امروزه به دليل رشد سريع جمعيت و توسعه شهرها، كمبود امكانات براي دفع زباله، و همچنين اثرات زيست‌محيطي و بيماري‌هاي ايجادشده درنتيجه‌ي روش‌هاي نامناسب دفع زباله، اهميت مطالعه و بررسي وضعيت جمع‌آوري، حمل‌ونقل،‌ دفع زباله و خصوصيات كمي و كيفي واثرات زيست‌محيطي زباله‌هاي جامد شهري بسيار احساس مي‌شود. هدف از مطالعه حاضر بررسي اثرات زيست‌محيطي روش‌هاي دفع پسماند شهري شهر كرمانشاه با استفاده از روش ارزيابي چرخه زندگي (Life cycle assessment) مي‌باشد. بدين منظور سه روش كمپوست‌سازي، بازيافت و دفن زباله كه از روش‌هاي غالب دفع زباله جامد شهري (Municipal solid waste) هستند، در كلان‌شهر كرمانشاه مورد بررسي قرار گرفت. بدين منظور در ابتدا اطلاعات مورد نياز براي تعيين خصوصيات كمي و مقدار زباله توليدي و تعيين اجزا فيزيكي تشكيل‌دهنده زباله (كاغذ، شيشه، پلاستيك و...) از شهرداري جمع‌آوري شد؛ سپس به‌منظور تكميل اين اطلاعات و بررسي اثرات زيست‌محيطي دفع زباله، روش ارزيابي چرخه زندگي مورداستفاده قرار گرفت؛ همچنين مرز سامانه، واحد عملكردي و سناريوهاي دفع تعيين شد و سپس در مرحله بعد، سياهه انتشار براي هريك از سناريوها فهرست شد. لازم به ذكر است كه در مرحله بعدي از به‌روزترين روش ارزيابي شاخص‌ها كه نرم‌افزار ايستك (EASETECH) مي‌باشد، براي محاسبه ميزان انتشارات و اثرات زيست‌محيطي استفاده شد و در پايان، نتايج تفسير شد. در اين پژوهش با دراختيار داشتن 1000 كيلوگرم پسماند جامد از سه سناريو كمپوست‌سازي، بازيافت و دفن در نرم‌افزار استفاده گرديد. نتايج نشان داد حدود حدود 50 درصد از آسيب‌هاي وارد بر سلامت انسان و محيط‌زيست ناشي از دفع پس از كمپوست مي‌باشد، زيرا راندمان آن حدود 40 درصد است همچنين راندمان دفع پس از مرحله بازيافت تنها 30 درصد است كه نشان از كم بودن مواد قابل بازيافت دارد. همچنين نتايج نشان داد حدود 76/94 كيلوگرم پسماند به صورت پلاستيك، كاغذ و منسوجات بازيافت شده‌اند كه منجربه كاهش 8/68 درصد هزينه‌ها شده است و پس از آن فرآيند MRF با 6/20 و فرآيند كمپوست با 12 درصد قرار دارند. طبق نتايج بدست‌آمده اين سه سناريو دوستدار محيط‌زيست نبوده و در پايان پژوهش پبشنهاداتي جهت بهبود مديريت پسماند در شهر كرمانشاه ارائه گرديد.

  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.

در ميان انرژي­هاي تجديدپذير، انرژي بادي به­خاطر عدم آلايندگي محيط­زيست و شرايط اقتصادي بهتر، امروزه بيشتر مورد توجه واقع ‌شده است. هدف از مطالعه حاضر، مدل­سازي يك سيستم بادي در كارخانه توليد روغن زيتون واقع در استان گيلان، به‌منظور تأمين انرژي از ديدگاه ارزيابي چرخه زندگي(Life cycle assessment)   مي‌باشد. در اين مطالعه با توجه به مقدار بالاي كشت زيتون و توليد روغن آن در منطقه، براي تعيين انرژي مصرفي در فرآيند توليد روغن زيتون؛ ابتدا اطلاعات لازم شامل مراحل توليد و تمامي نهاده­ها و انرژي­هاي مختلف بكار رفته در توليد روغن زيتون از كارخانه مورد مطالعه جمع­آوري گرديد و مقادير انرژي ورودي شاخص­هاي انرژي مصرفي محاسبه شد. سپس با توجه به سوخت و الكتريسيته مورد نياز براي توليد روغن زيتون در سامانه مرسوم، سامانه بادي مبتني بر نيروگاه منجيل مدل­سازي شد. در گام بعد با استفاده از روش ارزيابي چرخه زندگي ReCiPe2016، پس از تعيين مرز سامانه و واحد عملكردي براي كارخانه مذكور و تحليل سياهه توسط داده‌هاي جمع‌آوري‌شده و نيز اندازه­گيري پايگاه‌هاي داده به‌طور جامع براي هر دو سناريو پرداخته شد. نتايج برآن بود كل انرژي مصرفي27/4437 مگاژول است كه الكتريسيته با سهم 44/32 درصد انرژي برترين در ميان نهاده­ها براي توليد محصول مي­باشد. نتايج شاخص‌هاي انرژي نيز بيانگر آن بود كه بهره­وري انرژي 23/0 كيلوگرم بر مگاژول و انرژي ويژه 44/4 مگاژول بر كيلوگرم مي‌باشد. نتايج مدل­سازي سامانه‌ي بادي نيز نشان داد به‌طور متوسط تعداد 6-10×01/2 براي سامانه بادي جهت توليد برق با توجه به اقليم و شرايط آب و هوايي مورد نياز براي 1 تن روغن زيتون حاصل گرديد. ارزيابي چرخه زندگي محصول نشان داد در تمامي رده­هاي آسيب، سامانه بادي كمترين مقدار انتشارات را به خود اختصاص داده است. همچنين در دو سامانه مرسوم و بادي، پلي اتيلن ترفتالات بالاترين سهم را در انتشارات رده­هاي آسيب در توليد محصول دارا مي‌باشد. همچنين نتايج نشان داد در مجموع مقادير كل انتشارات سامانه مرسوم، بيشترين ميزان انتشار را به خود اختصاص داده است.     

