Journal Articles - Engineering - 2023
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PublicationA localized hybrid kernel meshless technique for solving the fractional Rayleigh–Stokes problem for an edge in a viscoelastic fluid( 2023)
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PublicationA NURBS-based IGA using zig-zag plate theory for nonlinear passive/semi-active damping analysis of laminated FG-CNTRC plates( 2023)T. Nguyen-Thoi, Duy-Khuong Ly, Sy-Ngoc Nguyen, Vinyas Mahesh, Chanachai Thongchom"In this article, a novel numerical approach based on electromechanical coupling isogeometric analysis employing a piecewise linear zig-zag function is proposed for modeling and analysis of smart constrained layer damping (SCLD) treatment in multilayer porous functionally graded graphene platelets-reinforced composite (PFG-GPRC) plates. The approach efficiently approximates the geometric, mechanical, and electric displacement fields by utilizing non-uniform rational B-splines (NURBS) basis functions. These basis functions are subsequently integrated with the zig-zag formulation to characterize the system dynamic and help handle both continuous/discontinuous material properties at all interfaces, as well as improve the effectiveness of global–local numerical solutions for the analysis of current structures. The multilayer PFG-GPRC plate model is designed to incorporate porous, uniformly, or non-uniformly distributed layers based on three different graphene platelet patterns. The analysis of the SCLD treatment encompasses an examination of the frequency response function of the damped structure under passive/hybrid mechanisms, taking into account"
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PublicationAir emission inventory and air quality modelling for developing countries: A case of Vinh Phuc province, Vietnam( 2023)
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PublicationAn electromechanical coupling isogeometric approach using zig-zag function for modeling and smart damping control of multilayer PFG-GPRC plates( 2023)"T. Nguyen Thoi , Duy-Khuong Ly, S. Kattimani Chanachai Thongchom""In this article, a novel numerical approach based on electromechanical coupling isogeometric anal ysis employing a piecewise linear zig-zag function is proposed for modeling and analysis of smart constrained layer damping (SCLD) treatment in multilayer porous functionally graded graphene platelets-reinforced com posite (PFG-GPRC) plates. The approach efficiently approximates the geometric, mechanical, and electric displacement fields by utilizing non-uniform rational B-splines (NURBS) basis functions. These basis func tions are subsequently integrated with the zig-zag formulation to characterize the system dynamic and help handle both continuous/discontinuous material properties at all interfaces, as well as improve the effective ness of global–local numerical solutions for the analysis of current structures. The multilayer PFG-GPRC plate model is designed to incorporate porous, uniformly, or non-uniformly distributed layers based on three different graphene platelet patterns. The analysis of the SCLD treatment encompasses an examination of the frequency response function of the damped structure under passive/hybrid mechanisms, taking into account"
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PublicationAn in-vitro evaluation of antifungal, anti-lungcancer (A549), and anti-hyperglycemic activities potential of Andrographis paniculata (Burm. f.) flower extract( 2023)
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PublicationAngle dependence of the electrical conductivity in high Tc superconductor( 2023)"Luu Huu Nguyen, Tran Cong Minh , Nguyen Chinh Cuong, Bui Duc Tinh""We use the time-dependent Ginzburg{Landau Lawrence{Doniach model with thermal noise to study angle dependence of electrical conductivity in high Tc superconductor. The electrical conductivity is calculated as temper- ature and magnetic eld in the homogeneous phase. When the magnetic eld is rotated in the plane indicated by the c axis and the direction of the electric current, the theoretical caculation based on the self-consistent Gaussian ap- proximation is used to compare with the experimental data of HgBa2CaCu2O6. Our caculations are in good agreement with the experimental data for temper- atures below Tc and above Tc. In terms of quality, the increase in resistivity brought on by a developing magnetic eld at constant strength is comparable to that brought on by a growing magnetic eld applied orthogonally to the layers. However, as the magnetic eld approaches an orientation parallel to the layers, the e ects of a perpendicular eld obviously di er from those of a tilted eld with the same normal component."
