In this paper,a class of new immersed interface finite element methods (IIFEM) is developed to solve elasticity interface problems with homogeneous and non-homogeneous jump conditions in two dimensions.Simple non-body...In this paper,a class of new immersed interface finite element methods (IIFEM) is developed to solve elasticity interface problems with homogeneous and non-homogeneous jump conditions in two dimensions.Simple non-body-fitted meshes are used.For homogeneous jump conditions,both non-conforming and conforming basis functions are constructed in such a way that they satisfy the natural jump conditions. For non-homogeneous jump conditions,a pair of functions that satisfy the same non-homogeneous jump conditions are constructed using a level-set representation of the interface.With such a pair of functions,the discontinuities across the interface in the solution and flux are removed;and an equivalent elasticity interface problem with homogeneous jump conditions is formulated.Numerical examples are presented to demonstrate that such methods have second order convergence.展开更多
Non-equilibrium turbulence phenomena have raised great interests in recent years. Significant efforts have been devoted to non-equilibrium turbulence properties in canonical flows, e.g., grid turbulence, turbulent wak...Non-equilibrium turbulence phenomena have raised great interests in recent years. Significant efforts have been devoted to non-equilibrium turbulence properties in canonical flows, e.g., grid turbulence, turbulent wakes, and homogeneous isotropic turbulence(HIT). The non-equilibrium turbulence in non-canonical flows, however, has rarely been studied due to the complexity of the flows. In the present contribution, a directnumerical simulation(DNS) database of a turbulent flow is analyzed over a backwardfacing ramp, the flow near the boundary is demonstrated, and the non-equilibrium turbulent properties of the flow in the wake of the ramp are presented by using the characteristic parameters such as the dissipation coefficient C and the skewness of longitudinal velocity gradient Sk, but with opposite underlying turbulent energy transfer properties. The equation of Lagrangian velocity gradient correlation is examined, and the results show that non-equilibrium turbulence is the result of phase de-coherence phenomena, which is not taken into account in the modeling of non-equilibrium turbulence. These findings are expected to inspire deeper investigation of different non-equilibrium turbulence phenomena in different flow conditions and the improvement of turbulence modeling.展开更多
Many recent laboratory experiments and numerical simulations support a non-equilibrium dissipation scaling in decaying turbulence before it reaches an equilibrium state.By analyzing a direct numerical simulation(DNS)d...Many recent laboratory experiments and numerical simulations support a non-equilibrium dissipation scaling in decaying turbulence before it reaches an equilibrium state.By analyzing a direct numerical simulation(DNS)database of a transitional boundary-layer flow,we show that the transition region and the non-equilibrium turbulence region,which are located in different streamwise zones,present different non-equilibrium scalings.Moreover,in the wall-normal direction,the viscous sublayer,log layer,and outer layer show different non-equilibrium phenomena which differ from those in grid-generated turbulence and transitional channel flows.These findings are expected to shed light on the modelling of various types of non-equilibrium turbulent flows.展开更多
Standard finite element approaches are still ineffective in handling extreme material deformation, such as cases of large deformations and moving discontinuities due to severe mesh distortion. Among meshfree methods d...Standard finite element approaches are still ineffective in handling extreme material deformation, such as cases of large deformations and moving discontinuities due to severe mesh distortion. Among meshfree methods developed to overcome the ineffectiveness, Reproducing Kernel Particle Method (RKPM) has demonstrated its great suitability for structural analysis.This paper presents applications of RKPM in elasto-plastic problems after a review of meshfree methods and an introduction to RKPM. A slope stability problem in geotechnical engineering is analyzed as an illustrative case. The corresponding numerical simulations are carried out on an SGI Onyx3900 supercomputer. Comparison of the RKPM and the FEM under identical conditions showed that the RKPM is more suitable for problems where there exists extremely large strain such as in the case of slope sliding.展开更多
Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ability.While,Traditional ELM cannot train massive data rapid...Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ability.While,Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence,high time and space complexity.In ELM,the hidden layer typically necessitates a huge number of nodes.Furthermore,there is no certainty that the arrangement of weights and biases within the hidden layer is optimal.To solve this problem,the traditional ELM has been hybridized with swarm intelligence optimization techniques.This paper displays five proposed hybrid Algorithms“Salp Swarm Algorithm(SSA-ELM),Grasshopper Algorithm(GOA-ELM),Grey Wolf Algorithm(GWO-ELM),Whale optimizationAlgorithm(WOA-ELM)andMoth Flame Optimization(MFO-ELM)”.These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression data.The proposed models applied to the predication of electricity loading data,that describes the energy use of a single residence over a fouryear period.In the hidden layer,Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of ELM.The best weights and preferences were calculated by these algorithms for the hidden layer.Experimental results demonstrated that the proposed MFO-ELM achieved 98.13%accuracy and this is the highest model in accuracy in tumor type classification gene expression data.While in predication,the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.展开更多
