본 연구는 한국 창조 신화가 한국인의 종교적 정체성과 세계관 형성에 어떠한 영향을 미쳐왔는지를 탐구한다. 또한, 이러한 전통적 신화들과 창세기의 성서적 창조 서사를 비교하여, 각 서사가 각기 다른 문화의 기원과 세계 인식에 어떤 영향을 주었는지를 밝히고자 한다. 아울러 이러한 토착 창조 신화들이 창세기 창조 이야기를 수용하는 데 어떻게 기여하였으며, 나아가 기독교의 전래와 확산에 어떠한 역할을 하였는지를 분석한다. 결론에서는 이러한 논의를 확장하여, 한국 교회의 생태신학적 성찰과 현대 선교 실천에 주는 함의를 제시한다.

A new aphid species record for South Korea, Greenidea (Trichosiphum) prunicola, is reported based on the collection of apterous viviparous females from Castanopsis sieboldii in Tongyeong-si in 2024. This discovery increases the number of known Greenidea species in South Korea to four. Detailed morphological descriptions, measurements, host plants, and distribution data for G. (T.) prunicola are provided. The species is characterized by its glossy reddish-brown body, a body length of 1.70–2.60 mm, a siphunculus that is 0.32–0.37 times the body length, and long, primarily bifurcated dorsal setae. Previous host records include Prunus spp., and its known distribution now includes South Korea, China, and India. A key to the four species of the genus Greenidea now known to be present in South Korea is also provided.

Pine wilt disease (PWD), caused by the pine wood nematode (Bursaphelenchus xylophilus), is a major threat to Pinus thunbergii forests in South Korea. Although climatic conditions are known to affect the spread of PWD, the specific influences of temperature and geography on nematode density and tree mortality remain unclear. This study assessed monthly PWN density and black pine mortality across three regions—two coastal (Geoje and Sacheon) and one inland (Jinju)—from 2021 to 2023. Nematode density and tree mortality consistently peaked in autumn across all regions. A strong positive correlation was observed between nematode density and tree mortality (r = 0.7468, p < 0.01), while temperature showed no significant correlation with either variable. These results indicate that PWD severity is more closely tied to nematode activity than to temperature alone, and that regional and seasonal variability must be considered in disease assessment. The findings highlight the need for region-specific monitoring and management strategies that prioritize high-risk periods, particularly autumn, when nematode activity and disease expression are most pronounced. This research provides essential data to support adaptive PWD control programs under changing climatic conditions.

Background: Stroke often results in hemiparesis, which leads to asymmetrical plantar pressure and impaired balance control. The gastrocnemius muscle plays a key role in plantar flexion and postural stability. Dysfuncion of this muscle is associated with decreased posterior foot pressure and increased fall risk in stroke patients. Objectives: To investigate the effects of gastrocnemius muscle stimulation using microcurrent stimulation therapy on plantar pressure distribution and functional balance in stroke patients. Design: Randomized controlled trial. Methods: Twenty chronic stroke patients were divided into an experimental group (microcurrent stimulation therapy+conventional rehabilitation therapy) and a control group (conventional rehabilitation therapy only). plantar pressure (posterior foot pressure), center of anteroposterior pressure displacement (CAP), and balance ability were measured using the berg balance scale (BBS) and functional reach test (FRT) before and after 8 weeks of intervention. Results: The experimental group showed statistically significant increases in posterior foot pressure and reductions in CAP after intervention. BBS and FRT scores also significantly improved. Although the control group showed some improvements, the changes were smaller and less consistent. Conclusion: Microcurrent stimulation targeting the gastrocnemius muscle improved plantar pressure symmetry and postural stability in stroke patients, suggesting its effectiveness as an adjunctive balance rehabilitation intervention.

Background: The Functional Movement Screen (FMS) is widely used for movement assessment but suffers from subjective scoring that leads to inconsistent evaluations. While previous studies have focused on reliability, the validity of AI-supported assessment remains unexplored. Objectives: To evaluate the reliability and validity of an AI-based motion analysis system using MediaPipe for three FMS movements. Design: Prospective reliability and validity study with repeated measures. Methods: Thirty healthy adults (age 23.4±2.8 years) performed three FMS tests (Deep Squat, Hurdle Step, Inline Lunge) recorded on video. Three evaluators (two experienced physical therapists and one novice) assessed recordings in three phases: Phase 1 involved traditional assessment by experts only to establish criterion reference, Phase 2 had all evaluators using AI support, and Phase 3 consisted of repeated AI-supported assessment. The AI system provided real-time visual feedback of joint angles and alignment through MediaPipe skeletal tracking. Results: Criterion validity showed strong agreement between traditional expert assessment and AI-supported assessment (r=0.94, P<.05). Inter-rater reliability improved from good (ICC=0.89) to excellent (ICC=0.91) with AI support. The novice evaluator achieved immediate expert-level performance with only 0.05 points difference from experts. Intra-rater reliability was excellent for all evaluators (ICC=0.84-0.89). Conclusion: The AI-based system demonstrated strong validity and improved reliability for fundamental movement assessment. While AI support enabled novice evaluators to achieve expert-level performance immediately, it may increase sensitivity to subtle movement variations. This technology shows promise for standardizing movement screening, though current limitations restrict its application to standing movements.

In this study, we analyzed the structural and mechanical properties of aluminum foams fabricated using aluminum powders of varying sizes and mixtures. The effects of sintering and pore structure at each size on the integrity and mechanical properties of the foams were investigated. Structural characteristics were examined using scanning electron microscopy and micro–computed tomography, while mechanical properties were evaluated through compression testing. The experimental results demonstrated that smaller powder sizes improved foam integrity, reduced porosity and pore size, and resulted in thinner cell walls. In combination, these effects increased compressive strength as the powder size decreased. The findings of this study contribute to the understanding and improvement of the mechanical properties of aluminum foams and highlight their potential for use in a wide range of applications.

