• Journal of Korean Medicine Rehabilitation
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• v.33 no.3
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• pp.67-78
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• 2023

Objectives To evaluate the evidence supporting the efficacy and safety of electroacupuncture for lumbar spinal stenosis. Methods We searched eight electronic databases (PubMed, EMBASE, Cochrane Library, Chinese Academic Journals, Research Information Sharing Service, ScienceOn, KMBASE, DBpia) and related two journals up to March 2023. We included randomized controlled trials of testing electroacupuncture for lumbar spinal stenosis patients. The methodological quality of relevant randomized controlled trials assessed by the Cochrane risk of bias tool. Results Among 90 articles that were searched, seven randomized controlled trials involving 474 participants were finally selected in this systematic review. Electroacupuncture was more effective on lumbar spinal stenosis compared with other treatments including analgesics, acupuncture, bed rest and exercise therapy, but showed ambiguous effect compared with physical therapy. Most of the side effects and adverse reactions were reported as minor and temporary. Conclusions Electroacupuncture for lumbar spinal stenosis was more effective than analgesics, acupuncture, bed rest and exercise therapy. In terms of safety, it was limited because there are many papers that do not mention side effects and adverse reactions related to electroacupuncture. Additional studies are needed to determine the effect of electroacupuncture on lumbar spinal stenosis.

• Smart Media Journal
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• v.11 no.7
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• pp.19-27
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• 2022

The artificial intelligence software market is expected to grow sixfold from 2020 to 2025. However, the software development process is not standardized and there is no standard for calculating the cost. Accordingly, each AI software development company calculates the input man-hours according to their respective development procedures and presents this as the basis for the development cost. In this study, the development stage of "artificial intelligence-based software" that learns with a large amount of data and derives and applies an algorithm was defined, and the required labor was collected by conducting a survey on the number of man-hours required for each development stage targeting developers. Correlation analysis and regression analysis were performed between the collected man-hours for each development stage, and a model for predicting the man-hours for each development stage was derived. As a result of testing the model, it showed an accuracy of 92% compared to the collected airborne effort. The man-hour prediction model proposed in this study is expected to be a tool that can be used simply for estimating man-hours and costs.

• Korean Medical Education Review
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• v.25 no.3
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• pp.243-257
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• 2023

This study aimed to analyze trends in research on depression among medical students in Korea and to conduct a meta-analysis to determine the average correlation coefficients between depression and related variables. In total, 38 quantitative studies (four theses and 34 journal articles) published between January 1995 and February 2023 were analyzed according to publication year, subjects, analysis methods, and measurement tools. Among them, 15 studies that provided numerical information on the relationships between depression and variables such as self-esteem, social support, grade point average (GPA), stress, and academic stress were selected for meta-analysis. The main findings of this study were as follows. First, quantitative research on depression among medical students began in earnest in 2009, and cross-sectional studies targeting first-year and second-year medical students were the most prevalent. Furthermore, the most commonly used analysis method was difference testing, and the Beck Depression Inventory was the most frequently used measurement tool. Second, the mean correlation coefficients between depression and stress, self-esteem, social support, academic stress, and GPA were 0.534, 0.532, 0.465, 0.390, and 0.102, respectively. The results for self-esteem, stress, and academic stress showed substantial heterogeneity, while those for social support and GPA showed little heterogeneity. These findings suggest that educational interventions, such as social support improvement programs, are necessary to prevent depression among medical students.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.125-134
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• 2024

