• Journal of Microbiology and Biotechnology
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• v.17 no.9
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• pp.1579-1584
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• 2007

5-Aminolevulinate (ALA) synthase (E.C. 2.3.1.37), which mediates the pyridoxal phosphate-dependent condensation of glycine and succinyl-CoA, encoded by the Rhodobacter sphaeroides hemA gene, enables Escherichia coli strains to produce ALA at a low level. To study the effect of the enhanced C4 metabolism of E. coli on ALA biosynthesis, NADP-dependent malic enzyme (maeB, E.C. 1.1.1.40) was coexpressed with ALA synthase in E. coli. The concentration of ALA was two times greater in cells coexpressing maeB and hemA than in cells expressing hemA alone under anaerobic conditions with medium containing glucose and glycine. Enhanced ALA synthase activity via coupled expression of hemA and maeB may lead to metabolic engineering of E. coli capable of large-scale ALA production.

• Environmental Engineering Research
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• v.21 no.2
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• pp.171-179
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• 2016

This study analyzes guidelines to select optimum number of grids to represent behavior of a given water system appropriately. The EFDC model was chosen as a 3-D hydrodynamic and water quality model and salt was chosen as a surrogate variable of pollutant. The model is applied to an artificial canal that receives salt water from coastal area and fresh water from a river from respective gate according to previously developed gate operation rule. Grids are subdivided in vertical and horizontal (longitudinal) directions, respectively until no significant changes are found in salinity concentrations. The optimum grid size was determined by comparing errors in average salt concentrations between a test grid systems against the most complicated grid system. MSE (mean squared error) and MAE (mean absolute error) are used to compare errors. The CFL (Courant-Friedrichs-Lewy) number was used to determine the optimum number of grid systems for the study site though it can be used when explicit numerical method is applied only. This study suggests errors seem acceptable when both MSE and MAE are less than unity approximately.

• KSBB Journal
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• v.23 no.4
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• pp.281-284
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• 2008

A simple and efficient microwave-assisted extraction procedure was developed and optimized for the extraction of paclitaxel from the plant cell cultures of Taxus chinensis. The biomass, immersed in a methanol-water mixture, was irradiated with microwaves in a closed-vessel system. The microwave-assisted extraction was compared with the existing conventional solvent extraction in terms of yield, extraction time, and solvent consumption. The use of microwave energy allows rapid recovery of paclitaxel from biomass and dramatically reduces extraction time and solvent usage compared to conventional solvent extraction. The paclitaxel was completely extracted from biomass by microwave-assisted extraction for 3 min at $50^{\circ}C$, for 6 min at $30^{\circ}C$ and $40^{\circ}C$, respectively.

• Microbiology and Biotechnology Letters
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• v.46 no.3
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• pp.277-287
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• 2018

Current reports suggest that obesity is a serious global health issue. Emerging evidence has predicted strong links between obesity and the human gut microbiota. However, only a few such studies have been conducted in Asia, and the gut microbiota of lean and obese adult Asians remains largely unexplored. Here, we investigated the potential relationship between gut microbiota, body massindex (BMI), and metabolic parameters in adults from Thailand, where obesity is increasing rapidly. Fecal and blood samples were collected from 42 volunteers who were allocated into lean, overweight, and obese groups. The fecal microbiota was examined by quantitative PCR analysis. Bacteroidetes, Firmicutes, and Staphylococcus spp. and methanogens were most abundant in lean volunteers. Overweight volunteers majorly harbored Christensenella minuta and Akkermansia muciniphila, ${\gamma}-Proteobacteria$, and bacteria belonging to the genus Ruminococcus. Methanogens and bacteria belonging to the phylum Bacteroidetes were negatively correlated with adiposity markers (BMI and waist circumference), but positive correlated with high-density lipoprotein, suggesting that they can be used as leanness markers. While some of our results agree with those of previous reports, results regarding the contributions of specific taxa to obesity were inconsistent. This is the first study to report the adult gut microbiota in Southeast Asian populations using molecular techniques and biochemical markers and provides a foundation for future studies in this field.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.508-519
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• 2021

In this study, a model was developed to predict the peak particle velocity (PPV) that affects people and the surrounding environment during blasting. Four machine learning models using the k-nearest neighbors (kNN), classification and regression tree (CART), support vector regression (SVR), and particle swarm optimization (PSO)-SVR algorithms were developed and compared with each other to predict the PPV. Mt. Yogmang located in Changwon-si, Gyeongsangnam-do was selected as a study area, and 1048 blasting data were acquired to train the machine learning models. The blasting data consisted of hole length, burden, spacing, maximum charge per delay, powder factor, number of holes, ratio of emulsion, monitoring distance and PPV. To evaluate the performance of the trained models, the mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were used. The PSO-SVR model showed superior performance with MAE, MSE and RMSE of 0.0348, 0.0021 and 0.0458, respectively. Finally, a method was proposed to predict the degree of influence on the surrounding environment using the developed machine learning models.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.179-179
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• 2023

This study suggests a new approach of water level forecasting for extended lead times using original data preprocessing with variational mode decomposition (VMD). Here, two machine learning algorithms including light gradient boosting machine (LGBM) and random forest (RF) were considered to incorporate extended lead times (i.e., 5, 10, 15, 20, 25, 30, 40, and 50 days) forecasting of water levels. At first, the original data at two water level stations (i.e., SW173 and SW269 in Bangladesh) and their decomposed data from VMD were prepared on antecedent lag times to analyze in the datasets of different lead times. Mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the performance of the machine learning models in water level forecasting. As results, it represents that the errors were minimized when the decomposed datasets were considered to predict water levels, rather than the use of original data standalone. It was also noted that LGBM produced lower MAE, RMSE, and MSE values than RF, indicating better performance. For instance, at the SW173 station, LGBM outperformed RF in both decomposed and original data with MAE values of 0.511 and 1.566, compared to RF's MAE values of 0.719 and 1.644, respectively, in a 30-day lead time. The models' performance decreased with increasing lead time, as per the study findings. In summary, preprocessing original data and utilizing machine learning models with decomposed techniques have shown promising results for water level forecasting in higher lead times. It is expected that the approach of this study can assist water management authorities in taking precautionary measures based on forecasted water levels, which is crucial for sustainable water resource utilization.

