• 대한한방내과학회지
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• 제41권6호
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• pp.1015-1029
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• 2020

Purpose: This study assessed the validity of a questionnaire (SEID-Q27) for diagnosis of chronic fatigue syndrome (CFS), designed based on the systematic exertion intolerance disorder (SEID) criteria. Methods: Two groups of participants were recruited: 1. a non-CFS control group: adult university personnel with a Chalder fatigue scale (CFQ) score ≥15, and 2. a CFS patient group: patients from an online CFS community diagnosed with CFS (diagnostic code of G93.3) with the symptoms present. The survey ran from September to October, 2020. The validity of the questionnaire was investigated by factor analysis and receiver operator characteristic (ROC) analysis. Results: Among the 35 adults surveyed, 30 (86%) participants had CFQ scores ≥15 (15 males and 15 females) and 5 (14%) had a CFS diagnosis (2 males and 3 females). The total mean score was significantly different between the two groups (CFS: 6.8±2.2 vs. control: 4.4±2.4, p<0.05). The area under the curve (AUC) was 0.827. With a cut-off point 62, the sensitivity was 60%, specificity 97%, and the discriminant ability of the survey was 79%. Conclusions: The SEID -Q27 appears to be a useful instrument for the diagnosis of CFS using SEID criteria. However, further large-scale studies are needed with greater numbers of participants.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.406-412
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• 2022

Storing large amounts of data has always been a big problem from the beginning of computing history. Big Data has made huge advancements in improving business processes by finding the customers' needs using prediction models based on web and social media search. The main purpose of big data stream processing frameworks is to allow programmers to directly query the continuous stream without dealing with the lower-level mechanisms. In other words, programmers write the code to process streams using these runtime libraries (also called Stream Processing Engines). This is achieved by taking large volumes of data and analyzing them using Big Data frameworks. Streaming platforms are an emerging technology that deals with continuous streams of data. There are several streaming platforms of Big Data freely available on the Internet. However, selecting the most appropriate one is not easy for programmers. In this paper, we present a detailed description of two of the state-of-the-art and most popular streaming frameworks: Apache Ignite and Hazelcast. In addition, the performance of these frameworks is compared using selected attributes. Different types of databases are used in common to store the data. To process the data in real-time continuously, data streaming technologies are developed. With the development of today's large-scale distributed applications handling tons of data, these databases are not viable. Consequently, Big Data is introduced to store, process, and analyze data at a fast speed and also to deal with big users and data growth day by day.

• Journal of Korean Neurosurgical Society
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• 제65권4호
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• pp.507-513
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• 2022

Objective : The goal of the following statewide age and gender-coordinated cohort study in Korea is to find out if there is a link between acute myocardial infarction (AMI) and Parkinson's disease (PD). Methods : Utilizing the National Health Insurance Sharing Service cohort, patient data were collected. Six thousand four hundred seventy-five individuals with PD were distinguished by utilizing the International Classification of Diseases 10 code G20 and have enrolled in the PD group. The number of participants decreased to 5259 after excluding 1039 patients who were hospitalized less than one time or who visited an outpatient clinic less than twice. Then, 26295 individuals were selected as part of the control group after case control matching was conducted through 1 : 5 age- and gender-coordinated matching. The Cox proportional hazard regression analysis and Kaplan-Meier method were utilized to analyze the likelihood of AMI in PD. Results : After controlling for age and gender, the hazard ratio of AMI in the PD group was 3.603 (95% confidence interval [CI], 2.837-4.577). After that, the following hazard ratio of AMI in the PD group was modified against for co-morbid medical disorders, resulting in 3.551 (95% CI, 2.795-4.511). According to a subgroup analysis, in males and females aged <65 and aged ≥65 and in the non-diabetes and diabetes, hypertension and non-hypertension, dyslipidemia and non-dyslipidemia subgroups, the AMI incidence rates were dramatically higher in the PD group compared to that of the control. Conclusion : Individuals with PD have a greater chance of AMI, according to this cross-national study.

• Nuclear Engineering and Technology
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• 제53권7호
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• pp.2184-2194
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• 2021

The Free-Piston Stirling engine (FPSE) is of interest for many research in aerospace due to its advantages of long operating life, higher efficiency, and zero maintenance. In this study, a 1-kW FPSE was proposed by analyzing the requirements of Space Reactor Power Systems (SRPS), of which performance was evaluated by developing a code through the Simple Analysis Method. The results of SAM showed that the critical parameters of FPSE could satisfy the designed requirements. The heater of the FPSE was designed with the copper rectangular fins to enhance heat transfer, and the parametric study of the heater was performed with Computational Fluid Dynamics (CFD) software STAR-CCM⁺. The Performance Evaluation Criteria (PEC) was used to evaluate the heat transfer enhancement of the fins in the heater. The numerical results of the CFD program showed that pressure drop and Nusselt number ratio had a linear growth with the height of fins, and PEC number decreased as the height of fins increased, and the optimum height of the fin was set as 4 mm according to the minimum heat exchange surface area. This paper can provide theoretical supports for the design and numerical analysis of an FPSE for SRPSs.

