Vinh University

Pham B.T., Jaafari A., Avand M., Al-Ansari N., Dinh Du T., Yen H.P., Phong T.V., Nguyen D.H., Le H.V., Mafi-Gholami D., Prakash I., Thi Thuy H., Tuyen T.T.

Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with ~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility maps provide a basis for developing more efficient fire-fighting strategies and reorganizing policies in favor of sustainable management of forest resources.

Minh H.V., Tien H.A., Sinh C.T., Thang D.C., Chen C., Tay J.C., Siddique S., Wang T., Sogunuru G.P., Chia Y., Kario K.

Insulin resistance (IR), a metabolic risk factor, is linked to the pathogenetic mechanism of primary hypertension. Detecting IR in the patients with hypertension will help to predict and stratify the added cardiovascular risk, institute appropriate IR management, and manage hypertension optimally. There are many methods for assessing IR, each with distinct advantages and disadvantages. The euglycemic insulin clamp and intravenous glucose tolerance test, gold standards for measuring IR, are used in research but not in clinical practice. Homeostatic model assessment (HOMA-IR), a method for assessing β-cell function and IR, is frequently applied presently, particularly in Asia. Besides, the triglyceride-glucose index (TyG) first published by South American authors showed a good correlation with the insulin clamp technique and HOMA-IR index. This simple, convenient, and low-cost TyG index is of research interest in many countries in Asia and can be used to screen for IR in the Asian hypertensive community.

Tran V., Thai D., Nguyen D.

This paper aims to develop a practical artificial neural network tool for predicting the axial compression capacity of circular concrete-filled steel tube columns with ultra-high-strength concrete. For this purpose, a nonlinear finite element analysis of circular concrete-filled steel tube columns with ultra-high-strength concrete was conducted and verified with experiments in the literature. Accordingly, a database of 768 finite element models was generated to use for developing the artificial neural network models. In this regard, the column length, the diameter of steel tube, the thickness of steel tube, yield and ultimate strength of steel tube, and compressive strength of concrete were considered as the input variables while the axial compression capacity was considered as an output variable. The performance of the proposed artificial neural network model was compared with the current structural design codes including AS/NZS 5100.6, Eurocode 4, AISC, and GB 50936. The comparative study indicated that the proposed artificial neural network model achieved a superior prediction compared to others. Ultimately, a graphical user interface tool was developed based on the proposed artificial neural network model to predict the axial compression capacity of circular concrete-filled steel tube columns with ultra-high-strength concrete for practical engineering design. • The proposed ANN model can accurately predict the axial compression capacity of CCFST columns with UHSC. • The proposed ANN model shows more accuracy than four notable existing design code formulas. • A GUI tool is developed based on the proposed ANN model for practical engineering design.

Thai H., Thuy Nguyen C., Thi Thach L., Thi Tran M., Duc Mai H., Thi Thu Nguyen T., Duc Le G., Van Can M., Dai Tran L., Long Bach G., Ramadass K., Sathish C.I., Van Le Q.

In this study, chitosan and alginate were selected to prepare alginate/chitosan nanoparticles to load the drug lovastatin by the ionic gelation method. The synthesized nanoparticles loaded with drug were characterized by Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), laser scattering and differential scanning calorimetry (DSC) methods. The FTIR spectrum of the alginate/chitosan/lovastatin nanoparticles showed that chitosan and alginate interacted with lovastatin through hydrogen bonding and dipolar-dipolar interactions between the C-O, C=O, and OH groups in lovastatin, the C-O, NH, and OH groups in chitosan and the C-O, C=O, and OH groups in alginate. The laser scattering results and SEM images indicated that the alginate/chitosan/lovastatin nanoparticles have a spherical shape with a particle size in the range of 50–80 nm. The DSC diagrams displayed that the melting temperature of the alginate/chitosan/lovastatin nanoparticles was higher than that of chitosan and lower than that of alginate. This result means that the alginate and chitosan interact together, so that the nanoparticles have a larger crystal degree when compared with alginate and chitosan individually. Investigations of the in vitro lovastatin release from the alginate/chitosan/lovastatin nanoparticles under different conditions, including different alginate/chitosan ratios, different solution pH values and different lovastatin contents, were carried out by ultraviolet-visible spectroscopy. The rate of drug release from the nanoparticles is proportional to the increase in the solution pH and inversely proportional to the content of the loaded lovastatin. The drug release process is divided into two stages: a rapid stage over the first 10 hr, then the release becomes gradual and stable. The Korsmeyer-Peppas model is most suitable for the lovastatin release process from the alginate/chitosan/lovastatin nanoparticles in the first stage, and then the drug release complies with other models depending on solution pH in the slow release stage. In addition, the toxicity of alginate/chitosan/lovastatin (abbreviated ACL) nanoparticles was sufficiently low in mice in the acute toxicity test. The LD50 of the drug was higher than 5000 mg/kg, while in the subchronic toxicity test with treatments of 100 mg/kg and 300 mg/kg ACL nanoparticles, there were no abnormal signs, mortality, or toxicity in general to the function or structure of the crucial organs. The results show that the ACL nanoparticles are safe in mice and that these composite nanoparticles might be useful as a new drug carrier.