Globally,hepatocellular carcinoma(HCC)is a frequently diagnosed malignancy with rapidly increasing incidence and mortality rates.Unfortunately,many of these patients are diagnosed in the advanced stages when locoregio...Globally,hepatocellular carcinoma(HCC)is a frequently diagnosed malignancy with rapidly increasing incidence and mortality rates.Unfortunately,many of these patients are diagnosed in the advanced stages when locoregional treatments are not appropriate.Before 2008,no effective drug treatments existed to prolong survival,until the breakthrough multi-tyrosine kinase inhibitor(TKI)sorafenib was developed.It remained the standard treatment option for advanced HCC for 10 years,with a battery of other candidate drugs in clinical trials failing to produce similar efficacy results.In 2018,the REFLECT trial introduced another multi-TKI,lenvatinib,which has non-inferior overall survival compared with sorafenib.Thus,offering patients and their treating physicians two effective treatment options.Recently,immunotherapy-based drugs,such as atezolizumab and bevacizumab,have shown promising results in patients with unresectable HCC.This review summarizes clinical trial and real-world data studies of sorafenib and lenvatinib in patients with unresectable HCC.We offer guidance on the optimal choice between the two treatments and discuss the potential of immunotherapy-based combination;when more data become available,this will likely make the choice between sorafenib and lenvatinib somewhat obsolete.展开更多
Motivated by the lack of a suitable constructive framework for analyzing popular stochastic models of Systems Biology, we devise conditions for existence and uniqueness of solutions to certain jump stochastic differen...Motivated by the lack of a suitable constructive framework for analyzing popular stochastic models of Systems Biology, we devise conditions for existence and uniqueness of solutions to certain jump stochastic differential equations (SDEs). Working from simple examples we find reasonable and explicit assumptions on the driving coefficients for the SDE representation to make sense. By “reasonable” we mean that stronger assumptions generally do not hold for systems of practical interest. In particular, we argue against the traditional use of global Lipschitz conditions and certain common growth restrictions. By “explicit”, finally, we like to highlight the fact that the various constants occurring among our assumptions all can be determined once the model is fixed. We show how basic long time estimates and some limit results for perturbations can be derived in this setting such that these can be contrasted with the corresponding estimates from deterministic dynamics. The main complication is that the natural path-wise representation is generated by a counting measure with an intensity that depends nonlinearly on the state.展开更多
The Burgers＇ equation with uncertain initial and boundary conditions is approximated using a Polynomial Chaos Expansion （PCE） approach where the solution is represented as a series of stochastic, orthogonal polynom...The Burgers＇ equation with uncertain initial and boundary conditions is approximated using a Polynomial Chaos Expansion （PCE） approach where the solution is represented as a series of stochastic, orthogonal polynomials. The resulting truncated PCE system is solved using a novel numerical discretization method based on spatial derivative operators satisfying the summation by parts property and weak boundary conditions to ensure stability. The resulting PCE solution yields an accurate quantitative description of the stochastic evolution of the system, provided that appropriate boundary conditions are available. The specification of the boundary data is shown to influence the solution; we will discuss the problematic implications of the lack of precisely characterized boundary data and possible ways of imposing stable and accurate boundary conditions.展开更多
We investigated the effects of prenatal bisphenol A (BPA) exposure on maternal thyroid hormones and fetal growth outcomes within a cohort from Saudi Arabia. In this prospective study, 672 pregnant women provided 1957 ...We investigated the effects of prenatal bisphenol A (BPA) exposure on maternal thyroid hormones and fetal growth outcomes within a cohort from Saudi Arabia. In this prospective study, 672 pregnant women provided 1957 urine samples, which were analyzed for BPA concentrations using UPLC-MS/MS throughout three trimesters. We recorded BPA detection rates and median concentrations, using mixed-effects models to examine the influence on maternal thyroid hormones, specifically free thyroxine (FT4) and thyroid-stimulating hormone (TSH). Additionally, we explored the impact on fetal growth markers such as head circumference (HC) and placental weight (PWT) through multivariable regression, adjusting for confounders. Findings indicated that BPA was present in over 95 % of samples, with a notable decrease in median concentrations from the 1st to the 3rd trimester. Higher BPA exposure correlated with a 2.96 % increase in FT4 levels and a 14.58 % reduction in TSH in the top exposure quartile. Fetal growth analysis showed a decrease of 3.8 % in HC and 15.3 % in PWT associated with high first-trimester BPA levels. Furthermore, FT4 levels in the first and 2nd trimesters mediated the relationship between BPA exposure and fetal growth outcomes by 21.1 % for PWT and 19.1 % for HC, while gestational age mediated 12.1 % of the change in HC. The study highlights significant disruptions in thyroid function and detrimental effects on fetal development due to high BPA exposure, underscoring the need for rigorous monitoring and preventive measures during pregnancy.展开更多
Over the past half century,a variety of computational fluid dynamics(CFD)methods and the direct simulation Monte Carlo(DSMC)method have been widely and successfully applied to the simulation of gas flows for the conti...Over the past half century,a variety of computational fluid dynamics(CFD)methods and the direct simulation Monte Carlo(DSMC)method have been widely and successfully applied to the simulation of gas flows for the continuum and rarefied regime,respectively.However,they both encounter difficulties when dealing with multiscale gas flows in modern engineering problems,where the whole system is on the macroscopic scale but the nonequilibrium effects play an important role.In this paper,we review two particle-based strategies developed for the simulation of multiscale nonequilibrium gas flows,i.e.,DSMC-CFD hybrid methods and multiscale particle methods.The principles,advantages,disadvantages,and applications for each method are described.The latest progress in the modelling of multiscale gas flows including the unified multiscale particle method proposed by the authors is presented.展开更多
