• Journal of Digital Convergence
• /
• v.20 no.2
• /
• pp.203-215
• /
• 2022

The study is to investigate research trends and knowledge structures in the Smart Farm field. To achieve the research purpose, keywords and the relationship among keywords were analyzed targeting 104 Korean academic journals related to the Smart Farm in KCI(Korea Citation Index), and topics were analyzed using the LDA Topic Modeling technique. As a result of the analysis, the main keywords in the Korean Smart Farm-related research field were 'environment', 'system', 'use', 'technology', 'cultivation', etc. The results of Degree, Betweenness, and Eigenvector Centrality were presented. There were 7 topics, such as 'Introduction analysis of Smart Farm', 'Eco-friendly Smart Farm and economic efficiency of Smart Farm', 'Smart Farm platform design', 'Smart Farm production optimization', 'Smart Farm ecosystem', 'Smart Farm system implementation', and 'Government policy for Smart Farm' in the results of Topic Modeling. This study will be expected to serve as basic data for policy development necessary to advance Korean Smart Farm research in the future by examining research trends related to Korean Smart Farm.

• Nuclear Engineering and Technology
• /
• v.54 no.3
• /
• pp.842-848
• /
• 2022

Parametric studies of heat transfer and fluid flow are very important research of interest because the design and operation of fluid flow and heat transfer systems are guided by these parametric studies. The safety of the system operation and system optimization can be determined by decreasing or increasing particular fluid flow and heat transfer parameter while keeping other parameters constant. The parameters that can be varied in order to determine safe and optimized system include system pressure, mass flow rate, heat flux and coolant inlet temperature among other parameters. The fluid flow and heat transfer systems can also be enhanced by the presence of or without the presence of particular effects including gravity effect among others. The advanced Generation IV reactors to be deployed for large electricity production, have proven to be more thermally efficient (approximately 45% thermal efficiency) than the current light water reactors with a thermal efficiency of approximately 33 ℃. SCWR is one of the Generation IV reactors intended for electricity generation. High Performance Light Water Reactor (HPLWR) is a SCWR type which is under consideration in this study. One-eighth of a proposed fuel assembly design for HPLWR consisting of 7 fuel/rod bundles with 9 coolant sub-channels was the geometry considered in this study to examine the effects of system pressure and mass flow rate on wall and fluid temperatures. Gravity effect on wall and fluid temperatures were also examined on this one-eighth fuel assembly geometry. Computational Fluid Dynamics (CFD) code, STAR-CCM+, was used to obtain the results of the numerical simulations. Based on the parametric analysis carried out, sub-channel 4 performed better in terms of heat transfer because temperatures predicted in sub-channel 9 (corner subchannel) were higher than the ones obtained in sub-channel 4 (central sub-channel). The influence of system mass flow rate, pressure and gravity seem similar in both sub-channels 4 and 9 with temperature distributions higher in sub-channel 9 than in sub-channel 4. In most of the cases considered, temperature distributions (for both fluid and wall) obtained at 25 MPa are higher than those obtained at 23 MPa, temperature distributions obtained at 601.2 kg/h are higher than those obtained at 561.2 kg/h, and temperature distributions obtained without gravity effect are higher than those obtained with gravity effect. The results show that effects of system pressure, mass flowrate and gravity on fluid flow and heat transfer are significant and therefore parametric studies need to be performed to determine safe and optimum operating conditions of fluid flow and heat transfer systems.

• Journal of Animal Reproduction and Biotechnology
• /
• v.37 no.4
• /
• pp.292-297
• /
• 2022

Direct injection of CRISPR/Cas9 into zygotes enables the production of genetically modified nonhuman primates (NHPs) essential for modeling specific human diseases, such as Usher syndrome, and for developing novel therapeutic strategies. Usher syndrome is a rare genetic disease that causes loss of hearing, retinal degeneration, and problems with balance, and is attributed to a mutation in MYO7A, a gene that encodes an uncommon myosin motor protein expressed in the inner ear and retinal photoreceptors. To produce an Usher syndrome type 1B (USH1B) rhesus macaque model, we disrupted the MYO7A gene in developing zygotes. Identification of appropriately edited MYO7A embryos for knockout embryo transfer requires sequence analysis of material recovered from a trophectoderm (TE) cell biopsy. However, the TE biopsy procedure is labor intensive and could adversely impact embryo development. Recent studies have reported using cell-free DNA (cfDNA) from embryo culture media to detect aneuploid embryos in human in vitro fertilization (IVF) clinics. The cfDNA is released from the embryo during cell division or cell death, suggesting that cfDNA may be a viable resource for sequence analysis. Moreover, cfDNA collection is not invasive to the embryo and does not require special tools or expertise. We hypothesized that selection of appropriate edited embryos could be performed by analyzing cfDNA for MYO7A editing in embryo culture medium, and that this method would be advantageous for the subsequent generation of genetically modified NHPs. The purpose of this experiment is to determine whether cfDNA can be used to identify the target gene mutation of CRISPR/Cas9 injected embryos. In this study, we were able to obtain and utilize cfDNA to confirm the mutagenesis of MYO7A, but the method will require further optimization to obtain better accuracy before it can replace the TE biopsy approach.

