Chang Jung Christian University

Eye-tracking technology was employed to examine how varying levels of media multitasking behaviours influence students' visual attention and learning performance in online education. Students' eye movements were tracked and recorded while they simultaneously engaged in online platforms and media activities in an English vocabulary course. Based on the media multitasking index, 46 participants were categorised into high or low media multitasking groups. The study compared key eye movement measures: latency of first fixation, duration of first fixation, total fixation duration, and fixation count across different regions of interest. Results revealed that students from the two groups showed different fixation sequences in visual attention. The main screen content attracted the attention of both groups first, and students spent more time focusing on this content while learning. Analytical results showed that the low-level group's learning performance significantly exceeded that of the high-level group in the subsequent English vocabulary test. The study provides eye-tracking evidence that students engaging in high media multitasking struggle to focus on instructional content, emphasising the need for strategies that effectively manage multitasking. There is also a critical need to teach students how to manage media distractions so as to enhance their academic performance in online learning.

Grid-forming (GFM) battery energy storage system (BESS) inverters are becoming a cornerstone of resilient microgrids, where severe voltage sags and abrupt operating shifts can challenge both voltage regulation and controller stability. Finite-set model predictive control (FS-MPC) offers fast transient response and multi-objective coordination, yet conventional designs rely on static cost-function weights that are typically tuned offline and may become suboptimal under disturbance-driven regime changes. This paper proposes a forecast-guided KAN-adaptive FS-MPC framework that (i) formulates the inner-loop predictive control in the stationary alpha beta frame, thereby avoiding PLL dependency and mitigating loss-of-lock risk under extreme sags, and (ii) introduces an Operating Stress Index (OSI) that fuses load forecasts with reserve-margin or percent-operating-reserve signals to quantify grid vulnerability and trigger resilience-oriented control adaptation. A lightweight Kolmogorov-Arnold Network (KAN), parameterized by learnable B-spline edge functions, is embedded as an online weight governor to update key FS-MPC weighting factors in real time, dynamically balancing voltage tracking and switching effort. Experimental validation under high-frequency microgrid scenarios shows that, under a 50% symmetrical voltage sag, the proposed controller reduces the worst-case voltage deviation from 0.45 p.u. to 0.16 p.u. (64.4%) and shortens the recovery time from 35 ms to 8 ms (77.1%) compared with static-weight FS-MPC. In the islanding-like transition case, the proposed method restores the PCC voltage within 18 ms, whereas the static baseline fails to recover within 100 ms. Moreover, the deployed KAN governor requires only 6.2 mu s per inference on a 200 MHz DSP, supporting real-time embedded implementation. These results demonstrate that forecast-guided adaptive weighting improves transient resilience and power quality while maintaining DSP-feasible computational complexity.

This study examines the influence of ecological responsibility, legitimation, and competitiveness on the intention of Taiwanese SMEs to adopt carbon reduction practices. It further investigates whether these intentions translate into actual implementation and explores the moderating role of employee green efficacy in this relationship. Data were collected through a questionnaire survey of Taiwanese SMEs. A total of 326 valid responses were analyzed using regression analysis to test the proposed hypotheses. Findings reveal that ecological responsibility is the most significant predictor of adoption intention, followed by competitiveness, while legitimation exerts the weakest effect. Employee green efficacy significantly moderates the relationship between intention and implementation, increasing the likelihood of action. These findings advance the literature on SME environmental strategy by extending Bansal and Roth's ecological responsiveness model through the integration of the theory of planned behavior alongside institutional and social cognitive perspectives. By highlighting the role of employee green efficacy in strengthening the translation of intentions into action, the study offers a theoretically enriched understanding of how cognitive and institutional mechanisms jointly drive SME carbon reduction. Practically, the results provide actionable insights for policymakers and SME managers seeking to facilitate effective transitions toward reduced carbon emissions.

Short-term load forecasting (STLF) is a core input to unit commitment and operating-reserve scheduling. However, average error metrics can mask tail failures during extreme events (e.g., typhoons and heatwaves) that drive the highest operational risk. This paper proposes a reproducible, resilience-oriented STLF framework for utility open data that decomposes demand dynamics into (i) a seasonal ARIMA stream for periodic memory and (ii) a Kolmogorov-Arnold Network (KAN) stream that learns an interpretable nonlinear correction of the ARIMA residual. Using Taipower open datasets (2023–2024) and stress-testing on Typhoon Gaemi (2024), the proposed ARIMA–KAN achieves 1.65% day-ahead MAPE and 2.35% week-ahead MAPE, while reducing worst-case typhoon MAPE from 12.4% (SARIMA) to 3.6%, shortening recovery time from 4.0 to 0.5 days, and decreasing degradation area from 25.5 to 2.8 %-days. In addition to accuracy and event-window resilience metrics, we provide interpretability via KAN spline functions and SHAP analysis to support operational trust and post-event diagnosis. The resulting pipeline is lightweight, auditable, and directly reusable for other utilities that publish load/reserve and meteorological open data.

Small and medium-sized enterprises (SMEs) are critical actors in promoting environmentally sustainable supply chains, particularly in emerging economies where their collective environmental footprint is substantial. Despite growing attention to green supply chain management (GSCM), research has predominantly focused on large firms, leaving the motivational drivers shaping GSCM implementation in SMEs underexplored. Addressing this gap, the present study develops and empirically tests a motivation-based framework to examine how four organizational motives, cost, market, ethical, and legitimacy, drive the depth of GSCM implementation in SMEs. In addition, environmental uncertainty is conceptualized as a key contextual contingency moderating the effectiveness of these motives. Drawing on survey data from Vietnamese SMEs, the findings reveal that all four motives positively influence implementation depth, with ethical motives exerting the strongest effect. Furthermore, environmental uncertainty significantly amplifies these relationships. By integrating multiple theoretical perspectives and emphasizing the contingent role of environmental uncertainty, this study advances GSCM research by providing a nuanced, context-sensitive understanding of how SMEs operationalize sustainability practices in dynamic and resource-constrained environments.