목적 : 본 연구의 목적은 Coronavirys Disease(COVID-19) 팬데믹을 거친 고령자의 사회적 건강의 수준의 변화궤적에 대한 잠재계층을 분류하고 잠재 계층별 특성을 분석함에 있다. 또한 이러한 사회적 건강에 영향을 미치는 예측요인을 파악하여 고령자의 사회적 건강을 증진을 위한 기초자료를 마련하고자 한다. 연구방법 : 국내 고령자의 사회적 친밀도에 따른 사회적 건강 유형을 파악하기 위해서 한국복지패널의 3 년차 종단자료를 토대로 분석하였으며, 연구대상자는 세 시점 모두를 응답한 2845명의 고령자를 대상으 로 하였다. 대상자중심접근인 성장혼합모형(Growth Mixture Model; GMM)을 적용하여 변화궤적에 따 른 잠재계층을 분석 하였고, 도출된 각 잠재유형별 특성을 파악하기 위해 χ2 분석, 분산분석을 실시하 였으며, 계층 간 차이를 유발하는 요인을 파악하기 위해 다항로지스틱 회귀분석을 실시하였다. 결과 : GMM 적용결과, 사회적 건강의 변화궤적에 대한 잠재계층은 최종 4개의 집단으로 저수준 감소-증 가 집단, 중수준 유지-증가 집단, 고수준-감소 집단, 고수준 유지’집단으로 분류되었다. 또한 사회적 건강 수준에 따라 여가만족도에서 차이가 나타나는 것으로 드러났으며, 그 외에도 연령 차이가 존재하였 다. 잠재계층분류에 영향을 미치는 영향변인을 검증한 결과, 특히 여성일수록, 종교를 가지고 있을수록, 여가만족도와 전반적 만족도가 모두 높을수록 고수준 유지 집단에 속할 확률이 높은 것으로 나타났다. 결론 : 국내 고령자의 사회적 건강은 시간이 지남에 따라 감소하는 궤적을 보이는 것으로 나타났다. 변화 궤적에 따라 4개의 집단으로 구분 지을 수 있으며, 각 잠재 유형별 연령과 여가 만족도 부분에서 집단별 차이가 드러났다.

The ‘Japanese Useful Fisheries Classification Table’, published in advance before the publication of the “Useful Fisheries of Japan”, is the first data to classify and introduce fisheries animals and plants in a single table. Therefore, it had received public attention immediately. However, The academy of animal studies at that time quickly pointed out that this classification table was a mixture of traditional and modern classifications, and that there were too many errors. However, Yoshio Tanaka, who was in charge of revising Yamamoto Arikatana, that wrote the classification table, was not an adherent of traditional taxonomy, nor was he ignorant of modern taxonomy. Nevertheless, the classification table, which was quite different from the well-known zoological classification at that time, was prepared. For example, the top classifications of marine organism are not at the same level, but rather a mixture of phylum and class, while the water insect contains several phylums, including Arthropoda, Echinodermata, Mollusca, and Coelenterata. As such, the method of classification of animals in the classification table was hard to understand in the zoological academy at that time. The reason for this unusual taxonomy was that the classification table showed classification of useful fisheries products, not intended to convey academic classification. In other words, it is not for the purpose of academic classification of all fishery products, but for the purpose of presenting standards that can be easily understood by those engaged in the collection, manufacture, and aquaculture of fishery products. This principle of ‘Useful Fisheries of Japan’ is also ascertained in the “Fishing Methods of Japan” and “Fishery Products of Japan”. Regarding the collection and processing of marine products, it could have shown how to catch whales, which are mammals, and how to dismantle whales and obtain oil and meat, according to academic classifications. However, the first appearances in the book include dried squid, abalone, shark fins, and sea cucumbers. In other words, the most important fishery products at that time are presented first. The contents of the classification table, which is considered somewhat bizarre, show where the purpose to compile Useful Fisheries of Japan.