• 한국수산과학회지
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• 제55권4호
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• pp.431-436
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• 2022

For the safety assessment of microbiological and chemical hazards in katsuobushi, fifteen samples of katsuobushi were purchased from supermarkets. The contamination levels of total viable bacteria, coliforms, Escherichia coli, and nine pathogenic bacteria [Staphylococcus aureus, Salmonella spp., Listeria monocytogenes, Bacillus cereus, Vibrio parahaemolyticus, Clostridium perfringens, Enterohemorrhagic E. coli (EHEC), Yersinia enterocolitica and Campylobacter jejuni/coli] were quantitatively or qualitatively assessed. Additionally, the heavy metals (total and methyl mercury) content, radioactivity (¹³¹ I, ¹³⁴ Cs⁺ and ¹³⁷ Cs) were quantitatively assessed. Microbial and chemical analyses were performed using standard methods in Korean food code. The contamination level of total viable bacteria was 2.70 (1.18-4.42) log CFU/g. Coliforms, E. coli and S. aureus were not detected in any samples. Other eight pathogenic bacteria were negative in all samples. The contamination levels of total and methyl mercury were 0.366 (0.227-0.481) and 0.120 (0.002-0.241) mg/kg, respectively. In addition, radioactivity was not detected in any samples. The results will be helpful in revitalizing domestic use and boosting exports of katsuobushi because the microbiological and chemical safety of katsuobushi has been assured. Furthermore, the results may be used as a basis for performing chemical and microbial risk assessments of katsuobushi.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.177-189
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• 2023

Malware detection is an increasingly important operational focus in cyber security, particularly given the fast pace of such threats (e.g., new malware variants introduced every day). There has been great interest in exploring the use of machine learning techniques in automating and enhancing the effectiveness of malware detection and analysis. In this paper, we present a deep recurrent neural network solution as a stacked Long Short-Term Memory (LSTM) with a pre-training as a regularization method to avoid random network initialization. In our proposal, we use global and short dependencies of the inputs. With pre-training, we avoid random initialization and are able to improve the accuracy and robustness of malware threat hunting. The proposed method speeds up the convergence (in comparison to stacked LSTM) by reducing the length of malware OpCode or bytecode sequences. Hence, the complexity of our final method is reduced. This leads to better accuracy, higher Mattews Correlation Coefficients (MCC), and Area Under the Curve (AUC) in comparison to a standard LSTM with similar detection time. Our proposed method can be applied in real-time malware threat hunting, particularly for safety critical systems such as eHealth or Internet of Military of Things where poor convergence of the model could lead to catastrophic consequences. We evaluate the effectiveness of our proposed method on Windows, Ransomware, Internet of Things (IoT), and Android malware datasets using both static and dynamic analysis. For the IoT malware detection, we also present a comparative summary of the performance on an IoT-specific dataset of our proposed method and the standard stacked LSTM method. More specifically, of our proposed method achieves an accuracy of 99.1% in detecting IoT malware samples, with AUC of 0.985, and MCC of 0.95; thus, outperforming standard LSTM based methods in these key metrics.

• Earthquakes and Structures
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• 제25권3호
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• pp.199-208
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• 2023

Seismic performance analysis is one of the fundamental steps in the design of new or retrofitting buildings. In the seismic performance analysis, the adapted spectral acceleration curve for a given site mainly governs the seismic behavior of buildings. Since every soil site (class) has a different impact on the spectral accelerations of input motions, different spectral acceleration curves have to be involved for every soil class that the building is located on top of. Modern seismic design codes (e.g., Eurocode 8, EC8, or Turkish Building Earthquake Code, TBEC) provide design response spectra for all the soil classes to be used in the building design or retrofitting. This research aims to evaluate the EC8 and TBEC based design response spectra using the spectra of real earthquake input motions that occurred (and were recorded at only soil classes A, B and C, no recording is available at soil class D) in a specific area in Turkey. It also conducts response spectrum analyses of 5, 10 and 13 floor reinforced concrete building models under EC8, TBEC and actual spectral response curves. The results indicate that the EC8 and especially TBEC given design response spectra cannot be able to represent the mean actual spectral acceleration curves at soil classes A, B and C. This is particularly observed at periods higher than 0.3 s, 0.42 s and 0.55 s for the TBEC design response spectra, 0.54 s, 0.65 s and 0.84 s for the EC8 design response spectra at soil classes A, B and C, respectively. This is also reflected to the shear forces of three building models, as actual spectral acceleration curves lead to the highest shear forces, followed by the shear forces obtained from EC8 and, then, the TBEC design response spectra.

