본 연구는 대학 교양영어 수업에서 실시한 영어 숏폼 제작 활동을 통 해 한국 이공계 대학생들이 어떠한 글로벌 사회 이슈를 선택하고 이를 어떤 방식으로 구현하며 그 과정에서 어떠한 글로벌 사회 인식을 형성하 는지를 탐구하는 데 목적이 있다. 연구 자료는 영어 숏폼 영상 및 스크 립트, 설문조사 자료, 학습자 저널로 구성되었으며 질적 내용 분석과 양 적 분석을 병행한 혼합 연구 방법을 적용하였다. 분석 결과 학습자들은 대중문화, 미디어 산업, 기술과 사회의 관계 등 다양한 글로벌 사회 이슈 를 선택하였으며 주제 구현 방식에서는 개인 의견 제시형이 가장 높은 비율을 차지하였다. 학습자들의 글로벌 인식의 특징으로는 글로벌 연결 성 인식의 확장, 구조적 문제 인식의 심화, 비판적 시민적 성찰로 나타났 다. 영어 숏폼 활동은 글로벌 사회 이해와 비판적 사고 경험 측면에서 긍정적인 인식 변화를 유도한 것으로 나타났다. 본 연구는 디지털 기반 영어 학습 환경에서 학습자 주도적 콘텐츠 제작 활동이 글로벌 사회 인 식 형성과 비판적 사고 함양에 기여할 수 있음을 실증적으로 제시한다.

This study investigated the impact of rainfall on the network performance and video transmission quality of smart CCTV systems deployed across 16 bridges over the Han River in Seoul. Using operational logs collected from September 22 to October 6, 2025 (n=254), a comparative analysis was performed between the wired (n=5) and wireless (n=11) network architectures. The results reveal that under rainfall conditions, wireless networks experienced critical performance degradation. Specifically, at a heavy rainfall intensity of 10 mm/h, the average latency (Ping) surged from 22.5 ms to 355.2 ms, while video frame rates (FPS) plummeted from 19.8 to 6.4. Notably, at a maximum rainfall intensity of 15 mm/h, the wireless network performance exhibited a 78.8% degradation compared with clear weather conditions, severely compromising real-time monitoring reliability. Conversely, the wired networks exhibited robustness, maintaining a Ping of approximately 20 ms and an FPS within the 19–20 range, regardless of weather conditions. A significant negative correlation (r = -0.81, p < 0.01) between Ping and FPS was identified, establishing increased network latency as the primary driver of video quality degradation. These findings provide a technical basis for implementing real-time operational thresholds at Ping 40 ms and FPS 15 as leading indicators to ensure surveillance reliability in a smart city infrastructure.

병충해의 조기 발견과 이에 따른 신속한 조치는 농업 생산성 유지와 생태계 보전에 있어 핵심적인 요소이다. 그러나 병충해 발생 초 기 단계에서는 일반적인 카메라나 센서를 통해 식생의 미세한 변화를 관측하는 데 한계가 있다. 이러한 한계를 극복하기 위해 초분광 모듈을 활용하여 파장대별 식물 데이터를 정밀하게 관측하고, 이를 딥러닝 모델에 적용함으로써 가로수 식생의 건강 상태를 판별하고 병충해 발생 여부를 조기에 확인할 수 있다. 이와 같은 방법을 통해 병충해에 대한 선제적 대응이 가능해지며, 결과적으로 피해의 확 산을 효과적으로 방지할 수 있다. 이러한 접근 방식은 농업 및 생태학 분야에서 식물의 건강 상태를 지속적으로 모니터링하고 보전하 기 위한 기술로 활발히 연구되고 있다.

본 연구는 RGB 카메라 기반 자세 추정 알고리즘을 활용하여 어깨 운동 중 보상 움직임을 실시간으로 검출하고, 관절 각도를 측정하는 디지털 트윈 재활 시스템을 구현하였다. 게임 인터페이스와 통합하여 실시간 바이오피드백을 제공하며, 파일럿 테스트 결과 사용자 만족도 4.3/5.0점을 기록하였다. 본 연구는 비접촉식 영상 분석 기술을 통한 접근성 높은 재활 게임 플랫폼의 가능성을 제시한다.

Purpose: This study aimed to provide a detailed understanding of nurses’ experiences with fall management in wards equipped with a video-based fall detection system. Methods: In-depth, semi-structured interviews were conducted with 10 nurses from an integrated nursing care ward at K Hospital in City C, where the system had been implemented. The interviews focused on nurses’ actual experiences and reflections regarding fall management. Data were systematically analyzed using Hsieh and Shannon’s conventional content analysis, which identified meaningful categories and themes. Results: The analysis identified six themes and 15 subthemes. The main themes were: Context of falls and limitations in management falls occurred through interactions between patient behaviors and environmental factors, while current assessment and management systems did not adequately address these complexities. Need for structured response processes after introducing video-based fall detection although video-based systems were implemented, fall recognition and responses remained experience-based and situation-dependent, highlighting the need for standardized, systematic procedures. Perceived limitations of video-based fall detection systems the system presented challenges such as delayed and false alarms, which reduced real-time responsiveness and affected clinical reliability. Practical benefits of video-based fall management and changes in nursing practice video verification improved the objectivity and accuracy of fall reporting, enhancing the consistency and systematization of nursing practice. Strategies for system use according to ward environment tailored use of the system based on ward characteristics and patient composition was suggested to optimize monitoring efficiency and fall prevention. Future directions for improved fall management strategies to enhance patient and caregiver awareness through video-based education and to improve ward environments were proposed as approaches for developing a preventive, smart-care model. Conclusion: The findings of this study indicate future directions and challenges for technology-based nursing practice in fall management, highlighting the need to develop new assessment frameworks, as well as educational and research strategies that reflect nurses’ experiences in diverse contexts, given the practical changes introduced by the video-based fall detection system and the limitations of current assessment tools.