   Abstract Energy, as one of the most important and necessary production factors, has a significant impact. Considering that fossil fuel resources are running out, researchers are looking to replace biodiesel fuel as a renewable biofuel with properties close to diesel. The current research seeks to model electronic nose data in predicting the oxidation stability and physical and chemical characteristics of biodiesel and to investigate the oxidation stability of biodiesel samples with the Rancimat device and to apply algorithms based on artificial and statistical intelligence. In this research, biodiesel fuels were prepared from different sources of rapeseed and sunflower oil and cooking waste with methanol and KOH (potassium hydroxide) catalyst. Each of the fuels is mixed with diesel fuel at a volume percentage of 2, 5, 10 and 20, with the help of an electronic nose system equipped with 10 sensors that can analyze the volatile components in the empty space of the sample container, physical and chemical characteristics such as density , viscosity along with their oxidation stability were analyzed by standard Rancimat method in different periods of time (each test in one month). Also, with different methods such as artificial neural network (ANN), principal component analysis (PCA), linear and quadratic discriminant analysis (LDA and QDA), support vector machine analysis (SVM) and response surface method (RSM) each month. was analyzed. The obtained results showed that for the classification and separation of pure fuels, the ANN method is able to separate pure fuels with 100% accuracy every four months. Other SVM classification methods in each four months were 94, 95, 82, 84 respectively, for QDA 100, 100, 100 and 99, in RSM these orders were 100, 93, 98 and 96 and LDA respectively in each month with accuracy 100, 98, 100 and 100% pure fuels were separated and categorized. Also, to identify and place types of pure fuels (D100, K100, WCO100, SUN100) in one group (Pure) and types of impure fuels in another group (Impure), ANN method with 100, 98.8, 100 and 100% accuracy is able to Separation of pure fuel from diesel-biodiesel combination. Keywords:

   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

   Sugar beet is one of the most suitable plants for sugar production. Sugar beet leaves produce sugar by using sunlight, and these sugars return to the root and are stored there. In general, leaves play the role of a sugar factory. The percentage of beet sugar is very important for both the farmer and the sugar factory, because the price of beet is paid based on this. The purpose of this research was to estimate the amount of sucrose in the sugar beet crop using the image processing method and to determine the best time to harvest the sugar beet crop. For this purpose, data was collected from an area of 1000 square meters of a sugar beet field, one month before the recommended harvest date and one month after that, every other day. Each time, 5 whole sugar beet plants were randomly picked and all the leaves were photographed. In order to estimate the amount of sugar beet and obtain the most suitable harvest time with the image processing method, it is necessary to have a suitable modeling between the harvest index and the harvest time of the product. The most important indicator of sugar beet harvest is sugar grade. For this purpose, using the decision tree method, we tried to select the most effective inputs from the features of the leaves and their images. Then a model was designed to find the maximum sugar level according to the harvest time using the RSM method. The best time to harvest sugar beet with the highest quality sugar was obtained on the 210th day after planting. In the tested area, sugar beet was harvested on the 215th day, which was almost consistent with the modeling calculations by the response surface. To design a decision, it is helpful to be able to inform the farmer of the approximate harvest time, the time elapsed from the day of planting to the harvest of each sample was deducted from the optimal harvest time that has the highest sugar content, and their difference is used as the output of the decision support model. The results of the decision tree showed that the average value of B corresponding to the smallest leaf area, the smallest leaf area value, and the largest leaf area value are suitable inputs for formulating a decision. For the design and modeling of decision support, three methods of response level modeling, Anfis and artificial neural networks were used. The value of R2 in the response surface method, Anfis and neural networks were 0.83, 0.832 and 0.80 respectively, and the Anfis method was selected as the best model with the highest accuracy.

  Among renewable energy sources, solar energy is the

   One of the most important topics in mycological sciences is the topic of identifying nonedible mushrooms and identifying them from edible mushrooms. Today, the number of people who get poisoned by consuming nonedible mushrooms is increasing. As a result, the detection and separation of edible mushrooms from nonedible ones is of great importance. Considering the research in this field and the need to perform high-cost and somewhat inaccessible tests such as GC, methods should be sought. It was an alternative to these tests. The method of artificial intelligence and electronic nose is a non-destructive and accessible method with a much lower cost compared to GC. In fact, the purpose of this experiment is to study an olfactory system to distinguish edible mushrooms from poisonous mushrooms based on electronic nose technology and to help maintain health and treat diseases and reduce poisoning caused by poisonous mushrooms. Therefore, in this research, an olfactory system was used to detect and identify edible and nonedible mushrooms. ANN methods, PCA principal component analysis, LDA linear discriminant analysis, QDA 2nd degree linear discriminant analysis and SVM support vector machine were used to analyze the data obtained from the olfactory system. The classification of the data obtained from the signals obtained from the sensor array showed that the LDA, QDA and ANN methods have a very good performance in separating mushrooms based on their edible and nonedible nature and high accuracy in classification. Obtained. The use of QDA method to separate and classify different types of edible and nonedible mushrooms was more effective and accurate than LDA method.

