• Animal Bioscience
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• v.36 no.1
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• pp.98-107
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• 2023

Objective: Current quail production is configured as an economic activity in scale. Advancements in quail nutrition have been limited to areas such as breeding and, automation of facilities and ambience. The objective of this study was to evaluate the performance responses, liver and oviduct morphometry, and liver histology of Japanese laying quails subjected to different levels of nitrogen-corrected apparent metabolizable energy (MEn). Methods: A completely random design was used that consisted of nine levels of MEn, six replicates, and five hens per cage with a total of 270 quails. The experimental period lasted for 10 weeks. The variables of performance were subjected to analysis of variance and then regression analysis using the broken-line model. The morphometric and histological variables were subjected to multivariate exploratory techniques. Results: The MEn levels influenced the responses to zootechnical performance. The broken-line model estimated the maximum responses for feed intake, egg production, egg weight, and egg mass as 3,040, 2,820, 1,802, and 2,960 kcal of MEn per kg of diet, respectively. Multivariate analysis revealed that the occurrence of hepatic steatosis and increased levels of Kupffer cells were not related to MEn levels. Conclusion: The level of 2,960 kcal/kg of MEn meets performance variable requirements without compromising hepatic physiology.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.12
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• pp.445-452
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• 2022

Class distribution of unbalanced data is an important part of the digital world and is a significant part of cybersecurity. Abnormal activity of unbalanced data should be found and problems solved. Although a system capable of tracking patterns in all transactions is needed, machine learning with disproportionate data, which typically has abnormal patterns, can ignore and degrade performance for minority layers, and predictive models can be inaccurately biased. In this paper, we predict target variables and improve accuracy by combining estimates using Synthetic Minority Oversampling Technique (SMOTE) and Light GBM algorithms as an approach to address unbalanced datasets. Experimental results were compared with logistic regression, decision tree, KNN, Random Forest, and XGBoost algorithms. The performance was similar in accuracy and reproduction rate, but in precision, two algorithms performed at Random Forest 80.76% and Light GBM 97.16%, and in F1-score, Random Forest 84.67% and Light GBM 91.96%. As a result of this experiment, it was confirmed that Light GBM's performance was similar without deviation or improved by up to 16% compared to five algorithms.

• Journal of the Korean Wood Science and Technology
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• v.50 no.4
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• pp.219-236
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• 2022

Wood-based nanocomposite electrode materials were synthesized for application in supercapacitors by mixing nanostructured hematite (Fe₂O₃) with highly porous activated carbon (AC) produced from the wood-waste of Pinus roxburghii. The AC was characterized using various instrumental techniques and the results showed admirable electrochemical properties, such as high surface area and reasonable porosity. Firstly, AC was tested as an electrode material for supercapacitors and it showed a specific capacitance of 59.02 Fg^-1 at a current density of 1 Ag^-1, cycle life of 84.2% after 1,000 cycles (at a current density of 3 Ag^-1), and energy density of 5.1 Wh/kg at a power density of 135 Wkg^-1. However, when the AC was composited with different ratios of Fe₂O₃ (1:1, 2:1, and 1:2), there was an overall improvement in its electrochemical performance. Among the 3 ratios, 2:1 (AC:Fe₂O₃) had the best specific capacitance of 102.42 Fg^-1 at 1 Ag^-1, cycle life of 94.4% capacitance after 1,000 cycles (at a current density of 3 Ag^-1), and energy density of 8.34 Wh/kg at a power density of 395.15 Wkg^-1 in 6 M KOH electrolyte in a 3-electrode experimental setup with a high working voltage of 1.55 V. Furthermore, when Fe₂O₃ was doubled, 1:2 (AC:Fe₂O₃), the electrochemical capacitive performance of the electrode twisted and deteriorated due to either the accumulation of Fe₂O₃ particles within the composite or higher bulk resistance value of pure Fe₂O₃.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.555-564
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• 2022

Recently, cyberattacks are using hacking techniques utilizing intelligent and advanced malicious codes for non-face-to-face environments such as telecommuting, telemedicine, and automatic industrial facilities, and the damage is increasing. Traditional information protection systems, such as anti-virus, are a method of detecting known malicious URLs based on signature patterns, so unknown malicious URLs cannot be detected. In addition, the conventional static analysis-based malicious URL detection method is vulnerable to dynamic loading and cryptographic attacks. This study proposes a technique for efficiently detecting malicious URLs by dynamically learning malicious URL data. In the proposed detection technique, malicious codes are classified using machine learning-based feature selection algorithms, and the accuracy is improved by removing obfuscation elements after preprocessing using Weighted Euclidean Distance(WED). According to the experimental results, the proposed machine learning-based malicious URL detection technique shows an accuracy of 89.17%, which is improved by 2.82% compared to the conventional method.

