04. Trường Đại học Khoa học tự nhiên (University of Science)
Tài nguyên nội sinh
Các bộ sưu tập số Luận văn Thạc sĩ, Luận án Tiến sĩ, Đề tài Báo cáo khoa học… của Thư viện Trường ĐH. Khoa học tự nhiên – ĐHQG-HCM được xây dựng nhằm hỗ trợ cho hoạt động giảng dạy và nghiên cứu của Nhà trường.
Duyệt 04. Trường Đại học Khoa học tự nhiên (University of Science) theo Kiểu tài liệu "Article"
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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 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 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 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 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-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 pso-sa scheme for improving the accuracy of fuzzy time series forecasting mo dels
Forecasting methods based on fuzzy time series have been examined intensively during the last few years. Three main factors which affct the accuracy of those forecasting methods are the length of intervals, the way of establishing fuzzy logical relationship groups, and defuzzifiation techniques. Many researchers focus on studying the methods of optimizing the length of intervals to improve forecasting accuracies by utilizing various optimization techniques. In line with that research trend, this paper proposes a hybrid algorithm combining particle swarm optimization with the simulated annealing technique (PSO-SA) to optimize the length of intervals to improve forecasting accuracies. The experimental results on the datasets of the “enrolments of the University of Alabama,” “killed in car road accidents in Belgium,” and the “spot gold in Turkey” have shown that the proposed forecasting model is more effctive than their counterparts.
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A hydrothermal pathway for TiO2-rGO heterojunction nanocomposite to enhance hydrogen peroxide evolution
In this study, titanium dioxide-reduced graphene oxide (TiO2-rGO(TGO)) nanocomposite was synthesized via hydrothermal pathway. The characterization of the fabricated material revealed an efficient incorporation of the two constituents, as well as a notable decrease in the band gap energy of TGO compared to that of pristine TiO2 (2.62 versus 3.15 eV, respectively), which can expand the absorption spectrum of the catalyst towards the visible region. Electrochemical studies also elucidated the contribution of the rGO substrate in prolonging the recombination rate of charge carriers, signifying a noticeable enhancement in the photocatalytic capability of the TGO composite. Meanwhile, the hydrogen peroxide evolution performance of the synthesized photocatalyst was relatively promising with a concentration of up to 158.34 μM after 180 min. Along with further examinations additionally showing the plausible formation and participation of reactive oxygen radicals during the photocatalytic scheme, the TGO material indicates a good potential for several practical applications, especially the generation of hydrogen peroxide under light irradiation.
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A key to Vietnamese polygonaceae genera, with note on extended distribution of persicaria runcinata in Vietnam
The present work provides a taxonomic key to the genera of Polygonaceae found in Vietnam. It also reports the first occurrence of Persicaria runcinata in Lai Chau province, extending its known distribution within the country. Detailed photographic illustrations of the specimens studied accompany the findings. This extended distribution of P. runcinata could hold significant conservation implications, emphasizing the importance of further research and conservation efforts in the region.
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A long-term dataset of in-situ temperature data reveals wind-induced up- and downwelling in the coastal waters of Nha Trang, South Central Vietnam
In this paper, the first long-term observation (2008–2019) of temperatures, not only from SST data, but also in situ in coral reefs (10 and 18 m depth) at Nha Trang Bay, South Central Vietnam, was investigated. The data showed that wind-induced upwelling during summer mainly govern the coastal region. In contrast, wind-induced downwelling was found during winter, visible in all three investigated water layers (SST, 10 and 18 m). In winter, the vertical mixing is strong and there is virtually no time-lag between the layers. In summer a scattering layer was formed, the phenomenon where a layer of water with different properties (such as temperature or salinity) is formed, blocking the sinking of water. In summer, correlations with air temperature were not significant, nor were correlations with night cooling, thus having implications for the distribution of nutrients and the health of the coral reefs. However, this was only the situation near the coast. Wavelet analysis shows that the short-term variability is significantly more substantial, caused by the shallow depth of the thermocline, which is much stronger affected by tidal and weather events than in winter. As a result of the combination of large yearly temperature variations (21 oC to 31oC) plus increased sediment deposition in the rainy seasons, reefs close to the shore are generally not well-developed. This paper strongly advocates for science-based monitoring of coral reef conditions and underscores the need for law enforcement within the Marine Protected Area of Nha Trang Bay.
