• Journal of Life Science
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• v.32 no.7
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• pp.510-522
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• 2022

In this study, to biosynthesize PHA with properties more similar to polypropylene, a Bacillus sp. EMK-5020 strain that biosynthesized poly (3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) was isolated from soil. Bacillus sp. EMK-5020 strain biosynthesized PHBV containing 1.3% 3-hydroxyvalerate (3HV) using reducing sugar contained in Makgeolli lees enzymatic hydrolysate (MLEH) as a single carbon source. As the amount of propionic acid, which was added as a second carbon source, increased, the content of 3HV also increased. PHBV containing up to 48.6% of 3HV was synthesized when 1.0 g/l of propionic acid was added. Based on these results, the strain was cultured for 72 hr in a 3 l fermenter using reducing sugar in MLEH (20 g/l) and propionic acid (1 g/l) as the main and secondary carbon sources, respectively. As a result, 6.4 g/l DCW and 50 wt% of PHBV (MLEH-PHBV) containing 8.9% 3HV were biosynthesized. Through gel permeation chromatography and thermogravimetric analysis, it was confirmed that the average molecular weight and the decomposition temperature of MLEH-PHBV were 152 kDa and 273℃, respectively. In conclusion, the Bacillus sp. EMK-5020 strain could biosynthesize PHBV containing various 3HV fractions when MLEH and propionic acid were used as carbon sources, and PHBV-MLEH containing 8.9% 3HV was confirmed to have higher thermal stability than standard PHBV (8% 3HV).

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.280-289
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• 2022

Purpose: In this study, the characteristics of fire occurrence according to ignition heat sources such as operating equipment, cigarette/lighter fire, and flame/fire were analyzed. Method: One-way ANOVA and cross-analysis were used to analyze the characteristics of fire occurrence by verifying the difference between the ignition environment, fire damage status and scale, and cause of ignition according to the ignition heat source. Result: The fire occurrence characteristics were analyzed through As a result of the analysis, it was found that fires caused by operating devices occurred more frequently on weekdays than other ignition heat sources, and the number of victims and the number of victims were the highest, so mobilization of firefighting power and property damage were the greatest. The initial ignition was generated by electric and electronic devices, and the combustion was expanded by the synthetic resin. For fires caused by cigarette and lighter fires, the most fires occurred on Saturdays and Sundays, and the mobilization of the police force was more characteristic than the mobilization of the firefighting force. In particular, it was found that the initial ignition and combustion expansion were caused by paper, wood, and hay. Fires caused by sparks and sparks occurred most frequently on Saturdays and Sundays, and initial ignition and combustion expansion were found to be caused by paper, wood, and hay. In particular, it showed the characteristic that it occurred in the place farthest from the fire station. The common characteristic of all ignition heat sources was that the fire occurred most frequently in the afternoon time, and the fire type was predominantly the building structure fire, and only the ignition point was burned the most. Conclusion: In order to prevent fire and minimize damage, it is necessary to analyze the tendency of fire occurrence and to prepare appropriate preparations according to the fire occurrence factors. In order to analyze the characteristics of fire occurrence using public data in the future, it is necessary to standardize disaster data and to open and activate data.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Journal of Life Science
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• v.31 no.12
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• pp.1094-1099
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• 2021

Hermetia illucens (Black soldier fly) is attracting attention as an environmental purification insect because it can supply a wide range of by-products of the agricultural food industry. Also, it has a potential feed for fish, birds, and pets due to a short life cycle and excellent nutritional components. Several pharmacological effects, including antimicrobial, of H. illucens have been reported. However, no study has focused on antiobesity effects of ethanol extract of H. illucens. In this study, we aimed to assess the anti-obesity effects of ethanol extract of H. illucens larvae (HIE) through inhibition of differentiation of 3T3-L1 preadipocytes into adipocytes. The amount of lipid accumulated in adipocytes was measured by oil red-O staining, and the inhibitory effect on adipogenesis was confirmed. The expression levels of factors related to adipocyte differentiation and fat synthesis were determined using Western blot analysis. Lipid droplet formation in adipocytes was remarkably inhibited by HIE. In addition, treatment with 400 ㎍/ml of HIE significantly reduced the expression levels of peroxisome proliferator-activated receptor γ and CCAAT/enhancer-binding protein α-transcription factors involved in adipocyte differentiation. Therefore, the results of this study indicate that HIE is a potential anti-obesity agent because it inhibits adipocyte differentiation.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.497-510
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• 2022

