• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.489-498
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• 2023

Advancements in hardware performance and computing technology have facilitated the progress of climate prediction models to address climate change. The Korea Meteorological Administration employs the GloSea6 model with supercomputer technology for operational use. Various universities and research institutions utilize the Low-GloSea6 model, a low-resolution coupled model, on small to medium-scale servers for weather research. This paper presents an analysis using Intel VTune Profiler on Low-GloSea6 to facilitate smooth weather research on small to medium-scale servers. The tri_sor_dp_dp function of the atmospheric model, taking 1125.987 seconds of CPU time, is identified as a hotspot. Nonlinear regression models, a machine learning technique, are applied and compared to existing functions conducting numerical operations. The K-Nearest Neighbors regression model exhibits superior performance with MAE of 1.3637e-08 and SMAPE of 123.2707%. Additionally, the Light Gradient Boosting Machine regression model demonstrates the best performance with an RMSE of 2.8453e-08. Therefore, it is confirmed that applying a nonlinear regression model to the tri_sor_dp_dp function during the execution of Low-GloSea6 could be a viable alternative.

• Animal Bioscience
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• v.37 no.6
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• pp.1001-1006
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• 2024

Objective: This study aimed to investigate the effect of Codonopsis pilosula polysaccharide (CPP) on the motility, mitochondrial integrity, acrosome integrity rate, and antioxidant ability of sheep sperm after preservation at 4℃. Methods: Semen from healthy adult rams were collected and divided into four groups with separate addition of 0, 200, 400, and 1,000 mg/L CPP. Sperm motility was analyzed using the Computer-Assisted Semen Analysis software after preservation at 4℃ for 24, 72, 120, and 168 h. Sperm acrosome integrity rate was analyzed by Giemsa staining at 24, 72, and 120 h, and mitochondrial membrane integrity was analyzed by Mito-Tracker Red CMXRos. The total antioxidant capacity (T-AOC) and malondialdehyde (MDA) content of spermatozoa were measured after 120 h of preservation. Results: The sperm viability and forward-moving sperm under 200 mg/L CPP were significantly higher than that in the control group at 72 h (61.28%±3.89% vs 52.83%±0.70%, 51.53%±4.06% vs 42.84%±1.14%), and 168 h (47.21%±0.85% vs 41.43%±0.37%, 38.68%±0.87% vs 31.68%±0.89%). The percentage of fast-moving sperm (15.03%±1.10% vs 11.39%±1.03%) and slow-moving sperm (23.63%±0.76% vs 20.29%±1.11%) in the 200 mg/L group was significantly higher than control group at 168 h. The mitochondrial membrane integrity of the sperm in the group with 200 mg/L CPP was significantly higher than those in the control group after storage at 4℃ for 120 h (74.76%±2.54% vs 65.67%±4.51%, p<0.05). The acrosome integrity rate in the group with 200 mg/L (87.66%±1.26%) and 400 mg/L (84.00%±2.95%) was significantly higher than those in the control group (80.65%±0.16%) after storage for 24 h (p<0.05). CPP also increased T-AOC and decreased the MDA concentration after preservation at 4℃ (p<0.05). Conclusion: Adding CPP could improve the T-AOC of sperm, inhibit lipid peroxidation, and facilitate semen preservation.

• Advances in nano research
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• v.16 no.4
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• pp.375-394
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• 2024

