The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches ...The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.展开更多
Spinal cord injury is a severe neurological condition with limited neuronal regeneration and functional recovery.Currently,no effective treatments exist to improve spinal cord injury prognosis.Neuronal guidance protei...Spinal cord injury is a severe neurological condition with limited neuronal regeneration and functional recovery.Currently,no effective treatments exist to improve spinal cord injury prognosis.Neuronal guidance proteins are a diverse group of molecules that play crucial roles in axon and dendrite growth during nervous system development.Increasing evidence highlights their regulatory functions in spinal cord injury.This review provides a brief overview of the modulation patterns of key neuronal guidance proteins in neuronal axon growth during nervous system formation and subsequently focuses on their roles in neuronal regeneration and functional recovery following spinal cord injury.Neuronal guidance proteins include,but are not limited to,semaphorins and their receptors,plexins;netrins and their receptors,deleted in colorectal cancer and UNC5;Eph receptors and their ligands,ephrins;Slit and its receptor,Robo;repulsive guidance molecules and their receptor,neogenin;Wnt proteins and their receptor,Frizzled;and protocadherins.Localized Netrin-1 at the injury site inhibits motor axon regeneration after adult spinal cord injury while promoting oligodendrocyte growth.Slit2 enhances synapse formation in the injured spinal cord of rats.EphA7 regulates acute apoptosis in the early pathophysiological stages of spinal cord injury,while ephrinA1 plays a role in the nervous system’s injury response,with its reduced expression leading to impaired motor function in rats.EphA3 is upregulated following spinal cord injury,promoting an inhibitory environment for axonal regeneration.After spinal cord injury,bidirectional activation of ephrinB2 and EphB2 in astrocytes and fibroblasts results in the formation of a dense astrocyte-meningeal fibroblast scar.EphB1/ephrinB1 signaling mediates pain processing in spinal cord injury by regulating calpain-1 and caspase-3 in neurons.EphB3 expression increases in white matter after spinal cord injury,further inhibiting axon regeneration.Sema3A,expressed by neurons and fibroblasts in the scar surrounding the injury,inhibits motor neuron and sensory nerve growth after spinal cord injury.Sema4D suppresses neuronal axon myelination and axon regeneration,while its inhibition significantly enhances axon regeneration and motor recovery.Sema7A is involved in glial scar formation and may influence serotonin channel remodeling,thereby affecting motor coordination.Given these findings,the local or systemic application of neuronal guidance proteins represents a promising avenue for spinal cord injury treatment.展开更多
Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–di...Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–discharge cycles and fixed-form physical constraints,which limit adaptability to different chemistries and real-world conditions.To address these issues,this study proposes an approach that extracts features from segmented state of charge(SOC)intervals and integrates them into an enhanced physics-informed neural network(PINN).Specifically,voltage data within the 25%–75%SOC range during charging are used to derive statistical,time–frequency,and mechanism-based features that capture degradation trends.A hybrid PINN-Lasso-Transformer-BiLSTM architecture is developed,where Lasso regression enables sparse feature selection,and a nonlinear empirical degradation model is embedded as a learnable physical term within a dynamically scaled composite loss.This design adaptively balances data-driven accuracy with physical consistency,thereby enhancing estimation precision,robustness,and generalization.The results show that the proposed method outperforms conventional neural networks across four battery chemistries,achieving root mean square error and mean absolute error below 1%.Notably,features from partial charging segments exhibit higher robustness than those from full cycles.Furthermore,the model maintains strong performance under high temperatures and demonstrates excellent generalization capacity in transfer learning across chemistries,temperatures,and C-rates.This work establishes a scalable and interpretable solution for accurate SOH estimation under diverse practical operating conditions.展开更多
Physics-informed neural networks(PINNs)are vital for machine learning and exhibit significant advantages when handling complex physical problems.The PINN method can rapidly predict ^(220)Rn progeny concentration and i...Physics-informed neural networks(PINNs)are vital for machine learning and exhibit significant advantages when handling complex physical problems.The PINN method can rapidly predict ^(220)Rn progeny concentration and is very important for regulating and measuring this property.To construct a PINN model,training data are typically preprocessed;however,this approach changes the physical characteristics of the data,with the preprocessed data potentially no longer directly conforming to the original physical equations.As a result,the original physical equations cannot be directly employed in the PINN.Consequently,an effective method for transforming physical equations is crucial for accurately constraining PINNs to model the ^(220)Rn progeny concentration prediction.This study presents an equation adaptation approach for neural networks,which is designed to improve prediction of ^(220)Rn progeny concentration.Five neural network models based on three architectures are established:a classical network,a physics-informed network without equation adaptation,and a physics-informed network with equation adaptation.The transport equation of the ^(220)Rn progeny concentration is transformed via equation adaption and integrated with the PINN model.The compatibility and robustness of the model with equation adaption is then analyzed.The results show that PINNs with equation adaption converge consistently with classical neural networks in terms of the training and validation loss and achieve the same level of prediction accuracy.This outcome indicates that the proposed method can be integrated into the neural network architecture.Moreover,the prediction performance of classical neural networks declines significantly when interference data are encountered,whereas the PINNs with equation adaption exhibit stable prediction accuracy.This performance demonstrates that the proposed method successfully harnesses the constraining power of physical equations,significantly enhancing the robustness of the resultant PINN models.Thus,the use of a physics-informed network with equation adaption can guarantee accurate prediction of ^(220)Rn progeny concentration.展开更多
Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce different...Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce differential equations,constitutive relations,and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models,particularly for solid and structural mechanics,where data are often limited or noisy.This review offers a comprehensive assessment of recent developments in PINNs,combining bibliometric analysis,theoretical foundations,application-oriented insights,and methodological innovations.A biblio-metric survey indicates a rapid increase in publications on PINNs since 2018,with prominent research clusters focused on numerical methods,structural analysis,and forecasting.Building upon this trend,the review consolidates advance-ments across five principal application domains,including forward structural analysis,inverse modeling and parameter identification,structural and topology optimization,assessment of structural integrity,and manufacturing processes.These applications are propelled by substantial methodological advancements,encompassing rigorous enforcement of boundary conditions,modified loss functions,adaptive training,domain decomposition strategies,multi-fidelity and transfer learning approaches,as well as hybrid finite element–PINN integration.These advances address recurring challenges in solid mechanics,such as high-order governing equations,material heterogeneity,complex geometries,localized phenomena,and limited experimental data.Despite remaining challenges in computational cost,scalability,and experimental validation,PINNs are increasingly evolving into specialized,physics-aware tools for practical solid and structural mechanics applications.展开更多
