HCMUS - Danh sách bài trích tạp chí (The List of Article)
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Duyệt HCMUS - Danh sách bài trích tạp chí (The List of Article) theo Nhan đề
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50 years field oriented control of three phase ac drives in the practice - The state-of-the-art
From DC motors (DCM) we know, it is possible to independently control the two currents of the flx and torque generating. Because the two DCM circuits are completely isolated, we obtain simple adjustment algorithms that require little computing time on the microprocessor. This paper aims to provide an overview of the development status of FOC in industrial practice. The content presented deals mainly with 3-phase induction motors.
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A balanced edge detector for aeromagnetic data
One of the most significant challenges in interpreting magnetic data is to mitigate the influence of the magnetization direction in the shape of the anomalies, especially when reduction to the pole fails to position them over the sources. We propose a balanced high-order filter that has low sensitivity to the direction of the resultant magnetization. We consider an edge-detector filter based on directional analytic signals of the vertically-integrated anomaly field. To ensure that anomalies from shallow and deep sources are equalized, we balance this filter with the magnitude of its two-dimensional Hilbert transform. The proposed filter is tested using aeromagnetic data from Apiai Terrane, Southern Brazil. The enhanced map is highly correlated with the study area's NE-SW trend of geological structures. Our results highlight the importance of using the directional analytical signals, which help reduce the influence of the magnetization direction, and the balancing filters, for equalizing the signal of shallow and deep causative sources.
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A closed-form solution for a queueing model of energy efficient Ethernet links
To save energy consumption of Ethernet switches, IEEE has standardized a new energyeffient operation for Ethernet links with a low-power state and transition mechanisms between the high-power state for transporting traff and the low-power state. In this paper, we propose a queueing model with the Markov Modulated Compound Poisson Process that is able to characterize backbone packet traffi We derive a closed-form solution for the stationary distribution of the proposed queueing model. We show that our model can capture an entire system where the transition times are constant.
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A colorimetric multiplex RPA approach for on-site dual monitoring of Vibrio parahaemolyticus and White Spot Syndrome Virus in Pacific Whiteleg shrimp
White spot syndrome virus (WSSV) and Vibrio parahaemolyticus represent the most prevalent and serious pathogens in Viet Nam's shrimp industry, annually inflicting major production losses. With no existing therapeutic measures, timely and accurate diagnosis is imperative for curbing outbreaks and limiting economic impacts through isolation and culling protocols. However, conventional techniques like polymerase chain reaction (PCR) and quantitative PCR require advanced laboratory infrastructure that is incompatible with rapid on-site pathogen surveillance.
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A combined approach for sustainable use of mountainous agricultural land: land suitability evaluation and ecological economic model
In the quest for poverty reduction, developing sustainable agriculture and stabilizing cultivation areas for specialty plants can be substantially improved by a new approach, which combines land suitability assessment and designs of ecological-economic models. The land suitability was assessed according to the FAO framework and the Vietnamese standard TCVN-8409:2012. The criteria were grouped in climate, topography, and soil physicochemical properties. The seven land utilization types selected for analysis included orange, banana, tea, turmeric, cinnamon, chinaberry, and chukrasia in mountainous regions of Thanh Hoa and Nghe An Provinces, Vietnam. The results showed that the most suitable lands (S1) for cultivating these crops were 169.916 ha for orange, 201.166 ha for banana, 107.636 ha for tea, 570,744 ha for turmeric, 233.987 ha for cinnamon, 633.306 ha for chinaberry, and 280.704 ha for chukrasia. Based on that foundation, the study designed three ecological models with economic efficiency and elaborated on six formations of the models. The analyses obtained from the research also allowed the proposal of six prioritized spaces for land use and environmental protection, which were illustrated on a 1:100,000 map of proposed agricultural land use. Therefore, this study provided a detailed and reliable approach for researchers and managers in agricultural land use planning. A proper procedure for the assessment of agricultural land in mountainous areas can be suggested from this research.
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A comparison of benzene-core PCF optical characteristics with hexagonal and circular lattices
This work compares the optical characteristics of benzene photonic crystal fiber with hexagonal lattice (H-PCF) and circular lattice (C-PCF). The difference in the radius between the rings is used to optimize effective area, nonlinearity, attenuation, and chromatic dispersion simultaneously. A series of numerical studies show that the effective mode area and attenuation are small for C-PCF and large for H-PCF geometries. That is the reason why the nonlinear coefficient of H-PCF is larger. Meanwhile, flat dispersion over a wide wavelength range is achieved mostly with H-PCFs in both dispersion modes. Our optimized fibers with near-zero flat dispersion, small loss, and large nonlinearity can enhance supercontinuum generation efficiency to generate a broad spectrum with high coherence.