The extensive utilization of concrete has given rise to environmental concerns, specifically concerning the depletion of river sand. To address this issue, waste deposits can provide manufactured-sand (MS) as a substitute for river sand. The objective of this study is to explore the application of machine learning techniques to facilitate the production of manufactured-sand concrete (MSC) containing stone nano-powder through estimating the splitting tensile strength (STS) containing compressive strength of cement (CSC), tensile strength of cement (TSC), curing age (CA), maximum size of the crushed stone (Dmax), stone nano-powder content (SNC), fineness modulus of sand (FMS), water to cement ratio (W/C), sand ratio (SR), and slump (S). To achieve this goal, a total of 310 data points, encompassing nine influential factors affecting the mechanical properties of MSC, are collected through laboratory tests. Subsequently, the gathered dataset is divided into two subsets, one for training and the other for testing; comprising 90% (280 samples) and 10% (30 samples) of the total data, respectively. By employing the generated dataset, novel models were developed for evaluating the STS of MSC in relation to the nine input features. The analysis results revealed significant correlations between the CSC and the curing age CA with STS. Moreover, when delving into sensitivity analysis using an empirical model, it becomes apparent that parameters such as the FMS and the W/C exert minimal influence on the STS. We employed various loss functions to gauge the effectiveness and precision of our methodologies. Impressively, the outcomes of our devised models exhibited commendable accuracy and reliability, with all models displaying an R-squared value surpassing 0.75 and loss function values approaching insignificance. To further refine the estimation of STS for engineering endeavors, we also developed a user-friendly graphical interface for our machine learning models. These proposed models present a practical alternative to laborious, expensive, and complex laboratory techniques, thereby simplifying the production of mortar specimens.

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1909-1917
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• 2021

Objective: Muscle quantity and quality can be measured with an automated system on CT. However, the effects of contrast phases on the muscle measurements have not been established, which we aimed to investigate in this study. Materials and Methods: Muscle quantity was measured according to the skeletal muscle area (SMA) measured by a convolutional neural network-based automated system at the L3 level in 89 subjects undergoing multiphasic abdominal CT comprising unenhanced phase, arterial phase, portal venous phase (PVP), or delayed phase imaging. Muscle quality was analyzed using the mean muscle density and the muscle quality map, which comprises normal and low-attenuation muscle areas (NAMA and LAMA, respectively) based on the muscle attenuation threshold. The SMA, mean muscle density, NAMA, and LAMA were compared between PVP and other phases using paired t tests. Bland-Altman analysis was used to evaluate the inter-phase variability between PVP and other phases. Based on the cutoffs for low muscle quantity and quality, the counts of individuals who scored lower than the cutoff values were compared between PVP and other phases. Results: All indices showed significant differences between PVP and other phases (p < 0.001 for all). The SMA, mean muscle density, and NAMA increased during the later phases, whereas LAMA decreased during the later phases. Bland-Altman analysis showed that the mean differences between PVP and other phases ranged -2.1 to 0.3 cm² for SMA, -12.0 to 2.6 cm² for NAMA, and -2.2 to 9.9 cm² for LAMA.The number of patients who were categorized as low muscle quantity did not significant differ between PVP and other phases (p ≥ 0.5), whereas the number of patients with low muscle quality significantly differed (p ≤ 0.002). Conclusion: SMA was less affected by the contrast phases. However, the muscle quality measurements changed with the contrast phases to greater extents and would require a standardization of the contrast phase for reliable measurement.

• Korean Journal of Radiology
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• v.22 no.4
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• pp.624-633
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• 2021

Objective: To evaluate the reliability of CT measurements of muscle quantity and quality using variable CT parameters. Materials and Methods: A phantom, simulating the L2-4 vertebral levels, was used for this study. CT images were repeatedly acquired with modulation of tube voltage, tube current, slice thickness, and the image reconstruction algorithm. Reference standard muscle compartments were obtained from the reference maps of the phantom. Cross-sectional area based on the Hounsfield unit (HU) thresholds of muscle and its components, and the mean density of the reference standard muscle compartment, were used to measure the muscle quantity and quality using different CT protocols. Signal-to-noise ratios (SNRs) were calculated in the images acquired with different settings. Results: The skeletal muscle area (threshold, -29 to 150 HU) was constant, regardless of the protocol, occupying at least 91.7% of the reference standard muscle compartment. Conversely, normal attenuation muscle area (30-150 HU) was not constant in the different protocols, varying between 59.7% and 81.7% of the reference standard muscle compartment. The mean density was lower than the target density stated by the manufacturer (45 HU) in all cases (range, 39.0-44.9 HU). The SNR decreased with low tube voltage, low tube current, and in sections with thin slices, whereas it increased when the iterative reconstruction algorithm was used. Conclusion: Measurement of muscle quantity using HU threshold was reliable, regardless of the CT protocol used. Conversely, the measurement of muscle quality using the mean density and narrow HU thresholds were inconsistent and inaccurate across different CT protocols. Therefore, further studies are warranted in future to determine the optimal CT protocols for reliable measurements of muscle quality.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1213-1224
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• 2021

