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Voxel event graph neural network for event-based human gait recognition 认领 引用
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作者 Guangyuan MA Xiaolin GONG +2 位作者 Jiangtao XU Jiandong GAO Zhaoxuan GUO 《Optoelectronics Letters》 EI 2026年第3期167-173,共7页
To improve the accuracy of event-based gait recognition,a method called voxel event graph neural network(VEGNN)is proposed.This method voxelizes the event stream and selects representative voxels as vertices of the gr... To improve the accuracy of event-based gait recognition,a method called voxel event graph neural network(VEGNN)is proposed.This method voxelizes the event stream and selects representative voxels as vertices of the graph.Then the edges are constructed based on spatio-temporal distance and temporal order constraints so that the event stream is constructed as a graph structure.Finally,a lightweight feature extraction network based on graph neural networks(GNNs)is used to efficiently capture spatio-temporal information and motion cues from the event graph.To evaluate our method,an event-based gait recognition dataset called Celex-Gait is created.Experimental results show that VEGNN achieves a gait recognition accuracy of 95.8%and 93.9%on the Celex-Gait dataset and the DVS128-Gait-Day dataset,respectively.Furthermore,compared to the state-of-the-art methods,VEGNN reduces the number of model parameters by 30%.This indicates that VEGNN achieves higher recognition accuracy with lower model complexity. 展开更多
关键词 selects representative voxels voxel event graph neural network vegnn event stream lightweight feature extraction network voxel event graph neural network graph structurefinallya graph neural networks gnns gait recognitiona
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Tensor Low-Rank Orthogonal Compression for Convolutional Neural Networks 认领 引用
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作者 Yaping He Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期227-229,共3页
Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression... Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression is crucial for deploying deep neural network(DNN)models on resource-constrained embedded devices. 展开更多
关键词 model compression convolutional neural network cnn which tensor low rank orthogonal compression deep neural network dnn models embedded devices convolutional neural networks
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Stem cell derived neural organoid approaches for neurological diseases 认领 引用
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作者 Rosalie Elvira Xiao-Xue Dong +6 位作者 Alfred Sun Xuyang Wai Hon Chooi Huck Hui Ng Hongyan Wang Yun-Cheng Wu Eng King Tan Zhi Dong Zhou 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第10期4495-4505,共11页
Traditional two-dimensional cultures and animal models often fall short in capturing the complexities of neurodevelopmental and neurodegenerative diseases.However,recently developed neural organoid approaches,three-di... Traditional two-dimensional cultures and animal models often fall short in capturing the complexities of neurodevelopmental and neurodegenerative diseases.However,recently developed neural organoid approaches,three-dimensional structures derived from human pluripotent stem cells,have become powerful tools for modeling human neuronal development and disease.Unlike traditional models,neural organoids provide significant insights and improved modeling capabilities.Here,we explore various types of neural organoids in disease modeling and outline distinct protocols for generating each type,including specific patterning methods,growth factors,and differentiation durations.The potential and advantages of co-culturing neural organoids with other cells and tissues are also discussed.While neural organoids have already made significant contributions to neuroscience research,future directions should focus on enhancing their maturation and functionality.The progression of neural organoids approaches will generate more accurate and comprehensive disease models,ultimately adding to our understanding of disease pathogenesis and paving the way for future precision therapies for neurological diseases. 展开更多
关键词 neural differentiation neural organoids neurogenesis neurological diseases stem cells three-dimensional models
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Novel 3D bioprinting approach for spinal cord injury repair using neural stem cells and TGF-β1 monoclonal antibody 认领 引用
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作者 Ming Deng Geliang Hu +8 位作者 Fan Liang Qi Liao Wanjun Ding Bin He Jianghua Ming Hui Huang Junqi Liu Zhaohong Kong Ping Xie 《Nano Materials Science》 EI CAS CSCD 2026年第3期625-640,共16页
