• Animal Bioscience
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• v.35 no.11
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• pp.1635-1648
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• 2022

Thermal stress due to extreme changes in the thermal environment is a critical issue in cattle production. Many previous findings have shown a decrease in feed intake, milk yield, growth rate, and reproductive efficiency of cattle when subjected to thermal stress. Therefore, selecting thermo-tolerant animals is the primary goal of the efficiency of breeding programs to reduce those adverse impacts. The recent advances in molecular genetics have provided significant breeding advantages that allow the identification of molecular markers in both beef and dairy cattle breeding, including marker-assisted selection (MAS) as a tool in selecting superior thermo-tolerant animals. Single-nucleotide polymorphisms (SNPs), which can be detected by DNA sequencing, are desirable DNA markers for MAS due to their abundance in the genome's coding and non-coding regions. Many SNPs in some genes (e.g., HSP70, HSP90, HSF1, EIF2AK4, HSBP1, HSPB8, HSPB7, MYO1A, and ATP1A1) in various breeds of cattle have been analyzed to play key roles in many cellular activities during thermal stress and protecting cells against stress, making them potential candidate genes for molecular markers of thermotolerance. This review highlights the associations of SNPs within these genes with thermotolerance traits (e.g., blood biochemistry and physiological responses) and suggests their potential use as MAS in thermotolerant cattle breeding.

• Coupled systems mechanics
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• v.11 no.1
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• pp.79-91
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• 2022

Models for water treatment processes include simulation, i.e., modelling of water quality, flow hydraulics, process controls and design. Water treatment processes are inherently dynamic because of the large variations in the influent water flow rate, concentration and composition. Moreover, these variations are to a large extent not possible to control. Mathematical models and computer simulations are essential to describe, predict and control the complicated interactions of the water treatment processes. An accurate description of such systems can therefore result in highly complex models, which may not be very useful from a practical, operational point of view. The main objective is to combine knowledge of the process dynamics with mathematical methods for processes estimation and identification. Good modelling practice is way to obtain this objective and to improve water treatment processes(its understanding, design, control and performance- efficiency). By synthesize of existing knowledge and experience on good modelling practices and principles the aim is to help address the critical strategic gaps and weaknessesin water treatment models application.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.213-220
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• 2023

Face recognition is a very important process in video monitoring and is a type of biometric technology. It is mainly used for identification and security purposes, such as ID cards, licenses, and passports. The recognition process has many variables and is complex, so development has been slow. In this paper, we proposed a face recognition method using CNN, which has been re-examined due to the recent development of computers and algorithms, and compared with the feature comparison method, which is an existing face recognition algorithm, to verify performance. The proposed face search method is divided into a face region extraction step and a learning step. For learning, face images were standardized to 50×50 pixels, and learning was conducted while minimizing unnecessary nodes. In this paper, convolution and polling-based techniques, which are one of the deep learning technologies, were used for learning, and 1,000 face images were randomly selected from among 7,000 images of Caltech, and as a result of inspection, the final recognition rate was 98%.

• The Korean Journal of Mycology
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• v.50 no.4
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• pp.255-262
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• 2022

In this study, the fungal strain KNUF-21-F39 was isolated from a declined apple tree (Malus domestica) in the Chungcheongbuk province in Korea. The strain KNUF-21-F39 presented a slow growth rate and a variety of macroconidia shapes and sizes ranging from ovoid to fusoid and 1- to 5-septate, primarily showing 3- and 4-septate, with "S" -shaped macroconidia rarely observed. The strain was identified based on morphological characteristics along with phylogenetic analysis performed using the internal transcribed spacer region (ITS) and partial sequences of translation elongation factor 1-α (tef1), RNA polymerase largest subunit (rpb1), and calmodulin (cal) genes. The fungal strain KNUF-21-F39 was identified as Fusarium diversisporum, which has not been previously reported in Korea. The ice nucleation activity (INA) of the strain was also evaluated, identifying the strain as positive for INA. This is the first report characterizing F. diversisporum as an IN-active fungal species.

• ETRI Journal
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• v.44 no.1
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• pp.135-146
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• 2022

The importance of big data analytics has become apparent with the increasing volume of data on the Internet. The amount of data will increase even more with the widespread use of Internet of Things (IoT). One of the most important application areas of the IoT is healthcare. This study introduces new real-time data analytics architecture for an IoT-based smart healthcare system, which consists of a wireless sensor network and a radio-frequency identification technology in a vertical domain. The proposed platform also includes high-performance data analytics tools, such as Kafka, Spark, MongoDB, and NodeJS, in a horizontal domain. To investigate the performance of the system developed, a diagnosis of Wolff-Parkinson-White syndrome by logistic regression is discussed. The results show that the proposed IoT data analytics system can successfully process health data in real-time with an accuracy rate of 95% and it can handle large volumes of data. The developed system also communicates with a riverbed modeler using Transmission Control Protocol (TCP) to model any IoT-enabling technology. Therefore, the proposed architecture can be used as a time-saving experimental environment for any IoT-based system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.460-462
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• 2021

