Identify the Attitudes of Agricultural Postgraduate Students towards Motivations on Entrepreneurial Actions from the Viewpoint of Three Universities of (Razi, Bu-Ali-Sina and Ramin) in Iran

This study was to identify the Attitudes of M.Sc students towards barriers of agricultural Entrepreneurship, in three Universities of (Razi, Bu-Ali-Sina and Ramin) in Iran. Statistical population of the study consisted of 650 agricultural M.Sc students in 2012 - 2013 educational year which of them 240 samples were selected through a stratified random sampling. Required data were collected by questionnaire and Validity of the research questions was verified by a panel of experts as well as reliability of the research tool was tested and the Cronbach's Alpha was calculated (?= 0.81). the Results of the frequency analysis indicated that from the students’ perspective such factors as personal, social, financial and educational factors like Desire for job security, support entrepreneurs, need for achievement, contact with entrepreneurs, close relations between university and successful businesses, desire to increase income and Considering needs of the labor market by agricultural university had the greatest entrepreneurial incentives of the respondents. Results of the factor analysis revealed that personal, social, financial and educational factors influence agricultural entrepreneurial intention. Keywords: agriculture, Entrepreneurship, incentive

The relationship between maternal mental health and communication skills in children in Shiraz, Iran.

OBJECTIVES: Child development is a significant issue in global public health, and maternal mental health (MMH) can have a remarkable effect on children's development of communication skills. We aimed to investigate the association between MMH and communication skills in a sample of Iranian children. METHODS: This study was conducted in Shiraz, Iran during 2016. In total, 640 mothers who lived in Shiraz and were registered in the Fars Birth Cohort (FBC) study were invited to attend the FBC clinic with their children. A trained physician evaluated MMH using the General Health Questionnaire (GHQ). Additionally, a trained nurse assessed the children's communication development status using the Ages and Stages Questionnaire for 60-month old children. RESULTS: The majority of the mothers were homemakers (82.8%) and had high school diplomas (38.9%). The mothers' mean age was 33.7±4.6 years. Seventy-nine (12.3%) children had delayed communication skills, but no significant association was found between children's communication skills and the mothers' total GHQ score (p=0.43). In total, 493 mothers (77.0%) had abnormal somatic symptoms, 497 (77.7%) had abnormal anxiety/insomnia, 337 (52.7%) had social dysfunction, and 232 (36.3%) suffered from depression. Logistic regression indicated that after adjusting for confounders, the odds of delayed communication skills were 3-fold higher among the children of mothers with abnormal somatic symptoms than among other children (p=0.01). CONCLUSIONS: The study results confirmed that MMH had a significant impact on children's communication skills. Moreover, maternal abnormal somatic symptoms exerted the strongest impact on the development of communication skills in 5-yearold children. KEYWORDS: Child development; Iran; Mental health; Questionnair

Predicting the energy and exergy performance of F135 PW100 turbofan engine via deep learning approach

In the present study, the effects of flight-Mach number, flight altitude, fuel types, and intake air temperature on thrust specific fuel consumption, thrust, intake air mass flow rate, thermal and propulsive efficiecies, as well as the exergetic efficiency and the exergy destruction rate in F135 PW100 engine are investigated. Based on the results obtained in the first phase, to model the thermodynamic performance of the aforementioned engine cycle, Flight-Mach number and flight altitude are considered to be 2.5 and 30,000 m, respectively; due to the operational advantage of supersonic flying at high altitude flight conditions, and the higher thrust of hydrogen fuel. Accordingly, in the second phase, taking into account the mentioned flight conditions, an intelligent model has been obtained to predict output parameters (i.e., thrust, thrust specific fuel consumption, and overall exergetic efficiency) using the deep learning method. In the attained deep neural model, the pressure ratio of the high-pressure turbine, fan pressure ratio, turbine inlet temperature, intake air temperature, and bypass ratio are considered input parameters. The provided datasets are randomly divided into two sets: the first one contains 6079 samples for model training and the second set contains 1520 samples for testing. In particular, the Adam optimization algorithm, the cost function of the mean square error, and the active function of rectified linear unit are used to train the network. The results show that the error percentage of the deep neural model is equal to 5.02%, 1.43%, and 2.92% to predict thrust, thrust specific fuel consumption, and overall exergetic efficiency, respectively, which indicates the success of the attained model in estimating the output parameters of the present problem