In this study, we propose a data-driven analytical framework for systematically analyzing the driving patterns of autonomous buses and quantitatively identifying risky driving behaviors at the road-segment level using operational data from real roads. The analysis was based on Basic Safety Message (BSM) data collected over 125 days from two Panta-G autonomous buses operating in the Pangyo Autonomous Driving Testbed. Key driving indicators included speed, acceleration, yaw rate, and elevation, which were mapped onto high-definition (HD) road maps. A hybrid clustering method combining self-organizing map (SOM) and k-means++ was applied, which resulted in eight distinct driving pattern clusters. Among these, four clusters exhibited characteristics associated with risky driving such as sudden acceleration, deceleration, and abrupt steering, and were spatially visualized using digital maps. These visualizations offer practical insights for real-time monitoring and localized risk assessment in autonomous vehicle operations. The proposed framework provides empirical evidence for evaluating the operational safety and reliability of autonomous buses based on repeated behavioral patterns. Its adaptability to diverse urban environments highlights its utility for intelligent traffic control systems and future mobility policy planning.

공유 유기 골격체(covalent organic frameworks, COF)는 기능성을 정밀하게 설계하고 제어할 수 있는 결정성 다 공성 소재로서, 차세대 연료전지 멤브레인으로 주목받고 있다. 표준 양성자 교환막인 나피온(Nafion)은 높은 비용과 좁은 가 용 범위 등의 한계에 직면해 있다. 본 논문은 COF를 다양한 고분자 매트릭스에 도입하여 이러한 단점을 극복하기 위한 최신 연구 전략을 심도 있게 다룬다. 특히 양성자 교환막 연료전지(proton exchange membrane fuel cells, PEMFC), 음이온 교환막 연료전지(anion exchange membrane fuel cells, AEMFC), 그리고 고온(high-temperature) PEMFC (HT-PEMFC)용 COF 기반 복합막의 설계와 성능 특성에 집중한다. 다양한 COF 기능화 및 복합화 전략을 통해 이온 전도도, 기계적 강도 및 운전 안정 성을 향상시킨 주요 연구들을 비평적으로 논하며, 연료전지의 전반적인 효율 향상에 대한 COF의 잠재력을 조명한다.

This study investigates how the term burnout was framed, transformed, and made to function in Korean public discourse from 2010 to 2024. Drawing on 9,206 news articles retrieved from the BigKinds database, the research applies an integrated analytical framework combining Entman’s (1993) four framing functions with Leventhal’s Common-Sense Model (CSM) of illness representation. Five dominant frames —medicalisation, individualisation, normalisation, risk framing, and metaphorisation— were identified through quantitative content analysis and qualitative coding with MAXQDA 2024 (qualitative and mixed-methods analysis software). Results show a marked shift from a medicalised frame in Period 1 (2010-2015) to an increasingly normalised frame in Period 3 (2021-2024), indicating a recontextualisation of burnout from an individual pathological condition to a shared everyday experience. Statistical analyses, including chi-square tests, interrupted time series analysis, and keyword network mapping, reveal significant temporal changes in frame distribution, functional complexity, and co-occurrence patterns. The framing functions were found to systematically structure CSM dimensions, shaping public perceptions of identity, causation, temporality, consequences, and controllability. These findings demonstrate that the reframing of burnout reflects not only shifts in linguistic representation but also broader socio-cultural and political reconfigurations, with implications for health communication, organisational policy, and public health strategies.

The semiconductor equipment industry is characterized by high technological intensity and a strong demand for customized functionality from client firms. This necessitates the collection and implementation of diverse client requirements, which in turn demands close cross-departmental collaboration and leads to highly complex project structures. To manage this complexity, many semiconductor-related companies operate global matrix organizations based on multiple reporting lines by region, function, and product. These structures entail intricate communication process and pose significant collaboration challenges. This study empirically investigates key factors influencing collaboration and project performance within global matrix organizations in the high-tech semiconductor equipment industry, applying the Technology-Organization-Environment (TOE) framework. The analysis identified significant effects of all examined factors on inter-organizational collaboration performance: three technological sub-factors (usability of communication tools, process standardization, and change management systems), two organizational sub-factors (clarity of roles and responsibilities, and robustness of information-sharing systems), and three environmental sub-factors (alignment of requirements, cultural understanding, and responsiveness to industry standards and regulations). Furthermore, the level of agile implementation was tested as a moderating variable in the relationships between TOE factors and collaboration performance. The results revealed significant moderating effects of agility in specific areas: the usability of communication tools and systematic change management (technological factors); information-sharing structure (organizational factor); and requirement alignment (environmental factor). These findings suggest that agile approaches do not operate as a single-factor solution but interact dynamically with various organizational conditions. By focusing on the underexplored dynamics of global matrix organizations in collaboration performance, this study provides a structured empirical analysis of their operational characteristics. Its theoretical contribution lies in offering an integrative perspective on technological, organizational, and environmental drivers of collaboration and project success, extending current research in high-tech project and organizational performance.

