n. asadzadeh

Large parts of Iran have a dry and semi-arid climate, which is why opportunities for fodder production in the country are limited. Providing feed from agricultural by-products, including potato vines, can be considered a suitable option to compensate for the limitations of traditional feed (e.g. alfalfa). Ardabil province has about 15-20% of the country's potato cultivation area, and with the annual production of about 800 thousand tons of potato tubers, it ranks second in the country. Therefore, there is great potential in this province to produce by-products related to potatoes. The present study was conducted to investigate the possibility and effect of replacing potato vines with regular forage in the diet of pregnant sheep (during the last 45 days of pregnancy).

In the first step of this research, the chemical compositions of alfalfa hay and potato vine including dry matter, organic matter, ash, crude protein, crude fat, neutral detergent fiber (NDF), and acid detergent fiber (ADF) were determined. In the next phase, 210 pregnant Moghani sheep were randomly assigned to seven treatments with three replicates and 10 sheep per replicate. In addition to the control treatment (pasture feeding), six treatments with different levels of potato vine (zero, 16, and 32%) were used in two food forms, mash and complete block. The mentioned diets were given to the animals in the last 45 days of pregnancy in the amount of 500 grams during the night feeding time. It should be noted that lactating ewes were not subjected to any treatment after lambing and the effect of an additional feeding period on milk production was examined after three months of lambing.

According to the results obtained, the percentage of dry matter, crude protein, crude fat, gross energy, and ADF in potato vine was not significantly different from alfalfa feed, but the NDF percentage in potato vine was lower than alfalfa and its ash content was more than alfalfa (P<0.05). No significant difference was observed in the amount of dry matter digestibility (DMD) and dry organic matter digestibility (DOMD) between the potato vine and alfalfa hay. Ewe weight gain during the supplementary feeding period was influenced by different treatments (P<0.05). A similar pattern was observed for other traits such as ewe weight after lambing, birth weight of lambs, weight gain before weaning, and, weaning weight of lambs. In the current study, the examined traits in sheep that were fed with complete block showed better performance compared to sheep fed with mash. In this feeding method, animals are not able to choose due to the uniform composition of food, which leads to a uniform effect on the rumen and thus reduces the fluctuation in ammonia release for more efficient use of non-protein nitrogen. Complete block feeding is an efficient method to increase voluntary feed intake in ruminants. It also improves nutrient utilization by stabilizing fermentation in the rumen. The increase in birth and weaning weight in lambs whose mothers were fed complete block diets showed that the use of supplementary feeding in the last weeks of gestation of ewes, when carried out appropriately, leads to improvement in fetal growth and proper development of the mammary gland, and increase in birth weight of the lamb and milk production. The different treatments have a significant impact on the amount of milk production so that the average daily milk production throughout the lactation period was lowest in the control treatment (without using any nutritional supplements) and highest in the block feeding treatment at 16% Potato vine. The results showed that using the complete block produced a higher profit compared to the mash.

Based on the results of the present research, the use of 16% potato vine as a replacement for part of the usual fodder in the form of a complete feed block resulted in profitability in the nomadic sheep farming system. Given the cheap price and availability compared to alfalfa fodder and wheat straw, especially in drought conditions and poor pastures, its consumption is economical.

Hot water injection is one of the common mechanisms to enhance the oil recovery from reservoirs. But, novel methods of enhanced oil recovery are used for oil recovery, and one of them is flooding polymeric solutions inside the reservoirs. The main purpose of this experimental study is to investigate the process of enhancing the recovery of viscose oil by injecting distilled water and polyacrylamide-based polymeric solution from a two-dimensional porous medium. In order to study the flow patterns during the injection of base fluid and polymer solution, injection of displacing fluids was performed at a constant flowrate of 0.4 mL/min. Also, to investigate the effect of injected fluid temperature on fluid-fluid displacement and enhanced oil recovery efficiency, displacing fluids were injected at temperatures of 25°C and 90°C. The results showed that by adding polyacrylamide to water, the viscosity of the displacing fluid increased significantly, which led to an increase in the capillary number, and enhanced oil recovery up to 65.2% when injected with 0.5%wt polyacrylamide at ambient temperature. Also, increasing the temperature and reducing the mobility ratio of displacing fluids and base oil, the efficiency of oil recovery by injecting 5000ppm polymer solution increased to 66.4%, which is the maximum rate of base oil recovery by injecting displacing fluids in present study.

