Agrophotovoltaic (APV) system is an integrated system producing crops as well as solar energy. Because crop production underneath Photovoltaic (PV) modules requires delicate management of crops, smart farming equipment such as real-time remote monitoring sensors (e.g., thermometers, irradiance sensors, and soil moisture sensors) is installed in the APV system. This study aims at introducing a simulation-based decision support system (DSS) for smart farming in an APV system. The proposed DSS is devised to provide a mobile application service, satellite image processing, real-time data monitoring, and simulation-based performance estimation. Particularly, an agent-based simulation (ABS) is used to mimic functions of an APV system so that a data-driven function and digital twin environment are implemented in the proposed system. The ABS model is validated with field data collected from an actual APV system at the Jeollanamdo Agricultural Research and Extension Services in South Korea. As a result, farmers and engineers enable to efficiently produce solar energy without causing harmful impact on regular crop production underneath PV modules. In addition, the proposed system will contribute to enhancement of the digital twin technology in the field of agriculture.

본 연구는 소비자가 경험재인 웹소설을 선택하는 상황에서 사실적 메시지와 평가적 메시지를 담은 온라인 리뷰를 보았을 때 더 유용하다고 판단하는 리뷰가 무엇인지 탐색하고, 이러한 유용성 평가의 차이에 대한 개인의 분석적 의사결정 성향 수준의 조절효과를 검증하기 위하여 실시되었다. 경험재를 구매하기 전 객관적 정보를 수집하는 소비 자의 성향에 근거하여 웹소설의 소비자는 사실적인 온라인 리뷰의 유용성을 더 높이 평가할 것으로 예상하였다. 또 한 인지적 성향에 따라 구분되는 의사결정 유형 중 분석적 의사결정자는 정확한 정보를 수집하여 논리적인 판단을 내린다. 따라서 분석적 의사결정 성향 수준이 높아짐에 따라 사실적인 온라인 리뷰의 유용성 평가가 높아질 것으로 예상하였다. 실험 1의 결과 사실적인 리뷰를 제시받은 집단이 평가적인 리뷰를 제시받은 집단보다 리뷰의 유용성을 높이 평가하였으며, 실제 웹소설 선택 상황과 유사하게 두 유형의 리뷰를 동시에 제시한 실험 2에서도 리뷰의 유용 성 평가에 대한 리뷰 메시지 유형의 주효과가 확인되었다. 또한 실험 2에서는 분석적 의사결정 성향 수준이 높아짐 에 따라 사실적인 리뷰의 유용성을 높이 평가하는 경향이 드러나 분석적 의사결정 성향 수준의 조절적 역할이 확인 되었다. 본 연구는 경험재인 웹소설을 선택하는 상황에서 리뷰 메시지 유형 및 소비자의 분석적 의사결정 성향 수준 이 소비자의 리뷰 유용성 평가에 영향을 끼친다는 사실을 확인함으로써 웹소설 소비자의 행동 양식을 밝혔다는 이론 적, 실무적 의의를 갖는다.

With the enforcement of the “Act on decisions on life-sustaining treatment for patients at the end of life” in February 2018, discussion on advanced care planning (ACP) has increased. However, as decisions on life-sustaining treatments are still made in the intensive care unit, deaths related to the suspension of life-sustaining treatment account for a large proportion of deaths in the intensive care unit. The nurses encounter challenges in supporting the patient's dignified death; they experience an ethical dilemma in the ambiguity due to a lack of guidance on legal responsibilities regarding decisions on life-sustaining treatment. In order for the nurses to perform as a supporter providing care to the patients and as a advocate during the process of decision-making on life-sustaining treatment, there should be a systemic change to ensure the nurses' participation. In addition, an open and continuous discussion should be proposed to cultivate nurses’ ethical sensitivity and moral courage. This paper reports two ethical examples related to the decisions on life-sustaining treatment occurred in intensive care units of a tertiary hospital.

This paper analyzed the correlation between injection molding factors through correlation analysis. In addition, the decision-tree model, which is a white box model with excellent explanatory power, was used to obtain optimal molding conditions that satisfy multiple constraint conditions. First, 243 data to be used in the experiment were created through a full factorial design. Second, a correlation analysis was conducted to understand the correlation. Third, to verify the decision-tree model, the prediction performance was evaluated using RMSE. As a result, good prediction performance was confirmed. A decision-tree experiment analysis was conducted. As a result of the progress, the same results as the correlation analysis were derived. Based on the previous analysis results, optimal molding conditions were applied to CAE. As a result, the amount of deformation in the multi-cavity could be improved by about 1.1% and 2.72% while satisfying the constraint.