Objective:To investigate the repair of spinal cord injury(SCI)in rats using 3D bioprinted decellularized extracellular matrix(dECM)hydrogel,neural stem cells(NSCs),and TGF-β1 monoclonal antibody.Methods:The spinal co... Objective:To investigate the repair of spinal cord injury(SCI)in rats using 3D bioprinted decellularized extracellular matrix(dECM)hydrogel,neural stem cells(NSCs),and TGF-β1 monoclonal antibody.Methods:The spinal cord-derived dECM hydrogel(SC-dECM-gel)was optimized for 3D printing.Rheological tests established a 3%concentration as optimal.In vitro tests assessed the effects of TGF-β1 monoclonal antibody on NSC viability and differentiation.A rat SCI model was treated with the printed gel,and recovery was monitored using the Basso-Beattie-Bresnahan test and evoked potentials.Results:The 3%SC-dECM-gel showed superior rheological properties.TGF-β1 monoclonal antibody enhanced NSC survival and differentiation in vitro.In vivo,the 3D bioprinted gel significantly improved motor function and promoted neuronal regeneration.Conclusion:3D bioprinted SC-dECM-gel loaded with NSCs and TGF-β1 monoclonal antibody effectively restores motor function and promotes regeneration in SCI rats,offering a promising approach for SCI treatment. 展开更多
关键词 Spinal cord injury 3D bioprinting Decellularized extracellular matrices Neural stem cells Transforming growth factor-beta 1 monoclonal antibody Neural regeneration Functional recovery
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Recognition and classification of microseismic signals based on Bayesian-optimized CNN-LSTM neural network 认领 引用
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作者 WU Yang LIU Jian-feng +4 位作者 WANG Chun-ping LIU Jun-jie JI Zhen-xing TIAN Cheng-yu XUE Fu-jun 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第6期2762-2787,共26页
Microseismic monitoring and signal recognition constitute critical technologies for accurately assessing rockburst risks and ensuring the safe construction of underground rock engineering.This study developed a"S... Microseismic monitoring and signal recognition constitute critical technologies for accurately assessing rockburst risks and ensuring the safe construction of underground rock engineering.This study developed a"Surface+Underground"microseismic intelligent monitoring system to evaluate dynamic disaster risks during construction at the Beishan High-level Radioactive Waste Geological Disposal Laboratory in China.The datasets of four typical one-dimensional time-domain microseismic signals,including rock fracture,blasting,TBM tunneling,and drilling,were constructed,and the BO-CNN-LSTM model is developed to identify and classify these signals..Based on the classification results,the typical time-frequency domain characteristics of the four types of signals are analyzed.The classification results of BO-CNN-LSTM,CNN,LSTM and CNN-LSTM models show that the recognition accuracy of the four models is 98.0%,85.7%,66.7%and 91.0%,respectively.Among all types,the four models demonstrate the highest effectiveness in identifying rock fracture and borehole signals.The findings further confirm that the BO-CNN-LSTM model efficiently recognizes and extracts microseismic signal features,demonstrating superior performance and stability in the classification task.Finally,the study suggests several future research directions,particularly in the areas of raw signal denoising,and automation of feature extraction. 展开更多
关键词 deep ground engineering microseismic monitoring signal classification neural network Bayesian optimization convolutional neural network(CNN) long short-term memory network(LSTM) BO-CNN-LSTM model
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Personalized Differential Privacy Graph Neural Network 认领 引用
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作者 Yanli Yuan Dian Lei +3 位作者 Chuan Zhang Zehui Xiong Chunhai Li Liehuang Zhu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期498-500,共3页
Dear Editor,This letter addresses the critical challenge of preserving privacy in graph learning without compromising on data utility.Differential privacy(DP)is emerging as an effective method for privacy-preserving g... Dear Editor,This letter addresses the critical challenge of preserving privacy in graph learning without compromising on data utility.Differential privacy(DP)is emerging as an effective method for privacy-preserving graph learning.However,its application often diminishes data utility,especially for nodes with fewer neighbors in graph neural networks(GNNs). 展开更多
关键词 graph neural networks gnns personalized differential privacy graph learning privacy preservation data utility preserving privacy graph neural network
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Structural Optimization of a Multi-Story Frame Structure Based on a Pre-Trained Physics-Informed Neural Network(PINN)Surrogate Model 认领 引用
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作者 An Xu Zhixiong Liu +4 位作者 Hua Rong Liang Han Wei Shi Jun Huang Jiyang Fu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期279-313,共35页
