بهمن طالبی

Improving students' academic buoyancy is a priority of education and health systems. The main purpose of this study was structural analysis of the relationship between health literacy, organizational support, school climate with students' academic buoyancy by teachers’ and school nurses’ job performance mediation in health promoting schools. The method of this correlation research is based on structural equation model. 160 health promoting schools were selected by stratified random sampling and two teachers, one health nurse and two students were randomly selected from each school. In total, 160 health nurses, 320 teachers and 960 students were studied. Annual teachers’ and school nurses’ job performance score, health literacy, organizational support, school climate and academic buoyancy questionnaires were used to collect data. Smart-PLS structural equation model has been used to analyze the data. The path coefficient of direct effect of organizational support on the performance of teachers and school nurses was 0.48, the correlation between performance with academic buoyancy was 0.74, the correlation between school climate and performance was 0.33 (P <0.05). The path coefficient of indirect effects of organizational support on academic buoyancy mediated by the performance was 0.36 and also the indirect correlation between school climate and academic buoyancy mediated by the performance was 0.24 (P <0.05). The direct effect of school climate, organizational support and health literacy on academic buoyancy is not significant (P> 0.05). Attention to improving the performance and organizational support of teachers leads to improving the academic buoyancy of students.

The purpose of this study was to present an optimal model Predicting Future Cash Flows optimized neural network with genetic (ANN+GA) and particle swarm algorithms (ANN+PSO). In this study, due to the nonlinear relationship among accounting information, we have tried to predict future cash flows by combining artificial intelligence algorithms. Variables of accruals components and operating cash flows were employed to investigate this prediction; therefore, the data of 137 companies listed in Tehran Stock Exchange during (2009-2017) were analysed. The results of this study showed that both neural network models optimized by genetic and particle swarm algorithms with all variables presented in this study (with 15 predictor variables) are able to provide an optimal model Predicting Future Cash Flows. The results of fitting models also showed that neural network optimized with particle swarm algorithm (ANN+PSO) has lower error coefficient (better efficiency and higher prediction accuracy) than neural network optimized with ge-netic algorithms (ANN+GA).