Coffee is a common drink made from roasted and ground coffee beans. The coffee plant is native to the subtropical regions of Africa and some islands in South and Southeast Asia. When the fruit of the coffee plant ripens, the coffee beans are harvested, processed and finally dried. Dried coffee beans are roasted to different degrees and depending on the desired aroma, different grades are considered for this product. Coffee is slightly acidic and can cause human irritation due to its high caffeine content. Coffee is one of the most valuable basic products, which is the second main commodity after oil. The detection of natural and unnatural impurities and additives in coffee is a constant concern, especially in relation to guaranteeing the quality of the product with the intentional or accidental addition of foreign substances that can harm the consumer, especially of an economic nature. Therefore, researchers are always trying to provide a suitable solution for detecting adulteration in coffee, which is of great importance considering the applicability of the method and obtaining the appropriate result for the tests, non-destructive and fast method. The purpose of this research was to use the olfactory machine system and artificial intelligence to detect fraud in Arabica coffee (Medium Dark). For this purpose, firstly, Arabica coffee beans from a reputable domestic company and samples of fake powders including roasted soybean powder, wheat flour Barley flour and Robusta coffee were prepared in the amount needed in the experiment. To carry out the experiments, Arabica coffee was mixed with adulterated powders with weight percentages of 10, 40, 30, 20 and 50%. For each sample of coffee and powders used for fraud, a 100% specific sample was considered. 10 grams of the mixture of each sample was added to 100 ml of boiling water and kept boiling for 2 minutes. Finally, it was kept at rest and away from heat for one minute until the particles settled and finally the supernatant was used to perform the smell test. After the step of sucking the smell of the sample by the olfactory device, the obtained data were analyzed by PCA, LDA and ANN methods. According to the obtained results, the ANN method provided a better classification than the LDA method.

   Abstract   Dairy products, renowned for their substantial nutritional value, are pivotal in general nutrition and the food industry. However, their considerable economic significance makes them susceptible to fraudulent practices. Standard deceptive techniques include dilution with water, substitution with different types of milk, fat content alteration, and cheese infusion into milk. Incorporating whey or whey solids into dairy products is a prevalent adulteration method. The notorious 2008 incident in China, involving the illicit adulteration of milk with melamine, resulted in kidney and urinary tract complications in 294,000 children, leading to six fatalities. Deliberate melamine contamination has been observed in milk, infant formula, pet food, and other items. These incidents have spurred the development of analytical methodologies for quantifying melamine in food and animal feed. This study employs an electronic olfaction technique to discern adulterated milk powder. Experimental protocols involve preparing and combining powdered milk, whey powder, and melamine at varying adulteration levels (10%, 20%, 30%, 40%, 50%). All tests are conducted across five different whey powder and melamine concentrations, employing both dry and water-boiled testing methods with the electronic nose apparatus. Data are subjected to rigorous analysis through Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Artificial Neural Network (ANN) methodologies. The PCA loading plot for dry tests reveals the substantial influence of MQ136, MQ3, and TGS2602 sensors on the principal component, whereas MQ9, MQ3, and TGS822 sensors exhibit the highest impact in detecting milk powder adulteration. LDA analysis yields an accuracy rate of 86.67% for dry tests and an impressive 95.15% for wet tests in dir="RTL" >   Keywords: Milk Powder, Adulteration, Electronic Nose, Whey Powder, Melamine, Chemometrics

