Inhibition of 11βHSD1 （11-beta-hydroxysteroid dehydrogenase 1） is a promising strategy in drug treatment of diabetes. Several 11βHSDI inhibitors have been proposed; however, their selectivity to 11βHSD1 over its ...Inhibition of 11βHSD1 （11-beta-hydroxysteroid dehydrogenase 1） is a promising strategy in drug treatment of diabetes. Several 11βHSDI inhibitors have been proposed; however, their selectivity to 11βHSD1 over its isozyme 11βHSD2 （11-beta-hydroxysteroid dehydrogenase 2） has not been fully reported. The authors sought to provide a short list of top potent and selective compounds along with their detailed binding modes and pharmacophore models, Molecular docking was used for initial screening of a set of 23 potent inhibitors reported by previous experimental studies. After that, selected promising entries were reassessed by molecular dynamics simulations, followed by hydrogen bond analysis. Pharmacophore models of all drug candidates and binding modes of some selected drugs were analyzed. Among the 23 compounds, only four inhibitors were identified as potent and selective drug candidates. Binding energies, 3D pharmacophores and binding modes of the four compounds with 11βHSDI are also discussed in detail in this study.展开更多
In this paper,Isogeometric analysis(IGA)is effectively integrated with machine learning(ML)to investigate the bearing capacity of strip footings in layered soil profiles,with a focus on a sand-over-clay configuration....In this paper,Isogeometric analysis(IGA)is effectively integrated with machine learning(ML)to investigate the bearing capacity of strip footings in layered soil profiles,with a focus on a sand-over-clay configuration.The study begins with the generation of a comprehensive dataset of 10,000 samples from IGA upper bound(UB)limit analyses,facilitating an in-depth examination of various material and geometric conditions.A hybrid deep neural network,specifically the Whale Optimization Algorithm-Deep Neural Network(WOA-DNN),is then employed to utilize these 10,000 outputs for precise bearing capacity predictions.Notably,the WOA-DNN model outperforms conventional ML techniques,offering a robust and accurate prediction tool.This innovative approach explores a broad range of design parameters,including sand layer depth,load-to-soil unit weight ratio,internal friction angle,cohesion,and footing roughness.A detailed analysis of the dataset reveals the significant influence of these parameters on bearing capacity,providing valuable insights for practical foundation design.This research demonstrates the usefulness of data-driven techniques in optimizing the design of shallow foundations within layered soil profiles,marking a significant stride in geotechnical engineering advancements.展开更多
This study describes an integrated framework in which basic aerospace engineering aspects(performance, aerodynamics, and structure) and practical aspects(configuration visualization and manufacturing) are coupled and ...This study describes an integrated framework in which basic aerospace engineering aspects(performance, aerodynamics, and structure) and practical aspects(configuration visualization and manufacturing) are coupled and considered in one fully automated design optimization of rotor blades. A number of codes are developed to robustly perform estimation of helicopter configuration from sizing, performance analysis, trim analysis, to rotor blades configuration representation. These codes are then integrated with a two-dimensional airfoil analysis tool to fully design rotor blades configuration including rotor planform and airfoil shape for optimal aerodynamics in both hover and forward flights. A modular structure design methodology is developed for realistic composite rotor blades with a sophisticated cross-sectional geometry. A D-spar cross-sectional structure is chosen as a baseline. The framework is able to analyze all realistic inner configurations including thicknesses of D-spar, skin, web, number and ply angles of layers of each composite part,and materials. A number of codes and commercial software(ANSYS, Gridgen, VABS, Pre VABS,etc.) are implemented to automate the structural analysis from aerodynamic data processing to sectional properties and stress analysis. An integrated model for manufacturing cost estimation ofcomposite rotor blades developed at the Aerodynamic Analysis and Design Laboratory(AADL),Aerospace Information Engineering Department, Konkuk University is integrated into the framework to provide a rapid and dynamic feedback to configuration design. The integration of three modules has constructed a framework where the size of a helicopter, aerodynamic performance analysis, structure analysis, and manufacturing cost estimation could be quickly investigated. All aspects of a rotor blade including planform, airfoil shape, and inner structure are considered in a multidisciplinary design optimization without an exception of critical configuration.展开更多
An effective hybrid optimization method is proposed by integrating an adaptive Kriging(A-Kriging)into an improved partial swarm optimization algorithm(IPSO)to give a so-called A-Kriging-IPSO for maximizing the bucklin...An effective hybrid optimization method is proposed by integrating an adaptive Kriging(A-Kriging)into an improved partial swarm optimization algorithm(IPSO)to give a so-called A-Kriging-IPSO for maximizing the buckling load of laminated composite plates(LCPs)under uniaxial and biaxial compressions.In this method,a novel iterative adaptive Kriging model,which is structured using two training sample sets as active and adaptive points,is utilized to directly predict the buckling load of the LCPs and to improve the efficiency of the optimization process.The active points are selected from the initial data set while the adaptive points are generated using the radial random-based convex samples.The cell-based smoothed discrete shear gap method(CS-DSG3)is employed to analyze the buckling behavior of the LCPs to provide the response of adaptive and input data sets.The buckling load of the LCPs is maximized by utilizing the IPSO algorithm.To demonstrate the efficiency and accuracy of the proposed methodology,the LCPs with different layers(2,3,4,and 10 layers),boundary conditions,aspect ratios and load patterns(biaxial and uniaxial loads)are investigated.The results obtained by proposed method are in good agreement with the literature results,but with less computational burden.By applying adaptive radial Kriging model,the accurate optimal resultsebased predictions of the buckling load are obtained for the studied LCPs.展开更多
This paper for first time proposes an isogeometric analysis (IGA) for free vibration response of bi-directional functionally graded (BDFG) rectangular plates in the fluid medium. Material properties of the BDFG plate ...This paper for first time proposes an isogeometric analysis (IGA) for free vibration response of bi-directional functionally graded (BDFG) rectangular plates in the fluid medium. Material properties of the BDFG plate change in both the thickness and length directions via power-law distributions and Mori-Tanaka model. The governing equation of motion of BDFG plate in the fluid-plate system is formulated basing on Hamilton's principle and the refined quasi three-dimensional (3D) plate theory with improved function f(z). The fluid velocity potential is derived from the boundary conditions of the fluid-plate system and is used to determine the added mass. The discrete system of equations is derived from the Galerkin weak form and numerically analyzed by IGA. The accuracy and reliability of the proposed solutions are verified by comparing the obtained results with those published in the literature. Moreover, the effects of the various parameters such as the interaction boundary condition, geometric parameter, submerged depth of plate, fluid density, fluid level, and the material volume control coefficients on the free vibration behavior of BDFG plate in the fluid medium are investigated in detail. Some major findings regarding the numerical results are withdrawn in conclusions.展开更多
