• Land and Housing Review
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• v.15 no.2
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• pp.39-56
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• 2024

This study addressed housing issues among various social problems of youth. With a focus on residential independence, this study analyzed the factors that lead youth to achieve residential independence. This study drew on nationwide data from the 'Youth Life Survey (2022)' with a sample size of 12,578. Binary logistic regression analysis was employed, with the dependent variable being residential independence. Key factors were as follows. The probability of residential independence was higher for men than women. Residential independence occurred mainly in non-metropolitan areas compared to metropolitan areas. Findings revealed that greater age, income, and assets facilitate achieving residential independence. In addition, public transport and cultural facilities were important for their residential independence, and it was found that the previous experience of residential independence had a positive effect. Policy implications derived from the findings are as follows. It is required to consider the heterogeneity and diversity of youth rather than implementing unitary policies. To ensure continuity and sustainability of self-reliance, long-term support programs are needed rather than temporary support. Moreover, it is required to offer public support comprehensively, instead of youth relying on support from personal networks, including their parents. An inclusive housing policy should be established to support youth for their residential independence in the future.

• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.27 no.4
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• pp.350-365
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• 2023

Backgrounds/Aims: Extra hepatic portal venous obstruction (EHPVO) is the most common cause of portal hypertension in Indian children. While endoscopy is the primary modality of management, a subset of patients require surgery. This study aims to report the short- and long-term outcomes of EHPVO patients managed surgically. Methods: All the patients with EHPVO who underwent surgery between August 2007 and December 2021 were retrospectively reviewed. Postoperative complications were classified after Clavien-Dindo. Binary logistic regression in Wald methodology was used to determine the predictive factors responsible for unfavourable outcome. Results: Total of 202 patients with EHPVO were operated. Mean age of patients was 20.30 ± 9.96 years, and duration of illness, 90.05 ± 75.13 months. Most common indication for surgery was portal biliopathy (n = 59, 29.2%), followed by bleeding (n = 50, 24.8%). Total of 166 patients (82.2%) had shunt procedure. Splenectomy with esophagogastric devascularization was the second most common surgery (n = 20, 9.9%). Nine major postoperative complications (Clavien-Dindo > 3) were observed in 8 patients (4.0%), including 1 (0.5%) operative death. After a median follow-up of 56 months (15-156 months), 166 patients (82.2%) had favourable outcome. In multivariate analysis, associated splenic artery aneurysm (p = 0.007), isolated gastric varices (p = 0.004), preoperative endoscopic retrograde cholangiography and stenting (p = 0.015), and shunt occlusion (p < 0.001) were independent predictors of unfavourable long-term outcome. Conclusions: Surgery in EHPVO is safe, affords excellent short- and long-term outcome in patients with symptomatic EHPVO, and may be considered for secondary prophylaxis.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.274-280
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• 2024

Machine learning techniques utilizing neural networks have been employed in various fields such as disease gene discovery and diagnosis, drug development, and prediction of drug-induced liver injury. Disease features can be investigated by molecular information of DNA. In this study, we developed a neural network to predict the length of DNA and the number of DNA species in mixture solution which are representative molecular information of DNA. In order to address the time-consuming limitations of gel electrophoresis as conventional analysis, we analyzed the dynamic data of a microfluidic concentrating device. The dynamic data were reconstructed into a spatiotemporal map, which reduced the computational cost required for training and prediction. We employed a convolutional neural network to enhance the accuracy to analyze the spatiotemporal map. As a result, we successfully performed single DNA length prediction as single-variable regression, simultaneous prediction of multiple DNA lengths as multivariable regression, and prediction of the number of DNA species in mixture as binary classification. Additionally, based on the composition of training data, we proposed a solution to resolve the problem of prediction bias. By utilizing this study, it would be effectively performed that medical diagnosis using optical measurement such as liquid biopsy of cell-free DNA, cancer diagnosis, etc.

• Annals of Occupational and Environmental Medicine
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• v.34
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• pp.23.1-23.14
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• 2022

