مطالب

Background: The modern increase in mechanical and sedentary lifestyles has led to a rise in musculoskeletal abnormalities within society. These physical irregularities may influence individual mental health.Aim: This study explores the correlation between upper body musculoskeletal abnormalities and mental health among female students at Payame Noor University, Bushehr.Material and Methods: A total of 173 female students were selected via convenience sampling for this cross-sectional study. The presence of scoliosis, forward head posture, kyphosis, and increased lordosis was assessed using a flexible ruler, the New York Test, a chessboard, and a scoliometer. Body mass index (BMI) and waist-to-hip ratio (WHR) were also measured. Participants completed the General Health Questionnaire (GHQ-28). Statistical analysis was conducted using one-sample t-tests and Pearson’s correlation coefficient, with a significance threshold set at P< 0.05.Results: The findings indicated that lordosis was significantly more prevalent among students aged 15 to 24 (t=31.5, P= 0.001) and those aged 25 and above (t=57.3, P= 0.003), compared to societal norms. Other assessed abnormalities were within normal ranges. Mental health vulnerabilities were noted in specific domains; however, no significant correlation was found between mental health status and the physical abnormalities studied.Conclusion: While lordosis was notably more common among the study's participants than in the general population, the incidence of other musculoskeletal abnormalities aligned with societal averages. No substantial impact of these physical conditions on mental health was observed. It is advisable to integrate educational initiatives and corrective exercise programs into the curriculum for educators, coaches, and students to promote overall well-being.

The purpose of this research is to investigate the prediction of corporate social responsibility reporting using financial ratios. To answer the research question, four prediction models of linear regression, K Nearest Neighbor, decision tree, and deep learning were investigated. Also, 61 financial ratios were used according to previous research using data related to listed and non-listed companies of Iran from the years 2012 to 2018. According to the re-sults obtained from the estimation of each of the proposed prediction mod-els, it can be stated that the k-nearest neighbor model has the lowest RMSE value, and in fact, this model predicts the amount of social responsibility with less error than other models. The linear regression model with the high-est RMSE value has a weaker performance than other models. LSTM model and decision tree respectively had the lowest RMSE value after the k-nearest neighbor model. As a result, since the LSTM model requires a large number of test sam-ples for deeper learning, it could not achieve high performance in the evaluated data set. Based on the investigations, it can be stated that the current research does not have a similar example inside or outside of Iran.

The bankruptcy of corporations causes huge losses for investors, managers, creditors, employees, suppliers, and customers. If someone understands the reason for the corporate's bankruptcy, then he can save the corporate from certain death with the necessary planning. Therefore, bankruptcy forecasting is the most important prerequisite for bankruptcy prevention. Due to this issue, the main aim of this article is the prediction of the economic bankrupt-cy of corporations in the Tehran Stock Exchange using group machine learn-ing algorithms. Financial ratios have been used as independent variables and healthy and bankrupt corporations as research dependent variables. The statistical population of the study is the information of financial statements of corporations on the Tehran Stock Exchange from the years 2004 to 2021. In this study, sampling is not used and corporations include two groups healthy and bankrupt. The bankrupt and non-bankrupt groups are selected based on the threshold of the Springate model. The research findings indicate that the accuracy of predicting the bankruptcy of corporations in the group learning model by stacking method is higher than other used models where the AUC and Accuracy Ratio were 0.9276 and 0.8247, respectively.

Seeking to promote multicultural and multi-linguistic interaction and peaceful co-existence, pluralistic education is a democratic construct that encompasses diversity in language, culture, mindset, understanding, socio-cultural orientation, and communicative styles. Moreover, teacher effectiveness is a crucial factor in English as a Foreign Language (EFL) teaching/learning. Combining the two concerns, this study aimed at exploring EFL teachers’ perceptions of pluralistic teacher effectiveness. It benefited from a basic interpretive design. A group of 30 Iranian male and female EFL instructors participated in the present study, selected through convenient sampling from State and Azad universities in Iran. A one-on-one semi-structured interview was used to collect data. The data were analyzed using thematic analysis. Data analysis revealed the following perceptions of pluralistic teacher effectiveness among EFL teachers : Building Verbal Relations with all Students, Identifying Learning Styles of all Students, Mastery over Content in Different Fields, Teaching through Different Teaching Methods or Strategies, Showing Care and Support to all Students, Teaching Reflectively, Being Accountable, Generating a Friendly Class Environment, Using Technology, Having Motivational Skills, Using Diverse Assessment Methods, and Enacting Classroom Fairness. The findings have some implications for EFL teachers, teacher educators, and teacher education curriculum planners.

This research aimed to rank the components of smart network governance in the Iranian banking system. This article discusses the prioritization of the governing elements of smart grids identified from the research conducted by the researcher is discussed. This research was carried out quantitatively and with a simple survey method. The participants of this research were managers and experts of the country's banking system. Participants were selected via purposive sampling with the criterion of at least 10 years of experience in banking management, at least a master's degree, and with the study field of preferably management. The data analysis was done using the method of network analysis process. Super Decision software was used for data analysis. The research results show that the components of the smart network governance model compiled in order of priority include behavioral factors, stability and economic prosperity, mechanism factors, legal considerations, structural factors, comprehensive development, and information and communication technology.