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.

Conventional antimicrobial air filters often conflate physical interception with true biochemical inactivation, posing secondary aerosolization risks during maintenance. This study developed a lactoferricin B-functionalized polypropylene (LfCF) filter to provide a dual-action mechanism: electrostatic capture and robust contact-killing against bioaerosols. To rigorously decouple these mechanisms, a polyallylamine binder-only (PP+PAA) control was incorporated. Dynamic penetration assays at 10 cm/s revealed that the 2.0 mg LfCF achieved significantly lower viable penetration rates for Escherichia coli (41.2%) and λ phage (46.0%) compared to the PP+PAA control (75.1% and 76.3%). This substantial gap demonstrates instantaneous sublethal injury upon aerodynamic impaction, defined here as “dynamic inactivation.” Crucially, time-dependent elution assays confirmed a >2 log reduction in viable counts for both retained E. coli and λ phage on LfCFs within 60 min, definitively validating its genuine contact-killing capability. Furthermore, the amphipathic lactoferricin B peptide maintained exceptional biocidal efficacy even under high-humidity conditions (70% RH), overcoming the electrostatic shielding typical of traditional biopolymers, without increasing aerodynamic pressure drop. Finally, field validation in a dental clinic demonstrated an 83.3% reduction in airborne viable bioaerosols. As a passive, self-sterilizing engineering control, the LfCF offers a highly reliable intervention for mitigating occupational bioaerosol exposures.