• Journal of Oriental Neuropsychiatry
• /
• v.32 no.2
• /
• pp.129-140
• /
• 2021

Psychotherapy in Korean Medicine is characterized not only by management of mental issues, but also a holistic perspective of the mind and body, which includes physical treatment. In this case report, we describe the efficacy of Korean psychotherapy for couples with physical symptoms of heartache, emotional tension and marital relationships by addressing the challenges at the Mind and Body levels. For the physical treatment of the couple, the wife was treated with a Bunshimgi-Eum and the husband was administered a Cheonwangbosimdan, combined with a psychiatric interview based on Korean Medicine. It involves listening to the couple's story, YiJungBeongi therapy, understanding and learning about vases and defense mechanisms, self-understanding and understanding of the husband through self-understanding and expansion, and husband's understanding of the wife's position, self-interpretation and acceptance. Based on counseling, the couple's personal characteristics and expansion for self-growth, the progress and results of the couple's challenges and relationship improvement in a relatively short period of time are presented. In response, we hope that the evidence based on Korean Psychotherapy supporting the counseling for couples will continue to accumulate. We would like to report and share a few opinions.

• Women's Health Nursing
• /
• v.28 no.3
• /
• pp.174-186
• /
• 2022

Purpose: This study aimed to investigate the effects of virtual reality used in maternal-child nursing clinical practicums on nursing students' competencies through a systematic review. Methods: The inclusion criteria were peer-reviewed papers in English or Korean presenting analytic studies of maternal-child nursing practicums using virtual reality. An electronic literature search of the Cochrane Library, CINAHL, EMBASE, ERIC, PubMed, and Research Information Sharing System databases was performed using combinations of the keywords "nursing student," "virtual reality," "augmented reality," "mixed reality," and "virtual simulation" from February 4 to 15, 2022. Quality appraisal was performed using the RoB 2 and ROBINS-I tools for randomized controlled trials (RCTs) and non-RCTs, respectively. Results: Of the seven articles identified, the RCT study (n=1) was deemed to have a high risk of bias, with some items indeterminable due to a lack of reported details. Most of the non-RCT studies (n=6) had a moderate or serious risk of bias related to selection and measurement issues. Clinical education using virtual reality had positive effects on knowledge, skills, satisfaction, self-efficacy, and needs improvement; however, it did not affect critical thinking or self-directed learning. Conclusion: This study demonstrated that using virtual reality for maternal-child nursing clinical practicums had educational effects on a variety of students' competencies. Considering the challenges of providing direct care in clinical practicums, virtual reality can be a viable tool that supplements maternal-child nursing experience. Greater rigor and fuller reporting of study details are required for future research.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.237-250
• /
• 2022

Structural health monitoring (SHM) plays an important role in ensuring the safety and functionality of critical civil infrastructure. In recent years, numerous researchers have conducted studies to develop computer vision and machine learning techniques for SHM purposes, offering the potential to reduce the laborious nature and improve the effectiveness of field inspections. However, high-quality vision data from various types of damaged structures is relatively difficult to obtain, because of the rare occurrence of damaged structures. The lack of data is particularly acute for fatigue crack in steel bridge girder. As a result, the lack of data for training purposes is one of the main issues that hinders wider application of these powerful techniques for SHM. To address this problem, the use of synthetic data is proposed in this article to augment real-world datasets used for training neural networks that can identify fatigue cracks in steel structures. First, random textures representing the surface of steel structures with fatigue cracks are created and mapped onto a 3D graphics model. Subsequently, this model is used to generate synthetic images for various lighting conditions and camera angles. A fully convolutional network is then trained for two cases: (1) using only real-word data, and (2) using both synthetic and real-word data. By employing synthetic data augmentation in the training process, the crack identification performance of the neural network for the test dataset is seen to improve from 35% to 40% and 49% to 62% for intersection over union (IoU) and precision, respectively, demonstrating the efficacy of the proposed approach.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.9
• /
• pp.267-277
• /
• 2022

Recently, the topics of SW liberal-arts education are diversifying, from 'Computational Thinking(CT)' to 'Programming, Data Analysis and Artificial Intelligence(AI)' in universities. We expect that the diversification of SW liberal-arts subjects does not just mean that the learning contents are different, but also differentiates the educational goals and educational effects of each subject. In this paper, we conducted a case study to analyze the educational effect according to the educational goals of two SW liberal-arts subjects, CT and Data Analysis Fundamentals(DA), for humanities college students. We confirmed that the educational effect of 'CT Efficacy' increased significantly in accordance with the common educational goal of 'Improving CT-based SW convergence competency' in both subjects. However, we also analyzed the difference in the educational effects of 'CT(the goal of basic SW education)' and 'DA(the goal of major-friendly SW education)', which have different subject goals. 'CT' mainly showed an educational effect on how to solve general daily problems, and 'DA' showed confidence in how to solve major problems along with general problems.

