• Tissue Engineering and Regenerative Medicine
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• 제15권6호
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• pp.673-697
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• 2018

BACKGROUND: Cartilage tissue engineering (CTE) aims to obtain a structure mimicking native cartilage tissue through the combination of relevant cells, three-dimensional scaffolds, and extraneous signals. Implantation of 'matured' constructs is thus expected to provide solution for treating large injury of articular cartilage. Type I collagen is widely used as scaffolds for CTE products undergoing clinical trial, owing to its ubiquitous biocompatibility and vast clinical approval. However, the long-term performance of pure type I collagen scaffolds would suffer from its limited chondrogenic capacity and inferior mechanical properties. This paper aims to provide insights necessary for advancing type I collagen scaffolds in the CTE applications. METHODS: Initially, the interactions of type I/II collagen with CTE-relevant cells [i.e., articular chondrocytes (ACs) and mesenchymal stem cells (MSCs)] are discussed. Next, the physical features and chemical composition of the scaffolds crucial to support chondrogenic activities of AC and MSC are highlighted. Attempts to optimize the collagen scaffolds by blending with natural/synthetic polymers are described. Hybrid strategy in which collagen and structural polymers are combined in non-blending manner is detailed. RESULTS: Type I collagen is sufficient to support cellular activities of ACs and MSCs; however it shows limited chondrogenic performance than type II collagen. Nonetheless, type I collagen is the clinically feasible option since type II collagen shows arthritogenic potency. Physical features of scaffolds such as internal structure, pore size, stiffness, etc. are shown to be crucial in influencing the differentiation fate and secreting extracellular matrixes from ACs and MSCs. Collagen can be blended with native or synthetic polymer to improve the mechanical and bioactivities of final composites. However, the versatility of blending strategy is limited due to denaturation of type I collagen at harsh processing condition. Hybrid strategy is successful in maximizing bioactivity of collagen scaffolds and mechanical robustness of structural polymer. CONCLUSION: Considering the previous improvements of physical and compositional properties of collagen scaffolds and recent manufacturing developments of structural polymer, it is concluded that hybrid strategy is a promising approach to advance further collagen-based scaffolds in CTE.

• Exercise Science
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• 제26권2호
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• pp.103-114
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• 2017

INTRODUCTION: Regulation of skeletal muscle protein mass is implicated not only in exercise performance but in metabolic health. Exercise in combination with nutrition, particularly dietary protein/amino acid intake, are the pragmatic approach that effectively induces muscle anabolic response (i.e., muscle hypertrophy) through regulating protein synthesis and breakdown. PURPOSE: The purpose of this review was to summarize available data on the effect of exercise intervention and amino acids intake on muscle protein synthesis and breakdown and provide an insight into development of an effective exercise intervention and amino acids supplements, applicable to training practice. METHODS: In this review, we have reviewed currently available data mainly from stable isotope tracer studies with respect to the effect of exercise intervention and protein or amino acid supplement on muscle protein anabolic response. CONCLUSIONS: Taken together, exercise alone may not be effective in achieving a positive net muscle protein balance due to the fact that protein breakdown still exceeds protein synthesis until nutrition intake such as protein/amino acids. It appears that muscle anabolic response increases in proportional to the amount of protein intake up to 20 - 35 g depending on quality of protein, age, differences on exercise intensity, duration, and frequency, and individual's training status

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.120-130
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• 2021

The spinal cord or CSF surgery is a very complex process. It requires continuous pre and post-surgery evaluation to have a better ability to diagnose the disease. To detect automatically the suspected areas of tumors and symptoms of CSF leakage during the development of the tumor inside of the brain. We propose a new method based on using computer software that generates statistical results through data gathered during surgeries and operations. We performed statistical computation and data collection through the Google Source for the UK National Cancer Database. The purpose of this study is to address the above problems related to the accuracy of missing hybrid KNN values and finding the distance of tumor in terms of brain cancer or CSF images. This research aims to create a framework that can classify the damaged area of cancer or tumors using high-dimensional image segmentation and Laplace transformation method. A high-dimensional image segmentation method is implemented by software modelling techniques with measures the width, percentage, and size of cells within the brain, as well as enhance the efficiency of the hybrid KNN algorithm and Laplace transformation make it deal the non-zero values in terms of missing values form with the using of Frobenius Matrix for deal the space into non-zero values. Our proposed algorithm takes the longest values of KNN (K = 1-100), which is successfully demonstrated in a 4-dimensional modulation method that monitors the lighting field that can be used in the field of light emission. Conclusion: This approach dramatically improves the efficiency of hybrid KNN method and the detection of tumor region using 4-D segmentation method. The simulation results verified the performance of the proposed method is improved by 92% sensitivity of 60% specificity and 70.50% accuracy respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권5호
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• pp.1814-1828
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• 2021

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from low-resolution. To reduce the noise of the enhanced image and improve the image quality, a super-resolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• 제47권4호
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• pp.239-248
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• 2021

