• Journal of agricultural medicine and community health
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• v.48 no.3
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• pp.165-177
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• 2023

Objectives: The objective of this study was to develop strategies aimed at reducing disparity of physical activity in urban community. Methods: The study was conducted in a urban vulnerable area, focusing on the establishment and operation of a community health organization through the residents' autonomy council. Training programs were provided to the members of the council to enhance their capabilities. The research project was planned and implemented using a living lab approach. Based on these activities, the health division of residents autonomy council was newly established. Results: The findings demonstrated the potential and feasibility of utilizing the residents' autonomy council as a community-led health organization. A health project model centered on the health division of the residents' autonomy council was developed. Conclusions: This study concluded that it is possible to effectively promote health projects to reduce the health disparity through the resident-led participation strategy on the residents' autonomy council in the community.

• Safety and Health at Work
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• v.13 no.4
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• pp.394-400
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• 2022

Background: Impacts of exposure are generally monitored and recorded after injuries or illness occur. Yet, absence of conventional after-the-effect impacts (i.e., lagging indicators), tend to focus on physical health and injuries, and fail to inform if workers are not exposed to safety and health hazards. In contrast to lagging indicators, leading indicators are proactive, preventive, and predictive indexes that offer insights how effective safety and health. The present study is to validate an extended Voluntary Protection Programs (VPP) that consists of six leading indicators. Methods: Questionnaires were distributed to 13 organizations (response rate = 93.1%, 1,439 responses) in Taiwan. Cronbach α, multiple linear regression and canonical correlation were used to test the reliability of the extended Voluntary Protection Programs (VPP) which consists of six leading indicators (safe climate, transformational leadership, organizational justice, organizational support, hazard prevention and control, and training). Criteria-related validation strategy was applied to examine relationships of six leading indicators with six criteria (perceived health, burnout, depression, job satisfaction, job performance, and life satisfaction). Results: The results showed that the Cronbach's α of six leading indicators ranged from 0.87 to 0.92. The canonical correlation analysis indicated a positive correlation between the six leading indicators and criteria (1st canonical function: correlation = 0.647, square correlation = 0.419, p < 0.001). Conclusions: The present study validates the extended VPP framework that focuses on promoting safety and physical and mental health. Results further provides applications of the extended VPP framework to promote workers' safety and health.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1720-1728
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• 2022

The industrial 4.0 era has been opened with the development of artificial intelligence technology, and the realization of smart farms incorporating ICT technology is receiving great attention in the livestock industry. Among them, the quality management technology of livestock products and livestock operations incorporating computer vision-based artificial intelligence technology represent key technologies. However, the insufficient number of livestock image data for artificial intelligence model training and the severely unbalanced ratio of labels for recognizing a specific defective state are major obstacles to the related research and technology development. To overcome these problems, in this study, combining oversampling and adversarial case generation techniques is proposed as a method necessary to effectively utilizing small data labels for successful defect detection. In addition, experiments comparing performance and time cost of the applicable techniques were conducted. Through experiments, we confirm the validity of the proposed methods and draw utilization strategies from the study results.

• Computers and Concrete
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• v.32 no.2
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• pp.217-232
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• 2023

Numerous studies have been performed on the behavior of pile foundations in cold regions. This study first attempted to employ artificial neural networks (ANN) to predict pile-bearing capacity focusing on pile data recorded primarily on cold regions. As the ANN technique has disadvantages such as finding global minima or slower convergence rates, this study in the second phase deals with the development of an ANN-based predictive model improved with an Elephant herding optimizer (EHO), Dragonfly Algorithm (DA), Genetic Algorithm (GA), and Evolution Strategy (ES) methods for predicting the piles' bearing capacity. The network inputs included the pile geometrical features, pile area (m²), pile length (m), internal friction angle along the pile body and pile tip (Ø°), and effective vertical stress. The MLP model pile's output was the ultimate bearing capacity. A sensitivity analysis was performed to determine the optimum parameters to select the best predictive model. A trial-and-error technique was also used to find the optimum network architecture and the number of hidden nodes. According to the results, there is a good consistency between the pile-bearing DA-MLP-predicted capacities and the measured bearing capacities. Based on the R2 and determination coefficient as 0.90364 and 0.8643 for testing and training datasets, respectively, it is suggested that the DA-MLP model can be effectively implemented with higher reliability, efficiency, and practicability to predict the bearing capacity of piles.

• 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.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.753-761
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• 2023

This study investigates strategies to increase learning efficiency for programming subjects to which flipped learning teaching method is applied targeting non-major students. Design a learner-centered flipped learning-based programming class and get strategies for effective application methods for field application. Also, the purpose is to explore the efficient application of the flipped learning teaching method to the computational thinking subject of liberal arts classes at this university. By applying the flipped learning teaching method, one of the innovative teaching methods, we consider ways to improve the quality of programming subject classes, the efficiency of practical education, and the improvement of learner achievement. The purpose of this study is to design an efficient learning model for software education targeting non-majors by applying various teaching methods and learning design models convergence away from the traditional teaching method.