Machine learning has become a common and powerful tool in materials research.As more data become available,with the use of high-performance computing and high-throughput experimentation,machine learning has proven pot...Machine learning has become a common and powerful tool in materials research.As more data become available,with the use of high-performance computing and high-throughput experimentation,machine learning has proven potential to accelerate scientific research and technology development.Though the uptake of data-driven approaches for materials science is at an exciting,early stage,to realize the true potential of machine learning models for successful scientific discovery,they must have qualities beyond purely predictive power.The predictions and inner workings of models should provide a certain degree of explainability by human experts,permitting the identification of potential model issues or limitations,building trust in model predictions,and unveiling unexpected correlations that may lead to scientific insights.In this work,we summarize applications of interpretability and explainability techniques for materials science and chemistry and discuss how these techniques can improve the outcome of scientific studies.We start by defining the fundamental concepts of interpretability and explainability in machine learning and making them less abstract by providing examples in the field.We show how interpretability in scientific machine learning has additional constraints compared to general applications.Building upon formal definitions in machine learning,we formulate the basic trade-offs among the explainability,completeness,and scientific validity of model explanations in scientific problems.In the context of these trade-offs,we discuss how interpretable models can be constructed,what insights they provide,and what drawbacks they have.We present numerous examples of the application of interpretable machine learning in a variety of experimental and simulation studies,encompassing first-principles calculations,physicochemical characterization,materials development,and integration into complex systems.We discuss the varied impacts and uses of interpretabiltiy in these cases according to the nature and constraints of the scientific study of interest.We discuss various challenges for interpretable machine learning in materials science and,more broadly,in scientific settings.In particular,we emphasize the risks of inferring causation or reaching generalization by purely interpreting machine learning models and the need for uncertainty estimates for model explanations.Finally,we showcase a number of exciting developments in other fields that could benefit interpretability in material science problems.Adding interpretability to a machine learning model often requires no more technical know-how than building the model itself.By providing concrete examples of studies(many with associated open source code and data),we hope that this Account will encourage all practitioners of machine learning in materials science to look deeper into their models.展开更多
In this work, we propose an efficient numerical method for computing the electrostatic interaction between two like-charged spherical particles which is governed by the nonlinear Poisson-Boltzmann equation. The nonlin...In this work, we propose an efficient numerical method for computing the electrostatic interaction between two like-charged spherical particles which is governed by the nonlinear Poisson-Boltzmann equation. The nonlinear problem is solved by a monotone iterative method which leads to a sequence of linearized equations. A modified central finite difference scheme is developed to solve the linearized equations on an exterior irregular domain using a uniform Cartesian grid. With uniform grids, the method is simple, and as a consequence, multigrid solvers can be employed to speed up the convergence. Numerical experiments on cases with two isolated spheres and two spheres confined in a charged cylindrical pore are carried out using the proposed method. Our numerical schemes are found efficient and the numerical results are found in good agreement with the previous published results.展开更多
We present a well-posed and discretely stable perfectly matched layer for the anisotropic(and isotropic)elastic wave equations without first re-writing the governing equations as a first order system.The new model is ...We present a well-posed and discretely stable perfectly matched layer for the anisotropic(and isotropic)elastic wave equations without first re-writing the governing equations as a first order system.The new model is derived by the complex coordinate stretching technique.Using standard perturbation methods we show that complex frequency shift together with a chosen real scaling factor ensures the decay of eigen-modes for all relevant frequencies.To buttress the stability properties and the robustness of the proposed model,numerical experiments are presented for anisotropic elastic wave equations.The model is approximated with a stable node-centered finite difference scheme that is second order accurate both in time and space.展开更多
The use of machine learning is becoming increasingly common in computational materials science.To build effective models of the chemistry of materials,useful machine-based representations of atoms and their compounds ...The use of machine learning is becoming increasingly common in computational materials science.To build effective models of the chemistry of materials,useful machine-based representations of atoms and their compounds are required.We derive distributed representations of compounds from their chemical formulas only,via pooling operations of distributed representations of atoms.These compound representations are evaluated on ten different tasks,such as the prediction of formation energy and band gap,and are found to be competitive with existing benchmarks that make use of structure,and even superior in cases where only composition is available.Finally,we introduce an approach for learning distributed representations of atoms,named SkipAtom,which makes use of the growing information in materials structure databases.展开更多
Heterostructure is an effective approach in modulating the physical and chemical behavior of materials. Here, the first-principles calculations were carried out to explore the structural, electronic, and carrier mobil...Heterostructure is an effective approach in modulating the physical and chemical behavior of materials. Here, the first-principles calculations were carried out to explore the structural, electronic, and carrier mobility properties of Janus MoSSe/GaN heterostructures. This heterostructure exhibits a superior high carrier mobility of 281.28 cm^(2)·V^(−1)·s^(−1) for electron carrier and 3951.2 cm^(2)·V^(−1)·s^(−1) for hole carrier. Particularly, the magnitude of the carrier mobility can be further tuned by Janus structure and stacking modes of the heterostructure. It is revealed that the equivalent mass and elastic moduli strongly affect the carrier mobility of the heterostructure, while the deformation potential contributes to the different carrier mobility for electron and hole of the heterostructure. These results suggest that the Janus MoSSe/GaN heterostructures have many potential applications for the unique carrier mobility.展开更多
