This study develops integrated evaluation indicators for demand responsive transport (DRT) operation services from a road traffic service perspective. The purpose is to support sustainable and efficient DRT service provision by considering the perspectives of users, operators, and local communities, while also reflecting road-based operational issues such as pick-up/drop-off management, curbside stopping, transfer-node linkage, and parking-demand reduction. While previous evaluations have often focused on operational efficiency, platform performance, ridership records, or user satisfaction, this study attempts to establish a comprehensive framework applicable to various DRT service types. DRT services were classified into four types according to temporal and spatial variability: shuttle-bus type, pre-call reservation type, real-time call reservation type, and dynamic call reservation type. Candidate evaluation indicators were derived through reviews of domestic and international DRT operation cases, public transport service-quality studies, MaaS performance studies, accessibility and equity evaluation studies, and existing evaluation practices. An analytic hierarchy process (AHP) survey was conducted with experts from research institutes, local governments, public agencies, and academia to identify the relative importance of indicators. The results indicate that convenience of booking methods and user satisfaction are mandatory common indicators for all DRT types. Safety, barrier-free service provision, driver courtesy and professionalism, revenue per operating cost, and operation time were identified as optional common indicators. In addition, arrival information change rate, changes in the number of users, operation distance, ride distance, service utilization rate, and parking space saving effect were suggested as type-specific indicators. The proposed indicators can support integrated monitoring of DRT operation services and provide practical information for service improvement, policy decisions, efficient public subsidy support, and road-space management.

Drowsy driving on highways is a critical traffic safety issue because the monotonous driving environment frequently induces driver fatigue, often leading to severe or fatal crashes. This study aims to develop a quantitative drowsy driving index (DDI) to proactively identify highrisk segments and establish effective safety management strategies. To achieve this, diverse static and dynamic traffic data along with weather conditions were integrated. We developed two distinct frequency models: a crash risk model setting drowsy driving crashes as the dependent variable, and a drowsiness-inducing risk model based on the frequency of drowsy driving behaviors. Poisson and negative binomial regression models were employed to account for the rare-event nature and overdispersion of crash data. Variables were carefully selected based on the variance inflation factor to prevent multicollinearity, and optimal models were determined using the Akaike information criterion. The final DDI was formulated by combining the quantified crash and drowsiness-inducing risks through a weight-based integration method. The analysis revealed a strong positive correlation between the proposed DDI and both actual drowsy driving crashes and behavioral frequencies. The proposed DDI framework offers a robust tool for monitoring and managing drowsy driving risks. Through public-private partnerships, this methodology can be utilized to provide real-time, location-based warnings and rest area guidance to drivers to significantly mitigate crash risks. Future research should expand the analysis to encompass a wider highway network and incorporate road geometry data, such as curve radii and gradients, to further enhance the precision and realism of the index.

We developed a device that can screen for shape and surface defects that may occur during the manufacturing process, and can be used for quality inspection during the production process. A non-contact vision system was used for shape quality inspection to check sphericity and concentricity. Shape size was measured to determine whether the defects were within acceptable limits. Even for products that passed shape quality inspection, surface defects could prevent valve sealing. Therefore, the sealing performance of the armature was verified by examining the pressure changes in the air supply section using high-pressure air pressure applied to each assembled valve. To assess the reliability of the inspection device for tip-type armature defects, various defective products were artificially mixed and tested in random order. The results showed that the 100% detection rate for armature defects confirmed its suitability for tip-type armature quality inspection.

도시 불투수 지역에서 발생하는 초기 강우 유출수는 고농도의 비점오염물질을 단기간에 하천으로 유입시켜 수질 악화를 초래한다. 본 연구에서는 우수받이 및 맨홀에 설치 가능한 다단 폴리프로필렌 섬유 여과장치를 개발하고, 실험실 및 현장자료를 기반으로 오염물질 제거 효율을 평가하였다. 섬유 여재 조합 실험을 통해 53 μm, 20 μm, 10 μm 구성의 3단 여과가 TSS, TN, TP 제거에 가장 효과적인 것으로 나타났다. 현장 초기유출수 분석 결과, 평균 제거 효율은 TSS 79.7%, TN 31.6%, TP 43.1%로 확인되었다. 또한 SWMM을 이용하여 학사마을 도시유역에 적용 시 25%, 50%, 100% 설치비율에 대해 TSS 47.3∼99.2%, TN 21.6∼49.4%, TP 23.6∼67.7%의 저감효과가 모의되었다. 본 연구의 결과는 공간 제약이 큰 도시지역에서 초기세척효과(first flush)를 제어하기 위한 실용적인 비점오염 관리기술로서 활용 가능성을 제시한다.