The novel coronavirus 2019 is called COVID-19 has outspread swiftly worldwide. An early diagnosis is more important to control its quick spread. Medical imaging mechanics, chest calculated tomography or chest X-ray, are playing a vital character in the identification and testing of COVID-19 in this present epidemic. Chest X-ray is cost effective method for Covid-19 detection however the manual process of x-ray analysis is time consuming given that the number of infected individuals keep growing rapidly. For this reason, it is very important to develop an automated COVID-19 detection process to control this pandemic. In this study, we address the task of automatic detection of Covid-19 by using a popular deep learning model namely the VGG19 model. We used 1300 healthy and 1300 confirmed COVID-19 chest X-ray images in this experiment. We performed three experiments by freezing different blocks and layers of VGG19 and finally, we used a machine learning classifier SVM for detecting COVID-19. In every experiment, we used a five-fold cross-validation method to train and validated the model and finally achieved 98.1% overall classification accuracy. Experimental results show that our proposed method using the deep learning-based VGG19 model can be used as a tool to aid radiologists and play a crucial role in the timely diagnosis of Covid-19.

• Journal of the Korea Institute of Building Construction
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• v.24 no.2
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• pp.227-238
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• 2024

This study addresses the escalating flood damages prompted by recent climate shifts characterized by extreme weather events and proposes rainwater infiltration blocks as a potential solution. Recognizing the limitations inherent in existing inundation simulation methods, we advocate for the integration of novel functionalities, particularly leveraging drone technology. Our research endeavors encompass experimental assessments of inundation and flooding simulation technologies. These evaluations are conducted within areas where rainwater infiltration storage blocks have been implemented, juxtaposed against existing programs utilizing Digital Elevation Models(DEM) and Digital Surface Models(DSM). Through this comparative analysis and a meticulous scrutiny of the adaptability of inundation and flooding simulation to real-world deployment scenarios, we ascertain the efficacy of the simulation program as a decision-making tool for identifying optimal sites for rainwater infiltration storage block installation.

• Korean Journal of Clinical Laboratory Science
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• v.56 no.2
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• pp.105-114
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• 2024

Clinical laboratories endeavor to secure quality by establishing effective quality management systems. However, laboratory environments are complex, and single quality control procedures may inadequately detect many errors. Patient-based real-time quality control (PBRTQC) is a laboratory tool that monitors the testing process using algorithms such as Bull's algorithm and several variables, such as average of normal, moving median, moving average, and exponentially weighted moving average. PBRTQC has many advantages over conventional quality control, including low cost, commutability, continuous real-time performance monitoring, and sensitivity to pre-analytical errors. However, PBRTQC is not easily implemented as it requires statistical algorithm selection, the design of appropriate rules and protocols, and performance verification. This review describes the basic concepts, methods, and procedures of PBRTQC and presents guidelines for implementing a patient-based quality management system. Furthermore, we propose the combined use of PBRTQC when the performance of internal quality control is limited. However, clinical evaluations were not conducted during this review, and thus, future evaluation is required.

• Physical Therapy Rehabilitation Science
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• v.13 no.2
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• pp.179-186
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• 2024

Objective: This study aims to assess the reliability and validity of the new hand-held spirometer as a potential substitute for traditional pulmonary function testing (PFT) devices. Design: Cross-sectional study. Methods: In this study, thirty healthy adults underwent spirometry using both the new hand-held spirometer and the MIR spirometer, which is a standard PFT device. Parameters including peak expiratory flow (PEF), forced expiratory volume in one second (FEV₁), and forced vital capacity (FVC) were measured and analyzed for validity and reliability. Inter-rater reliability and validity were evaluated through 95% limits of agreement (LOA) and intraclass correlation coefficients (ICC). Statistical analyses, including the Bland-Altman plots and the ICC, were utilized to assess agreement between the two devices. Results: The new hand-held spirometer exhibited a good agreement with intra-class coefficient (ICC [2,1]) ranging 0.762 to 0.956 and 95% LOA of -1.94 to 1.80 when compared with MIR. The test-retest reliability of the hand-held spirometer analyzed using - ICC [2,1] demonstrated a good level of consistency (ICC [2,1] =0.849-0.934). Conclusions: In conclusion, the study aimed to assess the potential of the new hand-held spirometer as a viable alternative to traditional PFT devices, with a specific focus on its reliability and validity in spirometric measurements. The new hand-held spirometer exhibited good test-retest reliability across all measured variables, suggesting its potential as a valid and reliable tool for simultaneous PFT measurements.