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A COMPARISON OF THREE VARIANT CALLING PIPELINES USING SIMULATED DATA
Advances in next generation sequencing allow us to do DNA sequencing rapidly at a relatively low cost. Multiple bioinformatics methods have been developed to identify genomic variants from whole genome or whole exome sequencing data. The development of better variant calling methodologies is limited by the difficulty of assessing the accuracy and completeness of a new method. Normally, computational methods can be benchmarked using simulated data which allows us to generate as much data as desired and under controlled scenarios. In this study, They compared three variant calling pipelines: Samtools/VarScan, Samtools/Bcftools, and Picard/GATK using two simulated datasets. The result showed a significant difference between the three pipelines in two cases. In Chromosome 6 dataset, GATK and Bcftools pipelines detected more than 90% of variants. Meanwhile, only 82.19% of mutations were detected by VarScan. In NA12878 datasets, the result showed GATK pipeline was more sensitive than Bcftools and Varscan pipeline. All pipelines showed a high Positive Predictive Value. Moreover, by a measure of run time, VarScan was the highest pipeline but GATK has an option for multithreading which is a way to make a program run faster. Therefore, GATK is more effective than Bcftools and Varscan to variant calling with a lower coverage dataset.
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A comprehensive review of rock dust for soil remineralization in sustainable agriculture and preliminary assessment of nutrient values in micronized porous basalt rock from Nghe An province, Viet Nam
This review delves into the utilization of rock powder as a mineral-derived fertilizer to support soil remineralization in the context of sustainable agriculture. Soil remineralization has emerged as a key strategy for ensuring long-term soil fertility and reducing the dependency on synthetic fertilizers. This review critically assesses the methodological approaches applied in various studies, taking into account factors such as local rock mineralogy, soil conditions, crop types, and nutrient uptake by plants.
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A comprehensive review of rock dust for soil remineralization in sustainable agriculture and reliminary assessment of nutrient values in micronized porous basalt rock from Nghe An province, Viet Nam
This review examines the use of rock powder as a mineral-derived fertilizer to promote soil remineralization in sustainable agriculture. Soil remineralization has emerged as a key strategy for ensuring long-term soil fertility and reducing reliance on synthetic fertilizers. This review critically assesses the methodological approaches employed in various studies, considering factors such as local rock mineralogy, soil conditions, crop types, and plant nutrient uptake. The review encompasses methods such as nutrient value analysis, nutrient release rate assessment, and crop cultivation experiments conducted in both laboratory and field settings, as well as implications for soil remineralization. Additionally, we present findings from a preliminary study on the nutrient content of basalt rock from Nghe An Province, Vietnam. This investigation focuses on assessing the nutrient values in a specific porous basalt material processed into micro- and nanometer-sized particles using a high-energy ball mill, thereby enhancing the efficiency of nutrient release. The study employs various analytical techniques, including XRD, XRF, SEM/EDS, and ICP-MS, to quantify macro- and micro-nutrient compositions. While basalt rock samples may exhibit limitations in providing macronutrients (like K) compared to commercial azomite rock, they offer advantages in supplying secondary macronutrients (such as Ca). This analysis provides insights into the potential use of micronized basalt rock for soil remineralization.
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A CONTRIBUTION TO THE TAXONOMIC KNOWLEDGE OF THE GENUS Thrinchostoma (Thrinchostoma s. str.) Saussure (Hymenoptera: Halictidae) FROM VIETNAM, WITH NEW RECORDS OF THREE SPECIES AND A KEY TO SPECIES
Four species of the little-known bee genus Thrinchostoma Saussure, 1890 from Vietnam are presented including Thrinchostoma (Thinchostoma s. str.) tonkinense Blüthgen, 1926 and three new records: T. (T.) flaviscapus Bülthgen, 1926; T. (T.) sladeni Cockerell, 1913; and T. (T.) yunnanense Niu & Zhu, 2016. The occurrence of T. (T.) tonkinense Blüthgen, 1926 in Vietnam is confirmed. A key to species and distribution map of four species of the genus Thrinchostoma are also provided for Vietnam.
