본 연구는 45도 방향전환 동작 시 주동다리와 비주동다리 간 지면반력 특성을 다차원적으로 비교하고, 비대칭 지수(Asymmetry Index, AI)를 정량화하여 개인별 변이성과 그 기능적 의미를 규명하 는데 목적이 있었다. 오른발이 주동다리인 건강한 대학생 38명을 대상으로 45도 방향전환 시 주동다리 와 비주동다리의 지면반력을 측정하였다. 집단 평균 수준에서는 대부분의 지면반력 변수(peak vertical, posterior, medial GRF, horizontal resultant, frontal/sagittal plane angles, VLR)에서 양측 간 유의한 차이가 나타나지 않았다(p>.05). 그러나 개인별 AI 분석 결과, 평균 22.6±16.6%(범위: 1.4-64.9%)로 높은 변이성을 나타내었으며, 71.1%가 잠재적 부상 위험 기준(10%)을 초과하였다. High AI 그룹 (n=27)은 Low AI 그룹(n=11)에 비해 peak posterior GRF(0.87 vs. 0.71, p=.009), horizontal resultant GRF(1.23 vs. 1.06, p=.004), sagittal plane angle(15.84 vs. 13.86°, p=.025)에서 유의하게 높은 수치 를 나타내었다. 이는 비대칭이 클수록 제동력과 수평면 힘 생성이 증가하며, 부상 위험이 증가될 수 있 음을 시사한다. 본 연구 결과는 집단 평균만으로는 개인의 비대칭을 평가하기 어려우며, 개인 맞춤형 평 가와 중재가 필요할 것으로 판단된다.

Due to cognitive differences, traditional perceptual engineering (KE) frequently relies too heavily on designers' experience in analyzing customers' emotional demands, which can result in product designs that deviate from users' expectations. This work suggests a thorough evaluation approach that combines the particle swarm optimization-support vector regression (PSO-SVR) model and perceptual engineering to increase the scientificity and precision of design choices. The approach first determines the subjective weights of users' emotional needs using spherical fuzzy hierarchical analysis (SFAHP). Next, it uses the entropy weighting method to determine the objective weights. Finally, it combines the subjective and objective data using game theory to produce a more rational evaluation system. Finally, the emotional prediction model based on PSO-SVR is constructed to realize the accurate mapping between emotional needs and design features. The empirical study shows that“speed”, “dynamic”and“luxury” are the core emotional demands of users, and the algorithm's prediction results are highly consistent with users' actual evaluations, which strongly verifies the accuracy of the model. Compared with the traditional KE method, the model better integrates subjective experience and objective data and provides more practical support for the design of flybridge yachts.

Malaria remains a significant public health issue, particularly in regions such as the Korean Demilitarized Zone (DMZ). Effective malaria control and prevention require precise prediction of mosquito density across both monitored and unmonitored areas. This study aimed to develop predictive models to estimate the abundance of malaria vector mosquitoes by integrating meteorological and geographical data. Data from mosquito surveillance sites and NASA MODIS land cover datasets acquired between 2009 and 2022 were utilized. Two predictive models, the Gradient Boosted Model (GBM) and Principal Component Regression (PCR), were employed and evaluated. Model performance was assessed using the coefficient of determination (R²). Results showed that PCR outperformed GBM in predictive accuracy, suggesting that PCR is more robust in handling multicollinearity among variables. However, both models did not show practically-usable level of prediction performance. This study provides a preliminary but foundational framework for extending predictive modeling to broader regions, thereby supporting malaria prevention efforts through improved risk mapping.