and various financial indicators, such as Net Present Value, Benefit-Cost Ratio, Internal Rate of Return, and Payback Period, to determine the economic feasibility of saffron production under controlled conditions in Mazandaran province. Data for calculating these financial indicators were collected through field surveys, questionnaires, interviews with experts and specialists from the Agriculture Jihad Organization of Mazandaran province, and by obtaining fixed and variable costs as well as income from the establishment of controlled environment spaces for saffron production (spaces with areas of 200 square meters, 1000 square meters, and 5000 square meters) from agricultural service centers. The collected data were analyzed and evaluated using COMFAR software version 3.3, and the results, including financial indicators and sensitivity analysis under different scenarios, were presented. The findings suggest that at a discount rate of 18% and an inflation rate of 35.06%, establishing a saffron production unit on a 200-square-meter plot is not financially viable within a seven-years. (one year for construction and six years for operation). However, for plots with areas of 1000 and 5000 square meters, the internal rate of return is 36.85% and 53.99%, respectively, making them financially viable. According to the project ranking test, increasing the plot area for saffron production under controlled conditions leads to higher financial profitability. Furthermore, based on the sensitivity analysis results, it was identified that increasing product sales income has a more significant impact on project profitability compared to reducing fixed and variable costs. Positive steps such as increasing production, effective marketing, and improving product quality can enhance profitability. [ABSTRACT FROM AUTHOR]

In light of the continuous and rapid changes in global competition, companies face the imperative of consistently introducing new products or expanding their existing product lines to maintain their competitive edge. Recognizing that numerous factors within the supply chain influence the production, design, distribution, and introduction of new products, understanding supply chain risks is crucial, spanning from the procurement of raw materials to the delivery of products to the market. Consequently, risk management stands as one of the most critical challenges within the supply chain, significantly impacting New Product Development (NPD) performance. This research seeks to answer the primary question: "How and to what extent do various supply chain risks affect newly developed products?" While prior research has employed various methods to evaluate and manage supply chain risks, few models have explored the interplay of these risks on each other and their influence on performance dimensions. In this study, based on a review of theoretical foundations and prior research within the clothing manufacturing sector, we identified dimensions of newly developed products and supply chain risks. We employed the Delphi technique through interviews to identify the most significant risks. Subsequently, we employed the Cross-Impact Analysis method to elucidate relationships between these factors. Finally, we utilized Bayesian networks to analyze the impact of identified risks on the performance of the selected new product, conducting sensitivity and scenario analyses. The findings indicate that environmental and supply risks are more likely to manifest than other risks, with three operational, distribution, and demand risks, influenced by environmental and supply risks, exerting the most direct impact on new product performance, particularly in the dimension of quality.IntroductionModern organizations recognize that traditional competitive strategies, such as improving quality and reducing costs, no longer suffice to remain competitive. Research has demonstrated that numerous new product development NPD projects face failure for various reasons. Effective risk identification and management, particularly concerning supply chain risks in NPD projects marked by a high degree of uncertainty, emerge as pivotal factors for NPD success. In this context, the clothing sector, characterized by a complex supply chain structure, has been extensively studied. However, prior research has predominantly examined existing risks individually, overlooking the interactions between risk components and their simultaneous effects on one or more project objectives. In this research, we not only assess the simultaneous impact of risks on product performance using the Bayesian network method, an effective approach in supply chain risk analysis, but also investigate the severity of risk impacts under different scenarios. This research addresses three primary objectives:Identification of supply chain risks in the clothing industry based on background research and case studies.Determination of interdependencies among variables using conditional modeling.Evaluation of the influence of supply chain risks on new product performance using the Bayesian network method under varying scenarios.Literature reviewNumerous researchers have investigated supply chain risks and their repercussions on product and organizational performance. Asgharenjad Nouri et al. (2021), in their article titled "The Effect of Risk Management on New Product Development in the Banking Industry," explored the impact of various risk indicators on new product development. Their results underscore the significant positive influence of managing all risk indicators, including technology, market, environment, finance, organizational resources, and commercialization, on new product development. Qazi et al. (2017), in their article titled “Supply Chain Risk Network Management,” prioritized risks and corresponding strategies through a case study involving semi-structured interviews. They initially identified organizational performance criteria and then linked them to relevant risks, using a matrix of expected profit to investigate the impact of risks on specified performance criteria. Subsequently, they employed the "weighted net evaluation" method to assess practical strategies.MethodologyIn conducting this research, we initially extracted supply chain risks and product performance dimensions from the existing literature. Subsequently, we employed the Delphi technique to select the most significant supply chain risks, providing indicators to participating experts through questionnaires with a 5-point Likert scale. We then used the Content Validity Ratio (CVR) index to confirm or reject the components derived from the questionnaires. In the next step, we used the Cross-Impact Analysis method, employing pairwise comparisons via questionnaires, to reveal relationships between the key risk criteria. Finally, we investigated the impact of identified risks on the performance of the selected new product within the supply chain of Happy Land factory using the Bayesian network method under various scenarios.Discussion and ResultsThe results from the Bayesian network analysis in this research demonstrate that environmental risk, as an external risk within Happy Land’s supply chain, exerts the most significant influence at the highest level of the Bayesian map. Subsequently, other risks, including economic risks, supplier risks, distribution risks, operational risks, and demand risks, are categorized in subsequent levels. Additionally, sensitivity analysis scenarios, depicted in the Tornado chart, reveal that supply chain risks have a substantial impact on performance criteria. According to this scenario analysis, the primary risk affecting quality and cost target nodes is operational risk, while the major risk affecting the product delivery time node is distribution risk, and the primary risk influencing profitability is demand risk. Results from both pessimistic and optimistic scenario analyses under the second scenario of the research indicate that in the pessimistic state, the presence of a high percentage of these risks significantly negatively impacts quality performance. Conversely, in optimistic scenarios, where these risk factors are not present, improvements in quality's functional dimension exhibit the most substantial impact.ConclusionWhen introducing a new product to the market, evaluating and managing supply chain uncertainties is essential due to the mutual influence of new product development and the supply chain. Supply chain risk management, which commences with the accurate identification and assessment of risks and proceeds with appropriate responses, is crucial for providing efficient and effective new products to the market. In addition to employing the Bayesian network method, a highly effective tool in supply chain risk analysis, we have endeavored to evaluate the simultaneous impact of risks on product performance and assess the severity of risk impacts under various scenarios, including optimistic, pessimistic, and sensitivity analyses. Scenario building proves to be an effective method for validating a developed model to measure the impact of risks under different conditions on target criteria.