This study presents a standalone diagnostic device for HEV high-voltage battery packs that communicates directly with the BMS outside the vehicle and enables quantitative verification of BMS SOC and SOH outputs. The prototype, developed for a Renault CMA HEV pack, activates the BMS via the low-voltage harness, reads key variables such as SOC, SOH, cell voltage and temperature, and pack voltage and current over CAN, and safely controls the pack’s high-voltage relay. Using a pack reported as 100% SOH by the BMS, constantcurrent discharge at about a 0.1 C-rate was performed in the SOC range from 30% to 45%, and for 5, 10 and 15-minute segments the usable energy estimated from the BMS SOC and the rated capacity showed mean values around 1.54kWh with a coefficient of variation of approximately 2-3%. The proposed BMS-linked evaluation equipment estimates the usable capacity within a tolerance consistent with the manufacturer’s nominal specification and can serve as a practical basis and tool for second-life evaluation of used high voltage battery packs.

국내 조직은 오랫동안 문화적·제도적 기반이 충분히 마련되지 않은 상태에서 외국의 인적자원 평가제 도를 무비판적으로 도입해 혼란을 겪어 왔다. 직무연구의 부재 속에서 직무분석을 전제로 한 평가방식을 적용하거나, 경쟁적 환경에도 불구하고 보수적·속인적 특성을 중심으로 한 평가를 지속한 사례가 대표적 이다. 특히 외환위기 이후 성과주의가 본격 도입되면서 연공중심의 기존 평가체계는 급격한 변화를 맞이 했고, 연봉제·인센티브제 등 새로운 보상체계의 확대는 팀워크 저하와 인간관계 훼손 등 부작용도 함께 초래하였다. 이러한 성과주의 체계가 조직 내에 안정적으로 정착하기 위해서는 무엇보다 평가시스템의 공정성·합리성·신뢰성 확보가 필수적이다. 본 연구는 (B)시설관리공단(FMA: Facilities Management Authority)의 인적자원 평가체계 운영 실태를 분석하고, 인사평가·내부평가·다면평가 각 영역에서 나타나 는 구조적 문제를 검토하여 개선방안을 제시하는 데 연구의 주안점을 두었다. 이론적 고찰과 실태분석 결과를 통합하여 인적자원평가의 타당성을 제고할 수 있는 대안을 도출하고, 조직 특성과 직무환경을 반 영한 전략적 인적자원 평가체계 구축을 위한 정책적 시사점을 제안하였다.

This pilot study aimed to develop a novel type of functional dietary management food for individuals with diabetes, utilizing a soup and bread format, and to evaluate its postprandial glycemic response and consumer acceptability. Two types of soup and bread meals—tomato vegetable soup and curry chicken soup—were formulated using chickpeas and low-fat meat and were compared with a commercially available diabetic management meal. In a randomized crossover design, 15 participants consumed one of three meals (experimental or commercial diabetic meal) on separate days. Blood glucose levels were measured at 0, 30, 60, and 120 minutes post-meal. Experimental meals showed stable glycemic responses evidenced by decreased Area Under the Curve (AUC) (Tomato 12839.0±1432.8, Curry 12683.0±1408.4) compared to commercial meals (15485.0±1870.5, p<0.05). Sensory evaluation revealed moderate acceptability, and subjective hunger, satiety, and food cravings were also assessed, but showed no significant differences among three types of meal. Although limited by the small sample size and short intervention duration, these findings provide preliminary evidence supporting the feasibility of tailored meal solutions for metabolic conditions. Further large-scale, long-term studies are warranted to validate these outcomes and optimize product development for diverse populations.

