본 논문은 칼 라너가 주장한 근본적 인간 경험의 개념이 인간 주체 안에서 이루어지는 초월적 경험을 이해하는데 있어서 매우 중요한 새로운 방법을 제시하고 있음을 연구하고자 한다. 계몽주의 시대 이후로 인간에 대한 새로운 이해의 시각이 대두되기 시작했으며, 특별히 인간이 주체적으로 의미를 형성하는데 중심적인 역할을 하기 시작했다. 신적 계시 보다는 사고하는 주체로서 인간의 내면적 자아가 어떤 측면에서 의미의 원천이 되는 경향을 보이기 시작했다. 라너의 사고의 중요성은 근본적 인간 경험에 대한 이해의 깊이를 심화 시켰다는 것과 초월의 개념을 초월의 유신론적 이해를 넘어서, 더 확장 시켰다는 것이다. 따라서 본 논문은 첫번째로 근본적 인간 경험이 모든 사람들에게 존재한다는 것과 동시에 신에 대한 경험을 피할 수 없다는 것을 보여 주고자 한다. 두번째로 인간은 질문이라는 역동성과 존재에 대한 선험적 인식을 통해서 초월 할 수 있다는 것을 도출할 것이다. 세번째로 에르네스트 블로흐와 발터 벤야민에서 드러나는 무신론적 초월의 개념이 초월의 유신론적 이해와 매우 연관이 깊다는 것을 연구 할 것이다.

목적: 코로나19의 상황에서 대학생을 대상으로 스마트폰을 포함한 디지털기기의 사용이 눈과 근육뼈대계 통증에 미치는 영향을 알아보고자 하였다. 방법: 2022년 11월 온라인 설문조사에 응답한 대학생 156명의 자료를 분석하였다. 설문은 대상자의 일반적 특성을 포함하여 코로나19 대유행 동안 디지털기기의 사용시간과 종류, 컴퓨터시각증후군(computer vision syndrome, CVS) 16항목, 그리고 근육뼈대계 관련 11문항으로 구성하였다. CVS의 빈도는 0~2점, 강도는 1~2점으로 평가하였고, 근육뼈대계 통증은 약한 증상, 경도, 그리고 심한 증상으로 구분하여 3점 척도로 평가하여 총점을 산출하였다. 결과: 일일 평균 디지털기기의 사용은 7.97±3.46시간으로 온라인 수업을 위해 노트북(47.4%), 태블릿(33.3%), 데스크톱(12.2%), 그리고 스마트폰(7.1%)을 사용하였다. 매일 규칙적으로 운동하는 경우는 대상자의 22.4% 이었다. 컴퓨터시각증후군 관련 증상은 건조감이 가장 높은 점수를 보였고, 근육뼈대계 항목은 목 통증이 가장 심했다. 디지털기 기의 사용시간이 증가할수록 몸통(목, 등, 허리)과 전체 근육뼈대 통증이 심해졌고, 노트북이나 스마트폰을 이용해서 온라인 수업을 듣는 경우에는 데스크톱을 사용하는 경우와 비교해 상지통증 점수가 높게 나타났다. 매일 규칙적인 운동을 하는 경우에는 하지 않는 경우에 비해 컴퓨터시각증후군과 근육뼈대 통증의 통증이 작았다. 결론 : 디지털기기의 장시간 사용과 온라인 수업 동안에 사용하는 기기의 종류는 눈과 근육뼈대계 통증에 영향 을 미쳤다. 본 연구결과를 통해 이러한 환경에서 향후 눈과 근육뼈대계의 융합적인 연구와 관리가 함께 필요함을 알 수 있었다.

