• Proceedings of the Plant Resources Society of Korea Conference
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• 2022.09a
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• pp.103-103
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• 2022

Ganghwaljetongyeum (GHJTY) is a complex herbal decoction comprising 18 plants; it is used to treat arthritis. In order to develop a new anti-arthritic herbal medication, we selected 5 out of 18 GHJTY plants by using bioinformatics analysis. The new medication, called ChondroT, comprised water extracts of Osterici Radix, Lonicerae Folium, Angelicae Gigantis Radix, Clematidis Radix, and Phellodendri Cortex. This study was designed to investigate its chondroprotective and anti-inflammatory effects to develop an anti-arthritic herb medicine. ChondroT was validated using a convenient and accurate high-performance liquid chromatography. photodiode array (HPLC-PDA) detection method for simultaneous determination of its seven reference components. The concentrations of the seven marker constituents were in the range of 0.81-5.46 mg/g. The chondroprotective effects were evaluated based on SW1353 chondrocytes and matrix metalloproteinase 1 (MMP1) expression. In addition, the anti-inflammatory effects of ChondroT were studied by Western blotting of pro-inflammatory enzymes and by enzyme-linked immunosorbent assay (ELISA) of inflammatory mediators in lipopolysaccharides (LPS)-induced RAW264.7 cells. ChondroT enhanced the growth of SW1353 chondrocytes and also significantly inhibited IL-1β-induced MMP-1 expression. However, ChondroT did not show any effects on the growth of HeLa and RAW264.7 cells. The expression of cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) was induced by LPS in RAW264.7 cells, which was significantly decreased by pre-treatment with ChondroT. In addition, ChondroT reduced the activation of NF-κB and production of inflammatory mediators, such as IL-1β, IL-6, PGE2, and nitric oxide (NO) in LPS-induced RAW264.7 cells. These results show that ChondroT exerted a chondroprotective effect and demonstrated multi-target mechanisms related to inflammation and arthritis. In addition, the suppressive effect was greater than that exhibited by GHJTY, suggesting that ChondroT, a new complex herbal medication, has therapeutic potential for the treatment of arthritis.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.4
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• pp.69-78
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• 2023

With the rapid growth of artificial intelligence, the demand for semiconductors is enormously increasing everywhere. To ensure the manufacturing quality and quantity simultaneously, the importance of automatic defect detection during the packaging process has been re-visited by adapting various deep learning-based methodologies into automatic packaging defect inspection. Deep learning (DL) models require a large amount of data for training, but due to the nature of the semiconductor industry where security is important, sharing and labeling of relevant data is challenging, making it difficult for model training. In this study, we propose a new framework for securing sufficient data for DL models with fewer computing resources through a divide-and-conquer approach. The proposed method divides high-resolution images into pre-defined sub-regions and assigns conditional labels to each region, then trains individual sub-regions and boundaries with boundary loss inducing the globally coherent and seamless images. Afterwards, full-size image is reconstructed by combining divided sub-regions. The experimental results show that the images obtained through this research have high efficiency, consistency, quality, and generality.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.1-7
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• 2023

This paper developed a training model to classify normal roads and flooded roads using artificial intelligence technology. We expanded the diversity of learning data using various data augmentation techniques and implemented a model that shows good performance in various environments. Transfer learning was performed using the CNN-based Resnet152v2 model as a pre-learning model. During the model learning process, the performance of the final model was improved through various parameter tuning and optimization processes. Learning was implemented in Python using Google Colab NVIDIA Tesla T4 GPU, and the test results showed that flooding situations were detected with very high accuracy in the test dataset.

