• The Korean Journal of Pain
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• v.36 no.2
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• pp.149-162
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• 2023

Chronic postsurgical pain (CPSP) is a multifactorial condition that affects a significant proportion of patients undergoing surgery. The prevention and management of CPSP require the identification of preoperative risk factors to screen high-risk patients and establish appropriate perioperative pain management plans to prevent its development. Active postoperative pain management should be provided to prevent CPSP in patients with severe pain following surgery. These tasks have become important for perioperative team members in the management of CPSP. This review article provides a comprehensive overview of the latest research on the role of perioperative team members in preventing and managing CPSP. Additionally, it highlights practical strategies that can be employed in clinical practice, covering the definition and risk factors for CPSP, including preoperative, intraoperative, and postoperative factors, as well as a risk prediction model. The article also explores various treatments for CPSP, as well as preventive measures, including preemptive analgesia, regional anesthesia, pharmacological interventions, psychoeducational support, and surgical technique modification. This article emphasizes the importance of a comprehensive perioperative pain management plan that includes multidisciplinary interventions, using the transitional pain service as an example. By adopting a multidisciplinary and collaborative approach, perioperative team members can improve patient outcomes, enhance patient satisfaction, and reduce healthcare costs. However, further research is necessary to establish targeted interventions to effectively prevent and manage CPSP.

• Journal of Korean Medicine for Obesity Research
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• v.17 no.1
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• pp.46-53
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• 2017

Health care big data is thought to be a promising field of interest for disease prediction, providing the basis of medical treatment and comparing effectiveness of different treatments. Korean government has begun an effort on releasing public health big data to improve the quality and safety of medical care and to provide information to health care professionals. By studying population based big data, interesting outcomes are expected in many aspects. To initiate research using health care big data, it is crucial to understand the characteristics of the data. In this review, we analyzed cases from inside and outside the country using clinical data registry. Based on successful cases, we suggest research method for evidence-based Korean medicine. This will provide better understanding about health care big data and necessity of Korean medicine data registry network.

• Journal of Distribution Science
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• v.15 no.9
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• pp.85-93
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• 2017

Purpose - The volume and valence of online word-of-mouth(eWOM) have become an important part of the retailer's market success for a wide range of products. This study aims to investigate how the growth of eWOM has generated the product's final financial outcomes in the introductory period influences. Research design, data, and methodology - This study uses weekly box office performance for 117 movies released in the South Korea from July 2015 to June 2016 using Korean Film Council(KOFIC) database. 292,371 posted online review messages were collected from NAVER movie review bulletin board. Using regression analysis, we test whether eWOM incurred during the opening week is valuable to explain the last of box office performance. Three major eWOM metrics were considered after controlling for the major distributional factors. Results - Results support that major eWOM variables play a significant role in box-office outcome prediction. Especially, the growth rate of the positive eWOM volume has a significant effect on the growth potential in sales. Conclusions - The findings highlight that the speed of eWOM growth has an informational value to understand the market reaction to a new product beyond valence and volume. Movie distributors need to take positive online eWOM growth into account to make optimal screen allocation decisions after release.

• Journal of KIBIM
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• v.9 no.2
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• pp.1-10
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• 2019

In this research, schedule optimization is defined as balancing the number of workers while keeping the demand and needs of the project resources, creating the perfect schedule for each activity. Therefore, when one optimizes a schedule, multiple potentials of schedule changes are assessed to get an instant view of changes that avoid any over and under staffing while maximizing productivity levels for the available labor cost. Optimizing the number of workers in the scheduling process is not a simple task since it usually involves many different factors to be considered such as the development of quantity take-offs, cost estimating, scheduling, direct/indirect costs, and borrowing costs in cash flow while each factor affecting the others simultaneously. That is why the optimization process usually requires complex computational simulations/modeling. This research attempts to find an optimal selection of daily maximum workers in a project while considering the impacts of other factors at the same time through OPEN BIM based multiple computer simulations in resource leveling. This paper integrates several different processes such as quantity take-offs, cost estimating, and scheduling processes through computer aided simulations and prediction in generating/comparing different outcomes of each process. To achieve interoperability among different simulation processes, this research utilized data exchanges supported by building SMART-IFC effort in automating the data extraction and retrieval. Numerous computer simulations were run, which included necessary aspects of construction scheduling, to produce sufficient alternatives for a given project.

• Journal of the Korean Geotechnical Society
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• v.39 no.10
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• pp.19-26
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• 2023

This research aimed to predict the seepage rate in the base rocks of a concrete face rockfill dam (CFRD) by conducting numerical analysis under various conditions. We examined the relationship between basic grouting and seepage, emphasizing the significance of the permeability coefficient of the grouting material and the rock. Moreover, we observed a decrease in seepage with an increase in the cross-sectional area of the dam. The results of this study provide essential input factors and outcomes of numerical analysis, incorporating various parameters, to inform the design phase. Additionally, our findings offer a dependable approach for calculating a reasonable seepage rate during both operational and maintenance phases.