A new approach for predicting forming limit curves(FLCs)at elevated temperatures was proposed herein.FLCs are often used to predict failure and determine the optimal forming parameters of automotive parts.First,a grap...A new approach for predicting forming limit curves(FLCs)at elevated temperatures was proposed herein.FLCs are often used to predict failure and determine the optimal forming parameters of automotive parts.First,a graphical method based on a modified maximum force criterion was applied to estimate the FLCs of 22MnB5 boron steel sheets at room temperature using various hardening laws.Subsequently,the predicted FLC data at room temperature were compared with corresponding data obtained from Nakazima's tests to obtain the best prediction.To estimate the FLC at elevated temperatures,tensile tests were conducted at various temperatures to determine the ratios of equivalent fracture strains between the corresponding elevated temperatures and room temperature.FLCs at elevated temperatures could be established based on obtained ratios.However,the predicted FLCs at elevated temperatures did not agree well with the corresponding FLC experimental data of Zhou et al.A new method was proposed herein to improve the prediction of FLCs at elevated temperatures.An FLC calculated at room tem-perature was utilized to predict the failure of Nakazima's samples via finite element simulation.Based on the simulation results at room temperature,the mathematical relationships between the equivalent ductile fracture strain versus stress triaxiality and strain ratio were established and then combined with ratios between elevated and room temperatures to calculate the FLCs at different temperatures.The predicted FLCs at elevated temperatures agree well with the corresponding experimental FLC data.展开更多
The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications.This study addresses the importance of estimating the friction angle and presents the develop...The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications.This study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJCSA-MLPnet.First of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal variances.This technique is expected to improve the accuracy of friction angle prediction models.The friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,i.e.FPA-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,i.e.YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJ-CSA-MLPnet.Among these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering scenarios.This improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.展开更多
This paper is devoted to the quasilinear equation ■where p > 2,Ω is a(bounded or unbounded) domain of R^N,w_1,w_2 are nonnegative continuous functions and f is an increasing function. We establish a Liouville typ...This paper is devoted to the quasilinear equation ■where p > 2,Ω is a(bounded or unbounded) domain of R^N,w_1,w_2 are nonnegative continuous functions and f is an increasing function. We establish a Liouville type theorem for nontrivial stable solutions of the equation under some mild assumptions on Ω,w_1, w_2 and f, which extends and unifies several results on this topic.展开更多
Thermoelectric materials have the ability to directly convert heat into electricity,which have been extensively studied for decades to solve global energy shortages and environmental problems.As a medium temperature(4...Thermoelectric materials have the ability to directly convert heat into electricity,which have been extensively studied for decades to solve global energy shortages and environmental problems.As a medium temperature(400-800 K)thermoelectric material,SnTe has attracted extensive attention as a promising substitute for PbTe due to its non-toxic characteristics.In this paper,the research status of SnTe thermoelectric materials is reviewed,and the strategies to improve its performance are summarized and discussed in terms of electrical and thermal transport properties.This comprehensive discussion will provides guidance and inspiration for the research on SnTe.展开更多
The use of machine learning to predict student employability is important in order to analyse a student’s capability to get a job.Based on the results of this type of analysis,university managers can improve the empl...The use of machine learning to predict student employability is important in order to analyse a student’s capability to get a job.Based on the results of this type of analysis,university managers can improve the employability of their students,which can help in attracting students in the future.In addition,learners can focus on the essential skills identified through this analysis during their studies,to increase their employability.An effectivemethod calledOPT-BAG(OPTimisation of BAGging classifiers)was therefore developed to model the problem of predicting the employability of students.This model can help predict the employability of students based on their competencies and can reveal weaknesses that need to be improved.First,we analyse the relationships between several variables and the outcome variable using a correlation heatmap for a student employability dataset.Next,a standard scaler function is applied in the preprocessing module to normalise the variables in the student employability dataset.The training set is then input to our model to identify the optimal parameters for the bagging classifier using a grid search cross-validation technique.Finally,the OPT-BAG model,based on a bagging classifier with optimal parameters found in the previous step,is trained on the training dataset to predict student employability.The empirical outcomes in terms of accuracy,precision,recall,and F1 indicate that the OPT-BAG approach outperforms other cutting-edge machine learning models in terms of predicting student employability.In this study,we also analyse the factors affecting the recruitment process of employers,and find that general appearance,mental alertness,and communication skills are the most important.This indicates that educational institutions should focus on these factors during the learning process to improve student employability.展开更多