• Health Policy and Management
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• v.33 no.4
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• pp.440-449
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• 2023

Background: This study aimed to analyze the prehospital process and reperfusion therapy process of acute ischemic stroke in Busan metropolitan area and examine the impact of living arrangement on the early management and functional outcomes of acute ischemic stroke (AIS). Methods: The patients who diagnosed with AIS and received reperfusion therapy at the Busan Regional Cardiovascular Center between September 2020 and May 2023 were selected. We investigated the patients' hospital arrival time (onset to door time) and utilization of 119 emergency ambulance services. Additionally, various time matrices related to reperfusion therapy after hospital were examined, along with the functional outcome at the 90-day after treatment. Results: Among the 753 AIS patients who underwent reperfusion therapy, 166 individuals (22.1%) were living alone. AIS patients living alone experienced significant delays in symptom detection (p<0.05) and hospital arrival compared to AIS patients with cohabitants (370.1 minutes vs. 210.2 minutes, p<0.001). There were no significant differences between the two groups in terms of 119 ambulance utilization and time metrics related with the reperfusion therapy. Independent predictors of prognosis in AIS patients were found to be age above 70, National Institutes of Health Stroke Scale score at admission, tissue plasminogen activator, living alone (odds ratio [OR], 1.785; 95% confidence interval [CI], 1.155-2.760) and interhospital transfer (OR, 1.898; 95% CI, 1.152-3.127). Delay in identification of AIS was shown significant correlation (OR, 2.440; 95% CI, 1.070-5.561) at living alone patients. Conclusion: This study revealed that AIS patients living alone in the Busan metropolitan region, requiring endovascular treatment, face challenges in the pre-hospital phase, which significantly impact their prognosis.

• Advanced Industrial SCIence
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• v.2 no.4
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• pp.11-16
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• 2023

The subway is a means of public transportation that plays an important role in the transportation system of modern cities. However, congestion often occurs due to sudden breakdowns and system outages, causing inconvenience. Therefore, in this paper, we conducted a study on failure prediction and prevention using machine learning to efficiently operate the subway system. Using UC Irvine's MetroPT-3 dataset, we built a subway breakdown prediction model using logistic regression. The model predicted the non-failure state with a high accuracy of 0.991. However, precision and recall are relatively low, suggesting the possibility of error in failure prediction. The ROC_AUC value is 0.901, indicating that the model can classify better than random guessing. The constructed model is useful for stable operation of the subway system, but additional research is needed to improve performance. Therefore, in the future, if there is a lot of learning data and the data is well purified, failure can be prevented by pre-inspection through prediction.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.59-64
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• 2024

As the aging population grows, the incidence of cancer is increasing. Skin cancer appears externally, but people often don't notice it or simply overlook it. As a result, if the early detection period is missed, the survival rate in the case of late stage cancer is only 7.5-11%. However, the disadvantage of diagnosing, serious skin cancer is that it requires a lot of time and money, such as a detailed examination and cell tests, rather than simple visual diagnosis. To overcome these challenges, we propose an Attention-based CNN model skin cancer classification system. If skin cancer can be detected early, it can be treated quickly, and the proposed system can greatly help the work of a specialist. To mitigate the problem of image data imbalance according to skin cancer type, this skin cancer classification model applies the Over Sampling, technique to data with a high distribution ratio, and adds a pre-learning model without an Attention layer. This model is then compared to the model without the Attention layer. We also plan to solve the data imbalance problem by strengthening data augmentation techniques for specific classes.

• Clinical and Experimental Vaccine Research
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• v.12 no.2
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• pp.97-106
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• 2023

