Thanh Thuy Nguyen

Particulate matter concentration mapping from MODIS satellite data: A Vietnamese case study

Particulate Matter (PM) pollution is one of the most important air quality concerns in Vietnam. In this study, we integrate ground-based measurements, meteorological and satellite data to map temporal PM concentrations at a 10 × 10 km grid for the entire of Vietnam. We specifically used MODIS Aqua and Terra data and developed statistically-significant regression models to map and extend the ground-based PM concentrations. We validated our models over diverse geographic provinces i.e., North East, Red River Delta, North Central Coast and South Central Coast in Vietnam. Validation suggested good results for satellite-derived PM2.5 data compared to ground-based PM2.5 (n = 285, r2 = 0.411, RMSE = 20.299 μg m−3 and RE = 39.789%). Further, validation of satellite-derived PM2.5 on two independent datasets for North East and South Central Coast suggested similar results (n = 40, r2 = 0.455, RMSE = 21.512 μg m−3, RE = 45.236% and n = 45, r2 = 0.444, RMSE = 8.551 μg m−3, RE = 46.446% respectively). Also, our satellite-derived PM2.5 maps were able to replicate seasonal and spatial trends of ground-based measurements in four different regions. Our results highlight the potential use of MODIS datasets for PM estimation at a regional scale in Vietnam. However, model limitation in capturing maximal or minimal PM2.5 peaks needs further investigations on ground data, atmospheric conditions and physical aspects.

Satellites may underestimate rice residue and associated burning emissions in Vietnam

In this study, we estimate rice residue, associated burning emissions, and compare results with existing emissions inventories employing a bottom-up approach. We first estimated field-level post-harvest rice residues, including separate fuel-loading factors for rice straw and rice stubble. Results suggested fuel-loading factors of 0.27 kg m−2 (±0.033), 0.61 kg m−2 (±0.076), and 0.88 kg m−2 (±0.083) for rice straw, stubble, and total post-harvest biomass, respectively. Using these factors, we quantified potential emissions from rice residue burning and compared our estimates with other studies. Our results suggest total rice residue burning emissions as 2.24 Gg PM2.5, 36.54 Gg CO and 567.79 Gg CO2 for Hanoi Province, which are significantly higher than earlier studies. We attribute our higher emission estimates to improved fuel-loading factors; moreover, we infer that some earlier studies relying on residue-to-product ratios could be underestimating rice residue emissions by more than a factor of 2.3 for Hanoi, Vietnam. Using the rice planted area data from the Vietnamese government, and combining our fuel-loading factors, we also estimated rice residue PM2.5 emissions for the entirety of Vietnam and compared these estimates with an existing all-sources emissions inventory, and the Global Fire Emissions Database (GFED). Results suggest 75.98 Gg of PM2.5 released from rice residue burning accounting for 12.8% of total emissions for Vietnam. The GFED database suggests 42.56 Gg PM2.5 from biomass burning with 5.62 Gg attributed to agricultural waste burning indicating satellite-based methods may be significantly underestimating emissions. Our results not only provide improved residue and emission estimates, but also highlight the need for emissions mitigation from rice residue burning.

Algorithmic approach to deadlock detection for resource allocation in heterogeneous platforms

An allocation of resources to a virtual machine specifies the maximum amount of each individual element of each resource type that will be utilized, as well as the aggregate amount of each resource of each type. An allocation is thus represented by two vectors, a maximum elementary allocation vector and an aggregate allocation vector. There are more general types of resource allocation problems than those we consider here. In this paper, we present an approach for improving parallel deadlock detection algorithm, to schedule the policies of resource which supply for resource allocation in heterogeneous distributed platform. Parallel deadlock detection algorithm has a run time complexity of O(min(m,n)), where m is the number of resources and n is the number of processes. We propose the algorithm for allocating multiple resources to competing services running in virtual machines on a heterogeneous distributed platform. The experiments also compare the performance of the proposed approach with other related work.

Deadlock Detection for Resource Allocation in Heterogeneous Distributed Platforms

An allocation of resources to a virtual machine specifies the maximum amount of each individual element of each resource type that will be utilized, as well as the aggregate amount of each resource of each type. An allocation is thus represented by two vectors, a maximum elementary allocation vector and an aggregate allocation vector. There are more general types of resource allocation problems than those we consider here. In this paper, we present an approach for improving deadlock detection algorithm, to schedule the policies of resource supply for resource allocation on heterogeneous. Deadlock detection algorithm using two way has run time complexity of O(min(m 1/2 ,n 2/3 ), where m is the number of resources and n is the number of processes. We propose the algorithm for allocating multiple resources to competing services running in virtual machines on a heterogeneous distributed platform. The experiments also compare the performance of the proposed approach with other related work.

