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In this research, general performance of Radial basis function (RBF) Artificial neural networks in experimental data on effect of the NiO, WO3, TiO2,ZnO and Fe2O3 nanoparticles in different temperatures and mass fractions on the viscosity of crude oil has been studied. The morphology and stability of the nanoparticles has been analyzed by DLS and TEM analysis, the results showed that the average diameter of the nanoparticles is from 10 to 30 nm which defers for different oxide nanoparticles. The general method for calculating the optimum span of the Isotropic Gaussian function with special algorithm for learning RBF networks, has been presented. This study's results declared that the RBF artificial neural networks, because of having strong academic basis and having the ability to filter the noises, has a good performance. With increase in temperature, the ratio of the viscosity of the nanofluids decreases compering to the viscosity of the basefluid. Also with increase in nanoparticles mass fraction the related viscosity increases boldly. For temperatures higher than 50°C, the related viscosity is less than the viscosity of the basefluid.

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Rice blast, caused by Pyricularia grisea, is one of the most important diseases of this crop in Iran and all over the world. To evaluate the relationship between spore population (SP) and meteorological factors, SP was measured daily using spore trap during growing seasons of 2006-2008 in Rasht and Lahijan regions (Guilan province, Iran). Weather data including precipitation, daily maximum and minimum temperatures, daily maximum and minimum relative humidity and duration of sunny hours were obtained from weather stations which were five kilometers away from the fields. The relationship between spore population and metrological factors was evaluated by Neurosolution 5.0 software. Weather data and spore population were considered as input and output data, respectively. In this study, multilayer perceptron neural network, regression model and Log(x + 1) transformation were performed. To evaluate the model efficiency, correlation coefficient and mean square error were used. The results showed that the correlation coefficient (r) and mean square error (MSE) parameters were 0.55 and 0.03 in Rasht and 0.1 and 0.03 in Lahijan, respectively. The results also showed the potential of this model for modeling SP using meteorological factors; however more data is needed for validation of this model. There has been no previous report on modeling the relationship between SP and meteorological data using artificial neural network in Guilan province (Iran).

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There is different methods for simulating river flow. Some of thesemethods such as the process based hydrological models need multiple input data and high expertise about the hydrologic process. But some of the methods such as the regression based and artificial inteligens modelsare applicable even in data scarce conditions. This capability can improve efficiency of the hydrologic modeling in ungauged watersheds in developing countries. This study attempted to investigate the capability of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for simulating the monthly river flow in three hydrometric stations of Pole-Almas, Nir, and Lai; which have different rate of river flow. The simulations are conducted using three input data including the precipitation, temperature, and the average monthly hydrograph (AMH). The study area islocated in the Gharasu Watershed, Ardabil Province, Iran. For this aim, six groupsof input data (M₁, M₂, … M₆) were defined based on different combinations of the above-mentioned input data. Theconducted simulations in Pole-Almas and Nir stations have presented an acceptable results; but in Lai station it was very poor. This different behavoirs was referred to the lower volume of flow and consequently irregularity and variability of flow in Lai station, which cause the decrease of accuracy in the simulation. The AMH parameter had an important role in increasing the accuracy of the simulations in Pole-Almas and Nir stations. The findings of this study showed that ANFIS is an efficient tool for river flow simulation; but in application of ANFIS, the selection and utilization of relevant and efficient input data will have a determinativerole in achieving to a successful modeling.

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Background: Soil salinization is a world-wide land degradation process in arid and semi-arid regions that leads to sever economic and social consequences.
Materials and Methods: We analyzed soil salinity by two statistical linear (multiple linear regression) and non-linear (artificial neural network) models using Landsat OLI data in Agh-Ghala plain located in north east of Iran. In situ soil electrical conductivity (EC) of 156 topsoil samples (depth of 0-15cm) was also determined. A Pearson correlation between 26 spectral indices derived from Landsat OLI data and in situ measured ECs was used to apply efficient indices in assessing soil salinity. The best correlated indices such as blue, green and red bands, intensity indices (Int1, Int2), soil salinity indices (Si1, Si2, Si3, Si11, Aster-Si), vegetation Indices (NDVI, DVI, RVI, SAVI), greenness and wetness indices were used to develop two models.
Results: Comparison between two estimation models showed that the performance of ANN model (R²=0.964 and RMSE=2.237) was more reliable than that of MLR model (R²=0.506 and RMSE=9.674) in monitoring and predicting soil salinity. Out of the total area, 66% and 55.8% was identified as non-saline, slightly and very slightly saline for ANN and MLR models, respectively.
Conclusions: This shows that remote sensing data can be effectively used to model and map spatial variations of soil salinity. 