Sistani cows are generally restless animals; therefore, controlling, treating, and weighing them is difficult. On the other hand, recording the weight of domestic animals, including Sistani cows, is inevitable, because it provides a good scale for management decisions in the herd such as balancing the diet, changing environmental conditions, or determining the time of slaughter of fattening animals. In addition to scales, various methods are commonly used to measure the body weight of large animals. Some of these methods include the use of weight-meters, appraisal assessments, and the use of mathematical models. One of the new methods for predicting livestock weight is artificial intelligence. Because some reports are indicating that artificial intelligence could facilitate the weighing process of animals, this study was performed to predict the body weight of Sistani cows using computer vision technology. The data required for this study were recorded in the Zahak breeding station located in Sistan and Baluchestan province of Iran. The recording operation involved the weighing and biometric measurement of about 190 Sistani cattle, including calves, heifers, and male and female animals, every three months during a year. At the time of weighing, images of the lateral view of each animal were taken and recorded using the CANON SX150IS digital camera. During this period, a total of 358 weight records of Sistani cows at different ages were recorded. The digital images were initially preprocessed using MATLAB software, and then some morphological features were extracted from each image. For predicting the weight of Sistani cows via the Artificial Neural Network (ANN), the extracted features of images were introduced to the ANN model as input and the weight of cows as output. The "feed-forward neural network", which was trained by the "error propagation" algorithm, was used to predict the weight of cows. The function used in the hidden layer of the ANN model was sigmoidal and in the output layer was linear. An ANN model which had the highest precision and lowest error was selected as the final model for predicting the animal weights. The criteria for selecting the best model were the highest determination coefficient (R2) and the lowest mean square error (MSE) compared to other available models. Out of 22 features extracted from each image, only 15 of them, which had a higher correlation with the body weight of cows at different ages, were selected as effective features. As result, equivalent diameter, major axis length, minor axis length, bounding box, convex area, filled area, area, perimeter, and the number of non-zero pixels of the image (NNZ) had the highest correlation with the cattle weights (P<0.01) and used as effective features to train the ANN model. The final ANN model had 15 neurons in the input layer including selected image features, 11 neurons in the hidden layer, and one neuron in the output layer including the weight of the cows. The precisions of the artificial neural network in the training, validation, and test phase were 0.974, 0.970, and 0.981, respectively. The results showed that the final ANN model had acceptable precision in all light, medium, and heavy-weight cows, and the size and the age of animals did not have a significant effect on the precision of the artificial neural network model. A correlation between the actual weight of Sistani cows and the weights predicted by the ANN model was 98.3%. The average error of the model in predicting the weight of cows was 1.11%. In the practical test, a 2.32 kg deviation was observed between the predictions of the ANN model and actual weights in Sistani cows. The accuracy of the ANN model for predicting the weight of Sistani cows in the present study is acceptable and within the range of the other reports. The proposed method based on image processing and ANN, had acceptable results in predicting the weight of Sistani cows. Given the difficulties of weighing Sistani cows as heavy livestock and sometimes the time-consuming process, it seems that the use of new technologies such as computer vision methods can be a good alternative to conventional weighing methods and facilitate and reduce recording costs of Sistani cows.