• Journal of Korean Physical Therapy Science
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• v.30 no.4
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• pp.29-43
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• 2023

Background: In the rehabilitation of stroke patients, regular physical activity is very important not only as a treatment for maximal functional recovery but also as a strategy to prevent the recurrence of stroke. The purpose of this study was to objectively measure the amount of physical activity in people with stroke, and to examine the differences in motor and cognitive function according to a level of physical activity. Design: A cross-sectional study. Methods: Physical activity (GENEActiv), motor function (Fugl-Meyer Assessment), cognitive function (Montreal Cognitive Assessment-Korean version), and the Korean version of Modified Barthel Index were evaluated in adult stroke patients with hemiplegia. Results: There was no statistically significant difference in the level of physical activity according to the motor and cognitive function. There was no statistically significant difference in motor and cognitive function according to the level of physical activity, but there was a statistically significant difference in the MBI (p<.01). Conclusion: As a result of the difference in the MBI according to the level of physical activity, it was found that the more moderate to vigorous physical activities are performed, the higher the independence in daily living. These results can be interpreted as that the more often you participate in physical activities such as physical therapy (gait training), the better your independence in ADL. Since regular physical activity participation of adult stroke patients can improve daily living performance, it is considered necessary to participate in physical activities such as continuous physical therapy.

• Journal of the Korean Society of Physical Medicine
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• v.18 no.4
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• pp.155-162
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• 2023

PURPOSE: The primary objective of this study was to develop a virtual reality-based vestibular rehabilitation system to enhance balance perception, target rehabilitation specialists, and evaluate its usability. A key goal was establishing a system refinement strategy based on the collected data. METHODS: We conducted a study involving ten adults aged 10 to 29 in Gwangju Metropolitan City to evaluate the usability of a virtual reality-based vestibular rehabilitation system to enhance balance perception. After introducing the product and explaining its use to the participants, balance assessments and training were conducted using computerized dynamic posturography (CDP) (also called the test of balance [TOB]). Subsequently, participants were given a questionnaire to evaluate subjective stability, operability, and satisfaction. Frequency analysis was utilized to determine the frequency of the variable values of the measurement items in the survey for descriptive statistics. RESULTS: We found that the average usability score was 2.587. When broken down by category, stability received an average rating of 2.725, operability scored an average of 2.783, and satisfaction averaged 2.454. These findings suggest that most participants experienced positive sentiments and considerable satisfaction. CONCLUSION: The study successfully developed a virtual reality-based vestibular rehabilitation system, which was an improvement over the previous model and addressed its shortcomings. The results show that users with vestibular impairments are satisfied and more engaged with this system, indicating that additional studies are warranted.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3383-3397
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• 2023

Scene graphs serve as semantic abstractions of images and play a crucial role in enhancing visual comprehension and reasoning. However, the performance of Scene Graph Generation is often compromised when working with biased data in real-world situations. While many existing systems focus on a single stage of learning for both feature extraction and classification, some employ Class-Balancing strategies, such as Re-weighting, Data Resampling, and Transfer Learning from head to tail. In this paper, we propose a novel approach that decouples the feature extraction and classification phases of the scene graph generation process. For feature extraction, we leverage a transformer-based architecture and design an adaptive calibration function specifically for predicate classification. This function enables us to dynamically adjust the classification scores for each predicate category. Additionally, we introduce a Distribution Alignment technique that effectively balances the class distribution after the feature extraction phase reaches a stable state, thereby facilitating the retraining of the classification head. Importantly, our Distribution Alignment strategy is model-independent and does not require additional supervision, making it applicable to a wide range of SGG models. Using the scene graph diagnostic toolkit on Visual Genome and several popular models, we achieved significant improvements over the previous state-of-the-art methods with our model. Compared to the TDE model, our model improved mR@100 by 70.5% for PredCls, by 84.0% for SGCls, and by 97.6% for SGDet tasks.

• Journal of Korean Academy of Nursing
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• v.54 no.1
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• pp.44-58
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• 2024

Purpose: This study aimed to apply a health partnership program using commercially available mobile health apps to improve cardiovascular risk factors in male employees and verify its effectiveness. Methods: Using a randomized control group pretest-posttest design, male employees with cardiovascular risk factors from five small and medium-sized workplaces were randomly assigned to an experimental group (n = 32) and a control group (n = 31). The experimental group was encouraged to use three mobile health apps for 12 weeks to acquire the necessary cardiovascular disease-related information and practice strengthening training, walking, and diet management appropriate to their level. They also received feedback on their weekly activities and motivational text messages from health partners. Hypotheses were tested using the SPSS WIN 22.0. Results: The experimental group showed a significant difference compared to the control group in terms of their perception of mobile health app (p < .05), self-efficacy for exercise and diet, self-management partnership, and cardiovascular disease prevention health behavior (p < .001). In particular, there were significant decreases in the body mass index, ratio, serum fasting blood sugar, total cholesterol, and triglyceride in the experimental group (p < .001); however, there was no significant difference in high-density lipoprotein-cholesterol. Conclusion: Intervention using mobile apps based on partnership with health managers is effective in improving the objective cardiovascular risk index in male employees; therefore, such intervention should be continuously used as a useful lifestyle modification strategy in the workplace.