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.2
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• pp.1-18
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• 2024

This study aimed to identify the optimal street tree planting method to improve the summer thermal environment in Seoul, Republic of Korea. The effects of road direction and street tree planting patterns on urban thermal environments using ENVI-met simulations were analyzed. The 68 scenarios were analyzed based on four road directions and 17 planting patterns. The results showed that tree planting had a reducing air temperature, mean radiant temperature, human thermal sensation (PET and UTCI). The most effective planting pattern among all scenarios was low tree height (6m), wide crown width (9m), high leaf area index (3.0), and narrow planting interval (8m). The largest improvement in the thermal environment was the northern sidewalk of the east-west road. Since this study used computer simulations, the difference from real urban spaces should be considered, and further research is needed through field measurement and consideration of more variables.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1054-1065
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• 2021

Objective: We implemented a novel resectable myocardial model for mock myectomy using a hybrid method of three-dimensional (3D) printing and silicone molding for patients with apical hypertrophic cardiomyopathy (ApHCM). Materials and Methods: From January 2019 through May 2020, 3D models from three patients with ApHCM were generated using the end-diastolic cardiac CT phase image. After computer-aided designing of measures to prevent structural deformation during silicone injection into molding, 3D printing was performed to reproduce anatomic details and molds for the left ventricular (LV) myocardial mass. We compared the myocardial thickness of each cardiac segment and the LV myocardial mass and cavity volumes between the myocardial model images and cardiac CT images. The surgeon performed mock surgery, and we compared the volume and weight of the resected silicone and myocardium. Results: During the mock surgery, the surgeon could determine an ideal site for the incision and the optimal extent of myocardial resection. The mean differences in the measured myocardial thickness of the model (0.3, 1.0, 6.9, and 7.3 mm in the basal, midventricular, apical segments, and apex, respectively) and volume of the LV myocardial mass and chamber (36.9 mL and 14.8 mL, 2.9 mL and -9.4 mL, and 6.0 mL and -3.0 mL in basal, mid-ventricular and apical segments, respectively) were consistent with cardiac CT. The volume and weight of the resected silicone were similar to those of the resected myocardium (6 mL [6.2 g] of silicone and 5 mL [5.3 g] of the myocardium in patient 2; 12 mL [12.5 g] of silicone and 11.2 mL [11.8 g] of the myocardium in patient 3). Conclusion: Our 3D model created using hybrid 3D printing and silicone molding may be useful for determining the extent of surgery and planning surgery guided by a rehearsal platform for ApHCM.

• Journal of the Korean Chemical Society
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• v.68 no.3
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• pp.160-175
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• 2024

The purpose of this study is to examine the effects of a Chemistry I class based on an artificial intelligence (AI) classification model. To achieve this, the research investigated the development and application of a class utilizing an AI classification model in Chemistry I classes conducted at D High School in Gyeongbuk during the first semester of 2023. After selecting the curriculum content and AI tools, and determining the curriculum-AI integration education model as well as AI hardware and software, we developed detailed activities for the program and applied them in actual classes. Following the implementation of the classes, it was confirmed that students' self-efficacy improved in three aspects: chemistry concept formation, AI value perception, and AI-based maker competency. Specifically, the chemistry classes based on text and image classification models had a positive impact on students' self-efficacy for chemistry concept formation, enhanced students' perception of AI value and interest, and contributed to improving students' AI and physical computing abilities. These results demonstrate the positive impact of the Chemistry I class based on an AI classification model on students, providing evidence of its utility in educational settings.

• Korean Journal of Radiology
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• v.22 no.1
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• pp.131-138
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• 2021

Objective: Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%). Materials and Methods: This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated. Results: Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001). Conclusion: DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.