Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug devel...Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug development has become a new trend,particularly in predicting drug-target associations.To address the challenge of drug-target prediction,AI-driven models have emerged as powerful tools,offering innovative solutions by effectively extracting features from complex biological data,accurately modeling molecular interactions,and precisely predicting potential drug-target outcomes.Traditional machine learning(ML),network-based,and advanced deep learning architectures such as convolutional neural networks(CNNs),graph convolutional networks(GCNs),and transformers play a pivotal role.This review systematically compiles and evaluates AI algorithms for drug-and drug combination-target predictions,highlighting their theoretical frameworks,strengths,and limitations.CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions.GCNs provide deep insights into molecular interactions via relational data,whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences.Network-based models offer a systematic perspective by integrating diverse data sources,and traditional ML efficiently handles large datasets to improve overall predictive accuracy.Collectively,these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy.This review summarizes the application of AI in drug development,particularly in drug-target prediction,and offers recommendations on models and algorithms for researchers engaged in biomedical research.It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.展开更多
Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI pre...Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features.In this study,we proposed KG-CNNDTI,a novel knowledge graph-enhanced framework for DTI prediction,which integrates heterogeneous biological information to improve model generalizability and predictive performance.The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm,which were further enriched with contextualized sequence representations obtained from ProteinBERT.For compound representation,multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated.The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor.Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods,particularly in terms of Precision,Recall,F1-Score and area under the precision-recall curve(AUPR).Ablation analysis highlighted the substantial contribution of knowledge graph-derived features.Moreover,KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease,resulting in 40 candidate compounds.5 were supported by literature evidence,among which 3 were further validated in vitro assays.展开更多
Computational approaches for predicting drug-target interactions(DTIs)are pivotal in advancing drug discovery.Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local an...Computational approaches for predicting drug-target interactions(DTIs)are pivotal in advancing drug discovery.Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local and global network information.To comprehensively consider network information,we propose DHGT-DTI,a novel deep learning-based approach for DTI prediction.Specifically,we capture the local and global structural information of the network from both neighborhood and meta-path per-spectives.In the neighborhood perspective,we employ a heterogeneous graph neural network(HGNN),which extends Graph Sample and Aggregate(GraphSAGE)to handle diverse node and edge types,effectively learning local network structures.In the meta-path perspective,we introduce a Graph Transformer with residual connections to model higher-order relationships defined by meta-paths,such as"drug-disease-drug",and use an attention mechanism to fuse information across multiple meta-paths.The learned features from these dual perspectives are synergistically integrated for DTI prediction via a matrix decomposition method.Furthermore,DHGT-DTI reconstructs not only the DTI network but also auxiliary networks to bolster prediction accuracy.Comprehensive experiments on two benchmark datasets validate the superiority of DHGT-DTI over existing baseline methods.Additionally,case studies on six drugs used to treat Parkinson's disease not only validate the practical utility of DHGT-DTI but also highlight its broader potential in accelerating drug discovery for other diseases.展开更多
Drug-protein interaction(DPI)prediction in drug discovery and new drug design plays a key role,but the traditional in vitro experiments would incur significant temporal and financial costs,cannot smoothly advance drug...Drug-protein interaction(DPI)prediction in drug discovery and new drug design plays a key role,but the traditional in vitro experiments would incur significant temporal and financial costs,cannot smoothly advance drug-protein interaction research,so many computer prediction models have emerged,and the current commonly used is based on deep learning method.In this paper,a deep learning model Computer-based Drug-Protein Interaction CBSG_DPI is proposed to predict drug-protein interactions.This model uses the protein features extracted by the Computed Tomography CT and Bert method and the drug features extracted by the SMILES2Vec method and input into the graph convolutional neural network(GCN)to complete the prediction of drug-protein interactions.The obtained results show that the proposed model can not only predict drug-protein interactions more accurately but also train hundreds of times faster than the traditional deep learning model by abandoning the traditional grid search algorithm to find the best parameters.展开更多
As a multidisciplinary phenomenon,panel aeroelasticity in shock-dominated flow is featured by two primary interactions:Fluid-Structure Interactions(FSIs)and Shock-Boundary Layer Interactions(SBLIs).The former raises s...As a multidisciplinary phenomenon,panel aeroelasticity in shock-dominated flow is featured by two primary interactions:Fluid-Structure Interactions(FSIs)and Shock-Boundary Layer Interactions(SBLIs).The former raises structural concerns,and the latter is of aerodynamic interest.Thus,panel aeroelasticity in shock-dominated flow represents a vital topic for the development and optimization of supersonic vehicles and propulsion systems.This review systematically summarizes recent advances in the methodologies applied to capture structural and fluid dynamics,including theoretical models,numerical simulations,and wind tunnel experiments.The application of data-driven modal decomposition,an advanced technique to extract physically crucial features,on the topic is introduced.From the perspective of FSIs,the distinctive aeroelastic behaviors in shock-dominated flow,including hysteresis phenomena and nonlinear responses,are highlighted.From the perspective of SBLIs,the modifications in their spatial and temporal characteristics imposed by the aeroelastic responses are emphasized.Motivated by the interaction between the shock waves and structural response,different strategies have been proposed to implement aeroelastic suppression and shock control,which have the potential to enhance structural safety and aerodynamic performance in the next generation of high-speed flight vehicles.展开更多
This study reveals the critical role of multiscale interaction within the westerly wind bursts(WWBs)west of the MJO convection in modulating the prediction skill for the November MJO event during the DYNAMO(Dynamics o...This study reveals the critical role of multiscale interaction within the westerly wind bursts(WWBs)west of the MJO convection in modulating the prediction skill for the November MJO event during the DYNAMO(Dynamics of the Madden–Julian Oscillation)field campaign.The characteristics of the MJO convection envelope are obtained by the largescale precipitation tracking method,and a novel metric is introduced to quantify the prediction skill for the MJO convection in the ECMWF reforecast.The ECMWF forecast exhibits approximately 17 days in skillful prediction for the MJO convection—significantly lower than that derived from the global measure.The reforecast ensembles are further classified into high and low skill catalogs based on the mean prediction skill during the observed WWBs period.High-skill ensembles exhibit significantly enhanced low-level westerlies,amplified MJO convection,and reduced spatial separation between the low-level westerlies and MJO convection during the WWBs period,indicating stronger coupling between the large-scale circulation and the convection.Mechanistic analysis reveals that enhanced westerlies in high-skill ensembles can transfer more high-frequency energy to the MJO convection through the flux convergence of interaction energy for MJO convection development,resulting in better prediction skill.展开更多