• Journal of Life Science
• /
• v.33 no.7
• /
• pp.593-604
• /
• 2023

The ocean accounts for over 70% of the Earth's surface and is a space of largely unexplored unknowns and opportunities. Korea is a peninsula surrounded by the sea on three sides, emphasizing the importance of marine research. The ocean has an extremely complex environment with immense biological diversity. In terms of microbiology, the marine environment has varying factors like extreme temperature, pressure, solar radiation, salt concentration, and pH, providing ecologically unique habitats. Due to this variety, marine organisms have very different phylogenetic classifications compared with terrestrial organisms. Although various microorganisms inhabit the ocean, studies on the diversity, isolation, and cultivation of marine microorganisms and the secondary metabolites they produce are still insufficient. Research on bioactive substances from marine microorganisms, which were rarely studied until the 1990s, has accelerated in terms of natural products from marine Actinomycetes since the 2000s. Since then, industries for bioplastic and biofuel production, carbon dioxide capture, probiotics, and pharmaceutical discovery and development of antibacterial, anticancer, antioxidant, and anti-inflammatory drugs using bacteria, archaea, and algae have significantly grown. In this review, we introduce current research findings and the latest trends in life science and biotechnology using marine microorganisms. Through this article, we hope to create consumer awareness of the importance of basic and applied research in various natural product-related discovery fields other than conventional pharmaceutical drug discovery. The article aims to suggest pathways that may boost research on the optimization and application of future marine-derived materials.

• Journal of Animal Science and Technology
• /
• v.65 no.4
• /
• pp.779-791
• /
• 2023

This study aimed to assess the effects of embryonic developmental stage, quality grade, and fresh or frozen/thawed conditions on the pregnancy rate and sex ratio of live offspring in Hanwoo (Bos taurus coreanae) cows. The quality and developmental stage of in vivo-derived (IVD) transferred embryos were evaluated using the standard criteria of the International Embryo Technology Society. The recipient cows were synchronized using conventional (estradiol benzoate and progesterone) protocols before embryo transfer. Embryos were transferred to 297 cows, and pregnancy was monitored for 60-70 days after embryo transfer. The pregnancy rates of fresh and frozen/thawed embryos were 56.90% and 52.49%, respectively. Pregnancy rates varied according to embryo quality (56.18% for grade 1 vs. 36.67% for grade 2). Pregnancy rates also varied by developmental stage and cryopreservation (67.86% vs. 63.49% for stage 4-1, 64.00% vs. 54.72% for 5-1, and 50.00% vs. 47.83% for 6-1, in fresh embryos vs. frozen/thawed embryos, respectively). For stage 7-1, the pregnancy rates were 72.73% for fresh embryos and 20.00% for frozen/thawed embryos. In 66 fresh embryos, the sex ratio of live offspring was 5:5, whereas it was 4(female):6(male) for frozen/thawed embryos among the 95 frozen/thawed embryos. The miscarriage rate was approximately 3% higher for frozen/thawed embryos than for fresh embryos (18.1% for fresh vs. 21.1% for frozen). Seasonal fertility rates were 33.3% in spring, 55.67% in summer, 52.8% in autumn, 60.0% in winter. The following male-to-female ratios were observed in different seasons: 6.7:3.3 in spring, 4.0:6.0 in summer, 5.5:4.5 in autumn, and 3.3:6.7 in winter. The current data revealed no significant differences in pregnancy rates between fresh and frozen/thawed IVD embryos. However, there was a lower pregnancy rate with advanced-stage frozen/thawed embryos (stage 7-1). The current study provides comprehensive results for the better optimization of embryo transfer in Hanwoo cattle to obtain the desired fertility rate, pregnancy rate, and sex ratio of calves. These results provide important insights into the factors that influence the viability and success of IVD embryo transfer in Hanwoo cows and may have practical applications for improving breeding programs and reducing production costs.

