Earticle

디지털 변혁과 더불어 기술 패권주의가 확대되고 있는 최근의 보안환경을 고려할 경우, 산업 체 수요에 기반한 산업보안 교과과정의 구성 및 운영 필요성이 그 어느때 보다도 크게 부각되고 있다. 이에 본 연구에서는 산업체에서 필요시 되는 산업보안 교과과정을 산업체 수요에 기반하 여 확인해 보기 위해 경남지역 소재 기업체에 근무하고 있는 산업보안 담당자들을 대상으로 주 요 산업보안 교과과정에 대한 인식도 조사를 수행하였다. 분석결과, 산업보안 담당자들은 모든 산업보안 교과과정이 중요도에 비해 기업 내외부에서 실제 수행되고 있는 교육 실행 정도가 낮 은 것으로 인식하고 있었으며, 특히 일부 교과과정에 대해서는 앞으로 교육 확대 및 축소 등이 필요하다고 인식하고 있음을 확인할 수 있었다. 끝으로 기업 규모에 따라서도 산업보안 교과과 정 수요가 상이할 수 있음을 확인할 수 있었는데 이와 같은 결과는 국내 산업보안 교육과정 설계 와 운영에 있어서 학교와 산업체 현장에서 발생할 수 있는 교과과정과 직무연관성간의 차이 (gap)를 극복해 볼 수 있는 대안을 제시하였다는 점에서 그 의미가 크다.

Along with the digital transformation, the expansion of technology hegemonism demands a new security environment. In other words, the need for the composition and operation of industrial security curriculum based on industry demand is becoming more prominent than ever before. In this study, the recognition survey was conducted to check the industrial security curriculum required by the industry. As a result of the analysis, industry security officers recognized that all industrial security curriculum were less effective in implementing education both inside and outside the company than in importance. In addition, it was confirmed that some curriculum in particular need to expand or reduce education in the future. Finally, it was confirmed that the demand for industrial security curriculum could depending on the size of the company. These results are significant in that they presented alternatives to overcome differences between curriculum and job relevance that may arise at schools and industrial sites in the design and operation of industrial security curriculum.

With the rise of bandwidth-intensive applications, such as video streaming and cloud services, efficient routing decision networks have become increasingly important. Bandwidth allocation issues arise from various causes. This paper examines the Bandwidth Starvation Problem (BSP), where routing decisions that insufficiently account for low-demand flows hinder high-demand flows. Current Reinforcement Learning (RL)-based hop-by-hop routing methods overlook bandwidth demand factors, worsening the BSP. We propose a bandwidth demand-oriented reward function and a Deep Reinforcement Learning (DRL) framework to address this challenge. Experiments on Topology Zoo topologies demonstrate proposed approach enhances throughput, utilization, and maximum service capacity over existing methods.

On-demand ride services and the rideshare infrastructure primarily focus on the minimization of travel time and cost. However, the safety of riders is overlooked by service providers. For driver authentication, existing identity management methods typically check the driving license, which can be easily stolen, forged, or misused. Further, background checks are not performed at all; instead, social profiles and peer reviews are used to foster trust, thereby compromising the safety and security of riders. Moreover, the present mechanism seems ineffective in discontinuing a malicious driver from offering the services. In this paper, we present DriverAuth?a fully transparent and easy-to-use authentication scheme for drivers that is based on common behavioral biometric modalities, such as hand movements, swipes, and touch-strokes while the drivers interact with the dedicated smartphone-based application for accepting the booking. A preliminary study of behavioral biometric-based approaches offers a usable verification mechanism on smartphones that could be a potential solution to improve the safety of riders in the emerging on-demand ride and the rideshare infrastructure.