In the present research, three data-driven models including M5P, REP tree, and random forest were used to estimate daily reference evapotranspiration. The abilities of these three models to estimate reference evapotranspiration were studied in single and combined modes. To this end, the daily meteorological data of five synoptic stations in Kerman province in the period from 2000 to 2020 were used. A combination of meteorological variables, using sensitivity analysis versus the reference evapotranspiration values ​​obtained from FAO-Penman-Monteith, was considered as input for each of the mentioned models. Finally, the accuracy of the mentioned models and empirical methods in estimating the evapotranspiration of the reference plant were compared using statistical indicators, and the superior model was selected. The results of validation data showed that the M5P model in the form of individually (RMSE = 0.083 and NS = 0.998 in Bam station) and the weighted averaging in the form of the ensemble (RMSE = 0.155 and NS = 0.994 in Bam and Sirjan stations) in all stations had better results for estimating evapotranspiration rates than other methods. In general, tree models, especially M5P, had better results in estimating daily evapotranspiration than empirical models.

The industry uses SCM440 steel extensively because of its many characteristics. Nevertheless, wear and vibration are two issues that this steel may bring about. Minimum Quantity Lubricant (MQL) technology is being employed extensively as the most efficient substitution approach in pursuit of a cost-effective alternative solution. Vibrations and acoustic emission signals work well for tracking surface roughness and tool wear. It is possible to use undesirable machining factors as parameters to study the behavior of the process. Regression analysis of several factors allows for the discovery of weaknesses and vulnerabilities in the machining process. Optimizing the production process and enhancing quality are possible with the use of effective factors. The two parameters that have the biggest effects on machining quality are feed rate and cutting depth. Feed rate is known to be sensitive to surface roughness, while cutting depth is recognized to be sensitive to tool wear. [ABSTRACT FROM AUTHOR]

Endmilling is a type of machining tool for chipping the surfaces of parts, which has received attention due to its wide application in industries such as molding. Therefore, today, the need of the industry to find the optimal parameters of the process is felt so that the quality of the desired surface can be achieved. In general, the selection of effective parameters in any milling process significantly affects the surface quality of a finished part. In this research, using E-fast statistical sensitivity analysis method, the simultaneous influence of input parameters including spindle speed, depth of cut, and feed rate on the output parameter of surface roughness for the samples has been investigated quantitatively. Machining experiments have been carried out under different cutting parameters as defined in steady state conditions for the milling tool. surface roughness and vibration rate of machining with non-linear quadratic forms; It has been modeled based on the cutoff parameters and its interactions through several regression analysis methods. The results of this research showed that the spindle speed time parameter is known as the most influential parameter on the surface roughness with 67% influence. It was also observed that the feed rate parameter with 30% effect of cutting depth with 3% are known as the second and third influencing parameters on surface roughness. [ABSTRACT FROM AUTHOR]

In this article, the effects of changing four different input parameters such as cutting speed, feed rate, feed force in Z direction and force in Y direction on the output of tool wear in the machining process of aluminum metal base composite have been investigated. To numerically examine the influence of each parameter on the desired composite machining process results, the E-fast sensitivity analysis procedure was used. E-fast method has a high speed in quantitative and qualitative data analysis. After conducting a sensitivity analysis, it was found that as the feed force increases in the X direction, the tool wear increases with a significant slope. It was also observed that this parameter (feed force in X direction) has the greatest impact on tool wear compared to other input parameters with an amount of 88%. The parameters of feed rate, feed force in Z direction and cutting speed are effective on tool wear with negligible rates of 8%, 3% and 1%, respectively. [ABSTRACT FROM AUTHOR]

The properties of metal -based composites, such as their high strength -to -weight ratio and good resistance to wear and fatigue, have caused a significant growth in their use in the aerospace, automotive, and aircraft industries. Magnesium -based composites have particularly attracted the attention of researchers in various fields, especially aerospace scientists, due to their lower density than other metal -based composite alloys such as titanium and aluminum. However, due to the presence of very abrasive reinforcing material in these materials, machining them is difficult and presents numerous challenges. Therefore, it to study the machining process of these is necessary composites and to examine the effect of the main turning parameters such as cutting speed, feed rate, and depth of cut on machining forces and surface roughness. Sobol's sensitivity analysis method was used for this purpose. Using this method, it was determined that the feed rate, cutting depth, and cutting speed have the greatest effect on the machining forces, respectively. Additionally, the feed rate has a greater effect on the surface roughness than the cutting depth and cutting speed. As the feed rate increases, the surface roughness and cutting forces increase. [ABSTRACT FROM AUTHOR]

In orthopedics, the drilling process is one of the most important steps in the preparation of medical tools and implants. Improving the performance of medical tools and implants is of great importance, because these tools and implants are used in the repair of bone and joint injuries. One of the ways to improve the efficiency of these tools and implants is to use titanium nitride nano coatings on drilling tools. This article is presented with the aim of experimental analysis and optimization of axial force in the orthopedic drilling process using a tool coated with titanium nitride nano coating by physical deposition method. The purpose of this research is to improve the performance and efficiency of this process by optimizing various parameters such as tool rotation speed, cutting depth and titanium nitride coating. For this purpose, experimental tests were conducted using the response surface method. the sensitivity analysis was also performed. The results have shown that the rotational speed, as the most effective parameter, has a lesser effect (45% effect) on the axial force in the case without nanocoating, while it shows a greater effect (73% effect) in the case with nanocoating. [ABSTRACT FROM AUTHOR]

Class A pan evaporation method as one of the most common methods for reference evapotranspiration (ET0) estimation has been widely used in the world due to its simplicity, relatively low cost, and ability to estimate daily ET. In this study, the performance of 8 empirical methods consisting of Allen and Pruitt (1991), Cuenca (1989), Snyder (1992), modified Snyder, Pereira, et al. (1995), Orang (1998), Raghuwanshi and Wallender (1998), and FAO/56 were analyzed to estimate class A pan coefficient and ET0 at Fasa synoptic station located in Fars province. The calculated pan evaporation coefficients from the above equations were compared with measured pan evaporation coefficients which were obtained from the ratio of evapotranspiration calculated by the FAO-Penman-Monteith method to the rate of evaporation from the pan. The results showed that all empirical methods did not predict pan coefficient values well (R2 < 0.3 and NRMSE > 0.25). The comparison results between ET0 from empirical methods and ET0 obtained from FAO-Penman–Monteith indicated that the FAO/56 method had the best performance (R2 = 0.72 and NRMSE = 0.3). To increase the accuracy of empirical pan coefficient equations, these equations were modified with eight years (2007-2015) of meteorological data from the Fasa synoptic station and validated using two years of independent data (2015-2017). The results showed that the accuracy of all empirical models was improved and the Cuenca equation with NRMSE = 0.16 and R2= 0.63 was selected as the best equation for pan coefficient estimation and ET0 (R2 =0.85; NRMSE =0.18) in Fasa region. The sensitivity analysis revealed that the estimated pan coefficient is more sensitive to wind speed, followed by relative humidity, fetch distance, the slope of the saturation vapor pressure curve, sunshine hours, and air pressure. According to statistical results and sensitivity analysis, an equation was expanded for the Fasa region and other areas with the same climate.