  Tomato paste is the main tomato product that is prepared commercially or traditionally at home. According to the definition of the World Health Organization, adding any unauthorized and harmful substances to the food basket of humans and animals is called fraud. Sometimes the profit seeker puts in food to reduce the production costs and get more profit and endangers human health. Therefore, it is necessary to use new methods with a fast and high response level to identify additives in the paste. It is completely reduced or destroyed, in this case such a food item is corrupted and it is called rotten. In this research, it is tried to be able to provide the nutritional health of the communities with the help of smelling machine, in addition to detecting corruption in tomato paste, by identifying common frauds, including the identification of unauthorized preservative additives in tomato paste. In this research, the work was carried out in two sections, examining the experiments of the fraud section and the experiments of the corruption section. In order to evaluate these changes, the physicochemical tests of the paste, which included the measurement of pH, acidity, Brix, sediment weight percentage and smell test, were performed. According to the results of the confusion matrix of LDA and C-SVM methods, both methods performed well in detecting different percentages of potassium sorbate and sodium benzoate. LDA also performed better than C-SVM with 100% recognition accuracy. C-SVM and PCA could distinguish with high accuracy the samples containing sodium benzoate and potassium sorbate in 0.1 and 0.05 percent. The PLS model was the best model for predicting acidity and the MLR model was the best model for predicting sediment weight percentage, Brix and pH. TGS2620 sensors,. MQ135, TGS2602 had the highest sensitivity in identifying adulterated tomato paste samples.

Coccinelidae with the English name, Ladybird, belong to the (Colleoptera: Coccinellidae) Both, adult insects and larvae of Coccinellidae are often predators of important agricultural pests and play an important role in controlling Aphids, Scale insects and even mites. In this research, the faunal survey of Coccinellidae and associated parasitoids in Kermanshah province were carried out during a trip to some parts of the province in 2020 to 2022. More than 4250 samples from different plant ecosystems were collected by multiple methods. Their identification was based on new and valid sources and keys. The species identified in the list below include 28 species and belong to 4 subfamilies: Chilocorinae (5 species: 3 genera: 1 tribe), Coccinelinae (11 species: 7 genera: 3 tribe), Scymninae (9 species: 5 genera : 4 tribe), Sticholotidinae (1 species : 1genera : 1 tribe). Four species of parasitoides related to ladybird has also been identified and presented separately in the list below. These two lists are related to a small number of ladybugs and their parasitoids in Kermanshah province. But surely the fauna of ladybugs and their natural enemies is much richer than this, so it is suggested to complete this study with more facilities in the future. In addition, the identification of two species: Nephus species A and B, which are probably new to the world and 4 parasitoid species is under identification prosses. Those has been sent abroad for identification and molecular work. The list of species is as follows: Key words: Biodiversity, Predator, Biological control, Checklist and Distribution Chilocorus bipustulatus (Linnaeus,1758), Exochomus quadripustulatus Linnaeus, 1758, Exochomus undulatus Weise, 1878, Parexochomus pubescens (Kuster, 1848), Parexochomus nigromaculatus (Goeze, 1777), Coccinella septempunctata Linnaeus,1758, Hippodamia variegata (Goeze, 1777), Oenopia coglobata (Linnaeus, 1758), Oenopia onica (Olivier , 1808), Propylae quatuoredecimpunctata Linnaeus, 1758, Adalia bipunctata linnaeus, 1758, Adalia decimpunctata Linnaeus, 1758, Ceratomegilla undecimnotata D.H. Shneider, 1793, Psyllobora vigintiduopunctata (Linnaeus, 1758), Coccinula elegantula (Weise, 1890), Coccinula redmita (Weise 1885), Stethorus gilvifrons Mulsant, 1850, Scymnus flavicolis Redtenbacher, 1843, Scymnus rubromaculatus Goeze,1777, Scymnus apetzi Mulsant, 1846, Scymnus subvillosus (Goeze, 1777), Scymnus pharaonis Motschulsky, 1851,   Clitostethus arcuatus (Rossi, 1794), Hyperaspis pseudopustulata Mulsant, 1853, Diomus rubidus Motscholsky, 1837, Pharoscymnus pharoides (Marsuel, 1868), Nephus sp A, Nephus sp B. Parasitoids: Encyrtidae: Homalotylus turkmenicus Myratseva, 1981, Ooencyrtus sp. Pteromalidae: Pachyneuron muscarum (Linnaeus, 1758) Aphelinidae: Marietta picta (Andre, 1878)  

   in this study, using three electric nose devices to detect and estimate the amount of oil odor per 500 km of car operation, refractometer to determine the concentration of each sample of oil, calorimeter to measure the amount of paint after every 500 km of operation, engine oil operation We estimated. We used PCA, LDA and neural network to >Based on the scoring and loading diagrams of various data (olfactory data, color data and integrated color, odor and brix data in both standard and normal methods) in the PCA principal component analysis method to detect distance or kilometers traveled. The results showed that the variance of the color data of the first and second major components was 98% and 1%, respectively. The most changes were observed in the color data. All samples of oils were well separated and >According to the neural network perturbation matrix, the 0.7372, R2 = 0.8677 and R2 = 0.6045, respectively, which are the values of R2 for the PLS model. For color change, brix and mileage were higher, and in general the data predicted in the PCR model were closer to the actual data Arshak, K., Moore, E., Lyons, G. M., Harris, J., Clfford, S. 2004. A review of gas sensors employed in electronic nose applications. Sensor Review. 24(2): 181-198.    Adibzadeh, A.; Dizaji, H.Z.; Aghilinategh, N. (2020) "Feasibility of Detecting Sugarcane Varieties by Electronic Nose Technique in Sugarcane Syrup". Iranian Biosystems Engineering journal. 51(1), 1-10. (In Farsi).   Https://doi.org/10.22059/IJBSE.2019.287027.665209.    Scott, S.M., James, D., Zulfiqur, A. 2007. Data analysis for electronic nose systems. Microchimica Acta. 156(3-4):  

