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The singular word "halū’a" in the Qur'an (Ma'arij/ 19), according to some commentators, and as it appears from different lexical and interpretive opinions, is one of the strange and difficult words to understand in the Qur'an, which its definition as "jazū’a" and "manū’a" in the next two verses has neglected scientists to accurately capture its meaning. The first goal of this research was to discover the main meaning of the "hala’a" and its various manifestations through lexical studies. The main question is how "halū’a" is related to "jaza’a" and "man’a", especially in their exaggerated form. The two well-known meanings and interpretations of halū’a, as "intensity of impatient" and "intensity of greed", fail to establish this connection. The present study by descriptive and analytic method clarifies that the central meaning of halū’a is "emptiness of the source from strength" which is accompanied by weakness on the one hand and on the other hand it appears in two branches: "lightness" and "severe hunger". Each of these is accompanied by a kind of restlessness and movement and in the presence of accelerating factors, various manifestations of speed, seriousness, exaggeration, etc. are manifested in this substance. In the Qur'an, halū’a is used for an anxious person devoid of faith, and refers to his exaggerated and intense action to evil and good; It means it has no direct relationship to the meaning of "jaza’a" and "man’a".
 

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Aims: This study aimed to investigate the predictors of health-related quality of life in pregnant women based on Pender’s health promotion model.
Instrument & Methods: In this descriptive correlational study, 300 pregnant women in the second and third trimesters of pregnancy were selected by simple random sampling from one of the community health centers of Yazd province. Data were collected through a demographic information questionnaire, a short-form health survey v.1, and a questionnaire based on Pender's model constructs. Finally, the obtained data were analyzed descriptively using SPSS 18 software.
Findings: The psychological dimension of quality of life was significantly correlated with perceived effects, perceived self-efficacy, and perceived benefits (p<0.05). Also, the physical dimension of quality of life had a significant correlation with perceived barriers, social support, affect, and benefits (p<0.05). The regression analysis results showed that the variables of perceived barriers, social support, positive and negative effect, and perceived benefits had significant effects on the physical dimension of health-related quality of life (p<0.002). Moreover, the variables of positive and negative affect, self-efficacy, and perceived benefits influenced the psychological dimension of health-related quality of life (p<0.001).
Conclusion: According to the results, perceived effect, perceived self-efficacy, and perceived benefits constructs are suitable for the psychological dimension of health-related quality of life, perceived barrier constructs, and social support effects. The perceived benefits are appropriate for predicting the physical dimension of quality of life.

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There are more than 37,000 hectares of land cultivated with date palm in Hormozgan Province producing around 145,000 tons of date annually. Therefore, increasing date yield and quality can ender positive effects, especially on farmers, income in the region. One of the most important agricultural practices is mineral nutrition nd soil fertility management that can improve yield and fruit quality of date palm. This research was conducted for a duration of three years to study the effect of essential nutrients application on Date Bunch Wilt and ry disordr (DBWD) which has caused large production reductions in recent years. Two palm groves of “Mordasang” cultivar were chosen as experimental sites. One was afflicted with DBWD and he other one healthy. Fertilizer treatments were applied on the basis of soil and leaf nalysis as follows: 1- Control (with no use of fertilizers) 2- Addition of balanced amounts of macro-nutrients and 3-Addition of balanced amounts of macro as well as micro-utrients. Theexperiment was arranged with a complete randomized block design of five replications. Maintenance operations consisted of pollination, weeding, disease and pest control. Such plant esponses as fruit yield, bunch number and percentage of bunches fflicted by DBWD were determined and compared among treatments. There was no significant difference observed between bunch numbers per tree in the fertilizer treatments. Application of ssential nutrient elements (treatments 2 and 3) resulted in a onsiderable increase in date yield and a decrease in dried bunches percentage.

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In order to engage students in higher-level thinking, cognitive activation (CA) strategies have been advanced and utilized in mathematics. CA develops when learners are challenged, confronted with conflicts, asked to think and explain clearly on their learning, and realize connections between new and previous content. Extending the theme to English language teaching (ELT), this study investigated Iranian English language teachers’ knowledge and practice of cognitive activation writing strategies (CAWS). In so doing, a model was proposed based on a questionnaire that was developed and validated in the present study. Through this scale, knowledge and practice of CAWS by 213 English as a foreign language (EFL) teachers were explored. During a pilot phase, the reliability of the questionnaire was calculated to be .78 for the knowledge section and .81 for the practice section. Two items in each section were removed after conducting exploratory factor analysis. Ultimately, the confirmatory factor analysis indicated that the proposed model was fit for the data. Five components were confirmed as constituents of CAWS for the knowledge section, and four components were identified for the practice section. The findings revealed that Iranian EFL teachers were already familiar with the CAWS and purportedly practiced them in their writing classes. Using the scale in the process of writing instruction can provide ELT practitioners with a helpful platform for relating to CA strategies and empowers learners to accomplish tasks such as problem-solving in their writing assignments, similar to what is practiced in mathematics.