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Research subject: Expandable Poly Styrene (EPS) has many applications. This polymer prepared by the radical polymerization. This material has many uses in packaging and insulation industries Some of the properties of this polymer like low mechanical strength caused its applications to be limited. By adding some materials, these properties can be improved. Styrene Butadiene Styrene (SBS) is from the materials that which by adding it to the EPS it can improve its quality.
Research approach: In this research, EPS having different percentages of SBS (0, 0.01, 0.02, 0.03) in different conversion percentages (0.6, 0.63, 0.66, 0.69) has been prepared. Different tests like Impact Test, Modular Melt Flow test, Vicat Softening Temperature test, Tensile at Break test, K-value test, Rochwell Hardness test and Elongation at Break test are done on the prepared polymer. Laboratory gained data has been simulated by Multi-Layer Perceptron (MLP) method of artificial neural networks (ANN) and the simulated data covers the laboratory data perfectly.
Main Results: Investigating the tests show that in constant percentages of SBS in EPS with increase in conversion percentage of EPS, the numerical amount of the tests increases except MFI test (low MFI number means better quality). Increase in SBS percentage in the EPS, increases the properties of polymer. In addition, the results of simulation show that the laboratory data covers the the simulated data perfectly. The data obtained from the results of this reasearch can be used for predicting the data for the points which has not been tested. Adding SBS in different weight percentages of poly styrene in different conversion percentages in order to increase the properties of poly styrene has been used for the first time in this research and the laboratory data results in points which has not been tested has been acquired by applications of ANN.

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Background: Prediction of future climate change is based on output of global climate models (GCMs). However, because of coarse spatial resolution of GCMs (tens to hundreds of kilometers), there is a need to convert GCM outputs into local meteorological and hydrological variables using a downscaling approach. Downscaling technique is a method of converting the coarse spatial resolution of GCM outputs at the regional or local scale. This study proposed a novel hybrid downscaling method based on artificial neural network (ANN) and particle swarm optimization (PSO) algorithm. Materials and Methods: Downscaling technique is implemented to assess the effect of climate change on a basin. The current study aims to explore a hybrid model to downscale monthly precipitation in the Minab basin, Iran. The model was proposed to downscale large scale climatic variables, based on a feed-forward ANN optimized by PSO. This optimization algorithm was employed to decide the initial weights of the neural network. The National Center for Environmental Prediction and National Centre for Atmospheric Research reanalysis datasets were utilized to select the potential predictors. The performance of the artificial neural network-particle swarm optimization model was compared with artificial neural network model which is trained by Levenberg–Marquardt (LM) algorithm. The reliability of the models were evaluated by using root mean square error and coefficient of determination (R²). Results: The results showed the robustness and reliability of the ANN-PSO model for predicting the precipitation which it performed better than the ANN-LM. It was concluded that ANN-PSO is a better technique for statistically downscaling GCM outputs to monthly precipitation than ANN-LM. Discussion and Conclusions: This method can be employed effectively to downscale large-scale climatic variables to monthly precipitation at station scale.

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Research subject: In recent decades, hybrid optimizations methods based on natural phenomenon have placed special position according to their capabilities in finding optimal solutions without expensive computational loads and disassociation on choosing initial points. Artificial Neural Network is used as one of the powerful tools of Artificial Intelligence for process simulation. The employment of the neural network in the modeling of m-Cresol alkylation process of with isopropanol as well as meta-heuristic methods in obtaining the optimal conditions for the catalyst and the reaction can prepare an effective step towards a high efficiency process.
Research approach: In the present study, the artificial neural network is applied to model alkylation of m‐Cresol with isopropanol process. In addition, the bee colony is employed in order to optimize the process yield. To verify its performance, the proposed method is used in prediction of the m‐Cresol conversion and Thymol selectivity of the alkylation process with isopropanol 120 data. In this process, the input variables are Weight Hourly Space Velocity (WHSV), pressure and temperature; m-cresol conversion and thymol selectivity are considered as the output variables of the neural network. Five hidden neurons are considered for the proposed neural network. 120 data is used to train the neural network. The meta-heuristic approach based on bee colony (BC) is applied to maximize the yield of the process.
Main results: The results confirm that the proposed method develops the accurate model with an R² value of greater than 97.5%. The maximum yield is obtained 28.9% by bee colony algorithm with adjustable variables that are WHSV of 0.062 hr^-1, the pressure of 1.5 bar and the temperature of 300^oC. In addition, in order to achieve the better performance of the optimization algorithm, the appropriate values of acceleration coefficient and population size are chosen 100 and 10 during the trial-and-error phase.