In structural optimization,data-driven surrogate models are often explored as alternatives to finite element analysis to reduce computational cost.However,conventional neural networks usually fail to capture key struc... In structural optimization,data-driven surrogate models are often explored as alternatives to finite element analysis to reduce computational cost.However,conventional neural networks usually fail to capture key structural characteristics and are limited to predicting global responses(e.g.,top displacement),but usually fail to achieve accurate internal force predictions with conventional training data volumes.As a result,most existing studies involving surrogate models did not concern internal force constraints.To address this issue,this study proposes a structural optimization framework based on a pre-trained Physics-Informed Neural Network(PINN)surrogate model.By embedding static equilibrium equation into the loss function,the model achieves higher predictive accuracy,particularly for internal forces,while pre-training accelerates convergence and enhances stability.Combined with an improved multi-swarm particle swarm optimization(MPSO)algorithm,the framework enables efficient optimization of multi-story frame structures under internal force and multiple other constraints.The application to a six-story frame structure validates its effectiveness:compared with a DNN-based model,the PINN-based model improves the coefficient of determination for internal force prediction from 0.8874 to 0.9937.These results demonstrate that the proposed method offers a promising approach for efficient optimization of multi-story frame structures. 展开更多
关键词 Structural optimization intelligent algorithms physics-informed neural networks(PINN) neural networks
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Partial hearing recovery after cochlear nerve surface transplantation of hair follicle-derived neural crest stem cells in neural hearing loss rat 认领 引用
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作者 Huidong Chen Yunlong Zhang +7 位作者 Huanzhi Wan Bingqian Yang Tianyi Liu Jie Wang Hua Liao Yuanyuan Zhang Zhijian Zhang Qingquan Hua 《Animal Models and Experimental Medicine》 CAS CSCD 2026年第7期1454-1468,共15页
Background:Stem cell therapy offers promise for the neurodegenerative diseases and has been explored for sensorineural hearing loss(SNHL).However,effective cell delivery strategies remain a critical challenge for SNHL... Background:Stem cell therapy offers promise for the neurodegenerative diseases and has been explored for sensorineural hearing loss(SNHL).However,effective cell delivery strategies remain a critical challenge for SNHL treatment.Methods:To address this need,we established an ouabain-induced SGN injured hearing loss model in rat and evaluated a novel transplantation strategy targeting the cochlear nerve surface via posterior occipital approach,designed to minimize cochlear structural damage and facilitate targeted cell delivery to Rosenthal's canal(RC).The temporal changes in glial cell densities within RC revealed a progressively deteriorating neural microenvironment,supporting early-stage intervention.Accordingly,hair follicle-derived neural crest stem cell(HFNCSC)transplantation was performed 3 or 4 days after modeling via two approaches:cochlear nerve surface transplantation(CNT)and round window transplantation(RWT).Results:CNT resulted in significant improvements in auditory function,as evidenced by reduced auditory brainstem response(ABR)thresholds,shortened waveⅠlatencies,and preserved waveⅠamplitudes post-transplantation.Transplanted cells were distributed along the nerve trunk and within RC.In contrast,RWT failed to improve auditory function and caused cochlear structural damage,with widespread cell dispersion in cochlear fluids.Notably,the CNT group exhibited significantly higher densities of TUJ1-positive neuron-like cells and glial cells in the RC,accompanied by enhanced myelin basic protein expression suggestive of remyelination.No such improvements were observed in the RWT group.Conclusions:These findings suggest that cochlear nerve surface transplantation enhances stem cell survival and auditory function recovery,and represents a promising delivery approach for stem cell-based therapy of SGN-related hearing loss. 展开更多
关键词 cochlear nerve surface approach hair follicle-derived neural crest stem cell neural hearing loss spiral ganglion neuron stem cell transplantation
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Dynamic regulation of the developmental establishment of the adult hippocampal neural stem cell pool 认领 引用
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作者 Feng Zhang Guo-li Ming Hongjun Song 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第6期2325-2326,共2页
The adult subventricular zone of the lateral ventricles and the subgranular zone in the hippocampal dentate gyrus(DG)are the two brain regions where neurogenesis occurs throughout life in the adult mammalian brain(Min... The adult subventricular zone of the lateral ventricles and the subgranular zone in the hippocampal dentate gyrus(DG)are the two brain regions where neurogenesis occurs throughout life in the adult mammalian brain(Ming and Song,2011).Adult quiescent hippocampal neural stem cells(NSCs)are bona fide stem cells and,when activated,give rise to newborn granule neurons in the adult brain,which play vital roles in learning,memory,mood,and affective cognition(Bonaguidi et al.,2011;Ming and Song,2011). 展开更多
关键词 dynamic regulation bona fide stem cells adult hippocampal neural stem cell pool hippocampal dentate gyrus dg newborn granule neurons neural stem cells nscs adult subventricular zone lateral ventricles
