This study proposed and empirically validated an integrated conceptual model combining protection motivation theory (PMT) and the theory of planned behavior (TPB) to explain the policy acceptance of special evacuation stair installation and the evacuation intentions of users in deep subway stations. An online survey was conducted among metropolitan subway riders (18 items total, 15 core items), and data were analyzed using SPSS Statistics 26.0 for exploratory factor analysis (EFA), multiple regression, K-means cluster analysis, and chi-square tests. EFA confirmed a four-factor structure—awareness, perceived feasibility and trust, behavioral intention, and policy acceptance—with Cronbach α ≥ 0.78 for all factors. Regression results indicated that attitude and perceived behavioral control significantly predicted behavioral intention (p < 0.001) that in turn demonstrated strong explanatory power for policy acceptance (p < 0.01). Cluster analysis identified three user typologies (“high awareness–high acceptance,” “moderate awareness–moderate acceptance,” and “low–awareness–low acceptance”), and chi-square tests revealed significant group differences in prior training and in-depth guidance participation (p < 0.05). The findings suggested that the integrated PMT–TPB model effectively captured the determinants of evacuation stair acceptance and intention, providing a foundation for tailored communication and training strategies.

본 연구는 주요 선진국의 아동·청소년 공공 전달체계를 비교 분석하여, 한국 전달체계의 구조적 문제를 진단하고 개선 방향을 제시하였다. 분석 결과, 대부분 국가는 아동·청소년을 통합된 정책 대상으로 설정하고 있으 며, 보편주의 복지 또는 다부처 협업 모델을 통해 서비스의 효율성과 연 계성을 제고하고 있다. 반면, 한국은 부처 간 분절성과 중복, 지역 간 이 질적 운영으로 통합적 접근이 제한되고 있다. 이에 본 연구는 정책 대상 의 통합, 중앙-지방 간 역할 재정립, 법·제도 기반 정비 등 체계적이고 단계적인 전달체계 개편의 필요성을 제안하였다.

본 연구는 19세기 러시아 작가 표도르 도스토예프스키의 소설 『카라마 조프가의 형제들』을 20세기 미국 정신과 의사 머레이 보웬의 가족체계이 론으로 분석한 학제간 연구이다. 소설 속 핵심 사건인 존손살인을 둘러 싼 카라마조프가의 문제를 보웬이 주장하는 가족체계의 역기능으로 접근 해 보려는 것이 연구의 목표이기도 하다. 보웬의 가족체계이론에 따르면, 카라마조프가는 만성불안이 지배하는 감정체계로, 분화수준이 낮은 아들 들이 서로 융합관계를 형성하고 이 과정에서 맺은 삼각관계로 아버지를 죽이는 역기능이 발생했다. 이러한 가족구성원 전체의 기능적 문제로 접 근하는 방식은 등장인물 각각의 알리바이를 추적하면서 누가 진짜 범인 인지를 모색하고, 회개와 갱생을 촉구하던 기존의 연구 방식에 새로운 이정표가 되어줄 것이다. 아울러 인륜을 저버린 한 가족에 대한 세밀한 묘사를 통해 당대 러시아 현실을 진단하고 미래 비전을 제시하고자 했던 작가의 세계관을 이해하는 데도 좋은 잣대가 되어줄 것이다.

There have been meaningful changes in column stirrup spacing by KDS 41 20 00 in 2022, which is to decrease one of the spacing limits from the minimum section dimension to half of the minimum section dimension. Decreased column stirrup spacing increases the seismic shear resistance of columns and the seismic performance of the entire building. Among the effects of the column stirrup spacing change, this study focused on deformation compatibility in the seismic design of building frame system buildings with ordinary shear walls for seismic design category D. The beams and columns in building frame systems shall satisfy moment and shear strength, or deformation capability induced by seismic design displacement for satisfaction of the deformation compatibility. The commentary of KDS 41 17 00 describes that the deformation compatibility check can be ignored if the members in moment frames are upgraded to intermediate section details. The study showed that the deformation compatibility of columns was satisfied without additional consideration if the building frame systems were designed by the decreased column spacing in KDS 41 20 00. However, beams adjacent to walls needed further consideration, such as the recommendation of commentary in the code.

In this paper, a water rescue mission system was developed for water safety management areas by utilizing unmanned mobility( drone systems) and AI-based visual recognition technology to enable automatic detection and localization of drowning persons, allowing timely response within the golden time. First, we detected suspected human subjects in daytime and nighttime videos, then estimated human skeleton-based poses to extract human features and patterns using LSTM models. After detecting the drowning person, we proposed an algorithm to obtain accurate GPS location information of the drowning person for rescue activities. In our experimental results, the accuracy of the Drown detection rate is 80.1% as F1-Score, and the average error of position estimation is about 0.29 meters.