• Journal of Ginseng Research
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• v.46 no.3
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• pp.357-366
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• 2022

Background: Withania somnifera (Solanaceae), generally known as Indian ginseng, is a medicinal plant that is used in Ayurvedic practice for promoting health and longevity. This study aims to identify the bioactive metabolites from Indian ginseng and elucidate their structures. Methods: Withanolides were purified by chromatographic techniques, including HPLC coupled with LC/MS. Chemical structures of isolated withanolides were clarified by analyzing the spectroscopic data from 1D and 2D NMR, and HR-ESIMS experiment. Absolute configurations of the withanolides were established by the application of NMR chemical shifts and ECD calculations. Anti-adipogenic activities of isolates were evaluated using 3T3-L1 preadipocytes with Oil Red O staining and quantitative real-time PCR (qPCR). Results: Phytochemical examination of the roots of Indian ginseng afforded to the isolation of six withanolides (1-6), including three novel withanolides, withasilolides GeI (1-3). All the six compounds inhibited adipogenesis and suppressed the enlargement of lipid droplets, compared to those of the control. Additionally, the mRNA expression levels of Fabp4 and Adipsin, the adipocyte markers decreased noticeably following treatment with 25 µM of 1-6. The active compounds (1-6) also promoted lipid metabolism by upregulating the expression of the lipolytic genes HSL and ATGL and downregulating the expression of the lipogenic gene SREBP1. Conclusion: The results of our experimental studies suggest that the withasilolides identified herein have anti-adipogenic potential and can be considered for the development of therapeutic strategies against adipogenesis in obesity. Our study also provides a mechanistic rationale for using Indian ginseng as a potential therapeutic agent against obesity and related metabolic diseases.

• Steel and Composite Structures
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• v.43 no.1
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• pp.19-30
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• 2022

The static strength and fatigue crack resistance of the aircraft skin structures depend on the materials used and joint type. Most of the commercial aircraft's skin panel structures are made from aluminium alloy and carbon fibre reinforced epoxy. In this study, the fatigue resistance of four joint configurations (metal/metal, metal/composite, composite/composite and composite/metal) with riveted, adhesive bonded, and hybrid joining techniques are investigated with experiments and finite element analysis. The fatigue tests were tension-tension because of the typical nature of the loads on aircraft skin panels susceptible of experimenting fatigue. Experiment results suggest that the fatigue life of hybrid joints is superior to adhesive bonded joints, and these in turn much better than conventional riveted joints. Thanks to the fact that, for hybrid joints, the adhesive bond provides better load distribution and ensures load-carrying capacity in the event of premature adhesive failure while rivets induce compressive residual stresses in the joint. Results from FE tool ABAQUS analysis for adhesive bonded and hybrid joints agrees with the experiments. From the analysis, the energy release rate for adhesive bonded joints is higher than that of hybrid joints in both opening (mode I) and shear direction (mode II). Most joints show higher energy release rate in mode II. This indicates that the joints experience fatigue crack in the shear direction, which is responsible for crack opening.

• Computers and Concrete
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• v.28 no.6
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• pp.549-557
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• 2021