   This study was performed to evaluate the relative suitability of susceptible and resistant wild mustard (Sinapis arvensis) biotypes to the herbicide terry benuron methyl (granstar) in Hamil and Markazi counties in Islamabad Gharb city, Kermanshah province during 2017-2017 crop in two greenhouse sections. And the laboratory was run. The results of this experiment showed that the resistance of different masses harvested from 9 altitudes (above sea level) did not show a statistically significant difference despite the increase in resistance at altitudes, and the recorded resistance index between 5.55 to 64 / 6 was obtained. The results of the effect of temperature on germination of susceptible and resistant masses of wild mustard showed that the response of susceptible and resistant masses was different at different temperatures and at 20 and 25 ° C sensitive masses had the highest percentage of germination and vigor. They were resistant to the mass. Germination rate also showed significant differences in different temperature treatments and at (5 and 20) ° C the germination rate of resistant masses was significantly higher than sensitive mass. At 25 ° C, the germination rate of the sensitive mass was significantly higher than that of the resistant mass. The results related to the effect of different osmotic potential also showed that in general wild mustard is sensitive to reducing the osmotic potential of soil and the percentage and speed of germination and vigor of sensitive masses at the osmotic potential of 0.1 and 0.2 MPa were the highest and In the osmotic potential of 1 and 1.2 reached zero. Acidity and resistant and sensitive masses of wild mustard have a significant effect on germination percentage and vigor, as well as different amounts of acidity have a significant effect on wild mustard seeds, so that resistant masses prefer acidic to neutral acidity. The results of dose-response experiment showed that the interaction of mass in the amount of trifenuron methyl on the germination percentage of wild mustard seeds was significant. The highest germination percentage occurred at concentrations of 0.25, 0.5, 1 and 2 in resistant masses, and in susceptible masses in control treatment and concentration of 0.25 and germination in both sensitive and resistant masses at concentrations of 8, 16 And 32 went to zero. Given the relative suitability of wild mustard sensitive stands in terms of germination, it seems that if no special management operations are carried out to reduce the germination of these stands, in the future the population of these stands will expand and in this case, the possibility of There are increasing problems by these masses. eywords: Stolactate synthase, Resistance, Relative suitability, Germination

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 >  

   Abstract Vegetable oils, whose fatty acid hydrocarbon chain structure is very similar to the hydrocarbon structure of petroleum products, are an ideal alternative to petroleum lubricants due to their properties such as renewability and biodegradability. Biolubricants are obtained by modifying the chemical structure of vegetable oils. In this research, a two-stage transesterification-polyol ester method was used to produce a biolubricant based on Camellina oil. The required heat energy for the reaction was supplied by a microwave with a mechanical stirrer. A vacuum pump connected to a condenser was used to complete the transesterification reaction. Methanol condensing and returning it to the reaction vessel. In the initial stage, Camellia oil reacts with methanol in the presence of KOH catalyst and the microwave device with a constant power of 800 watts was set to Medium mode (12 seconds on and 16 seconds off) and stirring is performed continuously. Methyl ester, trimethylpropane (TMP) alcohol and potassium carbonate catalyst (K2CO3) were used in the biolubricant process. The microwave was set to Low (5 seconds on and 30 seconds off) and stirring is done continuously. Nuclear-hydrogen magnetic resonance spectroscopy (H-NMR) was used to analyze the efficiency of the transesterification reaction. In the biodiesel production stage, time factors, catalyst weight percentage and molar ratio of methanol to oil, and in the biolubricant production stage, factors such as time, catalyst weight percentage and biodiesel molar ratio to trimethylol propane (TMP) were investigated using RSM method and Box Behnken experimental design. . For biodiesel reaction, the regression model was quadratic and had an coefficient of determination (R2), standard deviation (Std. Dev.) And coefficient of variation (C.V.) of 97.80%, 3.02, and 4.03%, respectively. The optimization results showed that the highest biodiesel yield of 95.31%   was achived at the reaction time of 5.85 min, catalyst concentration of 1.26%, alcohol to Camellina oil molar ratio of 6.91, and desirability of 0.95. For biolubricant reaction, the regression model was quadratic and had determination coefficient of 97.97%, standard deviation of 0.91 and coefficient of variation of 1%. The optimization results showed that the highest biolubricant efficiency was obtained in 67.8 min, catalyst concentration of 1.4 wt% and molar ratio of 3.5. Under such conditions, the reaction efficiency was 94.3% with a desirability of 0.975. Some properties of produced biodiesel and biolubricant were evaluated and compared with EN14214, ASTM D6751, ISO VG10 and ISO VG22 standards.

  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.     

  One of the traditional methods of distinguishing a healthy egg from an unhealthy one is by shaking the egg and recognizing its sound. But the detection of egg defects in the traditional way by humans is not accurate. It is essential to use more accurate methods in diagnosing egg health and to use smart tools to reduce the time and increase the efficiency of separating rotten eggs from healthy eggs. The aim of this study was to identify the defects of healthy and unhealthy eggs (including rotten, hatched) using sound waves using fuzzy neural inference system (ANFIS). In this study, the acoustic response of 90 eggs, including healthy, rotten, and hatched eggs, was examined by shaking at 200 and 400 beats per minute. After collecting the audio signals, the time domain analysis of the signals was performed through descriptive statistics and then the frequency domain analysis. After calculating the statistical characteristics and determining the best characteristics, the data were used for classification with ANFIS. Based on the results of the accuracy of the Enfis model in the two reciprocating velocities of the egg-shaking mechanism, it was 0.99 in mode 1 and 0.93 in mode 2, respectively. The results showed that the adaptive-neural-fuzzy inference system can be used well and with high accuracy in detecting rotten and hatched eggs from healthy eggs.