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A Robot Grasp Detection Method Based on Neural Architecture Search and Its Interpretability Analysis 认领 引用 被引量：1
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作者 Lu Rong Manyu Xu +5 位作者 Wenbo Zhu Zhihao Yang Chao Dong Yunzhi Zhang Kai Wang Bing Zheng 《Computers, Materials & Continua》 SCIE EI 2026年第4期1282-1306,共25页
Deep learning has become integral to robotics,particularly in tasks such as robotic grasping,where objects often exhibit diverse shapes,textures,and physical properties.In robotic grasping tasks,due to the diverse cha... Deep learning has become integral to robotics,particularly in tasks such as robotic grasping,where objects often exhibit diverse shapes,textures,and physical properties.In robotic grasping tasks,due to the diverse characteristics of the targets,frequent adjustments to the network architecture and parameters are required to avoid a decrease in model accuracy,which presents a significant challenge for non-experts.Neural Architecture Search(NAS)provides a compelling method through the automated generation of network architectures,enabling the discovery of models that achieve high accuracy through efficient search algorithms.Compared to manually designed networks,NAS methods can significantly reduce design costs,time expenditure,and improve model performance.However,such methods often involve complex topological connections,and these redundant structures can severely reduce computational efficiency.To overcome this challenge,this work puts forward a robotic grasp detection framework founded on NAS.The method automatically designs a lightweight network with high accuracy and low topological complexity,effectively adapting to the target object to generate the optimal grasp pose,thereby significantly improving the success rate of robotic grasping.Additionally,we use Class Activation Mapping(CAM)as an interpretability tool,which captures sensitive information during the perception process through visualized results.The searched model achieved competitive,and in some cases superior,performance on the Cornell and Jacquard public datasets,achieving accuracies of 98.3%and 96.8%,respectively,while sustaining a detection speed of 89 frames per second with only 0.41 million parameters.To further validate its effectiveness beyond benchmark evaluations,we conducted real-world grasping experiments on a UR5 robotic arm,where the model demonstrated reliable performance across diverse objects and high grasp success rates,thereby confirming its practical applicability in robotic manipulation tasks. 展开更多
关键词 Robotics grasping detection neural architecture search neural network interpretability
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Neural functional rehabilitation:Exploring neuromuscular reconstruction technology advancements and challenges 认领 引用 被引量：1
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作者 Chunxiao Tang Ping Wang +3 位作者 Zhonghua Li Shizhen Zhong Lin Yang Guanglin Li 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第1期173-186,共14页
Neural machine interface technology is a pioneering approach that aims to address the complex challenges of neurological dysfunctions and disabilities resulting from conditions such as congenital disorders,traumatic i... Neural machine interface technology is a pioneering approach that aims to address the complex challenges of neurological dysfunctions and disabilities resulting from conditions such as congenital disorders,traumatic injuries,and neurological diseases.Neural machine interface technology establishes direct connections with the brain or peripheral nervous system to restore impaired motor,sensory,and cognitive functions,significantly improving patients'quality of life.This review analyzes the chronological development and integration of various neural machine interface technologies,including regenerative peripheral nerve interfaces,targeted muscle and sensory reinnervation,agonist–antagonist myoneural interfaces,and brain–machine interfaces.Recent advancements in flexible electronics and bioengineering have led to the development of more biocompatible and highresolution electrodes,which enhance the performance and longevity of neural machine interface technology.However,significant challenges remain,such as signal interference,fibrous tissue encapsulation,and the need for precise anatomical localization and reconstruction.The integration of advanced signal processing algorithms,particularly those utilizing artificial intelligence and machine learning,has the potential to improve the accuracy and reliability of neural signal interpretation,which will make neural machine interface technologies more intuitive and effective.These technologies have broad,impactful clinical applications,ranging from motor restoration and sensory feedback in prosthetics to neurological disorder treatment and neurorehabilitation.This review suggests that multidisciplinary collaboration will play a critical role in advancing neural machine interface technologies by combining insights from biomedical engineering,clinical surgery,and neuroengineering to develop more sophisticated and reliable interfaces.By addressing existing limitations and exploring new technological frontiers,neural machine interface technologies have the potential to revolutionize neuroprosthetics and neurorehabilitation,promising enhanced mobility,independence,and quality of life for individuals with neurological impairments.By leveraging detailed anatomical knowledge and integrating cutting-edge neuroengineering principles,researchers and clinicians can push the boundaries of what is possible and create increasingly sophisticated and long-lasting prosthetic devices that provide sustained benefits for users. 展开更多