목적 : 온도감응성 나노구조체를 제조하고 이를 적용하여, 보관시에는 항생제가 용출되지 않고, 안구착용시에 만 온도감응성으로 항생제를 용출하는 스마트 콘택트렌즈를 제조하고자 한다. 방법 : 에멀젼중합하여 p(NIPAAm)-기반의 나노구조체를 합성하였고, 이를 샌드위치 공법을 통해 콘택트렌즈 에 도입하였다. Soaking 방법을 통해 항생제인 levofloxacin(LVF)를 콘택트렌즈에 탑재하여 온도에 따라 항생제 용출 특성을 분석하였다. 결과 : sodium n-dodecyl sulfate (SDS) 마이셀 템플레이트를 활용한 에멀젼중합을 통해 20-40 nm 크기의 온도감응형 p(NIPAAm)-기반의 나노구조체를 합성하였고 이는 TEM과 입도분석기를 통해 확인하였다. 샌드위치 공법을 통해 콘택트렌즈에 나노구조체를 도입하였고, soaking 방법을 통해 항생제를 렌즈안의 나노구조체에 탑재 하였다. 25 oC와 35 oC에서 각각 항생제의 방출 특성을 분석하였다. 상온에서는 항생제를 3 ug 이내로 방출하였 지만, 35 oC에서는 2시간이내에 대부분의 항생제를 방출하였고 10 ug까지 방출하였다. 결론 : 본 연구에서는 온도감응형 나노구조체를 합성하고, 이를 콘택트렌즈에 적용 및 항생제를 탑재하여, 온 도감응형 스마트 항생제용출 콘택트렌즈를 제조하였다. 온도감응형 나노구조체는 콘택트렌즈안에서 항생제를 성 공적으로 탑재할 수 있었고, 상온에서 상당량의 항생체를 보관하고, 온도증가시 10 ug까지의 많은 양의 항생제를 방출하였다. 본 연구결과는 약물전달용 스마트 안과의료기기 및 콘택트렌즈의 개발 및 상용화에 큰 역할을 할 것 으로 기대된다.

목적 : 반응성 청색광차단 염료를 하이드로겔 콘택트렌즈에 화학적으로 고정시켰고, 제조된 콘택트렌즈의 청색 광차단 기능의 분석 및 첨가된 염료를 정량하고자 한다. 방법 : Vinyl sulfone-기반의 반응성 염료인 Reactive Orange 16 Dye를 다량의 알콜 작용기를 함유하는 하 이드로겔 콘택트렌즈에 화학적으로 결합시켰다. 콘택트렌즈의 청색광차단 특성 및 염료의 정량은 UV-vis spectrophotometer를 이용하여 확인하였다. 결과 : 청색광차단기능의 Reactive Orange 16 Dye가 성공적으로 하이드로겔 콘택트렌즈에 결합되었다. UV-vis spectra 분석을 통해 염료를 함유한 콘택트렌즈들이 우수한 청색광차단 기능을 보임을 확인하였다. Beer-Lambert의 법칙을 이용하여, 콘택트렌즈에 첨가된 Reactive Orange 16 Dye를 정량하였으며, 반응 염료 의 농도 조절을 통해, 콘택트렌즈의 청색광차단율을 조절할 수 있었다. 결론 : 본 연구에서는 청색광차단 기능의 반응성 염료를 하이드로겔 콘택트렌즈에 화학적 결합을 통해 고정시 키고, 청색광차단 기능을 분석하였다. 화학적 반응에서 청색광차단 염료의 농도가 증가할수록, 380~500 nm 사 이의 청색광 파장 영역에서의 차단 세기와 첨가된 염료의 양이 같이 증가함을 확인하였다. 대량생산이 가능한 청 색광차단 콘택트렌즈의 개발은 기능성 안광학의료기기 개발에 큰 역할을 할 것으로 기대된다.

Due to the issue of the sustainability in transportation area, the number of electric vehicles has significantly increased. Most automakers have decided or planned to manufacture the electric vehicles rather than carbon fueled vehicles. However, there are still some problems to figure out for the electric vehicles such as long charging time, driving ranges, supply of charging stations. Since the speed of growing the number of electric vehicles is faster than that of the number of charging stations, there are lack of supplies of charging stations for electric vehicles and imbalances of the location of the charging stations. Thus, the location problem of charging stations is one of important issues for the electric vehicles. Studies have conducted to find the optimal locations for the charging stations. Most studies have formulated the problem with deterministic or hierarchical models. In this paper, we have investigated the fluctuations of locations and the capacity of charging stations. We proposed a mathematical model for the location problem of charging stations with the vehicle routing problem. Numerical examples provide the strategy for the location routing problems of the electric vehicles.

