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In many organizations, gender biases and stereotypes often occur, consciously or unconsciously, regardless of whether women assume leadership roles. In addition, women must overcome various challenges when assuming leadership roles or aspiring to be leaders. The present research has examined the management challenges of women managers in Khuzestan province from the perspective of experts. The research is qualitative in terms of development results, in terms of practical goals and in terms of data. The approach of the present study was mixed. Based on this, the present study in two levels includes the identification of concepts that were extracted by interviewing 17 women managers of Khuzestan province using the phenomenological method and also grading the concepts using the fuzzy Delphi method, with 10 experts and experts in political fields. Social, cultural, economic of Khuzestan province, an in-depth interview was conducted. The prioritization of women's management challenges based on the lived experience of the participants are: lack of protective laws, allocation of the lowest management level to women, traditional view of women, attendance at non-working hours of the office, ineffective management training courses. pay discrimination between male and female managers, emotional mood, lack of family cooperation, patriarchal view in organizations, low self-confidence, unwillingness to go on work missions, interference of family life with work, confronting female colleagues. which was categorized into 5 main concepts of glass ceiling, historical dominance of men over women, low self-confidence of women, incompatibility of customary and religious frameworks for the presence of women, disobedience of female colleagues. The structural space and organizational culture with the influence of the dominance and influence of the cultural system of the society as the most important, the most effective and the closest interactive environment of the world of work and employment of women is effective in their reaching managerial levels.

Maintaining the quality of tomato paste is very important for processing industry. Bacteria, fungal toxins and molds are factors that can cause food contamination and spoilage. The purpose of this research was to investigate the performance of electronic nose in detecting spoilage in tomato paste and also to investigate the changes of some important physicochemical properties due to spoilage in tomato paste. Bacillus subtilis bacteria and Penicillium and Aspergillus fungi were used to spoil tomato paste. Sampling for samples infected with bacteria was carried out in 4-hour intervals for 24 hours, and for samples infected with fungi, it was taken daily for one week. . Quadratic Discretion Analysis (QDA), Artificial Neural Network (ANN), Supppport Vector Regression (SVR), were the methods used to achieve this goal. The QDA method showed a good performance in the classification of bacteria and fungi and was able to detect bacterial growth and spoilage of tomato paste in 6 sampling times with 100% accuracy. Classification accuracy with the help of neural network for bacteria based on sampling time was 86.7% and for samples infected with fungi based on the type of fungus was 90%. The best prediction of the physicochemical properties of the sample infected with bacteria and fungi by the ANN model related to the properties of sediment weight percentage and acidity, respectively, and in the SVR model, it related to the properties of pH and acidity, respectively.

The use of corn oil in diets is due to its positive effects on cardiovascular and immune systems. Corn oil is composed of 99% triacylglycerol, with 59% unsaturated fatty acids and 13% saturated fatty acids. Of the unsaturated fatty acids, 24% contain a double bond. Because of this composition, corn oil can be a good alternative to other oils high in saturated fatty acids, as it reduces blood cholesterol levels.This study employed an electrical nasal system to detect the amount of palm oil present in corn oil. The properties extracted from the signals obtained by the device were processed using principal component analysis, artificial neural networks, infusion, and response surface methods. The results were then compared to find the best method for detecting palm oil levels in corn oil.

The required palm oil was obtained from the Nazgol Oil Agro-industrial Plant, while the corn oil was obtained from natural lubrication centers. To prepare samples with different percentages of palm oil, 75 grams of palm oil and corn oil with the specified percentages were mixed and stored in special containers.In the electrical nose system, ten metal oxide semiconductor sensors (MOS) were used to collect output data. Pre-processing operations were performed on this data using RSM, ANFIS, PCA, and ANN methods to estimate the percentage of palm oil in corn oil. The Unscrambler V.9 software, Design Expert 8.07.1, and MATLAB R2013a were used to analyze the results.

Based on the Score plot, PC-1 and PC-2 explain 53% and 25%, respectively, describing the variance between samples for a total of 78 data points. The analysis indicates that sensors 7 and 8 have minimal impact on the detection process and can be removed from the sensor array. When reducing the cost of the olfactory system's sensor array, sensor 6 plays a more significant role than other sensors in detecting corn oil with palm composition.According to the loading diagram of palm percentage in corn oil, the MQ6 sensor had the least effect in classifying different percentages of palm in corn oil and pattern identification. Out of all functional parameters (accuracy, sensitivity, and specificity), the RSM method is deemed more appropriate for determining the percentage of palm in corn oil.Regarding the separation of corn oil and palm oil by ANFIS, RSM, and ANN, the results in Table 3-1 indicate that the RSM method is better suited for classifying corn and palm oil.

