• Smart Structures and Systems
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• v.30 no.3
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• pp.327-332
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• 2022

In building envelope, transparent components play an important role. The structural glazing systems are the weak element of the casing in terms of mechanical resistance, thermal and acoustic insulation. In the present work, new structural glass panels with granular aerogel in interspace were investigated from different points of view. In particular, the mechanical characterization was carried out in order to assess the resistance to bending of the single glazing pane. To this end, a special instrument system was built to define an alternative configuration of the coaxial double ring test, able to predict the fracture strength of glass large samples (400 × 400 mm) without overpressure. The thermal and lighting performance of an innovative double-glazing façade with granular aerogel was evaluated. An experimental campaign at pilot scale was developed: it is composed of two boxes of about 1.60 × 2 m² and 2 m high together with an external weather station. The rooms, identical in terms of size, construction materials, and orientation, are equipped with a two-wing window in the south wall surface: the first one has a standard glazing solution (double glazing with air in interspace), the second room is equipped with the innovative double-glazing system with aerogel. The indoor mean air temperature and the surface temperature of the glass panes were monitored together with the illuminance data for the lighting characterization. Finally, a brief energy characterization of the performance of the material was carried out by means of dynamic simulation models when the proposed solution is applied to real case studies.

• Journal of The Korean Association of Information Education
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• v.26 no.1
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• pp.35-44
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• 2022

Previous studies showed that an improvement in attitude toward a subject affects academic achievement in the subject. The purpose of this study is to confirm that the AI education program causes changes in the AI attitude of gifted children in elementary school, and that the change in attitude leads to a change in literacy. In this study, we conducted an AI education program for gifted children, and analyze the effect of AI education program on the change in AI attitude AI literacy through pre- and post-test. The result of the analysis showed the statistically significant improvement of AI attitudes. In addition, through regression analysis it was found that the change in AI attitude has a statistically significant direct effect on the change in AI literacy. This study has a limitation in that self-evaluation was used to measure AI attitudes and literacy and conducted to a relatively small number of samples. Even though, it was also confirmed that the activities of making an AI programs experienced in real-life can cause changes in AI attitudes and literacy.

• Tuberculosis and Respiratory Diseases
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• v.84 no.3
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• pp.188-199
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• 2021

Background: Chronic obstructive pulmonary disease (COPD) is a common chronic respiratory disease with increased prevalence in the elderly. Telomeres are repetitive DNA sequences found at the end of the chromosome, which progressively shorten as cells divide. Telomere length is known to be a molecular marker of aging. This study aimed to assess the relationship between telomere length and the risk of COPD, lung function, respiratory symptoms, and emphysema index in Chronic Obstructive Pulmonary Disease in Dusty Areas (CODA) cohort. Methods: We extracted DNA from the peripheral blood samples of 446 participants, including 285 COPD patients and 161 control participants. We measured absolute telomere length using quantitative real-time polymerase chain reaction. All participants underwent spirometry and quantitative computed tomography scan. Questionnaires assessing respiratory symptoms and the COPD Assessment Test was filled by all the participants. Results: The mean age of participants at the baseline visit was 72.5±7.1 years. Males accounted for 72% (321 participants) of the all participants. The mean telomere length was lower in the COPD group compared to the non-COPD group (COPD, 16.81±13.90 kb; non-COPD, 21.97±14.43 kb). In COPD patients, 112 (75.7%) were distributed as tertile 1 (shortest), 91 (61.1%) as tertile 2 and 82 (55%) as tertile 3 (longest). We did not find significant associations between telomere length and lung function, exacerbation, airway wall thickness, and emphysema index after adjusting for sex, age, and smoking status. Conclusion: In this study, the relationship between various COPD phenotypes and telomere length was analyzed, but no significant statistical associations were shown.