Cytochrome c(cyt c)is released from mitochondria into the cytosol upon apoptotic stimulation,ultimately triggering programmed cell death.Recent studies have revealed that transfer RNA(tRNA)interacts with cyt c,impedin...Cytochrome c(cyt c)is released from mitochondria into the cytosol upon apoptotic stimulation,ultimately triggering programmed cell death.Recent studies have revealed that transfer RNA(tRNA)interacts with cyt c,impeding the formation of the apoptosome complex and thereby suppressing apoptosis.To elucidate the molecular mechanism underlying the interaction between cyt c and tRNA,nuclear magnetic resonance(NMR)-based chemical shift perturbation and intensity analysis were employed to characterize the binding interface between cyt c and tRNAphe.The findings demonstrate that cyt c primarily engages with tRNAphe through its 70–85Ω-loop and N-terminalα-helix.This interaction sterically hinders the accessibility of small molecules,such as H_(2)O_(2),to the hydrophobic pocket of cyt c,consequently attenuating its peroxidase activity.Furthermore,oxidative modification of cyt c,particularly the carbonylation of positively charged lysine residues,weakens this interaction.展开更多
Using observational and reanalysis datasets,this study explores the mechanisms by which the interactions among multi-timescale flows impacted the onset of rapid intensification(RI)of Typhoon Hato(2017).Hato(2017)forme...Using observational and reanalysis datasets,this study explores the mechanisms by which the interactions among multi-timescale flows impacted the onset of rapid intensification(RI)of Typhoon Hato(2017).Hato(2017)formed within a northwest–southeast-oriented synoptic-scale(with periods<10 days)wave train,concurring with a developing intraseasonal(10–90 days)oscillation and an elongated low-frequency(>90 days)monsoon trough in the western North Pacific.Impacted by continuously increasing vertical wind shear,the TC long maintained a highly asymmetric convective structure.Prior to RI onset,the synoptic-scale circulation and the inner-core asymmetric convection of Hato(2017)greatly strengthened,which are the key factors believed to trigger RI.A multi-timescale eddy kinetic energy budget indicates that the wind convergence associated with the intraseasonal circulation and monsoon trough led to barotropic energy conversion that largely enhanced the synoptic-scale cyclonic circulation.Besides,the pronounced increases in midlevel relative humidity(RH)and surface latent heat flux(LHF)were observed upshear before RI onset,which were primarily driven by the strong intraseasonal and synoptic-scale RH anomalies and the strengthened low-level wind speed,respectively.The increased LHF and midlevel RH,together with the enhanced downshear confluence between synoptic-scale and Intraseasonal Oscillation(ISO)/low-frequency winds,could have helped the intensification of asymmetric convection that supports RI onset.Overall,this study suggests that the interactions across multiple timescales may create favorable dynamic and thermodynamic conditions that promoted RI onset,offering new insights into RI processes for highly asymmetric tropical cyclones like Hato(2017).展开更多
Existing quantitative trait locus(QTL)mapping had low efficiency in identifying small-effect and closely linked QTL-by-environment interactions(QEIs)in recombinant inbred lines(RILs),especially in the era of global cl...Existing quantitative trait locus(QTL)mapping had low efficiency in identifying small-effect and closely linked QTL-by-environment interactions(QEIs)in recombinant inbred lines(RILs),especially in the era of global climate change.To address this challenge,here we integrate the compressed variance component mixed model with our GCIM to propose 3vGCIM for identifying QEIs in RILs,and extend 3vGCIM-random to 3vGCIM-fixed.3vGCIM integrates genome-wide scanning with machine learning,significantly improving power.In the mixed full model,we consider all possible effects and control for all possible polygenic backgrounds.In simulation studies,3vGCIM exhibits higher power(∼92.00%),higher accuracy of the estimates for QTL position(∼1.900 cM2)and effect(∼0.050),and lower false positive rate(∼0.48‰)and false negative rate(<8.10%)in three environments of 300 RILs each than ICIM(47.57%;3.607 cM2,0.583;2.81‰;52.43%)and MCIM(60.30%;5.279 cM2,0.274;2.17‰;39.70%).In the real data analysis of rice yield-related traits in 240 RILs,3vGCIM mines more known genes(57–60)and known gene-by-environment interactions(GEIs)(14–19)and candidate GEIs(21–23)than ICIM(27,2,and 7),and MCIM(21,1,and 3),especially in small-effect and linked QTLs and QEIs.This makes 3vGCIM a powerful and sensitive tool for QTL mapping and molecular QTL mapping.展开更多
The complex aerodynamic interaction between tandem tilt-wing and multi-rotor directly affects the wing surface flow and rotor thrust,making it a critical factor during the tilt transition process of this configuration...The complex aerodynamic interaction between tandem tilt-wing and multi-rotor directly affects the wing surface flow and rotor thrust,making it a critical factor during the tilt transition process of this configuration of rotorcraft.The aerodynamic interaction of tandem tilt-wing and multi-rotor is investigated based on the CFD method.The aerodynamic effect of multi tilt-rotor is simulated as virtual disk modeling by adding source terms to the Navier-Stokes equations,effectively reducing the calculation time while maintaining the accuracy of aerodynamic interaction calculations.Aerodynamic forces and flow field characteristics of the tandem tilt-wing and multi-rotor under different tilt angles are compared between cases with and without aerodynamic interaction.Furthermore,the differences in aerodynamic forces between dynamic tilt transition and fixed-angle conditions were compared.The results show that the aerodynamic interaction of multi-rotor obviously increases the lift of front tilt-wing at different tilt angles,the wing lift under interaction is increased by more than 40%compared with isolated wing at tilt angle of 15°for the computation in this paper,which is related to the increase of wing flow velocity and the suppression of flow separation caused by multi-rotor;the wing blocking effect will increase rotor thrust,especially near the tilt angles of 30°and 45°;the increases of rear wing lift and rear rotor thrust under aerodynamic interaction are not significant because of suppression by the front wing’s downwash;the unsteady effects during dynamic tilting have a relatively minor impact on aerodynamic interaction,with the aerodynamic forces on the rotors and wings during the dynamic tilting process showing little difference from those under corresponding fixed tilt angles.展开更多
Existing rehabilitation exoskeleton robots suffer from poor compatibility with the human limb coupling method,large internal power loss,and poor wearable performance,which seriously affect the rehabilitation ability o...Existing rehabilitation exoskeleton robots suffer from poor compatibility with the human limb coupling method,large internal power loss,and poor wearable performance,which seriously affect the rehabilitation ability of these robots.Therefore,this study proposes a variable stiffness humancomputer interaction contact unit module(VSHCUM)based on the granular jamming mechanism.It is characterized by a double-layer chamber structure:the inner layer is a granular chamber,and the outer layer is an air chamber.The interaction force is transmitted by embedding a rigid support in the inner layer.Unlike the common flexible-belt interactive contact unit,when the exoskeleton is bound to the patient's limb,vSHCUM can realize adaptive fitting of the patient's limb shape using the pressure change in the double-chamber structure.Simultaneously,by adjusting the vacuum level of the granular chamber,the stiffness of the interactive contact unit can be adjusted by a factor of more than five,and the internal work loss caused by self-pulling deformation during the auxiliary force transfer process can be reduced.展开更多
