This paper extends the one-dimensional(1D)nonlocal strain gradient integral model(NStraGIM)to the two-dimensional(2D)Kirchhoff axisymmetric nanoplates,based on nonlocal strain gradient integral relations formulated al...This paper extends the one-dimensional(1D)nonlocal strain gradient integral model(NStraGIM)to the two-dimensional(2D)Kirchhoff axisymmetric nanoplates,based on nonlocal strain gradient integral relations formulated along both the radial and circumferential directions.By transforming the proposed integral constitutive equations into the equivalent differential forms,complemented by the corresponding constitutive boundary conditions(CBCs),a well-posed mathematical formulation is established for analyzing the axisymmetric bending and buckling of annular/circular functionally graded(FG)sandwich nanoplates.The boundary conditions at the inner edge of a solid nanoplate are derived by L'H?spital's rule.The numerical solution is obtained by the generalized differential quadrature method(GDQM).The accuracy of the proposed model is validated through comparison with the data from the existing literature.A parameter study is conducted to demonstrate the effects of FG sandwich parameters,size parameters,and nonlocal gradient parameters.展开更多
To address the issue of disturbance compensation deviation in linear active disturbance rejection control(LADRC),a linear active disturbance rejection control method with reference to the integral chain model(LADRC-R)...To address the issue of disturbance compensation deviation in linear active disturbance rejection control(LADRC),a linear active disturbance rejection control method with reference to the integral chain model(LADRC-R)is proposed.By constructing an ideal control reference model,a dynamic correlation between output deviation and uncompensated disturbances is established,and a dual-loop compensation mechanism is designed.Based on theoretical analysis and frequency-domain characteristics of typical first/second-order systems,this method maintains the parameter-tuning advantages of LADRC while reducing disturbance effects by 50%and introducing no phase lag during low-frequency disturbance suppression.Simulations on second-order systems verify its robustness under parameter perturbations,gain mismatch,and complex disturbances,and an optimized design scheme for the deviation compensator is proposed to suppress discontinuous measurement noise interference.Finally,the engineering effectiveness of this method in precision motion control is validated on an electromagnetic suspension platform,providing a new approach to improving the control performance of LADRC in environments with uncertain disturbances.展开更多
In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and cha...In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.展开更多
Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(...Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].展开更多
Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying ...Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.展开更多
This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to use...This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.展开更多
The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is m...The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frame...Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.展开更多
Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of kn...Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of knowledge about the underlying modes of action and optimal treatment modalities,a thorough translational investigation of noninvasive brain stimulation in preclinical animal models is urgently needed.Thus,we reviewed the current literature on the mechanistic underpinnings of noninvasive brain stimulation in models of central nervous system impairment,with a particular emphasis on traumatic brain injury and stroke.Due to the lack of translational models in most noninvasive brain stimulation techniques proposed,we found this review to the most relevant techniques used in humans,i.e.,transcranial magnetic stimulation and transcranial direct current stimulation.We searched the literature in Pub Med,encompassing the MEDLINE and PMC databases,for studies published between January 1,2020 and September 30,2024.Thirty-five studies were eligible.Transcranial magnetic stimulation and transcranial direct current stimulation demonstrated distinct strengths in augmenting rehabilitation post-stroke and traumatic brain injury,with emerging mechanistic evidence.Overall,we identified neuronal,inflammatory,microvascular,and apoptotic pathways highlighted in the literature.This review also highlights a lack of translational surrogate parameters to bridge the gap between preclinical findings and their clinical translation.展开更多
Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in ...Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.展开更多
The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cereb...The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering.展开更多
