Environmental DNA (eDNA) has emerged as a promising tool for aquatic biodiversity monitoring, yet its collection in lentic ecosystems remains technically constrained by filtration capacity and field logistics. In this study, we applied a novel eDNA concentration system, QuickConcTM, to evaluate freshwater mussel diversity in lakes, and compared its performance with the conventional GF/F filtration method. Water samples were collected from four reservoirs at surface, mid, bottom, and waterside layers, and processed using both filtration techniques. Metabarcoding of mitochondrial 16S rDNA revealed that QuickConcTM captured a higher average number of amplicon sequence variants (ASVs) and exhibited greater species richness and diversity indices (Shannon and Simpson), although the differences were not statistically significant. QuickConcTM samples showed a greater capacity to detect rare taxa and to recover higher ASV richness in certain cases, suggesting its potential to enhance biodiversity resolution. Species composition remained consistent across methods, with Cristaria plicata and Sinanodonta lauta being dominant in both cases. However, slight spatial variations in species assemblages were observed between center and waterside sampling points, highlighting the influence of habitat heterogeneity on eDNA distribution. Overall, our results demonstrate that the QuickConcTM system offers a practical and efficient alternative to traditional filtration methods for eDNA-based freshwater mussel monitoring, particularly in environments with high suspended solids. The findings underline the need for adaptive sampling strategies that consider both methodological and ecological factors when designing eDNA surveys in lentic ecosystems.

본 연구에서는 부산대학교 천문대(Pusan National University Observatory; PNUO)의 0.5m 망원경을 이용해 외 계행성을 가진 것으로 알려진 항성 TOI-3653의 식현상을 관측한 결과를 제시한다. 관측은 협정세계시(UT) 기준 2 02 4 년 8월 12일 15시 2 8분부터 2 .2시간 동안 진행되었으며, 수집한 관측 데이터를 전처리한 뒤 TOI-3653의 주변 비교성 을 사용하여 비교 측광함으로써 광도 곡선을 얻을 수 있었다. 이렇게 얻은 광도 곡선을 EXOFAST 프로그램을 이용해 모형 맞추기한 결과, 우리가 관측한 식현상에서 약 1.7%의 감광이 발생했으며 이를 통해 모형 의존성이 존재하나 TOI- 3653이 목성의 3.15배 크기의 외계행성인 TOI-3653 b을 지니고 있다고 통계적으로 유의미( 2 = 1.08)하게 결론을 내릴 수 있었다. 본 결과는 PNUO 0.5 m 망원경이 외계행성 식현상에 따른 미세한 광도 변화를 감지할 수 있음을 보여주며, 향후 외계행성 연구에 유용한 관측 장비로 활용될 수 있음을 시사한다.

The Taskforce on Nature-related Financial Disclosures (TNFD) is a financial disclosure initiative dedicated to the protection of natural capital. It examines corporate activities related to natural capital through four key pillars: governance, strategy, risk and impact management, and metrics and targets. However, in the forestry sector, research analyzing the current state of natural capital and proposing corresponding strategies remains limited. Thus, this study explores potential applications by analyzing domestic and international corporate case studies and adoption criteria in the forestry field, using the official TNFD platform. The results indicate that a proactive response to international standards and domestic policy changes related to TNFD could play a decisive role in enabling forestry-related companies and institutions to secure global competitiveness.

현대 경영환경에서 조직의 성공을 위해서는 리더십의 역할이 중요하다. 리더십은 조직 구성원의 태도 와 행동에 영향을 미치며 혁신과 성과에 기여하도록 만든다. 한편 기업가정신은 진취성, 위험감수성, 혁신 성을 바탕으로 조직의 성공에 중요한 선행요인으로 다루어지고 있다. 이러한 특징에 따라 기업가정신을 바탕으로 하는 기업가적 리더십이 대두되었으며 관심이 커지고 있다. 본 연구는 국내, 해외에서 기업가적 리더십 연구가 어떻게 진행되고 있는지 살펴보고 나아가 기업가적 리더십의 주요 핵심주제들 간의 관계 를 분석하였다. 이를 위하여 국내, 해외의 학술지에 게재된 기업가적 리더십 논문 454편을 분석하였다. 먼저, 기업가적 리더십의 개념 및 특징을 알아보고 내용분석을 실시하였다. 구체적으로 연도별, 학술지별 연구동향을 파악하였다. 또한 핵심 연구주제인 주제어를 기준으로 내용분석을 실시하여 연구동향을 파악 하였다. 분석 결과, 첫째, 2015년 기업가적 리더십 측정문항이 개발된 이후 연구가 활발히 진행되고 있었으며 2018년 이후 매년 30편이 넘는 논문이 발표되고 있다. 둘째, “Journal of Small Business Management”, “Sustainability”, “European Journal of Innovation Management”, “벤처창업연구”, “리더 십연구” 에서 많이 발표되는 것으로 나타났다. 셋째, 중심성 분석결과, 기업가정신, 중소기업, 혁신, 창의 성과 같은 연구주제들이 활발히 연구되어지고 있는 것을 밝혔다. 반면, 조직성과, 직무성과, 조직몰입 등 과 같은 조직 및 구성원의 성과변수에 대한 연구는 부족한 것으로 나타났다. 본 연구의 결과는 기업가적 리더십의 흐름을 파악하였다. 또한 내용분석을 활용하여 기업가적 리더십의 핵심 주제어들 간의 관계를 구체적으로 제시하였으며 기업가적 리더십 연구의 지식구조를 면밀히 파악할 수 있었다. 이러한 결과는 기업가적 리더십 및 리더십 연구에 중요한 이론적, 실무적 함의가 있다고 할 수 있다.