   Food fraud has increased dramatically in recent years. In addition to affecting product quality and causing financial losses, food fraud also has adverse effects on consumer health. And it has raised a lot of concerns about food consumption. One of the most widely used foods in the food industry are edible oils. Which is used in cooking. Solid vegetable oils, or oils such as palm oil, are high in saturated fatty acids, which can cause high blood fats, high cholesterol, and eventually clogged arteries. Cardiovascular از are the most important causes of death. The oils used in the food basket of people should have at least 2% of saturated and trans fatty acids. One of the reasons for usingPalm oil is cheaper than other sesame, corn, soybean and sunflower oils, and due to higher prices of other oils than palm oil, the share of imports of this oil compared to other oils has increased in the last two years. In cooking, a variety of oils are used, each taken from a separate source From grains such as corn to fruits such as olives, nuts such as walnuts, almonds and hazelnuts, and seeds such as canola, safflower and sunflower. It is difficult to find a way to easily determine the quality of edible oils. Therefore, in recent years, olfactory, visual, taste and computer methods have been used more in the food industry. Electric nose is a new method that is considered in agriculture and food industry today In this study, a system has been used to detect and estimate the amount of palm in edible oils. The optoelectronic system consisted of ten metal oxide semiconductor sensors. During the test, the voltage response of the sensors at a certain schedule to inject the head space into the sensor housing and then clean the housing in the next step was done automatically and collected by the data system. Types of edible oils were prepared by mixing with palm oil in different proportions. The properties extracted from the signals obtained from the electrical nose were processed by principal component analysis and artificial neural network methods. Based on the results, the olfactory machine is able to detect palm oil   

   مقدمه: انرژي به‌عنوان يكي از مهم­ترين و ضروري ترين عوامل توليد ، داراي اثرات قابل‌توجهي است. با توجه به اين‌كه منابع   سوخت هاي فسيلي رو به اتمام است، پژوهشگران به دنبال جايگزين كردن سوخت بيوديزل كه يك سوخت زيستي قابل‌تجديد و داراي خواصي نزديك به گازوئيل است، مي­باشند. در آزمايش هاي مربوط به توليد بيوديزل و در كنترل كيفيت اين محصول از دستگاه GC استفاده مي­گردد و درصد بيوديزل توليدي را اندازه­گيري مي نمايند. استفاده از دستگاه GC ، به دليل هزينه بسيار بالاي آن در همه جا امكان پذير نيست لذا در اين تحقيق تلاش مي­شود درصد بيوديزل توليدي را با استفاده از بيني الكتريكي و تكنيك­هاي هوش مصنوعي تخمين زد. روغن انتخابي در اين تحقيق روغن پسماند به همراه روغن گياه رزماري مي باشد. اهداف: 1. توليد بيوديزل از روغن پسماند خوراكي و روغن گياه رزماري بوسيله ي واكنش ترانس استريفيكاسيون    2. تخمين درصد بيوديزل توليد شده با استفاده از بيني الكتريكي و تكنيك هاي هوش مصنوعي روش تحقيق: در ابتدا از روغن پسماند خوراكي و روغن گياه رزماري با استفاده از واكنش ترانس استريفيكاسيون و متانول ، بيوديزل توليد مي­شود. روغن پسماند خوراكي از آشپزخانه ها جمع آوري شده و پس از فيلتر شدن ، ناخالصي­هاي آن گرفته مي­شود و روغن گياه رزماري به آن افزوده مي شود . براي توليد بيوديزل پارامترهايي ازجمله مدت زمان همزني، مقدار متانول مصرفي ، سرعت همزني ، مدت زمان سيركوله و مقدار روغن رزماري مصرف شده پس از توليد بيوديزل، متغير و پارامترهايي از جمله   نوع كاتاليزور و نوع الكل ثابت در نظر گرفته   مي­شوند. الكل مورد استفاده در آزمايش حاضر متانول و كاتاليزگر KOH مي­باشد.    جدول 1 ماتريس آزمايش‌هاي توليد بيوديزل سطح 3 سطح2 سطح1 عامل 8:1 6:1 4:1 مقدار متانول مصرفي (مولي) 20 10 2 مدت زمان همزني 1200 900 600 سرعت همزني(دور بر دقيقه ) 9 6 3 مدت زمان سير كوله   (دقيقه ) 5/2 25/1 5/0 مقدار روغن رزماري مصرف شده (گرم)    براي پرهيز از اشتباه و به حداقل رساندن خطا در اندازه‌گيري‌ها مبناي محاسبات جرم در نظر گرفته مي­شود و با استفاده از ترازوي ديجيتال با دقت 01/0 گرم، مواد وزن خواهند شد. براي توليد بيوديزل مقدار 500 گرم از روغن را به ورودي   رآكتور مي­ريزيم و پس از تهيه­ي محلول پتاسيم متواكسيد با توجه به نسبت مولي الكل به روغن   ، محلول به روغن درون رآكتور اضافه مي­شود و آزمايش با شرايط نشان داده شده در جدول 1 توسط رآكتور انجام خواهند شد . پس از اتمام مراحل توليد بيوديزل ، روغن رزماري با نسبت­هاي مختلف به بيوديزل اضافه و از هر كدام از آزمايش ها نمونه گرفته خواهد شد .

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

   Integrated Pest Management (IPM) strategies is dependent on continuous monitoring of the pest population, this is not only time-consuming, but also highly dependent on human judgment and costly.On the other hand, traditional methods for identifying insects are time consuming and costly. Due to the expansion of the industry and its rapid growth, human beings have always sought to accelerate their work with greater accuracy. The use of artificial intelligence techniques instead of manual and human decision-making, in addition to increasing productivity, also has a high degree of accuracy. Pistachio is a commercial product, and many manufacturers of this product, are damaged by the insects each year. A group of pistachio's pests mainly feed on pistachio, which Idiocerus stali Fieber, 1868 (Hemiptera: Cicadellidae) is very important for this group. In this research, I. stali was selected as target insect for identification using image processing algorithm. Sticky yellow cards were used for collecting samples. To prepare the image processing algorithm, the color and shape characteristics of the objects were used.A total of 357 color properties and 20 shape's features for identification of I. stali were extracted by image processing algorithm. Color properties were divided into two categories of mean and standard deviation and characteristics related to vegetation indices. The mean and standard deviations are the average of the first, the second and the third component, the mean of the components of the first, second and third, the standard deviation of the first, the second   and the third component of 17 Color spaces such as RGB, HSV, YIQ, YCbCr, CMY, HSI, Improved YCbCr, L*a*b*, JPEG-YCbCr, YDbDr, Y  r, YUV, HSL, XYZ, Luv, LCH and CAT02 LMS. The characteristics of the vegetation indices are including the first component of the normalized RGB, the second component of the normalized RGB, the third component of the normalized RGB, the gray channel, the Excess Green, the Excess Red, Color index for vegetation cover extraction, the difference between the excess green and excess red parameters, Normalized Difference Index, Green Minus Blue Indicator, Red-Blue Contrast, Excess Red Index, Excess Green Indicator, and Excess Blue Indicator. shape's Characteristics used are also including length, width, area, perimeter, logarithm of length to width ratio, Ratio of the object's perimeter to the object surrounded by the rectangle's perimeter, width to length ratio, Area to length ratio, eccentricity, Orientation, Convex Area, Filled Area, Equivalent Diameter, Euler Number, Solidity, Extent, Elongation, Compression, Aspect Ratio and length to perimeter ratio. Artificial Neural Network hybrid method - Particle Swarm Optimization Algorithm (ANN-PSO) was used to select the effective features. The selected effective characteristicscolor space, normalized difference index for LCH color space, gray channel for color space YCbCr, second component index minus third component for color space YCbCr, area and mean of the first, The second and third components of color space Luv. The detection rate of the designed image processing algorithm is 99.72%. Artificial neural networks of multilayer perceptron have the ability to classify insects into two classes of I. stali and Anthaxia Sp. Eschscholtz, 1829 with a precision of 99.59 percent. The results showed the feasibility of the new method for identifying the pest insects without destroying them in the farm and in natural light conditions and in the shortest time and with very high accuracy.