This research was conducted to investigate the possibility of using visual machine technology in measuring the body dimensions of Sistani cows. For this purpose, the record of body dimensions including length, shoulder height, hip height and chest circumference of 179 heads of livestock in Zahak Sistani cow breeding station, were measured at different time points, using a tape meter. At recording time, digital images were taken using the canon camera from the lateral view of cattle from distance of 2 meters. Digital image processing and feature extraction were performed using Graphical Unit Interference of MATLAB software. The feature of digital images as input and different body dimensions of cows as output of Artificial Neural Network(ANN) were used in the training and modeling phase. The results showed that, out of 22 features extracted from the images of Sistani cows, the 15 effective features, such as Equivalent Diameter, Major Axis Length, Minor Axis Length, Bounding Box, Convex Area, Filled Area, Area, Perimeter, and the number of white pixels of image (NNZ) had a significant correlation with the body dimensions of the Sistani cow(p<0.01). Body dimensions of Sistani cows including body length, shoulder height, hip height, and chest girths were estimated with accuracy of 0.98, 0.97, 0.97and 0.98%, by the ANNs model, respectively. The results of the present research showed that Artificial Intelligence Technology can act as a suitable alternative to biometric evaluation of Sistani cows and save time and relevant costs.

The aim of this study was improvement of ram frozen semen efficiency using addition of glutathione antioxidant to freezing medium. In this experiment, semen samples were collected from five Zell rams and add to the extender containing 0, 0.5, 1, 2, 4 and 8 mM glutathione. In sperm quality evaluation, sperm samples were evaluated and motion parameters, membrane integrity, morphology, acrosome integrity, mitochondria activity, viability and lipid peroxidation were assessed. The results showed using 4 mM glutathione resulted in improving of sperm total and progressive motility, membrane integrity, mitochondria activity, acrosome integrity, and viability as well as reduction of apoptosis rate and lipid peroxidation. Using the treatment of 8 mM glutathione caused to reduction of sperm quality after thawing and the antioxidantd did not have any effect on sperm morphology. In conclusion, using 4 mM glutathione in freezing extender could be a suitable method to improve ram sperm reproductive performance after freeze-thaw process.

The purpose of the current research is to investigate the relationship between spiritual intelligence and its components with high school teachers’ job performance in Aslanduz city during the 2013-2014 academic year. This study is descriptive–correlational and its population includes high school teachers of Aslanduz city (N=70) who were selected through whole counting. The instruments included two questionnaires of Spiritual Intelligence and Patterson’s job performance. The data were analyzed by parametric tests of Kolmogorov-Smirnov, Pearson correlation, one sample t-test and multiple regression through SPSS- 20. The results showed that there is a significant and positive relationship between spiritual intelligence and teachers’ job performance. Also, analysis of the data revealed that spiritual intelligence could predict 71.2% of teachers’ job performance variance. Therefore, we can conclude that spiritual intelligence has important role to play in teachers’ job performance.

Considering the high prevalence of anxiety and the need to investigate the effectiveness of psychological interventions in helping people with anxiety, this study aimed to explore the effectiveness of acceptance- and commitment-based therapy on social adjustment, psychological well-being and self-criticism in people with anxiety in the city of Ahvaz. This study was of an educational trial type in which the research population consisted of all the people with anxiety referring to clinics in the city of Ahvaz. From this population, 40 persons were selected using the convenience sampling method as the sample and were then assigned to each of the experimental and control groups using the simple random sampling method. The research design was experimental with a pretest-posttest and a control group. Data were collected using the social adjustment questionnaire, psychological well-being questionnaire and self-confidence questionnaire. In the experimental group, an intervention-based treatment, based on admission and commitment, was performed one session a week for eight weeks, each session lasting 120 minutes. Both groups took a post-test at the end of the intervention and after a two-month follow up. Data were analyzed by multivariate analysis of covariance analysis using version 18 of the SPSS software. The results showed that the intervention in the experimental group was based on all the three variables of social adjustment (F = 8.74, p= 0.000), psychological well-being (F = 39.31, p= 0.000) and self-criticism (F = 34/92 and p=000/0). The results of data analysis indicate the effect of acceptance- and commitment-based therapy on improving the psychological status of people with anxiety. Therefore, the use of this therapeutic approach by clinical therapists is recommended in treating people with anxiety.