The neutron diffusion equation plays a pivotal role in nuclear reactor analysis.Nevertheless,employing the physics-informed neural network(PINN)method for its solution entails certain limitations.Conventional PINN app...The neutron diffusion equation plays a pivotal role in nuclear reactor analysis.Nevertheless,employing the physics-informed neural network(PINN)method for its solution entails certain limitations.Conventional PINN approaches generally utilize a fully connected network(FCN)architecture that is susceptible to overfitting,training instability,and gradient vanishing as the network depth increases.These challenges result in accuracy bottlenecks in the solution.In response to these issues,the residual-based resample physics-informed neural network(R2-PINN)is proposed.It is an improved PINN architecture that replaces the FCN with a convolutional neural network with a shortcut(S-CNN).It incorporates skip connections to facilitate gradient propagation between network layers.Additionally,the incorporation of the residual adaptive resampling(RAR)mechanism dynamically increases the number of sampling points.This,in turn,enhances the spatial representation capabilities and overall predictive accuracy of the model.The experimental results illustrate that our approach significantly improves the convergence capability of the model and achieves high-precision predictions of the physical fields.Compared with conventional FCN-based PINN methods,R 2-PINN effectively overcomes the limitations inherent in current methods.Thus,it provides more accurate and robust solutions for neutron diffusion equations.展开更多
This study aims to explore the impact of combined health education and dietary guidance on the speed of postoperative recovery in patients with gastrointestinal polyps.A specific number of patients who underwent gastr...This study aims to explore the impact of combined health education and dietary guidance on the speed of postoperative recovery in patients with gastrointestinal polyps.A specific number of patients who underwent gastrointestinal polyp resection were selected and randomly divided into a control group and an experimental group.The control group received routine nursing,while the experimental group implemented combined health education and dietary guidance on this basis.By comparing the recovery indicators of the two groups,it was found that the recovery speed of the experimental group was significantly faster than that of the control group,indicating that this combined intervention method can effectively promote patient recovery.展开更多
Physics-informed neural networks(PINNs),as a novel artificial intelligence method for solving partial differential equations,are applicable to solve both forward and inverse problems.This study evaluates the performan...Physics-informed neural networks(PINNs),as a novel artificial intelligence method for solving partial differential equations,are applicable to solve both forward and inverse problems.This study evaluates the performance of PINNs in solving the temperature diffusion equation of the seawater across six scenarios,including forward and inverse problems under three different boundary conditions.Results demonstrate that PINNs achieved consistently higher accuracy with the Dirichlet and Neumann boundary conditions compared to the Robin boundary condition for both forward and inverse problems.Inaccurate weighting of terms in the loss function can reduce model accuracy.Additionally,the sensitivity of model performance to the positioning of sampling points varied between different boundary conditions.In particular,the model under the Dirichlet boundary condition exhibited superior robustness to variations in point positions during the solutions of inverse problems.In contrast,for the Neumann and Robin boundary conditions,accuracy declines when points were sampled from identical positions or at the same time.Subsequently,the Argo observations were used to reconstruct the vertical diffusion of seawater temperature in the north-central Pacific for the applicability of PINNs in the real ocean.The PINNs successfully captured the vertical diffusion characteristics of seawater temperature,reflected the seasonal changes of vertical temperature under different topographic conditions,and revealed the influence of topography on the temperature diffusion coefficient.The PINNs were proved effective in solving the temperature diffusion equation of seawater with limited data,providing a promising technique for simulating or predicting ocean phenomena using sparse observations.展开更多
Accelerated aging tests are widely used to rapidly evaluate the durability of materials,of which thermal-oxidative aging is the most common approach.To quantitatively predict the effects of multiple coupled factors,th...Accelerated aging tests are widely used to rapidly evaluate the durability of materials,of which thermal-oxidative aging is the most common approach.To quantitatively predict the effects of multiple coupled factors,this study takes polyamide66 reinforced with glass fiber(PA66-GF)as a model system and proposed a high-precision paradigm for coupled thermal-oxidative aging.By integrating Arrhenius-type reaction kinetics with oxygen diffusion,a predictive formula that holistically captures the nonlinear synergistic effects of multiple factors was developed,thereby overcoming the limitations of traditional single-variable models.A systematic evaluation of the stepwise improved formulas through nonlinear fitting showed that the coefficient of determination(R^(2))increased from 0.223 to 0.803,elucidating the fundamental reason why conventional approaches fail in quantitative prediction.These formulae were further embedded as physical constraints into a physics-informed neural network(PINN),which further enhanced the predictive performance,with the proposed formula achieving a peak R^(2)of 0.946.The results highlight that robust data fitting alone is insufficient;the decisive factor for the success of PINN lies in whether the embedded formula faithfully reflects the underlying physical mechanisms.When applied to polyamide 6 reinforced with glass fiber(PA6-GF),the Formula-constrained PINN maintained a high level of accuracy(R^(2)=0.916),demonstrating its strong cross-system generalizability.In summary,this work establishes a robust hybrid physics-machine learning framework that combines high accuracy with transferability for predicting the thermal-oxidative aging behavior of composite material systems.展开更多
Physics-informed neural networks(PINNs)have been shown as powerful tools for solving partial differential equations(PDEs)by embedding physical laws into the network training.Despite their remarkable results,complicate...Physics-informed neural networks(PINNs)have been shown as powerful tools for solving partial differential equations(PDEs)by embedding physical laws into the network training.Despite their remarkable results,complicated problems such as irregular boundary conditions(BCs)and discontinuous or high-frequency behaviors remain persistent challenges for PINNs.For these reasons,we propose a novel two-phase framework,where a neural network is first trained to represent shape functions that can capture the irregularity of BCs in the first phase,and then these neural network-based shape functions are used to construct boundary shape functions(BSFs)that exactly satisfy both essential and natural BCs in PINNs in the second phase.This scheme is integrated into both the strong-form and energy PINN approaches,thereby improving the quality of solution prediction in the cases of irregular BCs.In addition,this study examines the benefits and limitations of these approaches in handling discontinuous and high-frequency problems.Overall,our method offers a unified and flexible solution framework that addresses key limitations of existing PINN methods with higher accuracy and stability for general PDE problems in solid mechanics.展开更多
