• Journal of Gastric Cancer
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• v.15 no.3
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• pp.159-166
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• 2015

Purpose: Laparoscopic total gastrectomy (LTG) is more complicated than laparoscopic distal gastrectomy, especially during a surgeon's initial experience with the technique. In this study, we evaluated the short-term outcomes of and learning curve for LTG during the initial cases of a single surgeon compared with those of open total gastrectomy (OTG). Materials and Methods: Between 2009 and 2013, 134 OTG and 74 LTG procedures were performed by a single surgeon who was experienced with OTG but new to performing LTG. Clinical characteristics, operative parameters, and short-term postoperative outcomes were compared between groups. Results: Advanced gastric cancer and D2 lymph node dissection were more common in the OTG than LTG group. Although the operation time was significantly longer for LTG than for OTG ($175.7{\pm}43.1$ minutes vs. $217.5{\pm}63.4$ minutes), LTG seems to be slightly superior or similar to OTG in terms of postoperative recovery measures. The operation time moving average of 15 cases in the LTG group decreased gradually, and the curve flattened at 54 cases. The postoperative complication rate was similar for the two groups (11.9% vs. 13.5%). No anastomotic or stump leaks occurred. Conclusions: Although LTG is technically difficult and operation time is longer for surgeons experienced in open surgery, it can be performed safely, even during a surgeon's early experience with the technique. Considering the benefits of minimally invasive surgery, LTG is recommended for early gastric cancer.

• Archives of Plastic Surgery
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• v.45 no.5
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• pp.395-402
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• 2018

Increased emphasis on competency-based learning modules and widespread departure from traditional models of Halstedian apprenticeship have made surgical simulation an increasingly appealing component of medical education. Surgical simulators are available in numerous modalities, including virtual, synthetic, animal, and non-living models. The ideal surgical simulator would facilitate the acquisition and refinement of surgical skills prior to clinical application, by mimicking the size, color, texture, recoil, and environment of the operating room. Simulation training has proven helpful for advancing specific surgical skills and techniques, aiding in early and late resident learning curves. In this review, the current applications and potential benefits of incorporating simulation-based surgical training into residency curriculum are explored in depth, specifically in the context of plastic surgery. Despite the prevalence of simulation-based training models, there is a paucity of research on integration into resident programs. Current curriculums emphasize the ability to identify anatomical landmarks and procedural steps through virtual simulation. Although transfer of these skills to the operating room is promising, careful attention must be paid to mastery versus memorization. In the authors' opinions, curriculums should involve step-wise employment of diverse models in different stages of training to assess milestones. To date, the simulation of tactile experience that is reminiscent of real-time clinical scenarios remains challenging, and a sophisticated model has yet to be established.

• Hwankyungkyoyuk
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• v.10 no.2
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• pp.51-62
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• 1997

The objective of this study was to review the meaning and problems of Scientific Knowledge and Ethical Value in Environmental Education. The ultimate goal of environmental education is shaping proenvironmental human behavior. The factors of human behavioral decision making are ideology, value, attitude and behavioral intentions. Ideology is a kind of belief system used by social groups to interpret their social world. The main elements of belief system are knowledge and value. The traditional thinking in education has been that we can change behavior by making human beings more knowledgeable and more valuable. In environmental education, the aim of scientific inquiry is to analysis cause-effect relation of human beings behavior and environmental phenomenon, and ethical education is to change the mind of human beings from zero-sum to positive-sum about the relations between human beings and natural environments. But, there are many problems of knowledge education and value education in environmental education. For example scientific knowledge without ethical value is dangerous to environment protection, and ethical value without scientific knowledge is vague. Therefore, we must recognize that the relationship of ethical value and scientific knowledge is not substitutional but complementary. The teaching-learning methods which can integrate knowledge and value in environmental education are rational decision making model. For this model, we can construct teaching contents with inquiry materials. To earn the benefits of specialization among several subjects in environmental education, social studies can focus on social science knowledge and decision making, science education can focus on pure natural science knowledge and scientific investigation, moral education can focus on problems of ethical value system, home economics can focus on practical action and environmental education(Environments in middle school, Ecology and Environments in high school) can integrate social-national science knowledge and ethical value in broad perspective about human beings and ecosystem. That is the method to protect from law of diminishing marginal utility of learning in environmental education.

• Journal of the Korean School Mathematics Society
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• v.8 no.4
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• pp.481-494
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• 2005

In this study, the goal is to spread profound knowledge and theory through providing with accumulated methods in mathematics education to the students who are relatively neglected in educational benefits. The process is divided into 3 categories: mathematics for obtaining common sense and intelligence, practical math for application, and math as a liberal art to elevate their characters. Furthermore, it includes the reasons for studying math, improving problem-solving skills, machinery application learning, introduction to code(cipher) theory and game theory, utilizing GSP to geometry learning, and mathematical relations to sports and art. Based on these materials, the next step(goal) is to train graduate students to conduct researches in teaching according to the teaching plan, as well as developing interesting and effective teaching plan for the remote high school learners.