• Korean Chemical Engineering Research
• /
• v.61 no.4
• /
• pp.542-549
• /
• 2023

Since the growing interest in surrogate modeling, there has been continuous research aimed at simulating nonlinear chemical processes using data-driven machine learning. However, the opaque nature of machine learning models, which limits their interpretability, poses a challenge for their practical application in industry. Therefore, this study aims to analyze chemical processes using Explainable Artificial Intelligence (XAI), a concept that improves interpretability while ensuring model accuracy. While conventional sensitivity analysis of chemical processes has been limited to calculating and ranking the sensitivity indices of variables, we propose a methodology that utilizes XAI to not only perform global and local sensitivity analysis, but also examine the interactions among variables to gain physical insights from the data. For the ammonia synthesis process, which is the target process of the case study, we set the temperature of the preheater leading to the first reactor and the split ratio of the cold shot to the three reactors as process variables. By integrating Matlab and Aspen Plus, we obtained data on ammonia production and the maximum temperatures of the three reactors while systematically varying the process variables. We then trained tree-based models and performed sensitivity analysis using the SHAP technique, one of the XAI methods, on the most accurate model. The global sensitivity analysis showed that the preheater temperature had the greatest effect, and the local sensitivity analysis provided insights for defining the ranges of process variables to improve productivity and prevent overheating. By constructing alternative models for chemical processes and using XAI for sensitivity analysis, this work contributes to providing both quantitative and qualitative feedback for process optimization.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.6
• /
• pp.993-1000
• /
• 2023

Recently, enviromentally friendly natural substances derived from plants have been attracting attention as cosmetic materials, and research on various physiological activities of natural substances is being actively conducted. This study investigated the antioxidant, anti-inflammatory, moisturizing, and antibacterial effects of three types of extracts of mixtures containing different mixing ratios, Polygala tenuifolia, Angelica dahurica, and Elsholtzia splendens, known to have various physiological activities. The mixing ratio is 7 conditions (M1, 1:1:1; M2, 0.5:1.5:1; M3, 1.5:0.5:1; M4, 0.1:0.95:0.95; M5, 0.5:0.5:2; M6, 0.95 :1.95:0.1; M7, 1.45:0.1:1.45), and the optimal mixing ratio was confirmed for use as a cosmetic material. DPPH and ABTS radical scavenging activities showed scavenging abilities of 75.37% and 99.19%, respectively, at 1,000 ㎍/mL of M6. At a concentration of 200 ㎍/mL of M6, it showed 50% of nitric oxide production inhibition compared to the lipopolysaccharide-treated that induced an inflammatory response. It was confirmed that M3 and M6 produced hyaluronic acid 1.47 and 1.49 times higher than the control at a concentration of 50 ㎍/mL, respectively. Through the disc diffiusion test, the clear zone was 9.75 mm at 8 ㎍/mL of M6, confirming the inhibition of growth of staplylococcus aureus strain. Based on the above results, it is believed that the mixed extract of Polygala tenuifolia, Angelica dahurica, and Elsholtzia splendens can be used as a functional natural material for cosmetics.

• Geomechanics and Engineering
• /
• v.35 no.2
• /
• pp.121-133
• /
• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• Journal of Marine Bioscience and Biotechnology
• /
• v.15 no.2
• /
• pp.41-48
• /
• 2023

The objective of the present study was to improve the biomass productivity of newly isolated freshwater green microalga Parachlorella sp. This was accomplished by culture conditions optimization, including CO₂ concentration, superficial gas velocity, and light intensity, in 0.5 L bubble column photobioreactors. The supplied CO₂ concentration and gas velocity varied from 0.032% (air) to 10% and 0.02 m/s - 0.11 m/s, respectively, to evaluate their effects on growth kinetics. Next, to maximize the production rate of Parachlorella sp., a lumostatic operation based on a specific light uptake rate (q_e) was applied. From these results, the optimal CO₂ concentration in the supplied gas and the gas velocity were determined to be 5% and 0.064 m/s, respectively. For the lumostatic operation at 10.2 µmol/g/s, biomass productivity and photon yield showed significant increases of 83% and 66%, respectively, relative to cultures under constant light intensity. These results indicate that the biomass productivity of Parachlorella sp. can be improved by optimizing gas properties and light control as cell concentrations vary over time.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.5
• /
• pp.935-940
• /
• 2023

Digital-twin technology is emerging as an innovative solution for all industries, including manufacturing and production lines. Therefore, this paper optimizes all the energy used in a biomass plant based on unused resources. We will then implement a digital-twin prototype for biomass plants and evaluate its performance in order to improve the efficiency of plant operations. The proposed digital-twin prototype applies a standard communication platform between the framework and the gateway and is implemented to enable real-time collaboration. and, define the message sequence between the client server and the gateway. Therefore, an interface is implemented to enable communication with the host server. In order to verify the performance of the proposed prototype, we set up a virtual environment to collect data from the server and perform a data collection evaluation. As a result, it was confirmed that the proposed framework can contribute to energy optimization and improvement of operational efficiency when applied to biomass plants.