Due to the difficulties in obtaining large deformation mining subsidence using differential Interferometric Synthetic Aperture Radar （D-InSAR） alone, a new algorithm was proposed to extract large deformation mining ...Due to the difficulties in obtaining large deformation mining subsidence using differential Interferometric Synthetic Aperture Radar （D-InSAR） alone, a new algorithm was proposed to extract large deformation mining subsidence using D-InSAR technique and probability integral method. The details of the algorithm are as follows：the control points set, containing correct phase unwrapping points on the subsidence basin edge generated by D-InSAR and several observation points （near the maximum subsidence and inflection points）, was established at first; genetic algorithm （GA） was then used to optimize the parameters of probability integral method; at last, the surface subsidence was deduced according to the optimum parameters. The results of the experiment in Huaibei mining area, China, show that the presented method can generate the correct mining subsidence basin with a few surface observations, and the relative error of maximum subsidence point is about 8.3%, which is much better than that of conventional D-InSAR （relative error is 68.0%）.展开更多
In this work,the static tensile and free vibration of nanorods are studied via both the strain-driven(Strain D)and stress-driven(Stress D)two-phase nonlocal models with a bi-Helmholtz averaging kernel.Merely adjusting...In this work,the static tensile and free vibration of nanorods are studied via both the strain-driven(Strain D)and stress-driven(Stress D)two-phase nonlocal models with a bi-Helmholtz averaging kernel.Merely adjusting the limits of integration,the integral constitutive equation of the Fredholm type is converted to that of the Volterra type and then solved directly via the Laplace transform technique.The unknown constants can be uniquely determined through the standard boundary conditions and two constrained conditions accompanying the Laplace transform process.In the numerical examples,the bi-Helmholtz kernel-based Strain D(or Stress D)two-phase model shows consistently softening(or stiffening)effects on both the tension and the free vibration of nanorods with different boundary edges.The effects of the two nonlocal parameters of the bi-Helmholtz kernel-based two-phase nonlocal models are studied and compared with those of the Helmholtz kernel-based models.展开更多
A torsional static and free vibration analysis of the functionally graded nanotube(FGNT)composed of two materials varying continuously according to the power-law along the radial direction is performed using the bi-He...A torsional static and free vibration analysis of the functionally graded nanotube(FGNT)composed of two materials varying continuously according to the power-law along the radial direction is performed using the bi-Helmholtz kernel based stress-driven nonlocal integral model.The differential governing equation and boundary conditions are deduced on the basis of Hamilton’s principle,and the constitutive relationship is expressed as an integral equation with the bi-Helmholtz kernel.Several nominal variables are introduced to simplify the differential governing equation,integral constitutive equation,and boundary conditions.Rather than transforming the constitutive equation from integral to differential forms,the Laplace transformation is used directly to solve the integro-differential equations.The explicit expression for nominal torsional rotation and torque contains four unknown constants,which can be determined with the help of two boundary conditions and two extra constraints from the integral constitutive relation.A few benchmarked examples are solved to illustrate the nonlocal influence on the static torsion of a clamped-clamped(CC)FGNT under torsional constraints and a clamped-free(CF)FGNT under concentrated and uniformly distributed torques as well as the torsional free vibration of an FGNT under different boundary conditions.展开更多
We present a study on the dynamic stability of porous functionally graded(PFG)beams under hygro-thermal loading.The variations of the properties of the beams across the beam thicknesses are described by the power-law ...We present a study on the dynamic stability of porous functionally graded(PFG)beams under hygro-thermal loading.The variations of the properties of the beams across the beam thicknesses are described by the power-law model.Unlike most studies on this topic,we consider both the bending deformation of the beams and the hygro-thermal load as size-dependent,simultaneously,by adopting the equivalent differential forms of the well-posed nonlocal strain gradient integral theory(NSGIT)which are strictly equipped with a set of constitutive boundary conditions(CBCs),and through which both the stiffness-hardening and stiffness-softening effects of the structures can be observed with the length-scale parameters changed.All the variables presented in the differential problem formulation are discretized.The numerical solution of the dynamic instability region(DIR)of various bounded beams is then developed via the generalized differential quadrature method(GDQM).After verifying the present formulation and results,we examine the effects of different parameters such as the nonlocal/gradient length-scale parameters,the static force factor,the functionally graded(FG)parameter,and the porosity parameter on the DIR.Furthermore,the influence of considering the size-dependent hygro-thermal load is also presented.展开更多
