• Journal of Korean Academy of Rural Health Nursing
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• v.16 no.2
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• pp.29-36
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• 2021

Purpose: The purpose of this study is to develop a living lab program to solve the musculoskeletal health problems of rural women and analyze its effects. Methods: The subjects included eight rural women and this study involved pretest and posttest designs for a single group. The program ran from July to August 2020 and consisted of one in-person training session and three weeks of management. The effectiveness of the program was evaluated by the change in the degree of pain experienced in the wrists, shoulders, and back, along with the general health status of the subjects. The Wilcoxon Sign-Rank test was used in the analysis. In addition, the program satisfaction was analyzed with five items based on the factors of the health belief model. Results: While the program seemed to have no significant impact on the health status of the subjects, all the participants did report reduced pain in their wrists, shoulders, and lower back. The 'sensitivity' and 'cue to action' metrics also increased with participation in the program. Conclusion: This program was effective in relieving some pain associated with the musculoskeletal problems in rural women. Therefore, such programs should be sustained and spread around community organizations

• Computers and Concrete
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• v.31 no.3
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• pp.185-195
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• 2023

Antalya is located south part of minor Asia, one of the biggest cities in Türkiye. As a result of population growth and vast migration to Antalya, many parts of the city that were not suitable for construction due to its geological conditions have become urban areas, and most of these urban areas are full of poorly engineered buildings. Poor engineering has been combined with unplanned urbanization, that causes utter vulnerability to disasters in Antalya. When an earthquake-prone city, Antalya faces with an earthquake risk, fear arises in society. To overcome this problem, it has become necessary to investigate the building stock, expressed in hundreds of thousands, in a fast and reliable way and then perform an urban transformation to create the perception of structural safety. However, the excessive building stock, labor, and economic problems made the implementation stage challenging and revealed the necessity of finding alternative solutions in the field. The present study presents a novel approach for assessment and model based on a rapid visual inspection method to transform areas under earthquake risk in Türkiye. The approach aimed to rank the interventions for decision-making mechanisms by making comparisons in the scale hierarchy. In the present study, to investigate the proposed approach, over 26,000 buildings were examined in Antalya, which is the fifth largest city in Türkiye that has a population of over 2.5 Million. In the results of the study, the risk classification was defined in the framework of building, block, street, neighborhood, and district scales.

• Restorative Dentistry and Endodontics
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• v.46 no.2
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• pp.29.1-29.11
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• 2021

Objectives: This study was conducted to compare the post-fracture survival rate of endodontically treated molar endodontically treated teeth (molar ETT) restored with resin composites or crowns and to identify potential risk factors, using a retrospective cohort design. Materials and Methods: Dental records of molar ETT with crowns or composite restorations (recall period, 2015-2019) were collected based on inclusion and exclusion criteria. The incidence of unrestorable fractures was identified, and molar ETT were classified according to survival. Information on potential risk factors was collected. Survival rates and potential risk factors were analyzed using the Kaplan-Meier log-rank test and Cox regression model. Results: The overall survival rate of molar ETT was 87% (mean recall period, 31.73 ± 17.56 months). The survival rates of molar ETT restored with composites and crowns were 81.6% and 92.7%, reflecting a significant difference (p < 0.05). However, ETT restored with composites showed a 100% survival rate if only 1 surface was lost, which was comparable to the survival rate of ETT with crowns. The survival rates of ETT with composites and crowns were significantly different (97.6% vs. 83.7%) in the short-term (12-24 months), but not in the long-term (> 24 months) (87.8% vs. 79.5%). Conclusions: The survival rate from fracture was higher for molar ETT restored with crowns was higher than for ETT restored with composites, especially in the first 2 years after restoration. Molar ETT with limited tooth structure loss only on the occlusal surface could be successfully restored with composite restorations.