• Korean Journal of Adult Nursing
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• v.28 no.5
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• pp.585-594
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• 2016

Purpose: The purpose of this study was to develop and test a predictive model on the male nurses' turnover intention. Methods: This study utilized the model-testing design based on the Price's causal model of turnover. This study collected data from 306 male nurses on a national scale with structured questionnaires measuring job opportunity, kinship responsibility, positive emotion, work autonomy, role conflict, work satisfaction, organizational commitment, and turnover intention. The data were analyzed using SPSS/WIN 22.0 program and AMOS 20.0 program. Results: As the outcomes satisfied the recommended level, the hypothetical model appeared to fit the data. Twenty-seven of the 38 hypotheses selected for the hypothetical model were statistically significant. 54.2% of turnover intention was explained by job opportunity, kinship responsibility, positive emotion, work autonomy, role conflict, work satisfaction and organizational commitment. Conclusion: The hypothetical model of this study was confirmed to be adequate in explaining and predicting male nurses' turnover intention. Findings from this study can be used to design appropriate strategies to decrease the male nurse's turnover intention.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1419-1436
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• 2020

For an energy harvesting sensor network, when the network lifetime is not the only primary goal, maximizing the network performance under environmental energy harvesting becomes a more critical issue. However, clustering protocols that aim at providing maximum information throughput have not been thoroughly explored in Energy Harvesting Wireless Sensor Networks (EH-WSNs). In this paper, clustering protocols are studied for maximizing the data transmission in the whole network. Based on a long short-term memory (LSTM) energy predictor and node energy consumption and supplement models, an uneven clustering protocol is proposed where the cluster head selection and cluster size control are thoroughly designed for this purpose. Simulations and results verify that the proposed scheme can outperform some classic schemes by having more data packets received by the cluster heads (CHs) and the base station (BS) under these energy constraints. The outcomes of this paper also provide some insights for choosing clustering routing protocols in EH-WSNs, by exploiting the factors such as uneven clustering size, number of clusters, multiple CHs, multihop routing strategy, and energy supplementing period.

• Psychiatry investigation
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• v.15 no.11
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• pp.1037-1045
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• 2018

Objective The present study aimed to identify risk factors for future SI and to predict individual-level risk for future or persistent SI among college students. Methods Mental health check-up data collected over 3 years were retrospectively analyzed. Students were categorized as suicidal ideators and non-ideators at baseline. Logistic regression analyses were performed separately for each group, and the predicted probability for each student was calculated. Results Students likely to exhibit future SI had higher levels of mental health problems, including depression and anxiety, and significant risk factors for future SI included depression, current SI, social phobia, alcohol problems, being female, low self-esteem, and number of close relationships and concerns. Logistic regression models that included current suicide ideators revealed acceptable area under the curve (AUC) values (0.7-0.8) in both the receiver operating characteristic (ROC) and precision recall (PR) curves for predicting future SI. Predictive models with current suicide non-ideators revealed an acceptable level of AUCs only for ROC curves. Conclusion Several factors such as low self-esteem and a focus on short-term rather than long-term outcomes may enhance the prediction of future SI. Because a certain range of SI clearly necessitates clinical attention, further studies differentiating significant from other types of SI are necessary.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.8
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• pp.145-155
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• 2022

Since 2014, the IFRS 9 has been the focus of the attention of many scholars across disciplines. The futuristic prediction of bank loan provision via a flexible ECL model has been observed as a game changer from the prior models offered in IAS 39. This study has two objectives; the first is to examine the impact on loan loss provisions (LLP), nonperforming loans (NPL), and the impairment loan losses (ILL) after the IFRS 9 in gulf banks. The second is to capture any variation in LLP, NPL, and ILL before and after IFRS9. The study used the two-way fixed effect model (TWFE) estimation and the DiD approach to attain its objectives. 54 gulf banks were selected from the periods between 2012 and 2020. The results indicate that LLP has significantly increased after the transition to IFRS 9, while the NPL has significantly decreased. The results did not capture a significant change in ILL after IFRS9 implementation. The results also indicate more consistency in LLP and NPL reporting after implementing the ECL model adopted in IFRS9. The study concluded that ECL model outcomes are in tandem with prior observation worldwide and pointed out some improvement opportunities for the future.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2343-2349
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• 1999

The hyper-geometric distribution software reliability growth model (HGDM) was recently developed and successfully applied to real data sets. The HGDM considers the sensitivity factor as a parameter to be estimated. In order to reflect the random behavior of the test-and-debug process, this paper generalizes the HGDM by assuming that the sensitivity factor is a binomial random variable. Such a generalization enables us to easily understand the statistical characteristics of the HGDM. It is shown that the least squares method produces the identical results for both the HGDM and the generalized HGDM. Methods for computing the maximum likelihood estimates and predicting the future outcomes are also presented.