abnormalities

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.

In this research, due to the importance of investing and especially investing in the stock market, we predicted the stock price return on the stock exchange through the Auto-Regressive Integrated Moving Average (ARIMA) and Recursive Neural Network (RNN) of long-short term memory (LSTM). Then, to reduce the risk of decision-making, we compared the predictive power of these two models to determine a better model. The research variable is the stock price of the top 20 (in market cap) companies on the stock exchange for the period of the 11th Feb 2015 to 22th Jan 2022. We considered the data of the last 10 days as experimental data and the previous data as educational data. Initially, we calculated the mean and standard deviation of the prediction error of both models; these criteria had less value for the LSTM recursive neural network model than the ARIMA model. To measure the significance of this difference in predictive power, we used Harvey, Liborne, and New Bold tests. The results showed that in predicting the stock prices of the top 20 companies of the stock exchange, the predictive power of the LSTM recursive neural network model was statistically and significantly higher than the ARIMA model which means better predition of stock prices and higher return for investors. In the end, it is believed that the LSTM model may have the best predictive ability, but it is greatly affected by the data processing.