The antioxidant activity of selenium-containing soybean peptides(SePPs)has been previously demonstrated,despite their limited absorption in the small intestine.This study investigates the antioxidant mechanism of a se...The antioxidant activity of selenium-containing soybean peptides(SePPs)has been previously demonstrated,despite their limited absorption in the small intestine.This study investigates the antioxidant mechanism of a selenium-containing tetrapeptide,Ser-Phe-Gln-SeM(SFQSeM),identified from SePPs,with particular emphasis on its interaction with the intestinal microbiota and its role in modulating host antioxidant defenses.The effects of SFQSeM were evaluated in a D-galactose-induced oxidative stress model and an antibiotictreated mouse model.SFQSeM supplementation significantly reduced the oxidative stress in D-galactosetreated mice.It also promoted the growth of beneficial bacteria and increased the levels of acetate,butyrate and lactate in the intestine(P<0.05).In the antibiotic-treated mouse model,depletion of the intestinal microbiota significantly reduced hepatic glutathione peroxidase(GSH-Px)activity(26.6%)and glutathione peroxidase 1(GPx-1)expression(48.77%)compared to normal mice supplemented with SFQSeM(P<0.05).In contrast to Na_(2)SeO_(3)and selenomethionine,SFQSeM effectively restored the diversity of the intestinal microbiota disrupted by antibiotics.Lactobacillus,Lachnospiraceae_NK4A136_group,and Muribaculaceae were identified as predominant bacteria in the SFQSeM group,and were strongly associated with increased hepatic GSH-Px activity and GPx-1 mRNA expression(P<0.05).In conclusion,intestinal microbiota enhances the antioxidant efficacy of SFQSeM by modulating microbial composition,producing active metabolites,and converting SFQSeM into a bioactive form of selenium.展开更多
Understanding the deterioration behaviors and mechanisms of rocks under thermo-hydromechanical(THM)interactions is crucial for mitigating slope instability.In this study,the physicomechanical properties of silty mudst...Understanding the deterioration behaviors and mechanisms of rocks under thermo-hydromechanical(THM)interactions is crucial for mitigating slope instability.In this study,the physicomechanical properties of silty mudstone subjected to THM interactions were investigated by triaxial tests.The underlying micro-mechanisms were revealed using microscopic tests.The triaxial test results indicate that the strength parameters of silty mudstone decrease by 89.50%(deformation modulus),78.15%(peak strength),70.58%(cohesion),and 48.65%(friction angle)under 16 THM cycles,a load of 300 kPa,and alternating between 0℃water immersion and 60℃drying.The SEM test results indicate that the deterioration of silty mudstone strength primarily results from hydrothermal-expansion softening and cracking driven by the TLHM interactions.The specimens manifest shear failure under confining pressure exceeding 140 kPa.Furthermore,a new constitutive model considering hydrothermalexpansion strain and non-linear deformation characteristics was developed.The discrepancy between the experimentally measured peak strength and the damage constitutive model prediction remains below 5%.The proposed model is verified to be in satisfactory agreement with the experimental results.The self-designed THM apparatus overcomes the limitations of traditional investigations,enabling simultaneous consideration of thermal,hydraulic,and mechanical interactions.展开更多
Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-...Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity.On the other hand,fixed-size convolution kernels are often used,which have weak perception ability for emotional regions of different scales.Therefore,this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling.In terms of space,multi-branch large-kernel stripe convolution is used to perceive emotional region features at different scales,and attention weights are generated for each scale feature.In terms of time,multi-layer odd-even down-sampling is performed on the time series,and oddeven sub-sequence interaction is performed to solve the problem of feature similarity,while reducing computational costs due to the linear relationship between sampling and convolution overhead.This paper was tested on CMU-MOSI,CMU-MOSEI,and Hume Reaction.The Acc-2 reached 83.4%,85.2%,and 81.2%,respectively.The experimental results show that the model can significantly improve the accuracy of emotion recognition.展开更多
Recycling of waste rubber(WR)is crucial for the sustainable development of the rubber industry.The enhancement of interfacial interactions is the main strategy for waste polymer recycling.However,there is a lack of me...Recycling of waste rubber(WR)is crucial for the sustainable development of the rubber industry.The enhancement of interfacial interactions is the main strategy for waste polymer recycling.However,there is a lack of methods for enhancing the interfacial interactions for WR recycling because WR contains abundant inert C―H bonds.Herein,we designed thioctic acid inverse vulcanization copolymers to endow recycled WR with dynamic disulfide interfacial interactions,significantly improving the mechanical properties of recycled WR.These disulfide interfacial interactions among the recycled WR tend to exchange,which dramatically increases the fractocohesive length and prevents stress concentration near the crack tips.When recycled WR is subjected to external stress,the loads are redistributed across a broad region of adjacent regions instead of being concentrated on a limited length scale,which resists crack propagation.This work effectively recycled WR,providing a strategy for solvent-free reaction-derived inverse vulcanization copolymers to improve the toughness of WR recycling.展开更多
For hypersonic air-breathing vehicles,the V-shaped leading edges(VSLEs)of supersonic combustion ramjet(scramjet)inlets experience complex shock interactions and intense aerodynamic loads.This paper provides a comprehe...For hypersonic air-breathing vehicles,the V-shaped leading edges(VSLEs)of supersonic combustion ramjet(scramjet)inlets experience complex shock interactions and intense aerodynamic loads.This paper provides a comprehensive review of flow characteristics at the crotch of VSLEs,with particular focus on the transition of shock interaction types and the variation of wall heat flux under different freestream Mach numbers and geometric configurations.The mechanisms governing shock transition,unsteady oscillations,hysteresis,and three-dimensional effects in VSLE flows are first examined.Subsequently,thermal protection strategies aimed at mitigating extreme heating loads are reviewed,emphasizing their relevance to practical engineering applications.Special attention is given to recent studies addressing thermochemical nonequilibrium effects on VSLE shock interactions,and the limitations of current research are critically assessed.Finally,perspectives for future investigations into hypersonic VSLE shock interactions are outlined,highlighting opportunities for advancing design and thermal management strategies.展开更多
