• Journal of Radiation Protection and Research
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• v.45 no.4
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• pp.163-170
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• 2020

Background: Gut microflora contributes to the nutritional metabolism of the host and to strengthen its immune system. However, if the intestinal barrier function of the living body is destroyed by radiation exposure, the intestinal bacteria harm the health of the host and cause sepsis. Therefore, this study aims to trace short-term radiation-induced changes in the mouse gut microflora-dominant bacterial genus, and analyze the degree of intestinal epithelial damage. Materials and Methods: Mice were irradiated with 0, 2, 4, 8 Gy X-rays, and the gut microflora and intestinal epithelial changes were analyzed 72 hours later. Five representative genera of Actinobacteria, Firmicutes, and Bacteroidetes were analyzed in fecal samples, and the intestine was pathologically analyzed by Hematoxylin-Eosin and Alcian blue staining. In addition, DNA fragmentation was evaluated by the TdT-mediated dUTP nick-end labeling (TUNEL) assay. Results and Discussion: The small intestine showed shortened villi and reduced number of goblet cells upon 8 Gy irradiation. The large intestine epithelium showed no significant morphological changes, but the number of goblet cells were reduced in a radiation dose-dependent manner. Moreover, the small intestinal epithelium of 8 Gy-irradiated mice showed significant DNA damaged, whereas the large intestine epithelium was damaged in a dose-dependent manner. Overall, the large intestine epithelium showed less recovery potential upon radiation exposure than the small intestinal epithelium. Analysis of the intestinal flora revealed fluctuations in lactic acid bacteria excretion after irradiation regardless of the morphological changes of intestinal epithelium. Altogether, it became clear that radiation exposure could cause an immediate change of their excretion. Conclusion: This study revealed changes in the intestinal epithelium and intestinal microbiota that may pave the way for the identification of novel biomarkers of radiation-induced gastrointestinal disorders and develop new therapeutic strategies to treat patients with acute radiation syndrome.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.2
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• pp.103-110
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• 2022

Degradation of handling qualities(HQs) due to bad weather or mechanical failure can pose a fatal risk to pilots unfamiliar with such situation. In particular, icing is an important issue to consider as it is a frequent cause of accidents. Most of the previous research works focuses on aerodynamic performance changes due to icing and the corresponding icing modeling or methods to prevent icing, whereas the present work attempts to actively compensate for HQ degradation due to icing on a full-scale helicopter through flight control law design. To this end, the present work first demonstrates HQ degradation due to icing using CONDUIT software, and subsequently presents a robust control design via the RS-LQR(Robust Servomechanism Linear Quadratic Regulation) procedure to compensate for the HQ degradation. Simulation results show that the proposed robust control maintains Level 1 HQ in the presence of icing.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• Smart Structures and Systems
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• v.29 no.1
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• pp.129-140
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• 2022

The effectiveness of system identification, damage detection, condition assessment and other structural analyses relies heavily on the accuracy and reliability of the measured data in structural health monitoring (SHM) systems. However, data anomalies often occur in SHM systems, leading to inaccurate and untrustworthy analysis results. Therefore, anomalies in the raw data should be detected and cleansed before further analysis. Previous studies on data anomaly detection mainly focused on just single type of data anomaly for denoising or removing outliers, meanwhile, the existing methods of detecting multiple data anomalies are usually time consuming. For these reasons, recognising multiple anomaly patterns for real-time alarm and analysis in field monitoring remains a challenge. Aiming to achieve an efficient and accurate detection for multi-type data anomalies for field SHM, this study proposes a pattern-recognition-based data anomaly detection method that mainly consists of three steps: the feature extraction from the long time-series data samples, the training of a pattern recognition neural network (PRNN) using the features and finally the detection of data anomalies. The feature extraction step remarkably reduces the time cost of the network training, making the detection process very fast. The performance of the proposed method is verified on the basis of the SHM data of two practical long-span bridges. Results indicate that the proposed method recognises multiple data anomalies with very high accuracy and low calculation cost, demonstrating its applicability in field monitoring.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.27-40
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• 2022