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A critical review on factors affecting the two-stage anaerobic digestion of biodegradable solid waste
Anaerobic digestion (AD) technology has attracted considerable attention from the scientific community and has become a crucial component of sustainable solid waste management systems. It is the only biotechnological process capable of converting highmoisture biomass waste into energy through a series of biochemical steps, including hydrolysis, acidogenesis, acetate production, and methane generation. Notably, the two-stage anaerobic digestion (TAD) process, which separates methanogenesis and hydrolysis into two distinct reactors, offers significant advantages over conventional methods. The performance of AD systems is influenced by various factors, including growth conditions (such as carbon-tonitrogen ratio, pH, and temperature), operational parameters (such as retention time and organic loading rate), feedstock pretreatment, and potential inhibitors. While these aspects have been widely studied in single-stage anaerobic digestion systems, research on their impact in TAD systems remains limited. This study aims to provide a comprehensive review of the factors affecting TAD systems. It synthesizes the latest research findings from recent years and discusses optimal operating conditions to enhance TAD performance.
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A data constrained approach for occupational silicosis detection on chest X-rays with few-shot learning
Occupational silicosis is a serious lung disease caused by long-term exposure to silica dust, mainly affcting workers in industries such as mining and construction. Diagnosis of silicosis is challenging due to subtle disease manifestations on chest X-rays (CXRs) and limited labeled medical data. Traditional deep learning models, such as Convolutional Neural Networks (CNNs), often require large datasets, which are often heavily expensive and time-consuming for collection and annotation, yet useful for specialized medical applications. To address these challenges, we present the use of FewShot Learning (FSL) to enable accurate detection of occupational silicosis with a minimal number of labeled examples. Our experimental results demonstrate that the FSL-based model achieves 84.4% accuracy and 46.0% mIoU in the 1-shot setting and 89.52% accuracy with 47.89% mIoU in the 4-shot setting. These fidings highlight the potential of FSL to improve diagnostic accuracy in datalimited environments, making it a viable solution for improving medical image analysis in resourceconstrained settings.
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A design of tuned mass damper with piezoelectric stack energy harvester and two-stage force amplification frame
The paper deals with a novel tuned mass damper (TMD) with an energy harvester consisting of a combination of piezo stacks and two-stage force amplification frames connected in series with TMD springs (TMD-2sPSFAFs). The governing equations of 2sPSFAF are established first, followed by those of TMD-2sPSFAF. It will be demonstrated that the reduced order model of the series combination of 2sPSFAF and TMD spring is an equivalent piezo stack energy harvester. Using the optimal results according to the fixed point theory, the conditions for selecting stiffnesses of piezo stacks and TMD spring are obtained. Next, a numerical examination of the electromechanical system reveals that the voltage amplitude curve has a fixed point independent of TMD spring stiffness. Furthermore, an effective stiffness of the TMD spring would be found to ensure that the peaks of mechanical magnification and voltage amplitude curves are of equal heights.
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A discovery of the genus Philoplitis Nixon, 1965 (Hymenoptera: Braconidae: Microgastrinae) in Vietnam, with description of two new species
The small microgastrine genus Philoplitis Nixon, 1965 from Vietnam is newly recorded. As a result, two new species are described and fully illustrated, viz. Philoplitis ipunctatus Long, sp. nov. and Philoplitis cariniferus Long, sp. nov.. Additionally, the genus Philoplitis is recorded for the first time for Vietnam, and the comparative characters of Philoplitis species summarized in a table.
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A Discussion on the Renaming of Cymodocea serrulata to Oceana serrulata: Implications for Seagrass Research and Conservation in Vietnam
The seagrass species previously classified as Cymodocea serrulata has been reclassified as Oceana serrulata. This reclassification is based on genetic analysis, which reveals a distinct separation of this species from others within the Cymodocea genus. This change will have implications for future research and taxonomy, highlighting the value of advanced genetic techniques in elucidating the relationships between seagrass species. Nevertheless, further analysis is warranted as new evidence emerges. The transition to the designation Oceana serrulata harmonizes traditional morphological descriptions with modern genetic phylogenetics, enabling a more precise classification.
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A fast overlapping community detection algorithm based on label propagation and social netwo rk graph clustering coefficient
In this paper, we present an algorithm to fid overlapping communities in very large social networks. The algorithm is based on the label propagation technique, and we fid the overlapping communities in the network by improving the clustering coeffient. Tests on a set of popular, standard social networks and certain real networks have shown the high speed and high effiency in fiding overlapping communities.