This study examines the dynamic response of two adjacent 9-and 20-story benchmark steel buildings subjected to six near-fault earthquake records.Two-dimensional numerical models were employed to account for the comple...This study examines the dynamic response of two adjacent 9-and 20-story benchmark steel buildings subjected to six near-fault earthquake records.Two-dimensional numerical models were employed to account for the complexities of structure-soil-structure interaction(SSSI).The research focuses on the separation gap between the buildings and the effects of pounding while considering Fixed Base(FB)and SSSI models,evaluated according to UBC 94 and ASCE 7-16 seismic codes.Key findings reveal that pounding occurs with the UBC 94 separation gap when earthquake frequency aligns with system frequency,leading to increased column stresses in the 9-story building.In contrast,the ASCE 7-16 standard effectively prevents pounding in both the FB and SSSI models.Additionally,drifts and displacements of lower floors in SSSI models exceed the allowable limits of ASCE 7-16,underscoring the impact of soil-structure interaction on seismic response.展开更多
Developing effective,versatile,and high-precision sensing interfaces remains a crucial challenge in human-machine-environment interaction applications.Despite progress in interaction-oriented sensing skins,limitations...Developing effective,versatile,and high-precision sensing interfaces remains a crucial challenge in human-machine-environment interaction applications.Despite progress in interaction-oriented sensing skins,limitations remain in unit-level reconfiguration,multiaxial force and motion sensing,and robust operation across dynamically changing or irregular surfaces.Herein,we develop a reconfigurable omnidirectional triboelectric whisker sensor array(RO-TWSA)comprising multiple sensing units that integrate a triboelectric whisker structure(TWS)with an untethered hydro-sealing vacuum sucker(UHSVS),enabling reversibly portable deployment and omnidirectional perception across diverse surfaces.Using a simple dual-triangular electrode layout paired with MXene/silicone nanocomposite dielectric layer,the sensor unit achieves precise omnidirectional force and motion sensing with a detection threshold as low as 0.024 N and an angular resolution of 5°,while the UHSVS provides reliable and reversible multi-surface anchoring for the sensor units by involving a newly designed hydrogel combining high mechanical robustness and superior water absorption.Extensive experiments demonstrate the effectiveness of RO-TWSA across various interactive scenarios,including teleoperation,tactile diagnostics,and robotic autonomous exploration.Overall,RO-TWSA presents a versatile and high-resolution tactile interface,offering new avenues for intelligent perception and interaction in complex real-world environments.展开更多
Cubic-shaped magnetic particles subjected to a dimensionless uniaxial anisotropy(Q=0.1)aligned with one of the crystallographic axes provide an ideal system for investigating magnetic equilibrium states.In this system...Cubic-shaped magnetic particles subjected to a dimensionless uniaxial anisotropy(Q=0.1)aligned with one of the crystallographic axes provide an ideal system for investigating magnetic equilibrium states.In this system,three fundamental magnetization configurations are identified:(i)the flower state,(ii)the twisted flower state,and(iii)the vortex state.This problem corresponds to standard problem No.3 proposed by the NIST Micromagnetics Modeling Group,widely adopted as a benchmark for validating computational micromagnetics methods.In this work,we approach the problem using a computational method based on direct dipolar interactions,in contrast to conventional techniques that typically compute the demagnetizing field via finite difference-based fast Fourier transform(FFT)methods,tensor grid approaches,or finite element formulations.Our results are compared with established literature data,focusing on the dimensionless parameterλ=L/l_(ex),where L is the cube edge length and l_(ex)is the exchange length of the material.To analyze equilibrium state transitions,we systematically varied the size L as a function of the simulation cell number N and intercellular spacing a,determining the criticalλvalue associated with configuration changes.Our simulations reveal that the transition between the twisted flower and vortex states occurs atλ≈8.45,consistent with values reported in the literature,validating our code(Grupo de Física da Matéeria Condensada-UFJF),and shows that this standard problem can be resolved using only interaction dipolar of a direct way without the need for sophisticated additional calculations.展开更多
Theintegration of human factors into artificial intelligence(AI)systems has emerged as a critical research frontier,particularly in reinforcement learning(RL),where human-AI interaction(HAII)presents both opportunitie...Theintegration of human factors into artificial intelligence(AI)systems has emerged as a critical research frontier,particularly in reinforcement learning(RL),where human-AI interaction(HAII)presents both opportunities and challenges.As RL continues to demonstrate remarkable success in model-free and partially observable environments,its real-world deployment increasingly requires effective collaboration with human operators and stakeholders.This article systematically examines HAII techniques in RL through both theoretical analysis and practical case studies.We establish a conceptual framework built upon three fundamental pillars of effective human-AI collaboration:computational trust modeling,system usability,and decision understandability.Our comprehensive review organizes HAII methods into five key categories:(1)learning from human feedback,including various shaping approaches;(2)learning from human demonstration through inverse RL and imitation learning;(3)shared autonomy architectures for dynamic control allocation;(4)human-in-the-loop querying strategies for active learning;and(5)explainable RL techniques for interpretable policy generation.Recent state-of-the-art works are critically reviewed,with particular emphasis on advances incorporating large language models in human-AI interaction research.To illustrate some concepts,we present three detailed case studies:an empirical trust model for farmers adopting AI-driven agricultural management systems,the implementation of ethical constraints in roboticmotion planning through human-guided RL,and an experimental investigation of human trust dynamics using a multi-armed bandit paradigm.These applications demonstrate how HAII principles can enhance RL systems’practical utility while bridging the gap between theoretical RL and real-world human-centered applications,ultimately contributing to more deployable and socially beneficial intelligent systems.展开更多
基金supported by grants from the National Natural Science Foundation of China(Grant No.:T2341008)Intelligent and Precise Research on TCM for Spleen and Stomach Diseases(20233930063).
文摘Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug development has become a new trend,particularly in predicting drug-target associations.To address the challenge of drug-target prediction,AI-driven models have emerged as powerful tools,offering innovative solutions by effectively extracting features from complex biological data,accurately modeling molecular interactions,and precisely predicting potential drug-target outcomes.Traditional machine learning(ML),network-based,and advanced deep learning architectures such as convolutional neural networks(CNNs),graph convolutional networks(GCNs),and transformers play a pivotal role.This review systematically compiles and evaluates AI algorithms for drug-and drug combination-target predictions,highlighting their theoretical frameworks,strengths,and limitations.CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions.GCNs provide deep insights into molecular interactions via relational data,whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences.Network-based models offer a systematic perspective by integrating diverse data sources,and traditional ML efficiently handles large datasets to improve overall predictive accuracy.Collectively,these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy.This review summarizes the application of AI in drug development,particularly in drug-target prediction,and offers recommendations on models and algorithms for researchers engaged in biomedical research.It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.