The aggregation behavior of the mixture of cetyltrimethylammonium chloride(CTAC), a cationic surfactant, and moxifloxacin hydrochloride(MFH), a fourth-generation fluoroquinolone antibiotic drug, has been studied using...The aggregation behavior of the mixture of cetyltrimethylammonium chloride(CTAC), a cationic surfactant, and moxifloxacin hydrochloride(MFH), a fourth-generation fluoroquinolone antibiotic drug, has been studied using the conductivity technique in aqueous and alcoholic(EtOH, 1-PrOH, and 2-BuOH)media. The study was performed at several temperatures between 298.15 and 323.15 K at 5 K intervals.The assembly has been characterized by evaluating the micellar parameters, such as the critical micelle concentration(CMC) and the counter ion binding(β), of the CTAC + MFH mixture. The values of the CMC for the assembly of the CTAC + MFH mixture were reliant on the composition of alcohols in the mixed solvents and the temperature. The CMC values of the CTAC + MFH mixture increased with increasing temperature;that is, assembly was delayed by increased temperature. The micellization of the CTAC + MFH mixed system was delayed in alcoholic media. The observed-ΔG0mvalues for the association of the CTAC + MFH mixed system demonstrated a spontaneous aggregation process under all study conditions.Based on the-ΔH^(0)_(m) and +ΔS^(0)_(m) values, the association of the CTAC + MFH mixture is exothermic and the interaction forces acting between the CTAC and MFH species are hydrophobic, ion–dipole, and electrostatic interactions. The transfer properties and enthalpy–entropy compensation were also assessed and described comprehensively.展开更多
Nowadays,air pollution is a big environmental problem in develop-ing countries.In this problem,particulate matter 2.5(PM2.5)in the air is an air pollutant.When its concentration in the air is high in developing countr...Nowadays,air pollution is a big environmental problem in develop-ing countries.In this problem,particulate matter 2.5(PM2.5)in the air is an air pollutant.When its concentration in the air is high in developing countries like Vietnam,it will harm everyone’s health.Accurate prediction of PM2.5 concentrations can help to make the correct decision in protecting the health of the citizen.This study develops a hybrid deep learning approach named PM25-CBL model for PM2.5 concentration prediction in Ho Chi Minh City,Vietnam.Firstly,this study analyzes the effects of variables on PM2.5 concentrations in Air Quality HCMC dataset.Only variables that affect the results will be selected for PM2.5 concentration prediction.Secondly,an efficient PM25-CBL model that integrates a convolutional neural network(CNN)andBidirectionalLongShort-TermMemory(Bi-LSTM)isdeveloped.This model consists of three following modules:CNN,Bi-LSTM,and Fully connected modules.Finally,this study conducts the experiment to compare the performance of our approach and several state-of-the-art deep learning models for time series prediction such as LSTM,Bi-LSTM,the combination of CNN and LSTM(CNN-LSTM),and ARIMA.The empirical results confirm that PM25-CBL model outperforms other methods for Air Quality HCMC dataset in terms of several metrics including Mean Squared Error(MSE),Root Mean Squared Error(RMSE),Mean Absolute Error(MAE),and Mean Absolute Percentage Error(MAPE).展开更多
To achieve carbon dioxide(CO_(2))storage through enhanced oil recovery,accurate forecasting of CO_(2) subsurface storage and cumulative oil production is essential.This study develops hybrid predictive models for the ...To achieve carbon dioxide(CO_(2))storage through enhanced oil recovery,accurate forecasting of CO_(2) subsurface storage and cumulative oil production is essential.This study develops hybrid predictive models for the determination of CO_(2) storage mass and cumulative oil production in unconventional reservoirs.It does so with two multi-layer perceptron neural networks(MLPNN)and a least-squares support vector machine(LSSVM),hybridized with grey wolf optimization(GWO)and/or particle swarm optimization(PSO).Large,simulated datasets were divided into training(70%)and testing(30%)groups,with normalization applied to both groups.Mahalanobis distance identifies/eliminates outliers in the training subset only.A non-dominated sorting genetic algorithm(NSGA-II)combined with LSSVM selected seven influential features from the nine available input parameters:reservoir depth,porosity,permeability,thickness,bottom-hole pressure,area,CO_(2) injection rate,residual oil saturation to gas flooding,and residual oil saturation to water flooding.Predictive models were developed and tested,with performance evaluated with an overfitting index(OFI),scoring analysis,and partial dependence plots(PDP),during training and independent testing to enhance model focus and effectiveness.The LSSVM-GWO model generated the lowest root mean square error(RMSE)values(0.4052 MMT for CO_(2) storage and 9.7392 MMbbl for cumulative oil production)in the training group.That trained model also exhibited excellent generalization and minimal overfitting when applied to the testing group(RMSE of 0.6224 MMT for CO_(2) storage and 12.5143 MMbbl for cumulative oil production).PDP analysis revealed that the input features“area”and“porosity”had the most influence on the LSSVM-GWO model's pre-diction performance.This paper presents a new hybrid modeling approach that achieves accurate forecasting of CO_(2) subsurface storage and cumulative oil production.It also establishes a new standard for such forecasting,which can lead to the development of more effective and sustainable solutions for oil recovery.展开更多
This article deals with the investigation of the effects of porosity distributions on nonlinear free vibration and transient analysis of porous functionally graded skew(PFGS)plates.The effective material properties of...This article deals with the investigation of the effects of porosity distributions on nonlinear free vibration and transient analysis of porous functionally graded skew(PFGS)plates.The effective material properties of the PFGS plates are obtained from the modified power-law equations in which gradation varies through the thickness of the PFGS plate.A nonlinear finite element(FE)formulation for the overall PFGS plate is derived by adopting first-order shear deformation theory(FSDT)in conjunction with von Karman’s nonlinear strain displacement relations.The governing equations of the PFGS plate are derived using the principle of virtual work.The direct iterative method and Newmark’s integration technique are espoused to solve nonlinear mathematical relations.The influences of the porosity distributions and porosity parameter indices on the nonlinear frequency responses of the PFGS plate for different skew angles are studied in various parameters.The effects of volume fraction grading index and skew angle on the plate’s nonlinear dynamic responses for various porosity distributions are illustrated in detail.展开更多
The quasi-biweekly oscillation （QBWO） is the second most dominant intraseasonal mode over the westem North Pacific （WNP） during boreal summer. In this study, the modulation of WNP tropical cyclogenesis （TCG） by ...The quasi-biweekly oscillation （QBWO） is the second most dominant intraseasonal mode over the westem North Pacific （WNP） during boreal summer. In this study, the modulation of WNP tropical cyclogenesis （TCG） by the QBWO and its association with large-scale patterns are investigated. A strong modulation of WNP TCG events by the QBWO is found. More TCG events occur during the QBWO＇s convectively active phase. Based on the genesis potential index （GPI）, we further evaluate the role of environmental factors in affecting WNP TCG. The positive GPI anomalies associated with the QBWO correspond well with TCG counts and locations. A large positive GPI anomaly is spatially correlated with WNP TCG events during a life cycle of the QBWO. The low-level relative vorticity and mid-level relative humidity appear to be two dominant contributors to the QBWO-composited GPI anomalies during the QBWO＇s active phase, followed by the nonlinear and potential