• Journal of The Korean Society of Clinical Toxicology
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• v.21 no.2
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• pp.135-142
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• 2023

Purpose: In patients with glufosinate poisoning, severe neurological symptoms may be closely related to a poor prognosis, but their appearance may be delayed. Therefore, this study aimed to determine whether the Acute Physiology and Chronic Health Evaluation II (APACHE II) score could predict the neurological prognosis in patients with glufosinate poisoning who present to the emergency room with alert mental status. Methods: This study was conducted retrospectively through a chart review for patients over 18 years who presented to a single emergency medical center from January 2018 to December 2022 due to glufosinate poisoning. Patients were divided into groups with a good neurological prognosis (Cerebral Performance Category [CPC] Scale 1 or 2) and a poor prognosis (CPC Scale 3, 4, or 5) to identify whether any variables showed significant differences between the two groups. Results: There were 66 patients (67.3%) with good neurological prognoses and 32 (32.8%) with poor prognoses. In the multivariate logistic analysis, the APACHE II score, serum amylase, and co-ingestion of alcohol showed significant results, with odds ratios of 1.387 (95% confidence interval [CI], 1.027-1.844), 1.017 (95% CI, 1.002-1.032), and 0.196 (95% CI, 0.040-0.948), respectively. With an APACHE II score cutoff of 6.5, the AUC was 0.826 (95% CI, 0.746-0.912). The cutoff of serum amylase was 75.5 U/L, with an AUC was 0.761 (95% CI, 0.652-0.844), and the AUC of no co-ingestion with alcohol was 0.629 (95% CI, 0.527-0.722). Conclusion: The APACHE II score could be a useful indicator for predicting the neurological prognosis of patients with glufosinate poisoning who have alert mental status.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.4274-4281
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• 2023

Blanket is of vital importance for engineering application of the fusion reactor. Nuclear heat deposition in materials is the main heat source in blanket structure. In this paper, the three-dimensional method for thermal-hydraulics/neutronics coupling analysis is developed and applied for the full-scale module of the helium-cooled ceramic breeder tritium breeding blanket (HCCB TBB) designed for China Fusion Engineering Test Reactor (CFETR). The explicit coupling scheme is used to support data transfer for coupling analysis based on cell-to-cell mapping method. The coupling algorithm is realized by the user-defined function compiled in Fluent. The three-dimensional model is established, and then the coupling analysis is performed using the paralleled Coupling Analysis of Thermal-hydraulics and Neutronics Interface Code (CATNIC). The results reveal the relatively small influence of the coupling analysis compared to the traditional method using the radial fitting function of internal heat source. However, the coupling analysis method is quite important considering the nonuniform distribution of the neutron wall loading (NWL) along the poloidal direction. Finally, the structure optimization of the blanket is carried out using the coupling method to satisfy the thermal requirement of all materials. The nonlinear effect between thermal-hydraulics and neutronics is found during the blanket structure optimization, and the tritium production performance is slightly reduced after optimization. Such an adverse effect should be thoroughly evaluated in the future work.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.531-550
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• 2023

Purpose: The unique nature of life-and-death healthcare services sets them apart from other service industries. While many studies exist on the relationship between healthcare services and customer satisfaction, most of them focus on mildly ill patients, ignoring the differences between critically ill and non-seriously ill patients. This study discusses the actual quality of healthcare services for patients who are facing life-threatening illnesses and are on life support, as well as their right to protection and dignity. Methods: The survey conducted to 149 patients with the four major illnesses: cancer, heart disease, brain disease and rare and incurable disease, those who have experiences with senior general hospitals. Results: The basic statistics of this study are adequate to represent the four major critical illnesses, and the reliability and validity of this study's hypotheses, which were measured by multiple items, were analyzed, and the internal consistency was judged to be high. In addition, it was found that the convergent validity was good and the discriminant validity was also secured. When examining the goodness of fit of the hypotheses, the SRMR, which is the standardized root mean square of residuals that measures the difference between the covariance matrix of the data variables and the theoretical covariance matrix structure of the model, met the optimal criteria. Conclusion: The academic implications of this study are differentiated from other studies by moving away from evaluating the quality of healthcare services for mildly ill patients and focusing on the rights and dignity of patients with life-threatening illnesses in four senior general hospitals. In terms of academic implications, this study enriches the depth of related studies by demonstrating the right to protection and dignity as a factor of patient-centeredness based on physical environment quality, interaction quality, and outcome quality, which are presented as sub-factors of healthcare quality. We found that the three quality factors classified by Brady and Cronin (2001) are optimized for healthcare quality assessment and management, and that the results of patients' interaction quality assessment can be used to provide a comprehensive quality rating for hospitals. Health and human rights are inextricably linked, so assessing the degree to which rights and dignity are protected can be a superior and more comprehensive measurement tool than traditional health level measures for healthcare organizations. Practical implications: Improving the quality of the physical environment and the quality of outcomes is an important challenge for hospital managers who attract patients with life and death conditions, but given the scale and economics of time, money, and human inputs, improving the quality of interactions and defining them as performance indicators in hospital quality management is an efficient way to create maximum value in the short term.