In order to improve rolled strip quality, precise plate shape control theory should be established. Roll flat- tening theory is an important part of the plate shape theory. To improve the accuracy of roll flattening c...In order to improve rolled strip quality, precise plate shape control theory should be established. Roll flat- tening theory is an important part of the plate shape theory. To improve the accuracy of roll flattening calculation based on semi infinite body model, especially near the two roll barrel edges, a new and more accurate roll flattening model is proposed. Based on boundary integral equation method, an analytical model for solving a finite length semi infinite body is established. The lateral surface displacement field of the finite length semi-infinite body is simulated by finite element method （FEM） and lateral surface displacement decay functions are established. Based on the boundary integral equation method, the numerical solution of the finite length semi-infinite body under the distribu ted force is obtained and an accurate roll flattening model is established. Different from the traditional semi-infinite body model, the matrix form of the new roll flattening model is established through the mathematical derivation. The result from the new model is more consistent with that by FEM especially near the edges.展开更多
The free energy at low temperature in 1D sine-Gordon-Thirring model with impurity coupling is studied by means of functional integrals method. For massive free sine-Gordon-Thirring model, free energy is obtained from ...The free energy at low temperature in 1D sine-Gordon-Thirring model with impurity coupling is studied by means of functional integrals method. For massive free sine-Gordon-Thirring model, free energy is obtained from perturbation expansion of functional determinant. Moreover, the free energy of massive model is calculated by use of an auxiliary Bose field method.展开更多
In this paper, collective excitations in the boson-fermion model are investigated by means of functional integration method. The equations of energy gap and excitation spectrum are derived. Moreover, the Bose energy s...In this paper, collective excitations in the boson-fermion model are investigated by means of functional integration method. The equations of energy gap and excitation spectrum are derived. Moreover, the Bose energy spectrum of zero wave vector Fermi fields is also calculated.展开更多
基金Project supported by the National Natural Science Foundation of China(No.12172169)the Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘This paper extends the one-dimensional(1D)nonlocal strain gradient integral model(NStraGIM)to the two-dimensional(2D)Kirchhoff axisymmetric nanoplates,based on nonlocal strain gradient integral relations formulated along both the radial and circumferential directions.By transforming the proposed integral constitutive equations into the equivalent differential forms,complemented by the corresponding constitutive boundary conditions(CBCs),a well-posed mathematical formulation is established for analyzing the axisymmetric bending and buckling of annular/circular functionally graded(FG)sandwich nanoplates.The boundary conditions at the inner edge of a solid nanoplate are derived by L'H?spital's rule.The numerical solution is obtained by the generalized differential quadrature method(GDQM).The accuracy of the proposed model is validated through comparison with the data from the existing literature.A parameter study is conducted to demonstrate the effects of FG sandwich parameters,size parameters,and nonlocal gradient parameters.
基金supported by the National Natural Science Foundation of China(Nos.62063009,52262050)the National Key Research and Development Program during the 14th 5-Year Plan(No.2023YFB4302100)the Major Science and Technology Research and Development Special Project in Jiangxi Province(No.20232ACE01011).
文摘To address the issue of disturbance compensation deviation in linear active disturbance rejection control(LADRC),a linear active disturbance rejection control method with reference to the integral chain model(LADRC-R)is proposed.By constructing an ideal control reference model,a dynamic correlation between output deviation and uncompensated disturbances is established,and a dual-loop compensation mechanism is designed.Based on theoretical analysis and frequency-domain characteristics of typical first/second-order systems,this method maintains the parameter-tuning advantages of LADRC while reducing disturbance effects by 50%and introducing no phase lag during low-frequency disturbance suppression.Simulations on second-order systems verify its robustness under parameter perturbations,gain mismatch,and complex disturbances,and an optimized design scheme for the deviation compensator is proposed to suppress discontinuous measurement noise interference.Finally,the engineering effectiveness of this method in precision motion control is validated on an electromagnetic suspension platform,providing a new approach to improving the control performance of LADRC in environments with uncertain disturbances.
基金the World Climate Research Programme(WCRP),Climate Variability and Predictability(CLIVAR),and Global Energy and Water Exchanges(GEWEX)for facilitating the coordination of African monsoon researchsupport from the Center for Earth System Modeling,Analysis,and Data at the Pennsylvania State Universitythe support of the Office of Science of the U.S.Department of Energy Biological and Environmental Research as part of the Regional&Global Model Analysis(RGMA)program area。
文摘In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.
文摘Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].
文摘Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.
基金funded by the Office of the Vice-President for Research and Development of Cebu Technological University.
