• The Korean Nurse
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• v.37 no.2
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• pp.53-62
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• 1998

ursing as a profession is characterized by its holistic, mind-body-spirit approach to the patient. Also, nurses have historically been the leaders in health education and promotion. Parish nursing has a great potential for providing primary preventive health care. services as well as assisting people to access the health care system. While working in the community, parish nurses see the church as the new arena for delivering health care services. The parish nurse program was introduced by Granger Westberg in 1984. The concept of parish nursing is based on several beliefs; health is multidimensional and affects all aspects of an individual-physical, psychological, social, and spiritaul being. Parish nursing is one model in which churches can cooperatively work with health care institutions to address the needs of their parishioners. The role of the parish nurse is emphasized in four basic area: a) health education, b) health counseling, c) referal services, and d) facilitation and organization of support groups within the congregation. The parish nurse programs work chiefly in congregation or commuity where a certain language of faith is ready at hand. This means that the parish nurse works in an ecology of meanings and care which encourages the drawing on the message of God's grace, the practices and habits it encourages. The parish nurse may be involved in the church's health ministries and may work on either paid or volunteer basis; however, one of the most important qualification of the parish nurse is to have the nursing knowledge and skills to practice within the standards of Nursing Practice Act. The completion of standards of practice for professional nurses practicing as parish nurses had been identified as a priority by the HMA Executive Board (1996, HMA). In conclusion, parish nursing promotes health and healing by empowering the faith community, family, or individual to incorporate health and healing practices. There are several preconditions that should proceed to establish the foundation for successful development of the parish nursing program in Korea. First, reciprocal relationship with home health nursing should be considered. Second, correct terms and concepts of parish nursing should be studied and understood. Third, systematic study and investigation should be followed for further development of parish nursing. Fourth, strengths and weaknesses of different models should be studied to develop proper model of parish nursing for Korean situation. Finally, consensus of standardized education program and corporation with various religious communities as well as health institutions should be established. When these preconditions are met, the role of parish nursing as a new program for the promotion of holistic health will be established.

• Nutrition Research and Practice
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• v.11 no.4
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• pp.319-326
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• 2017

BACKGROUND/OBJECTIVE: Effective weight reduction remains a challenge throughout the world as the prevalence of obesity and its consequences are increasing. This study aimed to determine the effects of an individualized nutrition counseling program (IC) matched with a transtheoretical model (TTM) for overweight and obese subjects. SUBJECTS/METHODS: Fifty overweight and obese subjects aged 19-60 years with a body mass index ${\geq}23kg/m^2$ were enrolled in the weight reduction study. They were randomized into two groups: Intervention group received an IC matched with a TTM; control group received an educational handbook. Body weight (BW), body fat (BF), waist circumference (WC), waist to height ratio (WHtR), stages of change (SOC), processes of change (POC), food intake, and physical activity (PA) were assessed at baseline and at 4, 8, and 12 weeks after program initiation in both groups. All data were analyzed by intention-to-treat, using SPSS software for hypothesis testing. RESULTS: Forty-five female subjects were included in the 12-week trial at Ramkhamhaeng Hospital, Bangkok, Thailand. The results showed significant weight loss ($1.98{\pm}1.75kg$; 3% loss of initial weight) in the intervention group at 12 weeks, compared to a $0.17{\pm}1.67kg$ loss in the control group. There were significant differences between intervention and control groups in BF mass ($-1.68{\pm}1.78$, $-0.04{\pm}1.62kg$); percentage BF ($-1.54{\pm}2.11$, $0.08{\pm}2.05$); WC ($-5.35{\pm}3.84$, $0.13{\pm}3.23cm$); WHtR ($-0.0336{\pm}0.02$, $-0.0004{\pm}0.02$), and energy consumption ($-405.09{\pm}431.31$, $-74.92{\pm}499.54kcal/day$) in the intervention and control groups, respectively. Intragroup SOC was improved in both groups. The POC for the weight management action (WMA) process was significantly different with POC scores increasing by $16.00{\pm}11.73$ and $7.74{\pm}14.97$ in the intervention and the control groups, respectively. PA level did not change in either group. CONCLUSIONS: The IC matched with a TTM resulted in reductions in BW, BF, and WC, thus reducing likely health risks by decreasing energy intake and inducing positive behavior changes while enhancing the WMA process.