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 Research subject: Low solubility of pharmaceutical compounds leads to increasing the required drug dosage and their side effects as well as reducing their therapeutic efficiency. Producing pharmaceutical micro/nanoparticles with homogenous morphology and narrow size distribution is one of the confirmed approaches for their solubility enhancement. So, selection and designing an appropriate method for this purpose is one of the most important research fields of pharmaceutical industries. Over the past three decades, supercritical carbon dioxide (sc-CO₂) based methods as a clean and green technologies have been received much attention in various fields of pharmaceutical industries. However, in order to design and development of these methods for producing micro/nanoparticles, determination of the compounds solubility in sc-CO₂ is essential.
Research approach: In this research, well known empirical models (Adachi and Lu, Ch and Madras, Hozahzbr et al., Bian et al., Mendez-Santiago-Teja), as well as the artificial neural network model were applied for prediction the solubility of six anticancer drugs (Aprepitant, 5-Fluorouracil, Imatinib mesylate, Capecitabine, Letrozole, Docetaxel) in sc-CO₂.
 In order to evaluate the accuracy of these models, a comparison was made between the calculated solubility values and the available experimental data, based on several statistical criteria, such as the average absolute relative deviation (AARD%), adjusted correlation coefficient (R_adj) and F-value.
Main results: According to obtained results, Adachi and Lu model with AARD% value of 12.12% and R_adj value of 0.97 provided acceptable results for solubility of mentioned drugs in sc-CO₂. Also, in comparison between empirical and artificial neural network models, the latter one with AARD% value of 1.65% and R_adj value of 0.9960 was appointed as the most appropriate model for correlation of drugs solubility data.

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The complex and stochastic nature of the electro-discharge machining (EDM) process has frustrated numerous attempts of physical modeling. In this paper two supervised neural networks, namely back propagation (BP), and radial basis function (RBF) have been used for modeling the process. The networks have three inputs of current (I), voltage (V) and period of pulses (T) as the independent process variables, and two outputs of material removal rate (MRR) and surface roughness (Ra) as performance characteristics. Experimental data, employed for training the networks and capabilities of the models in predicting the machining behavior have been verified. For comparison, quadratic regression model is also applied to estimate the outputs. The outputs obtained from neural and regression models are compared with experimental results, and the amounts of relative errors have been calculated. Based on these verification errors, it is shown that the radial basis function of neural network is superior in this particular case, and has the average errors of 8.11% and 5.73% in predicting MRR and Ra, respectively. Further analysis of machining process under different input conditions has been investigated and comparison results of modeling with theoretical considerations shows a good agreement, which also proves the feasibility and effectiveness of the adopted approach.

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Aims: Soil organic carbon (SOC) is contemplated as a crucial proxy to manage soil quality, conserve natural resources, monitoring CO2 and preventing soil erosion within the landscape, regional, and global scale. Therefore, the main aims of this study were to (1) determine the impact of terrain derivatives on the SOC distribution and (2) compare the different algorithms of topographic wetness index (TWI) calculation for SOC estimation in a small-scale loess hillslope of Toshan area, Golestan province, Iran. (3) Comparison between multiple linear regression (MLR) and artificial neural networks (ANN) methods for SOC prediction.
Materials & Methods: total of 135 soil samples were taken in different slope positions, i.e., shoulder (SH), backslope (BS), footslope (FS), and toeslope (TS). Primary and secondary terrain derivatives were calculated using digital elevation model (DEM) with a spatial resolution of 10 m × 10 m. To SOC estimation (dependent variable) was applied two models, i.e., MLR and ANN with terrain derivatives as the independent variables.
Findings: The results showed significant differences using Duncan’s test in where TS position had the higher mean value of SOC (25.90 g kg⁻¹) compared to SH (5.00 g kg⁻¹) and BS (12.70 g kg⁻¹) positions. The present study also revealed which SOC was more correlated with TWIMFD (Multiple-Flow-Direction) and TWIBFD (Biflow-Direction) than TWISFD (Single Flow Direction). The MLR and ANN models were validated by additional samples (25 points) that can be explain 65% and 76% of the total variability of SOC, respectively, in the study area.
Conclusion: These results indicated that the use of terrain derivatives is a beneficial method for SOC estimation. In general, an accurate understanding of TWIMFD is needed to better estimate SOC to evaluate soil and ecosystem related effects on global warming of as this hilly region at a larger scale in a future study.