This study aims to develop and evaluate a GPT-based English learning system that creates reading materials tailored to the Korean middle school first-grade English curriculum. To this end, this study adopted OpenAI’s MyGPTs platform and created the AI system through eight different developmental versions by iterating prompts and uploading relevant knowledge files. To evaluate its linguistic appropriateness, this study generated reading passages at three difficulty levels (low, medium, high) and compared them with eight textbook texts using seventeen key indices from Coh-Metrix 3. The results show that GPT-generated texts at the medium level most closely resembled actual textbook passages in sentence count, syntactic simplicity, lexical familiarity, and overall readability. Low-level outputs achieved the highest readability and concreteness scores, indicating suitability for beginner EFL learners, while high-level outputs featured greater syntactic complexity, longer sentences, and richer lexical diversity. The study also identified limitations in GPT’s consistent adherence to prescribed difficulty parameters, text-type variety, and adaptive difficulty adjustment based on learner performance.

Purpose: This study aims to develop and evaluate A4, a generative AI agent (Adaptive Aptitude Assessment by AI for Nursing Students), designed to support undergraduate nursing students in matching their personal traits with suitable clinical roles. Method: An exploratory research design was used with 71 nursing students and 65 clinical nurses. Developed using ChatGPT-4, the A4 agent classified nursing roles, identified required personal traits, and generated adaptive test items. Perceived person–job fit was measured via survey, and the data were analyzed using descriptive statistics and independent t-tests. Results: Among students, 80.3% agreed that the A4 results represented their characteristics, compared with 69.2% of nurses. Nursing students had higher perceived aptitude–fit scores (2.99 ± 0.62) than nurses (2.74 ± 0.69), indicating a significant difference (t = 2.19, p = .030). Conclusion: The findings indicate that the generative AI-based adaptive assessment, which incorporated clinical scenarios, was perceived by nursing students and clinical nurses as a tool that represents personal traits. This suggests its potential to support personalized career guidance in nursing education and serve as a supplementary tool for clinical practicum orientation.

바다의 사막화라 할 수 있는 갯녹음(barren ground)은 우리나라뿐만 아니라 미국, 일본 등 전세계적으로 발생하여 수산생물의 서식지 황폐화, 해양 탄소고 정 능력 약화, 해양생태계 순기능 상실이라는 부정적 영향을 미치고 있다. 갯 녹음의 발생원인은 기후변화, 해수온도 상승, 해양 산성화, 이산화탄소, 지하수, 조식동물, 해양오염 등 복합적인 요인으로 추정하고 있으나 현재까지는 정 확한 원인을 규명하지 못하고 있다. 갯녹음 대응을 위해 우리나라는 바다숲(marine forests) 조성사업을 시행하 고 있지만 갯녹음이 왜 발생하는지에 대한 원인을 명확하게 규명하지 못한채 사업이 진행되다 보니 갯녹음 발생이 줄어들지 않고 있다. 본 연구는 갯녹음의 유발요인을 찾기 위해 현재 해양환경관리를 위한 지표 (indicator)와 지수(index)를 살펴보고 바다숲 조성사업 시행에서 나타난 바다숲 조성사업 시행지침상의 평가항목, 천연 해조장 등급화상의 평가항목, 갯녹음의 진단 및 판정 기준상의 평가항목을 검토하고 이들 지표 및 지수에서 갯녹음 관 리를 위한 지표와 지수의 개발 방향에 대해 검토하였다.

Permeable blocks are effective in improving urban water circulation and alleviating heat islands and floods. However, they cause environmental problems owing to their dependence on cement and natural aggregates. In this study, a permeable block was developed, and its performance was verified using ferronickel slag (FNS) as a substitute. The block applied with FNS met the KS F 4419 standard, and an average flexural strength of 4 MPa and a permeability coefficient of 0.1 mm/s or more were secured. This study confirmed that natural aggregate collection could be suppressed, resource efficiency could be improved, greenhouse gas reduction could be achieved, and high value-added industrial byproducts could be used.

