Sun Moon University

Rotary kiln-based activated carbon production combines high-temperature operation with flammable/reducing gases, carbonaceous dust, and downstream off-gas treatment and acid/base washing, creating complex escalation pathways. This study prioritizes safety improvements by applying classical failure modes and effects analysis (FMEA) and a transparent Fuzzy-FMEA framework to 18 representative failure modes (six each for kiln/activation, acid/base handling, and atmosphere/control). Five experts evaluated Severity, Occurrence, and Detection on a 10-point scale. The fuzzy model used triangular membership functions (L/M/H), a monotonic 27-rule base, Mamdani max-min inference, and centroid defuzzification to compute a continuous fuzzy risk priority number (FRPN, 0-10). Classical FMEA identified dust explosion (RPN = 405), temperature control failure (RPN = 378), and off-gas leakage (RPN = 324) as the highest-ranked risks. Fuzzy-FMEA preserved the top-risk group while more strongly highlighting barrier-related risks, placing off-gas leakage, instrumentation/interlock failure, and electrostatic ignition control alongside dust explosion (FRPN 9.221-9.332). The rankings were strongly correlated (Spearman rho = 0.871; Kendall tau = 0.752), yet mid-risk items were rearranged (mean |Delta rank| = 2.06; max = 5), improving discrimination within tied RPN clusters. The five highest-priority scenarios were reconstructed into actionable engineering packages, including dust and ignition control, off-gas integrity linked to shutdown logic, interlock proof testing and bypass management, and independent protection layers for kiln temperature control.

ObjectivesThe older adult population is increasing worldwide, especially South Korea. Development of Western and Oriental combined intervention for health management of the older adults and research to examine its effect are needed. This study was to examine the effects of Western-Oriental combined intervention (health education, Oriental breathing, qigong: paldangeum, Fumanet cognition/exercise, meridian acupressure, and sharing opinions) on cognitive function, health state, depression, social support, and life satisfaction of the older adults living in the community.Study DesignA quasiexperimental pretest-posttest control group design was employed.MethodsStudy participants were a total of 68 aged 65 years or older residing in Seoul, South Korea (intervention: n = 34 and C\control: n = 34). Western-Oriental combined intervention was applied as twice a week, 90 min per session, for 20 sessions for 10 weeks. Measures included Mini-Mental State Examination, Korean version older adult health state evaluation scale, the shortened Korean version of the Geriatric Depression Scale, social support scale, and life satisfaction scale.Main FindingThe intervention group to which the Western-Oriental combined intervention was applied showed cognitive function, health state, depression, social support, and life satisfaction were statistically significantly improved than the control group without intervention.Conclusionand Implication: This study suggests that the Western-Oriental combined intervention is an effective intervention method for improving cognitive function, health state, depression, social support, and life satisfaction of the older adults living in the community. In the practice, health professionals or nurses can use the Western-Oriental combined intervention for health management of the older adults living in community.

Accurate and trustworthy prediction of Enzyme Commission (EC) numbers is critical for understanding enzyme functions and their roles in biological processes. Despite the success of recently proposed deep learning-based models, there remain limitations, such as low performance in underrepresented EC numbers, lack of learning strategy with incomplete annotations, and limited interpretability. To address these challenges, we propose a hierarchical interpretable transformer model, HIT-EC, for trustworthy EC number prediction. HIT-EC employs a four-level transformer architecture that aligns with the hierarchical structure of EC numbers, and leverages both local and global dependencies within protein sequences for this multi-label classification task. We also propose a learning strategy to handle samples associated with incomplete EC numbers. HIT-EC, as an evidential deep learning model, produces trustworthy predictions by providing domain-specific evidence through a biologically meaningful interpretation scheme. The predictive performance of HIT-EC is assessed by multiple experiments: a cross-validation with a large dataset, a validation with external data, and a species-based performance evaluation. HIT-EC shows statistically significant improvement in predictive performance when compared to the current state-of-the-art benchmark models. HIT-EC’s robust interpretability is further validated by identifying well-known conserved motifs and functional regions. HIT-EC is a robust, interpretable, and reliable solution for EC number prediction, with significant implications for enzymology, drug discovery, and metabolic engineering. Accurately predicting enzyme functions remains challenging, especially for underrepresented enzyme commission (EC) classes. Here, the authors introduce HIT-EC, a hierarchical interpretable transformer that improves EC number prediction and provides insight into sequence-function relationships.

BACKGROUND:Prior research on circadian rhythms have primarily focused on the risk of diabetes, with limited evidence on their impact in glycemic control among individuals with type 2 diabetes. This study investigated the association between Fitbit-derived circadian rhythm parameters and continuous glucose monitoring (CGM) metrics. METHOD:Data were analyzed from 122 insulin-treated patients with type 2 diabetes who concurrently wore real-time CGM devices (Dexcom G6) and activity trackers (Fitbit Inspire 2) for 10 days. Cosinor analyses were used to derive circadian parameters from wearable-based heart rate data. Associations between time-of-day-specific activity metrics and CGM outcomes were evaluated using partial Spearman correlations and multivariable logistic regression. RESULTS:Stronger circadian rhythmicity-characterized by greater amplitude and higher goodness-of-fit (R2)-was significantly associated with improved glycemic outcomes and reduced glucose variability. Higher daytime step counts and lower sedentary time were associated with reduced hyperglycemia and variability. Longer sleep duration was inversely associated with hypoglycemia (TBR <70) and glucose variability indices. Notably, circadian robustness (R2) and afternoon step counts emerged as independent predictors of achieving comprehensive CGM-based targets after adjusting for key clinical and behavioral confounders. CONCLUSIONS:In this cross-sectional exploratory analysis, greater daytime physical activity and stronger circadian rhythmicity were associated with improved glycemic control and reduced glucose variability. These findings are hypothesis-generating and support the need for prospective trials testing circadian-aligned behavioral interventions.

Student dropout undermines both student success and institutional performance. In recent years, various machine-learning models have been developed to predict dropout risk in higher education; however, existing studies remain constrained by four major gaps: (i) limited prediction horizons, (ii) first-year focus, (iii) inadequate handling of class imbalance, and (iv) lack of calibrated interpretability. To overcome these limitations, this study proposes a portable, generalizable machine-learning framework for long-term dropout prediction that systematically integrates modular components addressing each gap. Specifically, (i) rolling multi-semester windows generate horizon-specific targets several semesters in advance, (ii) an all-cohort student-semester dataset eliminates the first-year restriction by representing every enrolled cohort, (iii) a resampling-threshold optimization coupling mitigates class imbalance to improve sensitivity to rare events, and (iv) probability calibration with SHAP (SHapley Additive exPlanations)-based interpretability ensures reliable and transparent model outputs. Empirical evaluation on a university registry dataset demonstrates that the framework produces well-calibrated, temporally stable, and interpretable dropout-risk estimates. Because it relies solely on ubiquitous registry variables and off-the-shelf algorithms, the framework is designed to be portable and generalizable across higher-education institutions and suitable for integration into early-warning systems that support timely, data-driven student advising.