Abstract Introduction: Rainfall-runoff modeling is considered one of the important methods in the study of hydrology and environmental management, especially in urban areas, in which sudden floods lead to significant financial and human losses. Thereupon, various models have been developed for urban flood simulation, among which selecting the best, is of significant consideration in the literature. Methods: Runoff peak flow and hydroghs measured in three nodes of an urban drainage network were applied for both spatial and temporal evaluation of the SWMM hydrological model in EPA SWMM, ASSA and Bently SewerGEMS V8i softwares. Other rainfall-runoff models also evaluated were SCS TR-55, SCS TR-20, Rational, Dekalb Rational, Santabarbara UH models (in ASSA software) and SCS UH model (in SewerGEMS V8i software). The models were calibrated considering the measurements during three precipitation events, and then validated by the measured data of three other events. The Nash-Sutcliffe coefficient along with the BIAS coefficient, the Coefficient of Determination and the Root Mean Square Error were used as the efficiency indicators. Findings: The measured runoff peak flow and hydrograph were most compatible with those simulated by SWMM models of EPA SWMM, SewerGEMS V8i and ASSA softwares, respectively. Regarding the results of the partial parameter and the Spearman correlation coefficient methods, model outputs were most sensitive to the percentage of impervious areas, equivalent width, roughness coefficient of impervious areas, the depth of depression of impervious and pervious areas, the percentage of impervious areas without surface storage and the curve number, respectively. The model was not sensitive to the roughness of the permeable areas. The results suggest EPA SWMM as the software with more reliable simulation results for runoff management projects in the study area and urban basins alike.

Evaporation losses from free surface water in reservoir dams are one of the important processes in meteorology and hydrology. Each experimental methods estimates the evaporation based on the climatic conditions of each region. For this reason, calibration of the relationship between evaporation estimates in different regions are required (Vanzyl et al., 1989). experimental methods are common in engineering sciences. Most scholars, by modifying experimental methods and discovering new relationships, try to find a simple and high-level relationship to replace with the field methods. More than 50 experimental relationships have been proposed for estimating evapotranspiration by various researchers (Naorem and Devi, 2014). Despite the importance of evaporation in hydrology, less attention has been paid to it. However, with the correct estimation, it can be applied in water resource planning with fewer errors (Saadatkhah et al., 2002).In this research, the experimental methods of Mayer, Marciano, Shahthin, Henfer, Ivanov and USBR have been used to study the evaporation from the free surface of the Choghakhor lake, located in Chaharmahal and Bakhtiari province of Iran. The results have been compared with actual evaporation values from the free surface.

Long-term monitoring as well as the impacts of climate change on sand dune mobility play a crucial role in reducing their adverse effects, including creating dusty days, potential risks of reduced air quality, and other environmental and health risks. The main objectives of this study were to investigate the dust phenomenon and its origin, to analyze the mobility status of sand dunes using two indicators, to determine the effect of climatic conditions on mobility, and also to predict the effects of changes in climate parameters on sand dunes mobility in the strategic area of ​​Sarakhs, located on the northeastern border of the country, for a period of 27 years. The results on the number of dusty days showed that the local dust events at Sarakhs station had a significant increasing trend. The results of sand dune mobility using Lancaster and modified Lancaster indices showed that there was a significant relationship at 99% level with dusty days, based on which the ability to use the modified Lancaster index in this area is raised. It can also be acknowledged that in the study region, increasing environmental drought coming with the mobility of sand dunes increases the risk of desertification. Also, the results of sand mobility sensitivity analysis showed that the combined effect of the frequency of erosive winds or the potential of sand transport and evapotranspiration has the greatest effect on reducing and increasing sand activity in the future.

The review of the literature survey of transient stability constrained optimal power flow (TSCOPF) shows that only mechanical input power is used as a key parameter in the energy sensitivity approaches while generator’s voltage changes are not considered. Using AC-OPF, this paper considers the effect of generator voltage changes in the calculation of energy sensitivity. To evaluate the transient stability, a new index from the family of corrected hybrid techniques is proposed and applied. It is shown that the change of the proposed index against the mentioned key parameters is linear. Thus, to reduce the computational burden of the approach, this linear property is considered to define the transient stability constraint. The proposed framework is implemented on the New England 39-bus test network. A comparison of the obtained results with those in the literature survey shows that using the proposed approach, the operation cost is decreased from 61799.68 ($/h) to 60927.86 ($/h) which achieves 1.41% saving in the fuel cost while securing the network against considered contingency.