In order to maintain the quality of fruits and increase their shelf life, extensive research has been done on packaging methods, especially the use of nanomaterials in packaging. Due to the high medicinal and nutritional properties of garlic and also the sensitivity of its storage period after peeling, the packaging of this product is of great importance. In this study, garlic was packaged in two normal and nano films at temperatures of 25, 4   and -18   ° C and three modes of normal atmosphere, vacuum and modified atmosphere. Measured properties include mechanical properties (Fmax, Emod and deformation percentage), chemical properties (PH and TSS), colorimetric properties (L *, color change and browning index) as well as the amount of gas (O2 and CO2) inside It was packages. Data analysis was performed in two sections of 14 days including all three temperatures and 35 days including refrigerator temperature and freezer temperature. Data were analyzed by statistical methods and artificial neural network (ANN). The trend of changes during the storage period in mechanical properties (except deformation), the amount of CO2, TSS and L * decreased and in pH, the percentage of deformation, color change and browning index were reported as increasing. The results of statistical analysis showed that in the 14-day period, temperature changes had a significant effect on the measured parameters and in the 35-day period, temperature and the interaction of temperature and atmosphere had a significant effect on all parameters. The least changes in the measured properties occurred mainly in the nano film. In neural network (ANN) analysis, the output of the best model for the effect of treatments on properties, validation performance diagram, data regression coefficient (experimental, training and general) as well as data regression line fitting was measured.   

   Vegetables and summer vegetables play an important role in human health due to their high fiber and antioxidant properties. Green cucumber is one of the oldest cultivated vegetables and has a known history of more than five thousand years. Green cucumber with the scientific name of Cucumis Sativus belongs to the squash family Cucurbiteacae, which is one of the most important plant families and includes 90 genera and 750 species. Researchers have concluded that the use of urea fertilizer will increase the yield of vegetables, which has led to the indiscriminate use of urea fertilizer by farmers. The use of urea fertilizer on farms should be controlled because excessive use will not only increase yield but also cause nitrate accumulation. Due to the fact that vegetables and summer vegetables have the ability to absorb and retain large amounts of nitrite and nitrate, so the consumption of such products by humans can endanger health. For this purpose, criteria have been considered that label products with authorized consumption of pesticides and chemical fertilizers as a healthy product. Detection of urea fertilizer overdose in farms is done using existing technologies such as chromatography (GC) or spectrometer gas chromatography (GC / MS) which is very costly and time consuming and requires specialized users. has it. Therefore, it is necessary to look for an easy and low-cost solution that can perform the test in the shortest time. In this study, five levels of urea fertilizer in cucumber were classified using electronic nasal method and chemical analysis and by chemometric methods. Urea fertilizer levels were zero, 100, 200, 300 and 400 kg / ha. In each urea fertilizer level, two harvests were performed at intervals of four and five months after sowing. Electronic nose technology is a modern and advanced technology that has many applications in the agricultural industry. In this study, an olfactory machine with eight metal oxide semiconductor sensors was used to detect the amount of urea fertilizer used in green cucumber cultivation, and Kojeldal was used to measure phosphorus by spectroscopy, flame diffusion potassium and nitrogen. Odor machine data were analyzed and classified by ANN, SVM, LDA and QDA methods and chemical analysis by PCR, PLS and MLR methods.

  AbstractThe production of organic and quality food is one of the most important human challenges today. Tomato paste has been considered as a popular condiment in Iranian food due to its antioxidants, lycopene, vitamins and minerals. Many tomato paste factories maintain the quality of this product through thermal processes, pasteurization and chemical preservatives. Previous methods can affect the effective management and production of tomato paste. Cold plasma food processing is used to achieve the desired level of quality of a product, especially heat-sensitive food. This emerging technology and non-thermal, safe, healthy, environmentally friendly, cost-effective and low impact It is based on the internal structure of the product. Due to recent advances in plasma technology, this study will be conducted to investigate the effect of using cold plasma treatment on the quality parameters of tomato paste. The research method is that at first, samples of tomato paste prepared from a specific variety of tomatoes were prepared from the tomato paste factory in both traditional (without preservatives) and industrial (with preservatives) and in the laboratory directly below. Laminar hoods were exposed to plasma under relatively uniformly cold plasma using dielectric barrier (DBD) discharge at 3 repetitions and 2 time levels. Samples were subjected to atmospheric pressure and 3 voltage levels (maximum 12 kV) and with Constant frequency of about 6-7 kHz was irradiated, then the effect of cold plasma after 10 days on pH, dye and brix parameters was evaluated and after 5 days of incubation, Aspergillus niger mold was evaluated and the mean treatment was compared with Duncan's test was performed by statistical analysis using   16 software.The results showed that the application of atmospheric cold plasma on industrial tomato paste had no significant effect on the pH of the paste and increased the brix of tomato paste and decreased the number of colonies. Aspergillus niger molds and thus improved the quality of industrial tomato paste over the weeks.  Keywords: Tomato paste, Quality, Atmospheric cold plasma, Food processing