Fault detection in electromechanical systems plays a significant role in product quality and manufacturing efficiency during the transition to smart manufacturing. Because collecting a sufficient number of datasets under faulty conditions of the system is challenging in practical industrial sites, unsupervised fault detection methods are mainly used. Although fault datasets accumulate during machine operation, it is not straightforward to utilize the information it contains for fault detection after the deep learning model has been trained in an unsupervised manner. However, the information in fault datasets is expected to significantly contribute to fault detection. In this regard, this study aims to validate the effectiveness of the transition from unsupervised to supervised learning as fault datasets gradually accumulate through continuous machine operation. We also focus on experimentally analyzing how changes in the learning paradigm of the deep learning model and the output representation affect fault detection performance. The results demonstrate that, with a small number of fault datasets, a supervised model with continuous outputs as a regression problem showed better fault detection performance than the original model with one-hot encoded outputs (as a classification problem).

To support the International Maritime Organization’s (IMO) 2050 greenhouse gas reduction targets, hybrid propulsion energy management systems (EMS)—which integrate multi-energy coordination and dynamic scheduling—have become a critical pathway for enabling low-carbon transitions and improving energy efficiency in the maritime sector. This paper conducts a comprehensive and structured analysis of EMS technologies applied to ship hybrid propulsion systems. It evaluates the functional roles of EMS under varying system architectures, synthesizes mainstream energy management strategies, and identifies current technological bottlenecks, thereby contributing theoretical foundations for the green transformation of the shipping industry. The study first examines representative hybrid propulsion architectures, detailing their technical characteristics to clarify the functional positioning and optimization priorities of EMS in each configuration. It then reviews prevailing energy management and control strategies, with a focus on their integration with artificial intelligence (AI) and the emergence of adaptive and data-driven approaches. Finally, the paper identifies key challenges in hybrid propulsion EMS, proposes future research directions, and offers practical recommendations to support the advancement and implementation of intelligent energy management technologies in maritime applications.

Volatile fatty acids (VFAs) are designated as offensive odor substances, and they are known for their strong polarity and adsorptive properties, which can lead to significant losses during sample collection and analysis. This study evaluates two analytical methods currently outlined in the odor process test standards, alongside an analytical system utilizing adsorption tubes and another system that uses ion chromatography (IC). Furthermore, suitable analytical methods were proposed for analyzing concentrations below the odor threshold and emission limits. When assessing SPME-GC/FID, SPME-GC/MSD, TD-GC/MSD, and IC based on the internal quality control standards specified in the process test standards, all methods were found to have met these criteria. The absolute injection amounts (1 atm, 25oC) satisfying the emission limits ranged from 3 to 95 ng, while those that met the odor thresholds ranged from 0.2 to 6.5 ng. Based on these criteria, analytical systems suitable for the specified concentration range and odor thresholds were identified. The results are as follows. 1. The analytical systems confirmed to be suitable for quantifying limits were TD-GC/ MS and IC. 2. In terms of recovery and precision, both TD-GC/MSD and IC were found to be suitable. 3. Regarding detection limits, both previously mentioned systems were satisfactory. 4. Finally, concerning quantitation limits, both systems were adequate; however, TD-GC/MSD slightly exceeded the odor threshold analysis range for propionic acid by approximately 1.5 ng. The odor thresholds for the four VFAs were converted to absolute quantities (1 atm, 25oC), confirming that the IC system met the following criteria: (1) calibration range and curve, (2) accuracy and precision, and (3) instrumental detection and quantitation limits.

This study examines the risks posed by the on-site reactivity of hazardous chemicals, focusing on high-risk accident scenarios and response system improvements. Using cases like TATP and VX, it analyzes the accessibility and combination potential of precursor chemicals that are not inherently hazardous but can become highly dangerous under specific conditions. Scenario-based qualitative risk assessments reveal critical gaps in South Korea’s current safety management, including insufficient anticipatory regulations, limited detection capabilities for reactively synthesized agents, and fragmented inter-agency coordination. The study highlights the need for a proactive, integrated approach incorporating real-time precursor tracking, advanced detection technologies, and joint scenario-based response training. By shifting from static substance control to risk-based preparedness, this research offers strategic recommendations for enhancing chemical accident prevention and response in complex facility environments.

This study aims to explore the public perception of sports welfare by employing big data analysis techniques and to analyze it through a multi-layered lens grounded in Bronfenbrenner’s ecological systems theory. To this end, text mining software Textom and Ucinet 6 were utilized to examine online textual data related to “sports welfare” collected from May 2017 to February 2025. frequency analysis, TF-IDF analysis, degree centrality analysis, and CONCOR analysis were conducted. The results identified keywords such as “physical education.” “fitness.” “citizens.” “society.” “support.” “disability.” “voucher.” “university.” and “center.” as having high co-occurrence with sports welfare. CONCOR analysis revealed six major clusters: National Fitness 100 Service, Sports Voucher Program, Health Programs at Public Sports Centers, Community-Based Sports Welfare Environment, Training of Sports Welfare Professionals, and Support System Centered on the Korea Sports Promotion Foundation. This study suggests that the level of individual sports welfare can be enhanced through dynamic and interactive relationships between the individual and various environmental systems. Furthermore, to realize sustainable sports welfare, it is essential to develop long-term national strategies that holistically integrate all levels of the ecological systems from the micro system to the chrono system.