In this paper, a water rescue mission system was developed for water safety management areas by utilizing unmanned mobility( drone systems) and AI-based visual recognition technology to enable automatic detection and localization of drowning persons, allowing timely response within the golden time. First, we detected suspected human subjects in daytime and nighttime videos, then estimated human skeleton-based poses to extract human features and patterns using LSTM models. After detecting the drowning person, we proposed an algorithm to obtain accurate GPS location information of the drowning person for rescue activities. In our experimental results, the accuracy of the Drown detection rate is 80.1% as F1-Score, and the average error of position estimation is about 0.29 meters.

Ensuring operational safety and reliability in Unmanned Aerial Vehicles (UAVs) necessitates advanced onboard fault detection. This paper presents a novel, mobility-aware multi-sensor health monitoring framework, uniquely fusing visual (camera) and vibration (IMU) data for enhanced near real-time inference of rotor and structural faults. Our approach is tailored for resource-constrained flight controllers (e.g., Pixhawk) without auxiliary hardware, utilizing standard flight logs. Validated on a 40 kg-class UAV with induced rotor damage (10% blade loss) over 100+ minutes of flight, the system demonstrated strong performance: a Multi-Layer Perceptron (MLP) achieved an RMSE of 0.1414 and R² of 0.92 for rotor imbalance, while a Convolutional Neural Network (CNN) detected visual anomalies. Significantly, incorporating UAV mobility context reduced false positives by over 30%. This work demonstrates a practical pathway to deploying sophisticated, lightweight diagnostic models on standard UAV hardware, supporting real-time onboard fault inference and paving the way for more autonomous and resilient health-aware aerial systems.

목적: 건성안은 스트레스에 의해 영향을 받을 수 있는 대표적인 안과 질환이다. 본 연구는 제8기 국민건강영양조사 (2019–2021) 원시자료를 활용하여, 직업에 의한 스트레스와 건성안 간의 관련성을 분석하고자 하였다. 방법 : 국민건강영양조사 제8기 자료 중 건성안 진단 유무와 스트레스 인지 정도에 응답한 3,781명을 대상으로, 성별, 연령, 학력 등의 일반적 특성과 건성안 진단 유무, 스트레스 인지 정도, 직업군별 건성안 유병률 간의 관계를 분석하였다. 통계적으로 p<0.050을 유의수준으로 설정하였다. 결과 : 여성에서 남성보다 건성안 유병률이 높았으며, 젊은 연령층과 고학력자에게서 유병률이 유의하게 높았 다. 스트레스 인지 수준이 높을수록 건성안 발생률도 증가하였고, 직업군별로도 유의한 차이를 보였다. 결론 : 직업에 의한 스트레스는 건성안 유병률 증가와 통계적으로 유의한 관련이 있었다. 특히 스트레스 수준이 높은 직업군에서 건성안 발생률이 높았으며, 이에 따라 직업환경 개선 및 예방 대책 마련의 필요성이 확인되었다.

Italian ryegrass (Lolium multiflorum Lam., IRG) is a widely cultivated winter forage crop known for its high yield and nutritional value. This study evaluated the processing characteristics and feeding performance of IRG-based pellets in Hanwoo cattle (Bos taurus coreanae) and Korean native black goats (Capra hircus). IRG was harvested at the optimal growth stage and processed into two pellet formulations: IRG ≥80% (with up to 20% soybean meal) and 100% IRG. Feeding trials were conducted under ad libitum feeding conditions. Hanwoo cattle showed higher intake of 100% IRG pellets (7.9 kg/day/head) than IRG ≥80% pellets (7.5 kg/day/head, p<0.05), with similar average daily gain (0.9 ± 0.4 kg/day/head). Conversely, black goats exhibited significantly lower intake of IRG ≥80% pellets (54.6 g/day/head) compared to 100% IRG pellets (266 g/day/head), likely due to reduced palatability associated with soybean meal inclusion. These findings suggest that IRG pellets are suitable for Hanwoo cattle, while further optimization of pellet size and formulation is required to improve acceptance in goats. Future studies should assess long-term impacts on digestion, rumen fermentation, and metabolic responses.

본 연구에서 Greenidea (Trichosiphum) nigra를 국내에서 처음으로 보고한다. 이 종의 무시성충에 대한 형태학적 정보, 기주식물, 분포지역, 한국에 분포하는 털관진딧물속 종들의 분류키를 제공한다.

This study proposes a weighted ensemble deep learning framework for accurately predicting the State of Health (SOH) of lithium-ion batteries. Three distinct model architectures—CNN-LSTM, Transformer-LSTM, and CEEMDAN-BiGRU—are combined using a normalized inverse RMSE-based weighting scheme to enhance predictive performance. Unlike conventional approaches using fixed hyperparameter settings, this study employs Bayesian Optimization via Optuna to automatically tune key hyperparameters such as time steps (range: 10-35) and hidden units (range: 32-128). To ensure robustness and reproducibility, ten independent runs were conducted with different random seeds. Experimental evaluations were performed using the NASA Ames B0047 cell discharge dataset. The ensemble model achieved an average RMSE of 0.01381 with a standard deviation of ±0.00190, outperforming the best single model (CEEMDAN-BiGRU, average RMSE: 0.01487) in both accuracy and stability. Additionally, the ensemble's average inference time of 3.83 seconds demonstrates its practical feasibility for real-time Battery Management System (BMS) integration. The proposed framework effectively leverages complementary model characteristics and automated optimization strategies to provide accurate and stable SOH predictions for lithium-ion batteries.