关键词 agonist–antagonist myoneural interface biocompatibility brain–machine interface clinical anatomy neural machine interface neuroprosthetics peripheral nerve interface proprioception targeted muscle reinnervation targeted sensory reinnervation
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Multi-target neural circuit reconstruction and enhancement in spinal cord injury 认领 引用 被引量：3
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作者 Lingyun Cao Siyun Chen +2 位作者 Shuping Wang Ya Zheng Dongsheng Xu 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第3期957-971,共15页
After spinal cord injury,impairment of the sensorimotor circuit can lead to dysfunction in the motor,sensory,proprioceptive,and autonomic nervous systems.Functional recovery is often hindered by constraints on the tim... After spinal cord injury,impairment of the sensorimotor circuit can lead to dysfunction in the motor,sensory,proprioceptive,and autonomic nervous systems.Functional recovery is often hindered by constraints on the timing of interventions,combined with the limitations of current methods.To address these challenges,various techniques have been developed to aid in the repair and reconstruction of neural circuits at different stages of injury.Notably,neuromodulation has garnered considerable attention for its potential to enhance nerve regeneration,provide neuroprotection,restore neurons,and regulate the neural reorganization of circuits within the cerebral cortex and corticospinal tract.To improve the effectiveness of these interventions,the implementation of multitarget early interventional neuromodulation strategies,such as electrical and magnetic stimulation,is recommended to enhance functional recovery across different phases of nerve injury.This review concisely outlines the challenges encountered following spinal cord injury,synthesizes existing neurostimulation techniques while emphasizing neuroprotection,repair,and regeneration of impaired connections,and advocates for multi-targeted,task-oriented,and timely interventions. 展开更多
关键词 multi-targets nerve root magnetic stimulation neural circuit neuromodulation peripheral nerve stimulation reconstruction spinal cord injury task-oriented training timing transcranial magnetic stimulation
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mTORC1 and mTORC2 synergy in human neural development, disease, and regeneration 认领 引用 被引量：2
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作者 Navroop K.Dhaliwal Julien Muffat Yun Li 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第4期1552-1553,共2页
The mechanistic target of rapamycin(m TOR) is a serinehreonine kinase that plays a pivotal role in cellular growth, proliferation, survival, and metabolism. In the central nervous system(CNS), the mTOR pathway regulat... The mechanistic target of rapamycin(m TOR) is a serinehreonine kinase that plays a pivotal role in cellular growth, proliferation, survival, and metabolism. In the central nervous system(CNS), the mTOR pathway regulates diverse aspects of neural development and function. Genetic mutations within the m TOR pathway lead to severe neurodevelopmental disorders, collectively known as “mTORopathies”(Crino, 2020). Dysfunctions of m TOR, including both its hyperactivation and hypoactivation, have also been implicated in a wide spectrum of other neurodevelopmental and neurodegenerative conditions, highlighting its importance in CNS health. 展开更多
关键词 m tor neural development mtorc central nervous system cns mtor neurodevelopmental disorders neurodegenerative conditions
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Amelioration of behavioral and neural deficits in animal models of neurodegenerative disease by nanoformulations of curcumin and quercetin 认领 引用 被引量：3
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作者 Bridget Martinez Philip V.Peplow 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第8期3311-3322,共12页
Neurodegenerative diseases are increasing in prevalence due largely to aging populations worldwide and improved medical care for the elderly.Currently approved drugs can reduce some of the symptoms of neurodegenerativ... Neurodegenerative diseases are increasing in prevalence due largely to aging populations worldwide and improved medical care for the elderly.Currently approved drugs can reduce some of the symptoms of neurodegenerative diseases but cannot cure them.Inflammation is involved in the development and progression of neurodegenerative diseases,and oxidative stress is implicated in neurodegeneration associated with cognitive decline and age-related cognitive impairment.Polyphenols such as curcumin,quercetin,and resveratrol possess potent anti-inflammatory and antioxidant