In this paper,we present an immersed boundary method for simulating moving contact lines with surfactant.The governing equations are the incompressible Navier-Stokes equations with the usual mixture of Eulerian fluid ...In this paper,we present an immersed boundary method for simulating moving contact lines with surfactant.The governing equations are the incompressible Navier-Stokes equations with the usual mixture of Eulerian fluid variables and Lagrangian interfacial markers.The immersed boundary force has two components:one from the nonhomogeneous surface tension determined by the distribution of surfactant along the fluid interface,and the other from unbalanced Young’s force at the moving contact lines.An artificial tangential velocity has been added to the Lagrangian markers to ensure that the markers are uniformly distributed at all times.The corresponding modified surfactant equation is solved in a way such that the total surfactant mass is conserved.Numerical experiments including convergence analysis are carefully conducted.The effect of the surfactant on the motion of hydrophilic and hydrophobic drops are investigated in detail.展开更多
There are a number of interesting applications where modeling elastic and/or viscoelastic materials is fundamental,including uses in civil engineering,the food industry,land mine detection and ultrasonic imaging.Here ...There are a number of interesting applications where modeling elastic and/or viscoelastic materials is fundamental,including uses in civil engineering,the food industry,land mine detection and ultrasonic imaging.Here we provide an overview of the subject for both elastic and viscoelastic materials in order to understand the behavior of these materials.We begin with a brief introduction of some basic terminology and relationships in continuum mechanics,and a review of equations of motion in a continuum in both Lagrangian and Eulerian forms.To complete the set of equations,we then proceed to present and discuss a number of specific forms for the constitutive relationships between stress and strain proposed in the literature for both elastic and viscoelastic materials.In addition,we discuss some applications for these constitutive equations.Finally,we give a computational example describing the motion of soil experiencing dynamic loading by incorporating a specific form of constitutive equation into the equation of motion.展开更多
Many macroscopic equations are proposed to describe the rarefied gas dynamics beyond the Navier-Stokes level,either from the mesoscopic Boltzmann equation or some physical arguments,including(i)Burnett,Woods,super-Bur...Many macroscopic equations are proposed to describe the rarefied gas dynamics beyond the Navier-Stokes level,either from the mesoscopic Boltzmann equation or some physical arguments,including(i)Burnett,Woods,super-Burnett,augmented Burnett equations derived from the Chapman-Enskog expansion of the Boltzmann equation,(ii)Grad 13,regularized 13/26 moment equations,rational extended thermodynamics equations,and generalized hydrodynamic equations,where the velocity distribution function is expressed in terms of low-order moments and Hermite polynomials,and(iii)bi-velocity equations and“thermo-mechanically consistent"Burnett equations based on the argument of“volume diffusion”.This paper is dedicated to assess the accuracy of these macroscopic equations.We first consider the RayleighBrillouin scattering,where light is scattered by the density fluctuation in gas.In this specific problem macroscopic equations can be linearized and solutions can always be obtained,no matter whether they are stable or not.Moreover,the accuracy assessment is not contaminated by the gas-wall boundary condition in this periodic problem.Rayleigh-Brillouin spectra of the scattered light are calculated by solving the linearized macroscopic equations and compared to those from the linearized Boltzmann equation.We find that(i)the accuracy of Chapman-Enskog expansion does not always increase with the order of expansion,(ii)for the moment method,the more moments are included,the more accurate the results are,and(iii)macroscopic equations based on“volume diffusion"do not work well even when the Knudsen number is very small.Therefore,among about a dozen tested equations,the regularized 26 moment equations are the most accurate.However,for moderate and highly rarefied gas flows,huge number of moments should be included,as the convergence to true solutions is rather slow.The same conclusion is drawn from the problem of sound propagation between the transducer and receiver.This slow convergence of moment equations is due to the incapability of Hermite polynomials in the capturing of large discontinuities and rapid variations of the velocity distribution function.This study sheds some light on how to choose/develop macroscopic equations for rarefied gas dynamics.展开更多
The finite volume(FV)method is the dominating discretization technique for computational fluid dynamics(CFD),particularly in the case of compressible fluids.The discontinuous Galerkin(DG)method has emerged as a promis...The finite volume(FV)method is the dominating discretization technique for computational fluid dynamics(CFD),particularly in the case of compressible fluids.The discontinuous Galerkin(DG)method has emerged as a promising highaccuracy alternative.The standard DG method reduces to a cell-centered FV method at lowest order.However,many of today’s CFD codes use a vertex-centered FV method in which the data structures are edge based.We develop a new DG method that reduces to the vertex-centered FV method at lowest order,and examine here the new scheme for scalar hyperbolic problems.Numerically,the method shows optimal-order accuracy for a smooth linear problem.By applying a basic hp-adaption strategy,the method successfully handles shocks.We also discuss how to extend the FV edge-based data structure to support the new scheme.In this way,it will in principle be possible to extend an existing code employing the vertex-centered and edge-based FV discretization to encompass higher accuracy through the new DG method.展开更多
基金supported by the US ARO grants 49308-MA and 56349-MAthe US AFSOR grant FA9550-06-1-024+1 种基金he US NSF grant DMS-0911434the State Key Laboratory of Scientific and Engineering Computing of Chinese Academy of Sciences during a visit by Z.Li between July-August,2008.