In this study, we used an electronic multi-sensor system based on metal oxide sensors to analyze various aromatic compounds in different oil and palm samples and to detect the presence of palm. The system provided comparable information for classifying different samples of palm oils. Using PCA, ANN, ANFIS, and RSM methods, we evaluated the system's performance in differentiating and classifying various oil and palm samples.The results obtained from the loading diagrams for the detection of palm in corn oil indicated that the MQ6 sensor had the least impact on the detection process. Therefore, this sensor can be removed from the sensor array.Additionally, our analysis showed that using the RSM method is more effective in detecting different percentages of palm in corn oil. Overall, our study demonstrates the efficacy of the electronic multi-sensor system in analyzing different oil and palm samples and detecting the presence of palm.

The current research study with a qualitative approach and descriptive phenomenological method aims to investigate the experiences of Zanjani activists regarding the cultural contexts of development in Zanjan. Using targeted sampling and considering the theoretical saturation of data, 27 people from Zanjan participated in the research. Data were collected and implemented using semi-structured interviews. And then it was analyzed manually. The interview analysis method was based on the seven stages of the Colaisian methodology. Based on the results of the research, the realized cultural fields of development in Zanjan based on the lived experience of the participants are: the development of social and popular institutions, the sense of belonging to family and relatives, the facilitation of the media, the realization of social awareness regarding the position of women in society, the development of thinking Islamic and Iranian and the formation of collective self-awareness. Therefore, the people of Zanjan, along with their accepted traditions, are trying to rebuild their daily life according to global factors, which mostly through new sources and platforms such as global media, virtual spaces and information and communication technologies, as well as local factors, have changed their ways of life. Choose different options.

The main purpose of this research is to study, understand and conceptualize the concept of development culture and barriers to development in Zanjan, which in terms of the purpose of applied research and in terms of method, qualitative research with a phenomenological approach. The data collection tool is a semi-structured interview. The average duration of each interview was 30 to 60 minutes. Klaizi analysis method was used to analyze the data. The results showed that the spread of globalization and the advancement of technology and the culture of misuse of development tools such as the Internet, satellite, cyberspace, etc., has shaken the foundation of families, tendency to individualism and negative utilitarianism, as well as the opposition between tradition and modernity, extreme prejudice. Severe prejudice, from alienation and consumerism, the occurrence of cultural changes to cultural development, Lack of social demands, lack of attention to legalism and correct social behavior, failure to realize social, cultural, symbolic, human, economic, political and ... Capital renaissance , Other cultural barriers to development were developed from the results of the study.the  Conflict of interest

Adulteration, especially industrial type, is caused by the direct addition of natural or fermented syrups to honey. In this study, fennel honey was supplied from beekeepers located in Kangavar city.After ensuring the authenticity of honey, 39 samples of counterfeit honey were prepared by adding and stirring sucrose, fructose and a combination of 0.9 fructose-glucose syrups in natural honey at different levels of 0 to 100 wt%.Different samples were dissolved in water and their images were recorded using a camera.In order to process the images in each 33 monochrome channels, 15 parameters (495 parameters in total) were measured.  Few parameters were selected by sensitivity analysis using ANFIS fuzzy neural inference network, ANN artificial neural network and RSM response level. The explanation coefficient of the presented models for water-soluble samples was 0.9512, 0.9882 and 0.9904, respectively.Considering all the statistical error valuesof the RSM model, it was introduced as the best model to determine the amount of honey fraud in this method by the desirability function.

The common pistachio psylla, Agonoscena pistaciae (Hemiptera: Aph alaridae), is a key pest of pistachio trees. Nymphs and adults suck sap from leaves resulting in defoliating, falling flower buds, stopping tree growth and finally yield loss. The population dynamics of insects is influenced by several physical and biological factors such as temperature and natural enemies. Identifying the key factor in population dynamics is difficult due to the potential interactions between biological and environmental factors. Non-linear analysis methods such as artificial neural networks (ANNs) are suited to be applied in the ecosystem with non-linear and complex ecological data. These methods have been widely used as a robust information-processing instrument in many research fields, especially in predicting pest occurrence. For example, a neural model is used to predict bionomic variables related to the nutritional dynamics of blowflies.
In the present investigation, the seasonal abundance of A. pistaciae in a pistachio orchard was evaluated for two years. This study aimed to assess the performance of ANN in representing nonlinear dynamic data for common pistachio psyllid populations. To this end, back propagation ANN was implemented to evaluate the relationship between the pest occurrence and influential factors.
 