The capture zones of the continuous and pulsed guidance laws in the pursuit-evasion game are analytically discussed in this paper to provide deep insights into the capturability distinction between the continuous guid...The capture zones of the continuous and pulsed guidance laws in the pursuit-evasion game are analytically discussed in this paper to provide deep insights into the capturability distinction between the continuous guidance law and the pulsed guidance law.Specifically,first,in the pursuit-evasion game,various capture cases are defined regarding the Zero-Effort Miss distance(ZEM)to facilitate the capturability analysis.Then,for both the evader and the pursuer,the Linear-Quadratic Differential Game(LQDG)guidance laws concerning the continuous acceleration and the pulsed acceleration are converted into a unified form.In each capture case,the optimal solution existence conditions are derived,and the corresponding capture zones are formulated.The discussion on the capture zones shows that if the optimal solution exists,the distinction between the pulsed guidance law and the continuous guidance law can be neglected under small guidance effort weight.However,the capture zone of the continuous guidance law is larger than that of the pulsed guidance law with large pursuer guidance effort weight,but smaller with large evader guidance effort weight.Finally,various simulations are conducted to illustrate the distinction of the continuous and pulsed guidance laws,as well as the impact of the acceleration ratio and the time constant ratio on the capturability.展开更多
The nervous system processes a vast amount of information,performing computations that underlie perception,cognition,and behavior.During development,neuronal guidance genes,which encode extracellular cues,their recept...The nervous system processes a vast amount of information,performing computations that underlie perception,cognition,and behavior.During development,neuronal guidance genes,which encode extracellular cues,their receptors,and downstream signal transducers,organize neural wiring to generate the complex architecture of the nervous system.It is now evident that many of these neuroguidance cues and their receptors are active during development and are also expressed in the adult nervous system.This suggests that neuronal guidance pathways are critical not only for neural wiring but also for ongoing function and maintenance of the mature nervous system.Supporting this view,these pathways continue to regulate synaptic connectivity,plasticity,and remodeling,and overall brain homeostasis throughout adulthood.Genetic and transcriptomic analyses have further revealed many neuronal guidance genes to be associated with a wide range of neurodegenerative and neuropsychiatric disorders.Although the precise mechanisms by which aberrant neuronal guidance signaling drives the pathogenesis of these diseases remain to be clarified,emerging evidence points to several common themes,including dysfunction in neurons,microglia,astrocytes,and endothelial cells,along with dysregulation of neuron-microglia-astrocyte,neuroimmune,and neurovascular interactions.In this review,we explore recent advances in understanding the molecular and cellular mechanisms by which aberrant neuronal guidance signaling contributes to disease pathogenesis through altered cell-cell interactions.For instance,recent studies have unveiled two distinct semaphorin-plexin signaling pathways that affect microglial activation and neuroinflammation.We discuss the challenges ahead,along with the therapeutic potentials of targeting neuronal guidance pathways for treating neurodegenerative diseases.Particular focus is placed on how neuronal guidance mechanisms control neuron-glia and neuroimmune interactions and modulate microglial function under physiological and pathological conditions.Specifically,we examine the crosstalk between neuronal guidance signaling and TREM2,a master regulator of microglial function,in the context of pathogenic protein aggregates.It is well-established that age is a major risk factor for neurodegeneration.Future research should address how aging and neuronal guidance signaling interact to influence an individual’s susceptibility to various late-onset neurological diseases and how the progression of these diseases could be therapeutically blocked by targeting neuronal guidance pathways.展开更多
Autografting is the gold standard for surgical repair of nerve defects>5 mm in length;however,autografting is associated with potential complications at the nerve donor site.As an alternative,nerve guidance conduit...Autografting is the gold standard for surgical repair of nerve defects>5 mm in length;however,autografting is associated with potential complications at the nerve donor site.As an alternative,nerve guidance conduits may be used.The ideal conduit should be flexible,resistant to kinks and lumen collapse,and provide physical cues to guide nerve regeneration.We designed a novel flexible conduit using electrospinning technology to create fibers on the innermost surface of the nerve guidance conduit and employed melt spinning to align them.Subsequently,we prepared disordered electrospun fibers outside the aligned fibers and helical melt-spun fibers on the outer wall of the electrospun fiber lumen.The presence of aligned fibers on the inner surface can promote the extension of nerve cells along the fibers.The helical melt-spun fibers on the outer surface can enhance resistance to kinking and compression and provide stability.Our novel conduit promoted nerve regeneration and functional recovery in a rat sciatic nerve defect model,suggesting that it has potential for clinical use in human nerve injuries.展开更多
Cooperative guidance is a method for achieving combat objectives through information sharing and cooperative effects,and has emerged as a significant research area in the fields of missile guidance and systematic warf...Cooperative guidance is a method for achieving combat objectives through information sharing and cooperative effects,and has emerged as a significant research area in the fields of missile guidance and systematic warfare.This study presents a systematic review and analysis of current research on cooperative guidance.First,a bibliometric analysis is conducted on 513 articles using the Scopus database and CiteSpace software to assess keyword clustering,keyword cooccurrence,and keyword burst,and to later visualize the results.Second,fundamental theories of cooperative guidance,including relative motion modeling methods,algebraic graph theory,and multi-agent consensus theory,are summarized.Subsequently,an overview of current cooperative laws and corresponding analysis methods is provided,with categorization based on the cooperative structure and convergence performance.Finally,we summarize current research developments based on five perspectives and propose a developmental framework based on five layers(cyber,physical,decision,information,and system),discussing potential future advancements in cooperative terminal guidance.This framework emphasizes five key areas of research:networked,heterogeneous,integrated,intelligent,and group cooperations,with the goal of offering trends and insights for futurework.展开更多
In this paper,a novel guidance law is proposed which can achieve the desired impact speed and angle simultaneously for unpowered gliding vehicles.A guidance law with only impact angle constraint is used to produce the...In this paper,a novel guidance law is proposed which can achieve the desired impact speed and angle simultaneously for unpowered gliding vehicles.A guidance law with only impact angle constraint is used to produce the guidance profile,and its convergence in the varying speed scenario is proved.A relationship between flight states,guidance input and impact speed is established.By applying the fixed-time convergence control theory of error dynamics,an impact speed corrector is built with the above guidance profile,which can implement impact speed correction without affecting the impact angle constraint.Numerical simulations with various impact speed and angle constraints are conducted to demonstrate the performance of the proposed guidance law,and the robustness is also verified by Monte Carlo tests.展开更多