Background: The musculoskeletal disease (MSD) burden is an important health problem among Korean fishers. We aimed to investigate the indicators of the prevalence of MSD and contributions of significant indicators to MSD in Korean fishers. Methods: This cross-section study included 927 fishers (male, 371; female, 556) aged 40 to 79 years who were enrolled from 3 fishery safety and health centers. The outcome variable was one-year prevalence of MSD in 5 body parts (the neck, shoulder, hand, back, and knee). Independent variables were sex, age, educational attainment, household income, job classification, employment xlink:type, hazardous working environment (cold, heat, and noise), ergonomic risk by the 5 body parts, anxiety disorder, depression, hypertension, diabetes, and hyperlipidemia. The adjusted odds ratio of MSDs by the 5 body parts were calculated using multiple logistic regression analysis. We computed the population attributable fraction (PAF) for each indicators of MSDs using binary regression models. Results: The one-year prevalence of MSD in the neck, shoulder, hand, back, and knee was 7.8%, 17.8%, 7.8%, 27.2%, and 16.2% in males vs. 16.4%, 28.1%, 23.0%, 38.7%, and 30.0% in females, respectively. The ergonomic risk PAF according to the body parts ranged from 22.8%-59.6% in males and 22.8%-50.3% in female. Mental diseases showed a significant PAF for all body parts only among female (PAF 9.1%-21.4%). Cold exposure showed a significant PAF for the neck, shoulder, and hand MSD only among female (25.6%-26.8%). Age was not a significant indicator except for the knee MSD among female. Conclusions: Ergonomic risk contributed majorly as indicators of MSDs in both sexes of fishers. Mental disease and cold exposure were indicators of MSDs only among female fishers. This information may be important for determining priority risk groups for the prevention of work-related MSD among Korean fishers.

• Journal of the Korean Society of Radiology
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• v.18 no.4
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• pp.373-381
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• 2024

This study aimed to identify and assess the influence of risk factors on the frequency of thyroid nodules diagnosed through ultrasonography among adults visiting for health screenings. The study analyzed 210 adult men and women who underwent thyroid ultrasound at J Hospital in Busan. The differences in variables were analyzed using Chi-square and independent t-tests, and risk ratios were calculated using binary logistic regression. The results showed significant differences in gender, age, T3, Free T4, Uric acid, T-chol, HDL-C and LDL-C. The risk ratios for risk factors indicated that women had a 2.42 times higher risk compared to men, and the age groups 41-50 and over 61 had risks 2.32 and 2.22 times higher, respectively compared to those under 40. Conversely, and increase in T3 and Free T4 levels was associated with a decreased risk of 0.12 and 0.86 times, respectively, while lipid levels had negligible influence. Based on these findings, it is concluded that regular ultrasonography monitoring, rather than solely relying on biochemical markers, is crucial for the early detection and management of thyroid nodules.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.89-95
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• 2015

Traditional frequent pattern mining discovers valid patterns with no smaller frequency than a user-defined minimum threshold from databases. In this framework, an enormous number of patterns may be extracted by a too low threshold, which makes result analysis difficult, and a too high one may generate no valid pattern. Setting an appropriate threshold is not an easy task since it requires the prior knowledge for its domain. Therefore, a pattern mining approach that is not based on the domain knowledge became needed due to inability of the framework to predict and control mining results precisely according to the given threshold. Top-k frequent pattern mining was proposed to solve the problem, and it mines top-k important patterns without any threshold setting. Through this method, users can find patterns from ones with the highest frequency to ones with the k-th highest frequency regardless of databases. In this paper, we provide knowledge both on frequent and top-k pattern mining. Although top-k frequent pattern mining extracts top-k significant patterns without the setting, it cannot consider both item quantities in transactions and relative importance of items in databases, and this is why the method cannot meet requirements of many real-world applications. That is, patterns with low frequency can be meaningful, and vice versa, in the applications. High utility pattern mining was proposed to reflect the characteristics of non-binary databases and requires a minimum threshold. Recently, top-k high utility pattern mining has been developed, through which users can mine the desired number of high utility patterns without the prior knowledge. In this paper, we analyze two algorithms related to top-k high utility pattern mining in detail. We also conduct various experiments for the algorithms on real datasets and study improvement point and development direction of top-k high utility pattern mining through performance analysis with respect to the experimental results.

• The Journal of Korean Society for School & Community Health Education
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• v.17 no.1
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• pp.89-99
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• 2016