This study examined how 16 Chinese transformational structures are generated using generative AI from the perspective of learners whose native language is Korean. To summarize: (1) In weak AI models, using the zero-shot input method, Baidu generated 13 transformed Chinese Sentences, and Papago generated 11 transformed Chinese Sentences. (2) In strong AI models, using the prompt input method, WRTN generated 12 transformed Chinese Sentences, and Yuanbao generated 11 transformed Chinese Sentences. The possible reason why weak AI showed better results than strong AI may be because the analysis target was simple sentences. Baidu and Papago AI are programs specialized in translation. Therefore, under the same conditions as the experiment, it can posited that weak AI is more specialized than strong AI. Thus, it may be sufficient to utilize weak AI in current Chinese writing education. Nevertheless, for this research be applicable to Chinese writing education, the following additional analyses are necessary: (1) This study targeted ‘simple sentences.’ If applied to ‘complex sentence’ writing education, an analysis of whether weak AI remains useful is necessary. (2) An analysis of how to conduct education using Artificial Intelligence is required.

인공지능(artificial intelligence, AI)은 분리막 개발에 중대한 영향을 미치기 시작하며 소재 설계 및 성능 최적화를 위한 새로운 접근법을 제시하고 있다. 본 총설에서는 머신러닝(machine learning, ML)과 딥러닝(deep learning, DL) 기술에 중점을 둔 AI 기반 분리막 개발의 최근 발전상을 조명하고 있다. 이러한 도구는 데이터 기반 예측을 가능하게 하고, 제조 공 정을 개선하며, 소재 발굴을 가속화한다. 데이터 품질, 모델 해석 가능성, 실험 검증과 같은 주요 과제도 제시한다. 또한, AI 통합의 미래 전망을 개괄하고, 가스 분리, 청정에너지, 환경 응용 분야에서 분리막기술에 혁명을 일으킬 수 있는 AI의 잠재력 을 강조한다.

With the rapid expansion of personal mobility (PM) devices as urban transport alternatives, the associated safety risks have increased significantly. Although previous studies have offered insights into user behavior and accident traits, more integrated approaches that consider spatial and administrative contexts are required to better understand the factors affecting accident severity. This study investigated the factors influencing accident severity involving PM devices in Seoul, South Korea by employing a cross-classified multilevel model (CCMM) to account for both police jurisdiction and regional characteristics. Analyzing the 2021 data from the Traffic Accident Analysis System (TAAS), the model showed strong validity (ICC: 15.8%, DIC: 697.2), outperforming the logistic and hierarchical models. Key predictors of higher severity included crashes in non-standard areas (e.g., other than single roads or intersections), helmet non-use, and older age of victims and perpetrators. Violations, such as exceeding passenger capacity, were negatively associated with severity. Industrial areas and high subway station densities reduced the severity, reflecting the benefits of pedestrian-friendly infrastructure. Larger areas covered by police officers significantly increased the severity, revealing enforcement limitations. The 2021 Road Traffic Act revision has had no statistically significant impact. These results highlight the need for integrated policies that combine infrastructure improvements, enhanced enforcement, and behavioral changes to reduce the severity of PM-related accidents in urban environments.

