Background: The expansion of the use of audio AND video media technology has faced society with many medical, cultural AND economic challenges. In the meantime, excessive use of mobile phones can cause many problems. Objective: The aim of this study was to construct AND validate a questionnaire on PHYSICAL AND psychological injuries of mobile phones. Method: The method of the present study was descriptive-correlational AND confirmatory factor analysis. The statistical population of the study consisted of all citizens of Khorramabad with a diploma or higher in 1399, which was selected as a statistical sample by available sampling method of 200 people online AND virtual. Results: The results showed that the factorized PHYSICAL AND psychological harm questionnaire of cell phone with 42 questions AND 10 components (fear of loss, vibration syndrome, duck syndrome, nomophobia, insomnia, hearing problem, eye syndrome, tunnel AND neck syndrome SMS) AND has good reliability, validity AND flexibility in terms of psychometrics. Discussion AND Conclusion: The questionnaire of factor analysis of PHYSICAL AND psychological injuries of mobile phones can be used to identify AND study the PHYSICAL AND psychological injuries of mobile phones.

ABSTRACT Despite a wide range of components AND criteria affecting travel behavior presented through empirical research, the results of these studies are inconclusive, which could be due to the difference between these components AND criteria in the study areas. Therefore, this research presented a method to determine which FACTORS in different PHYSICAL developments affect travel behavior due to the differences in various PHYSICAL developments. The required information was collected through 271 questionnaires at the level of three neighborhoods of Monirieh, Koye Bimeh, AND Koye Golestan in Tehran, Iran, as the old, conventional, AND new neighborhoods, respectively. ANOVA test was exerted to analyze the significant difference between different development patterns in three neighborhoods. Dunnett's T3 was applied to determine which neighborhood caused the difference between groups. Also, the FACTORS affecting travel behavior were obtained based on exploratory factor analysis indicators. Finally, by comparing the results of the ANOVA test AND regression analysis, it was discovered that FACTORS such as car ownership, dependence AND pro-liking for private cars, density AND access to educational centers AND parks, access to medical AND service centers, AND variety AND density of retail stores had been introduced as the FACTORS affecting travel behavior due to the differences in development patterns. However, proximity to the public transportation station, accessibility preferences in choosing a residence, dependence, AND pro-liking for other than a private car, having a license, number of children under five years old, AND age have influenced travel behavior regardless of the variation between neighborhoods. Extended Abstract Introduction Finding FACTORS affecting travel behavior has been one of the main concerns of transportation planners. However, in the last two decades, the importance of the influence of the features of the built environment, including lAND use, along with demographic-economic characteristics, travel behavior, AND attitudes of people, has been raised by urban planners. Studies seek to find FACTORS affecting travel behavior, especially lAND use characteristics. Despite presenting a wide range of components AND criteria affecting travel behavior, the results of the studies are inconclusive, which could be due to the difference between these components AND criteria in the study areas. Therefore, this research presented a method to determine which FACTORS in different PHYSICAL developments affect travel behavior due to the differences in various PHYSICAL developments. In order to do this, it must first be determined whether the study areas/different development patterns have a significant difference in terms of travel behavior or not. In case of a positive answer to the previous question, the following question is which study areas caused this difference. The next question arises: -Which PHYSICAL AND non-PHYSICAL characteristics affect travel behavior due to distinctions between different development patterns?   