• Applied Microscopy
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• v.51
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• pp.20.1-20.12
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• 2021

We explored the link between mitochondrial biogenesis and mitochondrial morphology using transmission electron microscopy (TEM) in lymphoblasts of pediatric acute lymphoblastic leukemia (ALL) patients and compared these characteristics between tumors and control samples. Gene expression of mitochondrial biogenesis markers was analysed in 23 ALL patients and 18 controls and TEM for morphology analysis was done in 15 ALL patients and 9 healthy controls. The area occupied by mitochondria per cell and the cristae cross-sectional area was observed to be significantly higher in patients than in controls (p-value=0.0468 and p-value<0.0001, respectively). The mtDNA copy numbers, TFAM, POLG, and c-myc gene expression were significantly higher in ALL patients than controls (all p-values<0.01). Gene Expression of PGC-1α was higher in tumor samples. The analysis of the correlation between PGC-1α expression and morphology parameters i.e., both M/C ratio and cristae cross-sectional area revealed a positive trend (r=0.3, p=0.1). The increased area occupied by mitochondria and increased cristae area support the occurrence of cristae remodelling in ALL. These changes might reflect alterations in cristae dynamics to support the metabolic state of the cells by forming a more condensed network. Ultrastructural imaging can be useful for affirming changes occurring at a subcellular organellar level.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.3
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• pp.471-481
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• 2023

Digital twin is considered as a key technology of industry 4.0, thus being essential for the future of industrial production. Despite the significance, a systematic analysis of its technological landscape is lacking. This study aims to investigate the technological development trends and newly emerging technological convergence opportunities in the domain of digital twin by exploiting patent information derived from U SPTO. For this purpose, this study visualized and predicted the convergence dynamics among patent classification codes by adopting patent co-classification analysis and link prediction approach. The findings show that the number of digital twin-related patent applications has increased significantly since 2018. The CPC code G06F showed the highest eigenvector centrality, while G05B was characterized by highest betweenness centrality. According to the predictive model, 41 novel links were revealed, acting as potential technology convergence opportunities. These links were then categorized into 11 different domains. The most dominant category was "digital data processing and artificial intelligence", which could play a foundational role in the diffusion of digital twin technology. The presence of digital twin technology is dominant in manufacturing, but its applications are expected to expand, including "climate change", "healthcare" and "aerospace engineering". The derived insights can support R&D managers and policy makers in formulating R&D strategies and directing future R&D investment decisions.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.42.1-42.1
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• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.284-290
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• 2023

Domestic water supply and sewage facilities are rapidly aging and maintenance difficulties such as aging of operation and management personnel are overlapping, so Digital Twin technology is attracting attention as an intelligent means of process management. Digital twin projects for domestic water treatment processes include the smart sewage treatment project promoted by the Ministry of Environment, projects independently promoted by some local governments, and digital twin purification plant projects promoted by K-water. However, the content of digital twin promotion is different for each institution. Therefore, in the water treatment process, technological standardization and step-by-step implementation methods for digital twins must be preceded to reduce trial and error in future business promotion. This study aims to provide an efficient promotion plan by prescribing the digital twin element technology and composition method in the water treatment process and reviewing the contents currently being promoted by the Ministry of Environment, local governments, and K-Water individually.

• Clean Technology
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• v.30 no.1
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• pp.23-27
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• 2024

Bismuth is a promising anodic for Li-ion batteries (LIBs) due to its adequate operating voltage and high-volume capacity (3,765 mAh cm^-3). Nevertheless, inevitable volume expansion during Bi alloy reactions leads to severe capacity loss and cell destruction. To address this, a complex of bismuth alloy nanoparticles (Bi@NC) embedded in an N doping-carbon coating is fabricated via a simple pyrolysis method. Nano-sized bismuth alloys can improve the reaction dynamics through a shortened Li⁺-ion diffusion path. In addition, the N-doped carbon coating effectively buffers the volume change of bismuth during the extended alloy/dealloy reaction with Li⁺ ions and maintains an effective conductive network. Based on the Thermogravimetric analysis (TGA) showed high bismuth alloy loading (80.9 wt%) and maintained a high gravimetric capacity of 315 mAh g^-1 up to 100 cycles with high volumetric capacity of 845.6 mAh cm^-3.