intensity terms. These positive contributions to the GPI anomalies are partly offset by the negative contribution from the vertical wind shear. During the QBWO＇s inactive phase, the mid-level relative humidity appears to be the largest contributor, while weak contributions are also made by the nonlinear and low-level relative vorticity terms. Meanwhile, these positive contributions are partly cancelled out by the negative contribution from the potential intensity. The contributions of these environmental factors to the GPI anomalies associated with the QBWO are similar in all five flow patterns--the monsoon shear line, monsoon confluence region, monsoon gyre, easterly wave, and Rossby wave energy dispersion associated with a preexisting TC. Further analyses show that the QBWO strongly modulates the synoptic-scale wave trains （SSWs） over the WNP, with larger amplitude SSWs during the QBWO＇s active phase. This implies a possible enhanced （weakened） relationship between TCG and SSWs during the active （inactive） phase. This study improves our understanding of the modulation of WNP TCG by the QBWO and thus helps with efforts to improve the intraseasonal prediction of WNP TCG.展开更多
The main purpose of this paper is to present numerical results of static bending and free vibration of functionally graded porous(FGP) variable-thickness plates by using an edge-based smoothed finite element method(ES...The main purpose of this paper is to present numerical results of static bending and free vibration of functionally graded porous(FGP) variable-thickness plates by using an edge-based smoothed finite element method(ES-FEM) associate with the mixed interpolation of tensorial components technique for the three-node triangular element(MITC3), so-called ES-MITC3. This ES-MITC3 element is performed to eliminate the shear locking problem and to enhance the accuracy of the existing MITC3 element. In the ES-MITC3 element, the stiffness matrices are obtained by using the strain smoothing technique over the smoothing domains formed by two adjacent MITC3 triangular elements sharing an edge. Materials of the plate are FGP with a power-law index(k) and maximum porosity distributions(U) in the forms of cosine functions. The influences of some geometric parameters, material properties on static bending, and natural frequency of the FGP variable-thickness plates are examined in detail.展开更多
This article makes the first attempt in assessing the influence of active constrained layer damping(ACLD)treatment towards precise control of frequency responses of functionally graded skew-magneto-electroelastic(FGSM...This article makes the first attempt in assessing the influence of active constrained layer damping(ACLD)treatment towards precise control of frequency responses of functionally graded skew-magneto-electroelastic(FGSMEE)plates by employing finite element methods.The materials are functionally graded across the thickness of the plate in terms of modest power-law distributions.The principal equations of motion of FGSMEE are derived via Hamilton’s principle and solved using condensation technique.The effect of ACLD patches are modelled by following the complex modulus approach(CMA).Additionally,distinctive emphasis is laid to evaluate the influence of geometrical skewness on the attenuation capabilities of the plate.The accuracy of the current analysis is corroborated with comparison of previous researches of similar kind.Additionally,a complete parametric study is directed to understand the combined impacts of various factors like coupling fields,patch location,fiber orientation of piezoelectric patch in association with skew angle and power-law index.展开更多
Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines.To evaluate the quality of blasting,the size of rock distribution is used as a critical criterion in blasting operations.A...Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines.To evaluate the quality of blasting,the size of rock distribution is used as a critical criterion in blasting operations.A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage.Therefore,this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters,as well as the efficiency of blasting operation in open mines.Accordingly,a nature-inspired algorithm(i.e.,firefly algorithm-FFA)and different machine learning algorithms(i.e.,gradient boosting machine(GBM),support vector machine(SVM),Gaussian process(GP),and artificial neural network(ANN))were combined for this aim,abbreviated as FFA-GBM,FFA-SVM,FFA-GP,and FFA-ANN,respectively.Subsequently,predicted results from the abovementioned models were compared with each other using three statistical indicators(e.g.,mean absolute error,root-mean-squared error,and correlation coefficient)and color intensity method.For developing and simulating the size of rock in blasting operations,136 blasting events with their images were collected and analyzed by the Split-Desktop software.In which,111 events were randomly selected for the development and optimization of the models.Subsequently,the remaining 25 blasting events were applied to confirm the accuracy of the proposed models.Herein,blast design parameters were regarded as input variables to predict the size of rock in blasting operations.Finally,the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting.Among the models developed in this study,FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks.The other techniques(i.e.,FFA-SVM,FFA-GP,and FFA-ANN)yielded lower computational stability and efficiency.Hence,the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.展开更多
The above-threshold ionization of argon in an intense 70-fs,400-nm linearly polarized laser pulse has been investigated by the velocity map imaging techniques,combined with an attosecond-resolution quantum wave packet...The above-threshold ionization of argon in an intense 70-fs,400-nm linearly polarized laser pulse has been investigated by the velocity map imaging techniques,combined with an attosecond-resolution quantum wave packet dynamics method.There is a quantitative agreement in all dominant features between the experiment and the theory.Moreover,a peak-splitting phenomenon in the first energy peak has been observed at high pulse intensity.Further,through the theoretical analysis,an ac Stark splitting with evident resonant and nonresonant ionization pathways has been found to be the physical reason for the experimental observations.展开更多
文摘Inhibition of 11βHSD1 （11-beta-hydroxysteroid dehydrogenase 1） is a promising strategy in drug treatment of diabetes. Several 11βHSDI inhibitors have been proposed; however, their selectivity to 11βHSD1 over its isozyme 11βHSD2 （11-beta-hydroxysteroid dehydrogenase 2） has not been fully reported. The authors sought to provide a short list of top potent and selective compounds along with their detailed binding modes and pharmacophore models, Molecular docking was used for initial screening of a set of 23 potent inhibitors reported by previous experimental studies. After that, selected promising entries were reassessed by molecular dynamics simulations, followed by hydrogen bond analysis. Pharmacophore models of all drug candidates and binding modes of some selected drugs were analyzed. Among the 23 compounds, only four inhibitors were identified as potent and selective drug candidates. Binding energies, 3D pharmacophores and binding modes of the four compounds with 11βHSDI are also discussed in detail in this study.