The population density of ‎psyllids was monitored weekly by the yellow sticky trap for the adult and direct counting for ‎the nymph. After collecting the related data, the curves of the seasonal dynamic population of adults and nymphs were drawn. Then, the number of generations and duration activity of psyllid in each generation were determined. Multi-layer perceptron neural network ‎‎(MLP), genetic algorithm (GA) and multi-linear regression (MLR) were used to determine the relative significance of biotic (natural enemies) and ‎abiotic (weather variables) factors for predicting A. pistaciae density. An ANN model was designed by using the inputs (average temperature, average rainfall, average relative humidity, wind speed and direction,  and population of natural enemies), hidden layer (the number of neurons in the hidden layers determined by trial and error), and one neuron in the output layer (the occurrence amount for predicting the population). The Levenberg–Marquardt algorithm was used as the learning algorithm. The root mean square error (RMSE) and coefficient of determination (R2) were statistics, calculated for both the training and testing set for each iteration.

The population fluctuations of A. pistaciae on Akbari pistachio cultivar during 2015 and 2016 indicated that the psyllid populations in the field had five apparent peaks from late March to October. Agonoscena pistaciae in Rafsanjan county had six complete and one incomplete generation in 2007 and 2008 (9). The general population trends were similar over time within two years, but population densities of adults and nymphs were higher in 2016. Statistical comparison of weather variables between two years showed no significant difference. 
Several topologies were examined and the best result was obtained with 15 and 9 neurons in the first and second hidden layer for both adult and nymph in MLP method, respectively. In genetic algorithm, a hidden layer with 14 neurons for adult and 16 neurons for nymph was employed. The R2 values of MLR, MLP and GA methods (at test phase) were 0.32, 0.61, 0.73, respectively and the RMSE values were 31.79, 0.223 and 0.083, respectively for adult. In the prediction of the population density of the nymph by MLR, MLP and GA, the R2 values were obtained to be 0.22, 0.84, 0.88, respectively, and the RMSE values were 48.03, 0.051 and 0.051, respectively.

The R2 and RMSE values showed reliable performance of ANN and GA. The ANNs also modeled the numbers of the psyllid with high accuracy. In addition, the higher R2 and lower RMSE were obtained for MLP and GA methods relative to MLR. It has been reported in the related literature that the ANN consistently outperformed the statistical models. The ANN as a nonlinear predictor exhibited a high accuracy in predicting the richness of aquatic insect species in running waters by a set of four environmental variables (21). Based on the principal components analysis and back propagation artificial neural methods to analyze historical data on the population occurrence of Scirpophaga incertulas, the new model could improve the prediction accuracy, compared with other methods (27). It is worth noting that in regression models, the weak correlation between dependent and independent variables does always not imply that these variables are not associated, as they may have a nonlinear correlation.

Intelligent systems have received considerable attention as a modern modeling methods in recent years. These models is used for prediction and classification in situations where the classic statistical models are not able due to their constraints. This study is aimed to compare the ability of ANFIS and multi factor linear regression models for predicting density of all growing stages of Sunn pest. The data population fluctuation of Sunn pest in the years 2015 and 2016 on a farm with an area of one hectar in chadegan city was obtained. Predictor variables including variables sampling date, average temperature, average relative humidity, wind speed, wind direction, rainfall, height from sea level and degree- day were processed as input data to achive an output of number of developmental stages as response variable. In the ANFIS model, 70% of the data was assigned to training and 30% for validation. After network training and assessment of the best structure according to type, number of membership function and related rules with the use of MATLAB software, the appropriate model was selected based on statistical indices of, root mean square error (RMSE) and coefficient of determination (R2). After sensivity analysis the results showed that ANFIS method (RMSE= 0.051, R2= 0.97) had higher accuracy than multi linear regression (RMSE= 0.26, R2= 0.47) and better predicts the population fluctuation of Sunn pest Eurygaster integriceps.

The Sunn pest, Eurygaster integriceps Put. is one of the main pests of wheat and one of the most important plant protection problems in Iran. Multiple linear regression models have been used to predict the fluctuation of various pest populations by using environmental variables. Using intelligent systems to better estimate insect population fluctuations can lead to better results. Therefore, the current study was conducted to predict population fluctuation of the Sunn pest by using a neuro adaptive fuzzy inference system, a response surface method and multiple linear regression. This study was done during in 2015 and 2016 on two wheat farms each one with an area of one hectare in the Chadegan county. In these models, average temperature, average relative humidity, rainfall, wind speed and direction, sampling date, degree- day and altitude from sea level were used as response variables. The collected data randomly divided in two categories of training (70%) and testing (30%) and they used for train and test of ANFIS and response surface methodology. The accuracy of the prediction was evaluated by R2 and RMSE. The higher performance of the ANFIS model (R2= 0.93, RMSE= 0.0614) and RSM (R2 = 0.88, RMSE= 0.0836) resulted comparing to the multiple linear regression (R2 = 0.23, RMSE= 0.34). Also the results of sensitivity analysis indicated that the average of temperature, relative humidity, wind speed and date of sampling were the most important parameters for predicting density of adult Sunn pest.