文摘This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
基金supported by the National Natural Science Foundation of China(Grant No.72161034).
文摘Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.
基金funded by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation):project ID 431549029-SFB 1451the Marga-und-Walter-Boll-Stiftung(#210-10-15)(to MAR)a stipend from the'Gerok Program'(Faculty of Medicine,University of Cologne,Germany)。
文摘Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of knowledge about the underlying modes of action and optimal treatment modalities,a thorough translational investigation of noninvasive brain stimulation in preclinical animal models is urgently needed.Thus,we reviewed the current literature on the mechanistic underpinnings of noninvasive brain stimulation in models of central nervous system impairment,with a particular emphasis on traumatic brain injury and stroke.Due to the lack of translational models in most noninvasive brain stimulation techniques proposed,we found this review to the most relevant techniques used in humans,i.e.,transcranial magnetic stimulation and transcranial direct current stimulation.We searched the literature in Pub Med,encompassing the MEDLINE and PMC databases,for studies published between January 1,2020 and September 30,2024.Thirty-five studies were eligible.Transcranial magnetic stimulation and transcranial direct current stimulation demonstrated distinct strengths in augmenting rehabilitation post-stroke and traumatic brain injury,with emerging mechanistic evidence.Overall,we identified neuronal,inflammatory,microvascular,and apoptotic pathways highlighted in the literature.This review also highlights a lack of translational surrogate parameters to bridge the gap between preclinical findings and their clinical translation.
文摘Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.
基金supported by the Grant PID2021-126715OB-IOO financed by MCIN/AEI/10.13039/501100011033 and"ERDFA way of making Europe"by the Grant PI22CⅢ/00055 funded by Instituto de Salud CarlosⅢ(ISCⅢ)+6 种基金the UFIECPY 398/19(PEJ2018-004965) grant to RGS funded by AEI(Spain)the UFIECPY-396/19(PEJ2018-004961)grant financed by MCIN (Spain)FI23CⅢ/00003 grant funded by ISCⅢ-PFIS Spain) to PMMthe UFIECPY 328/22 (PEJ-2021-TL/BMD-21001) grant to LM financed by CAM (Spain)the grant by CAPES (Coordination for the Improvement of Higher Education Personnel)through the PDSE program (Programa de Doutorado Sanduiche no Exterior)to VSCG financed by MEC (Brazil)
文摘The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering.
基金Project (BK20130174) supported by the Basic Research Project of Jiangsu Province (Natural Science Foundation) Project (1101109C) supported by Jiangsu Planned Projects for Postdoctoral Research Funds,China+1 种基金Project (201325) supported by the Key Laboratory of Geo-informatics of State Bureau of Surveying and Mapping,ChinaProject (SZBF2011-6-B35) supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions,China
文摘Due to the difficulties in obtaining large deformation mining subsidence using differential Interferometric Synthetic Aperture Radar （D-InSAR） alone, a new algorithm was proposed to extract large deformation mining subsidence using D-InSAR technique and probability integral method. The details of the algorithm are as follows：the control points set, containing correct phase unwrapping points on the subsidence basin edge generated by D-InSAR and several observation points （near the maximum subsidence and inflection points）, was established at first; genetic algorithm （GA） was then used to optimize the parameters of probability integral method; at last, the surface subsidence was deduced according to the optimum parameters. The results of the experiment in Huaibei mining area, China, show that the presented method can generate the correct mining subsidence basin with a few surface observations, and the relative error of maximum subsidence point is about 8.3%, which is much better than that of conventional D-InSAR （relative error is 68.0%）.
基金the National Natural Science Foundation of China(No.12172169)the China Scholarship Council(CSC)(No.202006830038)。
文摘In this work,the static tensile and free vibration of nanorods are studied via both the strain-driven(Strain D)and stress-driven(Stress D)two-phase nonlocal models with a bi-Helmholtz averaging kernel.Merely adjusting the limits of integration,the integral constitutive equation of the Fredholm type is converted to that of the Volterra type and then solved directly via the Laplace transform technique.The unknown constants can be uniquely determined through the standard boundary conditions and two constrained conditions accompanying the Laplace transform process.In the numerical examples,the bi-Helmholtz kernel-based Strain D(or Stress D)two-phase model shows consistently softening(or stiffening)effects on both the tension and the free vibration of nanorods with different boundary edges.The effects of the two nonlocal parameters of the bi-Helmholtz kernel-based two-phase nonlocal models are studied and compared with those of the Helmholtz kernel-based models.