In recent years, the growing population and increasing demand has led to an increase in food adulteration by profiteers. Adulteration in food products, in addition to reducing product quality and financial losses, is also detrimental to the health of consumers, so it has caused concern and weakened consumer confidence. Vinegar is a solution of acetic acid and other chemicals, such as flavorings, produced from the fermentation of various fruits. Vinegar has an important role in human life and health due to its medicinal properties, cleansing, preparation of pickles and use in various foods as a condiment. Mixing natural vinegar with relatively cheaper natural vinegar, white market vinegar, water and using industrial acetic acid in the production of artificial vinegar are the most common methods of adulteration in the market. It is difficult to find a technique that can easily and reliably evaluate the quality parameters of vinegar. Therefore, in recent decades, researchers have turned to the use of visual, olfactory, taste and computer methods in the food industry. Electronic nose is one of the new methods that has recently been highly regarded by researchers in agriculture and food industry, especially in the field of food quality assessment. In this study, a portable system was developed and implemented to evaluate the detection of adulteration in two types of natural vinegar (grape and apple). The electronic nose system consisted of eight metal oxide semiconductor sensors. Counterfeit vinegar was prepared by mixing grape and apple vinegar and both with white market vinegar, acetic acid and water in different proportions. The features extracted from the signals obtained from the system were processed by principal component analysis (PCA), artificial neural network (ANN), linear resolution analysis (LDA) and quadratic linear resolution analysis (QDA). In the main component analysis, simultaneous comparison (17 groups) of grape vinegar with a total variance of 88% separation was performed and based on the loading diagram, TGS2620 and MQ136 sensors were introduced as the best sensors in the 0.999, MSE = 0.000136. Apple cider vinegar was 0.998, MSE = 0.000513. According to the titration results, it was proved that in PCA analysis, the acetic acid level did not affect the variance of the samples chewed with grape vinegar and the lower the level of acetic acid in the chewed samples with apple cider vinegar, the higher the percentage of variance between samples. According to LDA and QDA analyzes, the smaller the difference between the acetic acid level of vinegar and the substance with which it is mixed, the weaker its detection and >  

  تجزيه گرمايي زيست توده در محيطبدون اكسيژن يا با اكسيژن اندك را گرماكافت مي‌نامند كه محصول اين فرآيند دي اكسيدكربن، گازهاي سوختي، بخار قيري و جزء جامدي به نام بيوچار است. فرآيند گرماكافتراهي براي تبديل زيست توده به مواد با ارزشتر نظير بيوچار است. بيوچار ماده ايجامد و داراي محتواي كربن بالاست كه رايج ترين مورد استفاده آن در كشاورزي بهعنوان اصلاح كننده خاك است. محققان در سال هاي گذشته تاثير استفاده از بيوچار برخصوصيات فيزيكي وشيميايي خاك را مورد مطالعه قرار داده اند و مشخص شده است كهافزودن بيوچار به خاك كيفيت خاك را بهبود مي‌بخشد. خصوصيات فيزيكي و شيميايي بيوچارتحت تأثير عوامل مختلفي از جمله نوع مواد اوليه، شرايط واحد گرماكافت، سرعت گرمادهي،مدت زمان گرماكافت وللفعوامل متعدد ديگري قرار مي‌گيرد . دامنه گسترده فرآيندگرماكافت منجر به توليد بيوچارهايي   كه ازنظر خواص شيميايي و فيزيكي مختلفي نظير تركيب عنصري و خاكستر، وزن مخصوص، تخلخل،توزيع اندازه منافذ، سطح ويژه، pH، جذب و دفع آب و يون ها و بسياري خواص ديگر متفاوت هستند مي‌شود. هدفاز اين مطالعه، بررسي اثر تغيير دبي هوا و دماي محفظه در گرماكافت اكسايشي بسترثابت بر روي عملكرد بيوچار، محتواي خاكستر، وزن مخصوص و pH بود. بدين منظور يك دستگاه توليد بيوچار اكسايشي بسترثابت با قابليت تغيير در دماي محفظه و دبي هواي خروجي طراحي و ساخته شد.آزمايش‌هادر چهار دبي هواي 20، 25، 30 و 35 ليتر در دقيقه ونيز چهار دماي 350، 400، 450 و500 درجه سانتي‌گراد براي كاه و كلش گندم انجام شد. نتايج نشان داد كه افزايش دبيهواي   خروجي   از محفظه و افزايش دماي محفظه، سبب افزايش ميزانخاكستر وpH شد. درحالي كه تغيير اين پارامترها سبب كاهش وزن مخصوص ظاهري و عملكرد   بيوچارتوليدي شدند.     