The Sunn-pest, Eurygaster integriceps Put., is the most important insect pest of wheat and barley in Chadegan city, in Isfahan province. This study was conducted on a farm with an area of one hectare in 2015 and 2016 in Chadegan county. In this research with sampling regular population fluctuation and spatial distribution nymphal stage different by using Taylor’s power law and Iwao’s patchiness regression was calculated. Based on the results of Taylor’s power law in 2015 (except 5 nymphal stage) and 2016 the spatial distribution of nymphal instars different, was determined aggregated. In Iwao’s patchiness regression, the spatial distribution of nymphal stages different in 2015, was determined aggregated and in 2016 the spatial distribution except 3 and 4 nymphal instars, was determined aggregated. Upon the results of this research, appearance date 1st to 5th nymphal instars in 2015 were observed in 10th, 16th, 16th, 19th and 26th May respectively. appearance date 1st to 5th nymphal instars in 2016 were observed in 18th, 21th, 23th, 31th May and 4th Jun respectively. Maximum density population 2nd nymphal instar that is the best time for control sunn pest was observed in 2015 and 2016, 30th may (2.43±0.65) and 4th Jun, respectively

Common pistachio psylla, Agonoscena pistaciae, is a key pest of pistachio trees in Iran. Sampling was done from three pistachio orchards including Sefid, Akbari and Kale-Ghochi varieties. Sample units for nymphs and adults were leaf and a yellow sticky trap, respectively. The number of nymphs on leaf and adult the number captured by traps was recorded weekly. The densities of nymphs and adults were significantly affected by pistachio varieties. The spatial distribution pattern of A. pistaciae nymphs was determined by indices of variance to mean ratio and regression methods of Taylor and Iwao. Indices of variance to mean ratio showed that spatial distribution of common pistachio psyllid in 2015 on three pistachio varieties was a random distribution. In 2016 and pooled two years, on Sefid variety random distribution and on Akbari and Kalle-Ghuchi varieties aggregated distributions were indicated. The aggregation indices in Taylor's model and Iwao's patchiness regression on the three pistachio varieties and during two years were both significantly greater than one. The spatial distribution of nymphs at Taylor’s model on Sefid pistachio and aggregated on other varieties in 2015. In Iwao's patchiness regression, spatial distribution in 2015 and 2016 was random on Sefid variety and aggregate in other cases. However, the data obtained showed that Iwao's patchiness regression gave a better fitness with all data in comparison with Taylor's power law. The results can be used to improve the sampling program and implementation of pest management programs of this important pest.

The Sunn pest, Eurygaster integriceps Put. is the most important pest of wheat and barley in Iran. Many studies have shown that various biotic and abiotic environmental factors affect the population of this pest. In this study, the relationship between population density of the Sunn pest adult insect with different environmental factors including sampling date, average daily temperature, average relative humidity, wind speed, wind direction, height from sea level and degree-day was investigated. Field data were collected from two wheat farm of one-hectare in the city of Chadegan, Isfahan province. The used network type was multilayer perceptron with back propagation algorithm and the learning algorithm was Levenberg-Markvart. After sensitivity analysis due to the ease of the model and extraction of effectiveness of factors including four factors of sampling date, temperature, humidity and wind speed were selected. The results showed that a neural network with two hidden layer, 7 neuron in the first hidden layer and three neuron in the second hidden layer, as a sigmoid activation function, and a data percentage of 60, 30, 10 for training, testing and validation for prediction of population fluctuation of the Sunn pest adult insect is used (R2= 0.94).

