• Journal of Korean Neurosurgical Society
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• v.66 no.2
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• pp.113-120
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• 2023

Artificial intelligence (AI) is a field of computer science that equips machines with human-like intelligence and enables them to learn, reason, and solve problems when presented with data in various formats. Neurosurgery is often at the forefront of innovative and disruptive technologies, which have similarly altered the course of acute and chronic diseases. In diagnostic imaging, such as X-rays, computed tomography, and magnetic resonance imaging, AI is used to analyze images. The use of robots in the field of neurosurgery is also increasing. In neurointensive care units, AI is used to analyze data and provide care to critically ill patients. Moreover, AI can be used to predict a patient's prognosis. Several AI applications have already been introduced in the field of neurosurgery, and many more are expected in the near future. Ultimately, it is our responsibility to keep pace with this evolution to provide meaningful outcomes and personalize each patient's care. Rather than blindly relying on AI in the future, neurosurgeons should gain a thorough understanding of it and use it to enhance their patient care.

• Journal of Trauma and Injury
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• v.36 no.1
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• pp.3-7
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• 2023

Purpose: As resuscitative endovascular balloon occlusion of the aorta (REBOA) is performed in an extremely emergent situation, achieving competent clinical practice is mandatory. Although there are several educational courses that teach the REBOA procedure, there have been no reports evaluating the impact of training on clinical practice. Therefore, this study is aimed to evaluate the effects of the course on procedural performance during resuscitation and on clinical outcomes. Methods: Patients who were managed at a regional trauma center in Dankook University Hospital from August 2016 to February 2018 were included and were grouped as precourse (August 2016-August 2017, n=9) and postcourse (September 2017- February 2018, n=9). Variables regarding injury, parameters regarding REBOA procedure, morbidity, and mortality were prospectively collected and reviewed for comparison between the groups. Results: Demographics and REBOA variables did not differ between groups. The time required from arterial puncture to balloon inflation was significantly shortened from 9.0 to 5.0 minutes (P=0.003). There were no complications associated with REBOA after the course. Mortality did not show any statistical difference before and after the course. Conclusions: The endovascular training for REBOA pilot course, which uses a modified form of flipped learning, realistic simulation of ultrasound-guided catheter insertion and balloon manipulation, and competence assessment, significantly improved procedural performance during resuscitation of trauma patients.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.814-826
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• 2023

Sensor faults in nuclear power plant instrumentation have the potential to spread negative effects from wrong signals that can cause an accident misdiagnosis by plant operators. To detect sensor faults and make accurate accident diagnoses, prior studies have developed a supervised learning-based sensor fault detection model and an accident diagnosis model with faulty sensor isolation. Even though the developed neural network models demonstrated satisfactory performance, their diagnosis performance should be reevaluated considering real-time connection. When operating in real-time, the diagnosis model is expected to indiscriminately accept fault data before receiving delayed fault information transferred from the previous fault detection model. The uncertainty of neural networks can also have a significant impact following the sensor fault features. In the present work, a pilot study was conducted to connect two models and observe actual outcomes from a real-time application with an integrated system. While the initial results showed an overall successful diagnosis, some issues were observed. To recover the diagnosis performance degradations, additive logics were applied to minimize the diagnosis failures that were not observed in the previous validations of the separate models. The results of a case study were then analyzed in terms of the real-time diagnosis outputs that plant operators would actually face in an emergency situation.

• Korean Medical Education Review
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• v.25 no.2
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• pp.119-125
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• 2023

Pusan National University School of Medicine (PNUSOM) began analyzing the cohort of pre-medical students admitted in 2015 and has been conducting purposeful analyses for the past 3 years. The aim of this paper is to introduce the process of cohort establishment, cohort composition, and the utilization of cohort analysis results. PNUSOM did not initially form a cohort with a purpose or through a systematic process, but was able to collect longitudinal data on students through the establishment of a Medical Education Information System and an organization that supports medical education. Cohort construction at our university is different in terms of a clear orientation toward research questions, flexibility in cohort composition, and subsequent guideline supplementation. We investigated the relevance of admission factors, performance improvements, satisfaction with the educational environment, and promotion and failure rate in undergraduate students, as well as performance levels and career paths in graduates. The results were presented to the Admissions Committee, Curriculum Committee, Learning Outcomes Committee, and Student Guidance Committee to be used as a basis for innovations and improvements in education. Since cohort studies require long-term efforts, it is necessary to ensure the efficiency of data collection for graduate cohorts, as well as the validity and ethics of the study.

• The Journal of Economics, Marketing and Management
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• v.4 no.4
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• pp.25-28
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• 2016

In this paper we expand on the notion of "integration" in terms of the variety of ways in which it would manifest itself in business education. Our main argument is that "integration" is multidimensional and has been manifest in pedagogy, research and service dimensions of university programs for a long time. However, assessments of "integration" efforts have been spotty thus far and only recently are being formalized. We present several examples in business curriculum and with increased focus on formal assessments of "integration" efforts, business education will become more pragmatic. The goal of this paper is to unpack the broad construct of "integration," and discuss its historical and current manifestations in business education. Ultimately, we conclude that while the process of integrative thinking is well underway for a long time in business education, the assessment of outcomes of integrative thinking is just taking root through formal ETS tests. We believe that integrative thinking in business education is an ultimate indicator of the effectiveness of the business curriculum, as students skilled in this area will be best prepared for the real-life jobs in the market place.