   Abstract Pomegranate, known as Punica granatum L., belongs to the Punicaseae family. Iran holds the 60,000 hectares of land under cultivation and production of 800,000 tonnes, Iran is the world's first pomegranate producer. Pomegranate seeds can be made from water, grenadine, potion, syrup, jam, jelly and so on. Healthy and desirable food quality is currently playing an important role in the food industry.   Adulteration of fraud in the food industry has always challenged the scientific community. Hence, attention has been method on the use, smell, and taste and computer technology in the food industry over the last few decades. Electronic Nose is a new method that has recently been considered by researchers adulterated in pure grenadine. he response characteristics of the sensors to the volatile compounds of the samples were extracted and used as inputs to the pattern recognition model. 30 grams were tested for each sample. According to the results obtained for the mixture of pure grenadine with grape syrup, Palm sap 92 and 94% of variance by PCA method, in order to >      Key Words: Adulterant, Electronic Nose, Foodstuffs, grenadine, sensors.

,urea fertilizer,electronic nose,artificial neural net work ,basil,summer

   The present study seeks to identify and classify the biodiesel from various oils and alcohols using the olfactory machine technique and employing artificial intelligence and statistical algorithms. In this research, biodiesel fuels were prepared from different sources such as rapeseed oil-methanol (MK), corn oil-methanol (MZ), rapeseed oil-ethanol (EK), corn oil-ethanol (EZ) and combined fuel (EK & MZ). Each of these fuels were mixed in volume percentage of 2, 5, 10, 20 and 80 with diesel fuel. The data were collected with help of an electronic nose system equiped 8 metal oxide semiconductor sensors. The normalized data were analyzed by various methods such as artificial neural network (ANN), principal component analysis (PCA), linear and quadratic discriminant  analysis (LDA and QDA), support-vector machine (SVM) and response surface methodology (RSM). The results showed that ANN was able to classify pure fuels with a precision of 100%. Other classifier   methods, QDA, SVM, RSM and LDA, were categorized pure fuels with accuracy of 94.4, 93.3, 92.2 and 86.7 percent, respectively. Also, ANN method was able to identify and classify any pure fuels (MK100, MZ100, EK100, EZ100, EK & MZ100, G) in one group (Pure) and various disel-bidisel blends (B2, B5, B10, B20, B80) in the other group (Impure).   ANN and LDA were more powerful methods than other for idenfying   the fuels of B2, B5 and B20. The classification accuracy of both methods for B20 was 100%. For discriminant   of B5 and B2, the ANN method had an accuracy of 98.7%, while the LDA method had a precision of 100% and 97.3% respectively. By averaging the performance parameters of various models for the categories used in this study can be said that the ANN model had better performance with an average precision, sensitivity and specificity of 98.5, 98.8 and 99.5 percent than other models.