This study aimed to predict population fluctuation of sunn pest in the field using artificial neural network and multiple linear regression was performed. The data on population fluctuation of Sunn pest in years 2015 and 2016 on a farm with an area of one hectare in the city Chadegan was obtained. In this model of the variables sampling date, the average temperature, average relative humidity, wind speed, wind direction, rainfall as the input variables and population changes mother Sunn pest was used as the outcome variable. The network was used of type Multilayer Perceptron with back propagation algorithm and was learning method Levenberg Markvart. Results showed between these two models, artificial neural network with coefficient of determination 0.96 better than regression with coefficient of determination 0.40 population density of mother Sunn pest was predicted. After sensitivity analysis model for easier and factors more effective extraction, four factors: the number of days of the year, temperature, humidity and wind speed were selected. Neural network model was trained again using the four factor model and a model with 11 hidden layer gave the best result. The coefficient of determination testing stepe was 0.97 that was showed high accuracy relative to the multiple linear regression model with the coefficient of determination 0.43.

The purpose of the present study is to produce biodiesel from fish waste oil and methanol by combination of mechanical stirrer and microwave as a technique to accelerate this. In this research, a microwave system was used including microwave source, stirrer, spiral tube and decantor. With the help of this system, the effect of molar ratio of alcohol to oil (4 to 1, 6 to 1 and 8 to 1), catalyst concentration (0.5, 1 and 1.5 weight percent of oil), reaction time (5, 15 and 25 min) and microwave time (0.5, 1.5 and 2.5 min) on the conversion of fatty acid to methyl ester. For analyzing the obtained results, the response surface method and Box Behnken layout were used in Design Expert 10.0 software. After analyzing the data and optimizing the biodiesel production reaction, it was found that the highest percentage of biodiesel conversion (92.62%) was found in the catalyst concentration of 1.13%, the reaction time of 24.61, the molar ratio of alcohol to oil of 5.55 and the microwave time of 0.5 minute. The regression model between independent variables and dependent variable (percent conversion) was obtained as a quadratic equation with R2 = 0.9953.

Vibrations include a wide range of engineering sciences and discuss from different aspects. One of the aspects is related to various types of engines vibrations, which are often used as power sources in agriculture. The created vibrations can cause lack of comfort and reduce effective work and have bad influence on the health and safety. One of the important parameters of the diesel engine that has the ability to create vibration and knocking is the type of fuel. In this study, the effects of different blends of biodiesel, bioethanol and diesel on the engine vibration were investigated. As a result, a blend of fuels such as synthetic fuel that creates less vibration engine can be identified and introduced.
In this study, canola oil and methanol alcohol with purity of 99.99% and the molar ratio of 6:1 and sodium hydroxide catalyst with 1% by weight of oil were used for biodiesel production. Reactor configurations include: maintaining the temperature at 50 ° C, the reaction time of 5 minutes and the intensity of mixing (8000 rpm), and pump flow, 0.83 liters per minute. A Massey Ferguson (MF) 285 tractor with single differential (2WD), built in 2012 at Tractor factory of Iran was used for the experiment. To measure the engine vibration signals, an oscillator with model of VM120 British MONITRAN was used. Vibration signals were measured at three levels of engine speed (2000, 1600, 1000 rpm) in three directions (X, Y, Z). The analysis performed by two methods in this study: statistical data analysis and data analysis using Adaptive neuro-fuzzy inference system (ANFIS).
Statistical analysis of data: a factorial experiment of 10×3 based on completely randomized design with three replications was used in each direction of X, Y and Z that conducted separately. Data were compiled and analyzed by SPSS 19 software. Ten levels of fuel were including of biodiesel (5, 15 and 25%) and bioethanol (2, 4 and 6%), and diesel fuel.
Data analysis by ANFIS: ANFIS is the combination of fuzzy systems and artificial neural network so that it has both benefits. This system is useful to solve the complex non-linear problems in agricultural engineering applications such as systems involved in the soil, plant and air. ANFIS by linguistic concepts can establish and inference non-linear relationship between inputs and outputs. In this research, modeling was generally performed by Toolbox of ANFIS and coding in MATLAB too. Five important and effective factors in modeling were optimized until the best ANFIS model is obtained. The five factors were: type of input fuzzy sets, the number of input fuzzy sets, fuzzy set of output, methods of optimization and the number of epochs.
Based on the total vibration acceleration values for different fuels in different rpm, pure diesel (B5E4D91) had the highest vibration and the lowest vibration was seen in the mixed fuel of B25E4D71. Based on the results, two combined fuel of (B25E2D73, B25E4D71) have the lowest vibration and highest amount of biodiesel fuel (25%). After them, three combined fuels of (B5E2D83, B5E4D81, and B5E6D79) have created more vibration and the lowest amount of biodiesel fuel in this study (5%) has created the greatest amount of vibration. With increasing engine speed, the number of combustion courses and piston shock per unit of time increases. As a result, the engine body vibration increases. The results are consistent with results from other researchers.
In this study, motor vibration of MF285 tractors, by replacing a portion of diesel fuel with biodiesel produced from canola oil and bioethanol, was investigated. In the beginning, necessary biodiesel fuel was produced by research reactor in biodiesel workshop, and then different percentages of diesel and bio-ethanol were mixed to biodiesel and ten combined fuels were created. Finally the effect of different fuel combinations and different engine rotational speeds on the tractor engine vibrations was studied based on a factorial randomized complete block design and then analyzed and modeled by ANFIS. The results showed that the vibration of pure diesel fuel had the highest vibration. Also, with increasing biodiesel fuel blends, the amount of vibration reduced significantly. Increase in engine speed had direct effect on increasing the amount of vibration. Also by increasing the percent of bioethanol from 0 to 4%, the amount of vibration was reduced then vibration value increased by raising the percent of bioethanol. After modeling and analyzing, our results showed that the best fuel in terms of having the lowest vibration motor was B25E4D71.

