• Title/Summary/Keyword: Gulf of Thailand

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Species Identification of the Tropical Abalone (Haliotis asinina, Haliotis ovina, and Haliotis varia) in Thailand Using RAPD and SCAR Markers

  • Klinbunga, Sirawut;Amparyup, Piti;Leelatanawit, Rungnapa;Tassanakajon, Anchalee;Hirono, Ikuo;Aoki, Takashi;Jarayabhand, Padermsak;Menasveta, Piamsak
    • BMB Reports
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    • v.37 no.2
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    • pp.213-222
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    • 2004
  • A randomly amplified polymorphic DNA (RAPD) analysis was used to identify the species- and population-specific markers of abalone; Haliotis asinina, H. ovina, and H. varia in Thai waters. Fifteen species-specific and six population-specific RAPD markers were identified. In addition, an 1650 bp band (UBC195) that was restricted to H. ovina from the Gulf of Thailand (east) was also found. All of the specific RAPD markers were cloned and sequenced. Twenty pairs of primers were designed and specificity-tested (N = 12 and 4 for target and non-target species, respectively). Seven primer pairs (CUHA1, 2, 4, 11, 12, 13, and 14) were specifically amplified by H. asinina DNA, whereas a single pair of primers showed specificity with H. ovina (CUHO3) and H. varia (CUHV1), respectively. Four primer pairs, including CUHA2, CUHA12, CUHO3, and CUHV1, were further examined against 216 individuals of abalone (N = 111, 73, and 32, respectively). Results indicated the species-specific nature of all of them, except CUHO3, with the sensitivity of detection of 100 pg and 20 pg of the target DNA template for CUHA2 and CUHA12 and CUHV1, respectively. The species-origin of the frozen, ethanol-preserved, dried, and boiled H. asinina specimens could also be successfully identified by CUHA2.

Opportunities for Synergistic Collaboration between U.S. and Asia in Construction Business, Research, and Education

  • Liu, LiangY.
    • Proceedings of the Korean Institute Of Construction Engineering and Management
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    • 2007.11a
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    • pp.3-8
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    • 2007
  • "The world is flat" as the popular author Thomas L. Friedman declares in his best belling book about the progress of business globalization in the $21^{st}$ century [3]. Construction projects and related businesses have been global since the 1800's, but it has never seen such rapid transformation in both scope and depth as in the past decade. Construction projects today often bring together international design talents, construction management firms, local and international labor forces, and global suppliers. On a visit to Dubai in the United Arab Emirates on the Persian Gulf, a person will experience the complexity of globalization of modern construction projects- with Arab owners, European engineering and design companies, American construction management teams, Korean general contractors, Jordanian subcontractors, and labor forces from Thailand, Indonesia, Turkey, and Sri Lanka. A count of material suppliers reveals over 60 countries involved, covering all continents. Indeed construction projects are getting more and more complex and competitive, as is the project execution. The trend toward globalization poses both challenges and opportunities to construction and engineering companies competing on a global scale. While global competition may be a threat to many companies, there are, however, many opportunities for synergistic collaborations that can create win-win scenarios for construction business, research, and education. This paper presents some of the opportunities between the U.S. and Asia in business integration, research collaboration on technologies, and educational development, which may mutually benefit countries on either side of the Pacific.

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Water Level Prediction on the Golok River Utilizing Machine Learning Technique to Evaluate Flood Situations