Earth-to-Moon missions with low thrust-to-weight ratios present unique challenges for exoatmospheric guidance,and the existing algorithms are ineffective for the unprecedentedly long burn arcs and high orbital eccentr...Earth-to-Moon missions with low thrust-to-weight ratios present unique challenges for exoatmospheric guidance,and the existing algorithms are ineffective for the unprecedentedly long burn arcs and high orbital eccentricities.To address these challenges,a Long Burn Arc Powered Explicit Guidance(LBA-PEG)algorithm is developed and compared with the existing algorithms.In the proposed LBA-PEG algorithm,a fully numerical thrust prediction method is developed to accurately predict the highly nonlinear thrust effects over long burn arcs.Moreover,a real-time Newton correction method is proposed to correct the orbit injection point,remedying the position-velocity coupling induced by high orbital eccentricities.The comparison between the proposed algorithm and the existing algorithm shows that the proposed algorithm surpasses the existing ones by significantly enhancing fuel efficiency and improving tolerance to thrust decrease.The proposed LBA-PEG algorithm can adapt to a 65%thrust decrease,which is 12%–22%larger than that of the existing algorithms,and it can still reliably converge and complete the guidance mission even when the length of the burn arc exceeds 90°.The proposed LBA-PEG highlights the algorithm's adaptability for long burn arc missions,especially in critical scenarios such as manned Earth-to-Moon missions.展开更多
A segmented predictor-corrector method is proposed for hypersonic glide vehicles to address the issue of the slow computational speed of obtaining guidance commands using the traditional predictor-corrector guidance m...A segmented predictor-corrector method is proposed for hypersonic glide vehicles to address the issue of the slow computational speed of obtaining guidance commands using the traditional predictor-corrector guidance method.Firstly,an altitude-energy profile is designed,and the bank angle is derived analytically as the initial iteration value for the predictor-corrector method.The predictor-corrector guidance method has been improved by deriving an analytical form for predicting the range-to-go error,which greatly accelerates the iterative speed.Then,a segmented guidance algorithm is proposed.The above analytically predictor-corrector guidance method is adopted when the energy exceeds an energy threshold.When the energy is less than the threshold,the equidistant test method is used to calculate the bank angle command,which ensures guidance accuracy as well as computational efficiency.Additionally,an adaptive guidance cycle strategy is applied to reduce the computational time of the reentry guidance trajectory.Finally,the accuracy and robustness of the proposed method are verified through a series of simulations and Monte-Carlo experiments.Compared with the traditional integral method,the proposed method requires 75%less computation time on average and achieves a lower landing error.展开更多
For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models...For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing limitations.Physics-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational conditions.This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode levels.These parameters include available capacity,electrode capacities,and lithium inventory capacity.The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public dataset.The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 mV.This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.展开更多
Physics-informed neural networks(PINNs)have shown remarkable prospects in solving the forward and inverse problems involving partial differential equations(PDEs).The method embeds PDEs into the neural network by calcu...Physics-informed neural networks(PINNs)have shown remarkable prospects in solving the forward and inverse problems involving partial differential equations(PDEs).The method embeds PDEs into the neural network by calculating the PDE loss at a set of collocation points,providing advantages such as meshfree and more convenient adaptive sampling.However,when solving PDEs using nonuniform collocation points,PINNs still face challenge regarding inefficient convergence of PDE residuals or even failure.In this work,we first analyze the ill-conditioning of the PDE loss in PINNs under nonuniform collocation points.To address the issue,we define volume weighting residual and propose volume weighting physics-informed neural networks(VW-PINNs).Through weighting the PDE residuals by the volume that the collocation points occupy within the computational domain,we embed explicitly the distribution characteristics of collocation points in the loss evaluation.The fast and sufficient convergence of the PDE residuals for the problems involving nonuniform collocation points is guaranteed.Considering the meshfree characteristics of VW-PINNs,we also develop a volume approximation algorithm based on kernel density estimation to calculate the volume of the collocation points.We validate the universality of VW-PINNs by solving the forward problems involving flow over a circular cylinder and flow over the NACA0012 airfoil under different inflow conditions,where conventional PINNs fail.By solving the Burgers’equation,we verify that VW-PINNs can enhance the efficiency of existing the adaptive sampling method in solving the forward problem by three times,and can reduce the relative L 2 error of conventional PINNs in solving the inverse problem by more than one order of magnitude.展开更多
Current treatments for epilepsy can only manage the symptoms of the condition but cannot alter the initial onset or halt the progression of the disease. Consequently, it is crucial to identify drugs that can target no...Current treatments for epilepsy can only manage the symptoms of the condition but cannot alter the initial onset or halt the progression of the disease. Consequently, it is crucial to identify drugs that can target novel cellular and molecular mechanisms and mechanisms of action. Increasing evidence suggests that axon guidance molecules play a role in the structural and functional modifications of neural networks and that the dysregulation of these molecules is associated with epilepsy susceptibility. In this review, we discuss the essential role of axon guidance molecules in neuronal activity in patients with epilepsy as well as the impact of these molecules on synaptic plasticity and brain tissue remodeling. Furthermore, we examine the relationship between axon guidance molecules and neuroinflammation, as well as the structural changes in specific brain regions that contribute to the development of epilepsy. Ample evidence indicates that axon guidance molecules, including semaphorins and ephrins, play a fundamental role in guiding axon growth and the establishment of synaptic connections. Deviations in their expression or function can disrupt neuronal connections, ultimately leading to epileptic seizures. The remodeling of neural networks is a significant characteristic of epilepsy, with axon guidance molecules playing a role in the dynamic reorganization of neural circuits. This, in turn, affects synapse formation and elimination. Dysregulation of these molecules can upset the delicate balance between excitation and inhibition within a neural network, thereby increasing the risk of overexcitation and the development of epilepsy. Inflammatory signals can regulate the expression and function of axon guidance molecules, thus influencing axonal growth, axon orientation, and synaptic plasticity. The dysregulation of neuroinflammation can intensify neuronal dysfunction and contribute to the occurrence of epilepsy. This review delves into the mechanisms associated with the pathogenicity of axon guidance molecules in epilepsy, offering a valuable reference for the exploration of therapeutic targets and presenting a fresh perspective on treatment strategies for this condition.展开更多
基金supported by the National Natural Science Foundation of China(No.52207228)the Beijing Natural Science Foundation,China(No.3224070)the National Natural Science Foundation of China(No.52077208).
文摘The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.
基金supported by Shenzhen University General Hospital Scientific Research Project,No.SUGH2019QD002Shenzhen Science and Technology Development Foundation,No.20220810173216001(both to ZS).