Food basically must have nutrition and safety. Recently, a number of symptoms of food poisoning occurred in a kindergarten in Ansan, where food safety was suspected. Therefore, the safety of food is more demanding. In this paper, we propose a method to inprove the detector to secure food safety. The proposed method is to learn through the network of convolution neural network (CNN) and Faster region-CNN (Faster R-CNN) and test the images of normal and foreign products. As a result of testing through a deep learning model, the method that used Faster R-CNN in parallel with the existing foreign body detector algorithm showed better detection rate than other methods.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1708-1717
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• 2023

The grade analysis of lead-zinc ore is the basis for the optimal development and utilization of deposits. In this study, a method combining Prompt Gamma Neutron Activation Analysis (PGNAA) technology and machine learning is proposed for lead-zinc mine borehole logging, which can identify lead-zinc ores of different grades and gangue in the formation, providing real-time grade information qualitatively and semi-quantitatively. Firstly, Monte Carlo simulation is used to obtain a gamma-ray spectrum data set for training and testing machine learning classification algorithms. These spectra are broadened, normalized and separated into inelastic scattering and capture spectra, and then used to fit different classifier models. When the comprehensive grade boundary of high- and low-grade ores is set to 5%, the evaluation metrics calculated by the 5-fold cross-validation show that the SVM (Support Vector Machine), KNN (K-Nearest Neighbor), GNB (Gaussian Naive Bayes) and RF (Random Forest) models can effectively distinguish lead-zinc ore from gangue. At the same time, the GNB model has achieved the optimal accuracy of 91.45% when identifying high- and low-grade ores, and the F₁ score for both types of ores is greater than 0.9.

• Korean Journal of Pharmacognosy
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• v.54 no.1
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• pp.38-43
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• 2023

A simple and accurate assay was developed for the quantitative analysis of 1-deoxynojirimycin (1-DNJ) derived from the silkworm (Bombyx mori). Normal-phase high-performance liquid chromatography coupled with an evaporative light scattering detector (HPLC-ELSD) and a hydrophilic interaction liquid chromatography column was used. Various parameters were applied to optimize the analysis method. The limits of detection and quantification of 1-DNJ were 2.97 × 10^-3 and 9.00 × 10^-3 mg/mL, respectively. The calibration curve showed good linearity results. The concentration range and the r² value were 0.0625-1.0 mg/mL and 0.9997, respectively. The accuracy test demonstrated a significantly high recovery rate (89.95-103.22%). The relative standard deviation was ≤ 1.00%. Thus, a method for the accurate identification and quantitative analysis of 1-DNJ in silkworms was developed. Moreover, in this procedure, the process of derivatization of 1-DNJ, which was required in previous experiments, could be eliminated. This technique may be actively utilized for the development of pharmaceuticals and health functional foods using 1-DNJ.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.273-273
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• 2022

The increasing number of Westernized eating patterns based on wheat flour in Korea has led to an increase in the rate of diseases such as obesity and diabetes, which has become a social problem. Wheat consumption is increasing due to changes in eating habits, but domestic wheat has low price competitiveness and has stagnated recently, so it is necessary to secure new resources to differentiate from imported wheat. Resistant starch, a newly created resource in domestic wheat, can act as a prebiotic similar to dietary fiber in the body, inducing microbial changes in the gut and having beneficial effects on metabolic syndrome. Wheat research on resistant starch was carried out based on the breeding of high amylose. A genome-wide association study (GWAS) was used to perform SNP identification and expression analysis related to wheat amylose through phenotype and genotype. 561 wheat core collection gene sources were investigated for amylose content in wheat, and related genes were extracted and analyzed. In the GWAS analysis, the model formulas BLIMK, FarmCPU, GLM, MLM, and MLMM were used to derive results such as QQ plots and Manhattan plots through phenotypic data. Among these models, BLAST was conducted to find the association between the SNPs identified using FarmCPU and genes related to starch, and 15 were found. Using the identified markers, it becomes easier to develop and browse related wheat cultivars according to their amylose content.

• Korean Journal of Veterinary Research
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• v.63 no.4
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• pp.41.1-41.6
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• 2023

Many viruses can infect different types of birds, with poultry being the most susceptible. These viral diseases have a direct negative impact on the poultry industry, with significant economic losses. This study examined a group of the most important viruses that infect backyard chickens in 2 specific areas of Basrah Governorate, south of Iraq. The study analyzed avian influenza viruses (AIVs), Newcastle disease virus (NDV), and infectious bronchitis virus (IBV). Two hundred and ninety oropharyngeal swabs, 150 from Abu Al-Khasib and 140 from Shatt Al-Arab regions in the Basrah governorate, were obtained from backyard chickens with clear respiratory signs. The samples were subjected to viral RNA extraction, and the viral nucleic acids were detected using a reverse transcriptase polymerase chain reaction technique. The overall rate of viral infections was 74.8%, which varied depending on the type of virus: 15.8%, 31.3%, and 27.5% for AIV, NDV, and IBV, respectively. The NDV and IBV had much higher infection rates than that of AIV. In addition, the prevalence of AIV in the Shatt Al Arab district was significantly higher than in the Abul Khasib district. Moreover, there were no significant differences between the NDV and the IBV distributions in either of the targeted regions in this study.