properties.Nanoformulations of curcumin and quercetin can optimize their pharmacological effects in the treatment of neurodegenerative diseases.Nanocarriers play a crucial role in delivering drugs across the blood-brain barrier,thereby lowering the risk of peripheral side effects.Various nanoforms have been developed to induce bioavailability and solubility of curcumin and quercetin,including nanoparticles and nanoemulsions.The studies reviewed included 17 using curcumin nanoformulations and seven with quercetin nanoformulations and were tested in widely used animal models of Alzheimer’s disease,Parkinson’s disease,Huntington’s disease,and multiple sclerosis.Many of the curcumin and quercetin nanoformulations brought about improvements in learning and memory in behavioral tests of Alzheimer’s disease models and were effective in reducing oxidative stress in the brain.Both nanocurcumin and nanoquercetin decreased the levels of inflammatory markers in the brain.Nanocurcumin formulations improved motor behavior,gait,and memory in Parkinson’s disease models and increased dopaminergic neurons in the striatum and substantia nigra.Furthermore,nanocurcumin improved locomotor activity,memory,and learning,and the number of dendrites of medium spiny neurons in Huntington’s disease models.Nanocurcumin formulations decreased oxidative stress and inflammation in a model of demyelination.Several important limitations were identified in the studies reviewed and these need to be considered in future studies.Also,clinical trials could be performed using the currently available nanoforms of curcumin and quercetin. 展开更多
关键词 animal models behavioral deficits curcumin inflammation nanoformulations neural deficits neurodegeneration oxidative stress quercetin
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Experimental data-driven deep neural network modeling and prediction for the streaks of turbulent separated shear flow 认领 引用 被引量：1
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作者 Xingyu Ma Jiateng Pan +1 位作者 Yihong Liu Nan Jiang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第7期52-58,共7页
In this paper,we report a recent experimental study of streak structures in the turbulent separated shear flow by datadriven deep neural network.By applying spanwise-aligned tetrahedron vortex generators upstream of a... In this paper,we report a recent experimental study of streak structures in the turbulent separated shear flow by datadriven deep neural network.By applying spanwise-aligned tetrahedron vortex generators upstream of a plane backward-facing step,spanwise-aligned high-and low-speed streaks were generated within the separated shear layer behind the step.The velocity profiles of the shear flow were measured by single-probe hot-wire anemometer in both the streamwise-vertical and the streamwise-spanwise planes in the wind tunnel.Deep neural network models are trained and verified based on the experimental datasets.The input parameter sets include the vortex generator height,spanwise spacing,and the spatial coordinates within the measurement domain,while the output parameter sets are mean and root-mean-square velocities of the shear flow.Mean squared errors between the model-predicted and experimentally measured data are used for quality evaluation of different deep neural network model designs,among which the minimum error of the optimal design descends less than 1%.For other vortex generator parameters,which are not measured in the wind tunnel or used in the training,the model prediction provides reasonable mean velocity contours with streak structures.Thus,we find that the experimental data-driven modeling approach shows reliable robustness for nonlinear fitting of complex datasets as well as considerable generalization for turbulent coherent structures. 展开更多
关键词 Streak Experimental data-driven Deep neural network Vortex generator Backward-facing step
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Physics-informed neural networks for hidden boundary detection and flow field reconstruction 认领 引用 被引量：1
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作者 Yongzheng Zhu Weizheng Chen +1 位作者 Jian Deng Xin Bian 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第7期131-149,共19页
Simultaneously detecting hidden solid boundaries and reconstructing flow fields from sparse observations poses a significant inverse challenge in fluid mechanics.This study presents a physics-informed neural network f... Simultaneously detecting hidden solid boundaries and reconstructing flow fields from sparse observations poses a significant inverse challenge in fluid mechanics.This study presents a physics-informed neural network framework designed to infer the presence,shape,and motion of static or moving solid boundaries within a flow field.By integrating a body fraction parameter into the governing equations,the model enforces no-slipo-penetration boundary conditions in solid regions while preserving conservation laws of fluid dynamics.Using partial flow field data,the method simultaneously reconstructs the unknown flow field and infers the body fraction distribution,thereby revealing solid boundaries.The framework is validated across diverse scenarios,including incompressible Navier-Stokes and compressible Euler flows,such as steady flow past a fixed cylinder,an inline oscillating cylinder,and subsonic flow over an airfoil.The results demonstrate accurate detection of hidden boundaries,reconstruction of missing flow data,and estimation of trajectories and velocities of a moving body.Further analysis examines the effects of data sparsity,velocity-only measurements,and noise on inference accuracy.The proposed method exhibits robustness and versatility,highlighting its potential for applications when only limited experimental or numerical data are available. 展开更多
