Search Results (3)

This study comprehensively evaluates the impact of remote work intensity on employee well-being, productivity, and sustainable practices within the IT sector, utilizing a cross-sectional online survey of 1003 employees. Findings reveal that remote work consistently boosts self-rated health, enhances perceived productivity, and promotes the adoption of sustainable workplace practices, with these benefits largely consistent across gender and most age groups. However, its effect on perceived stress is complex and significantly age-dependent, showing increased stress for younger employees (under 25) while mid-career professionals (26–35) experience stress reduction. Perceived stress did not emerge as a statistically significant mediator in the remote work-productivity relationship, suggesting that positive effects on productivity are primarily driven by direct mechanisms such as increased autonomy and flexibility. This research contributes to the Job Demands-Resources and Self-Determination Theory by illuminating how digital work demands and psychological needs are experienced heterogeneously across demographics in the remote context. Practical implications emphasize the need for differentiated stress management strategies tailored to younger employees, as well as a broader promotion of remote work, to enhance sustainable behavior within organizations. Methodologically, the study highlights the value of utilizing large, non-probability datasets, along with carefully constructed proxy scales, and proposes the future integration of AI-powered analytics for deeper insights. Full article

Graduate employability remains a crucial challenge for higher education institutions, especially in developing economies. This study investigates the key academic and vocational factors influencing early employment outcomes among recent graduates at a public university in Vietnam’s Mekong Delta region. By leveraging predictive analytics, the research explores how data-driven approaches can enhance career readiness strategies. The analysis employed AI-driven models, particularly classification and regression trees (CARTs), using a dataset of 610 recent graduates from a public university in the Mekong Delta to predict early employability. The input factors included gender, field of study, university entrance scores, and grade point average (GPA) scores for four university years. The output factor was recent graduates’ (un)employment within six months after graduation. Among all input factors, third-year GPA, university entrance scores, and final-year academic performance are the most significant predictors of early employment. Among the tested models, CARTs achieved the highest accuracy (93.6%), offering interpretable decision rules that can inform curriculum design and career support services. This study contributes to the intersection of artificial intelligence and vocational education by providing actionable insights for universities, policymakers, and employers, supporting the alignment of education with labor market demands and improving graduate employability outcomes. Full article

This paper serves as a comprehensive “starter pack” for electrical diagnostic methods for power transformers. It offers a thorough review of electrical diagnostic techniques, detailing the required instrumentation and highlighting key research directions. The methods discussed include frequency response analysis, partial discharge testing, dielectric dissipation factor (tan delta), direct current (DC) insulation resistance, polarization index, transformer turns ratio test, recovery voltage measurement, polarization–depolarization currents, frequency domain spectroscopy, breakdown voltage testing, and power factor and capacitance testing. Additionally, the paper brings attention to less-explored electrical diagnostic techniques from the past decade. For each method, the underlying principles, applications, necessary instrumentation, advantages, and limitations are carefully examined, alongside emerging trends in the field. A notable shift observed over the past decade is the growing emphasis on hybrid diagnostic approaches and artificial intelligence (AI)-driven data analytics for fault detection. This study serves as a structured reference for researchers—particularly those in the early stages of their careers—as well as industry professionals seeking to explore electrical diagnostic techniques for power transformer condition assessment. It also outlines promising research avenues, contributing to the ongoing evolution of transformer diagnostics. Full article