The paper reviews various classifications and surgical techniques for the treatment of temporomandibular joint ankylosis. PubMed, EBSCO, Web of Science, and Google Scholar were searched using a combination of keywords. Articles related to classification, resection-reconstruction of the temporomandibular joint, and management of airway obstruction were considered and categorized based on the objectives. Seventy-nine articles were selected, which included randomized clinical trials, non-randomized controlled cohort studies, and case series. Though several classifications exist, most classifications are centered on the radiographic extent of the ankylotic mass and do not include the clinical and functional parameters. Hence there is a need for a comprehensive staging system that takes into consideration the age of the patient, severity of the disease, clinical, functional, and radiographic findings. Staging the disease will help the clinician to adopt a holistic approach in treating these patients. Interpositional arthroplasty (IA) results in better maximal incisal opening compared with gap arthroplasty, with no significant difference in recurrent rates. Distraction osteogenesis (DO) is emerging as a popular technique for the restoration of symmetry and function as well as for relieving airway obstruction. IA, with a costochondral graft, is recommended in growing patients and may be combined with or preceded by DO in cases of severe airway obstruction. Alloplastic total joint replacement combined with fat grafts and simultaneous osteotomy procedures are gaining popularity. A custom-made total joint prosthesis using CAD/CAM can efficiently overcome the shortcomings of stock prostheses.

• The Journal of the Convergence on Culture Technology
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• 제8권1호
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• pp.131-138
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• 2022

This study was to investigate the characteristics of nursing students' safety by reviewing literature reported in Korea. Methods: A total of 29 articles were selected by the combination with 'safety', 'safety*', 'patient safety', 'patient safety*' 'nursing students', and 'nursing * students' from the database(via DBpia, KCI, KISS, NANET, NDSL, NL, RISS). Results: Publication year of researchs are '2015-2019' 69.0%, publication journal 'non-nursing' 79.3%, mean of participants 242.4 persons, 'under 200 persons' 51.7%, number of participating area 'one region' 48.2%, number of participating schools 'over three schools' 42.3%, participating grade 'senior in colleges' 56.2%. Participant were calculated by G*Power and unapproved by Institutional Review of Board(IRB) were 58.6%. Most of statistics measures were 'regression' 75.9%, and authors' number were '2 co-authors' 58.6%. In keyword analysis, it has changed from knowledge-oriented to patient-centered. Conclusion: Future research will require research and policy support for the development of an integrated educational approach and intervention plan to strengthen the safety competency of nursing students.

• Journal of Technology Innovation
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• 제30권2호
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• pp.1-27
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• 2022

In recent years, the autonomous vehicle industry has changed drastically. So the needs and interests in predicting future technologies and market prospects of the autonomous vehicle field have been very increased. However, considering the characteristics of the automotive industry, which has various factors, complex correlation of them and big influence on each other, the study of systematic future forecasting methodologies are urgent and necessary which are applicable to autonomous vehicle industry. In this research, the two methods such as "Field Anomaly Relaxation" and "Multiple Perspective Concept" were analyzed and chosen, which are suitable to automotive industry. By the combination of two methods this research developed and examined the three future scenarios related to core technologies and industry trends. And these scenarios feasibility was verified by experts and evaluation checklist. This research has a contribution that this future scenario forecasting approach can be applied to the industries which have various volatility like the autonomous vehicle industry.

• International Journal of Computer Science & Network Security
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• 제22권8호
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• pp.25-34
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• 2022

Today, further education of adults in the UK is one of the developing areas of continuing education. The Open University with distance learning, in the process of which innovative forms and methods based on computer and telecommunication technologies are used, is particularly successful in the organization of additional education of the adult population. The advantages of distance learning, multimedia - the latest information technologies, which provide the combination of graphic images, video, sound with the help of modern computer tools, are noted. The basic principles and forms underlying the technologies and forms of work with the elderly are defined. The international experience of implementing "Universities of the Third Age" is summarized. The most widespread approach in adult education in Great Britain is informational. The use of computer technologies motivates a new paradigm in educational methods and strategies, which requires new approaches, forms of learning, and innovative ways of delivering educational materials to adult learners. Information technologies have gained great popularity in such activities as distance learning, online learning, assistance in the education management system, development of programs and virtual textbooks in various subjects, online search for information for the educational process, computer testing of students' knowledge, creation of electronic libraries, formation of a single scientific electronic environment, publication of virtual magazines and newspapers on pedagogical topics, teleconferences, expansion of international cooperation in the field of Internet education. The information technology of synchronous distance learning "online" has gained considerable popularity in the educational process today. A promising direction is the use of multimedia technologies in educational activities to create a design of a virtual computer environment by decoding audiovisual information.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• Journal of Korea Society of Industrial Information Systems
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• 제27권5호
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• pp.73-82
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• 2022

Chronic diseases such as hypertension require a differentiated approach according to age and life cycle. Chronic diseases such as hypertension require differentiated management according to the life cycle. It is also known that the cause of hypertension is a combination of various factors. This study uses machine learning prediction techniques to analyze various factors affecting hypertension by life cycle. To this end, a total of 35 variables were used through preprocessing and variable selection processes for the National Health and Nutrition Survey data of the Korea Centers for Disease Control and Prevention. As a result of the study, among the tree-based machine learning models, XGBoost was found to have high predictive performance in both middle and old age. Looking at the risk factors for hypertension by life cycle, individual characteristic factors, genetic factors, and nutritional intake factors were found to be risk factors for hypertension in the middle age, and nutritional intake factors, dietary factors, and lifestyle factors were derived as risk factors for hypertension. The results of this study are expected to be used as basic data useful for hypertension management by life cycle.