• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.389-394
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• 2021

In this work, we study the properties of molecular structure and boundary orbital functions of the DPHMP-AlH and propiophenone and butyrophenone, which are forms of alkoxy-amine-aluminum derivatives. Furthermore, we investigate the effect on the selective reduction of the final products (R), (S)-phenylpropanol and (R), (S)-phenylbutanol by calculating the stereoscopic and thermodynamic parameters of the transition state. Considering the three-dimensional molecular structural stability, the transition status of (S) types DPHMP-AlH and alkylphenone was found to be more stable, resulting in the selective reductions of DPHM-AlH and alkylphenone from this result: (S)-(1)-phenylpropanol and (S)-(1)-phenylbutanol was confirmed that the formation was advantageous.

• The Journal of Korean Institute of Information Technology
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• v.17 no.12
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• pp.21-28
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• 2019

Side effects of drugs mean harmful and unintended effects resulting from drugs used to prevent, diagnose, or treat diseases. These side effects can lead to patients' death and are the main causes of drug developmental failures. Thus, various methods have been tried to identify side effects. These can be divided into biological and systems biology approaches. In this study, we use systems biology approach and focus on using various phenotypic information in addition to the chemical structure and target proteins. First, we collect datasets that are used in this study, and calculate similarities individually. Second, we generate a set of features using the similarities for each drug-side effect pair. Finally, we confirm the results by AUC(Area Under the ROC Curve), and showed the significance of this study through a comparison experiment.

• The Plant Pathology Journal
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• v.37 no.2
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• pp.87-98
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• 2021

To establish an infection, fungal pathogens must recognize diverse signals from host surfaces. The rice blast fungus, Magnaporthe oryzae, is one of the best models studying host-pathogen interactions. This fungus recognizes physical or chemical signals from the host surfaces and initiates the development of an infection structure called appressorium. Here, we found that protein MoAfo1(appressorium formation, MGG_10422) was involved in sensing signal molecules such as cutin monomers and long chain primary alcohols required for appressorium formation. The knockout mutant (ΔMoafo1) formed a few abnormal appressoria on the onion and rice sheath surfaces. However, it produced normal appressoria on the surface of rice leaves. MoAfo1 localized to the membranes of the cytoplasm and vacuole-like organelles in conidia and appressoria. Additionally, the ΔMoafo1 mutant showed defects in appressorium morphology, appressorium penetration, invasive growth, and pathogenicity. These multiple defects might be partially due to failure to respond properly to oxidative stress. These findings broaden our understanding of the fungal mechanisms at play in the recognition of the host surface during rice blast infection.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.1
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• pp.13-19
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• 2021

In modern society, indoor activities time is increasing due to industrial development. Interest in indoor air quality is increasing as indoor activity time increases. The main causes of indoor air pollution are formaldehyde which a chemical cause, and fungi which a biological cause. Phytoncide effectively reduces Formaldehyde and Fungi. Charcoal which possess porous-structure has a good absorbance of pollutants. In this study, the authors manufactured functional cement matrix using by phytoncide and charcoal to remove formaldehyde and fungi. In this study, Functional cement matrix reduced formaldehyde and Fungi and effectively improve indoor air quality.

• Natural Product Sciences
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• v.26 no.4
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• pp.317-325
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• 2020

A new dimer stilbene [Monalittorin (1)] and ten known compounds [engeletin (2), aurantiamide acetate (3), lupeol (4), friedelin (5), quercetin (6), tiliroside (7), rutoside (8), astragalin (9), isoquercitrin (10) and quercimeritroside (11)] have been isolated from the leaves of Monanthotaxis littoralis (Annonaceae). The structures of these compounds were established by interpretation of their data, mainly, HR-TOFESIMS, 1-D NMR (1H and 13C) and 2-D NMR (1H-1H COSY, HSQC, HMBC and NOESY) and by comparison with the literature. The evaluation of their antimicrobial activities against three bacteria (Staphylococcus aureus ATCC 25923, Escherichia coli S2 (1) and Pseudomonas aeruginosa PA01) and three fungal strains (Candida albicans ATCC10231, Candida tropicalis PK233 and Cryptococcus neoformans H99) using broth micro dilution method, showed the largest antimicrobial activities of EtOAc fraction and compounds 1, 5, 6, 8 and 11 (MIC = 8 - 64 ㎍/mL). In addition, EtOAc fraction presented synergistic effect with Vancomycin and fluconazole against the tested microorganisms.

