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 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.

The expansion of online retail markets has driven the development of personalized product recommendation services leveraging platform-based product and customer data. Large retailers have implemented seller-oriented recommendation systems, where AI analyzes POS sales data to identify similar stores and recommend products not yet introduced but successful elsewhere. However, small and medium-sized retailers face challenges in adapting to rapidly evolving online market trends due to limited resources. This study proposes a recommendation algorithm tailored for small-scale retailers using sales data from an online shopping mall. We analyzed 600,000 transaction records from 13,607 sellers and 95,938 products, focusing on Beauty Supplies, Kitchenware, and Cleaning Supplies categories. Three algorithms—Attentional Factorization Machines (AFM), Deep Factorization Machines (DeepFM), and Neural Collaborative Filtering (NCF)—were applied to recommend top 10% weekly sales items, with an ensemble model integrating their strengths. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was employed, and performance was evaluated using AUC, Accuracy, Precision, and Recall metrics on separate training and test datasets. The ensemble model outperformed individual models across all metrics, while DeepFM excelled in Precision. These findings demonstrate that ensemble-based recommendation algorithms enhance recommendation accuracy for suppliers in large-scale online retail environments, offering practical implications for small-scale retailers.

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.