Pham B.T., Phong T.V., Nguyen H.D., Qi C., Al-Ansari N., Amini A., Ho L.S., Tuyen T.T., Yen H.P., Ly H., Prakash I., Tien Bui D.

Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.

Pham B.T., Jaafari A., Phong T.V., Yen H.P., Tuyen T.T., Luong V.V., Nguyen H.D., Le H.V., Foong L.K.

Improving the accuracy of flood prediction and mapping is crucial for reducing damage resulting from flood events. In this study, we proposed and validated three ensemble models based on the Best First Decision Tree (BFT) and the Bagging (Bagging-BFT), Decorate (Bagging-BFT), and Random Subspace (RSS-BFT) ensemble learning techniques for an improved prediction of flood susceptibility in a spatially-explicit manner. A total number of 126 historical flood events from the Nghe An Province (Vietnam) were connected to a set of 10 flood influencing factors (slope, elevation, aspect, curvature, river density, distance from rivers, flow direction, geology, soil, and land use) for generating the training and validation datasets. The models were validated via several performance metrics that demonstrated the capability of all three ensemble models in elucidating the underlying pattern of flood occurrences within the research area and predicting the probability of future flood events. Based on the Area Under the receiver operating characteristic Curve (AUC), the ensemble Decorate-BFT model that achieved an AUC value of 0.989 was identified as the superior model over the RSS-BFT (AUC = 0.982) and Bagging-BFT (AUC = 0.967) models. A comparison between the performance of the models and the models previously reported in the literature confirmed that our ensemble models provided a reliable estimate of flood susceptibilities and their resulting susceptibility maps are trustful for flood early warning systems as well as development of mitigation plans. • Developing three ensemble models for flood susceptibility mapping. • Performance test of Bagging, Decorate, and Random Subspace ensemble techniques. • Highest reliability of mapping coupling best first decision tree with Decorate. • Flood prevention activities primarily needed on 33% of the land area.

Tuyen T.T., Jaafari A., Yen H.P., Nguyen-Thoi T., Phong T.V., Nguyen H.D., Van Le H., Phuong T.T., Nguyen S.H., Prakash I., Pham B.T.

Fire is among the most dangerous and devastating natural hazards in forest ecosystems around the world. The development of computational ensemble models for improving the predictive accuracy of forest fire susceptibilities could save time and cost in firefighting efforts. Here, we combined a locally weighted learning (LWL) algorithm with the Cascade Generalization (CG), Bagging, Decorate, and Dagging ensemble learning techniques for the prediction of forest fire susceptibility in the Pu Mat National Park, Nghe An Province, Vietnam. A geospatial database that contained records from 56 historical fires and nine explanatory variables was employed to train the standalone LWL model and its derived ensemble models. The models were validated for their goodness-of-fit and predictive capability using the area under the receiver operating characteristic curve (AUC) and several other statistical performance criteria. The CG-LWL and Bagging-LWL models with AUC = 0.993 showed the highest training performance, whereas the Dagging-LWL ensemble model with AUC = 0.983 performed better than Decorate-LWL (AUC = 0.976), CG-LWL and Bagging-LWL (AUC = 0.972), and LWL (AUC = 0.965) for predicting the spatial pattern of fire susceptibilities across the study area. Our study promotes the application of ensemble models in forest fire prediction and enhances the researchers' understanding of the processes of model building. Although these four ensemble models were originally developed for the estimation of forest fire susceptibility, the models are sufficiently general to be used for predicting other types of natural hazards, such as landslides, floods, and dust storms, by considering local geo-environmental factors. • Developing four ensemble models for forest fire susceptibility mapping • Testing performance of CG, Bagging, Decorate, and Dagging ensemble learners • Reliable susceptibility mapping using the Dagging-LWL model (AUC = 0.983) • Providing insights for developing more advanced forest fire predictive models

Tran V., Kim S.