Purpose: This survey study aimed to investigate the current status, issues, and needs related to Clinical Endoscopy (CE), the official international journal of the Korean Society of Gastrointestinal Endoscopy (KSGE). Methods: A 10-item survey was emailed to domestic KSGE members between May 1 and May 15, 2023. The results were analyzed using descriptive statistics. Results: In total, 216 complete responses were analyzed. Most respondents (46.8%) read CEonce or twice monthly. The proportion of respondents who read the journal once or twice a year or did not read it at all was quite high, at 36.6%. The most informative article type was re-view articles (53%), and the least-read type was editorials (33%). Ninety-nine respondents (45.8%) stated that they did not want to submit their articles to CE because CE is not a Science Citation Index Expanded (SCIE) journal (38.4%). Eighty-nine respondents (41.2%) did not cite CE articles in their manuscripts. Furthermore, 41.2% of the respondents declined review invitations because they were too busy (73.0%). The two most common requests for CE were to increase the number of guidelines and review articles (38.0%) and to improve the journal quality (34.7%). Conclusion: Although CE is a representative journal of KSGE, the level of interest and concern for CE among society members was relatively low. Nonetheless, this survey offers valuable insights into the needs and current status of CE, paving the way for its further development. It is clear that more efforts and investments from the society and the editorial board are necessary.

본 연구는 중국 중소기업을 대상으로 AI 기반 수요예측이 경영성과에 미치는 영향을, 재고관 리 효율성과 의사결정 품질의 병렬 매개효과에 초점을 맞추어 실증적으로 분석하였다. 2025년 6–7월에 206개 기업을 대상으로 횡단면 설문을 실시하였으며, 구조방정식모형(SEM)과 PROCESS 매크로(Model 4, 부트스트랩 5,000회)를 활용해 모형을 추정하였다. 전체 모형 적합 도는 χ²/df=2.013, CFI=.971, TLI=.961, SRMR=.041, RMSEA=.070으로 수용 가능한 수준을 보였 다. 분석 결과, AI 기반 수요예측은 재고관리 효율성(β=.563, p<.001)과 의사결정 품질(β=.454, p<.001)에 유의한 정(+)의 영향을 미쳤으나, 경영성과에 대한 직접효과는 유의하지 않았다(β =.150, p=.101). 간접효과는 두 매개경로에서 모두 유의하였는데, 재고관리 경로의 효과는 B=0.0935(95% CI [0.0056, 0.1978]), 의사결정 품질 경로의 효과는 B=0.1716(95% CI [0.0796, 0.2784])로 확인되어 완전 매개가 성립하였고, 의사결정 품질 경로의 효과가 약 1.8배 더 강했 다. 본 모형은 경영성과 변동의 32.4%를 설명하였다(R²=.324). 이러한 결과는 기술 도입 그 자 체보다는, 특히 의사결정 품질의 고도화와 같은 조직 역량을 통해 AI 기반 수요예측의 성과 개선 효과가 주로 발생함을 시사한다. 따라서 관리자들은 AI 도입을 데이터 기반 의사결정의 제도화와 엄정한 재고관리 프로세스와 결합할 필요가 있다.

Purpose : This study investigates how AI-based demand forecasting affects firm performance in Chinese SMEs, focusing on the parallel mediating roles of inventory management efficiency and decision-making quality. Research design, data, methodology : A cross-sectional survey of 206 SMEs was conducted between June and July 2025. The research model was estimated using Structural Equation Modeling (SEM) and the PROCESS macro (Model 4) with 5,000 bootstrap resamples. Overall model fit was acceptable (χ²/df = 2.013, CFI = .971, TLI = .961, SRMR = .041, RMSEA = .070). Results : AI-based demand forecasting had significant positive effects on inventory management efficiency (β = .563, p < .001) and decision-making quality (β = .454, p < .001), but its direct effect on firm performance was not significant (β = .150, p = .101). Indirect effects were significant via both mediators—inventory management (B = 0.0935, 95% CI [0.0056, 0.1978]) and decision-making quality (B = 0.1716, 95% CI [0.0796, 0.2784])—indicating full mediation; the path through decision-making quality was approximately 1.8 times stronger. The model explained 32.4% of the variance in firm performance (R² = .324). Conclusions : Performance gains from AI-based demand forecasting arise primarily through organizational capabilities, particularly enhanced decision-making quality, rather than from technology adoption alone. Managers should complement AI deployment with processes that institutionalize data-driven decisions and disciplined inventory practices. Caution is warranted regarding generalizability due to the manufacturing-heavy, early-adopter sample; future research should broaden industry coverage and employ longitudinal designs.