Spiral conveyors effectively carry solid masses as free or partly free flow of materials. They create good throughput and they are the perfect solution to solve the problems of transport, due to their simple structure, high efficiency and low maintenance costs.
This study aims to investigate the performance characteristics of conveyors as function of auger diameter, rotational speed and handling inclination angle. The performance characteristic was investigated according to volumetric efficiency. In another words, the purpose of this study was obtaining a suitable model for volumetric efficiency changes of steep auger to transfer agricultural products. Three different diameters of auger, five levels of rotational speed and three slope angles were used to investigate the effects of changes in these parameters on volumetric efficiency of auger. The used method is novel in this area and the results show that performance by ANFIS models is much better than common statistical models.
The experiments were conducted in Department of Mechanical Engineering of Agricultural Machinery in Urmia University. In this study, SAYOS cultivar of wheat was used. This cultivar of wheat had hard seeds and the humidity was 12% (based on wet). Before testing, all foreign material was separated from the wheat such as stone, dust, plant residues and green seeds. Bulk density of wheat was 790 kg m-3. The auger shaft of the spiral conveyor was received its rotational force through belt and electric motor and its rotation leading to transfer the product to the output. In this study, three conveyors at diameters of 13, 17.5, and 22.5 cm, five levels of rotational speed at 100, 200, 300, 400, and 500 rpm and three handling angles of 10, 20, and 30º were tested.
Adaptive Nero-fuzzy inference system (ANFIS) is the combination of fuzzy systems and artificial neural network, so it has both benefits. This system is useful to solve the complex non-linear problems in agricultural engineering applications. ANFIS by linguistic concepts can establish and inference non-linear relationship between inputs and outputs. In this research, generally modeling was performed by using toolbox of ANFIS and coding in MATLAB software. Five important and effective factors in modeling were optimized until the best ANFIS model was obtained. The five factors were: type of fuzzy sets for inputs, number of fuzzy sets for inputs, type of fuzzy set for output, method of optimization and number of epochs. The statistical model was done by using SPSS and in the multivariate regression method. In multivariate linear regression in statistical model, the independent variables were auger blade diameter, rotational speed and the angle of slope of the auger and dependent variable was volumetric efficiency. The factorial test in randomized complete block design was conducted for variance analysis of volumetric efficiency. Mean Comparison of volumetric efficiency in different levels of factors was performed using Duncan' test in 5% level.
In this study, volumetric efficiency of spiral conveyors was investigated as a function of auger blade diameter, auger rotational speed and slope of transfer. The performance was measured in terms of volumetric efficiency using ANFIS and statistical models with SPSS. The results showed that: Volumetric efficiency almost decreased by increasing of rotational speed, for all three conveyors.
Maximum volumetric efficiency in all three spiral conveyors was in the speed range of 100 to 200 rpm.
Volumetric efficiency significantly reduced in all three spiral conveyors by increasing in rotational speed and slope of transferring in spiral conveyors.
Effect of spiral conveyor diameter on the volumetric efficiency in product transferring was irregular and no specific process is appeared.
The correlation coefficient between the actual and predicted values was obtained as 0.98 in ANFIS model and 0.94 in multivariate linear regression with SPSS which showed the ANFIS model was more accurate than statistical model.
Comparison between performances of spiral conveyor to transfer the seeds of wheat, with results by other researchers that has been reported for spiral conveyors with the same slope to transfer of corn kernels, was found that the angle effect on volumetric efficiency is quite significant. Therefore, it proves that performances of spiral conveyor are impressed by characteristics of transition material considerably.
The maximum volumetric efficiency was corresponded in rotational speed of 100 rpm, inclination angle of 10º, and blade diameter of 17.5 cm that it was approximately 29.11%.