文摘In this paper,a class of new immersed interface finite element methods (IIFEM) is developed to solve elasticity interface problems with homogeneous and non-homogeneous jump conditions in two dimensions.Simple non-body-fitted meshes are used.For homogeneous jump conditions,both non-conforming and conforming basis functions are constructed in such a way that they satisfy the natural jump conditions. For non-homogeneous jump conditions,a pair of functions that satisfy the same non-homogeneous jump conditions are constructed using a level-set representation of the interface.With such a pair of functions,the discontinuities across the interface in the solution and flux are removed;and an equivalent elasticity interface problem with homogeneous jump conditions is formulated.Numerical examples are presented to demonstrate that such methods have second order convergence.
基金Project supported by the National Natural Science Foundation of China(Nos.11572025,11772032,and 51420105008)the National Basic Research Program of China(No.2014CB046405)the U.K.Engineering and Physical Sciences Research Council(EPSRC)(Nos.EP/K024574/1 and EP/L000261/1)
文摘Non-equilibrium turbulence phenomena have raised great interests in recent years. Significant efforts have been devoted to non-equilibrium turbulence properties in canonical flows, e.g., grid turbulence, turbulent wakes, and homogeneous isotropic turbulence(HIT). The non-equilibrium turbulence in non-canonical flows, however, has rarely been studied due to the complexity of the flows. In the present contribution, a directnumerical simulation(DNS) database of a turbulent flow is analyzed over a backwardfacing ramp, the flow near the boundary is demonstrated, and the non-equilibrium turbulent properties of the flow in the wake of the ramp are presented by using the characteristic parameters such as the dissipation coefficient C and the skewness of longitudinal velocity gradient Sk, but with opposite underlying turbulent energy transfer properties. The equation of Lagrangian velocity gradient correlation is examined, and the results show that non-equilibrium turbulence is the result of phase de-coherence phenomena, which is not taken into account in the modeling of non-equilibrium turbulence. These findings are expected to inspire deeper investigation of different non-equilibrium turbulence phenomena in different flow conditions and the improvement of turbulence modeling.
基金Project supported by the National Natural Science Foundation of China(Nos.12002318,11572025,11772032,and 51420105008)the Science Foundation of North University of China(No.XJJ201929)。
文摘Many recent laboratory experiments and numerical simulations support a non-equilibrium dissipation scaling in decaying turbulence before it reaches an equilibrium state.By analyzing a direct numerical simulation(DNS)database of a transitional boundary-layer flow,we show that the transition region and the non-equilibrium turbulence region,which are located in different streamwise zones,present different non-equilibrium scalings.Moreover,in the wall-normal direction,the viscous sublayer,log layer,and outer layer show different non-equilibrium phenomena which differ from those in grid-generated turbulence and transitional channel flows.These findings are expected to shed light on the modelling of various types of non-equilibrium turbulent flows.
文摘Standard finite element approaches are still ineffective in handling extreme material deformation, such as cases of large deformations and moving discontinuities due to severe mesh distortion. Among meshfree methods developed to overcome the ineffectiveness, Reproducing Kernel Particle Method (RKPM) has demonstrated its great suitability for structural analysis.This paper presents applications of RKPM in elasto-plastic problems after a review of meshfree methods and an introduction to RKPM. A slope stability problem in geotechnical engineering is analyzed as an illustrative case. The corresponding numerical simulations are carried out on an SGI Onyx3900 supercomputer. Comparison of the RKPM and the FEM under identical conditions showed that the RKPM is more suitable for problems where there exists extremely large strain such as in the case of slope sliding.
文摘Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ability.While,Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence,high time and space complexity.In ELM,the hidden layer typically necessitates a huge number of nodes.Furthermore,there is no certainty that the arrangement of weights and biases within the hidden layer is optimal.To solve this problem,the traditional ELM has been hybridized with swarm intelligence optimization techniques.This paper displays five proposed hybrid Algorithms“Salp Swarm Algorithm(SSA-ELM),Grasshopper Algorithm(GOA-ELM),Grey Wolf Algorithm(GWO-ELM),Whale optimizationAlgorithm(WOA-ELM)andMoth Flame Optimization(MFO-ELM)”.These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression data.The proposed models applied to the predication of electricity loading data,that describes the energy use of a single residence over a fouryear period.In the hidden layer,Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of ELM.The best weights and preferences were calculated by these algorithms for the hidden layer.Experimental results demonstrated that the proposed MFO-ELM achieved 98.13%accuracy and this is the highest model in accuracy in tumor type classification gene expression data.While in predication,the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.