基金supported by the National Natural Science Foundation of China(Nos.82173746 and U23A20530)Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism(Shanghai Municipal Education Commission)。
文摘Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features.In this study,we proposed KG-CNNDTI,a novel knowledge graph-enhanced framework for DTI prediction,which integrates heterogeneous biological information to improve model generalizability and predictive performance.The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm,which were further enriched with contextualized sequence representations obtained from ProteinBERT.For compound representation,multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated.The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor.Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods,particularly in terms of Precision,Recall,F1-Score and area under the precision-recall curve(AUPR).Ablation analysis highlighted the substantial contribution of knowledge graph-derived features.Moreover,KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease,resulting in 40 candidate compounds.5 were supported by literature evidence,among which 3 were further validated in vitro assays.
基金the National Natural Science Foundation of China(Grant Nos.:62272288,U22A2041)Fundamental Research Funds for the Central Universities,Shaanxi Normal University(Grant No.:GK202302006)the Scientific Research Fund of Hunan Provincial Education Department of China(Grant No.:22B0097).
文摘Computational approaches for predicting drug-target interactions(DTIs)are pivotal in advancing drug discovery.Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local and global network information.To comprehensively consider network information,we propose DHGT-DTI,a novel deep learning-based approach for DTI prediction.Specifically,we capture the local and global structural information of the network from both neighborhood and meta-path per-spectives.In the neighborhood perspective,we employ a heterogeneous graph neural network(HGNN),which extends Graph Sample and Aggregate(GraphSAGE)to handle diverse node and edge types,effectively learning local network structures.In the meta-path perspective,we introduce a Graph Transformer with residual connections to model higher-order relationships defined by meta-paths,such as"drug-disease-drug",and use an attention mechanism to fuse information across multiple meta-paths.The learned features from these dual perspectives are synergistically integrated for DTI prediction via a matrix decomposition method.Furthermore,DHGT-DTI reconstructs not only the DTI network but also auxiliary networks to bolster prediction accuracy.Comprehensive experiments on two benchmark datasets validate the superiority of DHGT-DTI over existing baseline methods.Additionally,case studies on six drugs used to treat Parkinson's disease not only validate the practical utility of DHGT-DTI but also highlight its broader potential in accelerating drug discovery for other diseases.
文摘Drug-protein interaction(DPI)prediction in drug discovery and new drug design plays a key role,but the traditional in vitro experiments would incur significant temporal and financial costs,cannot smoothly advance drug-protein interaction research,so many computer prediction models have emerged,and the current commonly used is based on deep learning method.In this paper,a deep learning model Computer-based Drug-Protein Interaction CBSG_DPI is proposed to predict drug-protein interactions.This model uses the protein features extracted by the Computed Tomography CT and Bert method and the drug features extracted by the SMILES2Vec method and input into the graph convolutional neural network(GCN)to complete the prediction of drug-protein interactions.The obtained results show that the proposed model can not only predict drug-protein interactions more accurately but also train hundreds of times faster than the traditional deep learning model by abandoning the traditional grid search algorithm to find the best parameters.
基金supported by the National Natural Science Foundation of China(No.12372233)the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University,China(No.25GH01020005)the“111 Project”of China(No.B17037)。
文摘As a multidisciplinary phenomenon,panel aeroelasticity in shock-dominated flow is featured by two primary interactions:Fluid-Structure Interactions(FSIs)and Shock-Boundary Layer Interactions(SBLIs).The former raises structural concerns,and the latter is of aerodynamic interest.Thus,panel aeroelasticity in shock-dominated flow represents a vital topic for the development and optimization of supersonic vehicles and propulsion systems.This review systematically summarizes recent advances in the methodologies applied to capture structural and fluid dynamics,including theoretical models,numerical simulations,and wind tunnel experiments.The application of data-driven modal decomposition,an advanced technique to extract physically crucial features,on the topic is introduced.From the perspective of FSIs,the distinctive aeroelastic behaviors in shock-dominated flow,including hysteresis phenomena and nonlinear responses,are highlighted.From the perspective of SBLIs,the modifications in their spatial and temporal characteristics imposed by the aeroelastic responses are emphasized.Motivated by the interaction between the shock waves and structural response,different strategies have been proposed to implement aeroelastic suppression and shock control,which have the potential to enhance structural safety and aerodynamic performance in the next generation of high-speed flight vehicles.
基金sponsored by the National Natural Science Foundation of China(Grant Nos.U2442206,42205067,and 41922035)the National Key R&D Program of China(Grant No.2024YFC3013100)the Key Research Program of Frontier Sciences of CAS(Grant No.QYZDB-SSW-DQC017).
文摘This study reveals the critical role of multiscale interaction within the westerly wind bursts(WWBs)west of the MJO convection in modulating the prediction skill for the November MJO event during the DYNAMO(Dynamics of the Madden–Julian Oscillation)field campaign.The characteristics of the MJO convection envelope are obtained by the largescale precipitation tracking method,and a novel metric is introduced to quantify the prediction skill for the MJO convection in the ECMWF reforecast.The ECMWF forecast exhibits approximately 17 days in skillful prediction for the MJO convection—significantly lower than that derived from the global measure.The reforecast ensembles are further classified into high and low skill catalogs based on the mean prediction skill during the observed WWBs period.High-skill ensembles exhibit significantly enhanced low-level westerlies,amplified MJO convection,and reduced spatial separation between the low-level westerlies and MJO convection during the WWBs period,indicating stronger coupling between the large-scale circulation and the convection.Mechanistic analysis reveals that enhanced westerlies in high-skill ensembles can transfer more high-frequency energy to the MJO convection through the flux convergence of interaction energy for MJO convection development,resulting in better prediction skill.
基金financial support from National Key R&D Program of China(2018YFA0704002,2018YFE0202300,2023YFA1607500)National Natural Science Foundation of China(22174152,21991081,2204167,21505153,21675170,2147514621735007,and 22204167)+2 种基金Hubei Provincial Natural Science Foundation of China(2023AFA041)Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0540300)Funding of Wuhan Special Project for Knowledge Innovation(2023020201010085).