• Physical Therapy Korea
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• v.31 no.2
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• pp.174-181
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• 2024

Background: Effective management of clinical assessment tools is critical in stroke and brain injury rehabilitation research. Managing rehabilitation outcome measures (ROMs) scores and training therapists in multicenter randomized clinical trials (RCTs) is challenging. Objects: The aim of this study was to develop a web-based platform, the Korean Rehabilitation Outcome Measurement (KoROM), to address these limitations and improve both therapist training and patient involvement in the rehabilitation process. Methods: The development of the KoROM spanned from June 2021 to July 2022, and included literature and web-based searches to identify relevant ROMs and design a user-friendly platform. Feedback from six physical therapy and informatics experts during pilot testing refined the platform. Results: Several clinical assessment tools categorized under the International Classification of Functioning, Disability, and Health (ICF) model are categorized in the KoROM. The therapist version includes patient management, assessment tool information, and data downloads, while the patient version provides a simplified interface for viewing scores and printing summaries. The master version provides full access to user information and clinical assessment scores. Therapists enter clinical assessment scores into the KoROM and learn ROMs through instructional videos and self-checklists as part of the therapist standardization process. Conclusion: The KoROM is a specialized online platform that improves the management of ROMs, facilitates therapist education, and promotes patient involvement in the rehabilitation process. The KoROM can be used not only in multi-site RCTs, but also in community rehabilitation exercise centers.

• Computers and Concrete
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• v.34 no.2
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• pp.247-265
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• 2024

Measuring the fracture toughness of concrete in laboratory settings is challenging due to various factors, such as complex sample preparation procedures, the requirement for precise instruments, potential sample failure, and the brittleness of the samples. Therefore, there is an urgent need to develop innovative and more effective tools to overcome these limitations. Supervised learning methods offer promising solutions. This study introduces seven machine learning algorithms for predicting concrete's effective fracture toughness (K-eff). The models were trained using 560 datasets obtained from the central straight notched Brazilian disc (CSNBD) test. The concrete samples used in the experiments contained micro silica and powdered stone, which are commonly used additives in the construction industry. The study considered six input parameters that affect concrete's K-eff, including concrete type, sample diameter, sample thickness, crack length, force, and angle of initial crack. All the algorithms demonstrated high accuracy on both the training and testing datasets, with R² values ranging from 0.9456 to 0.9999 and root mean squared error (RMSE) values ranging from 0.000004 to 0.009287. After evaluating their performance, the gated recurrent unit (GRU) algorithm showed the highest predictive accuracy. The ranking of the applied models, from highest to lowest performance in predicting the K-eff of concrete, was as follows: GRU, LSTM, RNN, SFL, ELM, LSSVM, and GEP. In conclusion, it is recommended to use supervised learning models, specifically GRU, for precise estimation of concrete's K-eff. This approach allows engineers to save significant time and costs associated with the CSNBD test. This research contributes to the field by introducing a reliable tool for accurately predicting the K-eff of concrete, enabling efficient decision-making in various engineering applications.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.201-208
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• 2024

This study aimed to improve the EPL education platform, Rewond (rewond.com), which was built as a prototype, into an EPL educational tool based on Embodied Cognition. In the first phase, the heuristic evaluation, five experts were selected to evaluate the subject using evaluation items that included learning principles of Embodied Cognition. Based on the evaluation results, debriefing session analysis, and consultations with co-researchers, three improvement points were identified and specific modification plans were proposed. During the beta version development phase, the co-researchers implemented an increase in coding content, provided help for each content, and added a feature that allows progression to the next learning stage upon completion of the previous one. In the final usability testing phase, the usability of the beta version was tested with ten fourth-grade elementary school students.