• The Korean journal of internal medicine
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• v.33 no.6
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• pp.1210-1223
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• 2018

Background/Aims: The co-occurrence of obesity aggravates asthma symptoms. Diet-induced obesity increases helper T cell (TH) 17 cell differentiation in adipose tissue and the spleen. The 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitor pravastatin can potentially be used to treat asthma in obese patients by inhibiting interleukin 17 (IL-17) expression. This study investigated the combined effects of pravastatin and anti-IL-17 antibody treatment on allergic inflammation in a mouse model of obesity-related asthma. Methods: High-fat diet (HFD)-induced obesity was induced in C57BL/6 mice with or without ovalbumin (OVA) sensitization and challenge. Mice were administered the anti-IL-17 antibody, pravastatin, or both, and pathophysiological and immunological responses were analyzed. Results: HFD exacerbated allergic airway inflammation in the bronchoalveolar lavage fluid of HFD-OVA mice as compared to OVA mice. Blockading of the IL-17 in the HFD-OVA mice decreased airway hyper-responsiveness (AHR) and airway inflammation compared to the HFD-OVA mice. Moreover, the administration of the anti-IL-17 antibody decreased the leptin/adiponectin ratio in the HFD-OVA but not the OVA mice. Co-administration of pravastatin and anti-IL-17 inhibited airway inflammation and AHR, decreased goblet cell numbers, and increased adipokine levels in obese asthmatic mice. Conclusions: These results suggest that the IL-17-leptin/adiponectin axis plays a key role in airway inflammation in obesity-related asthma. Our findings suggest a potential new treatment for IL-17 as a target that may benefit obesity-related asthma patients who respond poorly to typical asthma medications.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.167-176
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• 2019

The conversational agent, which is at the forefront of the 4th industry, aims to personalize the user-centered focus in the future and holds an important position to have a hub that can be connected to various IoT devices. It is a challenge for interactive agents to recognize the user's emotions and provide the correct interaction to personalization. The study first I looked at emotional definitions and scientific and engineering approaches. Then I recognized through social perspectives what social function and what factors emotions have and how they can be used to understand emotions. Based on this, I explored how users can be discovered emotional social factors in communication. This research has shown that social factors can be found in the user's speech, which can be linked to the social meaning of emotions. Finally, I propose a model to discover social factors in user communication. I hope that this will help designer and researcher to study user-centered design and interaction in designing interactive agents.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.90-102
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• 2021

Technology evaluation is to assess the ability of technology commercialization entities to generate profits by using the subject technology, and domestic technology evaluation agencies have established and implemented their own evaluation systems. In particular, the recently developed technology evaluation model in the fields of marine and fishery does not sufficiently reflect the poor environment for technology development compared to other industries, so it does not pass the level of T4 rating, which is considered appropriate for investment. This is recognized as a challenge that occurs when the common evaluation indicators and evaluation scales used in other industries, and when the scoring system for T1 to T10 grading is similarly or identically utilized. Therefore, through this study, we intend to secure the appropriateness and reliability of the results of the comprehensive rating calculation by developing technology evaluation models and indicators that well explain the nine marine and fisheries industry classification systems. Based on KED and technology evaluation case data, AHP-based index weighting and Monte Carlo simulation-based rating system are applied and the results of case studies are verified. Through the proposed model, we aim to enhance the usability of R&D and commercialization support programs based on fast, convenient and objective evaluation results by applying to upcoming technology evaluation cases.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.99-113
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• 2023

In a customer-based industry, customer retention is the competitiveness of a company, and improving customer retention improves the competitiveness of the company. Therefore, accurate prediction and management of potential dormant customers is paramount to increasing the competitiveness of the enterprise. In particular, there are numerous competitors in the domestic card industry, and the government is introducing an automatic closing system for dormant card management. As a result of these social changes, the card industry must focus on better predicting and managing potential dormant cards, and better predicting dormant customers is emerging as an important challenge. In this study, the Recurrent Neural Network (RNN) methodology was used to predict potential dormant customers in the card industry, and in particular, Long-Short Term Memory (LSTM) was used to efficiently learn data for a long time. In addition, to redefine the variables needed to predict dormant customers in the card industry, Unified Theory of Technology (UTAUT), an integrated technology acceptance theory, was applied to redefine and group the variables used in the model. As a result, stable model accuracy and F-1 score were obtained, and Hit-Ratio proved that models using LSTM can produce stable results compared to other algorithms. It was also found that there was no moderating effect of demographic information that could occur in UTAUT, which was pointed out in previous studies. Therefore, among variable selection models using UTAUT, dormant customer prediction models using LSTM are proven to have non-biased stable results. This study revealed that there may be academic contributions to the prediction of dormant customers using LSTM algorithms that can learn well from previously untried time series data. In addition, it is a good example to show that it is possible to respond to customers who are preemptively dormant in terms of customer management because it is predicted at a time difference with the actual dormant capture, and it is expected to contribute greatly to the industry.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.542-549
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• 2023

