US-China rivalry in Southeast Asia region: a study on the South China Sea case

Southeast Asia is one of the places where fierce rivalry is taking place between the two leading powers in the world today - the US and China. The US-China rivalry in this region takes place in key fields, from politics - diplomacy, economy, security - defense to "soft power", the most prominent of which is the South China Sea issue. This article analyzes the strategic importance of the South China Sea in the policy of the US and China, the competition between the US and China in Southeast Asia in general, and the South China Sea in particular. To achieve this goal, the authors use research methods in international relations to analyze the main issues of the study. In addition to reviewing previous scholarly research and reviews, the authors use a comparative approach to assess the interactions between theory and data. The authors believe the data is important for accurately assessing the strategic competition between the US and China in Southeast Asia and the South China Sea. The rise of China in the early years of the XXI century strongly influenced the adjustment of the US policy in Southeast Asia and the powerful US-China rivalry in this region and the South China Sea. This rivalry is becoming increasingly complicated, and geopolitical conflicts between major powers are possible in the following years

Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices

Accurate crop yield forecasting is essential in the food industry’s decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, however, is that a one-fits-all prediction model is often employed over an entire region without considering subregional VCI and TCI’s spatial variability resulting from environmental and climatic factors. Furthermore, when using nonlinear ML, redundant VCI/TCI data present additional challenges that adversely affect the models’ output. This study proposes a framework that (i) employs higher-order spatial independent component analysis (sICA), and (ii), exploits a combination of the principal component analysis (PCA) and ML (i.e., PCA-ML combination) to deal with the two challenges in order to enhance crop yield prediction accuracy. The proposed framework consolidates common VCI/TCI spatial variability into their respective subregions, using Vietnam as an example. Compared to the one-fits-all approach, subregional rice yield forecasting models over Vietnam improved by an average level of 20% up to 60%. PCA-ML combination outperformed ML-only by an average of 18.5% up to 45%. The framework generates rice yield predictions 1 to 2 months ahead of the harvest with an average of 5% error, displaying its reliability

Apply deep learning to improve the question analysis model in the Vietnamese question answering system

Question answering (QA) system nowadays is quite popular for automated answering purposes, the meaning analysis of the question plays an important role, directly affecting the accuracy of the system. In this article, we propose an improvement for question-answering models by adding more specific question analysis steps, including contextual characteristic analysis, pos-tag analysis, and question-type analysis built on deep learning network architecture. Weights of extracted words through question analysis steps are combined with the best matching 25 (BM25) algorithm to find the best relevant paragraph of text and incorporated into the QA model to find the best and least noisy answer. The dataset for the question analysis step consists of 19,339 labeled questions covering a variety of topics. Results of the question analysis model are combined to train the question-answering model on the data set related to the learning regulations of Industrial University of Ho Chi Minh City. It includes 17,405 pairs of questions and answers for the training set and 1,600 pairs for the test set, where the robustly optimized BERT pre-training approach (RoBERTa) model has an F1-score accuracy of 74%. The model has improved significantly. For long and complex questions, the mode has extracted weights and correctly provided answers based on the question’s contents

Identifying biofilm forming bacteria in cow milk in Mekong Delta, Viet Nam

Bacterial biofilms are agglomeration of bacterial cells, stuck to the material surfaces of material in wet environments and formed by a self-produced matrix. The formation of bacterial biofilms is a great risk for the milk processing industry, as the survival of many bacterial species in cow milk may lead to many problems such as microbial spoilage, deterioration in quality, and consumer health risks. This study aimed to identify biofilm formation bacteria from cow milk. The experiment included isolation; biofilm forming assay in 96-well microtiter plates and the identification of microbial isolates using classical and molecular biological methods. A total of 14 bacterial isolates from 10 cow milk samples were evaluated for their biofilm formatting ability. Among them, four isolates were identified as moderate and strong biofilm producers. These four isolates belong to the genera Serratia and Aeromonas. Out of the 4 isolates, Serratia marcescens VL41 was classed as a strong biofilm producer while Aeromonas veronii ST15, Aeromonas sp. ST17, Serratia marcescens VL13 were classed as moderate biofilm producers respectively. The findings of this study suggest that it is necessary to discover the contamination causes and prevention of genera Serratia, and Aeromonas into cow milk

ARSENIC POLLUTION IN TUBE WELL WATER AT HANOI SUBURB VILLAGES

Joint Research on Environmental Science and Technology for the Eart

Antimicrobial resistance and molecular characterization of Escherichia coli isolated from bovine mastitis samples in Nghe An province, Vietnam

Background and Aim: Vietnam’s dairy sector is in its early phase of large-scale farming development. Therefore, mastitis in cows is always a concern to farm owners. This study aimed to determine the antimicrobial susceptibility, resistance, and virulence-related genes of Escherichia coli isolated from bovine mastitis in Nghe An province of Vietnam. Materials and Methods: Fifty E. coli strains were isolated from the clinical cases and subjected to this study. All isolates were tested for antimicrobial susceptibility by the disk-diffusion method, as described by the Clinical and Laboratory Standards Institute. Antimicrobial and virulence genes were confirmed by polymerase chain reaction with specific primers. Results: All isolates were resistant to lincomycin and sulfamethoxazole and sensitive to gentamicin, while other antimicrobials showed resistance from 2% to 90%. Multidrug resistance was confirmed in 46% of isolates, and none of them were identified as extended-spectrum beta-lactamase producers. From fifty strains tested for antimicrobial and virulence genes, six isolates harbored tetA, 6 tetB, 13 sul1, 15 sul2, 2 Intimin (eae), 1 iutA, and 3 stx2. Conclusion: Antimicrobial and multidrug resistances are the main virulence factors of E. coli isolated from bovine mastitis in Vietnam. The virulence genes encoding adhesion, siderophore, Shiga-toxin-producing, and antimicrobials resistant were first reported in Vietnam with low prevalence and contributed to the pathogenesis