Purpose: This study aimed to examine the effects of a disaster nursing education program using the Korean Triage and Acuity Scale (KTAS) on nursing students’ competency in emergency patient triage, core competencies, confidence in disaster nursing, and self-efficacy in disaster response. Methods: This study utilized a nonequivalent control group design. The experimental group (n=25) participated in a disaster nursing education program that incorporated the KTAS, whereas the control group (n=27) did not receive any intervention. Data were analyzed using descriptive statistics and t-tests. Results: The two groups differed significantly in both competency in emergency patient triage (t=3.47, p=.001) and confidence in disaster nursing (t=2.51, p=.015). Conclusions: This study indicates that a disaster nursing education program using the KTAS, a tool currently employed in clinical practice, rather than theory-based instruction alone, contributed to enhancing nursing students’ practical competencies. Such training can improve the emergency patient triage and confidence in disaster nursing required in emergency situations, ultimately enabling future nurses to better protect the lives and health of individuals affected by disasters.

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.

This study explores a pedagogical approach to learning modern Greek imperative forms using machine translation and evaluates its relevance in language education. While imperatives frequently appear in textbooks and exams, they present challenges for beginners, highlighting the need for effective instruction. Machine translation can serve as a practical learning aid in this context. The study h as tw o k ey a ims: e valuating t he q uality of G reek-to-Korean imperative sentence translations from Google Translate and DeepL, and identifying effective learning activities for helping students recognize and acquire imperative forms, specifically in instructional texts. The analysis shows that although machine translation captures core meanings, it struggles with contextually accurate expressions and complex syntax. The study suggests using machine translation to familiarize beginners with imperative forms and support intuitive learning. For more advanced learners, comparing machine and human translations can promote deeper grammatical understanding. Ultimately, machine translation can function not only as a translation tool but also as a means for linguistic analysis and grammar awareness in second language learning.

In this study, the effects of a hypothetical autonomous vehicle (AV)-exclusive roadway were estimated through a step-by-step approach using both microscopic and macroscopic simulations. First, the AV-exclusive roadway was classified into four types—entry lanes, mainlines, merging lanes, and intersections—and the C, α, and β values of the Bureau of Public Roads (BPR) function were estimated for each type through a microscopic simulation. These estimated values were then applied to a 3×3 (20 km) network, and a macroscopic simulation was conducted to compare the effectiveness of AVs and conventional vehicles (CVs) in terms of traffic volume and travel time.The analysis showed that for the same travel time, the traffic volume increased by more than 12% with AVs compared to that with CVs. Conversely, for the same traffic volume, the total travel time decreased by 11% for AVs. The estimated capacity of the AV-exclusive roadway, similar to the U-Smartway with a size of 3×3 (20 km), was approximately 400,000 vehicles, which was more than 140% higher than that of CVs. Assuming that each AV carries five passengers, up to two million people can be transported per day, indicating a significant potential benefit. However, these results were based on theoretical analyses using hypothetical networks under various assumptions. Future studies should incorporate more realistic conditions to further refine these estimations.

본 연구는 산업안전보건법 제57조, 시행규칙 제73조에 근거한 산업재해조사표와 관련하여 산업재해 예방 관점에서 산업재해조사표의 문제점을 경험적으로 확인하고 자율 안전보건 관리체계 구축을 위한 활용 방안을 제언하였다. 2017~2020년에 제출된 산업재해조사표 1,200건을 무선 추출하여 재해발생 개요 및 원인, 재발방지계획을 대상으로 작성된 내용 적정성을 분석하고 계량적 평정을 통해 업종과 규모별 내용작성 차이를 통계적으로 분석하 였다. 또한 일반적인 산업재해조사표 작성 사례를 작성방법과 비교하여 정리하였다. 연구 결과, 제조업과 건설업에 비해 기타업종은 평정점수가 낮게 나타났으며, 규모가 작은 사업 장일수록 평정점수가 낮게 나타나 산업재해조사표의 내용 적정성 수준이 상대적으로 낮은 것으로 확인되었다. 일반적인 작성 사례를 비교한 결과, 재해발생 개요 및 원인을 구체적으 로 작성하지 않는 경우와 더불어 사고의 원인과 결과를 단순화시켜 작성한 경우가 많았다. 산업재해조사표의 목적과 중요성을 고려할 때, 작성방법에 대한 교육과 홍보, 관련 업무의 인력 보충 및 업무처리 절차 개선, 그리고 산업재해 예방사업과의 연계가 필요하다.

