Background: The COVID-19 pandemic has had a substantial economic and psychological impact on workers in Saudi Arabia. The objective of the study was to assess the effects of the COVID-19 epidemic on the financial and mental well-being of Saudi employees in the Kingdom of Saudi Arabia. Purpose: The COVID-19 epidemic has resulted in significant economic and societal ramifications. Current study indicates that the pandemic has not only precipitated an economic crisis but has also given rise to several psychological and emotional crises. This article provides a conceptual examination of how the pandemic impacts the economic and mental health conditions of Saudi workers, based on contemporary Structural Equation Modeling (SEM) models. Method: The current study employed a qualitative methodology and utilized a sample survey strategy. The data was gathered from Saudi workers residing in major cities of Saudi Arabia. The samples were obtained from professionals such as managers, doctors, and engineers, as well as non-professionals like unskilled and low-skilled laborers, who are employed in various public and private sectors. A range of statistical tools, including Descriptive statistics, ANOVA, Pearson’s Correlation, Factor analysis, Reliability test, Chi-square test, and regression approach, were employed to analyze and interpret the results. Result: According to the data, the pandemic has caused a wide range of economic problems, including high unemployment and underemployment rates, income instability, and different degrees of pressure on workers to find work. Feelings of insecurity (about food and environmental safety), worry, dread, stress, anxiety, depression, and other mental health concerns have been generated by these challenges. The rate of mental health decline differs among demographics. Conclusions: The COVID-19 pandemic has universally affected all aspects of our lives worldwide. It resulted in an extended shutdown of educational institutions, factories, offices, and businesses. Without a question, it has profoundly transformed the work environment, professions, and lifestyles of billions of individuals worldwide. There is a high occurrence of poor psychological well-being among Saudi workers. However, it has been demonstrated that both economic health and mental health interventions can effectively alleviate the mental health burden in this population.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

This study aims to investigate the impact of dance training on the mental health of college students. Utilizing experimental research methods, we established an experimental group and a control group to compare changes in mental health dimensions—including anxiety, depression, self-esteem, and social skills—between the two groups before and after 12 weeks of dance training. The findings indicate that dance training significantly reduces levels of anxiety and depression, while also improving self-esteem and social skills, thereby enhancing social adaptability. These results provide empirical support for the use of dance as an intervention for mental health and offer new insights for mental health education in colleges and universities.

Technical Pedagogical Content Knowledge (TPACK) encompasses teachers’ understanding of the intricate interplay among technology, pedagogy, and subject matter expertise, serving as the essential knowledge base for integrating technology into subject-specific instruction. Over the decade, advancements in information technology have led to the consistent application of the TPACK framework within studies on instructional technology and technology-enhanced learning, significantly advancing the evolution of contemporary teacher education in technology integration. In this paper, we utilize the Teaching and Learning Knowledge of Subjects Based on Integrated Technology (TPACK) framework to administer a questionnaire survey to teacher trainees at Chinese colleges and universities. This survey aims to evaluate the current status of their integrated technology-based subject teaching and learning knowledge. Based on the research findings, we propose strategies aimed at enhancing the educational technology integration knowledge of students pursuing integrated technology courses in colleges and universities. Furthermore, we integrate the smart classroom setting to develop a comprehensive TPACK-integrated model teaching framework. Our final objective is to offer valuable references for the progress of modern teaching skills among education students in higher education institutions.

This research explores the role of social media in the political construction of identity, analyzing how these platforms mediate the expression and formation of individual and group political identities. The focus is on how social media changes the dynamics of communication and social interaction, facilitating the formation of “echo chambers” and increasing political polarization. Additionally, this study highlights challenges such as disinformation and the implications of social media for the health of democracy. As a researcher, I aim to highlight the broader implications of using social media in identity politics. By analyzing the impact of social media on political dynamics in Indonesia, this study reveals how social media influences public perception and political decisions. This study identifies how social media can be used as a tool to mobilize political support, but also how these platforms can spread disinformation and reinforce political polarization. Based on these concerns, researchers have not yet found research results that examine how social media specifically impacts the construction of political identity. This research aims to highlight how social media not only acts as a communication tool but also as a medium that influences the way individuals view and express their political identity. Through a qualitative approach, this study provides new insights into the impact of social media in contemporary political dynamics and the importance of digital literacy in addressing issues of identity politics in the digital era.