Alzheimer’s disease not only affects the brain,but also induces metabolic dysfunction in peripheral organs and alters the gut microbiota.The aim of this study was to investigate systemic changes that occur in Alzhei...Alzheimer’s disease not only affects the brain,but also induces metabolic dysfunction in peripheral organs and alters the gut microbiota.The aim of this study was to investigate systemic changes that occur in Alzheimer’s disease,in particular the association between changes in peripheral organ metabolism,changes in gut microbial composition,and Alzheimer’s disease development.To do this,we analyzed peripheral organ metabolism and the gut microbiota in amyloid precursor protein-presenilin 1(APP/PS1)transgenic and control mice at 3,6,9,and 12 months of age.Twelve-month-old APP/PS1 mice exhibited cognitive impairment,Alzheimer’s disease-related brain changes,distinctive metabolic disturbances in peripheral organs and fecal samples(as detected by untargeted metabolomics sequencing),and substantial changes in gut microbial composition compared with younger APP/PS1 mice.Notably,a strong correlation emerged between the gut microbiota and kidney metabolism in APP/PS1 mice.These findings suggest that alterations in peripheral organ metabolism and the gut microbiota are closely related to Alzheimer’s disease development,indicating potential new directions for therapeutic strategies.展开更多
Microstructure topology evolution during severe plastic deformation(SPD)is crucial for understanding and optimising the mechanical properties of metallic materials,though its prediction remains challenging.Herein,we c...Microstructure topology evolution during severe plastic deformation(SPD)is crucial for understanding and optimising the mechanical properties of metallic materials,though its prediction remains challenging.Herein,we combine discrete cell complexes(DCC),a fully discrete algebraic topology model-with finite element analysis(FEA)to simulate and analyse the microstructure topology of pure copper under SPD.Using DCC,we model the evolution of microstructure topology characterised by Betti numbers(β_(0),β_(1),β_(2))and Euler characteristic(χ).This captures key changes in GBNs and topological features within representative volume elements(RVEs)containing several hundred grains during SPD-induced recrystallisation.As SPD cycles increase,high-angle grain boundaries(HAGBs)progressively form.Topological analysis reveals an overall decrease in β_(0)values,indicating fewer isolated HAGB substructures,while β_(2) values show a steady upward trend,highlighting new grain formation.Leveraging DCC-derived RVE topology and FEA-generated plastic strain data,we directly simulate the evolution and spatial distribution of microstructure topology and HAGB fraction in a copper tube undergoing cyclic parallel tube channel angular pressing(PTCAP),a representative SPD technique.Within the tube,the HAGB fraction continuously increases with PTCAP cycles,reflecting the microstructure’s gradual transition from subgrains to fully-formed grains.Analysis of Betti number distribution and evolution reveals the microstructural reconstruction mechanism underpinning this subgrain to grain transition during PTCAP.We further demonstrate the significant influence of spatially non-uniform plastic strain distribution on microstructure reconstruction kinetics.This study demonstrates a feasible approach for simulating microstructure topology evolution of metals processed by cyclic SPD via the integration of DCC and FEA.展开更多
Fatigue properties of materials by Additive Manufacturing(AM) depend on many factors such as AM processing parameter, microstructure, residual stress, surface roughness, porosities, post-treatments, etc. Their evaluat...Fatigue properties of materials by Additive Manufacturing(AM) depend on many factors such as AM processing parameter, microstructure, residual stress, surface roughness, porosities, post-treatments, etc. Their evaluation inevitably requires these factors combined as many as possible, thus resulting in low efficiency and high cost. In recent years, their assessment by leveraging the power of Machine Learning(ML) has gained increasing attentions. A comprehensive overview on the state-of-the-art progress of applying ML strategies to predict fatigue properties of AM materials, as well as their dependence on AM processing and post-processing parameters such as laser power, scanning speed, layer height, hatch distance, built direction, post-heat temperature,etc., were presented. A few attempts in employing Feedforward Neural Network(FNN), Convolutional Neural Network(CNN), Adaptive Network-Based Fuzzy Inference System(ANFIS), Support Vector Machine(SVM) and Random Forest(RF) to predict fatigue life and RF to predict fatigue crack growth rate are summarized. The ML models for predicting AM materials' fatigue properties are found intrinsically similar to the commonly used ones, but are modified to involve AM features. Finally, an outlook for challenges(i.e., small dataset, multifarious features,overfitting, low interpretability, and unable extension from AM material data to structure life) and potential solutions for the ML prediction of AM materials' fatigue properties is provided.展开更多