This study addresses the limitations of traditional Failure Modes and Effects Analysis (FMEA), which heavily relies on expert judgment and lacks the ability to effectively incorporate unstructured failure history data such as warranty claims and maintenance records into the design stage. To overcome these challenges, we propose an automated FMEA framework based on a Retrieval-Augmented Generation (RAG) architecture integrated with a Local Large Language Model (LLM) in a secure, locally managed environment. The proposed system stores claim and test data in a vector database and leverages the LLM to retrieve and analyze relevant information, enabling automatic extraction of new failure modes and dynamic updates to FMEA documents. Additionally, the system recalculates Risk Priority Number (RPN) by adjusting severity, occurrence, and detection ratings when recurring failures are detected. To improve response quality, we applied prompt engineering and optimized chunking parameters during data retrieval. This research demonstrates the feasibility of achieving a life cycle-integrated quality enhancement framework throughout the product lifecycle while ensuring data security.

This study investigates the vibration characteristics of an aluminum subframe for small and high-speed vessels through modal and resonance analysis using the finite element method (FEM). Due to the low stiffness and damping of aluminum, concerns arise over structural resonance and fatigue. A 3D model based on actual design drawings was analyzed to extract six natural frequencies and corresponding mode shapes. Significant deformation was observed in the first and second modes (90.65 Hz, 110.60 Hz), which may overlap with operational frequencies. The fifth mode (263.70 Hz) showed high amplitude with Y-axis concentration, indicating lateral resonance vulnerability. Deformation ratios were used to identify dominant vibrational directions. Based on the findings, design strategies such as structural reinforcement, RPM adjustment, and damping device application were proposed to improve vibration safety in the early design stage.

Written examination for driver’s license certification plays a critical role in promoting road safety by assessing the applicants' understanding of traffic laws and safe driving practices. However, concerns have emerged regarding structural biases in multiple-choice question (MCQ) formats, such as disproportionate answer placement and leading linguistic cues, which may allow test-takers to guess the correct answers without substantive legal knowledge. To address these problems, this paper proposes a prompt-driven evaluation framework that integrates structural item analysis with response simulations using a large language model (LLM). First, we conducted a quantitative analysis of 1,000 items to assess formal biases in the answer positions and option lengths. Subsequently, GPT-based simulations were performed under four distinct prompt conditions: (1) safety-oriented reasoning without access to legal knowledge, (2) safety-oriented reasoning with random choices for knowledge-based questions, (3) performance-oriented reasoning using all available knowledge, and (4) a random-guessing baseline model to simulate non-inferential choice behavior. The results revealed notable variations in item difficulty and prompt sensitivity, particularly when safety-related keywords influence answer selection, irrespective of legal accuracy. The proposed framework enables a pretest diagnosis of potential biases in the MCQ design and provides a practical tool for enhancing the fairness and validity of traffic law assessments. By improving the quality control of item banks, this approach contributes to the development of more reliable knowledge-based testing systems that better support public road safety.

Business model(BM) innovation is widely known as a differentiated strategy and strategic framework for companies to secure a sustainable competitive advantage in an uncertain environment. While prior research has studied new business models in accordance with changes in manufacturing trends such as digitalization and servitization, empirical understanding of the dynamic processes of BM innovation is still lacking. This study addresses this gap by proposing an analytical framework of the BM innovation matrix that classifies companies' BM innovation cases into four types according to the degree of BM change and the influential level of the industry/market outcome through a critical literature review on business models and dynamics. Drawing on this framework, we conduct longitudinal case studies of leading global 3D printing firms to examine the dynamic processes and external environmental factors that shape the evolution of BM innovation. Our findings reveal previously underexplored patterns of co-evolution between firms’ business models and their broader industrial and market environments. This study has the significance of constructing a framework for dynamically analyzing BM innovation based on longitudinal case studies of emerging 3D printing companies. We presented implications for companies seeking successful commercialization of emerging technologies, such as the strategic usefulness of the BM innovation framework and the importance of co-evolution with industrial structure and environmental factors in the process of change.

본 연구는 걸밴드 QWER의 사례를 중심으로 K-Pop 걸그룹의 차별화 전 략을 ERRC(Eliminate-Reduce-Raise-Create) 모델을 활용하여 분석하였 다. QWER은 유튜버와 스트리머 출신의 온라인 콘텐츠 크리에이터들로 구 성된 4인조 걸밴드로, 기존과는 차별화된 접근법으로 주목받은 K-Pop 그 룹이다. 질적 연구방법인 내용분석을 통해 QWER의 결성 과정부터 2025년 2월까지의 활동을 분석한 결과, QWER은 ‘제거 전략’으로 전통적인 연습생 시스템, 통일된 이미지와 콘셉트, 기획사 주도의 철저한 관리 시스템에서 탈피했으며, 외모 중심주의, 과밀한 활동량, 과도한 변신에 대한 ‘감소 전 략’을 구사했다. ‘증가 전략’으로는 음악 제작 참여도, 라이브 연주 능력, 맞춤형 팬 소통을 강화했고, ‘창조 전략’으로는 온라인 콘텐츠 크리에이터 기반 발굴 시스템, 하이브리드 계약 모델, 이원화된 기획사 운영 시스템 등 새로운 요소들을 도입했다. 이러한 전략은 K-Pop 산업의 인재 발굴 패러 다임, 계약 및 조직 구조에 변화를 가져옴은 물론, 포화 상태에 이른 K-Pop 시장에서 가치 혁신을 통한 차별화의 중요성을 시사한다.

