This study forecasts electricity demand for Vietnam’s data center sector through 2030 in the context of rapid digitalization and the accelerating adoption of Artificial Intelligence (AI), both of which are expected to exert significant pressure on national power infrastructure. Using a baseline IT load of 524.7 MW in 2025 derived from industry market reports, the analysis employs a scenario-based approach with two growth trajectories: a high-growth case using a 16% CAGR and a market-aligned case using a 12.61% CAGR. Applying a Power Usage Effectiveness (PUE) value of 1.4, consistent with Vietnam’s green data center standards, projected electricity demand increases from 734.6 MW in 2025 to 1,542.8 MW under the high-growth scenario and 1,330.6 MW under the moderate-growth scenario by 2030, corresponding to increases of 110% and 81%, respectively. These findings indicate that the expansion of digital infrastructure will require proactive power system planning. The study highlights the importance of integrating renewable energy through Direct Power Purchase Agreements (DPPAs) and implementing stringent energy-efficiency standards to ensure the sustainable development of Vietnam’s data center ecosystem.

This study applies a first-order Markov chain to analyze and model the academic progression of 317 students from the Faculty of Education at Thu Dau Mot University, utilizing their semester Grade Point Averages (GPA) as the core data. Students' GPAs were methodologically classified into four distinct academic performance states: Weak (0–4.99), Average (5.0–6.99), Good (7.0–7.99), and Excellent (8.0–10.0). Transition matrices were constructed to capture the movements between these performance states across consecutive semesters. Descriptive analysis reveals a positive performance trend, specifically a frequent transition from the Average to the Good group, and a high level of stability observed within the Excellent group, particularly in the later stages of the program. A crucial Chi-square test for homogeneity revealed statistically significant differences, indicating that the learning process is non-homogeneous over time, reflecting fluctuations in student learning behavior. However, to fulfill the objective of forecasting the expected distribution of student performance in the subsequent semester, a weighted average transition matrix was computed, giving greater emphasis to the influence of more recent academic data. Forecasting results suggest that approximately 90% of students are expected to concentrate within the Good and Excellent categories, confirming a high standard of academic performance and providing valuable empirical evidence for targeted student support and curriculum management within the Faculty of Education.

Asia’s financial ecosystems, while distinct from Western paradigms, remain underexplored. This study integrates cultural finance, regime-switching machine learning, and ESG asymmetries into a novel analytical framework tailored to Asia’s unique financial architecture. We develop three models: a Hybrid LSTM-GARCH for crisis forecasting, a Bayesian Structural Equation Model capturing informal institutional dynamics, and a machine learning-enhanced Difference-in-Differences model to assess ESG impacts. Theoretically, we propose the Cultural-Statistical Nexus Framework, embedding sociocultural variables into predictive finance, the concept of institutional plasticity to explain regulatory divergence, and ESG Arbitrage Theory to highlight sustainability’s dual role as risk mitigator and speculative signal. Empirically, Confucian Risk Aversion reduces corporate leverage by 15 percent, ESG adoption lowers systemic risk but increases greenwashing, and hybrid models outperform conventional tools in FX crisis prediction. Practical implications include cultural-risk-adjusted capital buffers, AI-based liquidity tools, and region-specific ESG strategies, advancing a globally inclusive paradigm of financial science.