文摘In this paper,Isogeometric analysis(IGA)is effectively integrated with machine learning(ML)to investigate the bearing capacity of strip footings in layered soil profiles,with a focus on a sand-over-clay configuration.The study begins with the generation of a comprehensive dataset of 10,000 samples from IGA upper bound(UB)limit analyses,facilitating an in-depth examination of various material and geometric conditions.A hybrid deep neural network,specifically the Whale Optimization Algorithm-Deep Neural Network(WOA-DNN),is then employed to utilize these 10,000 outputs for precise bearing capacity predictions.Notably,the WOA-DNN model outperforms conventional ML techniques,offering a robust and accurate prediction tool.This innovative approach explores a broad range of design parameters,including sand layer depth,load-to-soil unit weight ratio,internal friction angle,cohesion,and footing roughness.A detailed analysis of the dataset reveals the significant influence of these parameters on bearing capacity,providing valuable insights for practical foundation design.This research demonstrates the usefulness of data-driven techniques in optimizing the design of shallow foundations within layered soil profiles,marking a significant stride in geotechnical engineering advancements.
基金supported by the National Foundation for Science and Technology Development (NAFOSTED) of Vietnam (No. 107.04-2012.25)
文摘This study describes an integrated framework in which basic aerospace engineering aspects(performance, aerodynamics, and structure) and practical aspects(configuration visualization and manufacturing) are coupled and considered in one fully automated design optimization of rotor blades. A number of codes are developed to robustly perform estimation of helicopter configuration from sizing, performance analysis, trim analysis, to rotor blades configuration representation. These codes are then integrated with a two-dimensional airfoil analysis tool to fully design rotor blades configuration including rotor planform and airfoil shape for optimal aerodynamics in both hover and forward flights. A modular structure design methodology is developed for realistic composite rotor blades with a sophisticated cross-sectional geometry. A D-spar cross-sectional structure is chosen as a baseline. The framework is able to analyze all realistic inner configurations including thicknesses of D-spar, skin, web, number and ply angles of layers of each composite part,and materials. A number of codes and commercial software(ANSYS, Gridgen, VABS, Pre VABS,etc.) are implemented to automate the structural analysis from aerodynamic data processing to sectional properties and stress analysis. An integrated model for manufacturing cost estimation ofcomposite rotor blades developed at the Aerodynamic Analysis and Design Laboratory(AADL),Aerospace Information Engineering Department, Konkuk University is integrated into the framework to provide a rapid and dynamic feedback to configuration design. The integration of three modules has constructed a framework where the size of a helicopter, aerodynamic performance analysis, structure analysis, and manufacturing cost estimation could be quickly investigated. All aspects of a rotor blade including planform, airfoil shape, and inner structure are considered in a multidisciplinary design optimization without an exception of critical configuration.
基金Vietnam National Foundation for Science and Technology Development(NAFOSTED)under Grant number 107.02-2019.330.
文摘An effective hybrid optimization method is proposed by integrating an adaptive Kriging(A-Kriging)into an improved partial swarm optimization algorithm(IPSO)to give a so-called A-Kriging-IPSO for maximizing the buckling load of laminated composite plates(LCPs)under uniaxial and biaxial compressions.In this method,a novel iterative adaptive Kriging model,which is structured using two training sample sets as active and adaptive points,is utilized to directly predict the buckling load of the LCPs and to improve the efficiency of the optimization process.The active points are selected from the initial data set while the adaptive points are generated using the radial random-based convex samples.The cell-based smoothed discrete shear gap method(CS-DSG3)is employed to analyze the buckling behavior of the LCPs to provide the response of adaptive and input data sets.The buckling load of the LCPs is maximized by utilizing the IPSO algorithm.To demonstrate the efficiency and accuracy of the proposed methodology,the LCPs with different layers(2,3,4,and 10 layers),boundary conditions,aspect ratios and load patterns(biaxial and uniaxial loads)are investigated.The results obtained by proposed method are in good agreement with the literature results,but with less computational burden.By applying adaptive radial Kriging model,the accurate optimal resultsebased predictions of the buckling load are obtained for the studied LCPs.