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This study was conducted to evaluate seasonal concentrations of macro-minerals in serum or plasma of sheep under grazing conditions to establish mineral deficiencies and excesses over a two-seasonal grazing period (fall-winter and spring-summer corresponding to the rainy and dry seasons) according to variations of year, region and animal classes in Sabalan, Iran. In each flock, ten sheep (38-50 kg body weight, three females groups: yearlings, growing and mature, in addition to one male group) were selected for the study purpose. Approximately 10 ml blood samples were collected in two stages by jugular vein, which were then centrifuged, and plasma or serum were obtained. Plasma phosphorous (P) was determined by the colorimetric method. Serum Ca and Mg were measured by atomic absorption spectrophotometry. Overall, means of serum Ca, Mg and plasma P concentrations were 11.73, 3.24 and 4.92 mg dl^-1 in dry season and 12.01, 3.17 and 5.3 mg dl^-1 in rainy season, respectively. Year, season and region significantly affected (P< 0.001) serum concentration of Ca but region had no effect (P> 0.05) on Plasma P. Unlike season, year effect on Mg concentration was significant (P< 0.05). These macro-mineral concentrations of serum and plasma were not different (P> 0.05) between animal classes. Ca and P deficiencies were higher in rainy than the dry season (6.24 vs. 3.38 and 16.52 vs. 15.2 percent, respectively). Although overall mean of serum Ca and P had higher values than the critical levels of these minerals in sheep blood, our results indicate that sheep in Sabalan need a common salt Ca and P supplement to enhance the blood level of these essential minerals in some sheep.

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Biodiesel fuel, which is produced by transesterification reaction between alcohol and vegetable oil/animal fat is proposed as a clean alternative to petro diesel fuel. Today, one of the new technologies to produce biodiesel is using ultrasonic energy that makes production faster, with improved quality and less expensive. Various factors that affect the design of ultrasonic reactors are ratio of vibrating rod diameter to reactor diameter, reactor height, depth of horn penetration into fluid and chamber characteristics (material and shape). In this paper, two parameters namely the ratio of vibrating rod diameter to reactor diameter and reactor height were studied in order to increase the reaction efficiency. In all performed tests, the horn diameter of 14 mm, molar ratio of alcohol to oil of 5 to 1, catalyst concentration of 0.7% wt?? oil, depth of horn penetration into fluid of 15 mm and a cylindrical reactor were used. Experimental design involved the use of Central Composite Design (CCD) and the statistical Response Surface Methodology (RSM). Considering the empirical model, a significant relationship was found between independent and dependent variables with a regression coefficient of 0.99. Taking into account the desirability of increasing the efficiency, the optimal function of reactor diameter and reactor height were 63 and 110 mm, respectively with a reaction yield of 87%. In order to verify the model, function responses in the defined area were tested with five replicates and the average efficiency of the reaction was 87.2%. The obtained model suggests the simultaneous reverse effects of reactor diameter and height on the reaction efficiency.

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To minimize the cost of maintenance and repair of rotating industrial equipment, one of the methods used is condition monitoring by sound analysis. This study was performed to diagnose the fault of a single-phase electric motor through machine learning method aiming to monitor its situation by sound analysis. Test conditions included healthy state, bearing failure, shaft imbalance and shaft wear at two speeds of 500 and 1400 rpm. A microphone was installed on the electric motor to record data. After data acquisition, signal processing and statistical analysis, the best characteristics were selected by PCA method and then the data were clustered by machine learning method and K mean algorithm. These features used in the ANFIS modeling process were common features selected in both electromotor speed situations. After evaluating the models, the best model had the highest accuracy value of 96.82%. The average accuracy was 96.71% for overall fault classification. The results showed that the analysis of acoustic signals and modeling process can be used to diagnose electromotor defects by machine learning method. Based on the obtained results, condition monitoring of the electromotor through acoustic analysis reduces its stop and continues its work process in the industry. The repair costs of the electromotor are reduced by its proper condition monitoring.

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The aim of this study was to determine the authenticity of honey by processing microscopic images and obtaining an algorithm for classifying various honey frauds. In this study, sucrose, fructose, and fructose-glucose solution at a ratio of 0.9 were used to make honey adulteration. The level of adulterated honey was based on the weight percentages of 2.5, 5, 7.5, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 by stirring. Different samples were imaged under a microscope. Each image was processed in 33 monochrome color spaces and 15 parameters were extracted from it. The three main and effective parameters of various color spaces were selected using sensitivity analysis for modeling honey fraud by adaptive Fuzzy Neural Inference System (ANFIS), Artificial Neural Network (ANN), and response surface methodology. Various criteria were used to evaluate the performance of the models such as coefficient of determination, mean square error, sum of squared estimate of errors, and mean absolute errors. The results showed that the determination coefficient and the mean square error of the artificial neural network model was 0.974 and 0.0024, respectively. Finally, using the desirability function, the artificial neural network model was selected as the best model due to less prediction error values and desirability of 0.948.