• Journal of International Academy of Physical Therapy Research
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• v.3 no.2
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• pp.413-421
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• 2012

Few studies address the use of manual muscle stretching to improve spinal active range of motion(AROM). There is evidence that' Hold-Relax'(HR) is effective for increasing ROM in the extremities, which leads the researchers to anticipate similar benefits in the spine. The purpose of this study is to investigate the effects of HR(trunk flexors) and active thoracic flexion and extension on thoracic mobility, specifically flexion and extension in healthy individuals. A convenience sample of 30 physical therapy students(22-38 years) were randomly assigned to intervention sequence 'A-B' or 'B-A', with at least 7 days between interventions. Intervention' A' consisted of HR of the ventral trunk musculature while 'B' consisted of thoracic flexion-extension AROM. Thoracic flexion and extension AROM were measured before and after each intervention using the double inclinometer method. Paired t-tests were used to compare AROM pre and post-intervention for both groups, and to test for carry-over and learning effects. There was a statistically significant increase(mean=$3^{\circ}$ ; p=0.006) in thoracic extension following HR of the trunk flexors. There were no significant changes in thoracic flexion following HR, or in flexion or extension following the AROM intervention. No carryover or learning effects were identified. HR may be an effective tool for improving AROM in the thoracic spine in pain free individuals. Further investigation is warranted with symptomatic populations and to define the minimal clinical difference(MCD) for thoracic spine mobility.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.1-14
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• 2010

The back-propagation neural network (BPN) has long been successfully applied in bankruptcy prediction problems. Despite its wide application, some major issues must be considered before its use, such as the network topology, learning parameters and normalization methods for the input and output vectors. Previous studies on bankruptcy prediction with BPN have shown that many researchers are interested in how to optimize the network topology and learning parameters to improve the prediction performance. In many cases, however, the benefits of data normalization are often overlooked. In this study, a genetic algorithm (GA)-based normalization transform, which is defined as a linearly weighted combination of several different normalization transforms, will be proposed. GA is used to extract the optimal weight for the generalization. From the results of an experiment, the proposed method was evaluated and compared with other methods to demonstrate the advantage of the proposed method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.71-82
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• 2021

In this study, a novel method for monitoring road pavements using the Mobile Mapping System (MMS) and a deep learning crack detection system was presented. Furthermore, an optimal maintenance method through economic analysis was presented targeting the pavement section of Sejong City. As a result of monitoring the pavement conditions, it was confirmed that the pavement ratings were good in the order of national highways, municipal roads, and roads of provinces. In addition, economic analysis using the pavement deterioration model showed that micro-surfacing, one of the preventive maintenance methods, is the most economical in terms of maintenance costs and user benefits. The results of this study are expected to be used as fundamental reference for local governments' infrastructure management plans.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.91-100
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• 2022

To support early intervention, it is necessary to develop programming system tools that enable accurate, valid, and reliable assessments and can help achieve reasonable, generalizable, and measurable goals. This study examined the Assessment, Evaluation, and Programming System (AEPS) used by associations of children with disabilities in Saudi Arabia to assess its suitability for children with intellectual disabilities. A group of 16 specialists with different professional backgrounds (including special education, physiotherapy, occupational therapy, speech therapy and psychology) from 11 associations of children with disabilities took part in semi-structured personal interviews. The study concluded that AEPS is generally suited for use with children with intellectual disabilities. However, its suitability depends on the type and severity of the child's disability. The more severe the disability, the less effective the AEPS is likely to be. On the basis of this finding the researchers formed interdisciplinary teams to organise and integrate the children's learning and assess the benefits of AEPS, including its accuracy and ability to achieve adaptive, cognitive, and social targets, enhance family engagement and learning and develop basic development skills. This study also identified obstacles associated with the use of AEPS. These include the lack of comprehensiveness and accuracy of the goal, lack of precision and non-applicability to large movements and the fact that it cannot be used with all children with intellectual disabilities. In addition, the research showed that non-cooperation within the family is a major obstacle to the implementation of the AEPS. The results of this study have several implications.

• Journal of the Korea Computer Graphics Society
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• v.29 no.1
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• pp.1-11
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• 2023

Deep-learning-based image segmentation is one of the most widely employed lane detection approaches, and it requires a post-process for extracting the key points on the lanes. A general approach for key-point extraction is using a fixed threshold defined by a user. However, finding the best threshold is a manual process requiring much effort, and the best one can differ depending on the target data set (or an image). We propose a novel key-point extraction algorithm that automatically adapts to the target image without any manual threshold setting. In our adaptive key-point extraction algorithm, we propose a line-level normalization method to distinguish the lane region from the background clearly. Then, we extract a representative key point for each lane at a line (row of an image) using a kernel density estimation. To check the benefits of our approach, we applied our method to two lane-detection data sets, including TuSimple and CULane. As a result, our method achieved up to 1.80%p and 17.27% better results than using a fixed threshold in the perspectives of accuracy and distance error between the ground truth key-point and the predicted point.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.72-81
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• 2023

This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.