- E-ISSN2586-6036
- KCI
E-ISSN : 2586-6036
Vol.8 No.5
Abstract
Purpose: Apply HEAR to driver-related human-error incidents in Korea—especially high-speed train operations—to identify dominant error–cause–outcome patterns and practical implications. Research design, data and methodology: Retrospective review of Korea Railroad Corporation records (2012–2021). We selected driver-related human-error cases and isolated high-speed operations. Errors were classified as execution, decision-making, situational judgment, or information perception; causes and outcomes were standardized. Descriptive statistics and cross-tabulations summarized distributions and frequent combinations. Results: By train type, incidents: conventional 45%, urban 39%, high-speed 16%. In the high-speed subset, decision-making errors predominated. Leading causes were improper work methods and negligent equipment handling; leading outcomes were service disruption, then worker injury and emergency stop. About 25% caused actual damage; injuries were approximately 20%. Conclusions: Priorities include refined SOPs and dual checks for high-risk tasks, simulator-based repetitive training with real-time risk feedback, improved working conditions (fatigue and cab air quality), and deployment of driver-assistance and automatic safety systems (e.g., ATS). The HEAR-based schema exposes a small set of decision-linked patterns explaining much of the risk in high-speed operations.
Abstract
This study investigates the impact of artificial intelligence (AI)-based e-business platforms on the adoption of personalized wellness services through a literature review. While prior studies have largely focused on evaluating the effectiveness of individual applications, this research examines the interplay of technological attributes, psychological factors, and human–AI interaction at the platform level. The findings reveal three key insights. First, personalization, trust, and interaction quality emerge as critical determinants of users’ intention to adopt wellness services. Second, perceived usefulness and engagement serve as mediating variables that link technological attributes to adoption intention and promote continuous use. Third, hybrid models that combine human coaching with AI demonstrate stronger potential to enhance user engagement and sustain long-term behavioral outcomes compared to AI-only services. These results contribute theoretically by extending the understanding of wellness service adoption mechanisms and offer practical implications for businesses developing AI-based platforms. Specifically, firms should focus on refining personalization algorithms, building trust through transparency and data protection, enhancing user engagement strategies, and designing hybrid service models that integrate human empathy with AI precision. Future research should validate these relationships empirically across diverse cultural and demographic contexts and different market environments to strengthen the generalizability of the findings.
Abstract
This study aims to examine the impact of integrating wearable-based digital wellness data with healthcare institutions on healthcare e-business innovation. Recent advancements in artificial intelligence, the Internet of Things, and cloud technology are accelerating the transformation of the healthcare industry. In particular, health data collected via wearable devices is emerging as a key factor reshaping the entire spectrum of diagnosis, prevention, and management systems. This data enables patient-centered care, personalized management, and remote monitoring by collecting and integrating patients' biometric information in real time. The research methodology involved analyzing major international literature from the past five years, with results organized into four categories. First, wearable data has fostered a platform ecosystem between healthcare institutions and companies. Second, it has supported patient-centered innovation by enhancing patient self-management and engagement. Third, privacy protection and lack of interoperability remain unresolved challenges. Fourth, it has been confirmed that new business models, such as wellness programs can be created. In conclusion, while wearable data is a driving force for healthcare e-business innovation, institutional reforms, collaborative governance, and securing social acceptance are necessary for widespread adoption. This study contributes to both theory and practice by clarifying how data-driven convergence between healthcare and digital business can generate sustainable value creation.