文摘Globally,hepatocellular carcinoma(HCC)is a frequently diagnosed malignancy with rapidly increasing incidence and mortality rates.Unfortunately,many of these patients are diagnosed in the advanced stages when locoregional treatments are not appropriate.Before 2008,no effective drug treatments existed to prolong survival,until the breakthrough multi-tyrosine kinase inhibitor(TKI)sorafenib was developed.It remained the standard treatment option for advanced HCC for 10 years,with a battery of other candidate drugs in clinical trials failing to produce similar efficacy results.In 2018,the REFLECT trial introduced another multi-TKI,lenvatinib,which has non-inferior overall survival compared with sorafenib.Thus,offering patients and their treating physicians two effective treatment options.Recently,immunotherapy-based drugs,such as atezolizumab and bevacizumab,have shown promising results in patients with unresectable HCC.This review summarizes clinical trial and real-world data studies of sorafenib and lenvatinib in patients with unresectable HCC.We offer guidance on the optimal choice between the two treatments and discuss the potential of immunotherapy-based combination;when more data become available,this will likely make the choice between sorafenib and lenvatinib somewhat obsolete.
文摘Motivated by the lack of a suitable constructive framework for analyzing popular stochastic models of Systems Biology, we devise conditions for existence and uniqueness of solutions to certain jump stochastic differential equations (SDEs). Working from simple examples we find reasonable and explicit assumptions on the driving coefficients for the SDE representation to make sense. By “reasonable” we mean that stronger assumptions generally do not hold for systems of practical interest. In particular, we argue against the traditional use of global Lipschitz conditions and certain common growth restrictions. By “explicit”, finally, we like to highlight the fact that the various constants occurring among our assumptions all can be determined once the model is fixed. We show how basic long time estimates and some limit results for perturbations can be derived in this setting such that these can be contrasted with the corresponding estimates from deterministic dynamics. The main complication is that the natural path-wise representation is generated by a counting measure with an intensity that depends nonlinearly on the state.
基金Supported by the US Department of Energy under the PSAAP Program
文摘The Burgers＇ equation with uncertain initial and boundary conditions is approximated using a Polynomial Chaos Expansion （PCE） approach where the solution is represented as a series of stochastic, orthogonal polynomials. The resulting truncated PCE system is solved using a novel numerical discretization method based on spatial derivative operators satisfying the summation by parts property and weak boundary conditions to ensure stability. The resulting PCE solution yields an accurate quantitative description of the stochastic evolution of the system, provided that appropriate boundary conditions are available. The specification of the boundary data is shown to influence the solution; we will discuss the problematic implications of the lack of precisely characterized boundary data and possible ways of imposing stable and accurate boundary conditions.
基金funded by the King Salman Center for Disability Research(RCS-0011-2017-PR)The project was approved by the Research Advisory Council,King Faisal Specialist Hospital,and Research Centre(RAC#2180005).
文摘We investigated the effects of prenatal bisphenol A (BPA) exposure on maternal thyroid hormones and fetal growth outcomes within a cohort from Saudi Arabia. In this prospective study, 672 pregnant women provided 1957 urine samples, which were analyzed for BPA concentrations using UPLC-MS/MS throughout three trimesters. We recorded BPA detection rates and median concentrations, using mixed-effects models to examine the influence on maternal thyroid hormones, specifically free thyroxine (FT4) and thyroid-stimulating hormone (TSH). Additionally, we explored the impact on fetal growth markers such as head circumference (HC) and placental weight (PWT) through multivariable regression, adjusting for confounders. Findings indicated that BPA was present in over 95 % of samples, with a notable decrease in median concentrations from the 1st to the 3rd trimester. Higher BPA exposure correlated with a 2.96 % increase in FT4 levels and a 14.58 % reduction in TSH in the top exposure quartile. Fetal growth analysis showed a decrease of 3.8 % in HC and 15.3 % in PWT associated with high first-trimester BPA levels. Furthermore, FT4 levels in the first and 2nd trimesters mediated the relationship between BPA exposure and fetal growth outcomes by 21.1 % for PWT and 19.1 % for HC, while gestational age mediated 12.1 % of the change in HC. The study highlights significant disruptions in thyroid function and detrimental effects on fetal development due to high BPA exposure, underscoring the need for rigorous monitoring and preventive measures during pregnancy.
基金National Numerical Windtunnel Project(Grant 2018-ZT3A05)National Natural Science Foundation of China(Grant No.11772034)Engineering and Physical Sciences Research Council(EPSRC,Grant No.EP/N016602/1).
文摘Over the past half century,a variety of computational fluid dynamics(CFD)methods and the direct simulation Monte Carlo(DSMC)method have been widely and successfully applied to the simulation of gas flows for the continuum and rarefied regime,respectively.However,they both encounter difficulties when dealing with multiscale gas flows in modern engineering problems,where the whole system is on the macroscopic scale but the nonequilibrium effects play an important role.In this paper,we review two particle-based strategies developed for the simulation of multiscale nonequilibrium gas flows,i.e.,DSMC-CFD hybrid methods and multiscale particle methods.The principles,advantages,disadvantages,and applications for each method are described.The latest progress in the modelling of multiscale gas flows including the unified multiscale particle method proposed by the authors is presented.
