Hanseo University

Accurate estimation of the State-of-Health (SOH) is essential for the reliable operation of lithium-ion batteries in electric vehicles and energy storage systems. However, conventional data-driven models often lack interpretability and show limited robustness under non-linear aging conditions. In this study, a physics-informed Transformer model is proposed for long-term SOH estimation by incorporating physically interpretable, degradation-sensitive indicators into a self-attention framework. Incremental Capacity Analysis (ICA)-derived features and thermal-gradient indicators are used as auxiliary inputs to provide physics-consistent inductive bias, enabling the model to focus on degradation-relevant regions of the charging trajectory. The proposed approach is validated using four lithium-ion battery cells exhibiting diverse aging behaviors, including severe non-linear capacity fade. Experimental results demonstrate that the proposed model consistently outperforms an LSTM baseline, achieving an RMSE below 1.5% even for the most degraded cell. Furthermore, attention map analysis reveals that the model autonomously emphasizes voltage regions associated with electrochemical phase transitions, providing clear physical interpretability. These results indicate that the proposed physics-informed Transformer offers a robust and explainable solution for battery health monitoring under practical aging conditions.

The prevalence of Internet addiction among college students has gained significant attention in recent years. Research has established a negative relationship between Internet addiction and life satisfaction, although the underlying mechanisms are not fully understood. The present study aims to examine the relationship between Internet addiction, grit, social support, and life satisfaction. A random sampling method was used to recruit 304 Chinese college students to complete a questionnaire that included measures of Young’s Internet Addiction Scale, Multidimensional Scale of Perceived Social Support, Satisfaction with Life Scale, and 12-Item Grit Scale. For data analysis, SPSS, PROCESS macro and AMOS 23 were used to conduct confirmatory factor analysis, descriptive statistics analysis, reliability analysis, correlation analysis, and moderated mediation analysis. The results revealed that Internet addiction was negatively correlated with life satisfaction, and social support plays a mediating role between them. Moreover, grit moderated the mediation effect of social support in the relationship between Internet addiction and life satisfaction. This suggests that Internet addicts with higher levels of grit are less likely to experience a significant decline in social support. The study provides a deeper insight into the mechanisms through which Internet addiction hampers life satisfaction, suggesting that the influence may be channeled through social support. However, fostering a strong sense of grit could serve as a protective factor against the adverse impacts of internet addiction on life satisfaction. The broader implications for both research and practical applications in the field are subsequently elaborated.

The catalyst materials 0N-Cu-MOF, 1N-Cu-MOF, and 2N-Cu-MOF were successfully synthesized usinga solvothermal method, and using different concentrations of nitrogen-modified Cu organic frameworks (xN-Cu-MOF).Characterizations using X-ray diffraction (XRD), scanning electron microscopy (SEM), fourier transform infrared (FT-IR) spectroscopy, X-ray photoelectron spectroscopy (XPS), and Brunauer-Emmett-Teller (BET) surface area analysis showed that 1N-Cu-MOF had the largest SSA and pore size among the three materials synthesized.1N-Cu-MOF exhibited the largest pore size and specific surface area among the three materials, which had a decisive effect on CO 2 reduction.In addition, stability and CO 2 reduction reaction (CO 2 RR) activity were evaluated by linear sweep voltmeter, cyclic voltmeter, electrochemical impedance spectroscopy, and time flow tests.Faradaic efficiency (FE) was determined by product analysis.Among the three catalyst materials, 1N-Cu-MOF showed the best catalytic performance at 50 mAcm -2 (maximum current density).The charge transfer resistance was 8.23 , the average current density was 19.9 mAcm -2 , and the FE of methane (CH 4 ) production showed a high efficiency of 70.45 % when tested for 12 h at an overpotential of -0.35 V (to-RHE).