Abstract
Purpose: This study aims to investigate the potential of recycling spent coffee grounds (SCG) as an auxiliary biofuel to improve energy efficiency and odor reduction in low-temperature sludge drying systems. Research Design & Data Collection: A pilot-scale closed-loop belt dryer equipped with an inverter-driven scroll compressor (30–200 Hz) was tested under varying compressor frequencies (130, 160, 180 Hz). Experimental data were collected for power use, temperature, pressure, and moisture content. Pilot-scale validation was supported by certified industrial testing (KTI and Daejeon Analytical Research Institute, 2025), confirming a mean SEC of 0.579 kWh kg⁻¹-H₂O and a final moisture of 7.4 wt %, both within design targets. Research Results: The system achieved an average Specific Energy Consumption (SEC) of 0.607 kWh/kg-H₂O and reduced final moisture content to 4.6 wt%, outperforming target thresholds of 0.64 kWh/kg-H₂O and 10 wt%. Operators also reported a noticeable reduction in odor emissions, suggesting SCG's adsorption capacity, though quantitative odor profiling remains a subject of future study. Conclusion: The findings confirm that SCG can serve as an effective auxiliary fuel, enhancing both energy efficiency and environmental performance. The developed system presents a scalable solution for sustainable sludge management within the framework of circular economy principles
Abstract
Purpose: Agroforestry can increase soil carbon and improve soil quality, so this study aimed to quantify soil organic carbon (SOC) storage and related soil properties in alfalfa–cherry and maize–cherry systems in Gilgit’s mountain valleys for climate change mitigation. Research design, data and methodology: We compared Danyore and Sultanabad by sampling soils at 0–20 cm and 20–40 cm and measuring pH, electrical conductivity, moisture, bulk density, soil organic matter, SOC, and SOC stocks; we then compared patterns by valley, system, and depth and examined Pearson correlations. Results: Results showed higher SOC stocks under alfalfa–cherry than maize–cherry in Danyore at 0–20 cm (37.74 vs 28.13 Mg ha⁻¹), stocks generally increased with depth except for maize–cherry in Sultanabad, SOC correlated positively with SOC stocks, bulk density was higher in Sultanabad and increased with depth, and pH, moisture, and texture differed by site and system. In discussion, the greater stocks under tree-based systems align with continuous litter inputs and management effects that favor SOC protection, and the higher topsoil SOC reflects typical depth gradients in agroforestry soils. Conclusions: We conclude that alfalfa–cherry agroforestry enhances soil quality and increases SOC storage in this high-altitude region, supporting its use as a local climate mitigation strategy and warranting continued monitoring of SOC stability.
Abstract
Purpose: This study investigates the effect of health literacy on life satisfaction among older adults, focusing on the mediating role of self-efficacy. Research design, data and methodology: Data from 3,444 adults aged 65 and older were analyzed using the 2021 Korea Health Panel. Structural equation modeling was applied to examine the causal relationships, controlling for gender, age, education, and chronic disease status. Results: Health literacy has a significant positive effect on life satisfaction. Self-efficacy partially mediates this relationship, with statistically significant indirect effects. Age and chronic disease negatively impact life satisfaction, while education has a positive association. Conclusions: Enhancing health literacy and self-efficacy among older adults can contribute to improved life satisfaction. The findings provide essential evidence for developing targeted health promotion interventions and policies. Future longitudinal studies are suggested to confirm these relationships.
Abstract
Purpose: This study investigates how population aging and household structure influence residential energy consumption patterns. The goal is to identify energy consumption behaviors that are specific to different age groups and energy sources, moving beyond traditional household-level analyses. Research design, data and methodology: Using the National Transfer Accounts (NTA) framework, we transform household-level energy consumption data from the Household Energy Panel Survey into individual-level estimates. We apply age-weighted allocation and smoothing techniques to create age profiles for electricity, city gas, district heating, and petroleum-based fuels. This enables meaningful comparisons across various life stages and household types. Results: Electricity consumption increases with age and remains high in later life, reflecting persistent needs for comfort and safety. District heating shows no clear age-related trend. In contrast, city gas consumption follows an inverted U-shaped pattern. Petroleum-based fuels remain essential for elderly and single-person households, particularly in detached homes. Single-person households exhibit the highest per capita energy use across all ages, indicating limited economies of scale. Conclusions: Population aging constitutes a structural driver of residential energy demand. Energy policies and forecasting models should incorporate age and household composition to improve precision. The age-specific profiles developed here provide empirical foundations for targeted energy welfare, efficiency enhancement, and sustainable policy design in aging societies.