Global concerns have grown regarding emerging infectious diseases (EIDs) caused by previously unknown pathogens. Considering that strengthening surveillance capacity for unknown diseases is one of the core capacities for preparedness and early response to EIDs, identifying areas with poor capacity could be beneficial to prioritize regions for the improvement of surveillance. In this regard, we aimed to develop prediction models to identify high risk areas for low surveillance capacity for unknown diseases in a global scale. Unexplained death events reported between 2015 and 2019 were collected from two internet-based surveillance systems, ProMED-mail and Global Public Health Intelligence Network. From the reports, the number of reported unexplained deaths at the first report and the time gap between death and report were extracted as measures for sensitivity and timeliness of surveillance capacity, respectively. Using geographical locations of the reports and published global scale spatial data, including demographic, socioeconomic, public health and geographical variables, we fitted two boosted regression tree models to predict regions with the low sensitivity and timeliness. The performance of prediction model for the low sensitivity showed moderate validity, but in terms of the model for timeliness, the performance was unreliable. Therefore, we provided predicted risk only for low sensitivity. The mean predicted risks of low sensitivity were, respectively, 45.2%, 37.4%, 12.5%, and 3.0% in low-income, lower middle-income, upper middle-income, and high-income countries. Enhancing surveillance capacity in low-income countries is highly required, given the predicted low level of sensitivity despite the importance of early response.

This paper addresses the maintenance optimization problem in multi-component systems in which parts are connected in series, carrying out several missions interspersed with scheduled finite breaks. Due to limited time or resources, maintenance actions can be only carried out on a limited set of components. The decision maker then has to decide which components to maintain to ensure a pre-specified performance level during next mission. Most of the existing models in the literature usually assume only one system and enough spare parts. However, there are situations in which maintenance is required for multiple systems of the same type. To overcome this restrictive assumption, this study optimizes the maintenance problem considering the lack of repair parts and cannibalism for many identical systems. This study presents two optimization models with different objectives to solve the problem and analyzes the results so that the decision maker can decide. The results of this study are expected to be used for the maintenance of multiple systems of the same type, such as swarm drones.

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., soil moisture sensors) and micro-climate monitoring sensors (e.g., thermometers and irradiance sensors) is installed in the APV system. This study aims at introducing a 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 performance estimation. Particularly, the real-time monitoring data is used as an input of the DSS system for performance estimation of an APV system in terms of production yields of crops and monetary benefit so that a data-driven function is implemented in the proposed system. The proposed DSS 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 smart farming technology in the field of agriculture.

Investors must adopt profitable investment opportunities to maximize their wealth. Almost all investment, finance, engineering economics textbooks explain that net present value (NPV) measures the profitability (or value) of investment opportunities in absolute size, and internal rate of return (IRR) measures the profitability of investment opportunities in relative proportions. However, NPV is a measure of the relative size of the return on investment opportunity to do-nothing alternative. Moreover, IRR can occur in multiple investment opportunities and may not exist. To make matters worse, IRR and NPV also have conflicting problems in accept-or-reject decisions. In this study, the reason why NPV and IRR cannot accurately measure the profitability of investment opportunities is identified, and fundamental characteristics that investment opportunity profitability measures should have are presented.

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

Transportation in urban area has been getting hard to fulfill the demand on time. There are various uncertainties and obstacles related with road conditions, traffic congestions, and accidents to interrupt the on-time deliveries. With this situation, the last mile logistics has been a keen issue for researchers and practitioners to find the best strategy of the problem. A way to resolve the problem is to use parcel lockers. Parcel locker is a storage that customers can pick up their products. Transportation vehicles deliver the products to parcel lockers instead of all customer sites. Using the parcel lockers, the total delivery costs can be reduced. However, the inconvenience of customer has to increase. Thus, we have to optimal solution to balance between the total delivery costs and customers' inconvenience. This paper formulates a mathematical model to find the optimal solution for the vehicle routing problem and the location problem of parcel lockers. Experimental results provide the viability to find optimal strategy for the routing problem as well as the location problem.

Iron deficiency is known to be a common nutritional disorder in many countries, especially among children, women of childbearing age and pregnant women. SUNACTIVE Fe-P80 is a new type of iron supplement that applies nanotechnlateology for the purpose of overcoming the disadvantages of food supplements. This study was conducted to investigate the potential adverse effects of a 28-day repeated oral dose of SUNACTIVE Fe-P80 in rats. SUNACTIVE Fe-P80 was administered once daily by gavage to Sprague-Dawley rats for 28 days at doses of 0, 500, 1,000, and 2,000 mg/kg/day. Additional recovery groups from the control and highdose groups were observed for a 14-day recovery period. At the scheduled termination, the animals were sacrificed, their organs weighed, and blood samples collected. There were no treatment-related effects in the context of clinical signs, body weight, food intake, ophthalmoscopy, urinalysis, necropsy findings, organ weights, and hematologic, serum biochemical and histopathological parameters at any dose tested. Under the present experimental conditions, the no-observed-adverse-effect level of SUNACTIVE Fe-P80 was ≥ 2,000 mg/kg/day in both the sexes, and no target organs were identified. Thus, the results suggest that SUNACTIVE Fe-P80 is relatively safe, as no treatment-related adverse effects were observed following a 28-day repeated oral dose experiment.