Agricultural reservoirs are essential structures for water supplies during dry period in the Korean peninsula, where water resources are temporally unequally distributed. For efficient water management, systematic and effective monitoring of medium-small reservoirs is required. Synthetic Aperture Radar (SAR) provides a way for continuous monitoring of those, with its capability of all-weather observation. This study aims to evaluate the applicability of SAR in monitoring medium-small reservoirs using Sentinel-1 (10 m resolution) and Capella X-SAR (1 m resolution), at Chari (CR), Galjeon (GJ), Dwitgol (DG) reservoirs located in Ulsan, Korea. Water detected results applying Z fuzzy function-based threshold (Z-thresh) and Chan-vese (CV), an object detection-based segmentation algorithm, are quantitatively evaluated using UAV-detected water boundary (UWB). Accuracy metrics from Z-thresh were 0.87, 0.89, 0.77 (at CR, GJ, DG, respectively) using Sentinel-1 and 0.78, 0.72, 0.81 using Capella, and improvements were observed when CV was applied (Sentinel-1: 0.94, 0.89, 0.84, Capella: 0.92, 0.89, 0.93). Boundaries of the waterbody detected from Capella agreed relatively well with UWB; however, false- and un-detections occurred from speckle noises, due to its high resolution. When masked with optical sensor-based supplementary images, improvements up to 13% were observed. More effective water resource management is expected to be possible with continuous monitoring of available water quantity, when more accurate and precise SAR-based water detection technique is developed.

• Journal of Appropriate Technology
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• v.6 no.2
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• pp.174-182
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• 2020

The permissible limit of ammonia concentration in drinking water recommended by the World Health Organization (WHO) is 1.5 mg/L. However, in the case of groundwater in Kathmandu, Nepal, the concentration of ammonia fluctuates dramatically from 0 to 120 mg/L at different locations and groundwater depths (Chapagain et al., 2010). Such a high concentration of ammonia causes aesthetic problems in drinking water, such as bad taste and odor; hence, prior treatment is required. In Kathmandu, half of the population utilizes groundwater, which is also employed for drinking water, but owing to a lack of knowledge of household water filters, residents of Kathmandu tend to depend greatly on commercially available jar water than on the installation of a proper household filtration method. Thus, in our study, we employed adsorption and reverse osmosis (RO) as two of the most viable decentralized/household treatment options to address the issue of high contamination of ammonia in drinking water. We evaluated their performances from technical and the economic perspectives using synthetically prepared groundwater at varying ammonia concentrations (50 mg/L and 15 mg/L). Consequently, it was found that adsorption via ion exchange (IE) resin was a comparatively better ammonia removal technology than RO, with 100% ammonia removal even after regeneration; the removal by RO was limited to up to 90%. Furthermore, our study suggests that IE is the most suitable ammonia removal technology for places with lower water consumption (< 50 L/day), whereas RO seemed to be a cost-effective technology for places with higher water consumption, where the daily water demand exceeds 50 L/day. Lastly, these assessments suggest that installing a suitable household treatment system would be more efficient and sustainable from both technical and economic points of view than purchasing commercially bottled water.