Medical AI, which has lately made significant advances, is playing a vital role, such as assisting clinicians with diagnosis and decision-making. The field of chest X-rays, in particular, is attracting a lot of attention since it is important for accessibility and identification of chest diseases, as well as the current COVID-19 pandemic. However, despite the vast amount of data, there remains a limit to developing an effective AI model due to a lack of labeled data. A research that used federated learning on chest X-ray data to lessen this difficulty has emerged, although it still has the following limitations. 1) It does not consider the problems that may occur in the Non-IID environment. 2) Even in the federated learning environment, there is still a shortage of labeled data of clients. We propose a method to solve the above problems by using the self-supervised learning model as a global model of federated learning. To that aim, we investigate a self-supervised learning methods suited for federated learning using chest X-ray data and demonstrate the benefits of adopting the self-supervised learning model for federated learning.

• Journal of Oriental Neuropsychiatry
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• v.33 no.3
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• pp.301-316
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• 2022

Objectives: To compare Korean medicine (KM) and traditional Chinese medicine (TCM) psychotherapy for anxiety. Methods: Databases including MEDLINE (via PubMed), EMBASE (via Elsevier), Cochrane Central Register of Controlled Trials, China National Knowledge Infrastructure, and Oriental Medicine Advanced Searching Integrated System were comprehensively searched. Prospective clinical studies on KM or TCM psychotherapy for patients with anxiety disorder or individuals with elevated anxiety levels published up to August 3, 2022 were reviewed. Psychotherapy was divided into counselling, art therapy, and meditation according to its characteristics. Results: A total of 12 clinical studies were reviewed, including nine randomized controlled trials. The most common disorder investigated was post-traumatic stress disorder. Ten studies used TCM psychotherapy and two used KM psychotherapy. As for differences between TCM psychotherapy and KM psychotherapy, TCM psychotherapy utilized pattern identification in the procedure more actively than KM psychotherapy. In addition, some TCM studies have attempted to directly converge Western psychotherapy (i.e., hypnosis) and Eastern psychotherapy (i.e., Taoin qigong therapy). In the case of KM psychotherapy, there was an attempt to incorporate psychotherapy with Sasang constitutional medicine. Reported effects of TCM psychotherapy and KM psychotherapy on anxiety were positive. Conclusions: Research status of KM psychotherapy and TCM psychotherapy for anxiety was investigated, revealing some of their characteristics, commonalities, and differences. Findings of this review have the potential to provide a clue to the development of conventional KM psychotherapy and new medical technology for KM psychotherapy.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.115-126
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• 2023

Due to the fluctuating random and periodical nature of renewable energy generation power quality disturbances occurred more frequently in power generation transformation transmission and distribution. Various power quality disturbances may lead to equipment damage or even power outages. Therefore it is essential to detect and classify different power quality disturbances in real time automatically. The traditional PQD identification method consists of three steps: feature extraction feature selection and classification. However, the handcrafted features are imprecise in the feature selection stage, resulting in low classification accuracy. This paper proposes a deep neural architecture based on Convolution Neural Network and Long Short Term Memory combining the time and frequency domain features to recognize 16 types of Power Quality signals. The frequency-domain data were obtained from the Fast Fourier Transform which could efficiently extract the frequency-domain features. The performance in synthetic data and real 6kV power system data indicate that our proposed method generalizes well compared with other deep learning methods.