The ionosphere is the upper part of the Earth's atmosphere, which is considered to be approximately 70 to 1000 km above the Earth's surface. Ionosphere modeling has been one of the goals of spatial geodesy since 1970. In many ionosphere modeling using satellite measurements such as GPS, total electron content (TEC) are used as observational input data. In recent years, modeling and prediction of the TEC have been considered by researchers with methods that have high speed and accuracy. One of the branches that has been able to show good capabilities in the field of estimation and modeling is machine learning methods (ML). Machine learning includes fuzzy inference systems (FIS), artificial neural networks (ANNs), genetic algorithms (GAs), support vector machines (SVMs), and evolutionary communications (ECs). Since 1993, with the advancement of computer technology, many new and hybrid algorithms, such as the adaptive neuro-fuzzy inference system (ANFIS), have been developed in ML. Another new effective approach in ML is the support vector regression (SVR) method. The SVR is a kernel-based ML method for classification and regression in which the risk of incorrect classification is minimized. The structure of an SVR network has a lot in common with the ANN, and the main difference is practically in the way of the training algorithm. In general, this method is divided into linear and nonlinear modes. In this paper, the TEC of the ionosphere is modeled and evaluated with ML models. Support vector regression (SVR) and artificial neural network (ANN) methods are used for local TEC modeling. In both models, the latitude and longitude of the GPS stations, day of the year (DOY), hours, AP, KP, DST, and F10.7 are considered an input vectors. Also, the value of VTEC is considered as the output of the models. The main innovation of this paper is in evaluating the effect of different physical parameters on the accuracy of ML models. Using observations of 15 GPS stations in the northwest of Iran from 193 to 228 in 2012, new models are evaluated. Also, the results of the new models are compared with the results of the global ionosphere map (GIM), the IRI2016, and NeQuick empirical models in two internal and one external control station. Statistical indices of root mean square error (RMSE), relative error, dVTEC, and correlation coefficient are used to evaluate the error of the models. Sensitivity analysis of SVR and ANN models to input parameters is performed and the importance of each physical parameter in spatio-temporal modeling of the ionosphere is investigated. The results obtained from this paper show that in both high and low geomagnetic and solar activities, the SVR model in internal control stations has a higher accuracy than other models. But at the external control station, the error of the SVR model is much higher than other models. Determining the parameters of the kernel function using observations at the territory of the studied network is the reason. Also, the sensitivity of SVR and ANN models is increased to the physical parameters F10.7, KP, DST, and AP, respectively. For precise local ionosphere modeling, the effect of these parameters must also be considered. [ABSTRACT FROM AUTHOR]

Class A pan evaporation method as one of the most common methods for reference evapotranspiration (ET0) estimation has been widely used in the world due to its simplicity, relatively low cost, and ability to estimate daily ET. In this study, the performance of 8 empirical methods consisting of Allen and Pruitt (1991), Cuenca (1989), Snyder (1992), modified Snyder, Pereira, et al. (1995), Orang (1998), Raghuwanshi and Wallender (1998), and FAO/56 were analyzed to estimate class A pan coefficient and ET0 at Fasa synoptic station located in Fars province. The calculated pan evaporation coefficients from the above equations were compared with measured pan evaporation coefficients which were obtained from the ratio of evapotranspiration calculated by the FAO-Penman-Monteith method to the rate of evaporation from the pan. The results showed that all empirical methods did not predict pan coefficient values well (R² < 0.3 and NRMSE > 0.25). The comparison results between ET0 from empirical methods and ET0 obtained from FAO-Penman–Monteith indicated that the FAO/56 method had the best performance (R² = 0.72 and NRMSE = 0.3). To increase the accuracy of empirical pan coefficient equations, these equations were modified with eight years (2007- 2015) of meteorological data from the Fasa synoptic station and validated using two years of independent data (2015- 2017). The results showed that the accuracy of all empirical models was improved and the Cuenca equation with NRMSE = 0.16 and R²= 0.63 was selected as the best equation for pan coefficient estimation and ET0 (R² =0.85; NRMSE =0.18) in Fasa region. The sensitivity analysis revealed that the estimated pan coefficient is more sensitive to wind speed, followed by relative humidity, fetch distance, the slope of the saturation vapor pressure curve, sunshine hours, and air pressure. According to statistical results and sensitivity analysis, an equation was expanded for the Fasa region and other areas with the same climate. [ABSTRACT FROM AUTHOR]

In this research the combination of laboratory tests and theoretical formula was used to determine the stiffness coefficients of these spacers that is related to a special fuel assembly. In order to validate the numerical model a spatial modal test set-up has been designed, constructed and set up to perform the modal test. The modal test results indicate that equivalent density of the dummy fuel rod depends on the natural mode of the rod. Then, the spring coefficients of the middle spacers and end constraints have been modified by comparing the results of numerical simulation with the results of modal tests defined in different supporting states of the hollow rod. The simulation results show that in addition to the middle spacers, also the end constraints have a significant effect on the natural frequencies of the rod. The Sensitivity Analysis results show that these coefficients depend on the natural mode of the rod. In addition, the results show that by updating the stiffness coefficients of the middle spacers and end constraints the estimation error of natural frequencies has been significantly reduced and the estimation of natural mode shapes based on the modal assurance criterion has been significantly improved. [ABSTRACT FROM AUTHOR]

Past earthquakes have shown that properly designed buildings using shear walls have great performance in dissipating a considerable amount of inelastic energy. It is notable that the reduction in seismic input energy occurs in plastic hinge area through inelastic deformation. Plastic hinge development in a RC shear wall in the areas with plastic behavior depends on the ground motions characteristics as well as shear wall details. One of the most generally used forms of structural walls is flanged RC wall. These types of shear walls have large in-plane and out-of-plane stiffnesses through flanges and can tolerate high shear stresses. Several international seismic design codes and guidelines have suggested special detailing to assure ductile response in this region. In this paper, the parameters that affect the length of plastic hinge region in the flanged RC walls were examined and the sensitivity of these parameters was evaluated with respect to the length of the plastic hinge region. Sensitivity analysis was conducted by independently variable parameters with one standard deviation away from their means. To this end, the Monte Carlo simulation, tornado diagram analysis, and first-order second moment method were employed to determine the uncertainties associated with analysis parameters. The results showed that among the considered design variables, the aspect ratio of the flanged RC wall (length to width ratio), length of flange to length of web (lf/lw), and axial load level were the most important design parameters in the plastic hinge region, while the yield strength of transverse reinforcements had the least effect on determining the length of this region.

This article investigates an operational orbit's design and sensitivity analysis for Earth observation (EO) missions in non-sun-synchronous orbits. Sun-synchronous orbits are the primary choice for deploying EO satellites, but in the absence of access to such orbits, alternative options can be considered, including multi-sun-synchronous orbits (MSSO) capable of repeating ground track (RGT).In this research, sets of such orbits are designed, taking into account the defined mission and considering the available altitude and inclination of the orbit. To achieve this, a constrained search problem is formulated, considering the constraints related to the characteristics of multi-sun-synchronicity and repeating ground tracks to search for orbit characteristics.Furthermore, to identify the allowable range of injection errors, a sensitivity analysis of the designed orbit's characteristics has been conducted to assess their sensitivity to uncertainties in injection accuracy during a case study investigation.