• Journal of Korean Academy of Nursing
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• v.53 no.1
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• pp.87-100
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• 2023

Purpose: This study aimed to develop a nursing simulation learning module for coronavirus disease 2019 (COVID-19) patient-care and examine its effects on clinical reasoning competence, clinical competence, performance confidence, and anxiety in COVID-19 patient care for nursing students. Methods: A non-equivalent control group pre- and post-test design was employed. The study participants included 47 nursing students (23 in the experimental group and 24 in the control group) from G City. A simulation learning module for COVID-19 patient-care was developed based on the Jeffries simulation model. The module consisted of a briefing, simulation practice, and debriefing. The effects of the simulation module were measured using clinical reasoning competence, clinical competence, performance confidence, and anxiety in COVID-19 patient-care. Data were analyzed using χ²-test, Fisher's exact test, t-test, Wilcoxon signed-rank test, and Mann-Whitney U test. Results: The levels of clinical reasoning competence, clinical competence, and performance confidence of the experimental group were significantly higher than that of the control group, and the level of anxiety was significantly low after simulation learning. Conclusion: The nursing simulation learning module for COVID-19 patient-care is more effective than the traditional method in terms of improving students' clinical reasoning competence, clinical competence, and performance confidence, and reducing their anxiety. The module is expected to be useful for educational and clinical environments as an effective teaching and learning strategy to empower nursing competency and contribute to nursing education and clinical changes.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.55-67
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• 2023

Lightweight face recognition models, as one of the most popular and long-standing topics in the field of computer vision, has achieved vigorous development and has been widely used in many real-world applications due to fewer number of parameters, lower floating-point operations, and smaller model size. However, few surveys reviewed lightweight models and reimplemented these lightweight models by using the same calculating resource and training dataset. In this survey article, we present a comprehensive review about the recent research advances on the end-to-end efficient lightweight face recognition models and reimplement several of the most popular models. To start with, we introduce the overview of face recognition with lightweight models. Then, based on the construction of models, we categorize the lightweight models into: (1) artificially designing lightweight FR models, (2) pruned models to face recognition, (3) efficient automatic neural network architecture design based on neural architecture searching, (4) Knowledge distillation and (5) low-rank decomposition. As an example, we also introduce the SqueezeFaceNet and EfficientFaceNet by pruning SqueezeNet and EfficientNet. Additionally, we reimplement and present a detailed performance comparison of different lightweight models on the nine different test benchmarks. At last, the challenges and future works are provided. There are three main contributions in our survey: firstly, the categorized lightweight models can be conveniently identified so that we can explore new lightweight models for face recognition; secondly, the comprehensive performance comparisons are carried out so that ones can choose models when a state-of-the-art end-to-end face recognition system is deployed on mobile devices; thirdly, the challenges and future trends are stated to inspire our future works.

• Journal of Microbiology and Biotechnology
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• v.33 no.9
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• pp.1162-1169
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• 2023

16S rRNA short amplicon sequencing-based microbiota profiling has been thought of and suggested as a feasible method to assess food safety. However, even if a comprehensive microbial information can be obtained by microbiota profiling, it would not be necessarily sufficient for all circumstances. To prove this, the feasibility of the most widely used V3-V4 amplicon sequencing method for food safety assessment was examined here. We designed a pathogen (Vibrio parahaemolyticus) contamination and/or V. parahaemolyticus-specific phage treatment model of raw oysters under improper storage temperature and monitored their microbial structure changes. The samples stored at refrigerator temperature (negative control, NC) and those that were stored at room temperature without any treatment (no treatment, NT) were included as control groups. The profiling results revealed that no statistical difference exists between the NT group and the pathogen spiked- and/or phage treated-groups even when the bacterial composition was compared at the possible lowest-rank taxa, family/genus level. In the beta-diversity analysis, all the samples except the NC group formed one distinct cluster. Notably, the samples with pathogen and/or phage addition did not form each cluster even though the enumerated number of V. parahaemolyticus in those samples were extremely different. These discrepant results indicate that the feasibility of 16S rRNA short amplicon sequencing should not be overgeneralized in microbiological safety assessment of food samples, such as raw oyster.

• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.28 no.2
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• pp.248-261
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• 2024

Backgrounds/Aims: This study aimed to investigate patterns and factors affecting recurrence after curative resection for pancreatic ductal adenocarcinoma (PDAC). Methods: Consecutive patients who underwent curative resection for PDAC (2011-21) and consented to data and tissue collection (Barts Pancreas Tissue Bank) were followed up until May 2023. Clinico-pathological variables were analysed using Cox proportional hazards model. Results: Of 91 people (42 males [46%]; median age, 71 years [range, 43-86 years]) with a median follow-up of 51 months (95% confidence intervals [CIs], 40-61 months), the recurrence rate was 72.5% (n = 66; 12 loco-regional alone, 11 liver alone, 5 lung alone, 3 peritoneal alone, 29 simultaneous loco-regional and distant metastases, and 6 multi-focal distant metastases at first recurrence diagnosis). The median time to recurrence was 8.5 months (95% CI, 6.6-10.5 months). Median survival after recurrence was 5.8 months (95% CI, 4.2-7.3 months). Stratification by recurrence location revealed significant differences in time to recurrence between loco-regional only recurrence (median, 13.6 months; 95% CI, 11.7-15.5 months) and simultaneous loco-regional with distant recurrence (median, 7.5 months; 95% CI, 4.6-10.4 months; p = 0.02, pairwise log-rank test). Significant predictors for recurrence were systemic inflammation index (SII) ≥ 500 (hazard ratio [HR], 4.5; 95% CI, 1.4-14.3), lymph node ratio ≥ 0.33 (HR, 2.8; 95% CI, 1.4-5.8), and adjuvant chemotherapy (HR, 0.4; 95% CI, 0.2-0.7). Conclusions: Timing to loco-regional only recurrence was significantly longer than simultaneous loco-regional with distant recurrence. Significant predictors for recurrence were SII, lymph node ration, and adjuvant chemotherapy.

• The Journal of Korean Academy of Prosthodontics
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• v.57 no.4
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• pp.342-349
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• 2019

Purpose: The purpose of this study was to retrospectively investigate the survival and success rate of implant-supported fixed prosthesis according to the materials in the posterior area. Other purposes were to observe the complications and evaluate the factors affecting failure. Materials and methods: Patients who had been restored implant prosthesis in the posterior area by the same prosthodontist in the department of prosthodontics, dental hospital, Chonbuk National University, in the period from January 2011 to June 2018 were selected for the study. The patient's sex, age, material, location, type of prosthesis and complications were examined using medical records. The KaplanMeier method was used to analyze the survival and success rate. The Log-rank test was conducted to compare the differences between the groups. Cox proportional hazards model was used to assess the association between potential risk factors and success rate. Results: A total of 364 implants were observed in 245 patients, with an average follow-up of 17.1 months. A total of 5 implant prostheses failed and were removed, and the 3 and 5 year cumulative survival rate of all implant prostheses were 97.5 and 91.0, respectively. The 3 and 5 year cumulative success rate of all implant prostheses were 61.1% and 32.9%, respectively. Material, sex, age, location and type of prosthesis did not affect success rate (P>.05). Complications occurred in the order of proximal contact loss (53 cases), retention loss (17 cases), peri-implant mucositis (12 cases), infraocclusion (4 cases) and so on. Conclusion: Considering a high cumulative survival rate of implant-supported fixed prostheses, regardless of the materials, implant restored in posterior area can be considered as a reliable treatment to tooth replacement. However, regular inspections and, if necessary, repairs and adjustments are very important because of the frequent occurrence of complications.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Distribution Research
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• v.5 no.2
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• pp.49-68
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• 2001

In this chapter, we summarize the results on the optimal location selection and present limitation and direction of research. In order to reach the objective, this study selected and tested the interaction model which obtains the value of co-ordinates on location selection through the optimization technique. This study used the original variables in the model, but the results indicated that there is difference in reality. In order to overcome this difference, this study peformed market survey and found the new variables (first data such as price, quality and assortment of goods, and the second data such as aggregate area, and area of shop, and the number of cars in the parking lot). Then this study determined an optimal variable by empirical analysis which compares an actual value of market share in 1988 with the market share yielded in the model. However, this study found the market share in each variables does not reflect a reality due to an assumption of λ-value in the model. In order to improve this, this study performed a sensitivity analysis which adds the λ value from 1.0 to 2.9 marginally. The analyzed result indicated the highest significance with the market share ratio in 1998 at λ of 1.0. Applying the weighted value to a variable from each of the first data and second data yielded the results that more variables from the first data coincided with the realistic rank on sales. Although this study have some limits and improvements, if a marketer uses this extended model, more significant results will be produced.