• The Journal of Korean Medicine
• /
• v.43 no.3
• /
• pp.49-64
• /
• 2022

Objectives: Peer role-play (PRP) has been used in health care training simulations because standardized patient training requires considerable time and expense. This study described the implementation of clinical simulation using PRP and examined the effect. Methods: Final year students from a single college of Korean medicine engaged in PRP as part of clinical skills practice. Education tools from clinical practice guidelines were used to structure the PRP. Communication competency was assessed with the Korean Version of the Self-Efficacy Questionnaire (KSE-12). Whether this training helped to achieve graduate outcomes was evaluated on a five-point scale. Results: Fifty-nine students (53.2%) participated in the survey. Among 12 items on the KSE-12, the score for "How certain are you that you are able to successfully listen attentively to the patient?" was the highest. Further, PRP was found to be helpful for self-directed learning, establishment of one's professional identity, and the ability to communicate and manage patients. Three themes ("Benefits of role-play", "The importance of positive feedback", "Limitations and problems of role-play"), 15 categories, and 16 central meanings were derived by categorizing learners' subjective opinions about PRP. Conclusions: Study findings indicate that PRP may contribute to improving communication skills and establishing a professional identity for future Korean medicine doctors. We suggest using PRP in clinical education in colleges of Korean Medicine.

• Advances in nano research
• /
• v.15 no.5
• /
• pp.451-466
• /
• 2023

Gastrointestinal cancer (GC) is a prevalent malignant tumor of the digestive system that poses a severe health risk to humans. Due to the specific organ structure of the gastrointestinal system, both endoscopic and MRI diagnoses of GIC have limited sensitivity. The primary factors influencing curative efficacy in GIC patients are drug inefficacy and high recurrence rates in surgical and pharmacological therapy. Due to its unique optical features, good biocompatibility, surface effects, and small size effects, nanotechnology is a developing and advanced area of study for the detection and treatment of cancer. Because of its deep location and complex surgery, diagnosing and treating gastrointestinal cancer is very difficult. The early diagnosis and urgent treatment of gastrointestinal illness are enabled by nanotechnology. As diagnostic and therapeutic tools, nanoparticles directly target tumor cells, allowing their detection and removal. XGBoost was used as a classification method known for achieving numerous winning solutions in data analysis competitions, to capture nonlinear relations among many input variables and outcomes using the boosting approach to machine learning. The research sample included 300 GC patients, comprising 190 males (72.2% of the sample) and 110 women (27.8%). Using convolutional neural networks (CNN) and artificial neural networks (ANN)-EXtreme Gradient Boosting (XGBoost), the patients mean± SD age was 50.42 ± 13.06. High-risk behaviors (P = 0.070), age at diagnosis (P = 0.037), distant metastasis (P = 0.004), and tumor stage (P = 0.015) were shown to have a statistically significant link with GC patient survival. AUC was 0.92, sensitivity was 81.5%, specificity was 90.5%, and accuracy was 84.7 when analyzing stomach picture.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.6
• /
• pp.721-728
• /
• 2023

This study was conducted with the aim of exploring the essence of the achievements experienced by nursing students while enhancing their problem-solving abilities through simulation practical training. The study participants included 13 fourth-year nursing students, and data were collected through individual interviews conducted after the simulation practical training. Data analysis followed the qualitative research method of content analysis, involving coding, categorization, and thematization of the data. The results of this study revealed that nursing students' achievement experiences through simulation practical training included the following processes: "confirming confidence through improvement," "acknowledging change," "experiencing nursing self-efficacy," and "getting closer to the goal of clinical practice." Furthermore, it is suggested that efforts should be made to implement efficient operation and evaluation tools through multifaceted and meticulous design to promote integrated learning through simulation practical training and to confirm the process of internalizing knowledge through reflection by nursing students.