• IMMUNE NETWORK
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• v.24 no.3
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• pp.21.1-21.16
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• 2024

IL-1, a pleiotropic cytokine with profound effects on various cell types, particularly immune cells, plays a pivotal role in immune responses. The proinflammatory nature of IL-1 necessitates stringent control mechanisms of IL-1-mediated signaling at multiple levels, encompassing transcriptional and translational regulation, precursor processing, as well as the involvement of a receptor accessory protein, a decoy receptor, and a receptor antagonist. In T-cell immunity, IL-1 signaling is crucial during both the priming and effector phases of immune reactions. The fine-tuning of IL-1 signaling hinges upon two distinct receptor types; the functional IL-1 receptor (IL-1R) 1 and the decoy IL-1R2, accompanied by ancillary molecules such as the IL-1R accessory protein (IL-1R3) and IL-1R antagonist. IL-1R1 signaling by IL-1β is critical for the differentiation, expansion, and survival of Th17 cells, essential for defense against extracellular bacteria or fungi, yet implicated in autoimmune disease pathogenesis. Recent investigations emphasize the physiological importance of IL-1R2 expression, particularly in its capacity to modulate IL-1-dependent responses within Tregs. The precise regulation of IL-1R signaling is indispensable for orchestrating appropriate immune responses, as unchecked IL-1 signaling has been implicated in inflammatory disorders, including Th17-mediated autoimmunity. This review provides a thorough exploration of the IL-1R signaling complex and its pivotal roles in immune regulation. Additionally, it highlights recent advancements elucidating the mechanisms governing the expression of IL-1R1 and IL-1R2, underscoring their contributions to fine-tuning IL-1 signaling. Finally, the review briefly touches upon therapeutic strategies targeting IL-1R signaling, with potential clinical applications.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.579-586
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• 2018

Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.6 no.3
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• pp.79-85
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• 2003

Human beings have pursued development and economic betterment; thus, enhancing convenience and prosperity. A flourish of human civilization upon the industrialization results a massive urbanization. However, human beings have connived the environmental importance in the course of rapid urbanization. The environmental quality now becomes one of the most important factors that determine the quality of life in a city. Many studies were proceeded about the heat island effect in large cities. In general, most studies have been done to investigate urban microclimate phenomena using meteorological network or AWS (automatic weather station) data. Those preceding studies do not seem to sufficiently reflect the and thus, failed to show regional representative. In this study, temporal Landsat TM satellite imageries of May 20, 1987 and May 21, 1999 were 뻐d in order to detect the surface temperature of the study area using the band 6 ($10.4{\mu}m{\sim}12.5{\mu}m$). The surface temperature distribution detected by the band 6 of Landsat TM was over layed with the land cover classification data in order to investigate the temperature difference of the paved road and the riparian areas of the stream. As a result, a surface temperature difference as much as $3^{\circ}C$ between the paved road and the riparian areas with vegetation was observed. This study concludes that the land cover change is one of the main causes of urban heat island effect which may be closely affected by the paved areas and roads. Besides, the change of the atmospheric temperature followed by the urban secular change could have been confirmed. In the case of Yangjaecheon stream which underwent a heavy environmental restoration in 1995, the temperature was decreased as much as $0.6^{\circ}C$ after the restoration. The results of this study is expected to contribute to develop an urban space in harmony with the healthy human life and the environment respecting the crucial role of vegetation to stabilize the urban environmental dynamics.

• Journal of the Mineralogical Society of Korea
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• v.19 no.4 s.50
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• pp.347-353
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• 2006

The atomic-nano scale structures of multi-component aluminosilicate glasses have not been well understood in spite of its implications fur dynamics and generation of magma in the natural system due to lack of suitable spectroscopic and scattering experiments. Here, we report O-17 MAS and isotropic projection of 3QMAS NMR spectra for quaternary Na-Ca silicate glasses $[(CaO)_x(Na_2O)_{1-x}]\;(A1_2O_3)_{0.5}(SiO_2)_6,\;CNAS)$ at 14.1 Tesla where atomic configurations around bridging oxygen (Si-O-Si, Si-O-Al) and non bridging oxygen (Na-O-Si, Ca-O-Si, (Na, Ca)-O-Si) are partially resolved. With increasing Na content, the fraction of Na-O-Si increases while those for bridging oxygens remain constant. The Na/Ca ratio apparently affects the peak widths of bridging oxygen peaks (e.g., Si-O-Si)) and thus the topological entropy as well as chemical shifts of the bridging oxygen peaks, implying that both BOs and NBOs are strongly interacting with network modifying cations The effect of cation field strength on the degree of Al-avoidance was also discussed.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.5
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• pp.563-569
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• 2012

The superheater and reheater tubes of a heavy-load fossil power plant boiler can be damaged by overheating, and therefore, the degree of overheating is assessed by measuring the oxide scale thickness inside the tube during outages. The tube temperature prediction from the oxide scale thickness measurement is necessarily accompanied by destructive tube sampling, and the result of tube temperature prediction cannot be expected to be accurate unless the selection of the overheated point is precise and the initial-operation tube temperature has been obtained. In contrast, if the tube temperature is to be predicted analytically, considerable effort (to carry out the analysis of combustion, radiation, convection heat transfer, and turbulence fluid dynamics of the gas outside the tube) is required. In addition, in the case of analytical tube temperature prediction, load changes, variations in the fuel composition, and operation mode changes are hardly considered, thus impeding the continuous monitoring of the tube temperature. This paper proposes a method for the short-term prediction of tube temperature; the method involves the use of boiler operation information and flow-network-analysis-based tube heat flux. This method can help in high-temperaturedamage monitoring when it is integrated with a practical tube-damage-assessment method such as the Larson-Miller Parameter.