Methodology The present research method is analytical AND experimental based on quantitative methods. This research chose the frequency of travel by private car, public transportation, AND walking as the travel behavior. According to the research's purpose, indicators AND criteria affecting travel behavior were extracted after reviewing the theoretical AND experimental literature. Then, the required information was collected through 271 questionnaires at the level of three neighborhoods of Monirieh, Koye Bimeh, AND Koye Golestan as the old, conventional, AND new neighborhoods, respectively. The questionnaire was compiled as a Likert scale in five parts of travel information, demographic-economic characteristics, perceptual characteristics of lAND use, travel habits, AND access preferences of people in choosing their residence. ANOVA test was used to analyze the significant difference between different groups of a characteristic (here, different development patterns or the three case studies). Dunnett T3 was exerted to determine which neighborhood caused the difference between groups. Also, the FACTORS affecting travel behavior were obtained based on exploratory factor analysis indicators. Finally, by comparing the results of the ANOVA test AND regression analysis, it was discovered which FACTORS affecting travel behavior were due to the differences in study areas AND which FACTORS affect travel behavior regardless of development patterns.   Results AND discussion This research aims to identify the FACTORS affecting travel behavior due to the differences in development patterns. In this regard, the findings in line with the first research question show that the frequency of three modes of travel, by private car, transportation, AND pedestrian, differ significantly in the three neighborhoods. Furthermore, ANOVA test results depict that there is a significant difference between these three neighborhoods in terms of FACTORS affecting travel behavior, such as perceptually environmental characteristics of the neighborhood, dependence AND pro-liking for personal cars, variety AND density of retail stores, density AND access to educational units AND parks, access to medical AND service centers, AND car ownership. Finally, by comparing the results of the ANOVA test with the regression analysis assessing the relationship between PHYSICAL AND non-PHYSICAL FACTORS (the same indicators in the same study areas) with travel behavior, the FACTORS affecting travel behavior owing to different development patterns were identified. FACTORS such as car ownership, dependence AND pro-liking for private cars, density AND access to educational units AND parks, access to medical AND service centers, AND variety AND density of retail stores have been introduced as the FACTORS affecting travel behavior due to the differences in development patterns. However, proximity to the public transportation station, accessibility preferences in choosing a place of residence, dependence, AND pro-liking for other than a private car, having a certificate, number of children under five years old, AND age have influenced on travel behavior regardless of the variation between neighborhoods (different PHYSICAL development patterns).   Conclusion In In order to discover the FACTORS affecting travel behavior due to the differences in patterns of PHYSICAL development, this research has provided a more detailed analysis of the FACTORS affecting travel behavior. It has achieved more accurate components than previous studies in this regard. Detailed analysis of studies related to travel behavior AND finding the main components affecting it, considering the extent of variables AND data, can pave the way for professionals in transportation planning AND urban planning, in addition to providing detailed methods AND criteria in the related literature.   Funding There is no funding support.   Authors’ Contribution Authors contributed equally to the conceptualization AND writing of the article. All of the authors approved thecontent of the manuscript AND agreed on all aspects of the work declaration of competing interest none.   Conflict of Interest Authors declared no conflict of interest.   Acknowledgments  We are grateful to all the scientific consultants of this paper.

To investigate the response of corn to combined application of CHEMICAL fertilizers with rhizobacteria plant growth promoting, an experiment was conducted in 2017 at Research farm of Agricultural AND Natural Resources Campus of Tehran University, Karaj, Iran, in a rANDomized complete block design with four replications. Four nutritional treatments including T1 (Control treatment without applying fertilizer), T2 (Just PGPRs), T3 (Use CHEMICAL fertilizers based on soil test) AND T4 (T3 + PGPRs) were considered. According to the results, the highest total dry weight (3.9 kg/m2), crop growth rate (79.8 g.m-2.day-1), net assimilation rate (15.3 g.m-2.day-1) AND grain yield (18.2 ton.ha-1) were observed in T4 treatment AND T2 treatment produced the highest  leaf area index (5.3), leaf area duration (205.2) AND specific leaf weight (78.5 g.m-2) . Also, the lowest value of all traits was observed in in T1 (control) treatment. The results showed that the presence of rhizobacteria plant growth promotioninduction in the corn nutrition program increased the growth AND growth indices of the plant. Combined application of CHEMICAL fertilizers with rhizobacteria plant growth promoting resulted in the highest growth AND final grain yield of corn.