文摘Cytochrome c(cyt c)is released from mitochondria into the cytosol upon apoptotic stimulation,ultimately triggering programmed cell death.Recent studies have revealed that transfer RNA(tRNA)interacts with cyt c,impeding the formation of the apoptosome complex and thereby suppressing apoptosis.To elucidate the molecular mechanism underlying the interaction between cyt c and tRNA,nuclear magnetic resonance(NMR)-based chemical shift perturbation and intensity analysis were employed to characterize the binding interface between cyt c and tRNAphe.The findings demonstrate that cyt c primarily engages with tRNAphe through its 70–85Ω-loop and N-terminalα-helix.This interaction sterically hinders the accessibility of small molecules,such as H_(2)O_(2),to the hydrophobic pocket of cyt c,consequently attenuating its peroxidase activity.Furthermore,oxidative modification of cyt c,particularly the carbonylation of positively charged lysine residues,weakens this interaction.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFF0807000)supported by the National Natural Science Foundation of China(Grant Nos.42305004,42175073 and 42175013)supported partly by the China Postdoctoral Science Foundation(Grant No.2023M743283).
文摘Using observational and reanalysis datasets,this study explores the mechanisms by which the interactions among multi-timescale flows impacted the onset of rapid intensification(RI)of Typhoon Hato(2017).Hato(2017)formed within a northwest–southeast-oriented synoptic-scale(with periods<10 days)wave train,concurring with a developing intraseasonal(10–90 days)oscillation and an elongated low-frequency(>90 days)monsoon trough in the western North Pacific.Impacted by continuously increasing vertical wind shear,the TC long maintained a highly asymmetric convective structure.Prior to RI onset,the synoptic-scale circulation and the inner-core asymmetric convection of Hato(2017)greatly strengthened,which are the key factors believed to trigger RI.A multi-timescale eddy kinetic energy budget indicates that the wind convergence associated with the intraseasonal circulation and monsoon trough led to barotropic energy conversion that largely enhanced the synoptic-scale cyclonic circulation.Besides,the pronounced increases in midlevel relative humidity(RH)and surface latent heat flux(LHF)were observed upshear before RI onset,which were primarily driven by the strong intraseasonal and synoptic-scale RH anomalies and the strengthened low-level wind speed,respectively.The increased LHF and midlevel RH,together with the enhanced downshear confluence between synoptic-scale and Intraseasonal Oscillation(ISO)/low-frequency winds,could have helped the intensification of asymmetric convection that supports RI onset.Overall,this study suggests that the interactions across multiple timescales may create favorable dynamic and thermodynamic conditions that promoted RI onset,offering new insights into RI processes for highly asymmetric tropical cyclones like Hato(2017).
基金supported by the National Natural Science Foundation of China(32270673 and 32470657).
文摘Existing quantitative trait locus(QTL)mapping had low efficiency in identifying small-effect and closely linked QTL-by-environment interactions(QEIs)in recombinant inbred lines(RILs),especially in the era of global climate change.To address this challenge,here we integrate the compressed variance component mixed model with our GCIM to propose 3vGCIM for identifying QEIs in RILs,and extend 3vGCIM-random to 3vGCIM-fixed.3vGCIM integrates genome-wide scanning with machine learning,significantly improving power.In the mixed full model,we consider all possible effects and control for all possible polygenic backgrounds.In simulation studies,3vGCIM exhibits higher power(∼92.00%),higher accuracy of the estimates for QTL position(∼1.900 cM2)and effect(∼0.050),and lower false positive rate(∼0.48‰)and false negative rate(<8.10%)in three environments of 300 RILs each than ICIM(47.57%;3.607 cM2,0.583;2.81‰;52.43%)and MCIM(60.30%;5.279 cM2,0.274;2.17‰;39.70%).In the real data analysis of rice yield-related traits in 240 RILs,3vGCIM mines more known genes(57–60)and known gene-by-environment interactions(GEIs)(14–19)and candidate GEIs(21–23)than ICIM(27,2,and 7),and MCIM(21,1,and 3),especially in small-effect and linked QTLs and QEIs.This makes 3vGCIM a powerful and sensitive tool for QTL mapping and molecular QTL mapping.
基金supported by the National Key Laboratory of Helicopter Aeromechanics Fund(No.2024-CXPT-GF-JJ-093-05).
文摘The complex aerodynamic interaction between tandem tilt-wing and multi-rotor directly affects the wing surface flow and rotor thrust,making it a critical factor during the tilt transition process of this configuration of rotorcraft.The aerodynamic interaction of tandem tilt-wing and multi-rotor is investigated based on the CFD method.The aerodynamic effect of multi tilt-rotor is simulated as virtual disk modeling by adding source terms to the Navier-Stokes equations,effectively reducing the calculation time while maintaining the accuracy of aerodynamic interaction calculations.Aerodynamic forces and flow field characteristics of the tandem tilt-wing and multi-rotor under different tilt angles are compared between cases with and without aerodynamic interaction.Furthermore,the differences in aerodynamic forces between dynamic tilt transition and fixed-angle conditions were compared.The results show that the aerodynamic interaction of multi-rotor obviously increases the lift of front tilt-wing at different tilt angles,the wing lift under interaction is increased by more than 40%compared with isolated wing at tilt angle of 15°for the computation in this paper,which is related to the increase of wing flow velocity and the suppression of flow separation caused by multi-rotor;the wing blocking effect will increase rotor thrust,especially near the tilt angles of 30°and 45°;the increases of rear wing lift and rear rotor thrust under aerodynamic interaction are not significant because of suppression by the front wing’s downwash;the unsteady effects during dynamic tilting have a relatively minor impact on aerodynamic interaction,with the aerodynamic forces on the rotors and wings during the dynamic tilting process showing little difference from those under corresponding fixed tilt angles.
基金Supported by National Key R&D Program of China(Grant Nos.2022YFC3601704,2023YFB4706100)National Natural Science Foundation of China(Grant Nos.U23A20338,62203149).
文摘Existing rehabilitation exoskeleton robots suffer from poor compatibility with the human limb coupling method,large internal power loss,and poor wearable performance,which seriously affect the rehabilitation ability of these robots.Therefore,this study proposes a variable stiffness humancomputer interaction contact unit module(VSHCUM)based on the granular jamming mechanism.It is characterized by a double-layer chamber structure:the inner layer is a granular chamber,and the outer layer is an air chamber.The interaction force is transmitted by embedding a rigid support in the inner layer.Unlike the common flexible-belt interactive contact unit,when the exoskeleton is bound to the patient's limb,vSHCUM can realize adaptive fitting of the patient's limb shape using the pressure change in the double-chamber structure.Simultaneously,by adjusting the vacuum level of the granular chamber,the stiffness of the interactive contact unit can be adjusted by a factor of more than five,and the internal work loss caused by self-pulling deformation during the auxiliary force transfer process can be reduced.