A new approach to the design and development of launchers is the use of advanced technologies to reduce design and development costs as much as possible. In this paper, an approach to reduce costs and increase reliability is proposed, which is based on the use of a non-turbo pump propulsion system (pressure-fed propulsion system) instead of a turbo pump propulsion system. For this purpose, the multidisciplinary conceptual design optimization of a two-stage launch vehicle with a pressure-fed propulsion system with the aim of sending max payload with a least gross mass to the orbit (500 km) in terms of structure, aerodynamics, propulsion, pressure vessels, simulation, and pitch program disciplines. Then, the sensitivity analysis was performed on the optimum launcher to determine the efficiency of the launcher at different orbital heights and the ability to carry a suitable payload.

Radiation damage calculation is very important for estimating the lifetime of nuclear power plant instruments. The purpose of the present study is to evaluate the sensitivity of the damage values to the neutron group energy, where the neutron flux rate is maximum. In the present study, we propose three neutron group energy (WIMS, CINDER, and OPENMC) to evaluate the sensitivity of radiation damage calculations. The obtained result from SPECTER code shows that the differences between these results and the standard values (ASTM E-693) for three group energy WIMS, CINDER and OPENMC are respectively 0.25E-04, 0.75E-04, and 1.74E-04 in the ¼ diameter of reactor pressure vessel (RPV). Therefore, the WIMS energy group is the most accurate spectrum compared to other spectrums in this region. Also, these differences are respectively 0.43E-04, 0.52E-04, and 1.86E-04 in the ¾ diameter of RPV. Moreover, the WIMS group energy is the most accurate spectrum in this region. This result shows that increasing the number of neutron energy group cause to reduce the difference between calculation of damage and its standard values. According to the results of calculated damage that induced by WIMS group energy in ¼ diameter of RPV, the values of yield strength at 1, 5, 10, 15, 20, 25, 30, 35 and 40 years are calculated by SPECOMP and SPECTER codes.

The review of the literature survey of transient stability constrained optimal power flow (TSCOPF) shows that only mechanical input power is used as a key parameter in the energy sensitivity approaches while generator's voltage changes are not considered. Using AC-OPF, this paper considers the effect of generator voltage changes in the calculation of energy sensitivity. To evaluate the transient stability, a new index from the family of corrected hybrid techniques is proposed and applied. It is shown that the change of the proposed index against the mentioned key parameters is linear. Thus, to reduce the computational burden of the approach, this linear property is considered to define the transient stability constraint. The proposed framework is implemented on the New England 39-bus test network. A comparison of the obtained results with those in the literature survey shows that using the proposed approach, the operation cost is decreased from 61799.68 ($/h) to 60927.86 ($/h) which achieves 1.41% saving in the fuel cost while securing the network against considered contingency. [ABSTRACT FROM AUTHOR]

The first production cycle in open-pit mines is the drilling of blast holes, which is a major part of the exploitation costs. Bits are considered as one of the most important parts of drilling in operational processes due to the type of application and high costs. Examining the parameters affecting the bits wear can reduce the effect of wear, prevent wasting time and additional costs of the drilling process. Therefore, in this study, first, by determining the effective parameters based on the results of univariate regression and Principal Component Analysis (PCA), the relationship between these factors and the tricone rotary bits wear has been determined through statistical methods. Then, 100 linear and nonlinear models were examined to predict the rate of the tricone rotary bits wears during drilling, of which only 21 models, including 6 linear models and 15 nonlinear models, were approved. The performance of these models is evaluated based on the root mean square error (RMSE), coefficient of determination (R2), Variance accounted for (VAF), and mean absolute percentage error (MAPE). then the priority of each method is determined based on performance evaluation and prioritization strategies. In this research, three strategies of prioritization, the Rank average method, the Borda method, the Copeland method, and finally the Aggregate of these methods have been used. The ranking results show that the best model for predicting the tricone rotary bits wear is the regression model with independent variables of Weight on Bits (WOB), Uniaxial Compressive Strength (UCS), and Geological Strength Index (GSI). According to the results of sensitivity analysis, the Rij value of the Weight on Bits (WOB) is 0.982, which is more than the other two parameters of the final model. Therefore, this parameter has the greatest effect on the tricone rotary bits wear and it is the most important parameter of the final model. [ABSTRACT FROM AUTHOR]

Sand dunes are one of the most dynamic geomorphic features of the earth's surface. Accordingly, identifying the factors affecting the mobility of sands and predicting their future status is essential to control dust. The study area was selected due to the location of Sabzevar city in the busiest railway line of the country, and the condition of sand dunes around this city. For this purpose, after calculating the percentage of erosive winds and drought index, the Lancaster index based on the average annual rainfall ratio and annual potential evapotranspiration for the period of 1990-2016 was calculated for analysis of sand dunes' mobility status. To predict the effect of climate change on the mobility of sand dunes, a sensitivity analysis test was carried out. The results show that the values ​​of the Lancaster index in this station did not show the inactive status in this span time period. The results of the analysis of the relationship between drought index and mobility of sand dunes show a significant effect of drought on the mobility and activity of sand dunes. Finally, the results of sensitivity analysis showed that if the frequency of erosive winds and evapotranspiration potential increases by 30%, the activity of sand dunes in the Sabzevar station will increase by 38%. Also, due to the potential of solid particles in creating dust, the location of sand dunes, and the study of local dust at Sabzevar station, the priority is to stabilize the hills located in the east of the study area.

Uncertainties are inevitable in the estimation of structural engineering issues and increase the cost of retrofitting. In the presence of uncertainties, the results of seismic evaluation are incorrect and create conservatism in acceptance criteria at structural performance levels. Considering and quantification of uncertainties in design and rehabilitation of structures reduces the existing conservatism and can lead to the economic design and rehabilitation of structures. In the seismic rehabilitation of structures, uncertainties have been studied on existing structures and have been applied by coefficients in guidelines. Adding a secondary system to rehabilitate of existing structure can enter uncertainties into the computation and can be effective for results of reliability. Therefore, in this study, reliability of rehabilitated steel moment frame with steel shear wall has been discussed in order to quantify the uncertainty of the steel shear wall. The selected structure is a nine-storey steel moment frame of SAC project, which was rehabilitated by steel shear wall. The studied structures were analyzed pre- and post-rehabilitation, with probabilistic variables considered for steel shear wall by OpenSees software. Based on results of incremental dynamic analysis and obtained fragility curves, the values of the reliability index have been obtained for the rehabilitated structure in the presence of uncertainties. Results showed that considering of uncertainties and reducing them can reduce the existing conservatism and the cost of rehabilitation.