Over the past decade, platelet-rich plasma (PRP) and platelet derivatives have been widely investigated in the field of regenerative medicine due to their high concentrations of platelet- related growth factors, cytokines, and other proteins. Recently, many clinical studies have suggested their regenerative therapeutic efficacy in treating several disorders in medical field. However, their therapeutic applications are not well characterized in veterinary medicine as in human and experimental animals. This article reviews functional roles of platelets, scientific concepts, and clinical use of PRP and platelet derivatives in veterinary medicine. It also presents guidelines for veterinary applications of PRP in the future.

During the global health emergency, like the COVID-19 pandemic, people attend to organizations dedicated to international well-being. This paper aims to identify the construction of the World Health Organization’s (WHO) multiple identities attested in four identity processes: indexicality, local-occasioning, positioning, and dialogism. Identifying the organization’s identities can foster identity processes and help to understand the relationality of identities. Qualitative data from the WHO director-general’s media remarks on COVID-19 were collected and analyzed using a corpus analysis tool, AntConc. The tool allowed an investigation of word frequency, sentence structure patterns, and the use of keywords in sentences. This analysis revealed that the WHO has negotiated and presented several identities in COVID-19 situations. The results suggest that although factors such as audience and situations affect identity change, these negotiated identities are still coherent with the organization’s original identity.

Various non-face-to-face services are being activated due to the influence of the Corona 19 virus around the world. However, unlike the rapid development of delivery services, social awareness of delivery services is causing many problems. Therefore, in this study, we analyze the quality attributes of delivery services from the consumer's point of view, and based on the results, we try to derive a direction for service improvement. In this study, quality factors were established through interviews and surveys with actual consumers, and quality attributes were classified through the Kano model and Timko’s customer satisfaction coefficient. “Attractive” is (‘Ease of ordering, Accurate delivery to the designated place’), “One Dimensional” is (‘Variety of payment methods, Accurate delivery on time, Accurate delivery of ordered food, Degree of non-deformation of packaging conditions, etc., Convenience of use time’), “Must be” is (‘Kindness of the delivery person’), “Reverse” is (‘provision of services, service response to order discrepancies’). This study has academic significance in that it compensated for the disadvantage of not being able to interpret the mathematical meaning of the Kano model with Teamco’s customer satisfaction coefficient. It also has practical implications in that it provides an indirect clue to future improvement directions.

본 연구는 자살을 생각하는 학생들의 대학생활 적응, 심리상담 실태, 그리고 자살생각 영향요인을 파악 하고자 한다. 편의표집이나 특정전공자들을 대상으로 할 경우 자살생각 대학생들의 실태를 파악하기 어려 운 제한점이 있다. 따라서 비임상군인 재학생 전수를 대상으로 실시한 실태조사 결과를 분석함으로써 대학 생 자살생각의 심각성을 파악하고자 했다. 본 연구는 서울 소재 A대의 <학생실태 및 요구도 조사>(2021년 5~6월 실시, 재학생의 74.8%인 4,129명 응답)의 데이터를 사용하였다. 본 연구는 응답결과 중에서 기본정 보, 정신건강, 상담, 학교생활에 관한 내용을 중심으로 ‘자살을 생각한 집단’과 ‘자살을 생각하지 않은 집단’ 간의 차이를 비교하여 분석하였다. 자살을 생각한 집단은 혼자 거주하는 비율이 높고, 흡연량이 많고, 스마 트폰 사용 장애를 겪는 나타났다. 그리고 그들은 입학만족도, 전공만족도, 자아존중감 및 교우관계에 대한 만족도가 낮았고, 학업스트레스가 높은 것으로 나타났다. 또한 그들은 교내보다는 교외에서 심리상담을 받 은 비율이 높은 것으로 나타났다. 한편, 자살생각에 영향을 미치는 요인으로는 성별, 학년, 흡연량, 입학만족 도, 학업스트레스, 자아존중감, 심리상담 여부 등인 것으로 확인되었다. 이런 연구결과를 토대로 본 연구는, 심리상담에 대한 접근성을 제고할 수 있는 상담시스템의 구축과 자살위험군에 대한 체계적인 관리시스템의 구축을 제언하였다.