The health of employees is so paramount for employee productivity. While emphasis is often placed on the physical health of employees, less emphasis is placed on the psychological or mental health of the employees. Similarly, it seems as if health challenges are more occurring in manufacturing industries, but the service organizations employees are as well susceptible to mental health challenges. Understanding the predictive factors to mental health challenges therefore becomes imperative. It is on this note that the present research examines how employee mental health is predicted by work safety measures like perceived workplace safety, work overload and pay satisfaction. The workplace safety variables include perception of job, co-worker, supervisor, management, and safety programs. A cross sectional survey method was adopted, using ex-post-facto research design. Data were gathered from 258 employees, including 150 (58.1%) females and 108 (41.9%) males of a non-governmental organization. Correlation and regression analyses were used to analyze data obtained from the standardized psychological scales that were administered. The results showed that mental health correlated positively with perceived job safety, but negatively with perceived co-worker, supervisor, management, safety programs and pay satisfaction. Workplace safety variables jointly predicted mental health, accounting for 23% variance, but only perceived job safety and supervisor safety were significant. The higher employees perceived job safety, the lower their mental health challenges. Similarly, the higher they perceived supervisor safety, the lower their mental health issues. Pay satisfaction accounted for 3% variance in mental health, and the higher the pay satisfaction, the lower the level of employee mental health issues. It is implied that the human resource unit of service organizations should intermittently examine their organizations to identify and prevent possible job and supervisor safety threats. Supervisors should be trained on how to be discrete in communicating safety measures to subordinates so that it will not boomerang to hamper mental health. The human resources unit should also intermittently organize workshop, training, and employee-assisted programs for younger and lower grade employees on adaptive mechanisms for reducing mental health challenges.

This study explores the determinants of control loss in eating behaviors, employing decision tree regression analysis on a sample of 558 participants. Guided by Self-Determination Theory, the findings highlight amotivation (β = 0.48, p < 0.001) and external regulation (β = 0.36, p < 0.01) as primary predictors of control loss, with introjected regulation also playing a significant role (β = 0.24, p < 0.05). Consistent with Self-Determination Theory, the results emphasize the critical role of autonomous motivation and its deficits in shaping self-regulation. Physical characteristics, such as age and weight, exhibited limited predictive power (β = 0.12, p = 0.08). The decision tree model demonstrated reliability in explaining eating behavior patterns, achieving an R² value of 0.39, with a standard deviation of 0.11. These results underline the importance of addressing motivational deficits in designing interventions aimed at improving self-regulation and promoting healthier eating behaviors.

The centers of trade and economic activities in the region of Southeast Asia rank from a huge and modern to a small and traditional pattern. Malacca and Singapore have been cases in point for huge and modern patterns, while the border areas in eastern Indonesia, East Malaysia, and the Philippines are the cases for small and traditional centers. This paper will argue that with global connectivity and regional dynamics, the small and traditional trade and economic centers could shift to modern ones. History records that the introduction of the Southeast Asian region by the outside world, especially in relation to trade and economic activities, was largely derived from the significant role played by the people in the mainland of Southeast Asia regarding the silk roads route and the role of the people in the insular or islands of Southeast Asia regarding the spice trade route in the premodern time. Later in the modern time in Southeast Asia, the role of Islam, the Europeans and the center trade of Malacca around the 17th and 18th centuries played a significant role. Indeed, huge trade centers like Malacca in the 17th C and 18th C and later by Singapore in the 9th C have been very important throughout the history of trade in the Southeast Asian region. However, we must not ignore the roles of the border areas in the Southeast Asian archipelago, especially in eastern Indonesia, East Malaysia, and the border region of the Philippines which have played a dominant role in trade and economic activities. These activities have been smaller and more traditional than the Malacca and Singapore cases, but economic activities could develop rapidly with the global connection and its interconnectivity. Besides, those border areas have also become an important key for security issues not only in the Southeast Asia region in particular but also in the Asia Pacific or Indo Pacific region as well. The security of the region of Southeast Asia and even Indo Pacific could be affected by the situation in those border areas. Interconnectivity is a challenge as well as an opportunity for these border areas to become the future of trade and economic activities within the region of Southeast Asia that also connects with the region of Indo Pacific, especially China, South Korea, and Taiwan. The planning of Indonesian capital movement to East Kalimantan will add opportunities for those border areas located near the proposed new capital. About the above issues, this paper will address several issues: firstly, the history of trade and economic activities in Malacca, Singapore, and the border areas in eastern Indonesia, East Malaysia, and the Philippines; secondly, the different patterns of trade and economic developments of the Malacca, Singapore, and the border areas in eastern Indonesia, East Malaysia, and Philippines; thirdly, the challenges and opportunities of the border areas in eastern Indonesia, East Malaysia, and the Philippines to develop bigger trade centers in the future; fourth, the interconnectivity of those border areas to Asia Pacific region. This paper uses an interdisciplinary approach in the fields of social sciences and humanities. With this study, it is hoped that a better understanding of regional dynamics will be obtained, especially in the border areas. The period that we use is from 1998 until present time regarding if there was changing policy due to the end of Old Order to the Reformation period of Indonesian government. As a result, the development of border areas had been in existence before the colonial time in which people moved freely and had trade contacts. Even though they used to have the same ethnic linkage, after the formation of a modern state where they have different citizenships, in reality they can relate to each other in harmony and peace because of the similarity of ethnic linkages they had in the past. Colonial powers intended to replace the powers of traditional kingdoms with the idea of civilizing the colonializ