• Journal of the Korean Society of Visualization
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• v.20 no.3
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• pp.136-145
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• 2022

Later flow immunoassay (LFIA) is a protein analytical method based on immunoreaction. On the LFIA based protein analytical method, bioreceptor molecule plays a key role, and so a system that evaluates and manages the binding affinity of bioreceptor is needed to secure detection reliability. In this study, Lateral Flow Immunoassay based rapid Bioreceptor Screening Method (rBSM) is presented that provide a simple and quick evaluating method for the binding affinity to the target protein of the antibody as model bioreceptor. To verify this evaluation method, Virus-like particles (VLP) and anti-VLP antibodies are selected as a model norovirus, which is target protein, and the candidate bioreceptors respectively. Among the 5 different candidate antibodies, appropriate antibody could be sorted out within 30 minutes through rBSM. In addition, selected antibodies were applied to two representative LFIA based techniques, sandwich assay and competitive assay. Among these methods, sandwich assay showed more effective VLP detection method. Through applying selected antibodies and techniques to the commercialized mass production lines, an VLP detecting LFIA kit was developed with a detection limit of 10¹² copies/g of VLPs in real samples. Since this proposed method in this study could be easily transformable into other combinations with bioreceptors, it is expected that this technique would be applied to LFIA kit development system and bioreceptor quality management.

• Fisheries and Aquatic Sciences
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• v.25 no.3
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• pp.151-157
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• 2022

Various approaches have been applied to transform aquaculture from a manual, labour-intensive industry to one dependent on automation technologies in the era of the fourth industrial revolution. Technologies associated with the monitoring of physical condition have successfully been applied in most aquafarm facilities; however, real-time biological monitoring systems that can observe fish condition and behaviour are still required. In this study, we used a video recorder placed on top of a fish tank to observe the swimming patterns of rock bream (Oplegnathus fasciatus), first one fish alone and then a group of five fish. Rock bream in the video samples were successfully identified using the you-only-look-once v3 algorithm, which is based on the Darknet-53 convolutional neural network. In addition to recordings of swimming behaviour under normal conditions, the swimming patterns of fish under abnormal conditions were recorded on adding an anaesthetic or lowering the salinity. The abnormal conditions led to changes in the velocity of movement (3.8 ± 0.6 cm/s) involving an initial rapid increase in speed (up to 16.5 ± 3.0 cm/s, upon 2-phenoxyethanol treatment) before the fish stopped moving, as well as changing from swimming upright to dying lying on their sides. Machine learning was applied to datasets consisting of normal or abnormal behaviour patterns, to evaluate the fish behaviour. The proposed algorithm showed a high accuracy (98.1%) in discriminating normal and abnormal rock bream behaviour. We conclude that artificial intelligence-based detection of abnormal behaviour can be applied to develop an automatic bio-management system for use in the aquaculture industry.

• Journal of Veterinary Science
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• v.21 no.3
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• pp.39.1-39.15
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• 2020

Background: There are various Helicobacter species colonizing the stomachs of animals. Although Helicobacter species usually cause asymptomatic infection in the hosts, clinical signs can occur due to gastritis associated with Helicobacter in animals. Among them, Helicobacter pylori is strongly associated with chronic gastritis, gastric ulcers, and gastric cancers. As the standard therapies used to treat H. pylori have proven insufficient, alternative options are needed to prevent and eradicate the diseases associated with this bacterium. Cheonwangbosim-dan (CBD), a traditional herbal formula that is popular in East Asia, has been commonly used for arterial or auricular flutter, neurosis, insomnia, and cardiac malfunction-induced disease. Objectives: The present study investigated the antimicrobial effect of CBD on H. pylori-infected human gastric carcinoma AGS cells and model mice. Methods: AGS cells were infected with H. pylori and treated with a variety of concentrations of CBD or antibiotics. Mice were given 3 oral inoculations with H. pylori and then dosed with CBD (100 or 500 mg/kg) for 4 weeks or with standard antibiotics for 1 week. One week after the last treatment, gastric samples were collected and examined by histopathological analysis, real-time quantitative polymerase chain reaction, and immunoblotting. Results: Our results showed that CBD treatment of AGS cells significantly reduced the H. pylori-induced elevations of interleukin-8, inducible nitric oxide synthase (iNOS), and cyclooxygenase-2 (COX-2). In the animal model, CBD treatment inhibited the colonization of H. pylori and the levels of malondialdehyde, inflammation, proinflammatory cytokines, iNOS, and COX-2 in gastric tissues. CBD also decreased the phosphorylation levels of p38 mitogen-activated protein kinase family. Conclusions: This study suggests that CBD might be a prospective candidate for treating H. pylori-induced gastric injury.