文摘Spinal cord injury is a severe neurological condition with limited neuronal regeneration and functional recovery.Currently,no effective treatments exist to improve spinal cord injury prognosis.Neuronal guidance proteins are a diverse group of molecules that play crucial roles in axon and dendrite growth during nervous system development.Increasing evidence highlights their regulatory functions in spinal cord injury.This review provides a brief overview of the modulation patterns of key neuronal guidance proteins in neuronal axon growth during nervous system formation and subsequently focuses on their roles in neuronal regeneration and functional recovery following spinal cord injury.Neuronal guidance proteins include,but are not limited to,semaphorins and their receptors,plexins;netrins and their receptors,deleted in colorectal cancer and UNC5;Eph receptors and their ligands,ephrins;Slit and its receptor,Robo;repulsive guidance molecules and their receptor,neogenin;Wnt proteins and their receptor,Frizzled;and protocadherins.Localized Netrin-1 at the injury site inhibits motor axon regeneration after adult spinal cord injury while promoting oligodendrocyte growth.Slit2 enhances synapse formation in the injured spinal cord of rats.EphA7 regulates acute apoptosis in the early pathophysiological stages of spinal cord injury,while ephrinA1 plays a role in the nervous system’s injury response,with its reduced expression leading to impaired motor function in rats.EphA3 is upregulated following spinal cord injury,promoting an inhibitory environment for axonal regeneration.After spinal cord injury,bidirectional activation of ephrinB2 and EphB2 in astrocytes and fibroblasts results in the formation of a dense astrocyte-meningeal fibroblast scar.EphB1/ephrinB1 signaling mediates pain processing in spinal cord injury by regulating calpain-1 and caspase-3 in neurons.EphB3 expression increases in white matter after spinal cord injury,further inhibiting axon regeneration.Sema3A,expressed by neurons and fibroblasts in the scar surrounding the injury,inhibits motor neuron and sensory nerve growth after spinal cord injury.Sema4D suppresses neuronal axon myelination and axon regeneration,while its inhibition significantly enhances axon regeneration and motor recovery.Sema7A is involved in glial scar formation and may influence serotonin channel remodeling,thereby affecting motor coordination.Given these findings,the local or systemic application of neuronal guidance proteins represents a promising avenue for spinal cord injury treatment.
基金supported by the Shanghai Pilot Program for Basic Research(22T01400100-18)the National Natural Science Foundation of China(22278127 and 12447149)+1 种基金the Fundamental Research Funds for the Central Universities(2022ZFJH004)the Postdoctoral Fellowship Program of CPSF(GZB20250159).
文摘Accurate state of health(SOH)estimation is essential for the safe and reliable operation of lithium-ion batteries.However,existing methods face significant challenges,primarily because they rely on complete charge–discharge cycles and fixed-form physical constraints,which limit adaptability to different chemistries and real-world conditions.To address these issues,this study proposes an approach that extracts features from segmented state of charge(SOC)intervals and integrates them into an enhanced physics-informed neural network(PINN).Specifically,voltage data within the 25%–75%SOC range during charging are used to derive statistical,time–frequency,and mechanism-based features that capture degradation trends.A hybrid PINN-Lasso-Transformer-BiLSTM architecture is developed,where Lasso regression enables sparse feature selection,and a nonlinear empirical degradation model is embedded as a learnable physical term within a dynamically scaled composite loss.This design adaptively balances data-driven accuracy with physical consistency,thereby enhancing estimation precision,robustness,and generalization.The results show that the proposed method outperforms conventional neural networks across four battery chemistries,achieving root mean square error and mean absolute error below 1%.Notably,features from partial charging segments exhibit higher robustness than those from full cycles.Furthermore,the model maintains strong performance under high temperatures and demonstrates excellent generalization capacity in transfer learning across chemistries,temperatures,and C-rates.This work establishes a scalable and interpretable solution for accurate SOH estimation under diverse practical operating conditions.
基金supported by the National Natural Science Foundation of China(Nos.12375310,118750356,and 62006110)Graduate Research and Innovation Projects of Hunan Province(CX20230964).
文摘Physics-informed neural networks(PINNs)are vital for machine learning and exhibit significant advantages when handling complex physical problems.The PINN method can rapidly predict ^(220)Rn progeny concentration and is very important for regulating and measuring this property.To construct a PINN model,training data are typically preprocessed;however,this approach changes the physical characteristics of the data,with the preprocessed data potentially no longer directly conforming to the original physical equations.As a result,the original physical equations cannot be directly employed in the PINN.Consequently,an effective method for transforming physical equations is crucial for accurately constraining PINNs to model the ^(220)Rn progeny concentration prediction.This study presents an equation adaptation approach for neural networks,which is designed to improve prediction of ^(220)Rn progeny concentration.Five neural network models based on three architectures are established:a classical network,a physics-informed network without equation adaptation,and a physics-informed network with equation adaptation.The transport equation of the ^(220)Rn progeny concentration is transformed via equation adaption and integrated with the PINN model.The compatibility and robustness of the model with equation adaption is then analyzed.The results show that PINNs with equation adaption converge consistently with classical neural networks in terms of the training and validation loss and achieve the same level of prediction accuracy.This outcome indicates that the proposed method can be integrated into the neural network architecture.Moreover,the prediction performance of classical neural networks declines significantly when interference data are encountered,whereas the PINNs with equation adaption exhibit stable prediction accuracy.This performance demonstrates that the proposed method successfully harnesses the constraining power of physical equations,significantly enhancing the robustness of the resultant PINN models.Thus,the use of a physics-informed network with equation adaption can guarantee accurate prediction of ^(220)Rn progeny concentration.
基金funded by National Research Council of Thailand(contract No.N42A671047).
文摘Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce differential equations,constitutive relations,and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models,particularly for solid and structural mechanics,where data are often limited or noisy.This review offers a comprehensive assessment of recent developments in PINNs,combining bibliometric analysis,theoretical foundations,application-oriented insights,and methodological innovations.A biblio-metric survey indicates a rapid increase in publications on PINNs since 2018,with prominent research clusters focused on numerical methods,structural analysis,and forecasting.Building upon this trend,the review consolidates advance-ments across five principal application domains,including forward structural analysis,inverse modeling and parameter identification,structural and topology optimization,assessment of structural integrity,and manufacturing processes.These applications are propelled by substantial methodological advancements,encompassing rigorous enforcement of boundary conditions,modified loss functions,adaptive training,domain decomposition strategies,multi-fidelity and transfer learning approaches,as well as hybrid finite element–PINN integration.These advances address recurring challenges in solid mechanics,such as high-order governing equations,material heterogeneity,complex geometries,localized phenomena,and limited experimental data.Despite remaining challenges in computational cost,scalability,and experimental validation,PINNs are increasingly evolving into specialized,physics-aware tools for practical solid and structural mechanics applications.