文摘Machine learning has become a common and powerful tool in materials research.As more data become available,with the use of high-performance computing and high-throughput experimentation,machine learning has proven potential to accelerate scientific research and technology development.Though the uptake of data-driven approaches for materials science is at an exciting,early stage,to realize the true potential of machine learning models for successful scientific discovery,they must have qualities beyond purely predictive power.The predictions and inner workings of models should provide a certain degree of explainability by human experts,permitting the identification of potential model issues or limitations,building trust in model predictions,and unveiling unexpected correlations that may lead to scientific insights.In this work,we summarize applications of interpretability and explainability techniques for materials science and chemistry and discuss how these techniques can improve the outcome of scientific studies.We start by defining the fundamental concepts of interpretability and explainability in machine learning and making them less abstract by providing examples in the field.We show how interpretability in scientific machine learning has additional constraints compared to general applications.Building upon formal definitions in machine learning,we formulate the basic trade-offs among the explainability,completeness,and scientific validity of model explanations in scientific problems.In the context of these trade-offs,we discuss how interpretable models can be constructed,what insights they provide,and what drawbacks they have.We present numerous examples of the application of interpretable machine learning in a variety of experimental and simulation studies,encompassing first-principles calculations,physicochemical characterization,materials development,and integration into complex systems.We discuss the varied impacts and uses of interpretabiltiy in these cases according to the nature and constraints of the scientific study of interest.We discuss various challenges for interpretable machine learning in materials science and,more broadly,in scientific settings.In particular,we emphasize the risks of inferring causation or reaching generalization by purely interpreting machine learning models and the need for uncertainty estimates for model explanations.Finally,we showcase a number of exciting developments in other fields that could benefit interpretability in material science problems.Adding interpretability to a machine learning model often requires no more technical know-how than building the model itself.By providing concrete examples of studies(many with associated open source code and data),we hope that this Account will encourage all practitioners of machine learning in materials science to look deeper into their models.
基金The research of the first author is supported by the Hong Kong Baptist University. The research of the second author is partially supported by a USA-AR0 grant 43751-MA and USA- NFS grants DMS0201094 and DMS-0412654. The third author is partially supported by CERG Grants of Hong Kong Research Grant Council, FRG grants of Hong Kong Baptist University, and an NSAF Grant （#10476032） of National Science Foundation of Chian.
文摘In this work, we propose an efficient numerical method for computing the electrostatic interaction between two like-charged spherical particles which is governed by the nonlinear Poisson-Boltzmann equation. The nonlinear problem is solved by a monotone iterative method which leads to a sequence of linearized equations. A modified central finite difference scheme is developed to solve the linearized equations on an exterior irregular domain using a uniform Cartesian grid. With uniform grids, the method is simple, and as a consequence, multigrid solvers can be employed to speed up the convergence. Numerical experiments on cases with two isolated spheres and two spheres confined in a charged cylindrical pore are carried out using the proposed method. Our numerical schemes are found efficient and the numerical results are found in good agreement with the previous published results.
文摘We present a well-posed and discretely stable perfectly matched layer for the anisotropic(and isotropic)elastic wave equations without first re-writing the governing equations as a first order system.The new model is derived by the complex coordinate stretching technique.Using standard perturbation methods we show that complex frequency shift together with a chosen real scaling factor ensures the decay of eigen-modes for all relevant frequencies.To buttress the stability properties and the robustness of the proposed model,numerical experiments are presented for anisotropic elastic wave equations.The model is approximated with a stable node-centered finite difference scheme that is second order accurate both in time and space.
文摘The use of machine learning is becoming increasingly common in computational materials science.To build effective models of the chemistry of materials,useful machine-based representations of atoms and their compounds are required.We derive distributed representations of compounds from their chemical formulas only,via pooling operations of distributed representations of atoms.These compound representations are evaluated on ten different tasks,such as the prediction of formation energy and band gap,and are found to be competitive with existing benchmarks that make use of structure,and even superior in cases where only composition is available.Finally,we introduce an approach for learning distributed representations of atoms,named SkipAtom,which makes use of the growing information in materials structure databases.
基金the Science Challenge Project(Grant No.TZ2018004)the National Natural Science Foundation of China(Grant Nos.51572016,U1530401,11747167,11804090,51472209,11774298,U1401241,and 21503012)+2 种基金the Natural Science Foundation of Hunan Province,China(Grant Nos.17C0626 and 2019JJ50148)a Tianhe-2JK computing time award at the Beijing Computational Science Research Center(CSRC)L.M.L.and G.T.acknowledge also support by the Royal Society Newton Advanced Fellowship scheme(Grant No.NAF\R1\0242).
文摘Heterostructure is an effective approach in modulating the physical and chemical behavior of materials. Here, the first-principles calculations were carried out to explore the structural, electronic, and carrier mobility properties of Janus MoSSe/GaN heterostructures. This heterostructure exhibits a superior high carrier mobility of 281.28 cm^(2)·V^(−1)·s^(−1) for electron carrier and 3951.2 cm^(2)·V^(−1)·s^(−1) for hole carrier. Particularly, the magnitude of the carrier mobility can be further tuned by Janus structure and stacking modes of the heterostructure. It is revealed that the equivalent mass and elastic moduli strongly affect the carrier mobility of the heterostructure, while the deformation potential contributes to the different carrier mobility for electron and hole of the heterostructure. These results suggest that the Janus MoSSe/GaN heterostructures have many potential applications for the unique carrier mobility.