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The present study aims at applying different methods for predicting spring inflow to the Amir Kabir reservoir in the Karaj river watershed, located to the northwest of Te-hran (Iran). Three different methods, artificial neural network (ANN), ARIMA time se-ries and regression analysis between some hydroclimatological data and inflow, were used to predict the spring inflow. The spring inflow accounts for almost 60 percent of annual inflow to the reservoir. Twenty five years of observed data were used to train or calibrate the models and five years were applied for testing. The performances of models were compared and the ANN model was found to model the flows better. Thus, ANN can be an effective tool for reservoir inflow forecasting in the Amir Kabir reservoir using snowmelt equivalent data.

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In today's competitive world, attention to the improvement and development of employees has become increasingly important. One of the procedures that organizations can use to improve their employees is implementation of assessment and development centers. But it should be noted that the implementation of improvement programs for each participants in the assessment center is time-consuming and costly. Accordingly, the aim of this study is reducing costs of assessment center implementation by segmenting the participants in assessment center and providing improvement programs for each sector. Sampling method is targeted and based on criteria and the sample consists of 75 employees. In this research, self-organizing maps have been used for segmentation of employees and the data were analyzed by Viscovery Profiler software. According to the findings, four sectors identified in the assessment center and they have been named: Talented, improvable employee (with a focus on competence), improvable employee (with focus on character) and insusceptible experts. Results of this segmentation can be used in five areas, including training, promotion, solve organizational challenges, recruitment and retention policies and rewards. Based on the results, for the areas that identified in the assessment center, suggestions are offered.

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  Citrus, especially orange, are of great important among agricultural products in the world. In this study thin-layer drying of orange (var. Thompson) was modeled using artificial neural network (ANN). An experimental dryer was used. Thin-layer of orange slices at five air temperatures (40, 50, 60, 70 & 80 ºC), three air velocities (0.5, 1 & 2 m/s) and three thicknesses (2, 4 & 6 mm) were artificially dried. Initial M.C. during all experiments was between 5.4 to 5.7 (g/g) (d.b.). Mass of samples were recorded and saved every 5 sec. using a digital balance connected to a PC. MLP with momentum and LM were used to train the ANNS. In order to develop ANN's models, temperatures, air velocity and time are used as input vectors and moisture ration as the output. Results showed a 3-6-1 topology for thickness of 2 mm, 3-7-1 topology for thickness of 4 mm and 3-5-1 topology for thickness of 6 mm, with LM algorithm and TANSIG activation function were able to predict moisture ratio withof  0.99906, 0.99919 and 0.99930 respectively. The corresponding MSE for this topology were 0.00013, 0.00012 and 0.00009 respectively.

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Stock market timing is a very difficult task because of the complexity of the market. Since there are various factors affecting the market and therefore it is not a simple task to predict future stock price and its trend. This paper aims to apply advanced tools and algorithms such as the artificial neural networks (ANN) to model nonlinear processes and predict future stock price and its trend. More specifically, this study explores the abilities of the ANN to enhance the effectiveness of the technical analysis indicators to predict stock trend signals. Using a sample of 50 companies in the Tehran Stock Exchange (TSE), the results indicate that the ANN is capable to predict the direction of the short term movement in the future stock price. After considering the transaction costs, the results confirm that there is not significant difference among the returns gained from the ANN method, buy and hold strategy, and the most profitable technical indicators in the market when the trend is increasing. While, the ANN model yields higher returns compared to buy and hold strategy in the market when the trend is decreasing. Nevertheless, in the case of decreasing trend, the finding confirms the trend indicators (moving averages) achieve the highest returns.

