Desmidiales (Conjugatophyceae, Charophyta) are commonly found in freshwater ecosystems and exhibit high species diversity, particularly in acidic wetlands, lakes, swamps, and peat bogs. They possess a distinctive morphology characterized by symmetrical semicells, and their wide variation in cell shape and size presents challenges in species identification due to high morphological plasticity. Although 832 species of Desmidiales have been reported in Korea, phylogenetic studies have been limited to only a few taxonomic groups. This study focused on investigating species-level relationships among Desmidiales using strains from the Freshwater Bioresources Culture Collection (FBCC), integrating morphological characteristics, ecological data, and original species descriptions. A total of 352 new plastid gene sequences were generated for phylogenetic analyses, including accD (30), atpA (42), atpB (22), ndhH (37), petA (37), psaA (32), psbA (44), psbC (1), psbD (39), rbcL (40), rpl2 (19), and rpoB (9). Among the 12 plastid genes analyzed, psbA showed the highest proportion of conserved sites (83.9%), while petA exhibited the highest proportion of variable sites (38.7%). Based on the combined phylogenetic analysis, Desmidiales were grouped into five major clades: Cosmarium Clade-1: Cosmarium punctulatum, Cosmarium sp. 1, Cosmarium Clade-2: C. blyttii, C. botrytis, C. costatum, C. ochthodes, C. pachydermum, C. subcostatum, C. subcrenatum, C. subprotumidum, C. trilobulatum, Cosmarium Clade-3: C. angulosum, C. formosulum, C. granatum, C. impressulum, C. norimbergense, C. regnellii, C. subtumidum, Cosmarium sp. 2, Staurastrum Clade-1: Staurastrum avicula var. lunatum, Staurastrum Clade-2: S. boreale, S. dispar, S. kouwetsii, S. margaritaceum, S. punctulatum. The newly generated sequence data from FBCC strains will serve as a valuable resource for accurate species identification and for exploring the molecular ecology of Desmidiales in freshwater ecosystems. This phylogenetic framework improves our understanding of Desmidiales species diversity in Korea and aids in achieving a more comprehensive taxonomic resolution within this algal order.

To support English speaking instruction and assessment in Korean schools, this study developed a taxonomy of English speaking tasks by analyzing the 2022 Revised English Curriculum, reviewing standardized English speaking tests, and referring to task classifications in the CEFR and ACTFL. Three modes of English speaking were identified in response to different communication contexts and purposes: imitative, interactional, and presentational. Tasks designed to elicit each mode were further classified by task type. Imitative speaking comprised two task types: ‘listen and repeat’ and ‘read aloud’. Interactional and presentational speaking each included three task types, based on the macro functions of language use: interpersonal, transactional, and evaluative/problem-solving. Each task type was further subdivided into categories and subcategories. The resulting taxonomy can be aligned with school grade levels in the curriculum by excluding tasks that exceed the relevant achievement standards and by adjusting topics, vocabulary, language forms, and task complexity according to students’ grade levels.

맵시혹나방(Meganola major (Hampson, 1891))은 구대륙 전반에 분포한다고 알려져 있으나, 주로 동양구에 집중 분포한다. 최근 전남지역 을 중심으로 종의 대발생이 확인되었으며, 이로 인해 배롱나무와 가래나무의 피해가 확인되고 있다. 본 종은 국내 분류학적 역사가 복잡한 바, 이번 논문을 통해 이 종의 분류학적 기록을 정확하게 파악하고자 한다. 또한 관찰을 근거로 간략한 생태적 정보 또한 제공하고자 한다.

This study presents a truck classification method using panoramic side-view images to meet the Ministry of Land, Infrastructure and Transport’s 12-category standard (types 4–12). The system captures a vehicle’s full side profile via a panoramic imaging device, ensuring complete wheel visibility. A YOLOv12-based deep learning model detects wheels, and image processing extracts their center coordinates. Pixel distances between adjacent wheels are calculated and normalized to determine axle spacing patterns, which, together with wheel count, are applied to a rule-based classifier. Tests on 1,200 real-world panoramic truck images (1,000 for training, 200 for testing) achieved a mean average precision of 96.1% for wheel detection and 90.5% overall classification accuracy. The method offers explainable classification through measurable structural features, supporting applications in smart tolling, road usage billing, overloading enforcement, and autonomous vehicle perception.

This paper reviews ordinal decision tree algorithms for ordinal classification, exploring theoretical foundations, key algorithms (MDT, QMDT), specialized splitting criteria (Ordinal Gini, Weighted Information Gain), and ensemble methods. It discusses applications in healthcare and social sciences, highlighting interpretability and flexibility while acknowledging overfitting and instability. As implications for future research, this study points out advantages such as interpretability and flexibility, and limitations such as overfitting and instability.

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.

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.

Sanghuang mushroom is highly valued for its medicinal potential, including anticancer, anti-inflammatory, and antioxidant activities, and has recently gained significant economic and pharmacological importance. Despite considerable taxonomic revisions within the genus Sanghuangporus, confusion persists in Korea due to the continued use of outdated names such as Phellinus linteus, P. baumii, and Inonotus baumii, as well as inconsistencies in common names. This study aimed to clarify the species diversity of Sanghuangporus in Korea through integrative approaches combining phylogenetic, morphological, and ecological analyses. Using four genetic markers (ITS, nLSU, RPB2, and TEF1), we analyzed 11 dried specimens preserved at the Seoul National University Fungus Collection and 74 fungal strains maintained by the Korean Agricultural Culture Collection. As a result, we identified eight Sanghuangporus species in Korea: S. baumii, S. mongolicus, S. quercicola, S. sanghuang, S. subbaumii, S. vaninii, S. weigelae, and a novel candidate species, Sanghuangporus sp. 1. Among these, S. mongolicus and S. quercicola were newly recorded species for Korea. By providing diagnostic traits and ITS barcode sequences, this study offers a reliable taxonomic framework for the accurate identification of Sanghuangporus species. It also supports future taxonomic studies, cultivar development, and applied research in pharmaceutical and functional bioactive materials.