본 연구는 정서 자극 유형 중 일상생활과 가장 유사한 정서 경험을 유발하는 자연주의적 자극인 영상 자극을 활용 하여 정서표상의 유사성과 개인 간 반응 일관성을 살펴보기 위해 수행되었다. 이를 위해 다차원척도법을 실시하여 영상 자극이 핵심정서 차원에 위치하는지 확인하고, 참가자 간 분류분석을 사용하여 영상들이 정서유형 별로 구분이 잘 이루어지는지, 영상 자극에 대한 참가자들의 정서표상이 일관적인지 검증하였다. 또한 참가자간 상관분석을 통해 각 영상 자극에 대한 정서표상이 참가자들간 유사한지 추가적으로 확인하였다. 다차원척도법 결과, 정서유발 영상들 이 정서가 차원에서 유의하게 구분되어 Russell(1980)의 핵심정서차원을 부분적으로 지지하였다. 분류분석 결과, 각 영상이 정서유형에 따라 잘 분류되고 예측되었다. 마지막으로, 참가자간 상관분석을 통해 정서 반응 일관성이 각 영상의 정서유형에 따라 다르게 나타남을 확인하였다. 본 연구는 영상 자극에 대한 정서표상과 정서 반응 일관성이 정서 유형에 따라 차이가 있음을 시사한다.

Recently, the importance of impact-based forecasting has increased along with the socio-economic impact of severe weather have emerged. As news articles contain unconstructed information closely related to the people’s life, this study developed and evaluated a binary classification algorithm about snowfall damage information by using media articles text mining. We collected news articles during 2009 to 2021 which containing ‘heavy snow’ in its body context and labelled whether each article correspond to specific damage fields such as car accident. To develop a classifier, we proposed a probability-based classifier based on the ratio of the two conditional probabilities, which is defined as I/O Ratio in this study. During the construction process, we also adopted the n-gram approach to consider contextual meaning of each keyword. The accuracy of the classifier was 75%, supporting the possibility of application of news big data to the impact-based forecasting. We expect the performance of the classifier will be improve in the further research as the various training data is accumulated. The result of this study can be readily expanded by applying the same methodology to other disasters in the future. Furthermore, the result of this study can reduce social and economic damage of high impact weather by supporting the establishment of an integrated meteorological decision support system.

Antimicrobials in human medicine are classified by The World Health Organization (WHO) into three groups: critically important antimicrobials (CIA), highly important antimicrobials (HIA), and important antimicrobials (IA). CIA are antibiotic classes that satisfy two main criteria: that they are the sole or the only available limited therapeutic option to effectively treat severe bacterial infections in humans (Criterion 1), and infections where bacteria are transmitted to humans from non-human sources or have the potential to acquire resistance genes from non-human sources (Criterion 2). WHO emphasizes the need for cautious and responsible use of the CIA to mitigate risk and safeguard human health. Specific antimicrobials within the CIA with a high priority for management are reclassified as “highest priority critically important antimicrobials (HP-CIA)” and include the 3rd generation of cephalosporins and the next generation of macrolides, quinolones, glycopeptides, and polymyxins. The CIA list is the scientific basis for risk assessment and risk management policies that warrant using antimicrobials to reduce antimicrobial resistance in several countries. In addition, the CIA list ensures food safety in the food industry, including for the popular food chain companies McDonald's and KFC. The continuous update of the CIA list reflects the advancement in research and emerging future challenges. Thus, active and deliberate evaluation of antimicrobial resistance and the construction of a list that reflects the specific circumstances of a country are essential to safeguarding food security.

This study aimed to categorize consumers using super app functional characteristics to identify demographic differences, and analyze shopping orientations by consumer type. This data can be used by fashion and beauty companies for product planning and marketing strategies. To categorize super app consumers, data were analyzed with SPSS v.26.0 software using frequency, factor, reliability K-mean cluster, and distributed analyses, one-way-ANOVAs, and Scheffe verification. Cross-analysis was conducted to correlate super app consumer types with demographic characteristics. One-way-ANOVAs and Scheffe verification were used to analyze the differences in shopping preferences between super app consumer groups. As a result of our analyses, super app consumers were classified into four types: the ration type, the low-use type, the multifunction type, and the habit type. There were statistically significant differences between these types in age, occupation, marital status, average monthly household income, and shopping impact factors. Five super app user shopping orientations were identified: brand pursuit, pleasure pursuit, trend pursuit, risk perception, and economic orientation. The differences in the preferred orientation between super app consumer types were found to be statistically significant. The majority of respondents were multifunction type consumers. This group used the super app most frequently and effectively. They also demonstrated the highest scores for all five of the shopping orientations. The classification of consumer types in this study will allow the fashion and beauty industries to utilize super apps for more targeted product design and marketing.

This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.