고속도로 2차 사고는 선행 사고(1차 사고) 또는 전방 고장 차량에 의해 교통흐름이 변화된 상황에서 발생하는 사고로, 이에 대한 효과적인 교통안전 관리전략이 필요하다. 그러나 일반사고에 비해 데이터 표본이 부족하여 신뢰성 있는 대응 전략 수립에 어려움이 있다. 본 연구는 고속도로에서 발생하는 2차 사고의 발생 주요 요인을 식별하고 예측하기 위해 BERT(Bidirectional Encoder Representations from Transformers) 기반 텍스트 분석 모델과 전통적 머신러닝 모델 (XGBoost, RandomForest, CatBoost)을 비교하였다. 교통사고 세부기록, 원클릭 속보자료 등 비정형 텍스트 및 정형 데 이터를 수집하고 1차 사고에 관한 시공간적 동적 변수를 통합하여 인공지능 기반의 사고 예측 프레임워크를 구축하였다. 특히, BERT 기반 모델을 통해 교통사고 문맥 정보를 고려하여 단어 삽입 및 대체 기법에 따른 2차사고 데이터 표본을 보완하였다. 또한, 설명가능한 AI(XAI) 기법을 활용하여 주요 사고 요인의 기여도를 시각적으로 해석하고 사고 예방 및 정책 수립에 필요한 정보를 제공하였다. 연구 결과, 제안된 하이브리드 접근법 기반 연구 프레임워크는 높은 정확도의 2 차 사고 발생 가능성 예측에 효과적이며, 교통사고관리시스템의 신뢰성과 효율성 향상에 핵심적인 기여를 할 것으로 기 대된다.

본 연구는 게임 IP의 음악 산업 확장 전략을 분석하기 위해 라이엇 게 임즈의 사례를 VRIO 프레임워크를 통해 분석했다. 연구 방법으로는 내용 분석과 사례분석을 활용했으며, 분석 결과는 다음과 같다. 첫째, 가치 (Value) 측면에서는 2023년 4,480억 원의 매출과 1,013억 원의 영업이익 을 달성했으며, 누적 스트리밍 100억 회를 기록하는 등 경제적 가치를 창출했다. 또한 룬테라 세계관의 11개 지역별 음악적 정체성 구축과 '아 케인' OST를 통한 IP 확장으로 브랜드 가치를 제고했다. 둘째, 희소성 (Rarity) 측면에서 세계 4위 음악 회사 BMG와의 글로벌 퍼블리싱 독점 계약을 통해 1,000개 이상의 음악 타이틀에 대한 권리를 확보했으며, 2023년 월드 챔피언십 640만 동시 시청자 달성으로 게임과 K-pop 팬덤 의 성공적 결합을 이뤄냈다. 셋째, 모방난이도(Imitability) 측면에서 가상 아이돌 제작에 필요한 수백만 달러의 초기 투자비용, 10년간 축적된 음악 프로덕션 노하우, 고도화된 기술 스택 등이 높은 진입장벽을 형성했다. 넷째, 조직(Organization) 측면에서 뮤직 슈퍼비전, 비즈니스 어페어, 전 략적 제품 총 3개의 부서로 구성된 전문화된 조직 구조를 갖추고, BMG SoundLab 운영과 개발자-음악가 협업 팀 시스템을 통해 혁신적인 운영 체계를 구축했다. 본 연구는 게임 IP의 음악 산업 확장이 가진 전략적 가 치와 실행 방안을 탐색적으로 분석했다는 점에서 의의가 있으며, 디지털 엔터테인먼트 산업의 융합 전략 수립에 실무적 시사점을 제공한다.

Lightweighting is crucial in various industries, especially for bicycles where weight and stiffness are key. Traditional materials like steel, aluminum, and carbon each have pros and cons. This study compares hybrid tubes made of aluminum and carbon composites with conventional aluminum tubes. Using structural analysis and experimental testing, the hybrid tubes showed a weight reduction of up to 17.25% and maintained acceptable deformation levels. Finite element analysis confirmed these findings, demonstrating the hybrid tubes' potential as superior bicycle frame materials. Future research should focus on long-term durability and fatigue characteristics.