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PublicationAsymptotic stability of evolution systems of probability measures for nonautonomous stochastic systems: Theoretical results and applications( 2023)
The limiting stability of invariant probability measures of time homogeneous transition semigroups for autonomous stochastic systems has been extensively discussed in the literature. In this paper we initially initiate a program to study the asymptotic stability of evolution systems of probability measures of time inhomogeneous transition operators for nonautonomous stochastic systems. Two general theoretical results on this topic are established in a Polish space by establishing some sufficient conditions which can be verified in applications. Our abstract results are applied to a stochastic lattice reaction-diffusion equation driven by a time-dependent nonlinear noise. A time-average argument and an extended Krylov-Bogolyubov method due to Da Prato and Röckner [Seminar on stochastic analysis, random fields and applications V, Birkhäuser, Basel, 2008] are employed to prove the existence of evolution systems of probability measures. A mild condition on the time-dependent diffusion function is used to prove that the limit of every evolution system of probability measures must be an evolution system of probability measures of the limiting equation. The theoretical results are expected to be applied to various stochastic lattice systems/ODEs/PDEs in the future.
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PublicationBiochar-Based Phosphorus Recovery from Different Waste Streams: Sources, Mechanisms, and Performance( 2023)
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PublicationBuilding the classification model based on the genetic algorithm and the improved Bayesianmethod( 2023)Dinh Pham-Toan, Tai Vo-Van"This study presents a classification model that incorporates significant enhancements based on the Bayesian method and genetic algorithm (BGA). Firstly, the prior probabilities in each iteration are determined using the ratio of the number of elements in each group, obtained through clustering techniques, to the total number of elements in the training set. Secondly, an automatic selection process optimizes the training set to minimize classification errors. Finally, the traditional genetic algorithm operators are improved by utilizing the Bayes error as the objective function. These improvements combine to create an effective classification model. Additionally, the BGA demonstrates effective performance on real data using the established MATLAB procedure. A numerical example illustrates the superiority of the proposed algorithm compared to existing methods. The study also applies the BGA for image classification using the Gabor filter, which extracts essential image features. The proposed model outperforms popular methods in classifying various numerical and image datasets. These applications highlight the potential of this study in real-world scenarios."
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PublicationContinuity with respect to the Hurst parameter of solutions to stochastic evolution equations driven by H-valued fractional Brownian motion( 2023)
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Publication"Data‑driven prediction of the shear capacity of ETS‑FRP‑strengthened beams in the hybrid 2PKT–ML approach"( 2023)Thai SonTran, Boonchai Stitmannaithum, LinhVan Hong Bui, Thanh‑Truong Nguyen.A new approach that combines analytical two-parameter kinematic theory (2PKT) with machine learning (ML) models for estimating the shear capacity of embedded through-section (ETS)-strengthened reinforced concrete (RC) beams is proposed. The 2PKT was first developed to validate its representativeness and confidence against the available experimental data of ETS-retrofitted RC beams. Given the deficiency of the test data, the developed 2PKT was utilized to generate a large data pool with 2643 samples. The aim was to optimize the ML algorithms, namely, the random forest, extreme gradient boosting (XGBoost), light gradient boosting machine, and artificial neural network (ANN) algorithm. The optimized ANN model exhibited the highest accuracy in predicting the total shear strength of ETS-strengthened beams and ETS shear contribution. In terms of predicting the total shear strength of ETS-strengthened beams, the ANN model achieved R2 values of 0.99, 0.98, and 0.96 for the training, validation, and testing data, respectively. By contrast, the ANN model could predict ETS shear contribution with high accuracy, with R2 values of 0.99, 0.99, and 0.97 for the training, validation, and testing data, respectively. Then, the effects of all design variables on the shear capacity of the ETS-strengthened beams were investigated using the hybrid 2PKT–ML. The obtained trends could well appraise the reasonability of the proposed approach.
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PublicationDependence of hole’s effective mass on the spin polarization and temperature in quantum well( 2023)Le Van Tan"The effective mass of holes (𝑚∗) in GaAs quantum wells is investigated as a function of spin polarization, temperature, and carrier density using the Hartree–Fock approximation. The results reveal 𝑚∗ initially decreases at low carrier densities before increasing with density. Additionally, as the interaction parameter increases, 𝑚∗ decreases rapidly. Especially, as the temperature rises to 200 K, 𝑚∗ asymptotically approaches a value of 0.45𝑚0, where 𝑚0 is the electron mass. These findings provide insights into the influence of Coulomb interaction and exchange energy on the effective mass of semiconductors across a wide range of temperatures, spin polarizations, and densities"
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PublicationEffect of Red Mud and Rice Husk Ash-Based Geopolymer Composites on the Adsorption of Methylene Blue Dye in Aqueous Solution for Wastewater Treatment( 2023)"Khoa Dang Nguyen,* Trang Tran Thu, Anh Thi Hoang Tran, Oanh Thi Kim Le, Suresh Sagadevan, and Noor Haida Mohd Kaus""In this study, geopolymer originating from locally industrial byproducts as red mud (RM) was successfully prepared in the presence of different loadings of rice husk ash (RHA) used for the adsorption of methylene blue (MB) in wastewater. During geopolymerization, various mixing amounts between RM and RHA were conducted when the weight ratio of binder solution/ activated alkali-metal solution (Na2SiO3/ NaOH 7 M) was 2.5 and the curing temperature was set at 60 °C for 24 h. As a result, the surface area value of the prepared geopolymer composited with RHA at 0 and 60% was increased from 19.2 to 29.5 m2/g, while the BJH pore size of the prepared geopolymer was reduced to 6.68 and 5.76 nm, respectively. In the dye removal test, higher additions of RHA in the RM-geopolymer maintained better retention of the MB ion due to the increase in the adsorption binding site. The maximum uptake amount of dyes performed at pH 8 was changed from 6.59 to 10.74 mg/g, while RHA was from 0 to 60% after 180 min of immersion in MB solution. The adsorption isotherms well obeyed the Langmuir model, as the relative coefficient R2 was 0.999. Based on these, the initial agricultural waste as RHA and industrial byproducts as RM were valued as functional materials used for dye treatment in wastewater."