The casting manufacturing process of aluminum automotive wheels often involves processing various wheel models during stages such as flow forming, machining, packaging, and delivery. Traditionally, separate equipment or production lines were required for each model, which led to higher facility investment costs and increased labor costs for classification. However, the implementation of machine learning-based model classification technology has made it possible to automatically and accurately distinguish between different wheel models, resulting in significant cost savings and enhanced production efficiency. Additionally, this approach helps prevent product mix-ups during the final inspection process and allows for the quick and precise identification of wheel models during packaging and delivery, reducing shipping errors and improving customer satisfaction. Despite these benefits, the high cost of machine learning equipment presents a challenge for small and medium-sized enterprises(SMEs) to adopt such technologies. Therefore, this paper analyzes the characteristics of existing machine learning architectures applicable to the automotive wheel manufacturing process and proposes a custom CNN(Convolutional Neural Network) that can be used efficiently and cost-effectively.

This study aims to identify latent classes among shared e-scooter users based on their characteristics and analyze the differences in personal and usage characteristics across these classes. Specifically, the study has the following key objectives: (1) to select variables related to the personal and usage characteristics of shared e-scooter users; (2) to collect data on the personal and usage characteristics of shared e-scooter users; (3) to derive the latent classes of shared e-scooter users; and (4) to test the differences in personal and usage characteristics across the identified latent classes. Variables related to the personal and usage characteristics of shared e-scooter users were selected based on a literature review. Through a survey, data on the personal and usage characteristics of shared e-scooter users were collected. A latent class analysis (LCA) was performed to derive the latent classes of shared e-scooter users. Finally, a chi-square analysis was conducted to test the differences in personal and usage characteristics across the latent classes of shared e-scooter users. The results of this study are as follows. The personal characteristics of shared e-scooter users were identified as age and sex, whereas the usage characteristics were identified as usage frequency, time periods of e-scooter usage, return/rental zones, return/rental places, and types of roads used. Data on sex, age, usage frequency, periods of e-scooter usage, and return/rental locations were collected from 278 shared e-scooter users. Based on information criterion, statistical validation, and the entropy index, four latent classes of shared e-scooter users were identified: “male users with a commuting purpose in business zones,” “male users with a homeward commuting purpose in residential zones,” “female users with a leisure purpose in park/green zones,” and “users in their 20s with a commuting purpose in residential zones.” The results of a chisquare analysis revealed statistically significant differences (p < 0.05) in the personal and usage characteristics across the latent classes. Shared e-scooter user types were classified through Latent Class Analysis (LCA), and differences in personal and usage characteristics were identified across the classes. The preferred usage environments and conditions for each class of shared e-scooter users are determined. Variables related to the return/rental zone and periods of e-scooter usage showed the most significant differences among the classes. These findings can contribute to the development of customized user policies and the improvement of services based on the characteristics of shared e-scooter users.

Natural populations of numerous species have decreased sharply in recent years, and a number of mammalian species are also now at elevated risk of extinction globally. The long-tailed goral Naemorhedus caudatus, a vulnerable and protected species designated by IUCN (International Union for Conservation of Nature) and CITES (the Convention on International Trade in Endangered Species of Wild Fauna and Flora), is distributed in Northeast Asia including the Korean Peninsula. In South Korea, the Seoraksan National Park is known as the largest core habitat for the long-tailed goral population. In this study, phylogenetic relationships and population genetic features of the Seoraksan goral population were analyzed using fecal samples with both mitochondrial DNA and microsatellite markers. We found that Seoraksan gorals were the most closely related to Russian population, and also found that a unique Seoraksan lineage evolved there. In addition, the Seoraksan goral population showed higher genetic diversity than other South Korean populations, suggesting that this population might represent the most ecologically and evolutionarily important remnants of the long-tailed goral in South Korea. The Seoraksan goral population had a low level of genetic differentiation and a rather single genetic structure, suggesting that non-negligible levels of gene flow might have occurred across populations. Moreover, microsatellite genotype-based individual identification estimated that the population size was ≥81 in the Seoraksan National Park. Findings of our study suggest that effective conservation and restoration actions are needed for long-term conservation of N. caudatus in this protected area.

한국산 복숭아거위벌레속(딱정벌레목: 바구미상과: 주둥이거위벌레과)의 분류학적 재검토를수행한 결과 애복숭아거위벌레(Rhynchites (Rhynchites) fulgidus Faldermann, 1835)를 국내 처음으로 확인하였다. 본 연구에서는 한국산 복숭아거위벌레속 2종(애복숭아거위벌레, 복숭아 거위벌레)의 형태학적 재기재를 제공한다. 또한, 한국산 복숭아거위벌레속 종들의 분류학사에 대한 고찰과 2종에 대한 분류 검색표를 제시한다.

한반도 애호랑밑빠진버섯벌레속(Baeocera Erichson, 1845)에 대하여 논의하고 검색표를 제공하였다. 참애호랑밑빠진버섯벌레(Baeocera choi Hoshina and Park, 2011)는 배애호랑밑빠진버섯벌레[Baeocera ventralis (Löbl, 1973)]의 동물이명 임이 밝혀졌다. 이들의 수컷생식기 형질 을 도해하고 근연종과 함께 동정에 유용한 형태형질을 제시한다.