• Fisheries and Aquatic Sciences
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• v.26 no.9
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• pp.548-557
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• 2023

The purposes of this research were to identify fish species using DNA barcodes or partial sequences of cytochrome b (Cytb) and to assess the diversity of fish in the Mae Tam reservoir, Phayao province, Thailand. Fish samples were collected 3 times, during the winter, summer, and rainy seasons, from 2 sampling sites using gillnets with 3 mesh sizes (30, 50, and 70 mm). A total of 34 representative samples were classified into 12 species, 7 families and 6 orders by morphological- and DNA barcoding-based identifications. However, one cichlid species, Cichlasoma trimaculatum, could only be identified using DNA barcoding. Family Cyprinidae had the greatest diversity, 50.00%. The diversity, richness and evenness indices ranged from 0.43-0.65, 0.64-1.46, and 0.27-0.40, respectively, indicating that fish diversity at both sampling sites was relatively low. A comparison of the catch per unit effort (CPUE) with 3 different mesh sizes found that the 50 mm mesh size was the best (474.80 ± 171.56 g/100 m²/night), followed by the 70 mm (417.41 ± 176.24 g/100 m²/night) and 30 mm mesh sizes (327.88 ± 115.60 g/100 m²/night). These results indicate that DNA barcoding is a powerful tool for species identification. Our data can be used for planning the sustainable management of fisheries resources in the Mae Tam reservoir.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1690-1707
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• 2021

Precipitation prediction during flood season has been a key task of climate prediction for a long time. This type of prediction is linked with the national economy and people's livelihood, and is also one of the difficult problems in climatology. At present, there are some precipitation forecast models for the flood season, but there are also some deviations from these models, which makes it difficult to forecast accurately. In this paper, based on the measured precipitation data from the flood season from 1993 to 2019 and the precipitation return data of CWRF, ANN cycle modeling and a weighted integration method is used to correct the CWRF used in today's operational systems. The MAE and TCC of the precipitation forecast in the flood season are used to check the prediction performance of the proposed algorithm model. The results demonstrate a good correction effect for the proposed algorithm. In particular, the MAE error of the new algorithm is reduced by about 50%, while the time correlation TCC is improved by about 40%. Therefore, both the generalization of the correction results and the prediction performance are improved.

• Journal of the Korean Home Economics Association
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• v.44 no.10
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• pp.91-100
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• 2006

This study analyzed the formal characteristics of traditional Jokakbo and modern works applying Jokakbo, and developed aroma chumoni representing the symbolic image and cultural identity of Bamboo at Dam-Yang, Mae-Wha at Kwang-Wang and San-Soo-You at Ku-Rae, all of which are in Chonnam. Initially, inform a theoretical point of view, the characteristics of modern works applying Jokakbo were investigated with regard to the pattern, color, fabric material and technique. Secondly, for the development of aroma chumoni, square and round patterns, representing the images of Bamboo, Mae-Wha and San-Soo-You, were applied. Relating to colors, green, pink, red purple, yellow, light yellow red and pale yellow were applied. With respect to the fabric materials, No-Bang and A-Sa, representing the lightness and coolness of the image, were used. When considering the technique, the traditional needle, rather than any other modern technique, was used.

• Food Science and Preservation
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• v.18 no.4
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• pp.546-551
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• 2011

Response surface methodology (RSM) was used for the microwave-assisted extraction (MAE) of the effective components of grape seed, such as its antioxidative and nitrite-scavenging abilities. Microwave power (2,450 MHz, 0-160W), ethanol concentration (0-100%), and MAE time (1-5 min) were used as independent variables (Xi) for the central composite design to yield 16 different MAE conditions. The optimum MAE conditions were predicted for the dependent variables of the extracts, such as the total phenolic content ($Y_1$) antioxidative ability ($Y_2$), and nitrite-scavenging ability ($Y_3$), depending on different microwave powers, ethanol concentrations, and MAE times. The determination coefficients ($R^2$) of the regression equations for the dependent variables ranged from 0.8024 to 0.9498. The maximal values of each dependent variable were predicted at different MAE conditions, as follows: 3.19% total phenolic content at 142.32W, 44.30% ethanol, and 4.36 min, and 1.22 antioxidative ability at 84.44W, 56.60% ethanol, and 3.28 min. More than 99.5% nitrite-scavenging ability was predicted at pH 1.2-3.0, 30.80-106.58W, 49.32-55.18% ethanol, and 3.72-4.58min, respectively. The results indicated that the total phenolic content and anti oxidative ability showed a higher correlation with each other in that they were more influenced by microwave power than by the other variables, while the nitrite-scavenging ability was largely influenced by the ethanol concentration.