본고는 역사학, 도상학, 서예학적 관점에서 공의석굴(鞏義石 窟)의 북조시기 조상제기(造像題記)를 심층적으로 고찰했다. 특 히 조상제기의 내용에 치중한 선행연구와 달리 조상제기의 필 법, 결구, 장법, 풍격 등에 대해 전면적이고 상세한 분석을 진 행해 다음과 같이 객관적으로 유의미한 결론을 도출해 냈다. 먼저, 본고는 역사적 분기와 조상제기의 구체적 특징을 종합 적으로 고찰해 공의석굴의 북조시기 조상제기를 단순히 역사적 분기로 구분한 선행연구의 한계를 극복하고 조상제기의 서풍에 대한 분석을 처음으로 진행했다. 둘째, 공의석굴은 지리적으로 특수한 위치에 있어 북조시기 조상제기의 서풍에서 다원적이고 다층적인 양상을 발견할 수 있다. 즉 여러 왕조의 교체, 문화의 다원화, 지역적 특성과 같 은 요소가 작용해 서풍의 다양성을 표출하고 있음을 증명했다. 셋째, 공의석굴의 북조시기 조상제기에서 위비체(魏碑體)는 줄곧 주류를 차지했고, 동시에 주변의 다양한 요소들을 받아들 여 탈중앙화 경향이 강하게 표출해 예술적 가치가 높은 서풍을 구현해 냈다. 마지막으로, 공의석굴 북조시기 조상제기의 필법, 결구, 장법, 풍격을 치밀하게 고찰해 다소 소박하지만 천진난만한 조상제기 의 서풍을 규명해 냈다. 당시 유행한 복고풍의 예서, 남조 서풍 의 영향 등으로 인해 장엄한 ‘낙양체(洛陽體)’나 연미한 남조의 ‘이왕(二王)’과는 구별되는 공의석굴 만의 지역적 자연미를 표 출하고 있음을 객관적으로 정리했다.

하천시설물은 정보의 관리주체가 국가하천과 지방하천 등에 따라 달라서 통합되어 관리되지 못하고 있는 점, 설계 및 시공 단계의 정보화 축적이 미흡하여 시설물 정보의 망실이 우려되는 점 등 하천분야로 BIM 도입을 하기 위해서는 해결해야 할 과제들이 산재해 있다. 또한, 이로 인해 유지운영단계에서의 시설물 정보 활용 수준 역시 상당히 미흡한 편이다. 따라서, 하천시설물의 효과적인 유지 운영을 위해서는 표준분류체계에 따라 시설물 정보를 정리함으로 데이터의 일관성을 확보하고 효율성을 증대시킬 필요가 있다. 본 연구에서는 이러한 하천분야의 시설물 특성을 고려하여 하천분야의 BIM 정보모델 도입과 3차원 기반의 하천시설의 효율적인 유지 운영 전환을 위한 BIM 표준분류체계를 적용하는 방안을 제시한다.

Natural uranium-contaminated soil in Korea Atomic Energy Research Institute (KAERI) was generated by decommissioning of the natural uranium conversion facility in 2010. Some of the contaminated soil was expected to be clearance level, however the disposal cost burden is increasing because it is not classified in advance. In this study, pre-classification method is presented according to the ratio of naturally occurring radioactive material (NORM) and contaminated uranium in the soil. To verify the validity of the method, the verification of the uranium radioactivity concentration estimation method through γ-ray analysis results corrected by self-absorption using MCNP6.2, and the validity of the pre-classification method according to the net peak area ratio were evaluated. Estimating concentration for 238U and 235U with γ-ray analysis using HPGe (GC3018) and MCNP6.2 was verified by 􀟙-spectrometry. The analysis results of different methods were within the deviation range. Clearance screening factors (CSFs) were derived through MCNP6.2, and net peak area ratio were calculated at 295.21 keV, 351.92 keV(214Pb), 609.31 keV, 1120.28 keV, 1764.49 keV(214Bi) of to the 92.59 keV. CSFs for contaminated soil and natural soil were compared with U/Pb ratio. CSFs and radioactivity concentrations were measured, and the deviation from the 60 minute measurement results was compared in natural soil. Pre-classification is possible using by CSFs measured for more than 5 minutes to the average concentration of 214Pb or 214Bi in contaminated soil. In this study, the pre-classification method of clearance determination in contaminated soil was evaluated, and it was relatively accurate in a shorter measurement time than the method using the concentrations. This method is expected to be used as a simple pre-classification method through additional research.