• Journal of Life Science
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• v.31 no.4
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• pp.418-424
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• 2021

Skin inflammation (dermatitis) is caused by varying skin damage due to ultraviolet radiation and microbial infection. Currently prescribed drugs for dermatitis include anti-histamine and steroid drug classes that soothe inflammation. However, incorrect or prolonged use of steroids can cause weakening of skin barriers as well as osteoporosis. Therefore, treating dermatitis with a drug that has minimal side effects is important. Statins, also known as 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors, are cholesterol-lowering drugs that have been widely treated for hyperlipidemia and cardiovascular diseases. Interestingly, recent studies have shown the anti-inflammatory effects of statins in both experimental and clinical models for of osteoarthritis. This study investigated the possible anti-inflammatory effects of atorvastatin and fluvastatin in human keratinocytes (HaCaT cells), which are crucial components of skin barriers. Stimulation of HaCaT cells with IL-1β increased the expression of the COX2 protein, a major player of inflammatory responses. However, this induction of the COX2 protein was downregulated by pretreatments with atorvastatin and fluvastatin. Treatment with IL-1ß-induced the upregulation of other inflammatory genes (such as iNOS and MMP-1) and these expressions were similarly lowered by these two statin drug treatments. Taken together, these results indicated that atorvastatin and fluvastatin can reduce IL-1β-induced inflammatory responses in HaCaT cells. In conclusion, the findings suggest that atorvastatin and fluvastatin can be potential modulators for ameliorating skin inflammation.

• Journal of the korean academy of Pediatric Dentistry
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• v.49 no.3
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• pp.300-309
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• 2022

This study aimed to evaluate the effects of two ginsenoside Rb1 (G-Rb1) loaded films on oral wound healing. Two types of G-Rb1 films, G-Rb1 loaded carboxymethyl cellulose (GCMC) film and G-Rb1 loaded hyaluronic acid (GHA) film, were developed. A total of 36 Sprague-Dawley rats were divided into 3 groups: control, GCMC, and GHA. After wound formation on midpalate, the control group was left without treatment, whereas the experimental groups had films attached. The specimen was analyzed clinically and histologically after 7 and 21 days. For clinical analysis, the area of incompletely re-epithelialized wound was measured. For histological analysis, the distance between the margins of the wound (soft tissue gap) was measured and the percentage of the collagen-stained area on the specimen was calculated. In clinical and soft tissue gap analysis, the GCMC group presented improved healing compared to the GHA group and the control at day 7 (p < 0.05). And, both GCMC (9.74 ± 10.12%) and GHA groups (19.50 ± 14.47%) presented greater collagen-positive pixels compared to control (0.89 ± 1.60%) at day 7 (p < 0.05). However, there were no differences in these parameters among the groups on day 21. Therefore, G-Rb1 loaded films improved oral wound healing.

• Korean Journal of Mineralogy and Petrology
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• v.35 no.4
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• pp.447-455
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• 2022

Fully dehydrated Tl₁₂-LTA (|Tl₁₂|[Si₁₂Al₁₂O₄₈]-LTA,Tl₁₂-A) was treated with 6.0×10³ Pa of ZrI₄ (g) at 623 K for 72 hr under anhydrous conditions. The crystal structure of product, |Zr_0.25I_1.5Tl₁₂|[Si₁₂Al₁₂O₄₈]-LTA, was determined by single-crystal crystallography using synchrotron X-radiation in the cubic space group Pm3m (a = 12.337(2) Å). It was refined using all data to the final error index (for the 712 unique reflections for which F_o> 4σ(F_o) R₁/wR₂= 0.055/0.189. In this structure, octahedral ZrI₆^2- ions center about 25% of the large cavities (Zr-I = 2.91(4) Å). Each coordinates to eight Tl⁺ ions and they are further bridged by Tl⁺ ions in the planes of 8-rings to form a cubic three-dimensional ZrI₆Tl₁₁⁹⁺ cationic cluster. About 1.5 Tl⁺ ions per unit cell moved to deeper side of sodalite cavity after reaction with ZrI₄(g). The remaining Tl⁺ ions occupy well-established cation positions near 6- and 8-rings.