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PublicationEffect of Gradient Descent Optimizers and Dropout Technique on Deep Learning LSTM Performance in Rainfall‑runoff Modeling( 2023)"Duong Tran Anh, Dat Vi Thanh, Hoang Minh Le, Bang Tran Sy, Ahad Hasan Tanim, Quoc Bao Pham, Thanh Duc Dang, Son T. Mai, Nguyen Mai Dang."Machine learning and deep learning (ML-DL) based models are widely used for rainfall runof prediction and they have potential to substitute process-oriented physics based numerical models. However, developing an ML model has also performance uncertainty because of inaccurate choices of hyperparameters and neural networks architectures. Thus, this study aims to search for best optimization algorithms to be used in ML-DL models namely, RMSprop, Adagrad, Adadelta, and Adam optimizers, as well as dropout tech niques to be integrated into the Long Short Term Memory (LSTM) model to improve forecasting accuracy of rainfall-runof modeling. A deep learning LSTMs were developed using 480 model architectures at two hydro-meteorological stations of the Mekong Delta, Vietnam, namely Chau Doc and Can Tho. The model performance is tested with the most ideally suited LSTM optimizers utilizing combinations of four dropout percentages respec tively, 0%, 10%, 20%, and 30%. The Adagrad optimizer shows the best model performance in the model testing. Deep learning LSTM models with 10% dropout made the best predic tion results while signifcantly reducing overftting tendency of the forecasted time series. The fndings of this study are valuable for ML-based hydrological models set up by identi fying a suitable gradient descent (GD) optimizer and optimal dropout ratio to enhance the performance and forecasting accuracy of the ML model.
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PublicationEvaluation of structural, optical, and magnetic properties of Gd doped MnFe2O4 nanoparticles( 2023)
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PublicationExistence and continuity results for Kirchhoff parabolic equation with Caputo–Fabrizio operator( 2023)
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PublicationExistence and continuity results for Kirchhoff parabolic equation with Caputo–Fabrizio operator( 2023)
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PublicationExistence and well-posed results for nonclassical diffusion systems with nonlocal diffusion( 2023)Nguyen Huy Tuana,b, Donal O’Reganc, Nguyen Huu Cand,∗, Mai Quang Vinhe"Our goal in this paper is to present well-posed results for nonclassical diffusion systems which have applications in population dynamics. First, we establish the existence and uniqueness of a mild solution to the initial value problem. The asymptotic behavior of the mild solution is also considered when the parameter tends to zero. Second, we obtain a local well-posedness result for nonclassical diffusion systems with a nonlocal time condition. The main idea to obtain the above theoretical results is to use Banach’s theorem and some techniques in Fourier series analysis. Some numerical tests are also presented to illustrate the theory."
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PublicationFirst-principles prediction of superconductivity in MgB3C3( 2023)
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PublicationGeometry-Based ElectronicModelsforAromaticityofAtomicClusters( 2023)Long VanDuong, Minh ThoNguyen"We presentthreedifferentelectronicmodelsforthearomaticcharacterofchemicalcompoundsbasedonmoleculargeometriesand applied tosometypicalsystems.Theformulationsarederivedfromthesolutionsofthewave-equationsdescribingthecorresponding geometricalmotifs.Thegeometriesconsideredincludetheribbon,thecirculardiskandthehollowcylinder.Theelectroncountrulefor each geometricalmotifisproposed"