Nasopharyngeal stenosis is defined as a morphological transition of narrowing at the nasopharyngeal region. A 2-yearold, castrated male, Korean short hair cat was referred to the animal medical center, Gyeongsang National University. According to clinical signs, diagnostic imaging, and physical examination, nasopharyngeal stenosis was diagnosed. The staphylectomy was performed using a CO2 laser, and there were not any post-operative complications. The patient was discharged in two days. This report describes the case of nasopharyngeal stenosis in cat and represents that laser ablation could be a good option for surgical management of the nasopharyngeal region with a low complication rate.

Expanding exports of small and medium-sized companies is crucial for the continuous growth of the Korean economy. Therefore, the government operates various support systems to enhance the export capabilities of these companies. This study aims to analyze the impact of the Korean government's flagship export support system, known as the export initiation support system, on the performance of participating domestic companies. A fixed effect model using panel data was applied to examine the characteristics of 11,099 companies that participated in the export initiation support system from 2016 to 2019. The analysis revealed that the number of exporting countries, employees, and previous export volume had a significant impact on the export amount of participating companies. However, contrary to expectations, the number of overseas marketing participation and the GCL (global competence level) test did not show a significant impact. This study is significant as it provides implications for the development of support projects tailored to the specific needs of small and medium-sized companies, with the goal of improving the export support system.

Recently, many studies have been conducted to improve quality by applying machine learning models to semiconductor manufacturing process data. However, in the semiconductor manufacturing process, the ratio of good products is much higher than that of defective products, so the problem of data imbalance is serious in terms of machine learning. In addition, since the number of features of data used in machine learning is very large, it is very important to perform machine learning by extracting only important features from among them to increase accuracy and utilization. This study proposes an anomaly detection methodology that can learn excellently despite data imbalance and high-dimensional characteristics of semiconductor process data. The anomaly detection methodology applies the LIME algorithm after applying the SMOTE method and the RFECV method. The proposed methodology analyzes the classification result of the anomaly classification model, detects the cause of the anomaly, and derives a semiconductor process requiring action. The proposed methodology confirmed applicability and feasibility through application of cases.

Recently, not only traditional statistical techniques but also machine learning algorithms have been used to make more accurate bankruptcy predictions. But the insolvency rate of companies dealing with financial institutions is very low, resulting in a data imbalance problem. In particular, since data imbalance negatively affects the performance of artificial intelligence models, it is necessary to first perform the data imbalance process. In additional, as artificial intelligence algorithms are advanced for precise decision-making, regulatory pressure related to securing transparency of Artificial Intelligence models is gradually increasing, such as mandating the installation of explanation functions for Artificial Intelligence models. Therefore, this study aims to present guidelines for eXplainable Artificial Intelligence-based corporate bankruptcy prediction methodology applying SMOTE techniques and LIME algorithms to solve a data imbalance problem and model transparency problem in predicting corporate bankruptcy. The implications of this study are as follows. First, it was confirmed that SMOTE can effectively solve the data imbalance issue, a problem that can be easily overlooked in predicting corporate bankruptcy. Second, through the LIME algorithm, the basis for predicting bankruptcy of the machine learning model was visualized, and derive improvement priorities of financial variables that increase the possibility of bankruptcy of companies. Third, the scope of application of the algorithm in future research was expanded by confirming the possibility of using SMOTE and LIME through case application.

The construction industry is considered to be a fatal accident industry, accounting for 28.5% of the total industrial accidents in 2017, as the number of industrial accidents in the construction industry has steadily increased over the past decade. So it is necessary to consider introducing Resilience Engineering, which is actively applied to risky industries around the world, to drastically reduce construction accidents. Although Resilience Engineering, which has emerged as the next-generation safety management centered on Hollnagel since the 2000s, claims the importance of strengthening Resilience abilities considering organizational structure and culture, most studies focus only on developing evaluation indicators. The purpose of this study is to analyze the impact of an organization's safety culture on its Resilience abilities in the construction industry. Specifically, it conducted empirical analysis on the impact of safety culture consisting of ‘communication, leadership and safety systems’ on the Resilience abilities(responding ability, monitoring ability, learning ability, anticipating ability), and the mediation relationship between leadership, communication, and safety system. The survey was conducted on construction workers, and an empirical analysis was conducted on the final 154 responses using SPSS 25 and Smart PLS 3. The results showed that the safety system had a significant impact on all Resilience Abilities, and communication had a significant impact on the remaining three except for anticipating ability among Resilience Abilities. On the other hand, leadership has been shown to have a significant impact on anticipating ability only. In the verifying of the mediation relationship between leadership, communication and safety systems, it was found that leadership affects all Resilience abilities by means of safety systems, but communication can only affect responding ability. This study has practical significance in that it suggests the need for policy-level efforts to introduce and apply Resilience Engineering and then expanded the effective safety management assessment of the construction industry in the future. Moreover, the academic implications are important in that the study attempted to expand the academic scope for a paradigm shift in the future as the safety culture has identified its impact on the Resilience abilities.