基金This research is funded by Vietnam National Foundation for Science and Technology Development(NAFOSTED)under Grant number 107.02-2019.330.
文摘This paper for first time proposes an isogeometric analysis (IGA) for free vibration response of bi-directional functionally graded (BDFG) rectangular plates in the fluid medium. Material properties of the BDFG plate change in both the thickness and length directions via power-law distributions and Mori-Tanaka model. The governing equation of motion of BDFG plate in the fluid-plate system is formulated basing on Hamilton's principle and the refined quasi three-dimensional (3D) plate theory with improved function f(z). The fluid velocity potential is derived from the boundary conditions of the fluid-plate system and is used to determine the added mass. The discrete system of equations is derived from the Galerkin weak form and numerically analyzed by IGA. The accuracy and reliability of the proposed solutions are verified by comparing the obtained results with those published in the literature. Moreover, the effects of the various parameters such as the interaction boundary condition, geometric parameter, submerged depth of plate, fluid density, fluid level, and the material volume control coefficients on the free vibration behavior of BDFG plate in the fluid medium are investigated in detail. Some major findings regarding the numerical results are withdrawn in conclusions.
基金funded by Vietnam National Foundation for Science and Technology Development(NAFOSTED)under Grant Number 107.02-2019.300.
文摘A new approach for predicting forming limit curves(FLCs)at elevated temperatures was proposed herein.FLCs are often used to predict failure and determine the optimal forming parameters of automotive parts.First,a graphical method based on a modified maximum force criterion was applied to estimate the FLCs of 22MnB5 boron steel sheets at room temperature using various hardening laws.Subsequently,the predicted FLC data at room temperature were compared with corresponding data obtained from Nakazima's tests to obtain the best prediction.To estimate the FLC at elevated temperatures,tensile tests were conducted at various temperatures to determine the ratios of equivalent fracture strains between the corresponding elevated temperatures and room temperature.FLCs at elevated temperatures could be established based on obtained ratios.However,the predicted FLCs at elevated temperatures did not agree well with the corresponding FLC experimental data of Zhou et al.A new method was proposed herein to improve the prediction of FLCs at elevated temperatures.An FLC calculated at room tem-perature was utilized to predict the failure of Nakazima's samples via finite element simulation.Based on the simulation results at room temperature,the mathematical relationships between the equivalent ductile fracture strain versus stress triaxiality and strain ratio were established and then combined with ratios between elevated and room temperatures to calculate the FLCs at different temperatures.The predicted FLCs at elevated temperatures agree well with the corresponding experimental FLC data.
文摘The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering applications.This study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJCSA-MLPnet.First of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal variances.This technique is expected to improve the accuracy of friction angle prediction models.The friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,i.e.FPA-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,i.e.YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and YJ-CSA-MLPnet.Among these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering scenarios.This improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.
基金supported by Vietnam National Foundation for Science and Technology Development(NAFOSTED)under Grant No.101.02-2017.307
文摘This paper is devoted to the quasilinear equation ■where p > 2,Ω is a(bounded or unbounded) domain of R^N,w_1,w_2 are nonnegative continuous functions and f is an increasing function. We establish a Liouville type theorem for nontrivial stable solutions of the equation under some mild assumptions on Ω,w_1, w_2 and f, which extends and unifies several results on this topic.
基金sponsored by the National Natural Science Foundation of China (Grant Nos.U1504511,11674083, and 12005194)。
文摘Thermoelectric materials have the ability to directly convert heat into electricity,which have been extensively studied for decades to solve global energy shortages and environmental problems.As a medium temperature(400-800 K)thermoelectric material,SnTe has attracted extensive attention as a promising substitute for PbTe due to its non-toxic characteristics.In this paper,the research status of SnTe thermoelectric materials is reviewed,and the strategies to improve its performance are summarized and discussed in terms of electrical and thermal transport properties.This comprehensive discussion will provides guidance and inspiration for the research on SnTe.
文摘The use of machine learning to predict student employability is important in order to analyse a student’s capability to get a job.Based on the results of this type of analysis,university managers can improve the employability of their students,which can help in attracting students in the future.In addition,learners can focus on the essential skills identified through this analysis during their studies,to increase their employability.An effectivemethod calledOPT-BAG(OPTimisation of BAGging classifiers)was therefore developed to model the problem of predicting the employability of students.This model can help predict the employability of students based on their competencies and can reveal weaknesses that need to be improved.First,we analyse the relationships between several variables and the outcome variable using a correlation heatmap for a student employability dataset.Next,a standard scaler function is applied in the preprocessing module to normalise the variables in the student employability dataset.The training set is then input to our model to identify the optimal parameters for the bagging classifier using a grid search cross-validation technique.Finally,the OPT-BAG model,based on a bagging classifier with optimal parameters found in the previous step,is trained on the training dataset to predict student employability.The empirical outcomes in terms of accuracy,precision,recall,and F1 indicate that the OPT-BAG approach outperforms other cutting-edge machine learning models in terms of predicting student employability.In this study,we also analyse the factors affecting the recruitment process of employers,and find that general appearance,mental alertness,and communication skills are the most important.This indicates that educational institutions should focus on these factors during the learning process to improve student employability.