During decommissioning and site remediation of nuclear power plant, large amount of wastes (including radioactive waste) with various type will be generated within very short time. Among those wastes, soil and concrete wastes is known to account for more than 70% of total waste generated. So, efficient management of these wastes is very essential for effective NPP decommissioning. Recently, BNS (Best System) developed a system for evaluation and classification of soil and concrete wastes from the generation. The system is composed of various modules for container loading, weight measurement, contamination evaluation, waste classification, stacking, storage and control. By adopting modular type, the system is good for dealing with variable situation where system capacity needs to be expanded or contracted depending on the decommissioning schedule, good for minimizing secondary waste generated during maintenance of failed part and also good for disassemble, transfer and assemble. The contamination evaluation module of the system has two sub module. One is for quick measurement with NaI(Tl) detector and the other is for accurate measurement with HPGe detector. For waste transfer, the system adopts LTS (Linear Transfer System) conveyor system showing low vibration and noise during operation. This will be helpful for minimizing scattering of dust from the waste container. And for real time positioning of waste container, wireless tag was adopted. The tag also used for information management of waste history from the generation. Once a container with about 100 kg of soil or concrete is loaded, it is moved to the weight measurement module and then it transfers to quick measurement module. When measured value for radioactivity concentration of Co- 60 and Cs-137 is more than 1.0 Bq/g, then the container is classified as waste for disposal and directly transferred to stacking and storage rack. Otherwise, the container is transferred to accurate measurement module. At the accurate module, the container is classified as waste for disposal or waste for regulatory clearance depending on the measurement result of 0.1 Bq/g. As the storage rack has a sections for disposal and regulatory clearance respectively, the classified containers will be positioned at one of the sections depending on the results from the contamination evaluation module. The system can control the movement of lots of container at the same time. So, the system will be helpful for the effective nuclear power plant decommissioning in view of time and budget.

Wolsong unit 1, the first PHWR (Pressurized Heavy Water Reactor) in Korea, was permanent shut down in 2019. In Korea, according to the Nuclear Safety Act, the FDP (Final Decommissioning Plan) must be submitted within 5 years of permanent shutdown. According to NSSC Notice, the types, volumes, and radioactivity of solid radioactive wastes should be included in FDP chapter 9, Radioactive Waste Management, Therefore, in this study, activation assessment and waste classification of the End shield, which is a major activation component, were conducted. MCNP and ORIGEN-S computer codes were used for the activation assessment of the End shield. Radioactive waste levels were classified according to the cooling period of 0 to 20 years in consideration of the actual start of decommissioning. The End shield consists of Lattice tube, Shielding ball, Sleeve insert, Calandria tube shielding sleeve, and Embedment Ring. Among the components composed for each fuel channel, the neutron flux was calculated for the components whose level was not predicted by preliminary activation assessment, by dividing them into three channel regions: central channel, inter channel, and outer channel. In the case of the shielding ball, the neutron flux was calculated in the area up to 10 cm close to the core and other parts to check the decrease in neutron flux with the distance from the core. The neutron flux calculations showed that the highest neutron flux was calculated at the Sleeve insert, the component closest to the fuel channel. It was found that the neutron flux decreased by about 1/10 to 1/20 as the distance from the core increased by 20 cm. The outer channel was found to have about 30% of the neutron flux of the center channel. It was found that no change in radioactive waste level due to decay occurred during the 0 to 20 years cooling period. In this study, activation assessment and waste classification of End Shield in Wolsong unit 1 was conducted. The results of this study can be used as a basis for the preparation of the FDP for the Wolsong unit 1.

Background: While efforts have been made to differentiate fall risk in older adults using wearable devices and clinical methodologies, technologies are still infancy. We applied a decision tree (DT) algorithm using inertial measurement unit (IMU) sensor data and clinical measurements to generate high performance classification models of fall risk of older adults. Objects: This study aims to develop a classification model of fall risk using IMU data and clinical measurements in older adults. Methods: Twenty-six older adults were assessed and categorized into high and low fall risk groups. IMU sensor data were obtained while walking from each group, and features were extracted to be used for a DT algorithm with the Gini index (DT1) and the Entropy index (DT2), which generated classification models to differentiate high and low fall risk groups. Model’s performance was compared and presented with accuracy, sensitivity, and specificity. Results: Accuracy, sensitivity and specificity were 77.8%, 80.0%, and 66.7%, respectively, for DT1; and 72.2%, 91.7%, and 33.3%, respectively, for DT2. Conclusion: Our results suggest that the fall risk classification using IMU sensor data obtained during gait has potentials to be developed for practical use. Different machine learning techniques involving larger data set should be warranted for future research and development.