基金supported in part by National Science Council of Taiwan under research grant NSC-97-2628-M-009-007-MY3 and MoE-ATU projectsupported by grants from the Natural Science and Engineering Research Council(NSERC)of Canada and the Mathematics of Information Technology and Complex Systems(MITACS)of Canada.
文摘In this paper,we present an immersed boundary method for simulating moving contact lines with surfactant.The governing equations are the incompressible Navier-Stokes equations with the usual mixture of Eulerian fluid variables and Lagrangian interfacial markers.The immersed boundary force has two components:one from the nonhomogeneous surface tension determined by the distribution of surfactant along the fluid interface,and the other from unbalanced Young’s force at the moving contact lines.An artificial tangential velocity has been added to the Lagrangian markers to ensure that the markers are uniformly distributed at all times.The corresponding modified surfactant equation is solved in a way such that the total surfactant mass is conserved.Numerical experiments including convergence analysis are carefully conducted.The effect of the surfactant on the motion of hydrophilic and hydrophobic drops are investigated in detail.
基金This research was supported in part by the Air Force Office of Scientific Research under grant number FA9550-09-1-0226The efforts of ZRK were supported in part by the Department of Education with a GAANN Fellowship under grant number P200A070386。
文摘There are a number of interesting applications where modeling elastic and/or viscoelastic materials is fundamental,including uses in civil engineering,the food industry,land mine detection and ultrasonic imaging.Here we provide an overview of the subject for both elastic and viscoelastic materials in order to understand the behavior of these materials.We begin with a brief introduction of some basic terminology and relationships in continuum mechanics,and a review of equations of motion in a continuum in both Lagrangian and Eulerian forms.To complete the set of equations,we then proceed to present and discuss a number of specific forms for the constitutive relationships between stress and strain proposed in the literature for both elastic and viscoelastic materials.In addition,we discuss some applications for these constitutive equations.Finally,we give a computational example describing the motion of soil experiencing dynamic loading by incorporating a specific form of constitutive equation into the equation of motion.
文摘Many macroscopic equations are proposed to describe the rarefied gas dynamics beyond the Navier-Stokes level,either from the mesoscopic Boltzmann equation or some physical arguments,including(i)Burnett,Woods,super-Burnett,augmented Burnett equations derived from the Chapman-Enskog expansion of the Boltzmann equation,(ii)Grad 13,regularized 13/26 moment equations,rational extended thermodynamics equations,and generalized hydrodynamic equations,where the velocity distribution function is expressed in terms of low-order moments and Hermite polynomials,and(iii)bi-velocity equations and“thermo-mechanically consistent"Burnett equations based on the argument of“volume diffusion”.This paper is dedicated to assess the accuracy of these macroscopic equations.We first consider the RayleighBrillouin scattering,where light is scattered by the density fluctuation in gas.In this specific problem macroscopic equations can be linearized and solutions can always be obtained,no matter whether they are stable or not.Moreover,the accuracy assessment is not contaminated by the gas-wall boundary condition in this periodic problem.Rayleigh-Brillouin spectra of the scattered light are calculated by solving the linearized macroscopic equations and compared to those from the linearized Boltzmann equation.We find that(i)the accuracy of Chapman-Enskog expansion does not always increase with the order of expansion,(ii)for the moment method,the more moments are included,the more accurate the results are,and(iii)macroscopic equations based on“volume diffusion"do not work well even when the Knudsen number is very small.Therefore,among about a dozen tested equations,the regularized 26 moment equations are the most accurate.However,for moderate and highly rarefied gas flows,huge number of moments should be included,as the convergence to true solutions is rather slow.The same conclusion is drawn from the problem of sound propagation between the transducer and receiver.This slow convergence of moment equations is due to the incapability of Hermite polynomials in the capturing of large discontinuities and rapid variations of the velocity distribution function.This study sheds some light on how to choose/develop macroscopic equations for rarefied gas dynamics.
基金The authors were supported in part by the ADIGMA project[3]and the Graduate School in Mathematics and Computing,FMB[16].
文摘The finite volume(FV)method is the dominating discretization technique for computational fluid dynamics(CFD),particularly in the case of compressible fluids.The discontinuous Galerkin(DG)method has emerged as a promising highaccuracy alternative.The standard DG method reduces to a cell-centered FV method at lowest order.However,many of today’s CFD codes use a vertex-centered FV method in which the data structures are edge based.We develop a new DG method that reduces to the vertex-centered FV method at lowest order,and examine here the new scheme for scalar hyperbolic problems.Numerically,the method shows optimal-order accuracy for a smooth linear problem.By applying a basic hp-adaption strategy,the method successfully handles shocks.We also discuss how to extend the FV edge-based data structure to support the new scheme.In this way,it will in principle be possible to extend an existing code employing the vertex-centered and edge-based FV discretization to encompass higher accuracy through the new DG method.