Abstract
Purpose: This study investigates the seasonal variation of odor components in traditional markets and examines the relationship between measured concentrations and public interest using both field monitoring and Google Trends data. The goal is to provide empirical and social evidence for developing effective odor management policies in urban commercial spaces. Research Design, Data, and Methodology: Field measurements of NH3, H2S, TVOC, and complex odor (D/T) were conducted in four traditional markets in Wonju, Korea, from 2021 to 2025. In parallel, Google Trends data for five related keywords were analyzed to capture patterns of public interest. Time-series analysis identified seasonal and annual trends, one-way ANOVA tested for seasonal differences, PCA determined the main contributors to complex odor, and correlation analysis compared field data with search volumes. Statistical analyses were performed using SPSS, R, and Python visualization tools. Research Results: NH3, and H2S displayed seasonal patterns consistent with search interest, indicating a strong link between environmental conditions and public perception. In contrast, TVOC concentrations increased by over 100% in 2025 without corresponding search interest, reflecting a management gap. Complex odor was consistently measured but rarely searched, revealing a terminology gap between experts and residents. Conclusion: Odor management policies appear effective for NH3, and H2S but insufficient for TVOC. Combining physical measurements with public awareness data offers a multidimensional understanding of urban odor issues and supports the need for tailored, long-term, and communication-sensitive management strategies.
Abstract
This study analyzed the impact of field-based safety training on workers’ risk-prevention behaviors. A total of 150 manufacturing and construction workers were divided into an experimental group (field-based training) and a control group (lecture-based training). Behavioral changes before and after training were measured through surveys and observations. The results showed that the experimental group demonstrated significant improvements compared to the control group in personal protective equipment (PPE) usage, risk-avoidance behaviors, and frequency of peer-to-peer safety interventions. These findings suggest that experiential learning in real working environments is effective in enhancing workers’ safety awareness and behaviors.Science and Scopus.
Abstract
Purpose: This study systematically analyzes the characteristics of human error accidents among railway trackside workers to identify dominant patterns and propose safety management strategies. Research design, data and methodology: This quantitative study analyzed 10 years (2012-2021) of KORAIL accident records. Incidents were classified using the Human Error Analysis and Reduction (HEAR) framework. The analysis primarily involved descriptive statistics to examine the distribution of error types, causes, and outcomes, with a supplementary job analysis. Results: The analysis revealed that incidents in the Facilities sector were twice as frequent as in the Electrical sector. The most prevalent error type was Decision-Making Error (56.9%), showing accidents primarily stem from flawed judgments and procedural violations rather than simple execution slips. Worker injury was the most common outcome, and accidents were concentrated in specific high-risk tasks like turnout maintenance and signal equipment inspection. Conclusions: This study concludes that human errors in trackside operations are systemic issues rooted in decision-making and procedural compliance. The findings support multi-faceted countermeasures, including enhanced scenario-based training, adopting advanced safety technologies to reduce cognitive load, improving fatigue management through scheduling, and robust safety feedback systems.
Abstract
Purpose: Mining in high mountain catchments can degrade downstream water quality by increasing suspended solids and mobilizing dissolved ions. This study evaluates how gemstone extraction at Chumar Bakhoor influences drinking-water indicators along a glacier–mine–community transect in Sumayar Nagar, Gilgit-Baltistan. Research design, data and methodology: A stratified design was implemented on 9 September 2024 with nine grab samples from glacier source waters, active mining flows, and downstream community channels. Physicochemical parameters were measured for pH, electrical conductivity, total dissolved solids, turbidity, hardness as calcium carbonate, a salinity proxy, chloride, and nitrite, and interpreted against WHO, PSQCA, and ANEQS guideline values. Results: Mean pH was near neutral at 7.40. Ionic strength rose sharply at the mine, with electrical conductivity peaking near 1,578 microsiemens per centimeter and a cross-site mean of 705.67. Total dissolved solids averaged 278 milligrams per liter and reached 564 to 566 in mining waters. Turbidity was greatest at the mine at 307 to 309 nephelometric turbidity units and averaged 153 across sites, far above potability criteria. Chloride remained low. Nitrite increased at the mine to 0.6 to 0.7 milligrams per liter, while glacier and community waters were near detection. Hardness was very low at about 2.5 to 2.7 milligrams per liter across sites. Conclusions: Mining is the dominant driver of observed deterioration; without treatment and improved practices, waters influenced by mining are unsuitable for direct consumption.
