본 연구는 COVID-19 팬데믹으로 인해 증폭된 사회 경제적 압박 에 대응하여, 한국교회 내에 확산된 목회자 이중직 사역을 복지선교 관점에서 해석하고 대안을 제시하고자 한다. 이에 이중직을 경험한 목회자 5인의 FGI(Focus Group Interview) 내러티브 분석과 본 연구의 공동 저자 3인의 공동자문화기술지 연구 방법을 활용하여 이중 직 목회자의 생생한 경험을 조사하였다. 이를 통해 복지선교 관점에서 목회자 이중직 사역이 더 넓은 의미에서 목회적‧선교적 가치가 보다 분명함을 확인하게 된다. 복지선교가 지향하는 약자를 돌보는 예수 그리스도의 복음적 가치 실현이 이중직 사역을 통해 이루어지도록 목회자의 역할을 재정의 하여, 지역사회의 영적‧사회적 측면에 대한 이중직 목회자의 지속적인 역량 강화를 위한 교육이 필수적으로 지원되 어야 함을 제시한다.

인공지능의 발전은 검색엔진, SNS, ChatGPT 등 다양한 분야에서 혁신을 이끌며 사회와 산업 전반에 변화를 가져오고 있다. 특히, 교 통 분야에서는 AI 기반 기술이 교통정보 수집 및 분석 방식에 변화를 주며, 새로운 활용 가능성을 제시하고 있다. 과거 육안 계수 방 식에 의존했던 교통량 조사는 현재 CCTV 영상과 딥러닝 객체 인식 기술을 활용해 신뢰성과 정확성이 크게 향상되었다. AI 기반 교통 솔루션의 도입으로 교통량 조사 데이터는 정책 수립, 운영 개선, 사회간접자본 건설 등 다양한 분야에서 중요한 기초 자료로 활용되고 있다. 이에 본 연구에서는 YOLO v8을 활용하여 차량 축 인식 기반 차종 분류의 정확성을 향상시키고, 기존 촬영 기법과 비교·분석을 통해 최적의 인식기법을 제시하고자 한다.

Zinc tin oxide (ZTO) thin films were deposited using atomic layer deposition (ALD) to ensure precise thickness control and uniformity. However, the low-temperature processing of ZTO often results in increased defect states, leading to degraded electrical performance. To address this issue, metal capping layers (Al or Au) were added to the ZTO active layer. The capping layers modulate electron energy levels at the interface, increase carrier density, and reduce interface traps, thereby improving electrical properties. Aluminum (Al) and gold (Au) were evaluated for their impact on key performance metrics, including electron mobility (μsat), threshold voltage (VT), subthreshold swing (SS), and on/off current ratio (ION/OFF). Results show that Al-capped ZTO thin-film transistors (TFTs) exhibited enhanced performance due to the lower work function of Al (4.0 eV), which facilitates electron injection and reduces contact resistance. In contrast, Au-capped ZTO TFTs showed decreased performance due to electron depletion caused by the higher work function of Au (5.1 eV). Optical analyses, including UPS and UV-Vis, revealed the band structure and work function of the ZTO thin films. This study concludes that the choice of capping material and its design parameters play a critical role in optimizing TFT performance, offering valuable insights for the development of next-generation high performance TFT devices.

This study evaluates adhesion strength under various conditions to ensure adhesion performance during asphalt-pavement maintenance. The adhesion performance of a tack coat varies under various conditions. Therefore, to evaluate its curing behavior, several tests, i.e., evaporation residue rate, tracking, tack-lifter, and shear bond strength tests, were conducted based on the type, amount, and curing time of the tack coat.The result of the evaporation residue rate test shows that, except for the SSC tack coat, RSC-4 and modified tack coats require similar curing times, even though the modified tack coats have a lower moisture content. Additionally, based on the evaporation residue rate, the tracking and track-lifter test results show that approximately 75% curing is required to prevent the loss of the tack coat during asphaltpavement maintenance. After maintenance work is completed, the shear bond strength was measured to evaluate the curing properties of the tack coat. The results show that the amount applied, curing degree, and shear bond strength are proportional, whereas the modified tack coat indicate a significant difference in the strength increase rate depending on the curing degree. Additionally, when dust is attached to the surface of the tack coat, the difference in strength exceeds 20%, depending on the attachment ratio.To achieve the best adhesion performance by the tack coat during maintenance work, the loss of the tack coat should be prevented by implementing the exact curing time determined experimentally, regardless of whether the tack coat is modified, and the surface where the tack coat is applied should be cleaned before application.

This study addresses the critical challenge of enhancing vehicle classification accuracy in traffic surveys by optimizing the conditions for vehicle axle recognition through artificial intelligence. With current governmental traffic surveys facing issues—particularly the misclassification of freight vehicles in systems employing a 12-category vehicle classification—the research proposes an optimal imaging setup to improve axle recognition accuracy. Field data were acquired at busy intersections using specialized equipment, comparing two camera installation heights under fixed conditions. Analysis revealed that a shooting height of 8.5m combined with a 50°angle significantly reduces occlusion and captures comprehensive vehicle features, including the front, side, and upper views, which are essential for reliable deep learning-based classification. The proposed methodology integrates YOLOv8 for vehicle detection and a CNN-based Deep Sort algorithm for tracking, with image extraction occurring every three frames. The axle regions are then segmented and analyzed for inter-axle distances and patterns, enabling classification into 15 categories—including 12 vehicle types and additional classes such as pedestrians, motorcycles, and personal mobility devices. Experimental results, based on a dataset collected at a high-traffic point in Gwangju, South Korea, demonstrate that the optimized conditions yield an overall accuracy of 97.22% and a PR-Curve AUC of 0.88. Notably, the enhanced setup significantly improved the classification performance for complex vehicle types, such as 6-axle dump trucks and semi-trailers, which are prone to misclassification under lower installation heights. The study concludes that optimized imaging conditions combined with advanced deep learning algorithms for axle recognition can substantially improve vehicle classification accuracy. These findings have important implications for traffic management, infrastructure planning, road maintenance, and policy-making by providing a more reliable and precise basis for traffic data analysis.

There are now many seismic observatory stations, excluding the acceleration monitoring network for infrastructures, of more than 300 operated by several public and governmental organizations across South Korea. The features of the site and properties of the stations were not investigated, and they have been assumed or guessed to estimate the site-specific seismic responses during the 2016 Gyeongju and 2017 Pohang earthquake events. For these reasons, various and intensive geotechnical and geophysical investigations have been conducted to quantify the site characteristics at 15 seismic stations selected in southeastern Korea. The VS profiles were, at first, obtained by performing only a downhole seismic test (DHT) at 7 stations, and were compared with those from a surface wave method. Then, the shear wave velocity (VS) profiles were deduced by combining three types of in situ seismic methods composed of a cross-hole seismic test, DHTs, and full-waveform sonic loggings at the 8 other stations, especially to complement the application limits of DHT and reduce the depth-dependent uncertainty in VS profile. The representative site characteristic profiles for each station regarding VS and VP with borehole stratigraphy and density were determined based on robust investigations. Various site parameters related to seismic responses at the seismic stations of interest were obtained for the site-specific geotechnical information, which would be useful to earthquake engineering practices.