Groundwater is the main source of drinking water in the semi-arid regions of Iran. Therefore, monitoring the quality of this valuable resource is vital. Hence, the purpose of this study was to investigate and prepare a groundwater quality map of Birjand located in the semi-arid region of eastern Iran using GIS-based geostatistical analysis with Drinking Groundwater Quality Index (DGWQI). For this purpose, the collected groundwater quality data of 27 agricultural wells during the years 2014-2019 were used. The results of spatial analysis show that 63% of the aquifer area was in the appropriate quality category, 18% in the poor category, 10% in the very poor category and 9% of the aquifer area was in the non-drinking category. Sensitivity analysis indicated that Mg2+, EC, and TDS parameters with the highest mean change index of 18.98, 20.68 and 19.04 were the most sensitive parameters in calculating DGWQI, respectively. Error evaluation calculated by R2 and RMSE methods on conventional kriging and spherical variogram models showed good performance for spatial analysis of all parameters including DGWQI, Mg2+, EC, and TDS. The DGWQI map shows that the western and southwestern parts of the aquifer do not have good quality conditions for extracting drinking water.

In the Ardabil Plain, groundwater is one of the most important sources of water supply. Therefore, quality protection and groundwater management are a research priority. Ardabil Plain is in danger due to agricultural activity's excessive consumption of fertilizers in the agricultural sector, livestock, and industrial centers effluent. Therefore, it is necessary to identify and monitor areas with high vulnerability potential. In this study, the vulnerability of the plain is first assessed using the DRASTIC method. The DRASTIC model is a general method and should calibrate in each area. So, this method was modified by two approaches. In the first approach, sensitivity analysis, and in the second approach, the amount of groundwater supply estimated by the WTF method was used. Then the vulnerability of Ardabil Plain was assessed. The results of the sensitivity analysis showed that the DRASTIC model in the Ardabil Plain area is susceptible to the parameters of Impact of vadose zone (I), Depth of groundwater (D), and aquifer media (A). The results also showed that the conventional model of the DRASTIC did not produce acceptable results compared to the Nitrate map. But calculating the amount of water recharge (R) by the WTF method, the correlation between the vulnerability maps and the Nitrate map was about 75%. In the second method, soil parameters (S) and hydraulic conductivity (C) did not have an acceptable correlation with the nitrate concentration in the groundwater of Ardabil Plain. These parameters were removed from the initial equation of the DRASTIC method, and the maps were prepared with the remaining parameters and with new weights and ratings, as a result of which the correlation between the new maps and the nitrate map reached about 30%. According to the WTF modified method for the DRASTIC model, the whole of the Ardabil Plain vulnerability maps in four areas was divided, including medium, high, very high, and infinitely highly vulnerable areas, and 25.8, 47.7, 15.1, and 11.4 percent, respectively.

In the present study, the performance of the 2D single-phase supersonic ejector with working fluid of air is simulated in ANSYS CFX. The aim is to investigate the velocity field, pressure distribution, primary nozzle flow regime, and entertainment ratio in different operational conditions. The primary pressure inlet with bar and the secondary inlet as an opening with bar at different outlet pressures are simulated. The turbulence model is used. The sidewalls are considered symmetry boundary conditions and the no-slip condition is applied to the ejector walls. Then, the ejector geometry is optimized using Multi-Objective Genetic Algorithm (MOGA) in order to reach greater efficiency. Optimization is performed considering geometric parameters including primary nozzle exit diameter, nozzle exit position, diameter, and length of the constant area section. Sensitivity analysis results show that the diameter of constant area section has major effect on the entertainment and pressure ratios as two objective functions. The nozzle exit position and external diameter of primary nozzle are the second and third dominant parameters that respectively influence the performance of the ejector, while the effect of constant area section length is negligible. Results indicate that by increasing the pressure ratio, the shock train moves upstream and at the design point, the last oblique shock is located in the exit of the constant area section, letting the remaining pressure recovery be done in the subsonic diffuser which reduces the pressure losses and increases the efficiency. Above the critical pressure ratio, due to the movement of the shock train to upstream weakening the shock strength, the suction pressure increases. Then, the pressure difference is reduced, leading to the lower secondary mass flow suction. The optimized ejector in the double choking condition has a % higher entertainment ratio and its operational range is enhanced by percent in comparison to the original geometry.

Due to drought and climate change, estimation and prediction of rainfall is quite important in various areas all over the world. In this study, a novel artificial intelligence (AI) technique (WGEP) was developed to model long-term rainfall (67 years period) in Anzali city for the first time. This model was combined using Wavelet Transform (WT) and Gene Expression Programming (GEP) model. Firstly, the most optimized member of wavelet families was chosen. Then, by analyzing the numerical models, the most accurate linking function and fitness function were selected for the GEP model. Next, using the autocorrelation function (ACF), the partial autocorrelation function (PACF) and different lags, 15 WGEP models were introduced. The GEP models were trained, tested and validated in 37, 20- and 10-years periods, respectively. Also, using sensitivity analysis, the superior model and the most effective lags for estimating long-term rainfall were identified. The superior model estimated the target function with high accuracy. For instance, correlation coefficient and scatter index for this model were 0.946 and 0.310, respectively. Additionally, lags 1, 2, 4 and 12 were proposed as the most effective lags for simulating rainfall using hybrid model. Furthermore, results of the superior hybrid model were compared with GEP model that the hybrid model had more accuracy.

Optimization of artificial intelligence (AI) models is a significant issue because it enhances the performance and flexibility of the numerical models. In this study, scour depth around bridge abutments with different shapes was estimated by means of ANFIS and ANFIS-Genetic Algorithm. In other words, the membership functions of the ANFIS model were optimized using the genetic algorithm, finding that the performance of ANFIS model was increased. Firstly, effective input parameters on the scour depth around bridge abutments were defined. Then, by using the input parameters, eleven ANFIS and ANFIS-GA models were produced. Next, the superior ANFIS and ANFIS-GA models were introduced by analyzing the numerical results. For example, the correlation coefficient and scatter index for ANFIS model were calculated to be 0.979 and 0.070; for ANFIS-GA, these were 0.986 and 0.056, respectively. In addition, the average discrepancy ratio (DRave) for ANFIS and ANFIS-GA models was 0.984 and 0.988, respectively. Also, it was shown that the ANFIS-GA models had more accuracy, as compared to the ANFIS models. Moreover, a sensitivity analysis showed that Froude number (Fr) and ratio of flow depth to radius of scour hole (h/L) were the most influential input parameters for simulating the scour depth around bridge abutments.

