This study compares Human Resource Development (HRD) in Vietnam and Malaysia, looking at their methods, problems, and institutional frameworks in the context of ASEAN economic integration and Industry 4.0. Based on Cho and McLean’s (2004) integrated HRD model, this paper looks at recent research (from 2018 to 2023) to look at important topics such globalization, demographic changes, vocational training alignment, and technology disruption. Vietnam has a vast workforce, but it still has problems with low productivity, skill mismatches, and not being ready for the global market. On the other hand, Malaysia’s institutional HRD structures are making more progress, even though its workforce is getting older and not everyone is adapting to digital transformation at the same rate. The study shows that we need HRD policies that are tailored to each industry, training that is delivered in a decentralized way, and stronger relationships between the public and commercial sectors. It also stresses how important it is for national HRD policies to include global competences and initiatives that help everyone learn new skills. The study adds a unique framework for comparing HRD and gives policymakers, educators, and practitioners useful information, even though it is constrained by its use of secondary data. Future study should use mixed-methods to confirm results and look into interventions that work in specific situations. The study shows that Vietnam and Malaysia need personalized, inclusive, and forward thinking HRD systems to produce strong and competitive workforces in the post-pandemic, digital driven global economy.

The role of trace gases in the storage of heat in the atmosphere of the Earth and in the exchange of energy between the atmosphere and outer space is discussed. The molar heat capacities of the trace gases water vapor, carbon dioxide and methane are only slightly higher than those of nitrogen and oxygen. The contribution of trace gases carbon dioxide and methane to heat storage is negligible. Water vapor, with its higher concentration and conversion energies, contributes significantly to the heat storage in the atmosphere. Most of the heat in the Earth’s atmosphere is stored in nitrogen and oxygen, the main components of the atmosphere. The trace gases act as converters of infrared radiation into heat and vice versa. They are receivers and transmitters in the exchange of energy with outer space. The radiation towards space is favored compared to the reflection towards the surface of the Earth with increasing altitude by decreasing the density of the atmosphere and condensation of water vapor. Predictions of the development of the climate over a century by extrapolation are critically assessed.

This study aims to investigate the enhancement in electrical efficiency of a polycrystalline photovoltaic (PV) module. The performance of a PV module primarily depends upon environmental factors like temperature, irradiance, etc. Mainly, the PV module performance depends upon the panel temperature. The performance of the PV module has an inverse relationship with temperature. The open circuit voltage of a module decreases with the increase in temperature, which consequently leads to the reduction in maximum power, efficiency, and fill factor. This study investigates the increase in the efficiency of the PV module by lowering the panel temperature with the help of water channel cooling and water-channel accompanied with forced convection. The two arrangements, namely, multi-inlet outlet and serpentine, are used to decrease the temperature of the polycrystalline PV module. Copper tubes in the form of the above arrangements are employed at the back surface of the panel. The results demonstrate that the combined technique is more efficient than the simple water-channel cooling technique owing to multi-heat dissipation and effective heat transfer, and it is concluded that the multi-inlet outlet cooling technique is more efficient than the serpentine cooling technique, which is attributed to uniform cooling over the surface and lesser pressure losses.

Simulation training in dental medical eduaction is a modern high-tech approach in providing quality higher education. Simulation training immerses students in realistic scenarios, allowing them to develop both technical and non-technical skills essential for effective patient care. This study highlights key contemporary issues in high-tech simulation training for dental education and consolidates its rationale and benefits. We searched the databases PubMed, Scopus, Web of Science, and ResearchGate. This review includes 36 articles published in English, Russian, and Ukrainian from 2020 to 2024. Non-peer-reviewed papers or those not published in indexed journals were not considered. Simulation training was found to impact integration of theory and practice, training a wide range of psychomotor skills, development of complex clinical competences, cultivating confidence, empathy and patient-oriented care, neuroplasticity of the brain and the cognitive load. Pedagogical benefits and the place of simulation training in the curriculum were also discussed.

The current business environment characterized by volatility, uncertainty, complexity, and ambiguity (VUCA) advances numerous challenges for organizations. To respond effectively to these changing demands, traditional approaches to solving problems often prove inadequate in this dynamic context. A new approach, the ProCESS methodology, was developed and tested in the last three years within an Erasmus+ consortium in four European countries. This approach stimulates unconventional thinking and the finding of creative solutions for real-world business challenges. The aim of this perspective paper is to present the research data collected in two Romanian companies by testing ProCESS methodology. In the discussion section, the paper highlights the potential of this methodology that uses various artistic tools like drawing, music, modeling, or meditation to encourage participants to tap into their sensory, emotional, and spiritual sides for finding new and unexpected solutions. The paper also discusses potential influences on organizational culture and employee well-being.

Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.