• Journal of Korean Orthopaedic Sports Medicine
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• v.2 no.2
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• pp.119-126
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• 2003

The shoulder is a complex joint and, by virtue of having a large range of motion, is inherently unstable, relying on the surrounding soft tissue structures for stability. The bony joint consists of the glenoid, acromion, and humoral head, while the soft tissues include the glenoid labrum, the glenohumeral ligaments. and coracoacromial ligament as well as the muscles of the rotator cuff, the long head of the biceps, and the scapulothoracic muscles. Dysfunction in any one of these components can cause shoulder problems. The throwing motion involves a series of phases that stress to their limits the dynamic and static restraints of the glenohumeral and scapulothoracic joints. . Therefore, maintaining a balance of proper biomechanical forces is essential to avoiding shoulder injuries in throwing athletes. Over the last decade, signficant advances have been made in the study and understanding of the shoulder mechanics, and pathophysiology of injury. Additionally, advances in surgical techniques, particularly arthroscopy , have aided in the diagnosis of and the developement of less invasive surgical treatments for injuries that do not respond to nonoperative measures. In this article, we reviewed the pathophysiology of injuries , diagnostic techniques, and surgical management of shoulder injuries in throwing athletes .

• Nuclear Medicine and Molecular Imaging
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• v.41 no.4
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• pp.335-336
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• 2007

Meniscus-like presentation of ulcerating gastric carcinoma on upper gastrointestinal series radiograph was first described in 1921 by Carman and has since been known as a useful differential diagnostic sign in radiology. In 1982 using then newly introduced computed tomography (CT) Widder and Mueller revisited the meniscus sign. Their study was primarily focused on a dynamic assessment of the demonstrability of the meniscus sign that largely depends on the judgment and technical skill of examiner, especially graded compression and patient positioning. One year earlier Balfe et al. assessed the diagnostic reliability of gastric wall thickening as observed on CT scan in adenocarcinoma, lymphoma and leiomyosarcoma and concluded that it is not a reliable finding. In contrast, however, Lee et al. recently emphasized that the wall thickness measurement on CT of exophytic carcinoma, myoma and ulcers was a useful diagnostic means. Thus, it appears that gastric wall thickening or mucosal heave-up is by itself not as reliable as the meniscus sign. The electronic search of world literature failed to disclose earlier report of this sign demonstrated by $^{18}F-FDG$ positron emission tomography and computed tomography (PET/CT). The present communication documents $^{18}F-FDG$ PET/CT finding of the meniscus sign as encountered in a case of ulcerating gastric carcinoma, the histological diagnosis of which was moderately differentiated tubular adenocarcinoma. Unlike most gastric tumors without ulceration that tend to unimpressively accumulate $^{18}F-FDG$ the present case of Borrmann type III gastric carcinoma demonstrated markedly increased $^{18}F-FDG$ uptake.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.372-381
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• 2006

The purpose of this study was to develop a process management system to manage ingot fabrication and improve ingot quality. The ingot is the first manufactured material of wafers. Trace parameters were collected on-line but measurement parameters were measured by sampling inspection. The quality parameters were applied to evaluate the quality. Therefore, preprocessing was necessary to extract useful information from the quality data. First, statistical methods were used for data generation. Then, modeling was performed, using the generated data, to improve the performance of the models. The function of the models is to predict the quality corresponding to control parameters. Secondly, rule extraction was performed to find the relation between the production quality and control conditions. The extracted rules can give important information concerning how to handle the process correctly. The dynamic polynomial neural network (DPNN) and decision tree were applied for data modeling and rule extraction, respectively, from the ingot fabrication data.

• The Journal of the Acoustical Society of Korea
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• v.29 no.1E
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• pp.38-44
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• 2010

This paper is based on our comments and proposed amendments to the documents, Annex A, Phantom for determining Maximum Depth of Penetration, and Annex B, Local Dynamic Range Using Acoustical Test Objects 87/400/CDV. IEC 61391-2 Ed. 1.0 200X, prepared by IEC technical Committee 87; Ultrasonics. The documents are concerned with the influence of microstructure of reference target material on the ultrasonic backscattering. Previous works on the attenuation due to backreflection and backscattering of reference target materials are reviewed. The drawback to the use of ungraded stainless steel and metallic materials without microstructural data such as, crystal structure, basic acoustic data of sound velocity and attenuation, grain size, roughness and elastic constants has been discussed. The analysis suggested that the insightful conclusion can be made by differentiating the influence arising from target size and microstructure on the backscattering measurements. The microstructural parameters are associated with physical, geometrical, acoustical and mechanical origins of variation with frequency. Further clarification of such a diverse source mechanisms for ultrasonic backscattering would make the target material and its application for medical diagnosis and therapy simpler and more reliable.