Background and Objectives: Environmental conditions in different geographical areas provide a basis for the spread of some diseases. Cutaneous leishmaniasis is a serious threat to public health and is one of the arthropod-borne diseases. The prevalence and distribution of this disease is affected by environmental and climatic factors. The aim of this study was to model the Spatio-temporal variations in the incidence rate of this disease based on environmental and ecological criteria. Methods: The northeast of Iran was selected as the study area. The data used in this study included vegetation, surface temperature, precipitation, evapotranspiration, soil moisture, digital elevation model and sunny hours. The artificial neural network method was used to model the spatio-temporal changes of cutaneous leishmaniasis. Results: Spatial variations in the incidence of the disease had a north-south trend and decreased from north to south. In addition, two foci were identified in the medium altitude areas in North and South Khorasan provinces. Temporal variations in the incidence of disease in the study period showed that the incidence rate decreased in the two identified foci from 2011 to 2016. Conclusion: The modeling results showed that the estimated regression coefficient was 0.92 for neural network based on all three types of data (training, validation, test) indicating good quality of constructed neural network. In addition, sensitivity analysis results showed that sunny hours and soil moisture were the most important factors in the model function.

Consecutive loads can be a serious threat to the safety of structures. Abrupt removal of elemental elements such as columns is one of the causes of progressive failure in structures. The local breakdown caused by the sudden removal of the column can affect the behavior of the structures of successive courts. One of the loads that threatens the safety of the structure over the life of the structure is the fire.When a structure can withstand the load, one of the main factors in the safety of structures against thermal loads. Local loopholes in the structure, including the sudden removal of barrier elements, can affect the stability of structures against fire load as two successive events. In this research, the susceptibility of the RC frame to the stability of the continuous loading of the column and the subsequent fire is compared to the random variables.The 7-story concrete frame is modeled after mechanical design and is subject to the sudden removal of the column in different situations. Several parameters have been considered as random variables, and two methods of Monte Carlo and second-order second-order sensitivity analysis (FOSM) have been used for sensitivity analysis. Sensitivity analysis in a concrete frame for the analysis of dynamic duct and thermal analysis has been done separately. The results show that in the sudden removal of the column, the dead load, and in the subsequent fire scenario, the dead load and reinforcement coatings have the most sensitivity to other parameters. Also, the maximum sensitivity error of the FOSM method was calculated to be 11% compared to the Monte Carlo method.

Groundwater contaminant transport modeling is a useful tool for identifying how pollutants fate and transport in porous aquifer environments. These models include several parameters, which are often estimated based on personal judgment or in the best case, based on limited field measurements. Therefore, the input data of simulation models are not accurate and contain several errors. The purpose of this study is parameter uncertainty and sensitivity analysis in the groundwater solute contaminants transport modeling using probability theory. First, governing equations for groundwater flow and solute contaminant transport have been presented. Then, using MOFLOW for modeling groundwater flow and MT3DMS for modeling solute contaminant transport in a hypothetical problem and using effective parameters in a case study (Qazvin plain), uncertainty analysis through the Monte Carlo method was done. To illustrate the uncertainty analysis, the Complementary Cumulative Distribution Functions (CCDF) of Chloride and Nitrate graphs have been computed. Then using random samples, generated in uncertainty analysis step, local and global sensitivity analysis of solute transport model parameters have been determined. Result: Using maximum concentration of solute contaminant as a model output, the results of the local sensitivity analysis show that the most sensitive parameters are hydraulic conductivity (K), decay rate constant (λ), porosity (θ), distribution coefficient (Kd), and dispersivity (D) respectively. While using time to maximum concentration as output variable, leads to the following order of sensitivity: K, Kd, θ, D, and λ. On the other hand, the global sensitivity analysis using maximum concentration shows that the order of sensitivity is: K, λ, θ, D, and Kd, and using time to maximum concentration it is: K, Kd, D, λ, and θ respectively. According to the CCDF of Chloride, concentrations of 5%, 50% and 95% equal 205.5, 196 and 185.4 mg/L respectively. Also, according to the CCDF of Nitrate the concentrations of 5%, 50%, and 95% equal 56, 54.125 and 51.5 mg/L respectively. All five parameters are sensitivite in solute transport modeling. The local and global sensitivity analysis show more or less the same results. In general, the sensitivity ranking of parameters is K, λ, Kd, θ, and D.

Groundwater vulnerability assessment is important in order to prioritize these resources from the perspective of exploitation, management and control of pollution in different areas. The purpose of this study was to evaluate the qualitative vulnerability of Birjand plain aquifer using DRASTIC-LU model. In this research, the DRASTIC base model with the land use parameter of the developed lands was used. In this method, the basic model parameters including groundwater depth, net nutrition, aquifer environment, soil type, topography, unsaturated area constituents, and hydraulic guidance were analyzed in GIS environment along with land use variable as a model development based on standard weights and the vulnerability zoning map was prepared. Vulnerability zoning map of DRASTIC-LU model showed that 62.27, 25.07, 17.17, and 2.38% of the area have low to medium, medium to high, low and high vulnerability, respectively. In addition, the sensitivity analysis of the model used to evaluate the assigned weights was performed. To validate the model, the correlation of the model with the nitrate concentration was performed; the obtained correlation of 86% indicated the appropriate correlation of this model with the nitrate concentration as an indicator of groundwater pollution.

In this research, the scour hole depth at the downstream of cross-vane structures with different shapes (i.e., J, I, U, and W) was simulated utilizing a modern artificial intelligence method entitled "Outlier Robust Extreme Learning Machine (ORELM)". The observational data were divided into two groups: training (70%) and test (30%). Then, using the input parameters including the ratio of the structure length to the channel width (b/B), the densimetric Froude number (Fd), the ratio of the difference between the downstream and upstream depths to the structure height (Δy/hst), and the structure shape factor (φ), eleven different ORELM models were developed for estimating the scour depth. Subsequently, the superior model and also the most effective input parameters were identified through the conduction of uncertainty analysis. The superior model simulated the scour values by the dimensionless parameters b/B, Fd, Δy/hst. For this model, the values of the correlation coefficient (R), the variance accounted for (VAF), and the Nash-Sutcliffe efficiency (NSC) for the superior model in the test mode were obtained 0.956, 91.378, and 0.908, respectively. Also, the dimensionless parameters b/B and Δy/hst were detected as the most effective input parameters. Furthermore, the results of the superior model were compared with the extreme learning machine model and it was concluded that the ORELM model was more accurate. Moreover, an uncertainty analysis exhibited that the ORELM model had an overestimated performance. Besides, a partial derivative sensitivity analysis (PDSA) model was performed for the superior model. [ABSTRACT FROM AUTHOR]