• 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.

• Mass Spectrometry Letters
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• v.13 no.3
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• pp.84-94
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• 2022

Neoadjuvant chemoradiotherapy (nCRT) is a standard therapy used for locally advanced rectal cancer prior to surgery, which can more effectively reduce the locoregional recurrence rate and radiation toxicity compared to postoperative chemoradiotherapy. The response of patients to nCRT varies, and thus, robust biomarkers for predicting a pathological complete response are necessary. This study aimed to identify possible biomarkers involved in the complete response/non-response of rectal cancer patients to nCRT. Comparative proteomic analysis was performed on rectal tissue samples before and after nCRT. Proteins were extracted for label-free proteomic analysis. Western blot and real-time PCR were performed using rectal cancer cell line SNU-503 and radiation-resistant rectal cancer cell line SNU-503R80Gy. A total of 135 up- and 93 down-regulated proteins were identified in the complete response group. Six possible biomarkers were selected to evaluate the expression of proteins and mRNA in SNU-503 and SNU-503R80Gy cell lines. Lyso-phosphatidylcholine acyltransferase 2, annexin A13, aldo-ketose reductase family 1 member B1, and cathelicidin antimicrobial peptide appeared to be potential biomarkers for predicting a pathological complete response to nCRT. This study identified differentially expressed proteins and some potential biomarkers in the complete response group, which would be further validated in future studies.

• Clinical and Experimental Pediatrics
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• v.64 no.12
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• pp.652-660
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• 2021

Background: Viral load and shedding duration are highly associated with the transmission of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. However, limited studies have reported on viral load or shedding in children and adolescents infected with sudden acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Purpose: This study aimed to investigate the natural course of viral load in asymptomatic or mild pediatric cases. Methods: Thirty-one children (<18 years) with confirmed SARS-CoV-2 infection were hospitalized and enrolled in this study. Viral loads were evaluated in nasopharyngeal swab samples using real-time reverse transcription polymerase chain reaction (E, RdRp, N genes). cycle threshold (Ct) values were measured when patients met the clinical criteria to be released from quarantine. Results: The mean age of the patients was 9.8 years, 18 (58%) had mild disease, and 13 (42%) were asymptomatic. Most children were infected by adult family members, most commonly by their mothers. The most common symptoms were fever and sputum (26%), followed by cough and runny nose. Nine patients (29%) had a high or intermediate viral load (Ct value≤30) when they had no clinical symptoms. Viral load showed no difference between symptomatic and asymptomatic patients. Viral rebounds were found in 15 cases (48%), which contributed to prolonged viral detection. The mean duration of viral detection was 25.6 days. Viral loads were significantly lower in patients with viral rebounds than in those with no rebound (E, P=0.003; RdRp, P=0.01; N, P=0.02). Conclusion: Our study showed that many pediatric patients with coronavirus disease 2019 (COVID-19) experienced viral rebound and showed viral detection for more than 3 weeks. Further studies are needed to investigate the relationship between viral rebound and infectiousness in COVID-19.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.181-192
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• 2022

The emergence of new malware is incapacitating existing signature-based malware detection techniques., and applying various anti-analysis techniques makes it difficult to analyze. Recent studies related to signature-based malware detection have limitations in that malware creators can easily bypass them. Therefore, in this study, we try to build a machine learning model that can detect and classify the anti-analysis techniques of packers applied to malware, not using the characteristics of the malware itself. In this study, the n-gram opcodes are extracted from the malicious binary to which various anti-analysis techniques of the commercial packers are applied, and the features are extracted by using TF-IDF, and through this, each anti-analysis technique is detected and classified. In this study, real-world malware samples packed using The mida and VMProtect with multiple anti-analysis techniques were trained and tested with 6 machine learning models, and it constructed the optimal model showing 81.25% accuracy for The mida and 95.65% accuracy for VMProtect.