基金Project supported by the National Natural Science Foundation of China(No.11672131)the Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘A torsional static and free vibration analysis of the functionally graded nanotube(FGNT)composed of two materials varying continuously according to the power-law along the radial direction is performed using the bi-Helmholtz kernel based stress-driven nonlocal integral model.The differential governing equation and boundary conditions are deduced on the basis of Hamilton’s principle,and the constitutive relationship is expressed as an integral equation with the bi-Helmholtz kernel.Several nominal variables are introduced to simplify the differential governing equation,integral constitutive equation,and boundary conditions.Rather than transforming the constitutive equation from integral to differential forms,the Laplace transformation is used directly to solve the integro-differential equations.The explicit expression for nominal torsional rotation and torque contains four unknown constants,which can be determined with the help of two boundary conditions and two extra constraints from the integral constitutive relation.A few benchmarked examples are solved to illustrate the nonlocal influence on the static torsion of a clamped-clamped(CC)FGNT under torsional constraints and a clamped-free(CF)FGNT under concentrated and uniformly distributed torques as well as the torsional free vibration of an FGNT under different boundary conditions.
基金Project supported by the National Natural Science Foundation of China(No.12172169)the Natural Sciences and Engineering Research Council of Canada(No.NSERC RGPIN-2023-03227)。
文摘We present a study on the dynamic stability of porous functionally graded(PFG)beams under hygro-thermal loading.The variations of the properties of the beams across the beam thicknesses are described by the power-law model.Unlike most studies on this topic,we consider both the bending deformation of the beams and the hygro-thermal load as size-dependent,simultaneously,by adopting the equivalent differential forms of the well-posed nonlocal strain gradient integral theory(NSGIT)which are strictly equipped with a set of constitutive boundary conditions(CBCs),and through which both the stiffness-hardening and stiffness-softening effects of the structures can be observed with the length-scale parameters changed.All the variables presented in the differential problem formulation are discretized.The numerical solution of the dynamic instability region(DIR)of various bounded beams is then developed via the generalized differential quadrature method(GDQM).After verifying the present formulation and results,we examine the effects of different parameters such as the nonlocal/gradient length-scale parameters,the static force factor,the functionally graded(FG)parameter,and the porosity parameter on the DIR.Furthermore,the influence of considering the size-dependent hygro-thermal load is also presented.
基金Item Sponsored by National Natural Science Foundation of China(51075353)
文摘In order to improve rolled strip quality, precise plate shape control theory should be established. Roll flat- tening theory is an important part of the plate shape theory. To improve the accuracy of roll flattening calculation based on semi infinite body model, especially near the two roll barrel edges, a new and more accurate roll flattening model is proposed. Based on boundary integral equation method, an analytical model for solving a finite length semi infinite body is established. The lateral surface displacement field of the finite length semi-infinite body is simulated by finite element method （FEM） and lateral surface displacement decay functions are established. Based on the boundary integral equation method, the numerical solution of the finite length semi-infinite body under the distribu ted force is obtained and an accurate roll flattening model is established. Different from the traditional semi-infinite body model, the matrix form of the new roll flattening model is established through the mathematical derivation. The result from the new model is more consistent with that by FEM especially near the edges.
基金The project supported by the Natural Science Foundation of Sichuan Normal University
文摘The free energy at low temperature in 1D sine-Gordon-Thirring model with impurity coupling is studied by means of functional integrals method. For massive free sine-Gordon-Thirring model, free energy is obtained from perturbation expansion of functional determinant. Moreover, the free energy of massive model is calculated by use of an auxiliary Bose field method.
基金The project supported by the Science Foundation of Sichuan Normal University
文摘In this paper, collective excitations in the boson-fermion model are investigated by means of functional integration method. The equations of energy gap and excitation spectrum are derived. Moreover, the Bose energy spectrum of zero wave vector Fermi fields is also calculated.