• Educational Technology International
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• v.13 no.2
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• pp.283-312
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• 2012

Problem-solving ability is one of the most important learning outcomes for students to compete and accomplish in a knowledge-based society. It has been empirically proven that visualization plays a central role in problem-solving. The best performing problem-solver might have a strong visualization tendency. However, there is little research as to what factors of visualization tendency primarily related to problem-solving ability according to phases of problem-solving. The purpose of this study is to identify the relationship between visualization tendency and problem-solving ability, to determine which factors of visualization tendency influence problem-solving ability in each phase of problem-solving, and to examine different problem-solving ability from the perspective of the levels of visualization tendency. This study has found out that visualization tendency has a significant correlation with problem-solving ability. Especially, Generative Visualization and Spatial-Motor Visualization as sub-visualization tendency were more strongly related to each phase of problem-solving. It indicates that visualization tendency to generate and operate mental processing can be considered a major cognitive skill to improve problem-solving ability. Furthermore, students who have high visualization tendency also have significantly higher problem-solving ability than students with low visualization tendency. It shows that the levels of visualization tendency can predict variables related to students' problem-solving ability.

• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1257-1274
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• 2023

Rotating machines heavily rely on an intricate network of interconnected sub-components, with bearing failures accounting for a substantial proportion (40% to 90%) of all such failures. To address this issue, intelligent algorithms have been developed to evaluate vibrational signals and accurately detect faults, thereby reducing the reliance on expert knowledge and lowering maintenance costs. Within the field of machine learning, Artificial Immune Systems (AIS) have exhibited notable potential, with applications ranging from malware detection in computer systems to fault detection in bearings, which is the primary focus of this study. In pursuit of this objective, we propose a novel procedure for detecting novel instances of anomalies in varying operating conditions, utilizing only the signals derived from the healthy state of the analyzed machine. Our approach incorporates AIS augmented by Dynamic Time Warping (DTW). The experimental outcomes demonstrate that the AIS-DTW method yields a considerable improvement in anomaly detection rates (up to 53.83%) compared to the conventional AIS. In summary, our findings indicate that our method represents a significant advancement in enhancing the resilience of AIS-based novelty detection, thereby bolstering the reliability of rotating machines and reducing the need for expertise in bearing fault detection.

• Journal of Pharmacopuncture
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• v.27 no.2
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• pp.142-153
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• 2024

Objectives: This study aimed to analyze the educational needs of interns and residents in Korean medicine as the first step in developing an education program to improve their research competencies. Methods: A mixed-method design, incorporating both quantitative and qualitative data collection methods, was used to investigate the educational needs for research competencies among interns and residents working in Korean medicine hospitals nationwide. Data were collected through online surveys and online focus group discussions (FGDs), and processed using descriptive statistical analysis and thematic analysis. The study results were derived by integrating survey data and FGD outcomes. Results: In total, 209 interns and residents participated in the survey, and 11 individuals participated in two rounds of FGDs. The majority of participants felt a lack of systematic education in research and academic writing in postgraduate medical education and highlighted the need for nationally accessible education due to significant disparities in the educational environment across hospitals and specialties. The primary barrier to learning research and academic writing identified by learners was the lack of knowledge, leading to time constraints. Improving learners' research competencies, relationship building, autonomy, and motivation through a support system was deemed crucial. The study also identified diverse learner types and preferred educational topics, indicating a demand for learner-centered education and coaching. Conclusion: This study provides foundational data for designing and developing a program on education on research competencies for interns and residents in Korean medicine and suggests the need for initiatives to strengthen these competencies.

• Wind and Structures
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• v.38 no.2
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• pp.147-160
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• 2024

The aerodynamic force is a significant component that influences the stability and safety of structures. It has unstable properties and depends on computer precision, making its long-term prediction challenging. Accurately estimating the aerodynamic traits of structures is critical for structural design and vibration control. This paper establishes an unsteady aerodynamic time series prediction model using Long Short-Term Memory (LSTM) network. The unsteady aerodynamic force under varied Reynolds number and angles of attack is predicted by the LSTM model. The input of the model is the aerodynamic coefficients of the 1 to n sample points and output is the aerodynamic coefficients of the n+1 sample point. The model is predicted by interpolation and extrapolation utilizing Unsteady Reynolds-average Navier-Stokes (URANS) simulation data of flow around a circular cylinder, square cylinder and airfoil. The results illustrate that the trajectories of the LSTM prediction results and URANS outcomes are largely consistent with time. The mean relative error between the forecast results and the original results is less than 6%. Therefore, our technique has a prospective application in unsteady aerodynamic force prediction of structures and can give technical assistance for engineering applications.

• Steel and Composite Structures
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• v.50 no.4
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• pp.443-458
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• 2024

The construction industry, one of the biggest producers of greenhouse emissions, is under a lot of pressure as a result of growing worries about how climate change may affect local communities. Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues connected to the manufacture of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete, which might be used in lieu of traditional concrete to reduce CO₂ emissions in the building industry. In the present work, the compressive strength (f_c) of GPC is calculated using random forests regression (RFR) methodology where natural zeolite (NZ) and silica fume (SF) replace ground granulated blast-furnace slag (GGBFS). From the literature, a thorough set of experimental experiments on GPC samples were compiled, totaling 254 data rows. The considered RFR integrated with artificial hummingbird optimization (AHA), black widow optimization algorithm (BWOA), and chimp optimization algorithm (ChOA), abbreviated as ARFR, BRFR, and CRFR. The outcomes obtained for RFR models demonstrated satisfactory performance across all evaluation metrics in the prediction procedure. For R² metric, the CRFR model gained 0.9988 and 0.9981 in the train and test data set higher than those for BRFR (0.9982 and 0.9969), followed by ARFR (0.9971 and 0.9956). Some other error and distribution metrics depicted a roughly 50% improvement for CRFR respect to ARFR.