   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.

   Abstract   Iran is one of the most susceptible regions of citrus cultivation, which had many advances in recent decades in the order cultivation and production of citrus, so that cultivated area and its amount annual production, of our country determined agreement among the top 10 countries in the world. in this research, after photography from Tangerines (ripe, half-ripe and unripe) using camera canon Pc1339-12.1 mega pixels and preprocessing operations, segmentation and >   Keywords: Citrus, >

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.  

  Today, due to the increasing population of the earth, the need for food supplies has increased. On the other hand, due to the reduction of water resources, water management needs, especially in the agricultural sector, are felt. For this purpose, several methods have been used to reduce water consumption, one of the most modern ones being precise irrigation, namely, the field moisture map. In this research, we attempted to extract from a sorghum field to a half-hectare wetland map by multi-capture and image processing. For this purpose, initial experiments were carried out to obtain the best height and best day of the day to be photographed. The farm was then divided into small pieces. Captured from each piece by a multi-capture. From different points of the farm, the samples were taken by recording the coordinates of the spots to obtain the percentage of moisture content of the plant in those points. Finally, the images were aggregated by the Arc GIS software Then the MATLAB software performed various image processing operations on the image. These operations include: plotting, separating the field, finding the neighborhood of points, extracting different data from neighboring points, modeling effective input parameters to estimate the plant's moisture content, applying model output to segmented images, aggregation, and so on. The best model obtained by neural networks consists of four roughness inputs, the mean values of the CM channel channel monochrome channel M, the mean values of the Y single channel channel of the CMY channel, the mean values of the channel B of the RGB channel, and the output of that percentage The weight of the plant was R2 and MSE of the model was 885/0 and 0/004, respectively. In the case of classification of the plant's moisture content into five classes of very watery, loose, moderate, low water and very low water, the perturbation matrix of the model was 90.5%.

  AbstractSignificant increases in the using of fossil fuels in internal combustion engines and the negative effects of emissions from burning these fuels into the environment have led to an increase in interest in alternative energy sources.On the other hand, the addition of MTBE to base gasoline, despite the increase in octane number, is destructive of the environment effects and malignant diseases, and has been eliminated from gasoline in developed countries for many years, but is still in use in our country. This research aims to reduce emissions in the gasoline engine and examines the amount of adsorbent of an alternative fuel, in the form of a mixture of 1-propanol and base gasoline in the gasoline pride engine. This engine has a Siemens fuel system. 1- Propanol was added to the base gasoline in various volumetric percentages (0, 5, 10, 15 and 20), and the rate of emission at 3 speeds of 1000, 2000 and 3000 rpm was measured by the MOTORSCAN emission Test device.This study was a factorial experiment in a completely randomized design with 6 treatments (normal gasoline for control treatment) and 3 replications.data analysis was performed using SAS statistical softwareandChecked out the effect of fuel type treatments and engine speed on exhaust emissions. Comparison of means with Duncans multiple range Test and error assumption 0.05 were used. The results showed that all modes, including fuel type treatments and different engine speeds, had a significant effect on the amount of emissions,Mutual effects of fuel type and engine speed were significant in both CO2 and UHC.The addition of 1- propanol in fuel in up to 20% reduced emissions of CO and UHC, but NOX changes were irregular and generally increased compared to conventional gasoline. The increase in engine speed led to a decrease in CO and UHC and increased NOX and CO2.Key words: internal combustion engine, MTBE, base gasoline, 1- propanol, exhaust emissions.

  Abstract    Today, the   urvey and analysis of uncertainties in any program is considered necessary,So that without considering and analyzing these uncertainties The occurrence of unpleasant situations that their events challenge the programs goals, Not waiting. These studies are conducted within the framework of risk management .In principle, the Monte Carlo simulation method is used as a tool for analyzing and integrating the various combinations of uncertainties. Uncertainty is integral component of hydrological and hydraulic models. Proper assessment of uncertainty in hydrological models may help to avoid high risk decisions, high cost of product-life cycle, over design structures. The aim of this study was to quantify the uncertainty of flood zoning maps for the reach of Seymwreh River. In this area due to heavy rains, floods cause a lot damage. First, the HEC-RAS flood zoning was calibrated based on F factor. Subsequently, using artificial data generation, 30 artificial data series were generated for the period of returns of 2, 5, 10, 25, 50 and 100 years. In Finally, the probability boundaries of the flood plain of the river were determined at the probability level of 90% and 10%, using the probability curve. The results showed that the increase in uncertainty bandwidth will increase as the maximum uncertainty bandwidth of 450 hectares is related to the flood with a return period of 100 years performed according to Monte Carlo simulation method with random sampling of the parameters space (floodplain and channel roughness coefficients). The model was run 500 times, and the results were evaluated comparing with observed area based on the F factor. Response surface curves obtained from sampling Monte Carlo showed that the highest performance of F when the coefficient of roughness for the channel and floodplain is 0/046 and 0/058. Then using the cumulative distribution function of flood zones, an uncertainty was reached at the upper and lower limits.      Keywords: Uncertainty,   Probability flood plain, Mont Carlo, Seymareh river