Objectives: This cross-sectional study aims to investigate the prevalence and factors relating to handwashing with soap among Korean adults. Methods: Study subjects consist of 755 adults who have been contacted in September 2013 via telephone surveys. The data collected has been analyzed using descriptive statistics, a chi-square test and a logistic regression analysis. A primary purpose is to understand the prevalence of handwashing with soap more than 8 times daily and for 30 seconds per wash among adults. Independent variables include socioeconomic levels, the participants' perception and knowledge of handwashing and their educational experiences relating to handwashing. Results: The overall percentile of people who wash their hands with soap 8 time per day for 30 seconds or more per wash was 16.0%, which is 121 people out of 755 study subjects. In univariate analysis, age, education levels, monthly average income, handwashing habits, perceptions relate to the importance of handwashing, self-assessment of handwashing, environment of public toilet, and the completion of handwashing education shows significant result. Significant differences also appear (p<0.05) in logistic regression analysis on binary variables. There is a strong correlation between daily frequency of handwashing and willingness to wash hands while outside. For example, people who wash their hands very often while outside are 2.24 times (95% C.I. 1.29-3.87) more likely to practice handwashing with soap 8 times per day for 30 seconds or more per wash than those people who only intermittently wash their hands while outside. Furthermore, people with general unwillingness to wash their hands while outside are 4.61 times (95% C.I. 1.22-3.28) less likely to practice handwashing with soap 8 times per day for 30 seconds or more per wash than those with general willingness. Conclusions: This study has been carried out to identify the decision factors in practicing handwashing with soap for Korean adults. In univariate analysis, age, education level, monthly average income, handwashing habits, handwashing self-assessment, public toilet environment, completion of handwashing education and so forth have been identified to be the decision factors. This study result shows that the overall level of cleanliness of public toilet perceives to be poor and it suggests that the environment of public toilet needs to be enhanced. As the handwashing habits and handwashing-self assessment have been identified to be the significant decision factors for handwashing, there search and approach in these factors need to be developed further.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Horticultural Science & Technology
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• v.29 no.6
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• pp.623-632
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• 2011

To increase the anti-carcinogens phenylethylisothiocyanate (PEITC), myrosinase (MYR), and glutathione S-transferase (GST), genes related to PEITC pathway were isolated and the gene expressions were regulated by Agrobacterium transformation. Isolated cDNAs, MYR, and GST genes were 1,647 bp and 624 bp, respectively, and the protein expression was confirmed through pET system. Thereafter, we constructed a sense-oriented over-expressing myrosinase (pBMY) and RNAi down-regulated GST (pJJGST) binary vectors for the Chinese cabbage transformation. After the transformation, thirteen over-expressing transgenic Chinese cabbage plants (IMS) with pBMY and five down-regulated ones (IGA) with pJJGST were selected by PCR analysis. Selected $T_0$ transgenic plants were generated to $T_1$ plants by self-pollination. Based on the Southern blot analysis on these $T_1$ transgenic plants, 1-4 copies of T-DNA were transferred to Chinese cabbage genome. Thereafter, RNA expression level of myrosinase gene or GST gene was analyzed through real-time RT PCR of IMS, IGA, and non-transgenic inbred lines. In case of IMS lines, myrosinase gene was increased 1.03-4.25 fold and, in IGA lines, GST gene was decreased by 26.42-42.22 fold compared to non-transgenic ones, respectively. Analysis of PEITC concentrations using GC-MS it showed that some IMS lines and some IGA lines increased concentrations of PEITC up to 4.86 fold and up to 3.89 fold respectively compared to wild type. Finally in this study IMS 1, 3, 5, 12, and 15 and IGA 1, 2, and 4 were selected as developed transgenic lines with increasing quantities of anti-carcinogen PEITC.

• Progress in Medical Physics
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• v.23 no.1
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• pp.8-14
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• 2012

In order to develop a Patient respiratory management system includinga biofeedback function for4-dimentional radiation therapy, this study investigated anoptimal tracking algorithmfor moving target using IR (Infra-red) camera as well as commercial camera. A tracking system was developed by LabVIEW 2010. Motion phantom images were acquired using a camera (IR or commercial). After image process were conducted to convert acquired image to binary image by applying a threshold values, several edge enhance methods such as Sobel, Prewitt, Differentiation, Sigma, Gradient, Roberts, were applied. The targetpattern was defined in the images, and acquired image from a moving targetwas tracked by matching pre-defined tracking pattern. During the matching of imagee, thecoordinateof tracking point was recorded. In order to assess the performance of tracking algorithm, the value of score which represents theaccuracy of pattern matching was defined. To compare the algorithm objectively, we repeat experiments 3 times for 5 minuts for each algorithm. Average valueand standard deviations (SD) of score were automatically calculatedsaved as ASCII format. Score of threshold only was 706, and standard deviation was 84. The value of average and SD for other algorithms which combined edge detection method and thresholdwere 794, 64 in Sobel, 770, 101 in Differentiation, 754, 85 in Gradient, 763, 75 in Prewitt, 777, 93 in Roberts, and 822, 62 in Sigma, respectively. According to score analysis, the most efficient tracking algorithm is the Sigma method. Therefore, 4-dimentional radiation threapy is expected tobemore efficient if threshold and Sigma edge detection method are used together in target tracking.