• Tuberculosis and Respiratory Diseases
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• v.82 no.2
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• pp.166-172
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• 2019

Background: The purpose of this study was to determine whether components of the ProVent model can predict the high medical costs in Korean patients requiring at least 21 days of mechanical ventilation (prolonged mechanical ventilation [PMV]). Methods: Retrospective data from 302 patients (61.6% male; median age, 63.0 years) who had received PMV in the past 5 years were analyzed. To determine the relationship between medical cost per patient and components of the ProVent model, we collected the following data on day 21 of mechanical ventilation (MV): age, blood platelet count, requirement for hemodialysis, and requirement for vasopressors. Results: The mortality rate in the intensive care unit (ICU) was 31.5%. The average medical costs per patient during ICU and total hospital (ICU and general ward) stay were 35,105 and 41,110 US dollars (USD), respectively. The following components of the ProVent model were associated with higher medical costs during ICU stay: age <50 years (average 42,731 USD vs. 33,710 USD, p=0.001), thrombocytopenia on day 21 of MV (36,237 USD vs. 34,783 USD, p=0.009), and requirement for hemodialysis on day 21 of MV (57,864 USD vs. 33,509 USD, p<0.001). As the number of these three components increased, a positive correlation was found betweeen medical costs and ICU stay based on the Pearson's correlation coefficient (${\gamma}$) (${\gamma}=0.367$, p<0.001). Conclusion: The ProVent model can be used to predict high medical costs in PMV patients during ICU stay. The highest medical costs were for patients who required hemodialysis on day 21 of MV.

• Journal of Korea Trade
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• v.25 no.5
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• pp.19-38
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• 2021

Purpose - This study aims to verify the direct causal relationship between cost competitiveness and global network competitiveness, which are the tangible service quality factors determined by the shipping company, which in turn affect the shipper's customer satisfaction. Additionally, we empirically investigate the intangible, related service qualities determined by shipping companies, such as operational competitiveness and customer relationship quality, and how these then positively affect customer satisfaction through the formation of trust. Therefore, we examine the mediating effect of trust formation among different contractors for shipping services. Design/methodology - In order to examine the shipping company's tangible and intangible service-qualities perceived by the shipper on customer satisfaction and the process of trust formation between contractors, we collected valid data from 114 respondents out of 200 distributed questionnaires. The respondents consisted of domestic freight forwarders who engage with domestic and international shipping and logistics agencies. Descriptive statistics, confirmatory factor analysis, reliability, convergent and discriminant validities, common method bias, and PLS-SEM (partial least square-structural equation model) were analyzed using the program STATA 16. Findings - The findings of this study are as follows. First, our results showed that all hypotheses assumed in this study had statistically significant supporting evidence. Second, it was found that the mediating effect of trust was significant in affecting the quality of intangible service- qualities for customer satisfaction. Third, through supplementary analysis, we found that the global network competitiveness of domestic shipping companies will increase in importance in the future. In conclusion, the theoretical and practical implications of these findings are presented. Originality/value - This study reaffirmed the traditional causal relationship between customer satisfaction and tangible service quality. Additionally, we also contribute to the literature on the understanding of the causal relationship between trust formation and customer satisfaction through intangible interactions from a long-term perspective.

• Journal of Animal Science and Technology
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• v.63 no.6
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• pp.1232-1246
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• 2021

Recently, the cattle genome sequence has been completed, followed by developing a commercial single nucleotide polymorphism (SNP) chip panel in the animal genome industry. In order to increase statistical power for detecting quantitative trait locus (QTL), a number of animals should be genotyped. However, a high-density chip for many animals would be increasing the genotyping cost. Therefore, statistical inference of genotype imputation (low-density chip to high-density) will be useful in the animal industry. The purpose of this study is to investigate the effect of the reference population size and marker density on the imputation accuracy and to suggest the appropriate number of reference population sets for the imputation in Hanwoo cattle. A total of 3,821 Hanwoo cattle were divided into reference and validation populations. The reference sets consisted of 50k (38,916) marker data and different population sizes (500, 1,000, 1,500, 2,000, and 3,600). The validation sets consisted of four validation sets (Total 889) and the different marker density (5k [5,000], 10k [10,000], and 15k [15,000]). The accuracy of imputation was calculated by direct comparison of the true genotype and the imputed genotype. In conclusion, when the lowest marker density (5k) was used in the validation set, according to the reference population size, the imputation accuracy was 0.793 to 0.929. On the other hand, when the highest marker density (15k), according to the reference population size, the imputation accuracy was 0.904 to 0.967. Moreover, the reference population size should be more than 1,000 to obtain at least 88% imputation accuracy in Hanwoo cattle.

