Background: As the length of stay for rehabilitation and medical care services for occupational injury patients continues to increase, this study aims to explore alternative solutions that can support the corporation's efforts to develop rehabilitation treatment programs in response. Objectives: By analyzing the most frequent diseases among occupational injury patients over the past five years and comparing the average length of stay (LOS) for each disease by disease type, occupation, gender, and type of accident, it is expected that the necessity for developing rehabilitation treatment programs for occupational injury patients can be identified. Furthermore, when applying the developed treatment programs, a reasonable LOS can be derived. Design: Patient clinical data lab. Methods: From January 2017 to December 2021, data from 292,423 occupational injury patients who submitted their initial medical treatment applications to the Korea Workers' Compensation & Welfare Service (KWCWS) and received approval for their occupational injuries were de-identified. After data preprocessing, the cases were categorized by diagnosis, and statistical analysis was conducted using Excel ver. 21.0. Results: In the intensive rehabilitation treatment process, inpatient and outpatient care show a very strong correlation with r=0.8817, followed by the total number of treatment days (r=0.6431) and the number of treatment sessions (r=0.6441). Conclusion: It is necessary to establish application criteria for injury groups that significantly impact the average medical care days and medical care benefits of industrial accident patients. These criteria should consider factors such as exceeding the average length of medical care for specific injury groups (orthopedic/neurological), gender differences (female patient group), discrepancies in inpatient and outpatient medical care benefits, the proportion of high disability grades (Grade 1-3), common injury (accident) types, and the top 1/2/3 associated injury groups by occupation type. Based on these criteria, simultaneous management of the provision timing of rehabilitation service programs and the average medical care days for the 50 most common injury groups is required.

이 연구는 인공지능(AI)이 문화콘텐츠와 미디어 산업에 미치는 영향을 다각적으로 분석하고, AI의 창의 적 생산, 미디어 소비 변화, 윤리적·사회적 쟁점에 대해 논의하였다. 그리고 AI가 콘텐츠 제작에서 창의성과 효율성을 높이며, 창작자와의 협력을 통해 새로운 형태의 콘텐츠 창출을 가능하게 한다는 점을 확인하였다. 또한 AI 기반 추천 시스템을 통해 개인화된 미디어 소비 경험이 제공됨으로써 사용자 만족도와 소비 지속 성이 강화된다는 사실을 밝혔다. 그러나 AI 도입은 저작권 문제, 알고리즘 편향성, 문화적 다양성 보존, 사회적 고용 변화 등 다양한 윤리적·사회적 과제도 야기하고 있다. 이를 해결하기 위해 법적, 기술적, 정책 적 접근이 필요하며, AI의 투명성과 공정성을 확보하는 것이 필수적임을 강조하였다. AI와 인간이 협력하 여 공정하고 신뢰할 수 있는 콘텐츠 제작을 지향할 때, AI는 문화콘텐츠와 미디어 산업의 발전에 이바지할 수 있을 것이다. 본 연구는 AI와 문화산업의 융합을 둘러싼 다양한 이슈를 종합적으로 검토함으로써 향후 지속 가능한 발전을 위한 방향성을 제시하고자 한다.

In this study used Computational Fluid Dynamic analysis to examine NOx reduction in hydrogen combustion, analyzing six conditions with varying air/fuel ratios, temperatures, and concentrations. Results were compared between two combustor shapes and previous experimental data. Findings showed increased air/fuel ratios decreased flame temperature and increased post-combustion O2. NOx emissions peaked at high temperatures and low O2. Numerical results aligned with previous experimental trends, validating the approach. Combustor shape differences, reflecting variations in fuel and air pipes, significantly affected flow rates and combustion positions. This reduced NOx emissions up to a certain air/fuel ratio, but excessive increases diminished this effect. The study highlights the complex relationship between combustor design, operating conditions, and NOx emissions. Further research is needed to optimize NOx reduction by considering pipe numbers and combustion locations. Future studies should explore various combustor geometries, fine-tune air/fuel ratios, and investigate additional parameters influencing NOx formation and reduction in hydrogen combustion systems.