The energy issue is one of the most importantprobmlems considered globally. Fossil fuels are the largest energy sourcecurrently. Increasing greenhouse gas emissions has led to climate change invarious climates. Increasing the environmental pollution caused by the use ofthese energies has led various communities and corporations to move towards renewableenergy sources. One of the alternative solutions for fossil fuels is using ofbiodiesel fuel. The major problem with the use of biodiesel is the low powerand torque generated compared to pure diesel. Therefore, the use of additivesto biodiesel is proposed to overcome this problem. In the present study, thediesel-biodiesel blens, nano-aditives in the presence of magnetic field wereused for improving the engine performance and reducing the emissions. The wastekitchen oil was used as source of biodiesel. The biodiesel ratio in fuelmixture is 0, 5 and 10 volume percentage of diesel. Also, used Nanomaterialswere cobalt and cerium nano-oxide. Two neodymium magnets - 42 grade were usedto affect the fuel. The magnetic field was placed on the fuel line and the fuelwas exposed to the magnetic field before entering the engine. In this study,the effect of six parameters as magnetic fields (0, 225 and 4500 Gauss),biodiesel (0, 5 and 10% volume of diesel), nano aditives of Cerium oxide (CeO2)and or Cobalt oxide (Co3O4) with concentration of 0, 20and 40 ppm, ratio of nanomaterials (Cerium oxide to Cobalt oxide (0, 50 and 100%), engine speed of 1200,1800 and 2400 rpm, and engine loads of 25, 50 and 75%using Box-Behnken experiment design and RSM method. Optimization of theparameters of the engine performance and emissions index indicate that the bestparameters were magnetic field of 1561.66 Gauss, concentration of nanomaterialsof 12.25 ppm, nanomaterials ratio of 56.37%, biodiesel ratio of 4.97, enginespeed of 1962 rpm and engine load of 16.14%. In these conditions, engine torqueof 67/14 Nm, engine power of 36.3 kW, brake special fuel consumption (BSFC) of2772.5 gr / kW.h, carbon monoxide (CO) of 135.0% vol., Carbon dioxide(CO2)of0.27%, unburned hydrocarbons (UHC) of 0.032 ppm and nitrogen oxide (NOx) of 4ppm with a desirability of 0.89 were obtained.  

  To evaluation of genetic diversity and recognize of resistant genotypes, the experiment done in randomize block designs with both irrigated and rain-fed conditions with 25 genotypes of bread wheat. This experiment was performed in the research field and physiology lab of Agriculture and Natural Resources of Razi University   during the year 2016-2017. Simple variance analysis of different agronomic, physiologic and biochemical traits showed that genotype effect for the most of traits was significant and too, combined analysis demonstrated that environment effect for the most of traits was significant. Different analysis i.e. Factor Analysis, Principle Component Analysis (PCA) and cluster analysis performed for all the measure traits. Based on correlation between drought resistant indices and grain yield in both conditions, MP, STI, GMP, HMP, MSTI, MRP,YI and REI were situation indices for the selection of the best genotypes in both irrigated and rain-fed conditions. Result of STS and ISI indices showed that genotype of eight number was superior. Genetic parameters, phenotypic correlation and genotypic correlation was calculated, current of correlations indicated that genotypic correlation higher than phenotypic correlations and this objective demonstration of genetic diversity higher in the genotypes. Result of GGE biplot analysis showed that irrigated condition 5, 15, 13,10,18, 11, 16 and24 genotypes was the best and result in the rain-fed condition18, 10, 15,17,11, 4, 16 and12 showed was the superior genotypes.Key words: bread wheat, resistance at drought, , physiologic traits,   arable traits, biochemical traits  

     In order to evaluate water deficit tolerance and genetic variation of 16 bitter vetch genotypes, two field experiments were conducted using randomized complete block design with three replications under non-stress and water deficit stress conditions at Research farm of Razi University, Kermanshah.   Days to flowering, days to maturity, grain filling period, plant height, number of branches, number of pods per plant, number of seeds per pod, number of seeds per plant, thousand seed weight, grain yield, biological yield and harvest index were measured in the both environments. Analysis of variance in each environment revealed significant variation among genotypes Combined variance analysis of the data showed that the effect of environment on most traits was significant. Different stress tolerance indices including stress susceptibility index (SSI), tolerance (TOL), geometric mean production (GMP), mean production (MP), stress tolerance index (STI), yield index (YI), yield stability index (YSI) and harmonic mean (HAM) were calculated based on yield data in the both coinditions. MP, GMP and STI indices showed positive significant correlation with yield in the both conditions, and YI and HAM indices were correlated with yield in stress conditions. Therefore, they were appropriate indices for selection of superior genotypes. Based on the biplot of the first two main components, genotypes 2, 3, 13 and 16 were identified as suitable entries for the both conditions. Genetic parameters were estimated for all the   measured traits. As a whole, phenotypic correlations were more than genetic correlations, indicating the effect of environment on the genotypes.Keywords: Genetic Diversity, Water Deficiency Stress, Bitter Vetch