이 논문은 회화 문화유산의 복원 현황과 AI 기술을 활용한 디지털 복원 방안을 제시한다. 국내에서는 디지털 복원 및 모사본 제작을 통해 회화 문화유산의 원형 복원을 한 사례, 국외에서는 AI를 활용한 복원 사례를 소개한다. AI는 회화의 패턴과 스타일을 학습하여 복원을 지원하 며, 복원 효과를 높일 수 있다. 이 과정에 기존에 제작된 원형 복원 모사본의 과정 및 이미지를 학습데이터로 활용 가능하다. 결론에서는 AI 기반 디지털 복원이 전통적 복원 방식과 조화를 이루어 문화유산의 원형 복원과 가치 보존에 기여할 수 있음을 강조한다. 특히, 고지도 및 산수화, 어진 및 초상화 관련 복원 등에서 강력한 도구로 작용할 수 있으며, 지속적인 기술 발전을 통해 더 많은 가능성을 열어갈 수 있다고 기대한다.

오늘날 글로벌 영상 제작 산업은 AI(인공지능)의 발달로 영 화, 방송 드라마의 서사 이미지는 더 이상 인간의 창의적인 독 창성에만 의존하지 않는다. 이 근간은 최근 넷플릭스와 같은 OTT 플랫폼의 확대로 관객들의 획기적인 영상 이미지 욕구 증대와 빠른 기술변화 속도, 관련 전문인력의 노동 생산성 저 하에 따른 제한적인 기술, 우리 인간-관객의 특정 판타지 욕망 확장의 결과라고 말할 수 있다. 영화나 방송 드라마의 기초가 되는 이야기조차도 이미 헐리우드 뿐만 아니라 국내에서도 AI 를 활용한 작업들의 움직임이 활발하다. 특히 OTT 플랫폼들의 경쟁이 치열해짐에 따라 영상 콘텐츠의 차별성이 사업 성공에 큰 영향을 미치기 때문이다. 그래서 영화나 방송 드라마의 스 토리를 창작함에 있어, AI가 인간의 도구일 뿐 아니라 ‘창작자’ 로서의 역할에 대한 질문도 대두되었다. 인류에게 AI는 분리된 개체가 아닌 미래를 견인하는 중요한 요인임을 일상에서 쉽게 경험하고 있다. 이제 AI는 인간의 고유 영역이었던 창작까지 진출하였고, 지금 이 순간에도 진화는 계속되고 있다. ‘미디어는 인간의 확장이다.’라고 말하는 ‘마셜 매클루언’의 말 처럼 미디어는 인간의 오감과 뇌의 영역까지 확장해 왔다. AI 와 미디어는 자연스럽게 교차, 미디어 영상에 접목되어 우리의 삶 속에 더욱 깊숙이 밀착되어 있다. 본 고는 디즈니 플러스의 오리지널 드라마 <비질란테> 제작 의 미술 부분, 특히 동 작품의 리얼리티 향상을 위해 필요한 주요 소품인 ‘마스크’ 제작과정을 사례로, 미술감독이 생성형 AI의 창의성에 어느 정도 의존하면서 어떻게 활용했는지에 대 해 연구하였다.

Purpose: This study aimed to develop and implement a multi-patient simulation (MPS) program for nursing students with no prior clinical practice experience. It also examined the effects of the program on the students’ communication competence and clinical reasoning ability. Methods: A one-group pretest-posttest design was used. The MPS program, consisting of four patient scenarios was applied to second-year nursing students with no prior clinical practice experience. Communication competence, clinical reasoning ability, and the perceived effectiveness of the multi-patient simulation program were measured using structured tools before and after the program. Results: Communication competence significantly improved after the MPS program, whereas clinical reasoning did not show a statistically significant difference. Perceived effectiveness of the MPS program was generally high, with the debriefing component scoring the highest. Confidence scores were relatively low, suggesting the need for level-appropriate scenario. Conclusion: The MPS program was effectively enhanced communication competence among preclinical nursing students. Although clinical reasoning scores did not improve significantly, the study highlights the importance of introducing realistic simulation experiences early in nursing education. Future research should focus on developing suitable clinical reasoning assessment tools for early year students and conducting randomized controlled trials to validate the effectiveness of customized MPS programs.