Companies widely use survey to identify customer requirements, but the survey has some problems. First of all, the response is passive due to pre-designed questionnaire by companies which are the surveyor. Second, the surveyor needs to have good preliminary knowledge to improve the quality of the survey. On the other hand, text mining is an excellent way to compensate for the limitations of surveys. Recently, the importance of online review is steadily grown, and the enormous amount of text data has increased as Internet usage higher. Also, a technique to extract high-quality information from text data called Text Mining is improving. However, previous studies tend to focus on improving the accuracy of individual analytics techniques. This study proposes the methodology by combining several text mining techniques and has mainly three contributions. Firstly, able to extract information from text data without a preliminary design of the surveyor. Secondly, no need for prior knowledge to extract information. Lastly, this method provides quantitative sentiment score that can be used in decision-making.

Recently, as the internet usage is increasing, accordingly generated text data is also increasing. Because this text data on the internet includes users’ comments, the text data on the Internet can help you get users’ opinion more efficiently and effectively. The topic of text mining has been actively studied recently, but it primarily focuses on either the content analysis or various improving techniques mostly for the performance of target mining algorithms. The objective of this study is to propose a novel method of analyzing the user’s requirements by utilizing the text-mining technique. To complement the existing survey techniques, this study seeks to present priorities together with efficient extraction of customer requirements from the text data. This study seeks to identify users’ requirements, derive the priorities of requirements, and identify the detailed causes of high-priority requirements. The implications of this study are as follows. First, this study tried to overcome the limitations of traditional investigations such as surveys and VOCs through text mining of online text data. Second, decision makers can derive users’ requirements and prioritize without having to analyze numerous text data manually. Third, user priorities can be derived on a quantitative basis.

Worldwide plant market keeps maintaining steady growth rate and along with this trend, domestic plant market and its contractors also maintain such growing tendency. However, in spite of its external growth, win-win growth of domestic material industry that occupies the biggest share in plant industry cost portion is extremely marginal in reality. Domestic plant material suppliers are required to increase awareness of domestic material brand by securing quality and reliability of international standard through improvement of design quality superior to that of overseas material suppliers. Improvement of design quality of plant material becomes an essential element, not an option, for survival of domestic plant industry and its suppliers. Under this background, in this study, priority and importance by each evaluation index was analyzed by materializing plant design stage through survey of experts and defining evaluation index by each design stage and based on this analysis result, evaluation index of stage-gate based decision-making process that may improve design quality of plant material was suggested. It is considered that by utilizing evaluation index of stage-gate based decision-making process being suggested in this study, effective and efficient decision-making of project decision-makers would be enabled and it would be contributory to improve design quality of plant material.

A sharing economy has emerged through today’s trust-building mechanisms, and a sharing economy is called a future economic model through a positive future market prospect. In this context, while the overseas sharing economic business is becoming a global trend, the domestic sharing economic business is busy following the global trend. The purpose of this study is to investigate the development direction of sharing economic business in Korea. First, the sharing economic cases of 50 oversea and domestic businesses were analyzed by time series analysis. Next, a cross-country analysis to analyze the business distribution and KCERN's sharing economic model through sharing economic cube model was conducted. Finally, profit model analysis through business case study and the relationship between the derived factors were investigated. As a result of the analysis, this study found comparative trends between overseas and domestic including differences in cultural and institutional environments and profit models. This study suggested directions for domestic sharing economy business.

As information technology advances, the penetration of smart devices connected to the Internet, such as smart phone and tablet PC, has rapidly expanded, and as sensor prices have fallen the Internet of Things has begun to be introduced in the industry. Today's industry is rapidly changing and evolving, requiring companies to respond to the new paradigm of business. In this situation, companies need to actively manage and maintain customer relationships in order to acquire loyal customers who bring them a high return. The purpose of this study is to suggest a method to manage customer relationship using real time IoT data including IoT product usage data, customer characteristics and transaction data. This study proposes a method of segmenting customers through RFM analysis and transition index analysis. In addition, a real-time monitoring through control charts is used to identify abnormalities in product use and suggest ways of differentiating marketing for each group. In the study, 44 samples were classified as 9 churn customers, 10 potential customers, and 25 active customers. This study suggested ways to induce active customers by providing after-sales benefit for product reuse to a group of churn customers and to promote the advantages or necessity of using the product by setting the goal of increasing the frequency of use to a group of potential customers. Finally, since the active customer group is a loyal customer, this study proposed an one-on-one marketing to improve product satisfaction.

VOCs have been used as the most definitive resource to reflect customer feedback when developing products and services. However, due to the development of the Internet and the emergence of SNS, VOC is no longer the only channel that represents customer opinions. There are also a number of studies showing that many customers express complaints through channels other than VOCs. In this paper, we analyze the difference between the official VOC data and the data collected through the external channel, and suggest ways to reflect the various opinions of customers. To do this, this study uses keyword analysis that can identify differences according to frequency through social network, modular analysis to distinguish topics according to centrality and similarity, and emotional analysis to confirm word polarity (positive and negative). The results of this study show that the opinions of the customers were different depending on channels such as VOCs and external channels. Therefore, the collected data through VOC as well as external channels should be used in order to reflect the opinions of customers. In particular, this paper confirms that the results of one channel may vary depending on the channel characteristics even for the same channel. This confirms that collecting voc only on certain channels may differ from what real customers require. Therefore, data collected through VOCs as well as external channels must be used to reflect various customer feedback.