Accurate drug-drug interaction (DDI) prediction is essential to prevent adverse effects, especially with the increased use of multiple medications during the COVID-19 pandemic. Traditional machine learning methods often miss the complex relationships necessary for effective DDI prediction. This study introduces a deep learning-based classification framework to assess adverse effects from interactions between Fluvoxamine and Curcumin. Our model integrates a wide range of drug-related data (e.g., molecular structures, targets, side effects) and synthesizes them into high-level features through a specialized deep neural network (DNN). This approach significantly outperforms traditional classifiers in accuracy, precision, recall, and F1-score. Additionally, our framework enables real-time DDI monitoring, which is particularly valuable in COVID-19 patient care. The model’s success in accurately predicting adverse effects demonstrates the potential of deep learning to enhance drug safety and support personalized medicine, paving the way for safer, data-driven treatment strategies.

The study documents the model of the knowledge transfer process between the University, the Vocational Training Center and the industrial actors. The research seeks to answer to the following questions. Where is new knowledge generated? Where does knowledge originate from? Is there a central actor? If so, which organization? Hypotheses tested by the research: H1: Knowledge starts from the higher education institution. H2: Most “new knowledge” is generated in universities and large multinational companies. H3: The university is a central actor in the knowledge flow, transmitting both hard and soft skills, as well as subject (‘know-what’), organizational (‘know-why’), use (‘know-how’), relational (‘know-who’), and creative (‘care-why’) knowledge. The aim of the research is to model the way of knowledge flow between the collaborating institutions. The novelty of this research is that it extends the analysis of the knowledge flow process not only to the actors of previous researches (higher education institutions, business organizations, and government) but also to secondary vocational education and training institutions. The methodology used in the research is the analysis of the documents of the actors investigated and the questionnaire survey among the participants. Knowledge transfer is the responsibility of the university and its partner training and business organizations. In vocational education and training, knowledge flows based on the knowledge economy, innovation and technological development are planned, managed and operational. The research has shown that knowledge is a specific good that it is indivisible in its production and consumption, that it is easy and cheap to transfer and learn.

Climate change is an important factor that must be considered by designers of large infrastructure projects, with its effects anticipated throughout the infrastructure’s useful life. This paper discusses how engineers can address climate change adaptation in design holistically and sustainably. It offers a framework for adaptation in engineering design, focusing on risk evaluation over the entire life cycle. This approach avoids the extremes of inaction and designing for worst-case impacts that may not occur for several decades. The research reviews case studies and best practices from different parts of the world to demonstrate effective design solutions and adjustment measures that contribute to the sustainability and performance of infrastructure. The study highlights the need for interdisciplinary cooperation, sophisticated modeling approaches, and policy interventions for developing robust infrastructure systems.