본 연구는 학령전기 아동을 둔 어머니를 대상으로 시뮬레이년 기반 응급상황 대처 교육에 대한 요구도를 파악하고자 하였다. 조사결과 지난 한해 동안 가정에서 발생한 응급상황 중 가장 많이 발생한 경우가 출혈(30.8%), 낙상 및 추락(9.6%), 중독(3.8%), 화상(21.2%), 질식(5.8%), 골절(7.7%), 고열(38.5%), 구토 (26.9%), 심폐소생술(3.8%), 벌레나 사람에 물린 경우(48.1%), 끼임 또는 협착(9.6%) 순으로 나타났다. 아동 응급처치시뮬레이션 교육프그램이 개발되어 진행 될 경우 참여 할 의사 있다고 답한 경우가 84.6%로 높게 나타났다. 본 연구 결과는 시뮬레이션 기반 응급처치 교육이 학령전기 아동 어머니에게 효과적인 교육 방법일 수 있음을 보여 주었다. 이와 같은 결과를 바탕으로 학령전기 자녀를 둔 어머니를 위해 시뮬레이션 기반 아동 응급상황 교육 프로그램을 개발하여 중재효과를 검증하는 실험 연구를 제언한다.

This study aimed to understand the need for simulation-based emergency response education among mothers with pre-school-aged children. According to the survey, the most common emergency situations at home in the past year were bleeding (30.8%), falls and drops (9.6%), poisoning (3.8%), burns (21.2%), choking (5.8%), fractures (7.7%), high fever (38.5%), vomiting (26.9%), CPR (3.8%), being bitten by bugs or humans (48.1%), and entrapment or constriction (9.6%). 84.6% expressed interest in participating if a child emergency care simulation training program were developed. The results indicate that simulation-based emergency care training could be effective for mothers of pre-school children. Based on these results, we suggest an experimental study to develop a simulation-based emergency care program for these mothers and verify its intervention effect.

본 연구는 뇌병변 장애 성인을 대상으로 장애 정도에 따른 보행 및 이동 지원 요구와 보조기기 사용의 차이를 분석하고자 하였다. 이를 위해 2023년 장애인 실태조사 자료를 활용하였으며, 총 167,094명을 분석하고 중증군과 경증군으로 분류하였다. 카이제곱 검정 결과, 중증군은 보행 및 이동 지원에 대한 의존도가 유의하게 높았으며, 휠체어, 전동 휠체어, 하지 보조기기의 사용률 또한 더 높게 나타났다. 이러한 결과는 장애 정도가 높을 수록 보조기기 필요성이 증가하며, 기능적 의존성이 경직과 불수의 운동과 밀접한 관련이 있음을 시사한다. 본 연구를 통해 개인의 기능 상태에 기반한 맞춤형 재활 전략과 보조기기 제공의 필요성이 확인되었다.

This study aimed to examine differences in mobility support needs and assistive device use according to disability severity among adults with brain lesion-related impairments. Using data from the 2023 National Survey on Persons with Disabilities in Korea, 167,094 individuals were included and classified into severe and mild groups. Chi-square test revealed that those in the severe group showed significantly higher reliance on walking and mobility assistance, as well as greater use of wheelchairs, electric wheelchairs, and lower limb orthoses. These findings indicate that assistive device needs increase with severity, and that functional dependence is strongly associated with spasticity and involuntary movement. Results support the need for individualized rehabilitation strategies and device provision based on functional status.

The rapid of electric vehicles (EVs) is challenges and opportunities for energy grid management and infrastructure planning. This research is aims to fill the knowledge gap by employing advanced analytical methods on a 503-day time series dataset from Thailand's EV charging stations. The dataset includes information on date, station name, connector type, energy consumed in kWh, payment in Baht, vehicle brand and model, as well as customer ID. This study focuses on three main objectives: (1) Forecasting daily energy demand with a focus on the top 5 stations in terms of kWh consumption to identify seasonality and trends, (2) Predicting daily revenue based on energy consumption, and (3) Conducting a Geo-Spatial Analysis to recommend optimal locations for installing new EV charging stations. The insights derived are expected to assist in efficient grid management, revenue planning, and strategic infrastructure deployment.