현재 국내 곤충 시장이 약 5천억 규모로 성장하면서 곤충 산업의 발전 잠재력이 큰 미래 산업으로 부각되고 있으며, 사료용, 약용, 식용, 학습애완용 등으로 그 영역을 넓혀 가고 있다. 곤충산업은 천적, 화분매개, 환경정화, 학습･애완, 식･약용 등 5가지를 기준으로 분류하고 있다. 그리고 현재 한국식품의약품안전처에서 인정한 식용곤충으로는 벼메뚜기, 누에 번데기, 누에 백강잠, 갈색거저리, 흰점박이꽃무 지, 쌍별귀뚜라미, 장수풍뎅이의 7가지 종이 있다. 2018년 들어 곤충 사육농가는 2,000개 농가를 넘어가며, 대다수가 흰점박이꽃무지 사육농가로 800개에서 점점 확대되고 있는 실정이다. 식용곤충을 이용한 건강식품이 다양한 형태로 국내 시장에 빠르게 확산된다면 국외로 수출도 가능할 것으로 전망된다. 학습･애완곤충의 국내 시장은 포화상태로 보이는 반면 식･약용과 사료용 분야에서는 곤충을 요리, 의약품, 사료 등의 다방면의 활용이 되고 있다. 이러한 활용으로 인해 지속적인 소비가 전망된다면 원재료의 원활한 수급을 위한 곤충사육 기반도 증가할 것으로 전망된다.

Poria cocos is an edible and pharmaceutical mushroom with a long history of medicinal use in Korea. For the last 30 years, the domestic cultivated supply of Poria cocos has been unable to meet consumer demand, so Poria cocos is collected in mountainous areas and also imported from China. Thus, to increase the supply of Poria cocos, many artificial cultivation methods have been studied. In this study, Poria cocos is cultivated under different environmental conditions using plastic bags and the results compared. When cultivating Poria cocos at different temperatures (20, 25, 30 and 35oC) and using different numbers of plastic bag layers (1, 2), the most efficient cultivation conditions were a temperature of 25-30oC and 2 plastic bag layers. The fastest growth was at 25-30oC, and the Poria cocos exhibited no weight change when cultivated using layers of plastic bags (1, 2).

The authors of this article compare American and Korean reactions to the persuasiveness of environmental advertising campaigns that include pledges. Findings indicate that environmental advertising effectiveness depends on how much effort recipients put into making environmental pledges prior to viewing the advertisements. Study 1 demonstrates that when environmental pledges requesting more effort precede ad messages, Americans are more persuaded but Koreans are less persuaded. Study 2 extends the findings and rules out an alternative explanation—mere-effort effect—by showing that the results are replicated only with an issue-relevant pledge, but not with an issue-irrelevant pledge.

High-quality β-silicon carbide (SiC) coatings are expected to prevent the oxidation degradation of carbon fibers in carbon fiber/silicon carbide (C/SiC) composites at high temperature. Uniform and dense β-SiC coatings were deposited on carbon fibers by low-pressure chemical vapor deposition (LP-CVD) using silane (SiH4) and acetylene (C2H2) as source gases which were carried by hydrogen gas. SiC coating layers with nanometer scale microstructures were obtained by optimization of the processing parameters considering deposition mechanisms. The thickness and morphology of β-SiC coatings can be controlled by adjustment of the amount of source gas flow, the mean velocity of the gas flow, and deposition time. XRD and FE-SEM analyses showed that dense and crack-free β-SiC coating layers are crystallized in β-SiC structure with a thickness of around 2 micrometers depending on the processing parameters. The fine and dense microstructures with micrometer level thickness of the SiC coating layers are anticipated to effectively protect carbon fibers against the oxidation at high-temperatures.

본 논문에서는 주성분 회귀법과 부분최소자승 회귀법을 비교하여 보여준다. 이 비교의 목적은 선형형태를 보유한 근적외선 분광 데이터의 분석에 사용할 수 있는 적합한 예측 방법을 찾기 위해서이다. 두 가지 데이터 마이닝 방법 론인 주성분 회귀법과 부분최소자승 회귀법이 비교되어 질 것이다. 본 논문에서는 부분최소자승 회귀법은 주성분 회귀법과 비교했을 때 약간 나은 예측능력을 가진 결과를 보여준다. 주성분 회귀법에서 50개의 주성분이 모델을 생 성하기 위해서 사용지만 부분최소자승 회귀법에서는 12개의 잠재요소가 사용되었다. 평균제곱오차가 예측능력을 측 정하는 도구로 사용되었다. 본 논문의 근적외선 분광데이터 분석에 따르면 부분최소자승회귀법이 선형경향을 가진 데이터의 예측에 가장 적합한 모델로 판명되었다.