Since the growing interest in surrogate modeling, there has been continuous research aimed at simulating nonlinear chemical processes using data-driven machine learning. However, the opaque nature of machine learning models, which limits their interpretability, poses a challenge for their practical application in industry. Therefore, this study aims to analyze chemical processes using Explainable Artificial Intelligence (XAI), a concept that improves interpretability while ensuring model accuracy. While conventional sensitivity analysis of chemical processes has been limited to calculating and ranking the sensitivity indices of variables, we propose a methodology that utilizes XAI to not only perform global and local sensitivity analysis, but also examine the interactions among variables to gain physical insights from the data. For the ammonia synthesis process, which is the target process of the case study, we set the temperature of the preheater leading to the first reactor and the split ratio of the cold shot to the three reactors as process variables. By integrating Matlab and Aspen Plus, we obtained data on ammonia production and the maximum temperatures of the three reactors while systematically varying the process variables. We then trained tree-based models and performed sensitivity analysis using the SHAP technique, one of the XAI methods, on the most accurate model. The global sensitivity analysis showed that the preheater temperature had the greatest effect, and the local sensitivity analysis provided insights for defining the ranges of process variables to improve productivity and prevent overheating. By constructing alternative models for chemical processes and using XAI for sensitivity analysis, this work contributes to providing both quantitative and qualitative feedback for process optimization.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Tuberculosis and Respiratory Diseases
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• v.57 no.5
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• pp.425-433
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• 2004

Background : Induction of oral tolerance (OT) has been known to prevent allergic inflammation in acute asthma model within 4 weeks. However it is remained whether induction of OT may effectively prevent allergic inflammation in chronic asthma model over 4 weeks. We observed the effect of induction of OT on allergic inflammation and airway remodeling in chronic asthma model up to 8 weeks. Methods : 5-week-old female BALB/c mice divided into 4 groups-control group, asthma group, low dose OT group, and high dose OT group. To induce oral tolerance mice were fed ovalbumin (OVA) before sensitization with OVA and aluminum hydroxide-1 mg for 6 consecutive days in the low dose OT group and 25 mg once in the high dose OT group. Mice in the asthma group were fed phosphate buffered saline instead of OVA. After sensitization followed by repeated challenge with aerosolized 1% OVA during 6 weeks, enhanced pause (Penh), inflammatory cells, IL-13, and IFN-${\gamma}$ levels in bronchoalveolar lavage (BAL) fluids as well as OVA-specific IgE, IgG1, and IgG2a levels in serum were measured. In addition the degree of goblet cell hyperplasia and peribronchial fibrosis were observed from lung tissues by PAS and Masson's trichrome stain. Results : Both OT groups showed a significant decrease in Penh, inflammatory cells, IL-13, and IFN-${\gamma}$ levels in BAL fluids as well as OVA-specific IgE, IgG1, and IgG2a levels in serum compared with the asthma group (P<0.05). In addition, the degree of goblet cell hyperplasia and peribronchial fibrosis were significantly attenuated in both OT groups compared with the asthma group (P<0.01). Conclusion : These results suggest that induction of OT may effectively prevent allergic inflammation as well as airway remodeling even in chronic asthma model up to 8 weeks.

• Clinical and Experimental Pediatrics
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• v.53 no.4
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• pp.481-487
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• 2010

Purpose: Recently many studies show early exposure during childhood growth to endotoxin (lipopolysaccharides, LPS) and/or early exposure to allergens exhibit important role in development of allergy including bronchial asthma. The aim of this study was to evaluate the role of endotoxin and allergen exposure in early life via the airways in the pathogenesis of allergic airways inflammation and airway hyperresposiveness (AHR) in mouse model of asthma. Methods: Less than one week-old Balb/c mice was used. Groups of mice were received either a single intranasal instillation of sterile physiologic saline, 1% ovalbumin (OVA), LPS or $1.0{\mu}g$ LPS in 1% OVA. On 35th day, these animals were sensitized with 1% OVA for 10 consecutive days via the airways. Animals were challenged with ovalbumin for 3 days on 55th days, and airway inflammation, hyperresponsiveness, and cytokine expression were assessed. Measurements of airway function were obtained in unrestrained animals, using whole-body plethysmography. Airway responsiveness was expressed in terms of % enhanced pause (Penh) increase from baseline to aerosolized methacholine. Lung eosinophilia, serum OVA-IgE and bronchoalveolar lavage (BAL) fluid cytokine levels were also assessed. ANOVA was used to determine the levels of difference between all groups. Comparisons for all pairs were performed by Tukey-Kramer honest significant difference test; $P$ values for significance were set to 0.05. Results: Sensitized and challenged mice with OVA showed significant airway eosinophilia and heightened responsiveness to methacholine. Early life exposure of OVA and/or LPS via the airway prevented both development of AHR as well as bronchoalveolar lavage fluid eosinophilia. Exposure with OVA or LPS also resulted in suppression of interleukin (IL)-4, 5 production in BAL fluid and OVA specific IgE in blood. Conclusion: These results indicate that antigen and/or LPS exposure in the early life results in inhibition of allergic responses to OVA in this mouse model of astham. Our data show that early life exposure with OVA and/or LPS may have a protective role in the development of allergic airway inflammation and development of allergen-induced airway responses in mouse model of asthma.