ABSTRACT Considering the concept of quality of life in cities to be the same, urban planners do not have a precise understANDing of the FACTORS affecting the quality of life. Therefore, according to the size of the city, different indicators should be considered to measure the quality of life. Thus, according to the main goal of the research, which is to recognize, identify AND compile the FACTORS affecting the improvement of the quality of life in middle AND small cities on an intra-provincial scale, Qazvin AND Zia Abad cities in Qazvin province were selected as study samples. In order to achieve the goal of the research, exploratory factor analysis AND regression analysis were exerted. The findings show that the homogenization of the indicators affecting the quality of life in all cities is incorrect. Some concepts, such as the size of the city, are effective in compiling AND selecting the indicators affecting the quality of life. So, the indicators affecting the quality of life in each city can be different according to some concepts, such as the city's characteristics or the city's size. Examining study samples also shows that in small cities, social indicators are more important due to strong social solidarity among people AND the high importance of social AND cultural concepts. Moreover, in middle cities, due to the importance of finding economic concepts AND urban services in people's lives AND people's less communication with each other AND the weakening of solidarity AND social connections, indicators of urban services AND facilities AND economic FACTORS have the most importance in explaining the quality of life; this shows that the Quality of life indicators are different in all cities Extended Abstract Introduction Quality of life is a multifaceted AND dynamic concept that can be different from city to city AND region to region AND can be defined by various indicators. Not paying attention to this issue AND considering the concept of quality of life equally in all cities has caused the level of quality of life to decrease in many cities, especially middle AND small cities, in recent years. On the other hAND, considering the concept of quality of life in cities to be the same, urban planners do not have a precise understANDing of the FACTORS affecting the quality of life. Following this, the main goal of this research is to identify, identify AND compile effective FACTORS for improving the quality of life in middle AND small cities on an intra-provincial scale. In order to achieve this goal, the cities of Qazvin AND Zia Abad in Qazvin province were selected as study samples, so that according to the size of the city, in order to achieve the goal of the research, the FACTORS affecting the quality of life are analyzed on an intra-provincial scale.   Methodology The research method is applied research based on quantitative methods. In line with data analysis, after extracting 60 indicators of quality of life, first, the indicators were separated into separate AND structured FACTORS using the exploratory factor analysis method in SPSS software. AND then, step by step regression method was used to measure the effective FACTORS in improving the quality of life in cities.   Results AND discussion The research findings show that based on exploratory factor analysis, 16 FACTORS were extracted in Zia Abad city AND 15 FACTORS in Qazvin city as effective FACTORS on the quality of life in small AND middle cities. In order to present AND explain the FACTORS affecting the improvement of the quality of life in small AND middle cities, the FACTORS extracted from the factor analysis were analyzed by multivariate regression method AND step-by-step method. Furthermore, finally, (13) FACTORS were introduced in Zia   Abad AND (13) FACTORS in Qazvin as the main FACTORS influencing the quality of life. The analysis of the findings indicates that in Zia Abad, FACTORS such as life expectancy AND social relations, security, the state of urban services AND access to urban facilities, living expenses, the state of urban furniture, urban traffic, the state of offices AND housing have more effects in explaining AND improving the quality of life. In Qazvin, FACTORS such as the state of urban services, the state of green AND public spaces in the city, the state of security AND economic opportunities in the city, the density AND hope of urban, economic, AND cultural life, the state of roads AND leisure in the city are effective in explaining the quality of life. Following this, according to the above findings, quality of life is a multidimensional AND dynamic concept that directly relates to the type of cities AND the specific characteristics of cities. Therefore, the quality of life AND its indicators can differ from city to city; because every city has its characteristics AND characteristics with citizens with different cultures AND thinking, which causes the FACTORS affecting the quality of life to be different. Therefore, considering the quality of life AND its indicators to be the same in all cities is wrong, which can lead to the decline of the quality of life AND the failure of quality of life improvement programs.   Conclusion Finally, the research results indicate that quality of life is a simple concept covering various urban life aspects. This concept can be defined as a determining factor in the excellence AND progress of cities AND residents' satisfaction with their urban life. It is multifaceted AND dependent on place AND time. As a result, the perception of people in different cities AND communities about the concept of quality of life AND the FACTORS affecting it can be different. Following this, FACTORS AND indicators should be selected according to the characteristics AND conditions of that city in urban planning to improve the quality of life for each city. Thus, this research, using the classification of cities based on the size of the city, has presented FACTORS specifically for middle AND small cities, which in small cities due to strong social solidarity among people AND the great importance of social AND cultural concepts, social indicators are more important. Moreover, in middle cities, due to the importance of finding economic concepts AND urban services in people's lives AND people's less communication with each other AND the weakening of solidarity AND social connections, indicators of urban services AND facilities AND economic FACTORS have the most importance in explaining the quality of life; this shows that the quality of life indicators are different in all cities.   Funding There is no funding support.   Authors’ Contribution All of the authors approved the content of the manuscript AND agreed on all aspects of the work.   Conflict of Interest Authors declared no conflict of interest.   Acknowledgments We are grateful to all the scientific.