본 연구는 장애인의 평생교육 참여의사에 영향을 미치는 요인을 밝히는 것에 주 목적이 있다. 이는 장애인 평생교육의 당위성과 실태에 관한 연구에 비해 상대적으로 부족한 장애인의 참여의사 영향요인을 통계적 으로 분석하는 연구의 필요성에서 출발하였다. 이를 위해 세종시 거주 성인 장애인 571명을 대상으로 설문조사를 실시하였고, 이 가운데 총 562부를 유효 설문지로 분석하였다. 로짓분석 결과 평생교육 참여 결정요인으로 장애인의 연령, 교육수준, 경제활동상태, 그리고 평생교육 참여경험 등이 통계적으로 유의미 하였다. 다음으로 장애유형별 로짓분석은 정신장애 유형을 제외한 모든 유형에서 평생교육 참여경험의 유의미성이 확인되었다. 이 같은 연구결과를 토대로 장애인의 평생교육 참여기회 제공을 위한 노력과 함께 장애유형에 따른 운영방안을 제안하였다.

With the rapid development of artificial intelligence, the use of artificial intelligence in the field of employment, especially in recruitment, is gradually increasing. Artificial intelligence judges applications for employment of applicants and analyzes interview videos to determine emotional intelligence, communication skills, cognitive ability, and problem-solving ability. However, there is a risk of discrimination in evaluating humans in hiring by these automated decisions made by artificial intelligence. Discrimination in hiring according to gender, race, or disability is prohibited by various laws such as the Constitution, Labor Standards Act, Framework Act On Employment Policy, and Employment Security Act in Korea. In particular, the Act On The Prohibition Of Discrimination Against Persons With Disabilities, Remedy Against Infringement Of Their Rights and the Act On The Employment Promotion And Vocational Rehabilitation Of Persons With Disabilities require special protection for people with disabilities. Discrimination against persons with disabilities can be divided into direct discrimination against persons with disabilities without justifiable grounds, and indirect discrimination that results in disadvantageous consequences for persons with disabilities by applying standards that do not take disabilities into account even though they are not formally treated unfavorable. When hiring according to automated decision-making, (i) intentional discrimination that intentionally sets applicants with elements of the disablity to be excluded from recruitment, (ii) making an automated decision not to hire the disabled through the existing data which prejudice against the disabled is already reflected, and (iii) if the existing data lacks or does not have information on the disabled, the data itself lacks representativeness, leading to distorted decisions, there is a risk that will be judged as discrimination for the disabled. In Korea, the Credit Information Use And Protection Act has proposed the definition of automated evaluation for owner of credit information and protection standards, but it is limited to the protection of credit information, and a recent amendment of the Personal Information Protection Act suggests protection measures from decisions made by automated systems, but it is criticized for the scope of protection being reduced than that of the GDPR. In addition, special protection measures for workers, such as the ILO’s Code of Practice for Protection of Workers’ Personal Data, are not mentioned separately, so it is necessary to devise legal protection measures in the hiring process according to automated decision-making of disabled workers.

본 연구는 간호대학생의 진로정체감과 진로의사결정유형이 진로준비행동에 미치는 영향을 파악 하기 위해 시행하였다. 연구대상자는 J도 소재 3,4학년 간호대학생 198명으로, 2021년 6월 14일부터 6월 27일까지 온라인 설문지를 이용하여 자료를 수집하였다. 자료는 SPSS/WIN 23.0 프로그램을 이용하여 기 술통계, 독립 t-test, one-way ANOVA, Pearson’s correlation, Hierarchical regression으로 분석하였다. 연구결과, 진로정체감 2.79점(범위 1∼4), 합리적 유형 3.78점(범위 1∼5), 직관적 유형 3.38점(범위 1∼5), 의존적 유형 3.01점(범위 1∼5), 진로준비행동 3.51점(범위 1∼5)이었다. 진로준비행동은 진로정체감 (r=.40, p<.001), 합리적 유형(r=.50, p<.001), 직관적 유형(r=.22, p=.002)과 양의 상관관계가 있었고, 의 존적 유형(r=-.20, p=.004)과는 음의 상관관계가 있었다. 진로준비행동에 영향을 미치는 요인은 진로정체 감(β=.23, p=.001), 합리적 유형(β=.31, p<.001), 직관적 유형(β=.27, p<.001), 의존적 유형(β=-.20, p=.002)으로 나타났다. 이 변수들은 진로준비행동을 24.6%p 설명하였다(F=11.93, p<.001). 본 연구결과를 바탕으로 간호대학생의 진로준비행동을 향상시키기 위해서 진로정체감 확립과 진로의사결정 유형에 따른 진로지도를 포함시킨 프로그램 개발 및 적용을 제안한다.

The Life-sustaining Treatment Decisions Act has became enforced in 2018. Patients' decision-making on life-sustaining treatments can change according to their health status and situations and thus should be understood as the process requiring continuous care. Therefore, it is necessary to build a support system to help patients' decision-making at end-of-life care including life-sustaining treatments. Nurses provide continuous care at the bedside and play a significant role in supporting patients' and their families' decision-making about end-of-life care. Based on reviewing literature and the enforcing process of the Life-sustaining Treatment Decisions Act, this paper described nurses' nursing care activities to support decision-making on life-sustaining treatments. The nursing care activities are 'empathetic support for the decision-making,' 'respect for patients and families as agents for the decision-making,' and 'support for the decision-making as a counselor.' It can emphasize the importance of a nurse's role as a support system for patients’ and their families' decision-making about end-of-life care.