基金funded by Institutional Fund Projects (IFPIP:515-961-1443)technical and financial support provided by the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia。
文摘The aggregation behavior of the mixture of cetyltrimethylammonium chloride(CTAC), a cationic surfactant, and moxifloxacin hydrochloride(MFH), a fourth-generation fluoroquinolone antibiotic drug, has been studied using the conductivity technique in aqueous and alcoholic(EtOH, 1-PrOH, and 2-BuOH)media. The study was performed at several temperatures between 298.15 and 323.15 K at 5 K intervals.The assembly has been characterized by evaluating the micellar parameters, such as the critical micelle concentration(CMC) and the counter ion binding(β), of the CTAC + MFH mixture. The values of the CMC for the assembly of the CTAC + MFH mixture were reliant on the composition of alcohols in the mixed solvents and the temperature. The CMC values of the CTAC + MFH mixture increased with increasing temperature;that is, assembly was delayed by increased temperature. The micellization of the CTAC + MFH mixed system was delayed in alcoholic media. The observed-ΔG0mvalues for the association of the CTAC + MFH mixed system demonstrated a spontaneous aggregation process under all study conditions.Based on the-ΔH^(0)_(m) and +ΔS^(0)_(m) values, the association of the CTAC + MFH mixture is exothermic and the interaction forces acting between the CTAC and MFH species are hydrophobic, ion–dipole, and electrostatic interactions. The transfer properties and enthalpy–entropy compensation were also assessed and described comprehensively.
文摘Nowadays,air pollution is a big environmental problem in develop-ing countries.In this problem,particulate matter 2.5(PM2.5)in the air is an air pollutant.When its concentration in the air is high in developing countries like Vietnam,it will harm everyone’s health.Accurate prediction of PM2.5 concentrations can help to make the correct decision in protecting the health of the citizen.This study develops a hybrid deep learning approach named PM25-CBL model for PM2.5 concentration prediction in Ho Chi Minh City,Vietnam.Firstly,this study analyzes the effects of variables on PM2.5 concentrations in Air Quality HCMC dataset.Only variables that affect the results will be selected for PM2.5 concentration prediction.Secondly,an efficient PM25-CBL model that integrates a convolutional neural network(CNN)andBidirectionalLongShort-TermMemory(Bi-LSTM)isdeveloped.This model consists of three following modules:CNN,Bi-LSTM,and Fully connected modules.Finally,this study conducts the experiment to compare the performance of our approach and several state-of-the-art deep learning models for time series prediction such as LSTM,Bi-LSTM,the combination of CNN and LSTM(CNN-LSTM),and ARIMA.The empirical results confirm that PM25-CBL model outperforms other methods for Air Quality HCMC dataset in terms of several metrics including Mean Squared Error(MSE),Root Mean Squared Error(RMSE),Mean Absolute Error(MAE),and Mean Absolute Percentage Error(MAPE).
文摘To achieve carbon dioxide(CO_(2))storage through enhanced oil recovery,accurate forecasting of CO_(2) subsurface storage and cumulative oil production is essential.This study develops hybrid predictive models for the determination of CO_(2) storage mass and cumulative oil production in unconventional reservoirs.It does so with two multi-layer perceptron neural networks(MLPNN)and a least-squares support vector machine(LSSVM),hybridized with grey wolf optimization(GWO)and/or particle swarm optimization(PSO).Large,simulated datasets were divided into training(70%)and testing(30%)groups,with normalization applied to both groups.Mahalanobis distance identifies/eliminates outliers in the training subset only.A non-dominated sorting genetic algorithm(NSGA-II)combined with LSSVM selected seven influential features from the nine available input parameters:reservoir depth,porosity,permeability,thickness,bottom-hole pressure,area,CO_(2) injection rate,residual oil saturation to gas flooding,and residual oil saturation to water flooding.Predictive models were developed and tested,with performance evaluated with an overfitting index(OFI),scoring analysis,and partial dependence plots(PDP),during training and independent testing to enhance model focus and effectiveness.The LSSVM-GWO model generated the lowest root mean square error(RMSE)values(0.4052 MMT for CO_(2) storage and 9.7392 MMbbl for cumulative oil production)in the training group.That trained model also exhibited excellent generalization and minimal overfitting when applied to the testing group(RMSE of 0.6224 MMT for CO_(2) storage and 12.5143 MMbbl for cumulative oil production).PDP analysis revealed that the input features“area”and“porosity”had the most influence on the LSSVM-GWO model's pre-diction performance.This paper presents a new hybrid modeling approach that achieves accurate forecasting of CO_(2) subsurface storage and cumulative oil production.It also establishes a new standard for such forecasting,which can lead to the development of more effective and sustainable solutions for oil recovery.
文摘This article deals with the investigation of the effects of porosity distributions on nonlinear free vibration and transient analysis of porous functionally graded skew(PFGS)plates.The effective material properties of the PFGS plates are obtained from the modified power-law equations in which gradation varies through the thickness of the PFGS plate.A nonlinear finite element(FE)formulation for the overall PFGS plate is derived by adopting first-order shear deformation theory(FSDT)in conjunction with von Karman’s nonlinear strain displacement relations.The governing equations of the PFGS plate are derived using the principle of virtual work.The direct iterative method and Newmark’s integration technique are espoused to solve nonlinear mathematical relations.The influences of the porosity distributions and porosity parameter indices on the nonlinear frequency responses of the PFGS plate for different skew angles are studied in various parameters.The effects of volume fraction grading index and skew angle on the plate’s nonlinear dynamic responses for various porosity distributions are illustrated in detail.