본 연구는 2016년 SM엔터테인먼트가 론칭한 다국적 보이그룹 NCT(Neo Culture Technology)의 유닛 시스템이 가진 차별화된 특성이 K-Pop 산 업에 미친 영향과 확산 과정을 분석하였다. 연구 방법은 질적 내용분석을 선택하였고, 로저스의 혁신 확산 이론의 네 가지 핵심 요소(혁신, 커뮤니케 이션 채널, 시간, 사회 시스템)를 분석 프레임워크로 활용하였다. 분석 결 과, NCT 유닛 시스템은 콘텐츠 다양화, 시장 확장성, 리스크 분산, 아티스 트 개발 측면에서 상대적 이점을 가진 혁신으로, SM의 전략적 커뮤니케이 션과 팬 커뮤니티의 정보 공유가 확산에 중요한 역할을 했음을 발견하였 다. 시간적 측면에서는 2016년부터 현재까지 초기 도입기, 확산 성장기, 급속 확산기, 안정화 단계로 이어지는 S자형 확산 곡선이 관찰되었다. 또 한 NCT 유닛 시스템은 K-Pop 산업의 기존 규범에 도전하며 아이돌 그룹 의 정체성 형성, 경력 관리, 글로벌 확장 전략, 인재 개발 방식에 변화를 가져왔다. 본 연구는 NCT 유닛 시스템이 K-Pop 그룹의 지속 가능한 성장 모델, 글로벌 시장 접근 전략, 유연한 인재 관리, 다층적 팬덤 참여, 미디 어 기술 적응, 비즈니스 모델 다각화 측면에서 K-Pop 산업의 미래 발전 방향에 중요한 시사점을 제공함을 확인하였다.

본 연구는 인공지능(AI) 기술의 발전이 음원 제작 시스템에 미치는 영향을 중심으로, 작곡, 편 곡, 믹싱, 마스터링의 핵심 제작 단계에서 인공지능 기술이 어떻게 적용되고 있는지를 체계적으로 분석하였다. 특히 뮤즈넷(MuseNet), 마젠타(Magenta), 수노(SUNO), 아이바(AIVA) 등 대표적인 인 공지능 작곡 도구의 기술 구조와 기능을 시대별로 비교함으로써, 음악 창작의 자동화 수준과 기술 적 한계를 실증적으로 조명하였다. 또한 하이브(HYBE), 에스엠(SM), 와이지(YG) 등 국내 주요 엔 터테인먼트 기업의 인공지능 기술 수용 사례를 통해, 산업 현장에서의 실제 활용 방식과 그로 인 한 제작 및 유통 시스템의 변화 양상을 분석하였다. 연구 결과, 인공지능은 음원 제작의 효율성과 확장성을 획기적으로 높이는 동시에, 콘텐츠 생산 방식과 산업 구조의 재편을 촉진하는 주요 요인 으로 작용하고 있음을 확인하였다. 본 연구는 이러한 변화를 바탕으로 향후 음악 산업이 나아가야 할 기술 통합 전략과 대응 방향에 대해 제언하고자 한다.

This study aims to develop an AI-based analysis system that aligns with the international trend of AI legislation, including the EU's AI Act, while also addressing the analytical needs of the public sector. The focus is on providing timely and objective information to policymakers and specialized researchers by exploring advanced analytical methodologies. As the complexity and volume of data rapidly increase in the modern policy environment, these methods have become essential for governments to obtain the objective information needed for critical decision-making. To achieve this, the study integrates machine learning, natural language processing (NLP), and Large Language Models (LLM) to create a system capable of meeting the analytical demands of government entities. The target dataset consists of “quantum” field data collected from South Korea's National R&D Information System (NTIS). Machine learning was applied to this data to assess the validity of the analysis, while BERTopic, a natural language analysis package, was used for text analysis. With the introduction of LLMs, the extracted information from machine learning and natural language analysis was not merely listed but also connected in meaningful ways to provide policy insights. This approach enhanced the transparency and reliability of AI analysis, minimizing potential errors or distortions in the data analysis process. In conclusion, this study emphasizes the development of a system that enables rapid and accurate information provision while maintaining compatibility with international AI regulations such as the AI Act. The use of LLMs, in particular, contributed to enhancing the system’s capabilities for deeper and more multifaceted analysis.

Truss structures, widely used in engineering, consist of straight members transferring axial forces. Traditional analysis methods like FEM and the Force Method become computationally expensive for large-scale and nonlinear problems. Surrogate models using Artificial Neural Networks (ANNs), particularly Physics-Informed Neural Networks (PINNs), offer alternatives but require extensive training data and computational resources. Variational Quantum Algorithms (VQAs) address these challenges by leveraging quantum circuits for optimization with fewer parameters. Variational Quantum Circuits (VQCs) based on Quantum Neural Networks (QNNs) utilize quantum entanglement and superposition to approximate high-dimensional data efficiently, making them suitable for computationally intensive tasks like surrogate modeling in structural analysis. This study applies QNNs to truss analysis using 6-bar and 10-bar planar trusses, assessing their feasibility. Results indicate that residual-based loss functions enable QNNs to make reliable predictions, with increased layers improving accuracy and a higher Q-bit count contributing to performance, albeit marginally.