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

Measurement of the Upper Tropospheric Density and Temperature Profiles in Hanoi Using a Raman Lidar

The nitrogen molecular density and temperature profiles of the upper troposphere are measured by a Raman lidar system in Hanoi over the range from 3 km to 19 km. The spatial and temporal resolutions of profiles are 60 m and 1h, respectively. The nitrogen molecular density profiles are directly calculated from the range-corrected lidar signal. The temperature profiles are derived from the molecular density profile based on the assumptions of the hydrostatic equilibrium, the ideal-gas law and a fixed nitrogen molecule ratio in the atmosphere. The results of our lidar measurements show good agreement with the MSISE-90 atmospheric model. The maximum errors of density and temperature measurements are 6% and 7%, respectively. We estimated the height of tropopause in Hanoi about 16 km from the derived temperature profile. The measured density and temperature profiles from this Raman lidar can be used for studying the trends and characteristics of the upper troposphere in Hanoi

Data Partitioning and Asynchronous Processing to Improve the Embedded Software Performance on Multicore Processors

Nowadays, ensuring information security is extremely inevitable and urgent. We are also witnessing the strong development of embedded systems, IoT. As a result, research to ensure information security for embedded software is being focused. However, studies on optimizing embedded software on multi-core processors to ensure information security and increase the performance of embedded software have not received much attention. The paper proposes and develops the embedded software performance improvement method on multi-core processors based on data partitioning and asynchronous processing. Data are used globally to be retrieved by any threads. The data are divided into different partitions, and the program is also installed according to the multi-threaded model. Each thread handles a partition of the divided data. The size of each data portion is proportional to the processing speed and the cache size of the core in the multi-core processor. Threads run in parallel and do not need synchronization, but it is necessary to share a general global variable to check the executing status of the system. Our research on embedded software is based on data security, so we have tested and assessed the method with several block ciphers like AES, DES, etc., on Raspberry PI3. The average performance improvement rate achieved was 59.09%

Co-infection of human parvovirus B19 with Plasmodium falciparum contributes to malaria disease severity in Gabonese patients

Background: High seroprevalence of parvovirus B19 (B19V) coinfection with Plasmodium falciparum has been previously reported. However, the impact of B19V-infection on the clinical course of malaria is still elusive. In this study, we investigated the prevalence and clinical significance of B19V co-infection in Gabonese children with malaria. Methods: B19V prevalence was analyzed in serum samples of 197 Gabonese children with P. falciparum malaria and 85 healthy controls using polymerase chain reaction (PCR), enzyme-linked immunosorbent assay (ELISA), and direct DNA-sequencing. Results: B19V was detected in 29/282 (10.28%) of Gabonese children. B19V was observed more frequently in P. falciparum malaria patients (14.21%) in comparison to healthy individuals (1.17%) (

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

Influence of alloying elements on the corrosion of fine art bronze in the strong corrosive environment

The influence of Sn, Zn, and Pb on the corrosion of fine art bronze in strong corrosive environment were investigated. The value of loss mass and the potential of the specimens were determined in the artificial sea-water environment. Corrosion of specimens was compared each to other to find the effect of the elements. The corrosion products were determined with the XRD/SEM/EDS analyses. The results shown that, Sn decreases the corrosion rate of the bronze alloy in marine environment while Zn increases it. The corrosion rates and potential curves showed that the corrosion rate of the bronzes reduced with immersion time. Alloy containing 0.7% Pb; 1.6% Zn; 9.7 % Sn; 88% Cu (in %wt.) has the corrosion rate as lowest. Keywords. Corrosion, strong corrosive environments, fine art bronze, Sn, Pb, Zn

Multichannel Photon Counting Lidar Measurements Using USB-based Digital Storage Oscilloscope

We present a simple method of making multichannel photon counting measurements of weak lidar signal from large ranges, using commonly available USB-based digital storage oscilloscopes. The single photon pulses from compact photomultiplier tubes are amplified and stretched so that the pulses are large and broad enough to be sampled efficiently by the USB oscilloscopes. A software interface written in Labview is then used to count the number of photon pulses in each of the prescribed time bins to form the histogram of LIDAR signal. This method presents a flexible alternative to the modular multichannel scalers and facilitate the development of sensitive lidar systems

Monitoring Cirrus Cloud and Tropopause Height over Hanoi Using a Compact Lidar System

Abstract. Cirrus clouds in the upper troposphere and the lower stratosphere have attracted great attention due to their important role and impact on the atmospheric radioactive balance. Because cirrus clouds are located high in the atmosphere, their study requires a high resolution remote sensing technique not only for detection but also for the characterization of their properties. The lidar technique with its inherent high sensitivity and resolution has become an indispensible tool for studying and improving our understanding of cirrus cloud. Using lidar technique we can simultaneously measure the cloud height, thickness and follow its temporal evolution. In this paper we describe the development of a compact and highly sensitive lidar system with the aim to remotely monitor for the first time the cirrus clouds over Hanoi (21001’42’’N, 105051’12’’W). From the lidar data collected during the year 2011. We derive the mean cloud height, location of cloud top, the cloud mean thickness and their temporal evolution. We then compare the location of the cloud top with the position of the tropopause determined the radiosonde data and found good that the distance between cloud top and tropopause remains fairly stable, indicating that generally the top of cirrus clouds is the good tracer of the tropopause. We found that the cirrus clouds are generally located at height between 11.2 to 15 km with average height of 13.4 km. Their thickness is between 0.3 and 3.8 km with average value of 1.7 km. We also compare the properties of cirrus cloud with that observed at other locations around the world based on lidar technique