• Smart Structures and Systems
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• v.13 no.5
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• pp.869-890
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• 2014

In this study, a damage detection approach using train-induced vibration response of the bridge is proposed, utilizing only direct structural analysis by means of introducing soft computing methods. In this approach, the possible damage patterns of the bridge are assumed according to theoretical and empirical considerations at first. Then, the running train-induced dynamic response of the bridge under a certain damage pattern is calculated employing a developed train-bridge interaction analysis program. When the calculated result is most identical to the recorded response, this damage pattern will be the solution. However, owing to the huge number of possible damage patterns, it is extremely time-consuming to calculate the bridge responses of all the cases and thus difficult to identify the exact solution quickly. Therefore, the soft computing methods are introduced to quickly solve the problem in this approach. The basic concept and process of the proposed approach are presented in this paper, and its feasibility is numerically investigated using two different train models and a simple girder bridge model.

• Investigative Magnetic Resonance Imaging
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• v.14 no.2
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• pp.145-150
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• 2010

We report the case of a 16-year-old boy with a solid pancreatic mass which proved to be a nonfunctioning, malignant pancreatic neuroendocrine tumor (PNET). In pediatric patients, malignant pancreatic tumors are rare, especially malignant PNET. When dynamic contrast enhanced MRI showed a well enhancing solid pancreatic tumor on arterial and delayed phases and combined with malignant features, such as vascular invasion, invasion of adjascent organs, and lymphadenopathy, we should include malignant pancreatic neuroendocrine tumor in the differential diagnosis of childhood pancreatic tumors.

• Journal of Genetic Medicine
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• v.13 no.2
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• pp.65-71
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• 2016

Cell-free nucleic acids (cf-NAs) originate in trophoblasts and are detected in the maternal plasma. Using innovative bioinformatic technologies such as next-generation sequencing, cf-NAs in the maternal plasma have been rapidly applied in prenatal genetic screening for fetal aneuploidy. Amniotic fluid is a complex and dynamic fluid that provides growth factors and protection to the fetus. In 2001, the presence of cf-NA in amniotic fluid was reported. Amniotic fluid is in direct contact with the fetus and is derived from fetal urine and maternal and fetal plasma. Therefore, these genetic materials have been suggested to reflect fetal health and provide real-time genetic information regarding fetal development. Recently, several studies evaluated the global gene expression changes of amniotic fluid cell-free RNA according to gestational age. In addition, by analyzing the transcriptome in the amniotic fluid of fetal aneuploidy, potential key pathways and novel biomarkers for fetal chromosomal aneuploidy were identified. Here, we review the current knowledge of cell-free RNA in amniotic fluid and suggest future research directions.

• Journal of the Korean Solar Energy Society
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• v.33 no.4
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• pp.89-93
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• 2013

In recent years, many investigations about photovoltaic power systems have been significantly carried out in the fields of renewable power energy. Such research area generally includes developments of highly efficient solar cells, advanced power conversion systems, and smart monitoring systems. A generic objective of fault detection and diagnosis techniques is to timely recognize unexpected faulty of dynamic systems so that economic demage occurred by such faulty is decreased by means of engineering techniques. This paper presents a novel fault detection approach for photovoltaic power arrays which are electrically connected in series and parallels. In the proposed fault detection scheme, we first measure all of photovoltaic modules located in each array by using electronic sense systems and then compare each measurement in turn to detect location of fault module through statistic computation algorithm. We accomplish real-time experiments to demonstrate our proposed fault detection methodology by using a test-bed system including two 20 watt photovoltaic modules.

• Journal of dental hygiene science
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• v.19 no.2
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• pp.77-85
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• 2019

In the past gut microbiome has been the main focus of microbiome research. Studies about the microbiome inside oral cavities and other organs are underway. Studies about the relationship between noninfectious diseases and periodontal diseases, and the negative effects of harmful oral microbes on systemic health have been published in the recent past. A lot of attention is being paid towards fostering a healthy oral microbial ecosystem. This study aimed to understand the roles and effects of the microbiome inside the human body can potentially help cure various diseases including inflammatory bowel diseases with no known cure such as Crohn's disease, atopic dermatitis, obesity, cancer, diabetes, brain diseases and oral diseases. The present study examined technological trends in the correlation between the human microbiome and diseases in the human body, interactions between the human body's immunity, the metabolic system, and the microbiome, and research trends in other countries. While it has been proven that human microbiome is closely correlated with human diseases, most studies are still in the early stage of trying to compare the composition of microbiomes between health and patient groups. Since the oral environment is a dynamic environment that changes due to not only food intake but also other external factors such as lifestyle, hygiene, and drug intake, it is necessary to continue in-depth research on the microbiome composition characteristics to understand the complex functions of oral microorganisms. Analyzing the oral microbiome using computational technology may aid in disease diagnosis and prevention.

• Smart Structures and Systems
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• v.30 no.3
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.