基金Financial support from the National Natural Science Foundation of China(32502106)One health Interdisciplinary Research Project,Institute of One Health Science,Ningbo University(NBUOH202502)the Ningbo Top Talent Project(215-432094250).
文摘The antioxidant activity of selenium-containing soybean peptides(SePPs)has been previously demonstrated,despite their limited absorption in the small intestine.This study investigates the antioxidant mechanism of a selenium-containing tetrapeptide,Ser-Phe-Gln-SeM(SFQSeM),identified from SePPs,with particular emphasis on its interaction with the intestinal microbiota and its role in modulating host antioxidant defenses.The effects of SFQSeM were evaluated in a D-galactose-induced oxidative stress model and an antibiotictreated mouse model.SFQSeM supplementation significantly reduced the oxidative stress in D-galactosetreated mice.It also promoted the growth of beneficial bacteria and increased the levels of acetate,butyrate and lactate in the intestine(P<0.05).In the antibiotic-treated mouse model,depletion of the intestinal microbiota significantly reduced hepatic glutathione peroxidase(GSH-Px)activity(26.6%)and glutathione peroxidase 1(GPx-1)expression(48.77%)compared to normal mice supplemented with SFQSeM(P<0.05).In contrast to Na_(2)SeO_(3)and selenomethionine,SFQSeM effectively restored the diversity of the intestinal microbiota disrupted by antibiotics.Lactobacillus,Lachnospiraceae_NK4A136_group,and Muribaculaceae were identified as predominant bacteria in the SFQSeM group,and were strongly associated with increased hepatic GSH-Px activity and GPx-1 mRNA expression(P<0.05).In conclusion,intestinal microbiota enhances the antioxidant efficacy of SFQSeM by modulating microbial composition,producing active metabolites,and converting SFQSeM into a bioactive form of selenium.
基金supported by“the National Natural Science Foundation of China(52378440,52078067,52078066,42477143,52408458)the Key Science and Technology Program in the Transportation Industry(2022-MS1-032,2022-MS5-125)+4 种基金the Natural Science Foundation of Hunan Province(2023JJ10045)the Outstanding Innovative Youth Training Program of Changsha City(kq2305023)Scientific Research Foundation of Hunan Provincial Education Department(24B0292)Water Resources Science and Technology Project of Hunan Province(XSKJ2023059-41)the Guangxi Key Research and Development Program(AB23075184)。
文摘Understanding the deterioration behaviors and mechanisms of rocks under thermo-hydromechanical(THM)interactions is crucial for mitigating slope instability.In this study,the physicomechanical properties of silty mudstone subjected to THM interactions were investigated by triaxial tests.The underlying micro-mechanisms were revealed using microscopic tests.The triaxial test results indicate that the strength parameters of silty mudstone decrease by 89.50%(deformation modulus),78.15%(peak strength),70.58%(cohesion),and 48.65%(friction angle)under 16 THM cycles,a load of 300 kPa,and alternating between 0℃water immersion and 60℃drying.The SEM test results indicate that the deterioration of silty mudstone strength primarily results from hydrothermal-expansion softening and cracking driven by the TLHM interactions.The specimens manifest shear failure under confining pressure exceeding 140 kPa.Furthermore,a new constitutive model considering hydrothermalexpansion strain and non-linear deformation characteristics was developed.The discrepancy between the experimentally measured peak strength and the damage constitutive model prediction remains below 5%.The proposed model is verified to be in satisfactory agreement with the experimental results.The self-designed THM apparatus overcomes the limitations of traditional investigations,enabling simultaneous consideration of thermal,hydraulic,and mechanical interactions.
基金supported,in part,by the National Nature Science Foundation of China under Grant 62272236,62376128in part,by the Natural Science Foundation of Jiangsu Province under Grant BK20201136,BK20191401.
文摘Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity.On the other hand,fixed-size convolution kernels are often used,which have weak perception ability for emotional regions of different scales.Therefore,this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling.In terms of space,multi-branch large-kernel stripe convolution is used to perceive emotional region features at different scales,and attention weights are generated for each scale feature.In terms of time,multi-layer odd-even down-sampling is performed on the time series,and oddeven sub-sequence interaction is performed to solve the problem of feature similarity,while reducing computational costs due to the linear relationship between sampling and convolution overhead.This paper was tested on CMU-MOSI,CMU-MOSEI,and Hume Reaction.The Acc-2 reached 83.4%,85.2%,and 81.2%,respectively.The experimental results show that the model can significantly improve the accuracy of emotion recognition.
基金financially supported by the National Natural Science Foundation of China(No.52363007)。
文摘Recycling of waste rubber(WR)is crucial for the sustainable development of the rubber industry.The enhancement of interfacial interactions is the main strategy for waste polymer recycling.However,there is a lack of methods for enhancing the interfacial interactions for WR recycling because WR contains abundant inert C―H bonds.Herein,we designed thioctic acid inverse vulcanization copolymers to endow recycled WR with dynamic disulfide interfacial interactions,significantly improving the mechanical properties of recycled WR.These disulfide interfacial interactions among the recycled WR tend to exchange,which dramatically increases the fractocohesive length and prevents stress concentration near the crack tips.When recycled WR is subjected to external stress,the loads are redistributed across a broad region of adjacent regions instead of being concentrated on a limited length scale,which resists crack propagation.This work effectively recycled WR,providing a strategy for solvent-free reaction-derived inverse vulcanization copolymers to improve the toughness of WR recycling.
基金funded by the Research Fund of National Key Laboratory of Aerospace Physics in Fluids,grant number 2024-APF-KFZD-01Guangdong Basic and Applied Basic Research Foundation,grant number 2025A1515012081+1 种基金National Natural Science Foundation of China,grant number 12002193Shandong Provincial Natural Science Foundation,China,grant number ZR2019QA018.
文摘For hypersonic air-breathing vehicles,the V-shaped leading edges(VSLEs)of supersonic combustion ramjet(scramjet)inlets experience complex shock interactions and intense aerodynamic loads.This paper provides a comprehensive review of flow characteristics at the crotch of VSLEs,with particular focus on the transition of shock interaction types and the variation of wall heat flux under different freestream Mach numbers and geometric configurations.The mechanisms governing shock transition,unsteady oscillations,hysteresis,and three-dimensional effects in VSLE flows are first examined.Subsequently,thermal protection strategies aimed at mitigating extreme heating loads are reviewed,emphasizing their relevance to practical engineering applications.Special attention is given to recent studies addressing thermochemical nonequilibrium effects on VSLE shock interactions,and the limitations of current research are critically assessed.Finally,perspectives for future investigations into hypersonic VSLE shock interactions are outlined,highlighting opportunities for advancing design and thermal management strategies.
