This study proposes a data-driven framework for analyzing freeway driving behavior using multiple real-world trajectory datasets, and applies it consistently to mainline and ramp sections. The four large-scale datasets—namely highD, exiD, NGSIM I-80, and NGSIM US- 101—were processed through a unified preprocessing pipeline that converted all variables to International System Units(SI), resampled trajectories to 10 Hz, applied Savitzky-Golay smoothing to speed, and removed physically implausible and statistical outliers based on joint physical-statistical criteria. For each vehicle, 24 summary features were constructed from six longitudinal indicators–speed, acceleration, deceleration, time headway (THW), distance headway (DHW), and time-to-collision (TTC)–using their minimum, maximum, mean, and standard deviation. Indicator distributions by road type were compared using relative frequency histograms with common binning; then, principal component analysis (PCA) and K-means clustering were applied independently to each dataset. The leading principal components revealed interpretable axes related to longitudinal driving intensity (speed and acceleration level), safety margin (THW/DHW/TTC), and onramp sections; responsiveness was characterized by acceleration-deceleration variability, as observed within the analyzed datasets. Cluster interpretation yielded four relative driving behavior categories–aggressive, responsive, stable, and defensive–defined within each dataset based on indicator levels and variability rather than absolute thresholds.

Since the National Emergency Management Agency’s seismic fragility function, developed in 2009, classified domestic buildings by structural type, numerous studies have used this classification. In 2021, the updated seismic fragility function adopted a slightly more complex classification logic, limited to concrete structures. Data for structural-type classification were derived from information in the building register, including primary use, floor area, permit date, and number of stories. To verify and improve the accuracy of the classification logic, a sample of approximately 1,800 from about 13,000 concrete buildings in a specific region was selected. Structural types classified by the logic were compared with those identified through road views provided by the Architecture HUB. The results confirmed that the existing classification logic requires revision to incorporate additional variables, including the sub-use of the building and the area-by-use on the first floor. The revised logic significantly improved classification accuracy by including those variables.

노린재과(Pentatomidae)에 속하는 꼬마갈색노린재속(Plautia Stål)은 중간 정도의 크기, 녹색 몸체, 그리고 갈색을 띠는 반시초(hemelytra) 가 특징인 속이다. 특히 갈색날개노린재와 같은 일부 종은 중요한 농업 해충으로 간주되어 이들의 방제에 대한 연구의 필요성이 높다. 본 연구는 한 국산 Plautia 속의 분류 현황을 검토하고 관련된 기생자(parasitoids) 정보를 제공하는 것을 목표로 했다. 재검토 결과, 한국에서 P. splendens Distant, 1900으로 알려졌던 꼬마갈색노린재는 P. himechabane Ishikawa and Moriya, 2019로 재동정되었다. 각 종들은 종별 사진과 암수 생식 기 구조를 기반으로 분류되었으며, 색상 변이와 암컷 저정낭(spermatheca)의 관찰 가능한 차이를 보여주는 그림이 포함되었다. 추가적으로, 알려 진 꼬마갈색노린재속에 대한 기생자의 목록을 검토했으며, 새로 발견된 두 종의 꼬마갈색노린재속 관련 응애에 대한 정보를 제공한다. 이 응애 중에 는 한국에서 처음으로 기록되는 Lobogynium 속이 포함된다.

In digital games, typography serves not only as a vehicle for conveying information but also as a crucial visual element that shapes the game’s identity and emotional atmosphere. However, prior research has predominantly focused on graphics, backgrounds, and character design, with systematic analyses of typographic expression remaining limited. This study concentrates on the emotional functions of typography in games by analyzing 25 PC games across five representative genres: role-playing (RPG), shooting (FPS/TPS), strategy (RTS/TBS), MOBA (AOS), and horror. The titles of these games were assessed using a seven-point scale based on typographic variables—weight, form, spacing, slant, baseline, and visual effects—and subsequently translated into emotional dimensions: robustness, stability, dynamism, traditionality, and fantasy. Based on this framework, genre-specific emotional typologies were identified. The results indicate that RPGs emphasize grandeur and mythic symbolism; FPS/TPS games highlight robustness and dynamism; strategy games exhibit order and stability; MOBAs convey competitive dynamism; and horror games strongly employ fantasy and anxiety. By classifying genre-specific emotional types of typography, this study expands the scope of game graphic design research to include textual expression. Practically, it provides design guidelines that help align typographic choices with genre-specific emotional characteristics. Nonetheless, the study is limited to PC games and a single-researcher evaluation, suggesting the need for future research to incorporate diverse platforms and user-based assessments.

항로표지는 해상에서 항행안전을 보장하기 위한 핵심 수단으로서, 국제적 표준화가 필수적인 분야이다. 국제항로표지기구 (International Organization for Marine Aids to Navigation, IALA)는 1980년대 초 해상부표식(Maritime Buoyage System, MBS)을 제정·채택하여 이 를 중심으로 전 세계 항로표지 표준화를 주도해왔으며, 해상부표식은 현재 국제해사기구(IMO)와의 협력을 통해 사실상 국제 규정으로 기 능하고 있다. 이에 따라 세계 각국은 IALA 기준을 자국 법령에 반영하여 항로표지의 통일성과 해상안전을 확보해 왔다. 우리나라도 항로 표지법과 해양수산부령 고시를 통해 IALA 기준을 수용하고 있으나, 분류체계의 명확성과 국제표준과의 용어 통일성 측면에서 보완과 정 비가 필요한 상황이다. 본 논문은 IALA의 역사적 배경과 문서 체계를 살펴보고, 해상부표식의 원칙과 국제법적 지위를 고찰하였다. 나아 가 우리나라의 항로표지법, 시행령, 시행규칙 및 해양수산부 고시상 항로표지 분류체계를 해상부표식과 비교·분석함으로써 국내 법령의 국제표준 반영 현황과 문제점을 도출하였다. 이를 바탕으로 항로표지법령에 IALA 표준 준수에 대한 명확한 법적 근거를 마련하고, 항로 표지 분류체계를 직관적으로 이해할 수 있도록 법령을 재편하는 방안을 제시하였다. 이를 통해 국제표준에 부합하는 체계를 구축하여 효 과적인 항로표지 운영 기반이 마련될 수 있을 것으로 기대된다.

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.

남방큰무당벌레(신칭)를 한국 미기록종으로 보고한다. 본 종은 국내 Harmornia속의 3종과는 둥글고 볼록한 체형, 앞가슴등판의 갈라진 점 모양, 각 딱지날개에 7개의 검은점이 1-3-2-1 배열로 나타나는 특징으로 쉽게 구별할 수 있다. 본 연구에서는 Harmonia속의 종 검색표, 진단형질과 DNA바코드 정보를 제공하고자 한다.

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.