In recent years, the rate of food fraud has increased significantly. Fraud in food products, in addition to affecting product quality and financial losses, has a negative impact on consumer health, thus much concern about the consumption of food products is needed. Cow ghee is one of the main souvenirs of Kermanshah city and is also used in cooking. Mixing cows ghee with vegetable oils, animal fat and margarine are common ways of cheating of this product. It is hard to find a technique that can easily and reliably measure the quality of the cow ghee. Now days, methods which use smell and taste technics are developed. Electrical nose is a new method that is used in agriculture and food industries, especially in the field of quality food research. In this research, a portable system was developed and implemented to evaluate the detection of adulteration in pure cow ghee. The electronic nose system was constructed based on eight metal oxide semiconductor sensors. During the tests, the voltage response of the sensors was collected by the data acquisition system. Different types of adulteration oils includes vegetable oil, fat oil and margarine at different level were prepared. The extracted properties of the signals obtained from the system were processed using principal component analysis method (PCA) and artificial neural network (ANN). According to the results obtained for the mixture of pure cow ghee with fat oil, vegetable oil, margarine, 97, 96 and 98% of variance by PCA method was obtained. Also, for the ANN method, the ltr">  

  AbstractNowadays, solar energy is one of the cheapest and available renewable energy sources. Among different uses of solar energy, solar collector is one of the most economical ways to use solar energy. In the present study, the outlet water temperature of the solar flat plate collector was modeled using artificial neural networks (ANN) and support vector regression (SVR) and compared with experimental data. Data was collected for 18 days. Water and Bohemite Nano- fluid (ALOOH) with a concentration of 0.2% by weight were used as operating fluid. In order to evaluation of the models, tow structures were tested for both artificial neural network and support vector regression. In the first structure the parameters were input flow, test time, environment temperature and inlet fluid temperature. While in the second structure inputs were input flow, test time, environment temperature, inlet fluid temperature, tow temperatures of absorber plate, and the glass cover temperature. Based on the results, coefficient of determination (R2) and root mean square error (RMSE) in the SVR method for pure water and the first structure, respectively were 0.978991 and 3.2508, respectively, and for the second structure, were 0.998715 and 0.1016, respectively. According to the results, R2 and RMSE for Boehmite Nano fluid and first structure were, 0.958303 and 6/68580, respectively. While these values for the second structure was equal to 0.965097 and 5.4765, respectively. Also, by influencing the type of input fluid as the input of the models, R2 and RMSE were 0.636978 and 281.8210, respectively, and for second structure were 0.939306 and 15.7420. Based on the result for modeling by artificial neural network and for pure water, R2 and RMSE for the first model were 0.99983 and 0.029084, respectively and for the second model were 0.99991 and 0.015617.0, respectively. Also, these values ??for the Bohemite Nano-fluid for the first structure were equal to 0.999 and 0.99896 and for the second structure were 0.9993 and 0.99927 respectively. With the effect of the type of operator fluid as input variable, the R2 and RMSE for the first structure ??were 0.99886 and 0.32567, respectively. Also, these values were 0.99934 and 0.32567, respectively, for the second structure.   Results indicated that the ANN model was better than SVR model for prediction of outlet temperature.   Also by increasing the input parameters, the accuracy of models was increased.Keywords: Artificial neural network, Nano fluid, Outlet fluid temperature, Solar flat plate collector, Support vector regression