基金supported by the Science and Technology on Reactor System Design Technology Laboratory(No.LRSDT12023108)supported in part by the Chongqing Postdoctoral Science Foundation(No.cstc2021jcyj-bsh0252)+2 种基金the National Natural Science Foundation of China(No.12005030)Sichuan Province to unveil the list of marshal industry common technology research projects(No.23jBGOV0001)Special Program for Stabilizing Support to Basic Research of National Basic Research Institutes(No.WDZC-2023-05-03-05).
文摘The neutron diffusion equation plays a pivotal role in nuclear reactor analysis.Nevertheless,employing the physics-informed neural network(PINN)method for its solution entails certain limitations.Conventional PINN approaches generally utilize a fully connected network(FCN)architecture that is susceptible to overfitting,training instability,and gradient vanishing as the network depth increases.These challenges result in accuracy bottlenecks in the solution.In response to these issues,the residual-based resample physics-informed neural network(R2-PINN)is proposed.It is an improved PINN architecture that replaces the FCN with a convolutional neural network with a shortcut(S-CNN).It incorporates skip connections to facilitate gradient propagation between network layers.Additionally,the incorporation of the residual adaptive resampling(RAR)mechanism dynamically increases the number of sampling points.This,in turn,enhances the spatial representation capabilities and overall predictive accuracy of the model.The experimental results illustrate that our approach significantly improves the convergence capability of the model and achieves high-precision predictions of the physical fields.Compared with conventional FCN-based PINN methods,R 2-PINN effectively overcomes the limitations inherent in current methods.Thus,it provides more accurate and robust solutions for neutron diffusion equations.
文摘This study aims to explore the impact of combined health education and dietary guidance on the speed of postoperative recovery in patients with gastrointestinal polyps.A specific number of patients who underwent gastrointestinal polyp resection were selected and randomly divided into a control group and an experimental group.The control group received routine nursing,while the experimental group implemented combined health education and dietary guidance on this basis.By comparing the recovery indicators of the two groups,it was found that the recovery speed of the experimental group was significantly faster than that of the control group,indicating that this combined intervention method can effectively promote patient recovery.
基金Supported by the National Key Research and Development Program of China(No.2023YFC3008200)the Independent Research Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(No.SML2022SP505)。
文摘Physics-informed neural networks(PINNs),as a novel artificial intelligence method for solving partial differential equations,are applicable to solve both forward and inverse problems.This study evaluates the performance of PINNs in solving the temperature diffusion equation of the seawater across six scenarios,including forward and inverse problems under three different boundary conditions.Results demonstrate that PINNs achieved consistently higher accuracy with the Dirichlet and Neumann boundary conditions compared to the Robin boundary condition for both forward and inverse problems.Inaccurate weighting of terms in the loss function can reduce model accuracy.Additionally,the sensitivity of model performance to the positioning of sampling points varied between different boundary conditions.In particular,the model under the Dirichlet boundary condition exhibited superior robustness to variations in point positions during the solutions of inverse problems.In contrast,for the Neumann and Robin boundary conditions,accuracy declines when points were sampled from identical positions or at the same time.Subsequently,the Argo observations were used to reconstruct the vertical diffusion of seawater temperature in the north-central Pacific for the applicability of PINNs in the real ocean.The PINNs successfully captured the vertical diffusion characteristics of seawater temperature,reflected the seasonal changes of vertical temperature under different topographic conditions,and revealed the influence of topography on the temperature diffusion coefficient.The PINNs were proved effective in solving the temperature diffusion equation of seawater with limited data,providing a promising technique for simulating or predicting ocean phenomena using sparse observations.
基金financially supported by the National Natural Science Foundation of China(No.22473032)。
文摘Accelerated aging tests are widely used to rapidly evaluate the durability of materials,of which thermal-oxidative aging is the most common approach.To quantitatively predict the effects of multiple coupled factors,this study takes polyamide66 reinforced with glass fiber(PA66-GF)as a model system and proposed a high-precision paradigm for coupled thermal-oxidative aging.By integrating Arrhenius-type reaction kinetics with oxygen diffusion,a predictive formula that holistically captures the nonlinear synergistic effects of multiple factors was developed,thereby overcoming the limitations of traditional single-variable models.A systematic evaluation of the stepwise improved formulas through nonlinear fitting showed that the coefficient of determination(R^(2))increased from 0.223 to 0.803,elucidating the fundamental reason why conventional approaches fail in quantitative prediction.These formulae were further embedded as physical constraints into a physics-informed neural network(PINN),which further enhanced the predictive performance,with the proposed formula achieving a peak R^(2)of 0.946.The results highlight that robust data fitting alone is insufficient;the decisive factor for the success of PINN lies in whether the embedded formula faithfully reflects the underlying physical mechanisms.When applied to polyamide 6 reinforced with glass fiber(PA6-GF),the Formula-constrained PINN maintained a high level of accuracy(R^(2)=0.916),demonstrating its strong cross-system generalizability.In summary,this work establishes a robust hybrid physics-machine learning framework that combines high accuracy with transferability for predicting the thermal-oxidative aging behavior of composite material systems.
基金Project supported by the Basic Science Research Program through the National Research Foundation(NRF)of Korea funded by the Ministry of Science and ICT(No.RS-2024-00337001)。
文摘Physics-informed neural networks(PINNs)have been shown as powerful tools for solving partial differential equations(PDEs)by embedding physical laws into the network training.Despite their remarkable results,complicated problems such as irregular boundary conditions(BCs)and discontinuous or high-frequency behaviors remain persistent challenges for PINNs.For these reasons,we propose a novel two-phase framework,where a neural network is first trained to represent shape functions that can capture the irregularity of BCs in the first phase,and then these neural network-based shape functions are used to construct boundary shape functions(BSFs)that exactly satisfy both essential and natural BCs in PINNs in the second phase.This scheme is integrated into both the strong-form and energy PINN approaches,thereby improving the quality of solution prediction in the cases of irregular BCs.In addition,this study examines the benefits and limitations of these approaches in handling discontinuous and high-frequency problems.Overall,our method offers a unified and flexible solution framework that addresses key limitations of existing PINN methods with higher accuracy and stability for general PDE problems in solid mechanics.
