The flora of the vascular plants in Ungseokbong County Park, Sancheonggun, Gyeongsangnam-do, was surveyed 11 times from April 2023 to September 2024. The surveys revealed 461 taxa, comprising 101 families, 288 genera, 417 species, 11 subspecies, 30 varieties, and three forms. Of these, two taxa were designated as endangered species, six taxa as rare plants, and 19 taxa as Korean endemic plants. The study also documented a total of 69 taxa as floristic regional plants, specifically two taxa of grade V, five taxa of grade IV, 11 taxa of grade III, 18 taxa of grade II, and 33 taxa of grade I. Furthermore, six taxa were identified as plants adaptable to climate change, comprising two northern species and four southern species. Forty-four taxa of alien plants and four taxa of ecosystem-disturbing plants were also found in this area. This study, based on reference specimens, provides fundamental data on the distribution and status of plants in Ungseokbong County Park and is expected to serve as a resource for the conservation and management of plant resources.

Passive acoustic monitoring (PAM) has emerged as an effective tool for studying underwater soundscapes and monitoring marine organisms. In this study, the biological sounds of three fish species that mainly inhabit or occur in the Korean coastal oceans, brown croaker (Miichthys miiuy), Pacific cod (Gadus macrocephalus), and small yellow croaker (Larimichthys polyactis) were recorded using the PAM method. The possibility of automatic classification was evaluated using a deep learning-based convolutional neural network (CNN) model based on the measured data. The biological fish sounds were recorded using hydrophones in the sea cage environments. The three fish species data were converted into spectrogram images and used as input for training and evaluating the CNN model. Gaussian noise was added to the test data to simulate low signal-to-noise ratio (SNR) environments. The model achieved high classification performance, with F1-score of about 96% on raw data and about 77% accuracy under signal-to-noise ratio conditions. These results suggest that CNN-based models be adequate for fish sound classification, even in acoustically complex underwater environments. Applying CNN models to classify and detect fish sounds can improve the automation and efficiency of PAM-based acoustic analysis, thereby improving the monitoring of fish populations, resource assessment, and ecological management in the future.

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.

재래흑염소는 다른 품종과의 유연관계, 기원 분석, 외래교잡염소와의 유전적 구조 및 평가 같은 유전학적 연구는 현재 부족한 실정이므로 본 연구는 재래흑염소와 외래교잡염소의 유전적 구조 및 혼합 양상 분석을 위해 실시하였다. 11개의 Microsatellite (MS) marker를 활용하여 재래흑염소 261두, 외래교잡염소 94두의 대립유전자형을 분석하였으며, 이를 통해 기초통계량 및 유전적 구조 및 혼합 양상 분석을 수행하였다. 그 결과 연구에 활용된 marker의 유전적 다양성 기초통계량 결과 재래흑염소의 observed heterozygosity, polymorphism information content (PIC) 수치가 각각 0.388 ~ 0.772, 0.362 ~ 0.754이며, 외래교잡염소 집단의 observed heterozygosity, PIC 수치는 각각 0.553 ~ 0.840, 0.664 ~ 0.915로 확인되어, 유전적 다양성 분석을 수행하기에 충분한 수치를 나타내었다. 또한, 재래흑염소 주요 대립유전자형 빈도와 외래교잡염소의 주요 대립유전자형 빈도를 비교하였을 때, marker별 대립유전자형의 빈도 분포에서 뚜렷한 차이를 보여, 두 집단 간, 재래흑염소 계통 간 대립유전자형 의 빈도 분포로 인한 유전적 구조의 차이를 확인하였다. 그리고 STRUCTURE 결과, K = 2가 최적의 K로 계산되었으며, 재래흑염소와 외래교잡염소 간의 유전적 혼합은 존재하지 않는 것으로 판단되었다. 따라서 본 연구 결과는 재래흑염소의 고유성을 제시할 수 있는 과학적 근거자료이며, 재래흑염 소 계통 간의 식별을 위한 기초 자료로 활용 가능할 것이다.