  Honey is a natural and sweet substance, which honeybees collect mainly from nectar flowers and process and storage in hives. Adulteration, especially industrial, is made by adding natural syrup or yeast directly to acids. Artificial honey is also made by mixing one or more types of sugar with acid. In this research, the accomplishment construction of a fluid-optimized imaging kit at the Agricultural faculty, Razi University, Kermanshah and fennel honey was also bought from bee keepers in Cangavar. After confirming the origin of honey, 39 samples of adulteration honey using sucrose syrup, fructose syrup and 0.9% fructose syrup with   mixed glucose percentage i   the honey   at 2.5, 5, 7.5, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100% .   then a natural honey sample, and an artificial honey sample were investigated and compared. In this study, three photographic methods including processing water-soluble honey images (DiW), honey imaging in a special box (black) using petri dish (PD) and microscopic imaging (M), and another adulteration detection method based on physicochemical properties (pH, TDS, EC and MC) and a combination of superior parameters of all mentioned methods was performed. The microscopic and TDS method was distinct from honey type. The (standard ?3). In combination method (C), by using the input parameters of the best model in all previous methods and performing sensitivity analysis, two parameters of (DiW) dissolution method and one parameter of microscopic imaging method (M) were selected and modeled using AFNIS, ANN, and RSM classification systems for hybridization and using the desirability function. The determination coefficient of RSM model was considered 0.9992. Among the best models in all five methods of this research, the RSM model was introduced in the combined method (C) with the least amount of statistical errors compared to other models with the most effective 0.9940 desirability function

  Abstract  Grape berry moth (GBM) Lobesia botrana Den. & Schiff. (Lepidoptera: Tortricidae) is one of the most important pests of vineyards in Iran that much damage each year in the different regions of Iran. In order to assess the population dynamics of the pest and the effect of altitude in population density of adults, pheromone trapping was performed in the three villages of Paveh city with different altitude. The results showed that this pest has four generations in the Dorisan village with 1605 meters altitude and it has three generations in Shamshir and Tazeabad villages respectively with 1700 and 1820 meters altitude. In Dorisan village appearance of the first moths began from 10th April. The first flight peak of this generation was occurred in 6th April with the mean of 24/5 moths and the second flight peak of that in 19th May with the mean of 27/5 moths, peak of adult population of second generation in 24th June with the mean of 42 moths, peak of adult population of third generation in 20th August with the mean of 51 moths and most population of fourth generation in 19th September with the mean of 34/5 moths. In Shamahir village appearance of the first moths started from 22th April and peak of flight of three consecutive generations respectively was occurred in 16th May, 10th June and 17th August and with the mean of 24, 15 and 31/5 moths. In Tazeabad village appearance of the first moths began from 19th April and peak of flight of three generations respectively was reported in 19th May, 3th July and 17th August and with the mean of 86, 139 and 276 moths in traps. T test results showed that between the average population density of pest in different villages there are significant differences.  Key words: grapvine, Lobesia botrana, population dynamism, Delta trap, Paveh region  

Emissions in motor vehicles, especially vehicles with spark ignition engines are the most important environmental parameters of air pollution in many countries. Given the global approach to reducing energy consumption and conservation of energy resources, especially fossil fuels in different countries, restrictions on vehicle fuel consumption has been defined at the international level. Reduction the exhaust gas temperature can be done by fuel enrichment which it will increase the fuel consumption. this problem can be solevd by cooled exhaust gas recirculation. This process will reduce pressure and temperature in the cylinder area during combustion, therefore the tendency to knock   will be reducted. In this study, using the ANSYS 13 software ( CFD section) a model of cooling radiator was analaysid and then based on the results form the model a recirculation circuit was built. The performance of engine temperature and produced emissions in five treatments includes without recirculation, recirculation without cooling and recirculation with cooling with three temperatures (15.4, 11.5 and 7.5 degrees) were test and evaluated. The results of variance analysis indicated a significant difference   for CO, CO2, HC, NOx, and inlet and outlet temperatures by changing the engine speed and recirculation type. Based on the results, the impact of changing the engine speed in the NOx emissions for all modes of recirculation was significant. Also, with increasing the engine speed,   the values of CO and HC emissions, and inlet and outlet air were increased. While with increaseing engine speed, the value of CO2 decreased.