  • Pheeranat Dornpunya;Watanasak Supaking;Hanisah Musor;Oom Thaisawasdi;Wasukree Sae-tia;Theethut Khwankeerati;Watcharaporn Soyjumpa
    • Proceedings of the Korea Water Resources Association Conference
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    • 2023.05a
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    • pp.31-31
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    • 2023
  • During December 2022, the northeast monsoon, which dominates the south and the Gulf of Thailand, had significant rainfall that impacted the lower southern region, causing flash floods, landslides, blustery winds, and the river exceeding its bank. The Golok River, located in Narathiwat, divides the border between Thailand and Malaysia was also affected by rainfall. In flood management, instruments for measuring precipitation and water level have become important for assessing and forecasting the trend of situations and areas of risk. However, such regions are international borders, so the installed measuring telemetry system cannot measure the rainfall and water level of the entire area. This study aims to predict 72 hours of water level and evaluate the situation as information to support the government in making water management decisions, publicizing them to relevant agencies, and warning citizens during crisis events. This research is applied to machine learning (ML) for water level prediction of the Golok River, Lan Tu Bridge area, Sungai Golok Subdistrict, Su-ngai Golok District, Narathiwat Province, which is one of the major monitored rivers. The eXtreme Gradient Boosting (XGBoost) algorithm, a tree-based ensemble machine learning algorithm, was exploited to predict hourly water levels through the R programming language. Model training and testing were carried out utilizing observed hourly rainfall from the STH010 station and hourly water level data from the X.119A station between 2020 and 2022 as main prediction inputs. Furthermore, this model applies hourly spatial rainfall forecasting data from Weather Research and Forecasting and Regional Ocean Model System models (WRF-ROMs) provided by Hydro-Informatics Institute (HII) as input, allowing the model to predict the hourly water level in the Golok River. The evaluation of the predicted performances using the statistical performance metrics, delivering an R-square of 0.96 can validate the results as robust forecasting outcomes. The result shows that the predicted water level at the X.119A telemetry station (Golok River) is in a steady decline, which relates to the input data of predicted 72-hour rainfall from WRF-ROMs having decreased. In short, the relationship between input and result can be used to evaluate flood situations. Here, the data is contributed to the Operational support to the Special Water Resources Management Operation Center in Southern Thailand for flood preparedness and response to make intelligent decisions on water management during crisis occurrences, as well as to be prepared and prevent loss and harm to citizens.

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Hexachlorobenzene Dechlorination Ability of Microbes from Canal and Estuary Sediments

  • Anotai, Jin;Voranisarakul, J.;Wantichapichat, W.;Chen, I.M.
    • Journal of Wetlands Research
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    • v.9 no.1
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    • pp.107-114
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    • 2007
  • This study aimed to investigate the hexachlorobenzene (HCB) dechlorinating ability of sediment microbes collected from a natural canal receiving secondary effluents from an industrial estate and nearby factories. Nine sites along the stream and one in the estuary in the Gulf of Thailand into which the canal spills were specified and sampling for sediment and water. Preliminary analysis of the sediments showed that the first four sites nearest to the discharging location were contaminated by HCB within the range of 0.18 to 1.25 ppm. Apart from that, 1,3,5-trichlorobenzene which has never been commercially produced or used in any manufacturing processes except for the transformation from higher chlorinated benzene was also identified in the range of 0.16 to 0.24 ppm. This suggested a possibility of sporadically HCB contamination in this stream. Of more important, people in the community along this canal earn their living by coastal fishery; hence, posing a risk of spreading HCB and its less chlorinated congeners via food chain from caught marine creatures to human. As a result, there is an urgent need to understand the behavior of HCB dechlorination in this stream sediment which can lead to a clean-up action in the future. Serum bottles with sediment slurries (sediment to water ratio of 1:1 (v/v) and filtered to remove particles larger than 0.7 mm) from each site were inoculated with 2 mg/l of HCB, kept anaerobically in the dark at room temperature without any nourishment, and analyzed for HCB and its less-chlorinated congeners every 6 days. Total chemical oxygen demand, suspended solids, and volatile suspended solids were in the range of 21,492-73,584, 158,100-518,100 and 6,000-32,700 mg/l, respectively. It was found that all sediment slurries began to dechlorinate HCB in 12 to 30 days and the HCB was completely removed within 42 to 60 days or so. On the other hand, there was no HCB dechlorination occurred in the controlled set which was sterilized by autoclaving prior to the addition of HCB. This implies that the HCB transformation was solely due to microorganisms' activities. HCB was dechlorinated principally via pentachlolobenzene to 1,2,3,5-tetrachlorobenzene and terminated at 1,3,5-trichlorobenzene which is the major pathway as reported by many researchers. Dichlorobenzene has not been detected in any samples within the dechlorination period of 60 days. The results indicate that the microbial matrix in the sediment of this stream has an outstanding capability to dechlorinate HCB. Existing substrates and nutrients which mainly sorbed onto the solid phase and the typical temperature in Thailand were sufficient and suitable to promote the activities of these HCB-dechlorinating microbes.

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