• Nuclear Engineering and Technology
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• 제55권5호
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• pp.1734-1741
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• 2023

This work presents the synthesis and preparation of a new glass system described by the equation of (70-x) B₂O₃-5TeO₂ -20SrCO₃-5ZnO -xBi₂O₃, x = 0, 1, 5, 10, and 15 mol. %, using the melt quenching technique at a melting temperature of 1100 ℃. The photon-shielding characteristics mainly the linear attenuation coefficient (LAC) of the prepared glass samples were evaluated using Monte Carlo (MC) simulation N-particle transport code (MCNP-5) at gamma-ray energy extended from 59 keV to 1408 keV emitted by the radioisotopes Am-241, Ba-133, Cs-137, Co-60, Na-22, and Eu-152. Furthermore, we observed that the Bi₂O₃ content of the glasses had a significantly stronger impact on the LAC at 59 and 356 keV. The study of the lead equivalent thickness shows that the performance of fabricated glass sample with 15 mol.% of Bi₂O₃ is four times less than the performance of pure lead at low gamma photon energy while it is enhanced and became two times lower the perforce of pure lead at high energy. Therefore, the fabricated glasses special sample with 15 mol.% of Bi₂O₃ has good shielding properties in low, intermediate, and high energy intervals.

• Genomics & Informatics
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• 제21권1호
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• pp.4.1-4.18
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• 2023

Coronavirus disease 2019 (COVID-19) is an inflammatory and infectious disease caused by severe acute respiratory syndrome coronavirus 2 virus with a complex pathophysiology. While COVID-19 vaccines and boosters are available, treatment of the disease is primarily supportive and symptomatic. Several research have suggested the potential of herbal medicines as an adjunctive treatment for the disease. A popular herbal medicine approved in the Philippines for the treatment of acute respiratory disease is Vitex negundo L. In fact, the Department of Science and Technology of the Philippines has funded a clinical trial to establish its potential as an adjunctive treatment for COVID-19. Here, we utilized network pharmacology and molecular docking in determining pivotal targets of Vitex negundo compounds against COVID-19. The results showed that significant targets of Vitex negundo compounds in COVID-19 are CSB, SERPINE1, and PLG which code for cathepsin B, plasminogen activator inhibitor-1, and plasminogen, respectively. Molecular docking revealed that α-terpinyl acetate and geranyl acetate have good binding affinity in cathepsin B; 6,7,4-trimethoxyflavanone, 5,6,7,8,3',4',5'-heptamethoxyflavone, artemetin, demethylnobiletin, gardenin A, geranyl acetate in plasminogen; and 7,8,4-trimethoxyflavanone in plasminogen activator inhibitor-1. While the results are promising, these are bound to the limitations of computational methods and further experimentation are needed to completely establish the molecular mechanisms of Vitex negundo against COVID-19.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.75-84
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• 2020

Modular construction is a construction method whereby prefabricated volumetric units are produced in a factory and are installed on site to form a building block. The construction productivity can be substantially improved by the manufacturing and assembly of standardized modular units. 3D printing is a computer-controlled fabrication method first adopted in the manufacturing industry and was utilized for the automated construction of small-scale houses in recent years. Implementing 3D printing in the fabrication of modular units brings huge benefits to modular construction, including increased customization, lower material waste, and reduced labor work. Such implementation also benefits the large-scale and wider adoption of 3D printing in engineering practice. However, a critical issue for 3D printed modules is the loading capacity, particularly in response to horizontal forces like wind load, which requires a deeper understanding of the building structure behavior and the design of load-bearing modules. Therefore, this paper presents the state-of-the-art literature concerning recent achievement in 3D printing for buildings, followed by discussion on the opportunities and challenges for examining 3D printing in modular construction. Promising 3D printing techniques are critically reviewed and discussed with regard to their advantages and limitations in construction. The appropriate structural form needs to be determined at the design stage, taking into consideration the overall building structural behavior, site environmental conditions (e.g., wind), and load-carrying capacity of the 3D printed modules. Detailed finite element modelling of the entire modular buildings needs to be conducted to verify the structural performance, considering the code-stipulated lateral drift, strength criteria, and other design requirements. Moreover, integration of building information modelling (BIM) method is beneficial for generating the material and geometric details of the 3D printed modules, which can then be utilized for the fabrication.