Purpose: The purpose of this study was to offer the baseline data for developing a systematic and high quality of clinical practice guideline by exploring how nurses utilize clinical guidelines and what they need for. Method: This study has been done with 242 nurses of a university hospital in Daegu using a self- administered questionnaire. The instrument used in this study was developed by researchers based on the results of the previous studies. Data analysis was done with SPSS 11.0 Program. Results: Nurses felt that clinical guidelines were not sufficiently disseminated to update their clinical knowledge education. Nurses showed the strong demand for developing clinical practice guidelines with the newest and systematic evidence. However, a relatively low number of nurses knew evidence-based nursing and evidence-based clinical guidelines. Conclusion: It is necessary to develop an educational program for evidence-based nursing and an evidence-based nursing clinical practice guideline for nurses and to explore the strategies for development and dissemination of evidence- based clinical practice guidelines to solve the urgent and frequent clinical problems.

목적: 본 연구는 직무요구-자원 모델에 기반 하여 중환자실 간호사의 소진 구조모형을 구축하고 검증하는 데 목적이 있다. 방법: 직무스트레스, 공감능력, 극복력, 직무만족, 공감 피로 및 소진간의 관계를 분석하기 위해 중환자실 간호사 414명의 설문자료를 사용하였다. 중환자실 간호사의 소진에 영향을 주는 변인들의 직, 간접 효과 분석을 위해 SPSS WIN 22.0과 AMOS 22.0 프로그램이 사용되었다. 결과: 최종적으로 수정된 가설적 모형의 적합도는 χ2=216.59, χ2/df=3.38, GFI=.93, AGFI=.89, NFI=.90, CFI=.93, RMSEA=.07, SRMR=.06으로 나타나 최적 모델 기준에 충족하였다. 직무만족과 공감피로는 다른 세 외생변수와 소진간의 매개효과를 갖는 것으로 나타났다. 결론: 본 연구 결과를 통해 직무만족을 향상시키고 공감피로를 줄이기 위해 지지 프로그램 개발과 행정 차원의 개선이 요구된다.

Purpose: The purpose of this study was to construct and test a structural equationmodel of burnout of the critical care nurses based on the job demand-resource model. Methods: A structured questionnaire was completed by 414 critical care nurses. The relationships between concepts of job stress, empathic ability, resilience, job satisfaction, compassion fatigue and burnout were analyzed. Using SPSSWIN 22.0 and AMOS 22.0 programs, the direct and indirect effects of factors affecting burnout among critical care nurses were calculated and modelled. Results: The modified model was yielded as follows: Chi-square= 216.59, GFI= .93, AGFI= .89, NFI= .90, CFI= .93, RMSEA= .07, SRMR= .06 and showed good fit indices. Job satisfaction and compassion fatigue had mediation effects between other three exogenous variables and burnout. Conclusion: The major findings of this study indicate that it is important to develop a support program for critical care nurse in order to improve their job satisfaction and ameliorate their compassion fatigue.

수요예측은 제품에 대한 수요량을 예측해 자원을 관리하기 위한 방법으로, 기업의 노동력과 예산 관리에 영향을 미 친다. 이러한 이유로 수요예측 모델의 성능 향상을 위한 연구가 주목을 받고 있다. 본 연구에서는 수요예측 성능 향 상을 위해 품목의 수요 패턴을 분석해 4가지 유형으로 구분하고, 각 유형에 적합한 모델을 제안한다. 성능 비교를 위해 사용한 데이터는 대한민국 공군 T-50 단일 기종의 수리 부속 품목의 분기 별 수요 데이터이다. 품목의 수요 패턴은 수요발생구간(average demand interval, ADI)과 변동 계수(coefficient of variation, CV)를 사용해 네 가지 smooth, lumpy, intermittent, erratic으로 구분하며 다양한 알고리즘으로 구현한 수요예측 모델의 성 능을 비교하기 위해 5가지 기계학습 알고리즘과 2 가지 딥러닝 알고리즘을 사용해 수요예측 모델을 구현한다. 기계 학습 알고리즘 중에는 앙상블 알고리즘인 random forest regression, adaboost, extra trees regression, bagging, gradient boosting regression 과 딥러닝 알고리즘인 long-short term memory(LSTM), deep neural network(DNN)을 사용한다. 수요 패턴에 따른 네 가지 유형에 적합한 모델을 선정해 수요예측 결과를 도 출한 경우가 일관된 모델을 사용한 경우에 비해 품목 정확도가 0.61%, 수량 정확도가 0.09 우수한 것을 확인할 수 있다. 제안하는 모델을 적용한다면 전문가의 효율적인 수요 관리가 이루어질 수 있을 것으로 기대한다.