Accurate estimation of vehicle exhaust emissions at urban intersections is essential to assess environmental impacts and support sustainable traffic management. Traditional emission models often rely on aggregated traffic volumes or measures of average speed that fail to capture the dynamic behaviors of vehicles such as acceleration, deceleration, and idling. This study presents a methodology that leverages video data from smart intersections to estimate vehicle emissions at microscale and in real time. Using a CenterNet-based object detection and tracking framework, vehicle trajectories, speeds, and classifications were extracted with high precision. A structured preprocessing pipeline was applied to correct noise, missing frames, and classification inconsistencies to ensure reliable time-series inputs. Subsequently, a lightweight emission model integrating vehicle-specific coefficients was employed to estimate major pollutants including CO and NOx at a framelevel resolution. The proposed algorithm was validated using real-world video data from a smart intersection in Hwaseong, Korea, and the results indicated significant improvements in accuracy compared to conventional approaches based on average speed. In particular, the model reflected variations in emissions effectively under congested conditions and thus captured the elevated impact of frequent stopand- go patterns. Beyond technical performance, these results demonstrate that traffic video data, which have traditionally been limited to flow monitoring and safety analysis, can be extended to practical environmental evaluation. The proposed algorithm offers a scalable and cost-effective tool for urban air quality management, which enables policymakers and practitioners to link traffic operations with emission outcomes in a quantifiable manner.

This study examined levels in self-directed learning (SDL) and learning engagement among 158 students at a college in Incheon, based on gender, video lecture usage, and English achievement level, using data collected through a Google survey. Pearson correlation coefficients and independent samples t-tests were conducted to investigate the relationships between variables and group differences. Female students scored significantly higher than male students in all subcomponents of SDL while no significant gender differences were found in learning engagement. Significant differences were observed in English achievement, learning action, cognitive engagement, and behavioral engagement between students who used video lectures and those who did not, suggesting that a weak blended learning environment can positively influence learning motivation. Although there was a clear performance gap between the high and low achievement groups, no statistically significant differences emerged in any subcomponent of SDL or learning engagement. Notably, approximately 70% of female students in the blended learning environment voluntarily utilized video lectures for various purposes such as previewing, reviewing, and clarifying contents, demonstrating active SDL. Qualitative interview data further supported these findings, providing concrete examples of SDL and learning engagement in practice.

The development of digital media has fundamentally transformed modes of visual expression and mechanisms of emotional communication, redefining the identity and performative function of fashion illustration, particularly within short-form content. This study thus aims to analyze the emotional performance strategies used in short-form video fashion illustration content and to interpret these strategies through an integrated framework combining performative image and emotional design theories. As a methodology, five actively operating English-language YouTube channels were selected, each recognized for producing fashion illustration content in short-form videos and holding a subscriber base of over 50,000. The study conducted a qualitative analysis of short-form videos from these channels, examining how they engage viewers emotionally across Norman’s three levels: visceral, behavioral, and reflective. The results revealed that short-form video fashion illustration content employs multi-layered emotional strategies: immediate aesthetic stimulation through visceral visual impact, behavioral immersion via sharing of the drawing process, and reflective meaning-making through storytelling and socio-cultural messages. Notably, these strategies extend beyond the mere display of images, positioning the illustration as an active agent that performs and elicits sensory, emotional, and social engagement from audiences. The study concludes that the convergence of performative image theory and emotional design offers a comprehensive lens to decode how fashion illustration short-form videos function both as visual art and as performative acts. These findings contribute theoretical and practical insights for future digital fashion content creation and research in the visual arts, highlighting how emotional experiences are strategically constructed in contemporary digital media contexts.

Background: Core stabilization exercises are a key component of exercises for the conservative treatment of adolescent idiopathic scoliosis (AIS). Objectives: To evaluate the effectiveness of two different home-based exercise instruction methods (leaflets versus video materials) for children with AIS performing core stabilization exercises. Design: A retrospective study. Methods: Pediatric outpatients diagnosed with AIS were assigned to either a leaflet group or a YouTube video group. They were instructed to perform core stabilization exercises at home daily, completing three sets per day for six months. Pre- and post-exercise (6 months) evaluations included X-rays to measure the Cobb angle and the degree of vertebral rotation. Additionally, endurance in maintaining the Superman and Bird-dog positions was assessed. Results: After 6 months of intervention, the leaflet and YouTube groups showed no significant differences regarding Cobb angle, rotational degree, or endurance in the Bird-Dog and Superman positions. However, within-group comparisons before and after the 6-month exercise period showed a significant improvement in Cobb angle in the leaflet group. If exercise leaflets are effectively utilised, they could facilitate the implementation of core stabilization exercises in children with AIS, potentially improving their prognosis. Conclusion: Providing exercise instruction via a leaflet may be more effective than using a YouTube video in facilitating adherence to core stabilization exercises and improving spinal alignment in children with AIS.