• Archives of Plastic Surgery
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• v.48 no.4
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• pp.417-426
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• 2021

Background Soft tissue coverage plays a vital role in replacing the vascularity of the underlying bone in Gustilo type IIIB fractures. The aim of this article was to evaluate the feasibility of local pedicled flaps in type IIIB fractures at a tertiary care center. Methods We included all cases of open Gustilo-Anderson type IIIB fractures of the tibia treated with local flap coverage from January 2017 to February 2019. We carried out a retrospective analysis to investigate the relationships of complications, hospital stay, and cost-effectiveness with the choice of flap, infective foci, site and size of the defect, and type of fixation. Results Out of 138 Gustilo type IIIB fractures analyzed in our study, 27 cases had complications, of which 19 (13.76%) involved flap necrosis, four (2.89%) were infections, three (2.17%) involved partial necrosis, and one (0.72%) was related to bone spur development. Flap complications showed a statistically significant association with the perforator flap category (propeller flaps in particular) (P=0.001). Flap necrosis showed a significant positive correlation with cases treated within 3 weeks after trauma (P=0.046). A significant positive correlation was also found between defect size and the duration of hospital stay (P=0.03). Conclusions Although local flaps are harvested from the same leg that underwent trauma, their success rate is at least as high as microvascular flaps as reported from other centers. Amidst the local flaps, complications were predominantly associated with perforator flaps.

• Advances in nano research
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• v.12 no.5
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• pp.515-527
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• 2022

The application of fibers in concrete obviously enhances the properties of concrete, also the application of natural fibers in concrete is raising due to the availability, low cost and environmentally friendly. Besides, predicting the mechanical properties of concrete in general and shear strength in particular is highly significant in concrete mixture with fiber nanocomposite reinforced concrete (FRC) in construction projects. Despite numerous studies in shear strength, determining this strength still needs more investigations. In this research, Adaptive Neuro-Fuzzy Inference System (ANFIS) have been employed to determine the strength of reinforced concrete with fiber. 180 empirical data were gathered from reliable literature to develop the methods. Models were developed, validated and their statistical results were compared through the root mean squared error (RMSE), determination coefficient (R²), mean absolute error (MAE) and Pearson correlation coefficient (r). Comparing the RMSE of PSO (0.8859) and ANFIS (0.6047) have emphasized the significant role of structural parameters on the shear strength of concrete, also effective depth, web width, and a clear depth rate are essential parameters in modeling the shear capacity of FRC. Considering the accuracy of our models in determining the shear strength of FRC, the outcomes have shown that the R² values of PSO (0.7487) was better than ANFIS (2.4048). Thus, in this research, PSO has demonstrated better performance than ANFIS in predicting the shear strength of FRC in case of accuracy and the least error ratio. Thus, PSO could be applied as a proper tool to maximum accuracy predict the shear strength of FRC.