Purpose: Rabies is a fatal but preventable disease with proper pre-exposure anti-rabies vaccination (ARV). Dogs, as household pets and strays, are the reservoir and vector of the disease, and dog bites have been associated with human rabies cases in Sri Lanka over the past few years. However, other susceptible species having frequent contact with humans may be a source of infection. One such species is sheep and immunity following ARV has never been tested in sheep reared in Sri Lanka. Materials and Methods: We have tested serum samples from sheep reared in the Animal Centre, Medical Research Institute of Sri Lanka for the presence of anti-rabies antibodies following ARV. Sheep serum samples were tested with Bio-Pro Rabies enzyme-linked immunosorbent assay (ELISA) antibody kits used for the first time in Sri Lanka and our results were verified by a seroneutralization method on cells (fluorescent antibody virus neutralization, FAVN test) currently recommended by World Organization for Animal Health and World Health Organization. Results: Sheep received annual ARV and maintained high neutralizing antibody titers in their serum. No maternal antibodies were detected in lamb around 6 months of age. Agreement between the ELISA and FAVN test, i.e., coefficient concordance was 83.87%. Conclusion: Annual vaccination in sheep has an effect on maintaining adequate protection against rabies by measurements of anti-rabies antibody response. Lambs need to be vaccinated earlier than 6 months of age to achieve protective levels of neutralizing antibodies in their serum. Introducing this ELISA in Sri Lanka will be a good opportunity to determine the level of anti-rabies antibodies in animal serum samples.

• Journal of Food Hygiene and Safety
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• v.39 no.2
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• pp.72-82
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• 2024

This study aimed to develop a scientifically and systematically standardized xylooligosaccharide analytical method that can be applied to products with various formulations. The analysis method was conducted using HPLC with Cadenza C₁₈ column, involving pre-column derivatization with 1-phenyl-3-methyl-5-pyrazoline (PMP) and UV detection at 254 nm. The xylooligosaccharide content was analyzed by converting xylooligosaccharide into xylose through acid hydrolysis. The pre-treated methods were compared and evaluated by varying sonication time, acid hydrolysis time, and concentration. Optimal equipment conditions were achieved with a mobile phase consisting of 20 mM potassium phosphate buffer (pH 6)-acetonitrile (78:22, v/v) through isocratic elution at a flow rate of 0.5 mL/min (254 nm). Furthermore, we validated the advanced standardized analysis method to support the suitability of the proposed analytical procedure such as specificity, linearity, detection limits (LOD), quantitative limits (LOQ), accuracy, and precision. The standardized analysis method is now in use for monitoring relevant health-functional food products available in the market. Our results have demonstrated that the standardized analysis method is expected to enhance the reliability of quality control for healthy functional foods containing xylooligosaccharide.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.1
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• pp.42-48
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• 2007

Purpose: The purpose of this study is to image metastaic lung melanoma model with optimal pre-conditions for animal handling by using $[^{18}F]FDG$ small animal PET and clinical CT. Materials and Methods: The pre-conditions for lung region tumor imaging were 16-22 h fasting and warming temperature at $30^{\circ}C$. Small animal PET image was obtained at 60 min postinjection of 7.4 MBq $[^{18}F]FDG$ and compared pattern of $[^{18}F]FDG$ uptake and glucose standard uptake value (SUVG) of lung region between Ketamine/Xylazine (Ke/Xy) and Isoflurane (Iso) anesthetized group in normal mice. Metastasis tumor mouse model to lung was established by intravenous injection of B16-F10 cells in C57BL/6 mice. In lung metastasis tumor model, $[^{18}F]FDG$ image was obtained and fused with anatomical clinical CT image. Results: Average blood glucose concentration in normal mice were $128.0{\pm}23.87$ and $86.0{\pm}21.65\;mg/dL$ in Ke/Xy group and Iso group, respectively. Ke/Xy group showed 1.5 fold higher blood glucose concentration than Iso group. Lung to Background ratio (L/B) in SUVG image was $8.6{\pm}0.48$ and $12.1{\pm}0.63$ in Ke/Xy group and Iso group, respectively. In tumor detection in lung region, $[^{18}F]FDG$ image of Iso group was better than that of Ke/Xy group, because of high L/B ratio. Metastatic tumor location in $[^{18}F]FDG$ small animal PET image was confirmed by fusion image using clinical CT. Conclusion: Tumor imaging in small animal lung region with $[^{18}F]FDG$ small animal PET should be considered pre-conditions which fasting, warming and an anesthesia during $[^{18}F]FDG$ uptake. Fused imaging with small animal PET and CT image could be useful for the detection of metastatic tumor in lung region.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.