This study aims to investigate the performance of three advanced data-driven models, namely multivariate adaptive regression spline (MARS), artificial neural network (ANN), and adaptive neural fuzzy inference system (ANFIS), for predicting the axial compression capacity of circular concrete-filled double-skin steel tube (CFDST) columns. For this purpose, 125 experimental data sets collected from the literature were used to develop the MARS, ANN, and ANFIS models. In this regard, the column length, the outer diameter, the outer thickness and yield strength of the outer steel tube, the inner diameter, the inner thickness and yield strength of the inner steel tube, and the compressive strength of concrete were considered as input variables, meanwhile, axial compression capacity was considered as the output variable. The performance of the three data-driven models was compared with six equations proposed by design codes and other authors. The comparisons showed that three data-driven models achieved more accuracy than previous equations, of which, the ANN model has an advantage over the ANFIS and MARS models. Finally, a graphical user-friendly interface (GUI) was developed to make the MARS, ANN, and ANFIS models become more attractive for practical use.

Wu G., Shi X., Phan H., Qu H., Hu Y., Yin G., Zhao X., Li X., Xu L., Yu Q., Yang H.

Sophisticated mechanically interlocked molecules (MIMs) with interesting structures, properties and applications have attracted great interest in the field of supramolecular chemistry. We herein report a highly efficient self-assembly of heterometallic triangular necklace 1 containing Cu and Pt metals with strong antibacterial activity. Single-crystal X-ray analysis shows that the finely arranged triangular necklace 1 has two racemic enantiomers in its solid state with intriguing packing motif. The superior antibacterial activity of necklace 1 against both standard and clinically drug-resistant pathogens implies that the presence of Cu(I) center and platinum(II) significantly enhance the bacterium-binding/damaging activity, which is mainly attributed to the highly positively charged nature, the possible synergistic effect of heterometals in the necklace, and the improved stability in culture media. This work clearly discloses the structure-property relationships that the existence of two different metal centers not only facilitates successful construction of heterometallic triangular necklace but also endows it with superior nuclease properties and antibacterial activities. Precise assembly of heterometallic complexes is a challenge. Here, the authors design a heterometallic triangular necklace through a highly efficient threading-and-ring-closing approach driven by metal-ligand coordination, which shows strong bacterium-binding and cell wall/plasma membrane-disrupting capacity for killing bacterial cells.

Nguyen-Van V., Tran P., Peng C., Pham L., Zhang G., Nguyen-Xuan H.

Lightweight cellular structures with porous architectures and controllable mechanical characteristics are promising candidates for a broad range of prefabricated engineering applications. A triply periodic minimal surface (TPMS) structure that is a naturally inspired continuous non-self-intersecting surface is a bioinspired cellular structure. In this work, we investigate a novel approach based on a combination of primitive-TPMS cells and cubic blocks along with lattice and gyroid-TPMS cells achieving 50% volume fraction cellular structures. Lightweight cellular specimens made of cement mortar with 3D printed sacrificial thermoplastic Polylactic Acid (PLA) moulds are subjected to uniaxial compressive loadings. Compression tests are carried out on the cement cubes, while tensile behaviours follow the simplified damage plasticity model, which is used to obtain the material properties for the input model data. Finite element (FE) analysis is employed to predict mechanical performances such as stress distributions, stress-strain curves, and the damage mechanisms of three representative cellular structures (primitive, lattice, and gyroid). Compressive experiment tests are conducted on these blocks and validated by the FE model. Results indicate that the mechanical responses of the cellular structure, wherein primitive cellular structures yield the highest compressive strength, could be predicted accurately through the FE analysis, and outcomes from both numerical models and experimental tests are validated.