Most of the open-source decision tree algorithms are based on three splitting criteria (Entropy, Gini Index, and Gain Ratio). Therefore, the advantages and disadvantages of these three popular algorithms need to be studied more thoroughly. Comparisons of the three algorithms were mainly performed with respect to the predictive performance. In this work, we conducted a comparative experiment on the splitting criteria of three decision trees, focusing on their interpretability. Depth, homogeneity, coverage, lift, and stability were used as indicators for measuring interpretability. To measure the stability of decision trees, we present a measure of the stability of the root node and the stability of the dominating rules based on a measure of the similarity of trees. Based on 10 data collected from UCI and Kaggle, we compare the interpretability of DT (Decision Tree) algorithms based on three splitting criteria. The results show that the GR (Gain Ratio) branch-based DT algorithm performs well in terms of lift and homogeneity, while the GINI (Gini Index) and ENT (Entropy) branch-based DT algorithms performs well in terms of coverage. With respect to stability, considering both the similarity of the dominating rule or the similarity of the root node, the DT algorithm according to the ENT splitting criterion shows the best results.

PURPOSES : The aim of this study is to develop a decision-making model for safety countermeasures based on the characteristics of water deer roadkills. METHODS : Through field investigation, 113 water deer roadkills with factors related to environment, geometry, and ecology were collected from May 2018 to April 2019. From the collected database, the characteristics of water deer roadkills were analyzed. An analytic hierarchy process was applied to establish a decision-making model to prevent water deer roadkills from appearing. RESULTS : The likelihood of water deer roadkills increases in summer and winter, road sections in suburban areas with low traffic volume, surrounding farmlands, and areas without illumination. The results show that factors such as safety, ecology, road geometry, policy consistency, and the willingness of the local government are critical factors for establishing the decision-making model. CONCLUSIONS : Appropriate safety countermeasures for water deer roadkills can be developed if the roadkill frequency, degree of sight distance restriction, speed limit for controlling overspeeding, roadkill severity, degree of forest area, traffic volume, and willingness of competent authorities are considered as essential variables.

본 연구는 데이터 마이닝 기반 의사결정 나무 분석을 적용해 Z세대 스포츠 소비 스타일을 탐색 하여 Z세대가 주도할 스포츠 소비 시장을 예측하기 위한 기초자료를 제공하고자 했다. 따라서 Z세대 중 만 19세 이상 남성 및 여성을 표본으로 선정해 본 조사를 실시했으며, 총 429명의 자료를 최종 분석에 사용했다. 자료처리는 SPSS statistics(ver. 21.0) 프로그램을 이용하여 빈도분석, 탐색적 요인분석, 재검사 신 뢰도 및 신뢰도 분석, 의사결정 나무 분석을 실시했다. 본 연구의 주요 결과는 다음과 같다. 첫째, 합리 효율성 지수가 높고, 심미적 소비 지수가 낮을 경우 여성 집단으로 분류될 확률이 96.8%로 나타났다. 반면에 합리 효율성과 가격 지향 지수가 낮을 경우 남성 집단으로 분류될 확률이 100%로 나타났다. 둘째, 브랜드 지향, 가격 지향, 합리 효율성 지수가 높을 경우 수도권 집단으로 분류될 확률이 97.3%로 나타났다. 앞서 제시한 결과와는 상반적으로 브랜드 지향, 기념 의례, 지위 상징 지수가 낮을 경우 이외 지역 집단으로 분 류될 확률이 82.1%로 나타났다. 셋째, 지위 상징, 유행 지향 지수가 높으며, 기능성 지수가 낮을 경우 일상 생활 및 패션 집단으로 분류될 확률이 77.6%로 나타났다. 이와 반대로 지위 상징 지수가 낮고, 소속감 유지, 소비 향유 지수가 높을 경우 운동 및 경기 집단으로 분류될 확률이 81.0%로 나타났다.

Investors aim to maximize the return rate for their own investment, utilizing various information as possible as they can access. However those investors, especially individual investors, have limitations of interpretation of the domain-specific information or even the acquisition of the information itself. Thus, individual investors tend to make decision affectively and frequently, which may cause a loss in returns. This study aims to analyze analysts’ target price and to suggest the strategy that could maximize individual’s return rate. Most previous literature revealed that the optimistic bias exists in the analysts’ target price and it is also confirmed in this study. In this context, this study suggests the upper limit of target rate of returns and the optimal value named ‘alpha(α)’ which performs the adjustment of proposed target rate to maximize excess earning returns eventually. To achieve this goal, this study developed an optimization problem using linear programming. Specifically, when the analysts’ proposed target rate exceeds 30%, it could be adjusted to the extent of 59% of its own target rate. As apply this strategy, the investors could achieve 1.2% of excess earning rate on average. The result of this study has significance in that the individual investors could utilize analysts’ target price practically.