Flexoelectricity is a two-way coupling effect between the strain gradient and electric field that exists in all dielectrics,regardless of point group symmetry.However,the high-order derivatives of displacements involv...Flexoelectricity is a two-way coupling effect between the strain gradient and electric field that exists in all dielectrics,regardless of point group symmetry.However,the high-order derivatives of displacements involved in the strain gradient pose challenges in solving electromechanical coupling problems incorporating the flexoelectric effect.In this study,we formulate a phase-field model for ferroelectric materials considering the flexoelectric effect.A four-node quadrilateral element with 20 degrees of freedom is constructed without introducing high-order shape functions.The microstructure evolution of domains is described by an independent order parameter,namely the spontaneous polarization governed by the time-dependent Ginzburg–Landau theory.The model is developed based on a thermodynamic framework,in which a set of microforces is introduced to construct the constitutive relation and evolution equation.For the flexoelectric part of electric enthalpy,the strain gradient is determined by interpolating the mechanical strain at the node via the values of Gaussian integration points in the isoparametric space.The model is shown to be capable of reproducing the classic analytical solution of dielectric materials incorporating the flexoelectric contribution.The model is verified by duplicating some typical phenomena in flexoelectricity in cylindrical tubes and truncated pyramids.A comparison is made between the polarization distribution in dielectrics and ferroelectrics.The model can reproduce the solution to the boundary value problem of the cylindrical flexoelectric tube,and demonstrate domain twisting at domain walls in ferroelectrics considering the flexoelectric effect.展开更多
Sintering,a well-established technique in powder metallurgy,plays a critical role in the processing of high melting point materials.A comprehensive understanding of structural changes during the sintering process is e...Sintering,a well-established technique in powder metallurgy,plays a critical role in the processing of high melting point materials.A comprehensive understanding of structural changes during the sintering process is essential for effective product assessment.The phase-field method stands out for its unique ability to simulate these structural transformations.Despite its widespread application,there is a notable absence of literature reviews focused on its usage in sintering simulations.Therefore,this paper addresses this gap by reviewing the latest advancements in phase-field sintering models,covering approaches based on energy,grand potential,and entropy increase.The characteristics of various models are extensively discussed,with a specific emphasis on energy-based models incorporating considerations such as interface energy anisotropy,tensor-form diffusion mechanisms,and various forms of rigid particle motion during sintering.Furthermore,the paper offers a concise summary of phase-field sintering models that integrate with other physical fields,including stress/strain fields,viscous flow,temperature field,and external electric fields.In conclusion,the paper provides a succinct overview of the entire content and delineates potential avenues for future research.展开更多
Accurate photovoltaic(PV)power forecasting ensures the stability and reliability of power systems.To address the complex characteristics of nonlinearity,volatility,and periodicity,a novel two-stage PV forecasting meth...Accurate photovoltaic(PV)power forecasting ensures the stability and reliability of power systems.To address the complex characteristics of nonlinearity,volatility,and periodicity,a novel two-stage PV forecasting method based on an optimized transformer architecture is proposed.In the first stage,an inverted transformer backbone was utilized to consider the multivariate correlation of the PV power series and capture its non-linearity and volatility.ProbSparse attention was introduced to reduce high-memory occupation and solve computational overload issues.In the second stage,a weighted series decomposition module was proposed to extract the periodicity of the PV power series,and the final forecasting results were obtained through additive reconstruction.Experiments on two public datasets showed that the proposed forecasting method has high accuracy,robustness,and computational efficiency.Its RMSE improved by 31.23%compared with that of a traditional transformer,and its MSE improved by 12.57%compared with that of a baseline model.展开更多
基金financially supported by the National Natural Science Foundation of China,No.823 74552 (to WP)the Science and Technology Innovation Program of Hunan Province,No.2022RC1220 (to WP)+1 种基金the Natural Science Foundation of Hunan Province of China,Nos.2020JJ4803 (to WP),2022JJ40723 (to MY)the Scientific Research Launch Project for New Employees of the Second Xiangya Hospital of Central South University (to MY)
文摘Alzheimer’s disease not only affects the brain,but also induces metabolic dysfunction in peripheral organs and alters the gut microbiota.The aim of this study was to investigate systemic changes that occur in Alzheimer’s disease,in particular the association between changes in peripheral organ metabolism,changes in gut microbial composition,and Alzheimer’s disease development.To do this,we analyzed peripheral organ metabolism and the gut microbiota in amyloid precursor protein-presenilin 1(APP/PS1)transgenic and control mice at 3,6,9,and 12 months of age.Twelve-month-old APP/PS1 mice exhibited cognitive impairment,Alzheimer’s disease-related brain changes,distinctive metabolic disturbances in peripheral organs and fecal samples(as detected by untargeted metabolomics sequencing),and substantial changes in gut microbial composition compared with younger APP/PS1 mice.Notably,a strong correlation emerged between the gut microbiota and kidney metabolism in APP/PS1 mice.These findings suggest that alterations in peripheral organ metabolism and the gut microbiota are closely related to Alzheimer’s disease development,indicating potential new directions for therapeutic strategies.