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A Meshfree-based topology optimization approach without calculation of sensitivity
This paper presents a novel topology optimization approach without calculation of sensitivity for the minimum compliance problems, based on the meshfree Radial Point Interpolation Method (RPIM). Relying on the algorithm of Proportional Topology Optimization (PTO), material is distributed using only information of the objective function (which is the elastic strain energy). Material properties are interpolated by the wellknown Solid Isotropic Material with Penalization (SIMP) technique; however the pseudo density (design variables) are not defied on the element center as usually encountered in fiite element-based approaches, but on integration points. Since no element exists in meshfree analysis, this would be a natural choice. More importantly, the number of integration points is in general larger than that of elements or that of nodes, resulting in higher resolution of the density fild. The feasibility and effiiency of the proposed approach are demonstrated and discussed via several numerical examples.
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A method of semantic-based image retrieval using graph cut
Semantic extraction from images is a topical problem and is applied in many diffrent semantic retrieval systems. In this paper, a method of image semantic retrieval is proposed based on a set of images similar to the input image. Since then, the semantics of the image are queried on the ontology by the visual word vector. The objects in each image are classifid and features extracted based on Mask R-CNN, then stored on a graph cut to extract semantics from the image. For each image query, a similar set of images is retrieved on the graph cut and then a set of the visual words is extracted based on the classes obtained from Mask R-CNN, as the basis for querying semantics of an input image on ontology by SPARQL query. On the basis of the proposed method, an experiment was built and evaluated on the image datasets MIRFLICKR-25K and MS COCO. Experimental results are compared with recently published works on the same data set to demonstrate the effctiveness of the proposed method. According to the experimental results, the semantic image retrieval method in the paper has improved the accuracy to 0.897 for MIRFLICKR-25K, 0.873 for MS COCO.
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A modified hard-templating for hollow mesoporous silica nanoparticles with suitable particle size and shortened synthesis time
In this study, HMSN was synthesized using hard-templating with some modifications to shorten the synthesis time and adjust the particle size to nearly 100 nm. The obtained HMSN particles showed high uniform morphology as spheres with hollow coremesoporous shell structure, having the particle diameter of about 90 nm, the hollow diameter of about 68 nm, and the mesoporous shell thickness of about 11 nm. The total time for the main reactions was shortened by more than half from 21 hours to 9 hours. Additionally, MTT assays revealed that the synthesized HMSN was biocompatible material. This modified hard-template method with shorter synthesis time and nearly 100 nm obtained particle diameter would be meaningful for scientific research and industrial scale production.
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A multivariate linear regression model for estimating chlorophyll-a concentration in Quan Son Reservoir (Hanoi, Vietnam) using Sentinel-2B Imagery
Monitoring chlorophyll-a concentration (Chla) in inland waters is vital for environmental assessment. This study develops an empirical multivariate linear regression (MLR) model to directly estimate Chla in Quan Son Reservoir using Sentinel-2B (S2B) Level 2A images. Regression analysis of a 68-point in-situ Chla dataset measured in Quan Son Reservoir between 2021 and 2023, in conjunction with the corresponding S2B reflectance data, reveals a significant correlation between Chla and a combination of the blue (B2), green (B3), and red (B4) bands (coefficient of determination, R² = 0.95). The Chla estimation model is validated using a 30-point in-situ dataset collected on various dates (R² = 0.87; the root-mean-squared error RMSE < 5%). Subsequently, the model is applied to ten S2B images acquired from 2021 to 2023, revealing Chla's spatio-temporal distribution across the reservoir. Two key trends emerge: (1) Chla is lower during winter (November and December) than in summer and early autumn (July and September), and (2) The distribution of Chla undergoes noticeable spatial changes, particularly in July, with elevated levels observed in areas characterized by tourist hotspots. This approach shows promise for monitoring Chla in similar inland waters.
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A new analytical approach of nonlinear thermal buckling of fg gplrc circular plates and shallow spherical caps using the fsdt and galerkin method
A new analytical approach for nonlinear thermal buckling of Functionally Graded Graphene Platelet Reinforced Composite (FG-GPLRC) circular plates and shallow spherical caps using the fist-order shear deformation theory (FSDT) is presented in this paper. The circular plates and shallow spherical caps are assumed to be subjected to uniformly distributed thermal loads. By applying the Galerkin method, the relations between thermal load-deflction are achieved to determine the postbuckling behavior and critical buckling loads of the considered structures. Special effects on the nonlinear thermal behavior of circular plates and shallow spherical caps with fie different material distribution laws, different Graphene platelet (GPL) mass fractions, and geometrical dimensions are explored and discussed in numerical examples.