关键词 Hidden moving boundary Flow reconstruction Inverse problem Physics-informed neural networks
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An innovative treatment for hypoxic-ischemic encephalopathy:Silk fibroin nanomaterials improve neural stem cell axon formation and facilitate cognitive improvement 认领 引用 被引量：1
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作者 Chao Han Shuna Chen +3 位作者 Zihan Shi Xin Guan Wei Zou Jing Liu 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第7期3017-3025,共9页
Stem cell therapy shows promise for treating brain injuries;neural stem cells in particular are capable of repairing damage by forming new nerve cells and supporting recovery.However,optimizing the implantation and fu... Stem cell therapy shows promise for treating brain injuries;neural stem cells in particular are capable of repairing damage by forming new nerve cells and supporting recovery.However,optimizing the implantation and functionality of these cells in damaged brain regions remains challenging.Silk fibroin,a natural protein derived from silkworm silk,is a biocompatible material with exceptional properties that are useful for tissue engineering.Its biodegradability,mechanical robustness,and ability to promote cell growth make it particularly valuable for biomedical applications.Silk fibroin nanomaterials,which comprise silk fibroin processed into nanostructures,offer enhanced surface area,improved loading capacity for bioactive molecules,and superior nanoscale interactions with cells compared with bulk silk fibroin materials.In this study,we first extracted human-derived neural stem cells from a 14-week-old human fetus.Then,neural stem cells were loaded with 1%silk fibroin nanomaterials,which was identified as the optimal concentration to support human-derived neural stem cell growth and release of neurotrophic factors.Finally,1%silk fibroin nanomaterials were implanted into a rat model of hypoxic-ischemic brain injury.The results showed that,compared with the treatment with human-derived neural stem cells alone,silk fibroin hydrogel carrying human-derived neural stem cells was significantly more effective at alleviating brain tissue damage,increasing neurotrophic factor secretion in the brain microenvironment,and promoting motor and cognitive function recovery.These findings suggest that silk fibroin nanomaterials loaded with human-derived neural stem cells could be used to treat hypoxic-ischemic encephalopathy.However,the mechanisms and related signaling pathways by which hydrogels combined with cells exert their reparative effects still require further in-depth investigation. 展开更多
关键词 astrocyte deactivation axonal development cognitive recovery hypoxic-ischemic brain injury motor function nerve regeneration neural stem cells neurotrophic factor silk fibroin hydrogel synapse formation
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Neural regulation of bone:from central neural circuits to peripheral innervation of the skeletal stem cell niche 认领 引用
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作者 Zihan Chen Zhengqiong Luo +2 位作者 Matthew B.Greenblatt Zuoxing Wu Ren Xu 《Bone Research》 SCIE CAS CSCD 2026年第3期742-757,共16页
The nervous system has emerged as a multi-scale regulator of bone biology,integrating central neural circuits with peripheral innervation to control skeletal homeostasis and repair.While bone remodeling is classically... The nervous system has emerged as a multi-scale regulator of bone biology,integrating central neural circuits with peripheral innervation to control skeletal homeostasis and repair.While bone remodeling is classically described as being governed by coupling between bone formation and resorption,neural signaling provides an additional hierarchical layer that links organismlevel cues to local skeletal stem/progenitor cell niches.This review presents a mechanistic framework for the neuro–bone regulatory network across three hierarchical levels.First,we examine central regulation,in which hypothalamic circuits integrate hormonal and metabolic signals via circumventricular organs to modulate endocrine outputs such as parathyroid hormone(PTH),thereby establishing circadian rhythms and systemic control of bone metabolism.Second,we analyze peripheral neural communication,where sensory inputs triggered by injury or inflammation,along with autonomic efferent signaling,includingβ-adrenergic pathways,directly influence osteolineage and stromal cells.These signals recalibrate cellular metabolic states,differentiation programs,and regenerative responses,linking pain perception with tissue repair mechanisms.Third,we investigate the bone marrow niche,where distinct subtypes of nerve fibers release a diverse array of neurotransmitters and or neuromodulators that shape the microenvironment of skeletal stem and progenitor cells(SSPCs)as well as downstream osteoprogenitors,thereby regulating proliferation,lineage commitment,and quiescence.Collectively,these findings delineate an integrated model of neural regulation of bone spanning central,peripheral,and local niche levels,providing a foundation for testable hypotheses in neuro-osteobiology. 展开更多