This study develops a model to determine the input rate of the chemical for coagulation and flocculation process (i.e. coagulant) at industrial water treatment plant, based on real-world data. To detect outliers among the collected data, a two-phase algorithm with standardization transformation and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is applied. In addition, both of the missing data and outliers are revised with linear interpolation. To determine the coagulant rate, various kinds of machine learning models are tested as well as linear regression. Among them, the random forest model with min-max scaled data provides the best performance, whose MSE, MAPE, R2 and CVRMSE are 1.136, 0.111, 0.912, and 18.704, respectively. This study demonstrates the practical applicability of machine learning based chemical input decision model, which can lead to a smart management and response systems for clean and safe water treatment plant.

Volatile organic compounds (VOCs) can adversely affect human and plant health by generating secondary pollutants such as ozone and fine particulate matter, through photochemical reactions, necessitating systematic management. This study investigated the distribution characteristics of gaseous VOCs in ambient air, with a focus on interpreting data from a photochemical pollution perspective. This paper analyzed the presence and concentration distribution of VOCs in industrial areas, identifying toluene, m-xylene, p-xylene, and n-octane as the most frequently detected components. Particularly, toluene was found to significantly contribute to the formation of ozone and fine particulate matter, highlighting the need for stricter regulation of this compound. Although n-octane and styrene were present in relatively low concentrations overall, their significant contributions to ozone generation and secondary organic aerosol formation, respectively, emphasize their importance in air pollution management.

This study quantitatively analyzes risks of industrial incidents to fisher on overseas tuna purse seiners and long liners. A Bayesian network is employed to analyze 478 cases of industrial incidents, comprising 401 cases from purse seiners and 77 cases from long liners, reported from 2019 to 2022. The highest risk of industrial incidents on purse seiners is attributed to diseases. Excluding diseases, risks are the highest during fishing: 5.31 times higher during catch handling, 2.05 times higher during maintenance, and 2.38 times higher during loading and unloading. The risk of industrial incidents caused by the hull is 9.50 times higher than those caused by fishing gear, 4.59 times higher than those caused by machinery, and 3.61 times higher than those caused by the caught fish. Among the types of industrial incidents, slips are the highest: 2.58 times higher than industrial incidents caused by being bump, 3.74 times higher than those caused by hit, and 3.94 times higher than those caused by imbalance and overexertion. For long liners, most industrial incidents are concentrated in diseases, with dental, musculoskeletal, skin, and respiratory diseases being the primary types of industrial incidents identified. This study aims to propose reduction measures for reducing the high-risk form of industrial incidents, specifically slips, and to present health management strategies for preventing diseases among fisher on overseas tuna fishing vessels. By addressing these aspects, this study seeks to contribute to the safety and sustainability of the overseas tuna fishing industry.

This study aims to predict return-to-work outcomes for workers injured in industrial accidents using a TabNet-RUSBoost hybrid model. The study analyzed data from 1,383 workers who had completed recuperation. Key predictors identified include length of recuperation, disability grade, occupation activity, self-efficacy, and socioeconomic status. The model effectively addresses class imbalance and demonstrates superior predictive performance. These findings underscore the importance of a holistic approach, incorporating both medical and psychosocial factors.

우리나라 제조 소기업은 온실가스 고배출 업종이 많고, 산재사망사고 발생 비중이 높아 수출의존도가 높은 국내 제조 소기업에 대한 ESG 이슈 관리가 점차 중요해 질 것이다. 본 연구는 제조 소기업의 저탄소 활동이 현장 작업자의 안전의식과 산업재해에 미치는 영향을 파악하고, 저탄소 활동이 안전, 고용 등의 영역에서 발생하는 부정적 영향을 감소 시켜 기업의 경쟁력을 향상시킬 수 있는 방안을 모색해 보았다. 연구에서는 제조 소기업의 저탄소 활동(저탄소 전략 및 시스템 활동, 온실가스 및 환경오염 분야 활동, 자원 및 에너 지 분야 활동)이 산업안전 인식 향상에 긍정적(+)인 영향을 미치는 것을 확인하고, 저탄소 활동에 참여한 기업들의 산업재해율이 감소하였으며, 매출과 고용이 증가하는 성과가 나 타난 것을 확인하였다. 따라서 정부는 제조 소기업의 저탄소 활동과 산업안전, 고용창출이 연계될 수 있도록 정책적인 지원을 통해 지속가능한 성장을 위한 핵심 경영 전략으로 자리 잡게 해야 한다.