• Applied Chemistry for Engineering
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• v.32 no.1
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• pp.91-96
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• 2021

This study was conducted to improve the operating conditions of an adsorption tower filled with potassium impregnated activated carbon for high hydrogen sulfide capture capacity. Heat treatment modified the surface properties of activated carbon, and ultimately determined its adsorption capacity. The activated carbon doped with potassium showed 57 times more adsorption at room temperature than that of using the raw adsorbent. It is believed that uniform pore formation and strong bonding of the potassium on the surface of carbon contributed to the chemical and physical absorption of hydrogen sulfide. The SEM analysis on the surface structure of various commercial carbons showed that the modification of surface properties through the heat treatment generated the destruction of pore structures resulted in the decrease of the absorption performance. The pressure drop across the activated carbon bed was closely related with the grain size and shape. The optimum size of irregularly shaped activated carbon granules was 2~4 mesh indicating economical feasibility.

• Composites Research
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• v.34 no.3
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• pp.174-179
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• 2021

Currently, fluid transfer steel pipes take a lot of time and expense to maintain all facilities due to new construction and painting or corrosion and aging. Therefore, this study was conducted for designing a CFRP pipe structure with high corrosion resistance and chemical resistance as a substitute for steel pipes. The helical/hoop pattern was cross-laminated to improve durability, and HNT was added to suppress the moisture absorption phenomenon of the epoxy. The HNT/CFRP pipe was manufactured by a filament winding process, and performed a mechanical property test, and a moisture absorption test in distilled water at 70℃. As a result, the highest bending strength was obtained when the hoop pattern was laminated with a thickness equivalent to 0.6% of the pipe. The 0.5 wt% HNT specimen had the highest moisture absorption resistance. Also, the delamination phenomenon at the interlayer interface was delayed, resulting in the lowest strength reduction rate.

• Korean Journal of Materials Research
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• v.31 no.6
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• pp.345-352
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• 2021

In this study, Ni nanoparticle supported by graphene oxide (GO) (Ni-GO) is successfully synthesized through hydrothermal synthesis and calcination, and Cr(VI) is extracted from aqueous solution. The morphology and structure of Ni-GO composites are characterized by scanning electron microscopy (SEM), trans mission electron microscopy (TEM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). High-resolution transmission electron microscopy (HRTEM) and XRD confirms the high dispersion of Ni nanoparticle after support by GO. Loading Ni on GO can obviously enhance the stability of Ni-GO composites. It can be calculated from TGA that the mass percentage of Ni is about 60.67 %. The effects of initial pH and reaction time on Cr(VI) removal ability of Ni-GO are investigated. The results indicate that the removal efficiency of Cr(VI) is greater than that of bared GO. Ni-GO shows fast removal capacity for Cr(VI) (<25 min) with high removal efficiency. Dynamic experiments show that the removal process conforms to the quasi-second order model of adsorption, which indicates that the rate control step of the removal process is chemical adsorption. The removal capacity increases with the increase of temperature, indicating that the reaction of Cr(VI) on Ni-GO composites is endothermic and spontaneous. Combined with tests and characterization, the mechanism of Cr(VI) removal by rapidly adsorption on the surface of Ni-GO and reduction by Ni nanoparticle is investigated. The above results show that Ni-GO can be used as a potential remediation agent for Cr(VI)-contaminated groundwater.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• Journal of Sensor Science and Technology
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• v.31 no.2
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• pp.102-106
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• 2022

Currently, the demand for real-time monitoring of water quality has increased dramatically. Total organic carbon (TOC) analysis is a suitable method for real-time analysis compared with conventional biochemical oxygen demand (BOD) and chemical oxygen demand (COD) methods in terms of analysis time. However, this method is expensive because of the complicated internal processes involved. The photocatalytic titanium dioxide (TiO2)-based TOC method is simpler as it omits more than three preprocessing steps. This is because it reacts only with organic carbon (OC) without extra processes. We optimized the rate between the TiO2 photocatalyst and binder solution and the TiO2 concentration. The efficiency was investigated under 365 nm UV exposure onto a TiO2 coated substrate. The optimized conditions were sufficient to apply a real-time monitoring system for water quality with a short reaction time (within 10 min). We expect that it can be applied in a wide range of water quality monitoring industries.