• The Journal of Bigdata
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• v.8 no.2
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• pp.165-175
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• 2023

LNG is a representative imported cargo at Incheon Port and has a relatively high contribution to the increase/decrease in overall cargo volume at Incheon Port. In addition, in the view point of nationwide, LNG is the one of the most important key resource to supply the gas and generate electricity. Thus, it is very essential to identify the factors that have impact on the demand fluctuation and build the appropriate forecasting model, which present the basic information to make balance between supply and demand of LNG and establish the plan for power generation. In this study, different to previous research based on macroscopic annual data, the weekly demand of LNG is converted from the cargo volume unloaded by LNG carriers. We have identified the periodicity and correlations among internal and external factors of demand variability. We have identified the input factors for predicting the LNG demand such as seasonality of weekly cargo volume, the peak power demand, and the reserved capacity of power supply. In addition, in order to predict LNG demand, considering the characteristics of the data, time series prediction with weekly LNG cargo volume as a dependent variable and prediction through an artificial neural network model were made, the suitability of the predictions was verified, and the optimal model was established through error comparison between performance and estimates.

• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2799-2805
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• 2024

Energy-resolved neutron imaging is an effective way to investigate the internal structure and residual stress of materials. Different sample sizes have varying requirements for the detector's imaging field of view (FOV) and spatial resolution. Therefore, a dual-mode energy-resolved neutron imaging detector was developed, which mainly consisted of a neutron scintillator screen, a mirror, imaging lenses, and a time-stamping optical fast camera. This detector could operate in a large FOV mode or a high spatial resolution mode. To evaluate the performance of the detector, the neutron wavelength spectra and the multiple spatial resolution tests were conducted at CSNS. The results demonstrated that the detector accurately measured the neutron wavelength spectra selected by a bandwidth chopper. The best spatial resolution was about 20 ㎛ in high spatial resolution mode after event reconstruction, and a FOV of 45.0 mm × 45.0 mm was obtained in large FOV mode. The feasibility was validated to change the spatial resolution and FOV by replacing the scintillator screen and adjusting the lens magnification.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• Food Science and Preservation
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• v.16 no.6
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• pp.810-816
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• 2009

To identify effective food packaging compounds that could significantly affect the freshness of stored food, the efficiency and performance of porous polypropylene film containing mustard oil as a freshness maintenance ingredient was studied by GC-MS analysis and storage testing of bread. AITC (allyl-isothiocyanate)-emitting properties of films impregnated with mustard oil were evaluated by GC-MS. AITC was extracted from mustard oil, and used as a vapor as an effective antimicrobial agent. Films were prepared under four different conditions (the film types were abbreviated 25SF1, 25SF2, 50LF, and IAF) and the amounts of AITC inside vinyl packs constructed using the four films were measured. The results showed that the 25SF2 film (width 25 mm, length 20 cm) yielded a greater amount of AITC than did the 50LF film (width 50 mm, length 20 cm). We confirmed that the amount of gas emission showed better between layer and layer of the film side than the internal film. In storage testing using various films at $35^{\circ}C$ for 25 days, 25SF2 film provided excellent preservation of bread compared with 50LF film. This was in line with the fact that 25SF2 film yielded the highest amount of AITC. Emission capacities AITC of 2 cm film were measured using bottles various volumes (43 mL, 500 mL, 1000 mL) and both closed and open systems. The AITC content of the film in 43 mL bottle was much higher than that yielded by other films in the closed system, and AITC was rapidly emitted, with relatively low residual gas emission after 4 days in an open system. Mustard oil is a useful freshness maintenance ingredient hence, analysis of AITC emission kinetics from various films were helpful to develop films with optimal antimicrobial effects, and will allow application of such films in food packaging systems.

• Journal of Global Scholars of Marketing Science
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• v.19 no.3
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• pp.28-36
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• 2009