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

AbstractEnergy as one of the most important economic and enviromental factors of production has significant effects and is consididered as a main pillars of development of contries. By considering of coming to the end of fossil fuel, the all countries in the world has an attention to biofuels among all biofuel, has been focused on biodiesel. Production and quality of this fuel is very important issue. Estimination of the trans estrification reaction efficiency is a very important issue in the biodiesel researchs. Therefore in this study evaluated the image processing method and used from response surface methodology (RSM), adaptive neuro-fuzzy inference system (ANFIS) And artificial Neural Network (ANN) to estimination reaction yield. In this study, first biodiesel was produced under different conditions (reaction time, alcohol type (ethanol and methanol), catalyst type (KOH and NaOH)and production method) and reaction yield was achived in the wide   range. Then by using the three methods: 1- Microscopic images 2- Using a special image processing box before centrifugation 3- Using a special image processing box aftre centrifugation, photos was taken from biodiesel sampel in similar conditions. Then by using the image processing provided color channel images in 5 different modes microscopic photos, special compartment photos before centrifugation, combination all methods before centrifugation, special compartment photos after centrifugation   and combination all methods of taking images. By using three modeling methods ANN, RSM and ANFIS, was estimated reaction efficiency properly. The results showed the best way, using of the special image processing compartment without centrifugation by ANFIS modeling method and this method was able to accurately evaluate the trans estrification reaction efficiency with R- squared 0.983, mean squared error 0.002226, mean absolute error 0.02927 and sum squared error 0.12466 performe the best performance in the estimation reaction efficiency. The use of ANN method was able to accurately estimate the reaction efficiency. The use of image processing and ANFIS modeling method, by reducing the cost of analysis compared to the conventional method gas chromatography (GC), estimated reaction efficiency properly.Key words: Biodiesel, Image processing, Gas chromatography (GC), ANN method, Trans esterification, RSM method, ANFIS method.  

  Aquaculture is a good source of protein for the proper combination of essential amino acids. According to the Worlds Food Organization, the annual production of aquaculture has steadily increased over the last seven decades and has risen from around 20 million tons in the early 1950 to 170 million tons in 2015 (FAO, 2016). Generally speaking, public awareness of the importance of nutrition has increased, and the production of important nutritionally proteins to meet the growing demand of the population is one of the most important issues of human societies. In order to control the quality of food in post-harvest technology in the agricultural industry, the availability of visual information from all levels is essential. And since most of the methods for measuring the quality of agricultural products are destructive, time-consuming and expensive, the visual system is considered as a non-destructive tool and is used to control the quality of agricultural products. This research was conducted to determine the algorithm for calendering using image processing. 36 rainbow trout in a weighing range between 200 and 800 grams were prepared randomly. Imaging of samples placed at environment temperature and in the refrigerator every 4 hours and the fish kept in the freezer was carried out weekly until complete degradation. In the image processing step, 527 parameters were extracted from each image of the fish, among which, according to the images and histograms, eight effective parameters were determined using the sensitivity analysis for modeling. Three algorithms ANFIS, ANN and RSM were used for grading and modeling. In the modeling the past time of hunting fishes placed at frizer,refrigerator and environment, the best of three models was ANFIS algorithm with correlation coefficients of 0.982627, 0.988024 and 0.988094, respectively. But in the modeling past time of hunting for all enviornments, the ANN algorithm with the correlation coefficient of 0.981391 was the best model. Fuzzy Inference System was used to grading trout. This decision-making system consists of three FIS proportional to the location of fish storage. Each FIS had two inputs including: past time of hunting in hours and size in pixels and one output as a grade of fish quality.Keywords: detection algorithm, image processing, salmon, duration of storage

ABSTRACTEnergy has an undeniable role in quotidian life of human. The growing and wasteful use of fossil fuels in the industry and tra  ortation has causes a lot of environmental pollution, and on the other hand these fuels are coming to an end. For this purpose, the production of biofuels and alternative fuels with less pollutants in the present age has of great importance. Biodiesel is one of the most highly regarded fuels and can be obtained for different renewable raw materials such as vegetable oils and animal fats. In the this study, with the aim of optimizing the production of this fuel the continuous biodiesel production system was made with the combination of microwave and magnetic fields technology. The system used in the present study included a micro wave reactor and magnetic field intensity, the force of gravity was also used to move the material from the reservoir into the reactor. The waste oil was used to produce biodiesel using the base Trans Esterification technique. The material was exposed with the magnetic field uniformly before and after entering the reactor. In this study, the effect of four treatments magnetic field intensity (0, 225 and 450 milli tesla), micro wave power (400, 328, and 1181 watts), the combination of two KOH and NaOH catalysts Constant concentration of 1% (0, 50 and 100% KOH), and the combination of two ethanol and methanol alcohols in constant molar ratio 6 : 1 (0, 50 and 100% ethanol) by using the Box-Behnken test and response surface methodology, biodiesel was studied and a significant conversion percentage of 96.197 %   was obtained. All independent parameters had a significant effect on the reaction efficiency. However, the type of alcohol and magnetic field had the greatest effect on the efficiency of the reaction. By using of   adaptive neuro-fuzzy inference system (ANFIS), the predicted yield reaction and   with response surface methodology (RSM)   were compared. The results of the R- squared factor for both methods were 0.994 and 0.957, respectively. This result indicated the high ability of each method, especially the ANFIS method for estimating reaction yield. Optimized parameters achivef from Design Expert software for full conversion (96.197%) are magnetic field of 331 (mT), microwave power 6176.3 (W), KOH 32.33% + 67.66% NaOH catalyst and 80.45% Methanol + 55.59% Ethanol alcohol.Key word : Biodiesel, Microwave, Magnetic field, waste oil, Transesterification, RSM method, ANFIS method.