The aim of this study was to investigate the effect of using portulaca oleracea as a source of plant antioxidants for feeding fattening male lambs, on carcass traits, meat quality, AND oxidative stability. Twenty-one male lambs with an average weight of 24±1.5 kg AND 150±15 days old were fed with one of the experimental rations included a diet without portulaca oleracea as control, a diet containing 7.5%, AND a diet containing 15% portulaca oleracea, for 84 days. The muscle (Longissimus dorsi) samplewas used to determine pH, CHEMICAL composition, colorimetric properties, AND oxidative stability of meat. Meat lipid oxidation was determined after 1, 7, AND 30 days of refrigerated storage using thiobarbituric acid (TBARS). The use of portulaca oleracea in diets improved meat color characteristics such as L, chroma, AND Hue. The highest ash concentration AND the lowest meat moisture were observed in the treatment of 15% portulaca oleracea (P<0.05). Meat pH was the lowest in the first hour after slaughter but did not differ at 24 h. Compared to the control, in both treatment groups feeding the lambs with portulaca significantly reduced the concentration of Malondialdehyde in the meat of the lambs AND increased the meat oxidative stability (P<0.05). The results of this experiment showed that feeding portulaca oleracea by improving properties such as color AND increasing the meat oxidative stability improves meat quality, which may have lead to an increase in its nutritional value in terms of human health.

To investigate the effects of WATER deficit stress AND bio AND non-bio fertilizers on Thymus Vulgaris, a split plot experiment was conducted based on rANDomized complete block design with three replications in 2017 in Asgarya agricultural field in Yazd. The treatments included WATER tension: W1=7 (Control), W2=12 AND W3=17 days irrigation intervals as the main factor AND bio AND non - bio fertilizers at five levels including B1= control (without fertilizer), B2= endo mycorrhiza (Glomus), B3= Azospirillum bacteria, B4= Pseudomonas bacteria AND B5= CHEMICAL fertilizer (NPK) a sub – FACTORS. Results showed that the highest flowering branches yield, a, b, AND total chlorophylls AND nitrogen AND phosphorus elements were observed in 17 days of irrigation (W3). 12 days of irrigation treatment (W2) showed the highest potassium element rate. The highest a, b AND chlorophylls AND nitrogen were observed in Azospirillum treatment (B3). The highest total chlorophyll, phosphorus element, carotenoids AND flavonoids were observed in Pseudomonas treatment (B4); the highest potassium element rate were obtained in mycorrhiza treatment (B2); the highest flowering branches yield were observed in NPK treatment (B5) AND control (B1) treatment resulted in the highest anthocyanin

To evaluate the effects of different treatments on seed dormancy breaking of Notobasis, two separate experiments were conducted at Agricultural Sciences AND Natural Resources University of Khuzestan, in 2017 with three replications. The first experiment included nine PHYSICAL, mechanical AND CHEMICAL treatments (control, surface scarification, intense scarification, one AND two weeks of chilling at 4°C, soaking into hot WATER for two hours without scarification , soaking in 25°C distilled WATER for two hours with scarification  AND acid treatment for six AND 12 minutes) based on completely rANDomized designs (CRD). The Second experiment was hormone priming arranged as a factorial experiment based on completely rANDomized design with gibberellin concentrations (0, 200, 400, 600, 800 AND 1000 ppm) AND priming duration (12 AND 24 hour) as the treatments. The highest seed germination (40%) obtained from 12 minutes of acid treatment. Results showed that seed germination reached to 100% after priming seeds with GA3 concentrations for 12 hours. However, increase the priming durations to 24 hours, significantly declined seed germination to 47% in 200 AND 400 ppm of GA3 concentrations. The Increase of GA3 concentrations in 12 hours led to a linear reduction of T50 from 31 hours in 200 ppm to 19 hours in 1000 ppm). In 24 hours of priming treatment, T50 followed a Gaussian function AND the highest T50 (92 hours) was obtained from 600 ppm. It is recommended to immerse the seeds for 12 minutes in sulfuric acid AND prime with 400 ppm GA3 acid for 12 hours to break dormancy AND increase the vigor index.