• Journal of the Korean Dietetic Association
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• v.28 no.2
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• pp.67-84
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• 2022

This study aimed to explore the status of food service outsourcing behavior of long-term care institutions (LTCIs) through a cross-sectional survey using a questionnaire administered between July 16th and August 7th, 2020. The survey respondents were either dietitians or facility managers, who worked at 731 nursing homes, 477 group homes, and 673 day-care centers. Approximately 25.9% of nursing homes, 11.7% of group homes, and 33.1% of day-care centers used a managed-services company to operate their food service units. The main reason for outsourcing food service by nursing homes was related to the staffing of dietitians and cooks, whereas group homes and day-care centers outsourced food services due to factors relating to meal costs and the cooking process. Almost all the LTCIs entered into private contracts for outsourced food services. Only a few food service contracts included the types of meals, nutrition standards such as protein and calories per meal, and the parameter or ratio of food cost. Of the respondents, 84.5% from nursing homes, 87.5% from group homes, and 87.1% from day-care centers agreed that the quality of outsourced food services of the LTCIs should be regulated. Meals are essential for maintaining the health and functional status of LTCI users. As more LTCIs outsource their food services, we suggest the following: (1) Increasing the minimum dietitian staffing standards for LTCIs as per the Welfare of Senior Citizens Act and requiring at least one dietitian for every nursing home, (2) Making it mandatory to use a standard food service contract template when drafting food service contract, and (3) Developing realistic standards for food service operations considering the size and operation type of the LTCIs.

• Journal of Animal Science and Technology
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• v.64 no.3
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• pp.531-538
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• 2022

In Italy, buffalo mozzarella is a largely sold and consumed dairy product. The fraudulent adulteration of buffalo milk with cheaper and more available milk of other species is very frequent. In the present study, Fourier transform infrared spectroscopy (FTIR), in combination with multivariate analysis by partial least square (PLS) regression, was applied to quantitatively detect the adulteration of buffalo milk with cow milk by using a fully automatic equipment dedicated to the routine analysis of the milk composition. To enhance the heterogeneity, cow and buffalo bulk milk was collected for a period of over three years from different dairy farms. A total of 119 samples were used for the analysis to generate 17 different concentrations of buffalo-cow milk mixtures. This procedure was used to enhance variability and to properly randomize the trials. The obtained calibration model showed an R² ≥ 0.99 (R² cal. = 0.99861; root mean square error of cross-validation [RMSEC] = 2.04; R² val. = 0.99803; root mean square error of prediction [RMSEP] = 2.84; root mean square error of cross-validation [RMSECV] = 2.44) suggesting that this method could be successfully applied in the routine analysis of buffalo milk composition, providing rapid screening for possible adulteration with cow's milk at no additional cost.

• Journal of The Korean Society of Grassland and Forage Science
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• v.42 no.4
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• pp.270-275
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• 2022

Livestock production costs are heavily influenced by the cost of feed, The use of domestically grown forages is more desirable for livestock feed production. As part of this study, triticale, which is an extremely palatable and easily cultivable crop in Korea, was used to produce low moisture silage bales with lactic acid bacteria (LAB) and then stored for different periods. We examined the nutrient content of silage, such as crude protein (CP), acid detergent fiber (ADF) and neutral detergent fiber (NDF), as well as their organic acids, including lactic acid, acetic acid, butyric acid, at different storage periods. The nutrient content of silages, such as crude protein, ADF, and NDF, did not change significantly throughout storage periods. Organic acid data indicated that lactic acid concentrations increased with increasing moisture contents and storage periods up to nine months. However, further extending storage to 12 months resulted in a reduction in the lactic acid content of all silages as well as an increase in their pH. Based on the present results, it suggested that the production of low moisture silage with the LAB may be able to preserve and maintain its quality without altering its nutritional composition. Also, the lactate content of the silage remained significant for at least nine months.

• Journal of Platform Technology
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• v.11 no.3
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• pp.3-11
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• 2023

Real-time control systems are growing rapidly as infrastructure technologies such as IoT and mobile communication develop and services that value real-time such as factory management and vehicle operation checks increase. Various solutions have been proposed to increase the time sensitivity of this system, but most real-time control systems are currently composed of local servers and multiple clients located in control stations, which are transmitted to local servers where control systems are located. In this paper, we proposed an edge computing-based real-time control model that can reduce the time it takes for the bus information system, one of the real-time control systems, to provide the information to the user at the time it collects the information. Simulating the existing model and the edge computing model, the edge computing model confirmed that the cost for users to receive data is reduced from at least 10% to up to 80% compared to the existing model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3099-3120
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• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.