基金jointly supported by the National Natural Science Foundation of China(Grant Nos.41675072,41305050,41275093,41475091 and 41305039)the National Basic Research Program of China(Grant Nos.2013CB430301,2013CB430103 and 2015CB452803)+5 种基金the Jiangsu Provincial Natural Science Fund Project(Grant No.BK20150910)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Grant No.14KJA170005)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)the Project of Global Change and Air–Sea Interaction(Grant No.GASI-03-IPOVAI-04)the base funding of the Atlantic Oceanographic and Meteorological Laboratory(AOML)Earth System Modelling Center Contribution Number 117
文摘The quasi-biweekly oscillation （QBWO） is the second most dominant intraseasonal mode over the westem North Pacific （WNP） during boreal summer. In this study, the modulation of WNP tropical cyclogenesis （TCG） by the QBWO and its association with large-scale patterns are investigated. A strong modulation of WNP TCG events by the QBWO is found. More TCG events occur during the QBWO＇s convectively active phase. Based on the genesis potential index （GPI）, we further evaluate the role of environmental factors in affecting WNP TCG. The positive GPI anomalies associated with the QBWO correspond well with TCG counts and locations. A large positive GPI anomaly is spatially correlated with WNP TCG events during a life cycle of the QBWO. The low-level relative vorticity and mid-level relative humidity appear to be two dominant contributors to the QBWO-composited GPI anomalies during the QBWO＇s active phase, followed by the nonlinear and potential intensity terms. These positive contributions to the GPI anomalies are partly offset by the negative contribution from the vertical wind shear. During the QBWO＇s inactive phase, the mid-level relative humidity appears to be the largest contributor, while weak contributions are also made by the nonlinear and low-level relative vorticity terms. Meanwhile, these positive contributions are partly cancelled out by the negative contribution from the potential intensity. The contributions of these environmental factors to the GPI anomalies associated with the QBWO are similar in all five flow patterns--the monsoon shear line, monsoon confluence region, monsoon gyre, easterly wave, and Rossby wave energy dispersion associated with a preexisting TC. Further analyses show that the QBWO strongly modulates the synoptic-scale wave trains （SSWs） over the WNP, with larger amplitude SSWs during the QBWO＇s active phase. This implies a possible enhanced （weakened） relationship between TCG and SSWs during the active （inactive） phase. This study improves our understanding of the modulation of WNP TCG by the QBWO and thus helps with efforts to improve the intraseasonal prediction of WNP TCG.
基金funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under Grant number 107.02-2019.330。
文摘The main purpose of this paper is to present numerical results of static bending and free vibration of functionally graded porous(FGP) variable-thickness plates by using an edge-based smoothed finite element method(ES-FEM) associate with the mixed interpolation of tensorial components technique for the three-node triangular element(MITC3), so-called ES-MITC3. This ES-MITC3 element is performed to eliminate the shear locking problem and to enhance the accuracy of the existing MITC3 element. In the ES-MITC3 element, the stiffness matrices are obtained by using the strain smoothing technique over the smoothing domains formed by two adjacent MITC3 triangular elements sharing an edge. Materials of the plate are FGP with a power-law index(k) and maximum porosity distributions(U) in the forms of cosine functions. The influences of some geometric parameters, material properties on static bending, and natural frequency of the FGP variable-thickness plates are examined in detail.
文摘This article makes the first attempt in assessing the influence of active constrained layer damping(ACLD)treatment towards precise control of frequency responses of functionally graded skew-magneto-electroelastic(FGSMEE)plates by employing finite element methods.The materials are functionally graded across the thickness of the plate in terms of modest power-law distributions.The principal equations of motion of FGSMEE are derived via Hamilton’s principle and solved using condensation technique.The effect of ACLD patches are modelled by following the complex modulus approach(CMA).Additionally,distinctive emphasis is laid to evaluate the influence of geometrical skewness on the attenuation capabilities of the plate.The accuracy of the current analysis is corroborated with comparison of previous researches of similar kind.Additionally,a complete parametric study is directed to understand the combined impacts of various factors like coupling fields,patch location,fiber orientation of piezoelectric patch in association with skew angle and power-law index.
基金supported by the Center for Mining,Electro-Mechanical research of Hanoi University of Mining and Geology(HUMG),Hanoi,Vietnamfinancially supported by the Hunan Provincial Department of Education General Project(19C1744)+1 种基金Hunan Province Science Foundation for Youth Scholars of China fund(2018JJ3510)the Innovation-Driven Project of Central South University(2020CX040)。
文摘Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines.To evaluate the quality of blasting,the size of rock distribution is used as a critical criterion in blasting operations.A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage.Therefore,this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters,as well as the efficiency of blasting operation in open mines.Accordingly,a nature-inspired algorithm(i.e.,firefly algorithm-FFA)and different machine learning algorithms(i.e.,gradient boosting machine(GBM),support vector machine(SVM),Gaussian process(GP),and artificial neural network(ANN))were combined for this aim,abbreviated as FFA-GBM,FFA-SVM,FFA-GP,and FFA-ANN,respectively.Subsequently,predicted results from the abovementioned models were compared with each other using three statistical indicators(e.g.,mean absolute error,root-mean-squared error,and correlation coefficient)and color intensity method.For developing and simulating the size of rock in blasting operations,136 blasting events with their images were collected and analyzed by the Split-Desktop software.In which,111 events were randomly selected for the development and optimization of the models.Subsequently,the remaining 25 blasting events were applied to confirm the accuracy of the proposed models.Herein,blast design parameters were regarded as input variables to predict the size of rock in blasting operations.Finally,the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting.Among the models developed in this study,FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks.The other techniques(i.e.,FFA-SVM,FFA-GP,and FFA-ANN)yielded lower computational stability and efficiency.Hence,the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.
基金Project supported by the National Natural Science Foundations of China (Grant Nos. 10874096 and 20633070)
文摘The above-threshold ionization of argon in an intense 70-fs,400-nm linearly polarized laser pulse has been investigated by the velocity map imaging techniques,combined with an attosecond-resolution quantum wave packet dynamics method.There is a quantitative agreement in all dominant features between the experiment and the theory.Moreover,a peak-splitting phenomenon in the first energy peak has been observed at high pulse intensity.Further,through the theoretical analysis,an ac Stark splitting with evident resonant and nonresonant ionization pathways has been found to be the physical reason for the experimental observations.