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The evolution of financial data shows a high degree of volatility of the series, coupled with increasing difficulties of forecasting financial variables. Some alternative forecasting methods, based on the literature review, have been developed, which can be particularly useful in the analysis of financial time series. Despite of the numerous time series forecasting models, the accuracy of time series forecasting is fundamental to many decision processes. Selecting an efficient technique in unique situations is very difficult task for forecasters. Many researchers have integrated linear and nonlinear methods in order to yield more accurate results. In practice, it is difficult to determine the time series under study are generated from a linear or nonlinear underlying process while many aspects of economic behavior may not be pure linear or nonlinear. Although both ARIMA and Artificial Neural Networks (ANNs) models have the flexibility in modeling a variety of problems, none of which is universally the best model used indiscriminately in every forecasting situation. In this paper, based on the foundations of ARIMA and ANNs models, a hybrid method is proposed to forecast exchange rate. Empirical results indicate that integrating linear and nonlinear ARIMA and Artificial Neural Networks (ANNs) models can be an effective way to improve forecasting accuracy achieved by either of the above linear and nonlinear models used separately.

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“Selecting right Person for the right job” in the organizations seems to be the most important managerial issue. Traditionally, this is realized through a “simple Job- Person Match”, which is an “Individual view”. This methodology, by some trends and paradigm shift in human resource management (i.e. Team Working), should be evolved to a “Group viwe”. In this study, “interpersonal interactions” is added to the traditional one. First, the suggested model for personnel selection and placement is formulated into a Quadratic mathematical form and then a Hopfield neural network has been used to solve it by using Matlab software, a well known and validated software for neural nets. The results show, there is a significant difference between the mean of solutions by traditional view (based on individual level) and the mean of solutions by the new suggensted one (based on group level): t(19) = - 10.966, P-Value=0.000.

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  The mechanical losses which occur in agricultural products are the damage's which imposed on country economy. It is important to investigate the bruising phenomena, as an index of mechanical losses for loss reduction and optimization of harvest and postharvest machinery. In the current study, by means of an impact pendulum apparatus and by conducting a series of impact tests, the effects of temperature (0, 10, 20 and 30⁰C), variety (Golden Delicious and Red Delicious), padding surface (corrugated carton, rubber and galvanized iron) and kinetic energy (300, 600 and 900 mJ) were investigated on rate of apple bruise. Statistical results showed that the effect of temperature, variety, padding surface and impact energy were significant on the mean value of bruise volume at 1% of statistical level. By increasing temperature, the bruised volume was decreased, whereas it increased by increase of energy level in both varieties. While, the Golden Delicious had more strength than Red Delicious. Also, the maximum rate of bruised volume was related to Red Delicious in contacting to galvanized iron and the minimum rate was related to Golden Delicious in contacting to corrugated carton. Prediction of bruised volume was provided using artificial neural network based on four factors of: temperature, impact energy, padding surface and variety. Multilayer perceptron of neural networks were used for prediction of bruised volume. In comparison with other topologies, algorithm Levenberg-Marquardt had better performance with structure 1-26-4 and logsigmoid transfer function in hidden layer. Based on this algorithm, the mean of prediction accuracy in training, evaluation and testing process was equal to 92.48, 88.94 and 87.72 percent, respectively. In addition, the correlation coefficient (R) was calculated equal to 0.975 for linear regression between predicted model and experimental data.  

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Fine-blanking is an effective and economical shearing process which offers a precise and clean cutting edge finish, eliminates unnecessary secondary operations and increases quality. Fine-blanking process utilizes triple-action tools: a punch, a stripper with an indented V-ring and a Counter punch (ejector) to generate a highly compressive stress state. The deformation is more violent and localized than that of any other metal forming operations. Therefore it is difficult to fully understand the mechanism of the process. This study investigates the effect of V-ring indenter, clearance of die, Force of holder and Counter punch, etc on state of stress, quality and accuracy of production. Some parameters have both positive and negative effect on quality of production and the life of the tool. Utilizing V-Ring indenter in Die will increase quality of production and life of the tool. Also Artificial Neural Networks was used to simulate Fine-Blanking process. It has been shown that booth of FEM and ANN is suitable for simulating and forecast of effect of the parameters on production.