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A framework for flood depth using hydrodynamic modeling and machine learning in the coastal province of Vietnam
Flood models based on traditional hydrodynamic modeling encounter significant difficulties with real-time predictions, require enormous computational resources, and perform poorly in data-limited regions. The difficulties are compounded as flooding worldwide worsens due to the increasing frequency of short-term torrential rain events, making it more challenging to predict floods over the long term. This study aims to address these challenges by developing a rapid flood forecasting model combining machine learning algorithms (support vector regression, XGBoost regression, CatBoost regression, and decision tree regression) with hydrodynamic modeling in Quang Tri province in Vietnam. 560 flood depth locations were obtained by hydrodynamic modeling, and several locations measured in the field were used as input data for the machine learning models to build a flood depth map for the study area. The statistical indices used to evaluate the performance of the four proposed models were the receiver operating characteristic (ROC) curve, area under the ROC curve, root mean square error, mean absolute error, and coefficient of determination (R). The results showed that all four models successfully constructed a flood depth map for the study area. Among the four proposed models, CatBoost regression performed best, with an R³ value of 0.86. This was followed by XiBoost regression (R-0.84), decision tree regression (R-0.72), and then support vector regression (R-0.7). This integration of hydrodynamic modeling and machine learning complements the framework in much of the existing literature. It can provide decision-makers and local authorities with an advanced flood warning tool and contribute to improving sustainable development strategies in this and similar regions.
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A high effective CuInS2 photocatalyst for oxidative desulfurization of dibenzothiophene in fuel oil
In this study, CuInS2 materials were synthesized by hydrothermal method. The effect of different reaction times was investigated. The structure and morphology of the materials were characterized by XRD, SEM, TEM, UV-Vis and EDX measurement methods. The obtained results showed that with a reation time of 24 h, the CuInS2 sample (CIS-24) had an average particle size of about 50 nm and a band gap energy value of Eg = 1.48 eV, with the ability to strongly absorb visible light. The photocatalytic activity of CIS-24 was investigated by the degradation of dibenzothiophene under visible light irradiation. The photocatalytic efficiency reached a maximum of 97.9 % at an initial concentration of 500 ppm after 5 h of reaction at 70 oC with a catalyst dosage of 50 mg and 1.0 mL of H2O2 as an oxidant.
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A high-efficiency positive buck–boost converter with three-mode selection circuit and soft start circuit
Positive Buck-Boost converters are considered the optimal choice for battery-powered applications that require high power conversion efficiency and extend battery life. The converter offers versatility in processing input voltages over a wide range thanks to its three operating modes: Boost, Buck, and Buck-Boost (Bck-Bst) while maximizing battery capacity. The efficiency of the converter can be severely affected by a severe ripple in the output voltage and current when transitioning between modes. To reduce the output instability and output ripple during operation, a three-mode selection circuit that uses a very small delay between mode transitions is proposed to minimize the above effects. The proposed chip was designed and implemented on CMOS 0.18 µm process. In addition, a high peak efficiency of 95.6 % can be achieved under the conditions of a wide input range of 2.5 V - 5 V.
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A high-resolution climate experiment over part of Vietnam and the Lower Mekong Basin: performance evaluation and projection for rainfall
This study first evaluates the performance of three model experiments in representing rainfall over part of Vietnam and the Lower Mekong Basin for the historical period 1986-2005. The three experiments include the Coupled Model Intercomparison Project Phase 5 (CMIP5) EC-EARTH Global Climate Model (GCM) and two downscaling runs based on a regional climate model at 25km resolution with the GCM forcing (RCM-25km) and at 5km resolution with the RCM-25km forcing (RCM-5km). Verifications against observations show that the experiments generally capture the spatial distribution of climatological rainfall. While the GCM well represents the observed average rainfall cycles, its coarse resolution limits its capability in reproducing extreme rainfall values. The downscaling experiments do not clearly show their advantage in simulating average rainfall but exhibit significant added values when representing extreme rainfall in the study region. The RCM-5km does not outperform its driving 25km experiment in representing the mean and extreme rainfall values, suggesting that having a better resolution may not compensate for having a good model configuration with appropriate physical schemes. Analysis of climate projection for the far future period 2080-2099 under two representative concentration pathways (RCP) scenarios, RCP4.5 and RCP8.5, reveals that the downscaling experiments can modify the change direction of future rainfall obtained with the GCM. While the EC-EARTH GCM generally projects wetter tendencies of up to 50%, the downscaling experiments project a general decrease of down to -50% under both scenarios over the study domain. Regarding extreme rainfall, the annual maximum 1-day rainfall amount (RX1day) is projected to increase for the three experiments. The simple daily intensity index (SDII) future changes follow those of the annual rainfall values.