基金support from Outstanding Youth Fund of Jiangsu Province(BK20240077)Key Project(Provincial-Municipal Joint)of Jiangsu Province(BK20243044)+2 种基金Fundamental Research Funds for the Central Universities(NE2024001)National Youth Talents Programof Chinaa project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘Microstructure topology evolution during severe plastic deformation(SPD)is crucial for understanding and optimising the mechanical properties of metallic materials,though its prediction remains challenging.Herein,we combine discrete cell complexes(DCC),a fully discrete algebraic topology model-with finite element analysis(FEA)to simulate and analyse the microstructure topology of pure copper under SPD.Using DCC,we model the evolution of microstructure topology characterised by Betti numbers(β_(0),β_(1),β_(2))and Euler characteristic(χ).This captures key changes in GBNs and topological features within representative volume elements(RVEs)containing several hundred grains during SPD-induced recrystallisation.As SPD cycles increase,high-angle grain boundaries(HAGBs)progressively form.Topological analysis reveals an overall decrease in β_(0)values,indicating fewer isolated HAGB substructures,while β_(2) values show a steady upward trend,highlighting new grain formation.Leveraging DCC-derived RVE topology and FEA-generated plastic strain data,we directly simulate the evolution and spatial distribution of microstructure topology and HAGB fraction in a copper tube undergoing cyclic parallel tube channel angular pressing(PTCAP),a representative SPD technique.Within the tube,the HAGB fraction continuously increases with PTCAP cycles,reflecting the microstructure’s gradual transition from subgrains to fully-formed grains.Analysis of Betti number distribution and evolution reveals the microstructural reconstruction mechanism underpinning this subgrain to grain transition during PTCAP.We further demonstrate the significant influence of spatially non-uniform plastic strain distribution on microstructure reconstruction kinetics.This study demonstrates a feasible approach for simulating microstructure topology evolution of metals processed by cyclic SPD via the integration of DCC and FEA.
基金the support from the National Science and Technology Major Project, China (No. J2019IV-0014-0082)the National Key Research and Development Program of China (No. 2022YFB4600700)+1 种基金the National Overseas Youth Talents Program, China, the Research Fund of State Key Laboratory of Mechanics and Control for Aerospace Structures, China (No. MCMS-I-0422K01)a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, China。
文摘Fatigue properties of materials by Additive Manufacturing(AM) depend on many factors such as AM processing parameter, microstructure, residual stress, surface roughness, porosities, post-treatments, etc. Their evaluation inevitably requires these factors combined as many as possible, thus resulting in low efficiency and high cost. In recent years, their assessment by leveraging the power of Machine Learning(ML) has gained increasing attentions. A comprehensive overview on the state-of-the-art progress of applying ML strategies to predict fatigue properties of AM materials, as well as their dependence on AM processing and post-processing parameters such as laser power, scanning speed, layer height, hatch distance, built direction, post-heat temperature,etc., were presented. A few attempts in employing Feedforward Neural Network(FNN), Convolutional Neural Network(CNN), Adaptive Network-Based Fuzzy Inference System(ANFIS), Support Vector Machine(SVM) and Random Forest(RF) to predict fatigue life and RF to predict fatigue crack growth rate are summarized. The ML models for predicting AM materials' fatigue properties are found intrinsically similar to the commonly used ones, but are modified to involve AM features. Finally, an outlook for challenges(i.e., small dataset, multifarious features,overfitting, low interpretability, and unable extension from AM material data to structure life) and potential solutions for the ML prediction of AM materials' fatigue properties is provided.