Demand forecasting is a way to manage resources by forecasting demands for products, so it has direct impacts on corporate resources and budget management. Based on these reasons, research on improving forecasting performances of demand forecasting models. In this research, 4 demand patterns for items were analyzed to improve demand prediction performance, and the optimal model was proposed. The data used to compare the performance were the demand data from each quarter for maintenance items for a T-50 aircraft of Republic of Korea air force. First, the demand patterns for the items adopted average demand interval(ADI) and coefficient of variation(CV) and were categorized into smooth, lumpy, intermittent, and erratic items. In this research, to compare the performance of demand forecasting models derived from different algorithms, 5 types of machine learning algorithms and 2 types of deep learning algorithms were used to construct demand forecasting models. In machine learning algorithms, there are ensemble learning such as random forest regression, adaboost, extra trees regression, bagging, gradient boosting regression and deep learning algorithm such as long-short term memory(LSTM) and deep neural network(DNN). We can confirm that item accuracy is 0.61% and quantity accuracy is 0.09% better than that of consistent models when the demand forecast results are derived by selecting models suitable for four types according to demand patterns. We expect that efficient demand management by experts will be achieved if the application of the proposed model.

Forecasting long-term mobile service demand is inevitable to establish an effective frequency management policy despite the lack of reliability of forecast results. The statistical forecasting method has limitations in analyzing how the forecasting result changes when the scenario for various drivers such as consumer usage pattern or market structure for mobile communication service is changed. In this study, we propose a dynamic model of the mobile communication service market using system dynamics technique and forecast the future demand for long-term mobile communication subscriber based on the dynamic model, and also experiment on the change pattern of subscriber demand under various scenarios.

KOBIS통합전산망으로부터 관객 수와 매출액에 관한 영화별 × 광역시도별 × 주별 자료를 채취한 후, 영화에 대한 수요를 확률계수 로짓모형으로 설정하여추정하고, 수요의 가격탄력성을 중점적으로 분석하였다. 영화별 × 광역시도별 × 주별 단위로 추정된 총 13,077개의 가격탄력성 추정치는 -8.585 ∼ -7.402 × 10-6 범위에 넓게 분포하여 영화는 개별 상품에 대한소비자 선호가 아주 다채로운 상품에 해당함을 확인할 수 있었다. 추정치의 표본 평균값은 -0.890, 표본 중위값은 -0.610으로서 한국을 대상으로 한 거시적연구가 보고한 추정치보다는 더 탄력적이지만 국외를 대상으로 한 미시 및 거시적 연구가 보고한 추정치보다는 더 비탄력적이었다.

From KOBIS Database compiling administrative and marketing information on every movie on screen in Korea, we constructed weekly data on movie-goers and sales at the individual movie/ province level. We then estimated a random coefficients logit model for cinema demand at the individual movie/week/province level. The 13,077 own price elasticity estimates range widely from -8.585 to -7.402 × 10-6 , manifesting colorful preferences of individual moviegoers. Sample mean and median of these estimates are -0.890 and -0.610, which are less elastic than the estimates from micro-level and macro-level studies on Spanish, German and Australian markets, and far more elastic than the estimate from a macro-level study on Korean market.

This study analyzes both locations and demands for preschool in the city of Jeonju, Jeolabuk-do, Korea through measuring the accessibility to preschools within the city and then addresses the issue of its spatial disparity. In order to deal with this problem, this paper calculates the index of its accessability using 2-Step Floating Catchment Area(2SFCA) and network analysis. Then, the level of accessibility is divided into five categories: Level 1(Highly Satisfactory), Level 2(Satisfactory), Level 3 (Accessible), Level 4(Accessible but Deficient), and Level 5(Not Accessible). Finally, the measures and policies appropriate for each level are made to solve inadequate distribution. The results are as follows. While the ratio of supply to demand is more than 70% in total, demands within Level 1 and Level 2 category accounts only for 25.2% out of the total demand, showing that there is serious inadequate distribution and spatial disparity. Therefore, it is recommended that the areas categorized Level 1 and 2 are appropriate to maintain the current preschool services. However, the areas in Level 3 should enhance its accessibility and the regions belonging to Level 4 should try to make accessibility improvement as well as capacity expansion of preschools. Lastly, the areas in Level 5 should provide additional preschools to satisfy its basic demands.