  Pinus trees die-back is one of the most serious problems that presently affects the Kermanshah pinus trees. To study the cause of die-back pinus trees, infected branches were collected during growing seasons, transferred to the laboratory and processed within 24 hours. Small pieces, approximately 4 mm in size, of discolored tissues were surface disinfected by immersing in 1.5 % solution on NaOCl for 30 sec, rinsed in sterile distilled water and plated on potato dextrose agar (PDA) and malt extract agar (MEA) amended with chloramphenicole (25 ?g/ml). Two hundred eighteen fungal isolates were recovered from pinus trees showing dieback symptoms. The most common fungi isolated from most diseased pinus were include Microsphaeropsi  olivacea (16 isolates), Microsphaeropsi  rotea (eight isolates) and Microdiplodia sp. (seven isolates). Numerous isolates of Penicillium, Aspergillus,.Alternaria, Rhizopus Cladosporium sphaerospermum and Trichoderma were always associated with diseased pinus in Kermanshah province. The fungi were inoculated to detached stem of pinus tree. Pathogenicity of all isolates of M.  olivacea, M.  rotea and Microdiplodia  sp. were confirmed on pinus trees by artificial inoculation on detached stems in the laboratory. Disease symptoms on the detached stems in the laboratory appeared as canker. To confirm of   morphological identification, genomic DNA was extracted and a nuclear rDNA region, containing the internal transcribed spacers 1, 2 and 5.8S gene of rDNA (ITS) were amplified and PCR products were sequenced. Amplicon was purified, sequenced and submitted to the GenBank. The resulting sequence was submitted to a BLAST search to find most similar sequences in GenBank. The search results showed highest similarity of Iranian isolates to other isolates from GenBank.. Voucher specimens deposited in fungal collection of the Department of Plant Protection, Razi University, Kermanshah, Iran

  AbstractGalium aparine is a problematic weed, which has become increasingly difficult to control with herbicides in Iran. The aim of this study was to screen selected putative-resistant populations of G. aparine for resistance to auxinic herbicides 2,4-D+MCPA and ALS–inhibiting herbicides sulfosulfuron, tribenuron-methyl, mesosulfuron-methyl+iodosulfuron-methyl-sodium. Populations of G. aparine were collected from different wheat fields in the west of Kermanshah, where herbicide-use pattern is typical for Iran. Herbicide resistance to premixed herbicide 2,4-D+MCPA was confirmed in several populations. More populations of G. aparine showed cross-resistance to ALS-inhibiting herbicides examined in this research. Some populations were found to have developed multiple resistant to both auxinic and ALS herbicides. Generally, the level of resistance to ALS-inhibitor herbicides was higher than that of auxin analog herbicides.   

  Nowadays for users, quality of fruits and vegetables is do important like produsers. Most of users want to pay more money for better products. In Iran usually old methods of agriculture products in >In this reaserch 200 samplese of local pomegranate from Kermanshah prepared by chance. Then after first prepration, their fisical qualeties were measured. Dufferent part of pomegranates captured by a special case. Then grains of pomegranate were separated and captured them too. PH number and suger amount of that was measured. Taste of that in 5 level: very sweet, sweet, subacid, sour and very soure, tasted by panel members. In producing picture stage with due attention to explains 482 parameters of every pomegranate obtained. In 14 parameter with help of sensitivity analysis for every indicator, determined some parameter for modulation, for grading and modulation 3 parameter (ANFIS, ANN and RSM) used. For classificating pomegranate in 3 level number 1, 2 and 3, ANFIS model with 98. 5% correlate coefficient was best model.   In modulation for estimating suger amount of pomegranate water ANN model with 0.988 correlate coefficient was best model. . In modulation for estimating PH number of pomegranate water ANFIS model with 0.991 correlate coefficient was best model. .   In modulation for estimating taste of pomegranate ANN model with 0.955 correlate coefficient was best model. . In modulation for grading of the grain pomegranate ANN model with 0.98% correlate coefficient was best model. In modulation for beforehand pomegranate weight ANFIS model with 0.998 correlate coefficient was best model. . In modulation for beforehand pomegranate volume ANN model with 0.995 correlate coefficient was best model.

     The reservoirs are operated and designed in several aims such as, water demand supply, hydropower energy generation and flood control reduction. Optimization of reservoir operation is one of the most important issues in water basin. In this study, optimization of reservoir operation to obtain the maximum agriculture water supply and hydropower energy production are considered. As the rainfall is the stochastic phenomenon and has the positive effect on reservoir inflow, hydropower optimization is applied by using chance constrained linear programming (CCLP). Maroon Reservoir is located on east western of Khozestan province and the aims of this reservoir are agriculture water supply of Jayazan, Behbahan,Shadegan and Khalafabad, flood controlling and hydropower generation about 150 Megawatt(MW). Monthly inflow of 52 years (1953-2004) is used to modeling of experimental Weibul probability distribution for each month. Annual energy production of Maroon reservoir is obtained by Lingo 16.0 software for a known agriculture water supply (P) and installed capacity 150 MW. Then, the best probability distribution of reservoir inflow is obtained by Easyfit software 6.0 for each month. The results showed that the maximum P are calculated 86% and 88% and also the annual hydropower energy are 114.86 and 111.55 Giga watt-hour(GWh) using experimental Weibul probability distribution and the best probability distribution of reservoir inflow, respectively. Thus, obtaining the best Inflow probability distribution does have insignificant effect on optimization results since in this study it’s less than 2% on agriculture water supply and hydropower generation. So, considering only the experimental Weibul probability distribution of reservoir inflow is sufficient in CCLP approach.

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.