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A high-sensitivity hydrogen gas sensor based on Ag nanoparticle-decorated porous Co3O4 nanorods
In this report, Ag nanoparticle-decorated cobalt carbonate hydroxide microflowers were synthesized using a simple hydrothermal approach without using surfactants, which were used as precursors to prepare Ag nanoparticle-decorated porous Co3O4 nanorods through an annealing process. The porous Co3O4 nanorods were composed of small primary nanoparticles with a size of ~ 10 nm, and their surface was decorated with uniform Ag nanoparticles (~ 10 nm). Sensors based on the porous Co3O4 nanorods decorated with Ag nanoparticles have higher sensitivity and selectivity to H2 gas than other reduced gases, as well as rapid response-recovery times. The enhanced H2 sensing properties of the sample may be attributed to the excellent catalytic features of Ag nanoparticles and unique porous Co3O4 nanorods. The results demonstrated the potential of Ag nanoparticle-decorated porous Co3O4 nanorods as sensing materials for the detection of hydrogen gas at low temperatures.
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A HOMOZYGOUS VARIANT IN G6PC IN A VIETNAMESE PATIENT WITH GLYCOGEN STORAGE DISEASE TYPE IA
Glycogen storage diseases (GSDs) are rare inherited metabolic disorders characterized by the absence of required enzymes for the glycogen degradation metabolism. GSD can be divided into more than 12 types based on enzyme deficiency and affected tissues, in which glycogen storage disease type Ia (GSD1a or von Gierke disease) is a liver-affecting form. GSD1a is an autosomal recessive inherited disease caused by mutations in the G6PC gene on chromosome 17q21. The present study reports a Vietnamese family with a 6-month-old male patient diagnosed with type Ia glycogen storage disease. A homozygous variant in the G6PC gene (NM_000151.3: c.518T>C; p.L173P) was detected in the proband using a comprehensive glycogen storage disease panel. This variant has been previously reported in ClinVar (Accession ClinVar: VCV000640818.3). The segregation of the variant was confirmed in ten people of a 3-generation family using Sanger sequencing. The results showed both parents were heterozygous for the variant. In addition, the variant c.518T>C in the G6PC gene was predicted to be deleterious using in silico prediction tools (SIFT, PolyPhen-2, Proven, REVEL, and MutPred2). Our results could help doctors decide on appropriate treatment and diet for the disease. Moreover, the study is also a contribution to molecular studies on GSD1a.
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A hybrid model using the pretrained bert and deep neural networks with rich feature for extractive text summarization
Deep neural networks have been applied successfully to extractive text summarization tasks with the accompany of large training datasets. However, when the training dataset is not large enough, these models reveal certain limitations that affct the quality of the system’s summary. In this paper, we propose an extractive summarization system based on a Convolutional Neural Network, an Encoder-Decoder, and a Fully Connected network for sentence selection. The pretrained BERT multilingual model is used to generate embedding vectors from the input text. These vectors are combined with TF-IDF values to produce the input of the text summarization system. Redundant sentences from the output summary are eliminated by the Maximal Marginal Relevance method. Our system is evaluated with both English and Vietnamese languages using CNN and Baomoi datasets, respectively. Experimental results show that our system achieves better results compared to existing works using the same dataset. It confims that our approach can be effctively applied to summarize both English and Vietnamese languages.
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A hybrid multi-constraint lagrangian relaxation based aggregated cost based segment routing in qos aware software defined networks
The Industrial Internet is a cornerstone of Industry 4.0, emphasizing the need for high reliability and low latency in network control. It enables the integration of diverse IoT sensing and actuation devices for efficient monitoring and management of industrial systems. SoftwareDefined Networking (SDN), a modern network architecture, offers centralized control by logically separating the control and data planes. This global view facilitates flexible network administration, optimized performance, and enhanced adaptability. However, meeting the increasing demands for efficient and dependable network services in SDNs has made Quality of Service (QoS) assurance a critical challenge. This study introduces a novel approach Hybrid Multi-constraint Lagrangian Relaxation-based Aggregated Cost (HMLR-AC) Segment Routing for QoS-aware routing in SDNs. HMLR-AC combines the strengths of Lagrangian Relaxation (LR) and Aggregated Cost (AC) to enhance routing decisions. Additionally, it incorporates an H-permissible Paths Routing Scheme (HPRS) that effectively routes traffic within cardinality constraints. Utilizing real-time traffic insights and a global network perspective, HMLR-AC dynamically adapts routing strategies. Simulation results demonstrate notable improvements in QoS parameters, proving HMLR-AC as a promising solution for future QoS-driven SDN infrastructures.