基金co-supported by the National Natural Science Foundation of China(Nos.U24B20157,62203031)the Natural Science Foundation of Beijing Municipality,China(No.4242041)+2 种基金the Natural Science Foundation of Zhejiang Province,China(No.LY24F030002)the Aeronautical ScienceFoundation of China(No.2024Z066051001)the Fundamental Research Funds for the Central Universities of China。
文摘The capture zones of the continuous and pulsed guidance laws in the pursuit-evasion game are analytically discussed in this paper to provide deep insights into the capturability distinction between the continuous guidance law and the pulsed guidance law.Specifically,first,in the pursuit-evasion game,various capture cases are defined regarding the Zero-Effort Miss distance(ZEM)to facilitate the capturability analysis.Then,for both the evader and the pursuer,the Linear-Quadratic Differential Game(LQDG)guidance laws concerning the continuous acceleration and the pulsed acceleration are converted into a unified form.In each capture case,the optimal solution existence conditions are derived,and the corresponding capture zones are formulated.The discussion on the capture zones shows that if the optimal solution exists,the distinction between the pulsed guidance law and the continuous guidance law can be neglected under small guidance effort weight.However,the capture zone of the continuous guidance law is larger than that of the pulsed guidance law with large pursuer guidance effort weight,but smaller with large evader guidance effort weight.Finally,various simulations are conducted to illustrate the distinction of the continuous and pulsed guidance laws,as well as the impact of the acceleration ratio and the time constant ratio on the capturability.
基金supported by JSPS(KAKENHI:21K06205,23K06937,24K23419)AMED(to JYK,SaY,TM,SiY,YT,and NH)JYW had long been supported by the NIH.
文摘The nervous system processes a vast amount of information,performing computations that underlie perception,cognition,and behavior.During development,neuronal guidance genes,which encode extracellular cues,their receptors,and downstream signal transducers,organize neural wiring to generate the complex architecture of the nervous system.It is now evident that many of these neuroguidance cues and their receptors are active during development and are also expressed in the adult nervous system.This suggests that neuronal guidance pathways are critical not only for neural wiring but also for ongoing function and maintenance of the mature nervous system.Supporting this view,these pathways continue to regulate synaptic connectivity,plasticity,and remodeling,and overall brain homeostasis throughout adulthood.Genetic and transcriptomic analyses have further revealed many neuronal guidance genes to be associated with a wide range of neurodegenerative and neuropsychiatric disorders.Although the precise mechanisms by which aberrant neuronal guidance signaling drives the pathogenesis of these diseases remain to be clarified,emerging evidence points to several common themes,including dysfunction in neurons,microglia,astrocytes,and endothelial cells,along with dysregulation of neuron-microglia-astrocyte,neuroimmune,and neurovascular interactions.In this review,we explore recent advances in understanding the molecular and cellular mechanisms by which aberrant neuronal guidance signaling contributes to disease pathogenesis through altered cell-cell interactions.For instance,recent studies have unveiled two distinct semaphorin-plexin signaling pathways that affect microglial activation and neuroinflammation.We discuss the challenges ahead,along with the therapeutic potentials of targeting neuronal guidance pathways for treating neurodegenerative diseases.Particular focus is placed on how neuronal guidance mechanisms control neuron-glia and neuroimmune interactions and modulate microglial function under physiological and pathological conditions.Specifically,we examine the crosstalk between neuronal guidance signaling and TREM2,a master regulator of microglial function,in the context of pathogenic protein aggregates.It is well-established that age is a major risk factor for neurodegeneration.Future research should address how aging and neuronal guidance signaling interact to influence an individual’s susceptibility to various late-onset neurological diseases and how the progression of these diseases could be therapeutically blocked by targeting neuronal guidance pathways.
基金supported by the National Natural Science Foundation of China,No.82202718the Natural Science Foundation of Beijing,No.L212050the China Postdoctoral Science Foundation,Nos.2019M664007,2021T140793(all to ZL)。
文摘Autografting is the gold standard for surgical repair of nerve defects>5 mm in length;however,autografting is associated with potential complications at the nerve donor site.As an alternative,nerve guidance conduits may be used.The ideal conduit should be flexible,resistant to kinks and lumen collapse,and provide physical cues to guide nerve regeneration.We designed a novel flexible conduit using electrospinning technology to create fibers on the innermost surface of the nerve guidance conduit and employed melt spinning to align them.Subsequently,we prepared disordered electrospun fibers outside the aligned fibers and helical melt-spun fibers on the outer wall of the electrospun fiber lumen.The presence of aligned fibers on the inner surface can promote the extension of nerve cells along the fibers.The helical melt-spun fibers on the outer surface can enhance resistance to kinking and compression and provide stability.Our novel conduit promoted nerve regeneration and functional recovery in a rat sciatic nerve defect model,suggesting that it has potential for clinical use in human nerve injuries.
基金supported by the National Natural Science Foundation of China(No.62173274)the National Key R&D Program of China(No.2019YFA0405300)+4 种基金the Natural Science Foundation of Hunan Province of China(Nos.2021JJ10045 and 2025JJ60072)the Open Research Subject of State Key Laboratory of Intelligent Game(No.ZBKF-24-01)the Postdoctoral Fellowship Program of CPSF(No.GZB20240989)the China Postdoctoral Science Foundation(No.2024M754304)the Aeronautical Science Foundation of China(No.2023Z005030001).
文摘Cooperative guidance is a method for achieving combat objectives through information sharing and cooperative effects,and has emerged as a significant research area in the fields of missile guidance and systematic warfare.This study presents a systematic review and analysis of current research on cooperative guidance.First,a bibliometric analysis is conducted on 513 articles using the Scopus database and CiteSpace software to assess keyword clustering,keyword cooccurrence,and keyword burst,and to later visualize the results.Second,fundamental theories of cooperative guidance,including relative motion modeling methods,algebraic graph theory,and multi-agent consensus theory,are summarized.Subsequently,an overview of current cooperative laws and corresponding analysis methods is provided,with categorization based on the cooperative structure and convergence performance.Finally,we summarize current research developments based on five perspectives and propose a developmental framework based on five layers(cyber,physical,decision,information,and system),discussing potential future advancements in cooperative terminal guidance.This framework emphasizes five key areas of research:networked,heterogeneous,integrated,intelligent,and group cooperations,with the goal of offering trends and insights for futurework.
