Objective: This study assessed the prevalence of psychological disorders and their correlation with health-promoting lifestyles among Chinese college students. Method: We used the Chinese version of the Depression Anxiety Stress Scales-21 (DASS-21) and the Health Promoting Lifestyle Profile II (HPLP-II) questionnaires. Gender and major differences were analyzed with the chi-square test, and multiple logistic regression explored the relationship between HPLP and psychological disorders. Results: Among 17,636 students, low prevalence rates were observed for stress (4.0%), depression (7.2%), and anxiety (15.4%). Females and students in humanities and social sciences reported higher rates of multiple psychological disorders. Higher HPLP scores were inversely correlated with depression (OR = 0.479, 95% CI: 0.376–0.609), anxiety (OR = 0.480, 95% CI: 0.408–0.565), and stress (OR = 0.821, 95% CI: 0.636–1.060) after adjusting for confounders. Conclusions: The study found low overall prevalence of psychological disorders, with higher rates among females and humanities/social sciences majors. Higher HPLP scores, particularly in interpersonal relationships and nutrition, are associated with a lower risk of mental disorders.

This review focuses on ferrites, which are gaining popularity with their unique properties like high electrical resistivity, thermal stability, and chemical stability, making them suitable for versatile applications both in industry and in biomedicine. This review is highly indicative of the importance of synthesis technique in order to control ferrite properties and, consequently, their specific applications. While synthesizing the materials with consideration of certain properties that help in certain methods of preparation using polyol route, green synthesis, sol-gel combustion, or other wise to tailor make certain properties shown by ferrites, this study also covers biomedical applications of ferrites, including magnetic resonance imaging (MRI), drug delivery systems, cancer hyperthermia therapy, and antimicrobial agents. This was able to inhibit the growth of all tested Gram-negative and positive bacteria as compared with pure ferrite nanoparticles without Co, Mn or Zn doping. In addition, ferrites possess the ability to be used in environmental remediation; such as treatment of wastewater which makes them useful for high-surface-area and adsorption capacity due heavy metals and organic pollutants. A critical analysis of functionalization strategies and possible applications are presented in this work to emphasize the capability of nanoferrites as an aid for the advancement both biomedical technology and environmental sustainability due to their versatile properties combined with a simple, cost effective synthetic methodology.

The hopes and aspirations of Law No. 6/24 on Village autonomy has faced several problems and challenges. These problems and challenges arose when the village government had to undertake various delegated tasks assigned by the regency, provincial, and central governments. As a result, the village is preoccupied with delegated tasks assigned by supra-village authorities, straining its resources and budget. The shift in focus resulted the village government are unable to perform their main tasks and responsibilities. This situation is akin to the Village Head functioning as a state employee. Stunting is one of the assignment programs that causes various problems and instrumentalizes villages. This process involves mobilizing village institutions, human resources, and budgets to ensure the program’s success. This study employed exploratory-qualitative approach to investigate the challenges arising from the stunting program’s implementation in Ngargosari Village. The research informants included the village head, village officials, posyandu cadres, community leaders, and program beneficiaries. The data were gathered through in-depth interviews were validated and reconfirmed using Focus Group Discussions. Furthermore, an in-depth analysis was carried out to obtain findings related to village instrumentalization in the stunting program. The findings revealed that the stunting program’s implementation involved mobilizing village institutions, resources, and budgets. The village government lacked bargaining power against supra-village policies, despite their alignment with local values and wisdom. The central government dictated the system, procedures, mechanisms, and methods for handling stunting in a centralized manner, disregarding local wisdom and the authority of village governments as outlined in Law Number 6 of 2014 on Villages. Consequently, the stunting program represents a form of village instrumentalization akin to the New Order era, with centralistic initiatives that relegate village heads to the role of state employees.

This study aims to examine the role of automotive industry development in the regional growth of Hungarian counties. Through word frequency analysis, the counties were grouped, and their unique characteristics were highlighted. Some counties already play a prominent role in the domestic automotive industry hosting established Original Equipment Manufacturers (OEMs), a significant number of automotive suppliers and high R&D and innovation potential. Another group includes counties that currently lack a significant automotive industry and did not identify it as a key focus area for future development. Additionally, an intermediate group has also emerged, including counties where the automotive industry is either in its early stages of investment, or such development is prioritized in regional planning documents. The study details the direction of automotive development in counties where the industry plays a significant role, focusing on labor market characteristics and human resource development. The findings have significant implications for the future of the automotive industry in these counties, underlining the urgent and immediate need for well-managed and well-established human resource development and ensuring effective partnership to realize its full potential in the automotive industry.

“This paper’s purpose l is to determine whether certain firm-specific factors have an influence on the catering theory of dividend in the MENA region.” The catering theory of dividend related to the dividend policy by the different companies used in our paper to explain the decision by managers. The sample includes 600 non-financial firms listed stocks in the Stock Exchange of 6 countries from MENA region during the years 2010–2019. Catering theory explains why managers initiate (continue) to distribute dividends. A high dividend premium encourages managers to increase the level of dividend payment and explains why firms pay dividends or do not pay them thereafter. Investors should increase their demand for dividends to push managers to comply. Investors show their preference for dividend to self control, satisfaction and increase their profit. “This could be the catering incentive of the firm to decide to pay dividends”. Even although the result Investor preference for dividend is explained by different factors related to the firms characteristics from each firms is different from markets, it can be the evidence supporting the catering theory of dividend, not only in well-developed markets, but also in emerging markets such as our country.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.