文摘This study examines the dynamic response of two adjacent 9-and 20-story benchmark steel buildings subjected to six near-fault earthquake records.Two-dimensional numerical models were employed to account for the complexities of structure-soil-structure interaction(SSSI).The research focuses on the separation gap between the buildings and the effects of pounding while considering Fixed Base(FB)and SSSI models,evaluated according to UBC 94 and ASCE 7-16 seismic codes.Key findings reveal that pounding occurs with the UBC 94 separation gap when earthquake frequency aligns with system frequency,leading to increased column stresses in the 9-story building.In contrast,the ASCE 7-16 standard effectively prevents pounding in both the FB and SSSI models.Additionally,drifts and displacements of lower floors in SSSI models exceed the allowable limits of ASCE 7-16,underscoring the impact of soil-structure interaction on seismic response.
基金supported by the National Natural Science Foundation of China(General Program)under Grant 52571385National Key R&D Program of China(Grant No.2024YFC2815000 and No.2024YFB3816000)+12 种基金Open Fund of State Key Laboratory of Deep-sea Manned Vehicles(Grant No.2025SKLDMV07)Shenzhen Science and Technology Program(WDZC20231128114452001,JCYJ20240813112107010 and JCYJ20240813111910014)the Tsinghua SIGS Scientific Research Startup Fund(QD2022021C)the Dreams Foundation of Jianghuai Advance Technology Center(2023-ZM 01 Z006)the Ocean Decade International Cooperation Center(ODCC)(GHZZ3702840002024020000026)Shenzhen Key Laboratory of Advanced Technology for Marine Ecology(ZDSYS20230626091459009)Shenzhen Science and Technology Program(No.KJZD20240903100905008)the National Natural Science Foundation of China(No.22305141)Pearl River Talent Program(No.2023QN10C114)General Program of Guangdong Province(No.2025A1515011700)the Guangdong Innovative and Entrepreneurial Research Team Program(2023ZT10C040)Scientific Research Foundation from Shenzhen Finance Bureau(No.GJHZ20240218113600002)Tsinghua University(JC2023001).
文摘Developing effective,versatile,and high-precision sensing interfaces remains a crucial challenge in human-machine-environment interaction applications.Despite progress in interaction-oriented sensing skins,limitations remain in unit-level reconfiguration,multiaxial force and motion sensing,and robust operation across dynamically changing or irregular surfaces.Herein,we develop a reconfigurable omnidirectional triboelectric whisker sensor array(RO-TWSA)comprising multiple sensing units that integrate a triboelectric whisker structure(TWS)with an untethered hydro-sealing vacuum sucker(UHSVS),enabling reversibly portable deployment and omnidirectional perception across diverse surfaces.Using a simple dual-triangular electrode layout paired with MXene/silicone nanocomposite dielectric layer,the sensor unit achieves precise omnidirectional force and motion sensing with a detection threshold as low as 0.024 N and an angular resolution of 5°,while the UHSVS provides reliable and reversible multi-surface anchoring for the sensor units by involving a newly designed hydrogel combining high mechanical robustness and superior water absorption.Extensive experiments demonstrate the effectiveness of RO-TWSA across various interactive scenarios,including teleoperation,tactile diagnostics,and robotic autonomous exploration.Overall,RO-TWSA presents a versatile and high-resolution tactile interface,offering new avenues for intelligent perception and interaction in complex real-world environments.
基金CAPES,CNPq,and FAPEMIG(Brazilian Agencies)for their financial support。
文摘Cubic-shaped magnetic particles subjected to a dimensionless uniaxial anisotropy(Q=0.1)aligned with one of the crystallographic axes provide an ideal system for investigating magnetic equilibrium states.In this system,three fundamental magnetization configurations are identified:(i)the flower state,(ii)the twisted flower state,and(iii)the vortex state.This problem corresponds to standard problem No.3 proposed by the NIST Micromagnetics Modeling Group,widely adopted as a benchmark for validating computational micromagnetics methods.In this work,we approach the problem using a computational method based on direct dipolar interactions,in contrast to conventional techniques that typically compute the demagnetizing field via finite difference-based fast Fourier transform(FFT)methods,tensor grid approaches,or finite element formulations.Our results are compared with established literature data,focusing on the dimensionless parameterλ=L/l_(ex),where L is the cube edge length and l_(ex)is the exchange length of the material.To analyze equilibrium state transitions,we systematically varied the size L as a function of the simulation cell number N and intercellular spacing a,determining the criticalλvalue associated with configuration changes.Our simulations reveal that the transition between the twisted flower and vortex states occurs atλ≈8.45,consistent with values reported in the literature,validating our code(Grupo de Física da Matéeria Condensada-UFJF),and shows that this standard problem can be resolved using only interaction dipolar of a direct way without the need for sophisticated additional calculations.
基金funded by the U.S.Department of Education under Grant Number ED#P116S210005the National Science Foundation under Grant Numbers 2226936 and 2420405.
文摘Theintegration of human factors into artificial intelligence(AI)systems has emerged as a critical research frontier,particularly in reinforcement learning(RL),where human-AI interaction(HAII)presents both opportunities and challenges.As RL continues to demonstrate remarkable success in model-free and partially observable environments,its real-world deployment increasingly requires effective collaboration with human operators and stakeholders.This article systematically examines HAII techniques in RL through both theoretical analysis and practical case studies.We establish a conceptual framework built upon three fundamental pillars of effective human-AI collaboration:computational trust modeling,system usability,and decision understandability.Our comprehensive review organizes HAII methods into five key categories:(1)learning from human feedback,including various shaping approaches;(2)learning from human demonstration through inverse RL and imitation learning;(3)shared autonomy architectures for dynamic control allocation;(4)human-in-the-loop querying strategies for active learning;and(5)explainable RL techniques for interpretable policy generation.Recent state-of-the-art works are critically reviewed,with particular emphasis on advances incorporating large language models in human-AI interaction research.To illustrate some concepts,we present three detailed case studies:an empirical trust model for farmers adopting AI-driven agricultural management systems,the implementation of ethical constraints in roboticmotion planning through human-guided RL,and an experimental investigation of human trust dynamics using a multi-armed bandit paradigm.These applications demonstrate how HAII principles can enhance RL systems’practical utility while bridging the gap between theoretical RL and real-world human-centered applications,ultimately contributing to more deployable and socially beneficial intelligent systems.