• Journal of dental hygiene science
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• v.22 no.4
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• pp.249-255
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• 2022

Background: Sexual dimorphism is important for sex determination in the field of forensics. However, sexual dimorphism is commonly assessed using cone beam computed tomography (CBCT) rather than three-dimensional (3D) modeling software; therefore, studies using a more accurate measurement approach are necessary. This study assessed the sexual dimorphism of the MS using a 3D modeling program to obtain information that could contribute to the fields of surgery and forensics. Methods: The CBCT data of 60 patients (age, 20~29 y; 30 males and 30 females) admitted to the Department of Orthodontics at the Dankook University School of Dentistry were provided in Digital Imaging and Communications in Medicine (DICOM) format. The left MS and right MS were modeled based on the DICOM files using the Mimics (version 22; Materialise, Leuven, Belgium) 3D program and converted to stereolithography (STL) files used to measure the width, length, and height of the MS, infraorbital foramen (IOF), right MS, and left MS. The average of three repeated measurements was calculated, and a reliability test was performed to ensure data reliability (Cronbach's α=0.618). A canonical discriminant analysis was performed using a standard approach (left: Box's M=0.096; right: Box's M=0.115). Results: Males had greater values for all parameters (MS width, MS length, MS height, IOF, right MS, left MS) than females. The discriminant analysis identified six independent variables (MS width, MS height, MS length, IOF, right MS, left MS) that could identify sex. The left MS and right MS correctly identified the sex of 81.7% and 71.7% of the patients, respectively, with the left MS having higher accuracy. Conclusion: This study confirmed that, for Korean individuals, the left MS has a better ability to identify sex than the right MS. These results may contribute to sex identification in the fields of surgery and forensics.

• Restorative Dentistry and Endodontics
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• v.48 no.1
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• pp.4.1-4.10
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• 2023

Objectives: This study aimed to compare the torsional and cyclic fatigue resistance of ProGlider (PG), WaveOne Gold Glider (WGG), and TruNatomy Glider (TNG). Materials and Methods: A total of 15 instruments of each glide path system (n = 15) were used for each test. A custom-made device simulating an angle of 90° and a radius of 5 millimeters was used to assess cyclic fatigue resistance, with calculation of number of cycles to failure. Torsional fatigue resistance was assessed by maximum torque and angle of rotation. Fractured instruments were examined by scanning electron microscopy (SEM). Data were analyzed with Shapiro-Wilk and Kruskal-Wallis tests, and the significance level was set at 5%. Results: The WGG group showed greater cyclic fatigue resistance than the PG and TNG groups (p < 0.05). In the torsional fatigue test, the TNG group showed a higher angle of rotation, followed by the PG and WGG groups (p < 0.05). The TNG group was superior to the PG group in torsional resistance (p < 0.05). SEM analysis revealed ductile morphology, typical of the 2 fracture modes: cyclic fatigue and torsional fatigue. Conclusions: Reciprocating WGG instruments showed greater cyclic fatigue resistance, while TNG instruments were better in torsional fatigue resistance. The significance of these findings lies in the identification of the instruments' clinical applicability to guide the choice of the most appropriate instrument and enable the clinician to provide a more predictable glide path preparation.

• Biomedical Science Letters
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• v.28 no.4
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• pp.317-321
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• 2022

Due to COVID-19 pandemic, wearing face masks is obligatory to prevent respiratory virus transmissions in the community. However, there are few studies of the desirable number of wearing a face mask, and how to store them for reuse. Therefore, in this study, a survey was conducted among 208 healthy adults, and 27 kf-94 masks worn for 1, 2, and 3 days were collected. To estimate the risk of bacterial contamination, we analyzed the extent of bacterial contamination of the BHI medium and 16S rRNA gene sequencing. With an increase in the number of days of using the mask, the degree of bacterial contamination of the used mask gradually increased. As a result of 16S rRNA PCR performed for strain identification, Staphylococcus, known as a pathogenic bacterium, was identified the most. In conclusion, we found that wearing a cotton KF mask provides an optimal environment for microbes, which are related to the skin and respiratory system, to thrive. Therefore, it is also important to reduce the risk of bacterial infection of the face mask with appropriate sterilization methods.