이 논문은 에밀리 디킨슨의 시에서 성경의 자연스러운 인용과 영적 진리의 탐색을 왜곡하거나 의도적으로 간과한 일부 비판들을 수정하고자 한다. “원주,” “중심,” “산문,” “가능성,” 그리고 “은둔” 등 디킨슨이 그녀의 시와 편지에서 직 접 언급한 은유들의 의미와 상호 연관성을 분석함으로써 종교적 신앙과 성서적 진리 탐구를 구현하는 디킨슨의 시를 재평가한다. “비스듬히 말하기”를 우회적 이고 은유적인 진술인 시 쓰기와 동일한 것으로 보고 디킨슨이 이 전략을 통해 자신의 영성을 어떻게 숨기고 동시에 드러내는지를 고찰한다. 제도화된 종교에 대한 거부와 신 앞에서의 인간 존재의 한계에 대한 좌절을 다루는 시들 속에서, 디킨슨이 성서적 진리에 기반한 진정한 개인적 영성을 끊임없이 추구하고 있음 을 읽을 수 있었다. 실제로, 종교와 신에 대한 회의와 저항을 담은 시들은 오히 려 성서적 진리에 접근하기 위한 디킨슨의 우회적 여정이었음이 확인된다.

This study aims to propose operational strategies for temperature management in wholesale fish markets, the first stage of the seafood distribution structure, in light of the increasing consumption of seafood and the government's strategy to revitalize the seafood consumption market. To identify areas for improvement in efficient cold chain management at wholesale markets, an analysis was conducted based on a survey assessing the importance and satisfaction levels among industry stakeholders. The survey results highlighted key reinforcement factors, such as ensuring the separation and safety of workspaces, securing low-temperature auction facilities, improving outdated wholesale market infrastructure, and raising workers' awareness of quality and hygiene management. Priority improvement areas included enhancing hygiene-related facilities, standardizing and regulating equipment, establishing temperature monitoring and management systems, implementing government policies for hygiene improvement in wholesale markets, and providing subsidies. The findings of this study are expected to serve as a reference for the adoption of seafood cold chain policies.

본 연구는 그리스 고고·미술사 분야에서 레거시 데이터의 디지털화와 인공지능(AI) 기술의 활용이 가져온 학술적 전환의 양상을 분석한 다. 주요 아카이브 및 데이터베이스/데이터뱅크의 구축 사례를 살펴보고, 이를 바탕으로 이루어진 융복합 연구의 성과를 소개한다. 특히 고대 마케도니아 벽화 복원, 금석문 해독, 탄화된 파피루스 해석 등 AI 기반 연구 사례를 검토하며, 데이터 편향, 해석의 불투명성, 윤리적 쟁점 등 기술 활용의 한계와 위험성을 비판적으로 고찰한다. 결론적으로, 디지털 및 AI 기술의 발전 속에서 국내 연구자가 전통적 학문 역량과 AI 리터러시를 어떻게 조화시켜야 하는지를 제시하며, 향후 고고·미술사 연구의 지속 가능성과 대응 전략을 모색한다.

The purpose of this study is to develop customized suit designs incorporating flower and bird painting patterns from Korean folk painting and to propose differentiation strategies for customized fashion design in Korea. The research was conducted in three stages. First, concepts and types of customization were examined through theoretical analysis, identifying the characteristics of customized design. Second, flower and bird painting patterns found in Korean folk painting were classified according to their symbolic meanings and developed into digital patterns. Third, flower and bird painting patterns from Korean folk painting were utilized in the suit designs following the principles of customized design. The results are as follows. Customizations were classified into three types: simple combination, selective adjustment, and creative assembly. The characteristics of customized design were identified as customer participation and product modularity. The symbolic meanings of the flower and bird motifs were categorized into six themes: happiness, wealth, longevity, fertility, love, and protection against evil. The resulting digitally developed patterns sought to integrate traditional cultural elements into customized fashion design. The final suit designs demonstrated hyper-individuation through modular assembly, enabling both customer participation design and product modularity. Detachable elements that modularize the jacket structure enhanced customizability while promoting sustainable and eco-conscious design practices.

This study investigates a vision-based autonomous landing algorithm using a VTOL-type UAV. VTOL (Vertical Take-Off and Landing) UAVs are hybrid systems that combine the forward flight capability of fixed-wing aircraft with the vertical take-off and landing functionality of multirotors, making them increasingly popular in drone-based industrial applications. Due to the complexity of control during the transition from multirotor mode to fixed-wing mode, many companies rely on commercial software such as ArduPilot. However, when using ArduPilot as-is, the software does not support the velocity-based GUIDED commands commonly used in multirotor systems for vision-based landing. Additionally, the GUIDED mode in VTOL software is designed primarily for fixed-wing operations, meaning its control logic must be modified to enable position-based control in multirotor mode. In this study, we modified the control software to support vision-based landing using a VTOL UAV and validated the proposed algorithm in simulation using GAZEBO. The approach was further verified through real-world experiments using actual hardware.