While existing literature has focused extensively on the strengths and weaknesses of the Chaebol and their ownership and governance, there have been few studies of Korean non-Chaebol firms. However, Lee, Lee and Pennings (2001) did not specifically investigate the competitive strategies that non-Chaebol firms use to survive against the Chaebol in the domestic Korean market. The motivation of this paper is to document, through four exploratory case studies, the successful competitive strategies of non-Chaebol Korean companies against the Chaebol and then offer some propositions that may be useful to other entrepreneurial firms as well as public policy makers. Competition and cooperation as conceptualized by product similarity and cooperative inter.firm relationship respectively, are major dimensions of firm.level marketing strategy. From these two dimensions, we develop the following $2{\times}2$ matrix, with 4 types of competitive strategies for non-Chaebol companies against the Chaebol (Fig. 1.). The non-Chaebol firm in Cell 1 has a "me-too" product for the low-end market while conceding the high-end market to a Chaebol. In Cell 2, the non-Chaebol firm partners with a Chaebol company, either as a supplier or complementor. In Cell 3, the non-Chaebol firm engages in direct competition with a Chaebol. In Cell 4, the non-Chaebol firm targets an unserved part of the market with an innovative product or service. The four selected cases such as E.Rae Electronics Industry Company (Co-exister), Intops (Supplier), Pantech (Competitor) and Humax (Niche Player) are analyzed to provide each strategy with richer insights. Following propositions are generated based upon our conceptual framework: Proposition 1: Non-Chaebol firms that have a cooperative relationship with a Chaebol will perform better than firms that do not. Proposition 1a; Co-existers will perform better than Competitors. Proposition 1b: Partners (suppliers or complementors) will perform better than Niche players. Proposition 2: Firms that have no product similarity with a Chaebol will perform better than firms that have product similarity. Proposition 2a: Partners (suppliers or complementors) will perform better than Co.existers. Proposition 2b: Niche players will perform better than Competitors. Proposition 3: Niche players should perform better than Co-existers. Proposition 4: Performance can be rank.ordered in descending order as Partners, Niche Players, Co.existers, Competitors. A team of experts was constituted to categorize each of these 216 non-Chaebol companies into one of the 4 cells in our typology. Simple Analysis of Variance (ANOVA) in SPSS statistical software was used to test our propositions. Overall findings are that it is better to have a cooperative relationship with a Chaebol and to offer products or services differentiated from a Chaebol. It is clear that the only profitable strategy, on average, to compete against the Chaebol is to be a partner (supplier or complementor). Competing head on with a Chaebol company is a costly strategy not likely to pay off for a non-Chaebol firm. Strategies to avoid head on competition with the Chaebol by serving niche markets with differentiated products or by serving the low-end of the market ignored by the Chaebol are better survival strategies. This paper illustrates that there are ways in which small and medium Korean non-Chaebol firms can thrive in a Chaebol environment, though not without risks. Using different combinations of competition and cooperation firms may choose particular positions along the product similarity and cooperative relationship dimensions to develop their competitive strategies-co-exister, competitor, partner, niche player. Based on our exploratory case-study analysis, partner seems to be the best strategy for non-Chaebol firms while competitor appears to be the most risky one. Niche players and co-existers have intermediate performance, though the former do better than the latter. It is often the case with managers of small and medium size companies that they tend to view market leaders, typically the Chaebol, with rather simplistic assumptions of either competition or collaboration. Consequently, many non-Chaebol firms turn out to be either passive collaborators or overwhelmed competitors of the Chaebol. In fact, competition and collaboration are not mutually exclusive, and can be pursued at the same time. As suggested in this paper, non-Chaebol firms can actively choose to compete and collaborate, depending on their environment, internal resources and capabilities.

• Korean Security Journal
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• no.41
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• pp.37-66
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• 2014

In recent years, with the development of computers and electronics, electronics and communication technology in a growing and each part is dependent on the cross-referencing makes all electronic equipment is obsolete due to direct or indirect damage EMP. Korea and the impending standoff North Korea has a considerable level of technologies related to the EMP, EMP weapons you already have or in a few years, the development of EMP weapons will complete. North Korea launched a long-range missile and conducted a nuclear test on several occasions immediately after, when I saw the high-altitude nuclear blackmail has been strengthening the outright offensive nuclear EMP attacks at any time and practical significance for the EMP will need offensive skills would improve. At this point you can predict the damage situation of Korea's security reality that satisfy the need, more than anything else to build a protective system of the EMP. The scale of the damage that unforeseen but significant military damage and socio-economic damage and fatalities when I looked into the situation which started out as a satellite communications systems and equipment to attack military and security systems and transportation, finance, national emergency system, such as the damage elsewhere. In General, there is no direct casualties reported, but EMP medical devices that rely on lethal damage to people who can show up. In addition, the State power system failure due to a power supply interruption would not have thought the damage would bring State highly dependent on domestic power generation of nuclear plants is a serious nuclear power plant accident in the event of a blackout phenomenon can lead to the plant's internal problems should see a forecast. First of all, a special expert Committee of the EMP, the demand for protective facilities and equipment and conduct an investigation, he takes fits into your budget is under strict criteria by configuring the contractors should be sifting through. He then created the Agency for verification of performance EMP protection after you have verified the performance of maintenance, maintenance, safety and security management, design and construction company organized and systematic process Guard facilities or secret communications equipment and perfect for the EMP, such as protective equipment maneuver system should take.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.