• International Journal of Computer Science & Network Security
• /
• v.23 no.10
• /
• pp.135-146
• /
• 2023

An effective educational program warrants the inclusion of an innovative construction which enhances the higher education efficacy in such a way that accelerates the achievement of desired results and reduces the risk of failures. Educational Decision Support System (EDSS) has currently been a hot topic in educational systems, facilitating the pupil result monitoring and evaluation to be performed during their development. Insufficient information systems encounter trouble and hurdles in making the sufficient advantage from EDSS owing to the deficit of accuracy, incorrect analysis study of the characteristic, and inadequate database. DMTs (Data Mining Techniques) provide helpful tools in finding the models or forms of data and are extremely useful in the decision-making process. Several researchers have participated in the research involving distributed data mining with multi-agent technology. The rapid growth of network technology and IT use has led to the widespread use of distributed databases. This article explains the available data mining technology and the distributed data mining system framework. Distributed Data Mining approach is utilized for this work so that a classifier capable of predicting the success of students in the economic domain can be constructed. This research also discusses the Intelligent Knowledge Base Distributed Data Mining framework to assess the performance of the students through a mid-term exam and final-term exam employing Multi-agent system-based educational mining techniques. Using single and ensemble-based classifiers, this study intends to investigate the factors that influence student performance in higher education and construct a classification model that can predict academic achievement. We also discussed the importance of multi-agent systems and comparative machine learning approaches in EDSS development.

• Geomechanics and Engineering
• /
• v.36 no.5
• /
• pp.465-474
• /
• 2024

Uniaxial compressive strength (UCS) is a critical geomechanical parameter that plays a significant role in the evaluation of rocks. The practice of indirectly estimating said characteristics is widespread due to the challenges associated with obtaining high-quality core samples. The primary aim of this study is to investigate the feasibility of utilizing the gene expression programming (GEP) technique for the purpose of forecasting the UCS for various rock categories, including Schist, Granite, Claystone, Travertine, Sandstone, Slate, Limestone, Marl, and Dolomite, which were sourced from a wide range of quarry sites. The present study utilized a total of 170 datasets, comprising Schmidt hammer (SH), porosity (n), point load index (Is(50)), and P-wave velocity (Vp), as the effective parameters in the model to determine their impact on the UCS. The UCS parameter was computed through the utilization of the GEP model, resulting in the generation of an equation. Subsequently, the efficacy of the GEP model and the resultant equation were assessed using various statistical evaluation metrics to determine their predictive capabilities. The outcomes indicate the prospective capacity of the GEP model and the resultant equation in forecasting the unconfined compressive strength (UCS). The significance of this study lies in its ability to enable geotechnical engineers to make estimations of the UCS of rocks, without the requirement of conducting expensive and time-consuming experimental tests. In particular, a user-friendly program was developed based on the GEP model to enable rapid and very accurate calculation of rock's UCS, doing away with the necessity for costly and time-consuming laboratory experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.10
• /
• pp.3010-3039
• /
• 2024

This research delves into the realm of Maritime Cybersecurity, focusing on the application of Artificial Intelligence (AI) mechanisms, namely K-Nearest Neighbors (KNN), Random Forest (RF), and Artificial Neural Networks (ANN). The maritime industry faces evolving cyber threats, necessitating innovative approaches for robust defense. The maritime sector is increasingly reliant on digital technologies, making it susceptible to cyber threats. Traditional security measures are insufficient against sophisticated attacks, necessitating the integration of AI mechanisms. This research aims to evaluate the effectiveness of KNN, RF, and ANN in fortifying maritime cybersecurity, providing a proactive defense against emerging threats. Investigate the application of KNN, RF, and ANN in the maritime cybersecurity landscape. Assess the performance of these AI mechanisms in detecting and mitigating cyber threats. Explore the adaptability of KNN, RF, and ANN to the dynamic maritime environment. Provide insights into the strengths and limitations of each algorithm for maritime cybersecurity. The study employs these AI algorithms to analyze historical maritime cybersecurity data, evaluating their accuracy, precision, and recall in threat detection. Results demonstrate the effectiveness of KNN in identifying localized anomalies, RF in handling complex threat landscapes, and ANN in learning intricate patterns within maritime cybersecurity data. Comparative analyses reveal the strengths and weaknesses of each algorithm, offering valuable insights for implementation. In conclusion, the integration of KNN, RF, and ANN mechanisms presents a promising avenue for enhancing maritime cybersecurity. The study underscores the importance of adopting AI solutions to the maritime domain's unique challenges. While each algorithm demonstrates efficacy in specific scenarios, a hybrid approach may offer a comprehensive defense strategy. As the maritime industry continues to evolve, leveraging AI mechanisms becomes imperative for staying ahead of cyber threats and safeguarding critical assets. This research contributes to the ongoing discourse on maritime cybersecurity, providing a foundation for future developments in the field.