Biofuels are considered as one of the largest sources of renewable fuels or replacement of fossil fuels. Combustion of plant-based fuels is the indirect use of solar energy. Biofuels significantly have less pollution than other fossil fuels and can easily generate from residual plant material. Waste and residues of foods and wastewater can also be a good source for biofuel production.
Transesterification method (one of biodiesel production methods) is the most common forms to produce mono-alkyl esters from vegetable oil and animal fats. The procedure aims are reduction the oil viscosity during the reaction between triglycerides and alcohol in the presence of a catalyst or without it. In this study, the method of transesterification with alkaline catalysts is used that it is the most common and most commercial biodiesel production method.
In this study, configurations of made hydrodynamic cavitation reactor were studied to measure biodiesel fuel quality and enhanced device performance with optimum condition. The Design Expert software and response surface methodology were used to get this purpose.
Transesterification method was used in this study. The procedure aims were reduction of the oil viscosity during the reaction between triglycerides and alcohol in the presence of a catalyst or without it. Materials needed in the production of biodiesel transesterification method include: vegetable oil, alcohol and catalysts. The used oil in the production of biodiesel was sunflower oil, which was used 0.6 liters per each test in the production process base on titration method. Methanol with purity of 99.8 percent and the molar ratio of 6:1 to oil was used based on titration equation and according to the results of other researchers. The used catalyst in continuous production process was high-purity sodium hydroxide (99%) that it is one of alkaline catalysts. Weight of hydroxide was 1% of the used oil weight in the reaction.
Response surface Three important settings of reactor were considered to optimize reactor performance, which include: inlet flow to reactor, reactor rotational speed and the fluid cycle time in the system. Each set was considered at three levels. The factorial design was used to the analysis without any repeat, there will be 27 situations that because of the cost of analysis per sample by GC, practically not possible to do it. Therefore, response surface methodology was used by Design Expert software. In the other words, after defining the number of variables and their boundaries, software determined the number of necessary tests and the value of the relevant variables.
Three parameters include the inlet flow to reactor, reactor rotational speed and the fluid cycle time in the system were considered as input variables and performance of reactor as outcome in analyzing of extracted data from the reactor and GC by Design Expert software. The results of tests and optimization by software indicated that in 3.51 minutes as retention time of the raw material of biodiesel fuel in the system, the method of transesterification reaction had more than 88% Methyl ester and this represents an improvement in reaction time of biodiesel production. This method has very low retention time rather than biodiesel fuel production in conventional batch reactors that it takes 20 minutes to more than one hour.
According to the researches, efficiency of biodiesel fuel production in hydrodynamic cavitation reactors is higher than ultrasonic reactors so in this study, the settings of hydrodynamic reactor were investigated so that the settings were optimized in production of biodiesel fuel. Sunflower oil was used in this research. The molar ratio of Methanol to oil was 6 to 1 and sodium hydroxide as a catalyst was used. Three important settings of reactor were considered which include: inlet flow to reactor, reactor rotational speed and the fluid cycle time in the system. The results were analyzed by gas chromatography. The results showed that at 8447 rpm of reactor speed, inlet flow of reactor at 0.86 liters per minute and 1.02 minute of circulation time, the best performance of reactor were created. The flash point, kinematic viscosity and density of biodiesel in this study were 172 ° C, 2.4 square millimeters per second and 861 kg per cubic meter, respectively. Maximum and minimum performances of hydrodynamic cavitation reactor in biodiesel production were 6.19 and 1.13 mg kJ-1, respectively.

In this study, the MF285 tractor engine vibrations resulting from the use of different combinations of biodiesel, bioethanol and diesel as fuel were studied. Engine vibrations in three directions and three engine speeds: 1000, 1600 rpm 2000 and ten fuel level was measured. To investigate the influence of measured parameters on engine vibration of factorial design in a randomized complete block design and response surface methodology were used. The results showed that vibration caused by the use of different combinations was less than of vibration caused by consumption of diesel fuel. The results showed that with increasing biodiesel fuel blends significantly reduce the vibration and the vibration increases with increasing engine speed. The vibration increased by increasing the bioethanol until 4% but then decreased. After optimization, the best fuel in terms of having the lowest vibration motor was B25E6D69.