Nowadays, strengthening of buildings is an inclusive and effective field in civil engineering that is not only applicable to the buildings but also it can be developed for the bridges. Rehabilitation and strengthening of structures are highly recommended for the existing structures due to the alter in codes and provisions as well as buildings' use change. Extensive surveys have been conducted in this field in the world that propose wide variety of methods for strengthening of structures. In recent years, more specific researches have been carried out that present novel materials for rehabilitation beside proposing methods and performing techniques. In the current study, a novel technique for developing the bending capacity of reinforced concrete beams to enhance their performance as well as rehabilitating and reforming the performance of reinforced concrete beams with nonstandard lap splices, has been proposed. In this method, a post-tensioned concrete layer is added to the side face of the concrete beams built in 1:1 scale. Results reveals that addition of the post-tensioned layer enhances the beams' performance and covers their weaknesses. In this method, 18 reinforced concrete beams were prepared for the bending test which were subjected to the four-point pushover test after they were reinforced. The testing process ended when the samples reached complete failure status. Results show that the performance and flexural capacity of reinforced beams without lap splice is improved 22.7% compared to the samples without the post-tensioned layer, while it is enhanced up to at least 80% compared to the reinforced beams with nonstandard lap splice. Furthermore, the location of plastic hinges formation was transformed from the beam's mid-span to the 1/3 of span's end and the beam's cracking pattern was significantly improved.

• The Korean Journal of Pain
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• v.35 no.4
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• pp.403-412
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• 2022

Background: Most pain management techniques for challenging procedures are still performed under the guidance of the C-arm fluoroscope although it is sometimes difficult for even experienced clinicians to understand the modified three-dimensional anatomy as a two-dimensional X-ray image. To overcome these difficulties, the development of a virtual simulator may be helpful. Therefore, in this study, the authors developed a virtual simulator and presented its clinical application cases. Methods: We developed a computer program to simulate the actual environment of the procedure. Computed tomography (CT) Digital Imaging and Communications in Medicine (DICOM) data were used for the simulations. Virtual needle placement was simulated at the most appropriate position for a successful block. Using a virtual C-arm, the authors searched for the position of the C-arm at which the needle was visualized as a point. The positional relationships between the anatomy of the patient and the needle were identified. Results: For the simulations, the CT DICOM data of patients who visited the outpatient clinic was used. When the patients revisited the clinic, images similar to the simulated images were obtained by manipulating the C-arm. Transforaminal epidural injection, which was difficult to perform due to severe spinal deformity, and the challenging procedures of the superior hypogastric plexus block and Gasserian ganglion block, were successfully performed with the help of the simulation. Conclusions: We created a pre-procedural virtual simulation and demonstrated its successful application in patients who are expected to undergo challenging procedures.

• Journal of Sasang Constitutional Medicine
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• v.34 no.3
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• pp.31-40
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• 2022

Objectives Sasang constitutional medicine is a traditional Korean medicine that classifies humans into four constitutions in consideration of individual differences in physical, psychological, and physiological characteristics. In this paper, we proposed a method to classify Taeeum person (TE) and Non-Taeeum person (NTE), Soeum person (SE) and Non-Soeum person (NSE), and Soyang person (ST) and Non-Soyang person (NSY) using a convolutional neural network with only facial images. Methods Based on the convolutional neural network VGG16 architecture, transfer learning is carried out on the facial images of 3738 subjects to classify TE and NTE, SE and NSE, and SY and NSY. Data augmentation techniques are used to increase classification performance. Results The classification performance of TE and NTE, SE and NSE, and SY and NSY was 77.24%, 85.17%, and 80.18% by F1 score and 80.02%, 85.96%, and 72.76% by Precision-Recall AUC (Area Under the receiver operating characteristic Curve) respectively. Conclusions It was found that Soeum person had the most heterogeneous facial features as it had the best classification performance compared to the rest of the constitution, followed by Taeeum person and Soyang person. The experimental results showed that there is a possibility to classify constitutions only with facial images. The performance is expected to increase with additional data such as BMI or personality questionnaire.

• Journal of the Society of Disaster Information
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• v.18 no.3
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• pp.433-443
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• 2022

Purpose: In this paper, we propose a study of deep learning algorithms that estimate and predict sarcopenia by exploiting the high penetration rate of smart devices. Method: To utilize deep learning techniques, experimental data were collected by using the inertial sensor embedded in the smart device. We implemented a smart device application for data collection. The data are collected by labeling normal and abnormal gait and five states of running, falling and squat posture. Result: The accuracy was analyzed by comparative analysis of LSTM, CNN, and RNN models, and binary classification accuracy of 99.87% and multiple classification accuracy of 92.30% were obtained using the CNN-LSTM fusion algorithm. Conclusion: A study was conducted using a smart sensoring device, focusing on the fact that gait abnormalities occur for people with sarcopenia. It is expected that this study can contribute to strengthening the safety issues caused by sarcopenia.