Abstract This study investigates the multiscale temporal dynamics of near surface temperature variability in Ho Chi Minh City, Vietnam, utilizing a comprehensive three-year hourly meteorological dataset comprising 28,508 observations. A unified analytical framework integrating time domain, frequency domain,... This study investigates the multiscale temporal dynamics of near surface temperature variability in Ho Chi Minh City, Vietnam, utilizing a comprehensive three-year hourly meteorological dataset comprising 28,508 observations. A unified analytical framework integrating time domain, frequency domain, and time frequency techniques was applied to robustly isolate periodicities and assess structural stability. Methodologies included Fast Fourier Transform (FFT), Fisher's g-test, Monte Carlo surrogate modeling, stationarity diagnostics (ADF and KPSS tests), autocorrelation analysis, classical seasonal decomposition, and Continuous Wavelet Transform (CWT) coupled with multi resolution decomposition. Results reveal a highly significant, deterministic 24-hour diurnal cycle governing temperature, relative humidity, and precipitation, primarily driven by intense solar forcing and regional land sea breeze regimes. Conversely, mean sea level pressure is dominated by low frequency seasonal fluctuations of approximately 1.08 years. Stationarity tests confirm the dataset's inherent non stationarity, while autocorrelation diagnostics reveal strong temporal persistence and long memory. Furthermore, wavelet scalograms and multi-resolution decomposition illustrate that while long term climatic forcing accounts for the overwhelming majority of statistical variance, short term weather anomalies introduce critical high frequency fluctuations. These findings underscore the limitations of standalone linear models for environmental forecasting. The identified hierarchical temporal structure strongly supports the development of hybrid predictive architectures. Future forecasting systems should integrate wavelet based multi resolution feature extraction, routing long term trends through deep learning frameworks while optimizing short term residuals via machine learning ensembles to significantly enhance urban climate and energy demand predictions. Read More Read Less

Abstract This study investigates the impact of digital transformation on green innovation and examines whether managerial myopia constrains digital innovation activities. Grounded in Upper Echelons, Resource Based, and Dynamic Capability theories, the research addresses critical gaps regarding causal... This study investigates the impact of digital transformation on green innovation and examines whether managerial myopia constrains digital innovation activities. Grounded in Upper Echelons, Resource Based, and Dynamic Capability theories, the research addresses critical gaps regarding causal identification and the interplay between technological capabilities and managerial cognition. Utilizing a longitudinal dataset of Chinese listed manufacturing firms from 2011 to 2022, the analysis employs rigorous endogeneitycorrection techniques, including lagged variable estimation, Two-Stage Least Squares instrumental variable approaches, and Propensity Score Matching combined with Difference in Differences. The findings consistently demonstrate that digital transformation exerts a significant positive causal effect on green innovation, with results remaining robust across all identification strategies. Conversely, managerial myopia exhibits no statistically significant impact on digital innovation, suggesting that digital initiatives are largely insulated from short-term managerial orientations due to competitive pressures, institutional mandates, and relatively rapid operational payoffs. These results challenge conventional assumptions that managerial short-termism uniformly suppresses innovation, highlighting the strategic necessity of digital transformation in modern manufacturing instead. The study contributes to sustainability and corporate governance literature by providing robust empirical evidence on how digital capabilities drive environmental innovation while clarifying the limited role of managerial cognition. In practice, the findings underscore the importance of policy support for digital infrastructure and technology adoption as effective pathways to achieving longterm corporate sustainability and green technological advancement. Read More Read Less

Abstract The rapid growth of online learning has generated vast educational data, yet existing Knowledge Tracing (KT) research predominantly focuses on predictive accuracy. This narrow focus often neglects the underlying mechanisms of learning progression, latent cognitive state transitions,... The rapid growth of online learning has generated vast educational data, yet existing Knowledge Tracing (KT) research predominantly focuses on predictive accuracy. This narrow focus often neglects the underlying mechanisms of learning progression, latent cognitive state transitions, and the structural organization of curricula. To address these critical limitations, this study proposes an integrated, multi-method analytical framework applied to the large-scale EdNet KT1 dataset. The framework synergistically combines Item Response Theory, Hidden Markov Modeling, Learning Transition Networks, Community Detection, Dynamic Time Warping trajectory clustering, Transformer-based Knowledge Tracing, and SHAP-based explainable AI. The converging quantitative results reveal a highly homogeneous learning environment within the dataset, characterized by universal learner mastery, strong network connectivity, and a unified knowledge structure without distinct subcommunities. Despite this data homogeneity, the framework successfully demonstrates its capacity to map complex learning dynamics from multiple analytical perspectives. Crucially, SHAP analysis confirms that the deep learning model's predictions are driven by pedagogically meaningful features, such as historical correctness and item difficulty, rather than opaque statistical artifacts. By transcending traditional single-method approaches, this research provides a comprehensive, multidimensional understanding of learning dynamics. It effectively bridges the gap between abstract algorithmic prediction and actionable educational insights, ultimately offering a robust blueprint for developing transparent, trustworthy, and adaptive personalized learning interventions in modern intelligent educational systems. Read More Read Less