Social، technical and economic factors in addition to environmental، soil and climate factors affect crop yield and cultivation. This study was implemented to know the impact of age، experience and literacy level of farmers as social factors and access to water supply، roads، silo، labor، tractors and machinery and conservation tillage as technical-mechanization factors on crop yield. Fuzzy rule-based inference system converts the complex decision-making problems to the smaller criteria and makes easier the multi-criteria evaluation process. So we decided to use fuzzy approach to modeling the social and technical-mechanization indices. The main disadvantage of fuzzy systems is their inability to learn. So، the optimization of fuzzy systems is the most important step in its implementation. Genetic algorithm (GA) approach is used as a complementation of fuzzy model to optimize fuzzy rules. One method to optimize the fuzzy rules is Pittsburgh method in GA. In this method، one gene is used for every rule and the gene value finds out the rule. The kind of membership function will have a great impact on the result. The kinds of membership function for fuzzy sets involve triangular، trapezoidal، generalized bell، Gaussian، Gaussian combination، Sigmoidal، product of two sigmoidal، difference between two sigmoidal، Π، Z and S shapes. The objectives of this study are: 1- providing two fuzzy models for the social and technical-mechanization indices for wheat production 2- optimizing the fuzzy rules and the type of membership function for the fuzzy set. Fuzzy toolbox of MATLAB software ver. 7. 8. 0 (R2009a) was used to design fuzzy model. Fuzzy inference system (FIS) used in this study was Mamdani type that is based on if-then rules. The age، experience and literacy level of farmers were selected as input data for fuzzy social model. Access to water supply، roads، silage، labor، tractors and machinery and conservation tillage equipment were selected as input data for fuzzy technical-mechanization model. Mamdani fuzzy inference system was used to design models. Fuzzy rules were written by a mechanization expert knowledge. To correct written rules، the method of Pittsburgh in GA was used to optimize the fuzzy rules for all FISs. Then، a program was written in MATLAB software to get the best combination of membership functions to achieve the best result. The program tested 24 kinds of combined membership functions for medial and side fuzzy sets of input variables. The result was the best when the relationship between obtained index and crop yield had the highest value of the correlation coefficient (R2)، minimum value of mean square error (MSE) and mean absolute error (MAE). So the fuzzy-GA model will produce the social and technical-mechanization indices while the fuzzy rules of model have been optimized and the best combination of membership functions has been selected. The coefficients of determination were obtained 0. 11 for fuzzy social model and 0. 51 for technical-mechanization model before optimization of fuzzy rules. The error of fitness function decreased with rising generation numbers of GA until the best answer was obtained. After optimization of fuzzy rules by genetic algorithm، these values ​​increased to 0. 50 and 0. 71 for the fuzzy social and technical-mechanization models، respectively. This result showed that optimizing the fuzzy rules had a significant impact on results of models. After implementation of the written program، to select the best type of membership functions for fuzzy input variables، coefficient of determination varied from 0. 14 to 0. 51 and 0. 1 to 0. 73 for the fuzzy social and technical- mechanization models، respectively. This result showed that the effect of social factors on wheat yield was less than technical-mechanization factors and yield can be predicted by technical-mechanization factors with more accuracy than social factors. In the social model for input of experience، the lowest MSE and the highest R2 belong to a FIS with three fuzzy sets and S، Π and Z-shaped membership functions for the right، medial and left fuzzy sets، respectively. In the technical model for input of road availability، the lowest MSE and the highest R2 belong to a FIS with three fuzzy sets and s، trapezoid and z- shaped membership functions for the right، medial and left fuzzy sets، respectively. These results showed that the type of membership functions for fuzzy sets had considerable importance for the accuracy of the model. It can be concluded that the accuracy of the fuzzy model with optimized rules by GA and the best type of membership function for fuzzy sets are considerable. Effect of technical-mechanization factors on wheat yield was more than social factors. This result also showed the strength of fuzzy–GA method in modeling of such issues.

Drafts of major primary tillage implements were measured in a clay loam soil using a tension loadcell. Implements consisted of a moldboard, a disk and a chisel plow each equipped with one tillage unit. A photoelectric speed sensor was used for actual forward speed measurements. During the study, the effects of forward speed and tillage depth on draft requirements were investigated. A general regression equation was developed to predict the specific draft of these implements as based on the Standard ASAE, D497.5. Results of statistical analysis indicated a significant increase in draft for all the three implements with an increase in depth of operation. The values of specific draft for the three tested tillage implements were significantly affected by forward speed. Coefficients of specific draft equations were determined through regression analysis. Specific draft equation coefficients (A,B and C) were obtained as: 256.26, 2.28 and12.11 from moldboard plow; 152.28, 3.79 and 9.66 for disk plow; and 26.9, 12.18 and 0.13 for chisel plow respectively. High values of R-square (R2) for specific draft equations in the case of each implement showed that the models are able to present a good prediction of the implement's required draft.