关键词 neuro bone regulatory network peripheral innervation nervous system central neural circuits bone remodeling neural regulation mechanistic framework bone biology
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Artificial Neural Network-Based Prediction and Validation of Drill Flank Wear in GFRP Machining for Sustainable and Smart Manufacturing 认领 引用 被引量：1
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作者 Sathish Rao Udupi Gururaj Bolar +1 位作者 Manjunath Shettar Ashwini Bhat 《Computers, Materials & Continua》 SCIE EI 2026年第6期903-918,共16页
Glass fiber-reinforced polymer composites(GFRPCs)are extensively utilized in the aerospace,automotive,and structural sectors;nevertheless,their heterogeneous and abrasive characteristics result in rapid tool wear duri... Glass fiber-reinforced polymer composites(GFRPCs)are extensively utilized in the aerospace,automotive,and structural sectors;nevertheless,their heterogeneous and abrasive characteristics result in rapid tool wear during drilling.Drill flank wear among various wear mechanisms notably influences hole quality and dimensional accuracy.This research investigates the impact of spindle speed,feed rate,and drill diameter on flank wear during dry drilling of GFRPC laminates with high-speed steel(HSS)twist drills.A full-factorial design with 81 experiments is used to create a comprehensive dataset.ANOVA indicates that spindle speed is the dominant factor affecting wear changes,accounting for 74.43%,followed by feed rate(15.80%)and drill diameter(6.16%).A linear regression model demonstrates reasonable statistical sufficiency(R2=0.964),but it falls short in reflecting nonlinear interactions.Hence,an artificial neural network(ANN)model is developed to improve prediction.The multilayer feed-forward ANN with a 3-10-6-1 architecture,trained using the Levenberg-Marquardt optimization algorithm,achieves excellent predictive accuracy,with high correlation and low root-mean-square error.Model validation was achieved through independent confirmation experiments,yielding a mean absolute percentage error of only 2.27%,with all predictions falling within the permissible wear range.The findings indicate that ANN-based modeling provides a reliable framework for capturing the complex nonlinear relationships governing tool wear in GFRPC drilling and serves as a viable soft sensor for tool condition monitoring,process optimization,and sustainable,data-driven manufacturing. 展开更多
关键词 Glass fiber-reinforced polymer composites machining and drilling drill flank wear prediction artificial neural networks(ANN) soft computational modeling
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Learning Laws for Deep Convolutional Neural Networks With Guaranteed Convergence 认领 引用 被引量：1
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作者 Sitan Li Chien Chern Cheah 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期170-185,共16页
Convolutional neural networks(CNNs)have shown remarkable success across numerous tasks such as image classification,yet the theoretical understanding of their convergence remains underdeveloped compared to their empir... Convolutional neural networks(CNNs)have shown remarkable success across numerous tasks such as image classification,yet the theoretical understanding of their convergence remains underdeveloped compared to their empirical achievements.In this paper,the first filter learning framework with convergence-guaranteed learning laws for end-to-end learning of deep CNNs is proposed.Novel update laws with convergence analysis are formulated based on the mathematical representation of each layer in convolutional neural networks.The proposed learning laws enable concurrent updates of weights across all layers of the deep convolutional neural network and the analysis shows that the training errors converge to certain bounds which are dependent on the approximation errors.Case studies are conducted on benchmark datasets and the results show that the proposed concurrent filter learning framework guarantees the convergence and offers more consistent and reliable results during training with a trade-off in performance compared to stochastic gradient descent methods.This framework represents a significant step towards enhancing the reliability and effectiveness of deep convolutional neural network by developing a theoretical analysis which allows practical implementation of the learning laws with automatic tuning of the learning rate to guarantee the convergence during training. 展开更多
关键词 Convergence convolution neural networks(CNNs) end-to-end learning online learning
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