基金supported by the National Natural Science Foundation of China(No.52175214)。
文摘In this paper,a novel guidance law is proposed which can achieve the desired impact speed and angle simultaneously for unpowered gliding vehicles.A guidance law with only impact angle constraint is used to produce the guidance profile,and its convergence in the varying speed scenario is proved.A relationship between flight states,guidance input and impact speed is established.By applying the fixed-time convergence control theory of error dynamics,an impact speed corrector is built with the above guidance profile,which can implement impact speed correction without affecting the impact angle constraint.Numerical simulations with various impact speed and angle constraints are conducted to demonstrate the performance of the proposed guidance law,and the robustness is also verified by Monte Carlo tests.
文摘Earth-to-Moon missions with low thrust-to-weight ratios present unique challenges for exoatmospheric guidance,and the existing algorithms are ineffective for the unprecedentedly long burn arcs and high orbital eccentricities.To address these challenges,a Long Burn Arc Powered Explicit Guidance(LBA-PEG)algorithm is developed and compared with the existing algorithms.In the proposed LBA-PEG algorithm,a fully numerical thrust prediction method is developed to accurately predict the highly nonlinear thrust effects over long burn arcs.Moreover,a real-time Newton correction method is proposed to correct the orbit injection point,remedying the position-velocity coupling induced by high orbital eccentricities.The comparison between the proposed algorithm and the existing algorithm shows that the proposed algorithm surpasses the existing ones by significantly enhancing fuel efficiency and improving tolerance to thrust decrease.The proposed LBA-PEG algorithm can adapt to a 65%thrust decrease,which is 12%–22%larger than that of the existing algorithms,and it can still reliably converge and complete the guidance mission even when the length of the burn arc exceeds 90°.The proposed LBA-PEG highlights the algorithm's adaptability for long burn arc missions,especially in critical scenarios such as manned Earth-to-Moon missions.
基金National Natural Science Foundation of China(Nos.61773387 and 62022061).
文摘A segmented predictor-corrector method is proposed for hypersonic glide vehicles to address the issue of the slow computational speed of obtaining guidance commands using the traditional predictor-corrector guidance method.Firstly,an altitude-energy profile is designed,and the bank angle is derived analytically as the initial iteration value for the predictor-corrector method.The predictor-corrector guidance method has been improved by deriving an analytical form for predicting the range-to-go error,which greatly accelerates the iterative speed.Then,a segmented guidance algorithm is proposed.The above analytically predictor-corrector guidance method is adopted when the energy exceeds an energy threshold.When the energy is less than the threshold,the equidistant test method is used to calculate the bank angle command,which ensures guidance accuracy as well as computational efficiency.Additionally,an adaptive guidance cycle strategy is applied to reduce the computational time of the reentry guidance trajectory.Finally,the accuracy and robustness of the proposed method are verified through a series of simulations and Monte-Carlo experiments.Compared with the traditional integral method,the proposed method requires 75%less computation time on average and achieves a lower landing error.
基金supported by the Beijing Natural Science Foundation(Grant No.L223013)。
文摘For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing limitations.Physics-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational conditions.This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode levels.These parameters include available capacity,electrode capacities,and lithium inventory capacity.The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public dataset.The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 mV.This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.
基金supported by the National Natural Science Foundation of China(Grant No.92152301)the National Key Research and Development Program of China(Grant No.2022YFB4300200)the Shaanxi Provincial Key Research and Development Program(Grant No.2023-ZDLGY-27).
文摘Physics-informed neural networks(PINNs)have shown remarkable prospects in solving the forward and inverse problems involving partial differential equations(PDEs).The method embeds PDEs into the neural network by calculating the PDE loss at a set of collocation points,providing advantages such as meshfree and more convenient adaptive sampling.However,when solving PDEs using nonuniform collocation points,PINNs still face challenge regarding inefficient convergence of PDE residuals or even failure.In this work,we first analyze the ill-conditioning of the PDE loss in PINNs under nonuniform collocation points.To address the issue,we define volume weighting residual and propose volume weighting physics-informed neural networks(VW-PINNs).Through weighting the PDE residuals by the volume that the collocation points occupy within the computational domain,we embed explicitly the distribution characteristics of collocation points in the loss evaluation.The fast and sufficient convergence of the PDE residuals for the problems involving nonuniform collocation points is guaranteed.Considering the meshfree characteristics of VW-PINNs,we also develop a volume approximation algorithm based on kernel density estimation to calculate the volume of the collocation points.We validate the universality of VW-PINNs by solving the forward problems involving flow over a circular cylinder and flow over the NACA0012 airfoil under different inflow conditions,where conventional PINNs fail.By solving the Burgers’equation,we verify that VW-PINNs can enhance the efficiency of existing the adaptive sampling method in solving the forward problem by three times,and can reduce the relative L 2 error of conventional PINNs in solving the inverse problem by more than one order of magnitude.
基金supported by the National Natural Science Foundation of China,Nos. 81760247, 82171450the Scientific Research Foundation for Doctors of the Affiliated Hospital of Zunyi Medical University,No.(2016)14 (all to HH)。
文摘Current treatments for epilepsy can only manage the symptoms of the condition but cannot alter the initial onset or halt the progression of the disease. Consequently, it is crucial to identify drugs that can target novel cellular and molecular mechanisms and mechanisms of action. Increasing evidence suggests that axon guidance molecules play a role in the structural and functional modifications of neural networks and that the dysregulation of these molecules is associated with epilepsy susceptibility. In this review, we discuss the essential role of axon guidance molecules in neuronal activity in patients with epilepsy as well as the impact of these molecules on synaptic plasticity and brain tissue remodeling. Furthermore, we examine the relationship between axon guidance molecules and neuroinflammation, as well as the structural changes in specific brain regions that contribute to the development of epilepsy. Ample evidence indicates that axon guidance molecules, including semaphorins and ephrins, play a fundamental role in guiding axon growth and the establishment of synaptic connections. Deviations in their expression or function can disrupt neuronal connections, ultimately leading to epileptic seizures. The remodeling of neural networks is a significant characteristic of epilepsy, with axon guidance molecules playing a role in the dynamic reorganization of neural circuits. This, in turn, affects synapse formation and elimination. Dysregulation of these molecules can upset the delicate balance between excitation and inhibition within a neural network, thereby increasing the risk of overexcitation and the development of epilepsy. Inflammatory signals can regulate the expression and function of axon guidance molecules, thus influencing axonal growth, axon orientation, and synaptic plasticity. The dysregulation of neuroinflammation can intensify neuronal dysfunction and contribute to the occurrence of epilepsy. This review delves into the mechanisms associated with the pathogenicity of axon guidance molecules in epilepsy, offering a valuable reference for the exploration of therapeutic targets and presenting a fresh perspective on treatment strategies for this condition.