Aligning Multi Sequences on GPUs

Implementing Multi Sequence Alignment (MSA) problem using the method of progressive alignment is not feasible on common computing systems; it takes several hours or even days for aligning thousands of sequences if we use sequential versions of the most popular MSA algorithm - Clustal. In this paper, we present our parallel algorithm called CUDAClustal, a MSA parallel program. We have paralleled the first stage of the algorithm Clustal and achieved a significant speedup when compared to the sequential program running on a computer of Pentium 4 3.0 GHz processor. Our tests were performed on one GPU Geforce GTX 295 and they gave a great computing performance: the running time of CUDAClustal is smaller approximately 30 times than Clustal for the first stage. This shows the large benefit of GPU for solving the MSA problem and its high applicability in bioinformatics.

Satellite Aerosol Optical Depth over Vietnam - An Analysis from VIIRS and CALIOP Aerosol Products

Satellite aerosol products are useful to address a variety of questions relating to the atmosphere, climate change, air pollution, and human health. Thus, their evaluation followed by validation in different regions of the world can help in refining the products. In this study, VIIRS (2012–2015) and CALIPSO (2006–2015) aerosol products are analyzed and compared for seasonal trend and aerosol subtypes at Nghia Do, Nha Trang, and Bac Lieu AERONET stations located in the north, central, and southern regions of Vietnam, respectively. At Nghia Do station, VIIRS AOD captured the northern seasonal trends well with low errors, and high correlation coefficients. CALIPSO aerosol subtypes have shown polluted dust, biomass burning, polluted continental, clean continental, and desert dust coinciding with the northern climate conditions, agricultural burning, and long-range transport. At Nha Trang station, VIIRS AOD performed poorly with no seasonal trends, large errors, and low correlation coefficients. However, aerosol subtype analysis revealed marine aerosol, polluted continental, polluted dust, biomass burning, and desert dust events over the Nha Trang which are mostly explained by location, local climate conditions, and vegetation burning. For Bac Lieu station, VIIRS AOD quality is the lowest compared to AERONET AOD. No seasonal trend has been captured and the errors are extremely high in rainy and dry seasons at this station. CALIPSO aerosol subtypes are marine aerosol, polluted continental, polluted dust, biomass burning, and clean continental which could be explained by location, heat island, and local paddy rice seasonality. In overall, evaluation of VIIRS and CALIPSO aerosol products over Vietnam provides useful insights on their utility and potential applications in aerosol and air quality research.

Using CPR Metric to Detect and Filter Low-Rate DDoS Flows

TCP-targeted low-rate distributed denial-of-service (LDDoS) attacks pose a serious challenge to the reliability and security of the Internet. Among various proposed solutions, we are particularly interested in the Congestion Participation Rate (CPR) metric and the CPR-based approach. Through a simulation study, we show that the existing algorithm cannot simultaneously achieve high TCP throughput while under attack and good fairness performance for new legitimate TCP flows in normal times. We then propose a new version of the CPR-based approach to overcome the tradeoff. Simulation results show that it preserves TCP throughput while under attack fairly well, yet maintains fairness for new TCP flows in normal times.

Deadlock prevention for resource allocation in model nVM-out-of-1PM

The cloud computing paradigm shift brings about disruptive changes to the traditional business models in the infocomm sector, especially for existing software licensing models and server purchase or lease models. Vietnam Posts and Telecommunications Group (VNPT) have ramped up their cloud computing efforts as a strategic response to protect their current market share. This paradigm shift also has major separability on Vietnams infocomm industry, which is heavily dependent on these traditional VNPT. Since 2014, Viet Nam has started offering services in domestic and international network in the country. The Global Data Services Joint Stock Company (GDS) is a joint venture between NTT Communications (NTT Com) and Vietnam Posts Telecommunications (VNPT). Although, a number of studies were published by third parties on how Vietnam has fared in its cloud journey. While such studies use different definitions and yardsticks, their results nonetheless are instructive in helping us to understand the progress Vietnam has made and to highlight areas for improvement. We have a few studies resources allocation such infrastructure as a services. With the aim to share research solutions and seek opportunities for local businesses who want to invest in infrastructure cloud from the Open Source duty. In this paper, we proposes an algorithm deadlock prevention the model n VM - out - of 1 PM to separate and manage resources. The algorithms for allocating multiple resources to competing services running in virtual machines on a heterogeneous distributed platforms. This paper presents an algorithm prevention deadlock used to reschedule the policies of resource supply for resource allocation. In the current scenario, deadlock prevention algorithm using method global state prevention based. We have implemented and performed our algorithm proposed by using CloudSim simulator.

Comparison of various image fusion methods for impervious surface classification from VNREDSat-1

Impervious surface is an important indicator for urban development monitoring. Accurate urban impervious surfaces mapping with VNREDSat-1 remains challenging due to their spectral diversity not captured by individual PAN image. In this artical, five multi-resolution image fusion techniques were compared for classification task of urban impervious surface. The result shows that for VNREDSat-1 dataset, UNB and Wavelet tranform methods are the best techniques reserving spatial and spectral information of original MS image, respectively. However, the UNB technique gives best results when it comes to impervious surface classification especially in the case of shadow area included in non-impervious surface group.