Introduction: As a metropolitan area in Iran, Tehran is exposed to damage from air pollution due to its large population and pollutants from various sources. Accordingly, research on damage induced by air pollution in this city seems necessary. The main purpose of this study was to forecast ozone in the city of Tehran. Considering the hazards of ozone (O3) gas on human health and the environment and its ascending trend over the past decades, it is also essential to study and predict its quantities in the air. Forecasting ozone in the air can be further used to prevent and control pollution by authorities. Material and Methods: Using an analytical-applied research method, this study was to predict ozone gas in this metropolitan area via daily ozone data of air quality measurement stations, traffic variables, green space, as well as time factors such as one-day time delay. In this regard, an artificial neural network (ANN) model was employed to forecast ozone concentration using the MATLAB software. Results: The results of the ANN model were compared with a linear regression one. Correlation coefficient and root-mean-square error (RMSE) of the ANN model were subsequently compared with R2=0.734 and RMSE=0.56 as well as R2=0.608 and RMSE=11.69 regression equations. Conclusion: It was concluded that the error in the ANN model was smaller than that in the regression one. According to the results of the sensitivity analysis of the season parameters, the length of sunshine hours had the most significant effect on the amount of ozone gas in Tehran air.

Given the fact that the DRASTIC index is ineffective in addressing the saltwater uprising issue in coastal plains, in the present study, three factors including land use, distance to shoreline, and differences between groundwater and sea level were added to the DRASTIC index. The proposed modification to DRASTIC was validated using the measured electrical conductivity (EC) data gathered from groundwater monitoring wells throughout the Talesh Plain. The results showed that the coefficient of correlation between the map of EC over the region and the modified DRASTIC was 0.52, while for the original DRASTIC, the coefficient was 0.45, thereby implying a stronger relationship between EC and the modified DRASTIC in the Talesh Plain. Sensitivity analysis also showed that DRASTIC and the modified DRASTIC were the most sensitive to, respectively, depth to groundwater (D) and land use (Lu). According to the single-parameter sensitivity analysis results, depth to water table and net recharge were the most effective parameters in DRASTIC, whereas the modified DRASTIC was the most sensitive to land use and depth to groundwater. It could be concluded that modifying the DRASTIC index would result in decreasing the area of very high and high vulnerable classes, and the area classified as low and moderate vulnerable could be increased.

The progressive collapse because it can be sustained the whole structure and the safety of the lives of residents threatens that has been the focus of scientific circles over the past few years. In the progressive collapse determining the key element that most potential for failure is of great importance. Four structural steel structures with two frames and dual systems are sensitivity analyzed with two structural systems in rectangular and L- shaped planes. Based on the results of the pushdown analysis in steel moment frame with rectangular and L-shaped planes and the dual system with middle braces, the corners column and the dual system (middle and corner braces) with a rectangular plan and in the dual system (corner braces) with the L- plan the internal column has the most potential for collapse. The results show that higher-heightened structures have a better performance against progressive collapse, and the increase in the height of the models in the dual system does not have much effect on reducing progressive collapse.

The heterogeneous temporal and spatial distribution of fresh water and rapid population growth in recent decades have led to problems in supplying required water resources for various uses. Therefore, new mathematical models have been developed to study the complex hydrological processes. In this study, SWAT model in Samalqan catchment with area of 1148 km2 was used to simulate the flow. In the modeling process, the basin was divided into 21 sub-basins and 402 hydrological units. Modeling of watersheds has uncertainties due to limited access to enough hydrological data. For this purpose, model calibration and validation were performed using SUFI2 algorithm for 1995 to 2012 and 2012 to 2014, respectively. Based on the sensitivity analysis results, the parameters RCHRG_DP (value of penetration into the deep aquifer), GWQMN (amount of water in the shallow aquifer to produce the base stream), ALPHA_BF (groundwater reaction coefficient), SOL_AWC (soil available water capacity) and CN (SCS curve number) had the most effect. The results showed that the values of Nash-Sutcliffe index and R2 coefficient were 0.65 to 0.80 for calibration period and 0.40 to 0.65 for validation period. Out of the total inflow into the basin, 87% is spent on evapotranspiration, 3.2% for surface runoff, 3% for infiltration and the rest is related to changes and moisture storage in soil. Since no estimation of water balance components in this basin has been done so far, this research can provide useful information about the water balance of the basin and help to plan water resources more accurately.

In this study, using the results of 100 PDA dynamic loading tests obtained from different projects and using three types of artificial neural networks (ANN), the loading capacity of a single pile is evaluated. Initially, the Perstron multilayer neural network was used as one of the most widely used neural networks. In the following, a combination of neural-fuzzy networks is used from the nephrophysical network, and at the end, the neural network is used as a function of the radial basis of the successful network in nonlinear problems. Unlike conventional behavioral models, neural network-based models do not explain how input parameters affect output. In this research, by performing sensitivity analysis on the optimal structure of the models introduced in each stage, an attempt has been made to examine this ambiguity to some extent. Also, introducing the relationships governing a neural network model can give engineers more confidence in using them to facilitate analysis and design.

The estimation of reference crop evapotranspiration (ETo) is one the important factors in hydrological studies, irrigation planning, and water resources management. This study attempts to explore the possibility of predicting this key component using three different methods in the Sistan plain: Generalized Linear Models (GLM), Random Forest (RF) and Gradient Boosting Trees (GBT). The maximum and minimum temperature, mean temperature, maximum and minimum humidity, mean humidity, rainfall, sunshine hours, wind speed, and pan evaporation data were applied for years between 2009 to 2018. Using various networks, the ETo as output parameter was estimated for different scenarios including the combination of daily scale meteorological parameters. In order to evaluate the capabilities of different models, results were compared with the ETo calculated by FAO Penman-Monteith as the standard method. Among studied scenarios, M1 covering the maximum number of input parameters (10 parameters) showed the highest accuracy for GBT model, with the lowest RMSE (0.633) and MAE (0.451) and the maximum coefficient of regression (R = 0.993). Air temperature was found as the most sensitive parameters during sensitivity analysis of studied models. It indicated that accuracy and precision of temperature data can improve the results. Application of the GBT model could decrease the time consumed to run the model by 70%. Therefore, the GBT model is recommended for estimation of ETo in the Sistan plain.