This study reports the development of a new alfalfa (Medicago sativa L.) variety, ‘Alfaone’, at the Forage Production Systems Division, National Institute of Animal Science, Rural Development Administration, Korea, from 2015 to 2023. The variety originated from an artificial cross between Xun Lu (maternal parent) and RadarⅡ Brand (paternal parent), followed by pedigree selection and performance testing. The elite line ‘MsCB01’ was subsequently released as ‘Alfaone’. Regional adaptability trials were conducted for two years (2022–2023) across four representative sites in Korea (Cheonan, Pyeongchang, Jeongeup, and Jinju) to evaluate agronomic traits, forage yield, and quality. Evaluated characteristics included plant height, regrowth ability, winter survival, and lodging resistance. The average dry matter yield of ‘Alfaone’ was 20,811 kg/ha, approximately by about 3% higher than that of the standard cultivar ‘Vernal’ (20,236 kg/ha). Yield superiority was particularly evident in Pyeongchang, suggesting excellent cold tolerance and winter hardiness. Assessment of forage nutritive traits indicated that ‘Alfaone’ was comparable to ‘Vernal’, demonstrating that its yield advantage did not come at the expense of quality. Overall, ‘Alfaone’ is a promising cultivar that combines high productivity with strong adaptability to unfavorable environments, particularly cold-prone regions. Its release is expected to promote the expansion of alfalfa cultivation, enhance forage self-sufficiency, and reduce dependence on imported hay in Korea.

Current portable reference equipment used to evaluate the performance of vehicle detectors can collect traffic volume and speed only for the outermost lanes in each direction. Passing vehicles on the other lanes are manually counted by reviewing the recorded videos. Consequently, only traffic volume—without vehicle speed—is used as a reference value. This method is time-consuming for comparing the performance data from the equipment with the reference data and can compromise the performance evaluation. This study aims to enhance the efficiency of vehicle detection system (VDS) performance evaluations by developing multilane portable reference equipment that can accurately collect traffic information for lanes beyond the outermost lane or for more than two lanes. This study introduced the core technologies of multilane portable reference equipment and compared and analyzed the measurement accuracy of the developed equipment against data from fixed reference equipment operated by the Intelligent Transportation System (ITS) Certification and Performance Evaluation Center, following ITS performance evaluation criteria. The data from the fixed reference equipment were considered the true values, providing a basis for evaluating the accuracy of the measurements by the developed equipment. First, the accuracy of the vehicle length was determined by driving four test vehicles, each measuring 7,085 mm in length, 24–29 times in each lane. The accuracy was calculated by comparing the vehicle length data obtained from the fixed reference equipment with the actual vehicle length. A confidence interval was established for this accuracy. To assess the accuracy of the speed and occupancy time in relation to the accuracy of the analyzed vehicle length, we evaluated the error range of the vehicle length according to variations in speed and occupancy time. This analysis was based on the following relationship equation: “vehicle length = speed × occupied time – sensor spacing.” The analysis used data from approximately 16,000 vehicles, including the speed, occupancy time, and vehicle length, collected between 8:00 am and 12:00 pm on August 8, 2024. The principle behind measuring traffic volume and vehicle speed using multilane portable reference equipment involves detecting a vehicle by analyzing the time difference between the driver and passenger tires. The vehicle speed was calculated using the installation angle of the tire detection sensor and trigonometric functions. An analysis of the measurement accuracy revealed that the traffic volume accuracy of the outermost lane (the fourth lane) was 100% during both day and night. The speed accuracy was 98.8% during the day and 97.7% at night, representing the highest performance in these metrics. Additionally, the traffic volume accuracy for the innermost lane (the first lane), as measured by the detection sensor from the third lane, was more than 99.3% at all times, with a speed accuracy exceeding 96% during the day and night, that also demonstrated excellent results. The analysis results indicated that the multilane portable reference equipment developed in this study was suitable for evaluating the VDS performance. This equipment allowed the collection of traffic volume and speed data from all lanes, rather than only the outermost lanes. This capability enabled consistent analysis for each lane and enhanced efficiency by reducing the analysis time. Additionally, this is expected to improve the reliability of the performance evaluations.

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.