基金funded by the National Natural Science Foundation of China(Grant No.12272020)Beijing Natural Science Foundation(Grant No.JQ21001)+1 种基金S.W.acknowledges support from the Fundamental Research Funds for the Central Universities(Grant No.YWF-23-SDHK-L-019)M.Y.acknowledges support from the National Natural Science Foundation of China(Grant Nos.12302134,12272173,and 11902150).
文摘Flexoelectricity is a two-way coupling effect between the strain gradient and electric field that exists in all dielectrics,regardless of point group symmetry.However,the high-order derivatives of displacements involved in the strain gradient pose challenges in solving electromechanical coupling problems incorporating the flexoelectric effect.In this study,we formulate a phase-field model for ferroelectric materials considering the flexoelectric effect.A four-node quadrilateral element with 20 degrees of freedom is constructed without introducing high-order shape functions.The microstructure evolution of domains is described by an independent order parameter,namely the spontaneous polarization governed by the time-dependent Ginzburg–Landau theory.The model is developed based on a thermodynamic framework,in which a set of microforces is introduced to construct the constitutive relation and evolution equation.For the flexoelectric part of electric enthalpy,the strain gradient is determined by interpolating the mechanical strain at the node via the values of Gaussian integration points in the isoparametric space.The model is shown to be capable of reproducing the classic analytical solution of dielectric materials incorporating the flexoelectric contribution.The model is verified by duplicating some typical phenomena in flexoelectricity in cylindrical tubes and truncated pyramids.A comparison is made between the polarization distribution in dielectrics and ferroelectrics.The model can reproduce the solution to the boundary value problem of the cylindrical flexoelectric tube,and demonstrate domain twisting at domain walls in ferroelectrics considering the flexoelectric effect.
基金supported by the National Science and TechnologyMajor Project,China(No.J2019-IV-0014-0082)the National Key Research and Development Program of China(No.2022YFB4600700)+1 种基金the National Overseas Youth Talents Program,China,the Research Fund of State Key Laboratory of Mechanics and Control for Aerospace Structures,China(No.MCMS-I-0422K01)a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions,China.
文摘Sintering,a well-established technique in powder metallurgy,plays a critical role in the processing of high melting point materials.A comprehensive understanding of structural changes during the sintering process is essential for effective product assessment.The phase-field method stands out for its unique ability to simulate these structural transformations.Despite its widespread application,there is a notable absence of literature reviews focused on its usage in sintering simulations.Therefore,this paper addresses this gap by reviewing the latest advancements in phase-field sintering models,covering approaches based on energy,grand potential,and entropy increase.The characteristics of various models are extensively discussed,with a specific emphasis on energy-based models incorporating considerations such as interface energy anisotropy,tensor-form diffusion mechanisms,and various forms of rigid particle motion during sintering.Furthermore,the paper offers a concise summary of phase-field sintering models that integrate with other physical fields,including stress/strain fields,viscous flow,temperature field,and external electric fields.In conclusion,the paper provides a succinct overview of the entire content and delineates potential avenues for future research.
基金Top Leading Talents Project of Gansu Province(B32722246002).
文摘Accurate photovoltaic(PV)power forecasting ensures the stability and reliability of power systems.To address the complex characteristics of nonlinearity,volatility,and periodicity,a novel two-stage PV forecasting method based on an optimized transformer architecture is proposed.In the first stage,an inverted transformer backbone was utilized to consider the multivariate correlation of the PV power series and capture its non-linearity and volatility.ProbSparse attention was introduced to reduce high-memory occupation and solve computational overload issues.In the second stage,a weighted series decomposition module was proposed to extract the periodicity of the PV power series,and the final forecasting results were obtained through additive reconstruction.Experiments on two public datasets showed that the proposed forecasting method has high accuracy,robustness,and computational efficiency.Its RMSE improved by 31.23%compared with that of a traditional transformer,and its MSE improved by 12.57%compared with that of a baseline model.
