Metal organic framework(MOF) assembled with coordination bonds has the disadvantage of poor stability that limits its application in the field of stationary phase,while covalent organic framework(COF)assembled through...Metal organic framework(MOF) assembled with coordination bonds has the disadvantage of poor stability that limits its application in the field of stationary phase,while covalent organic framework(COF)assembled through covalent bonds exhibits excellent structural stability.It has been shown that the stationary phases prepared by combining MOF and COF can make up for the poor stability of MOF@SiO_(2),and the MOF/COF composites have superior chromatographic separation performance.However,the traditional methods for preparing COF/MOF based stationary phases are generally solvent thermal synthesis.In this study,a green and low-cost synthesis method was proposed for the preparation of MOF/COF@SiO_(2) stationary phase.Firstly,COF@SiO_(2) was prepared in a choline chloride/ethylene glycol based deep eutectic solvent(DES).Secondly,another acid-base tunable DES prepared by mixing p-toluenesulfonic acid(PTSA)and 2-methylimidazole in different proportions was introduced as the reaction solvent and reactant for rapid synthesis of MOF/COF@SiO_(2).Compared with the toxic transition metal-based MOFs selected in most previous studies,a lightweight and non-toxic S-zone metal(calcium) based MOF was employed in this study.PTSA and calcium will form the calcium/oxygen-containing organic acid framework in acidic DES,which assembles with terephthalic acid dissolved in basic DES to form MOF.The strong hydrogen bonding effect of DES can facilitate rapid assembly of Ca-MOF.The obtained Ca-MOF/COF@SiO_(2) can be used for multi-mode chromatography to efficiently separate multiple isomeric/hydrophilic/hydrophobic analytes.The synthesis method of Ca-MOF/COF@SiO_(2) is green and mild,especially the use of acid-base tunable DES promotes the rapid synthesis of non-toxic Ca-MOF/COF@silica composites,which offers an innovative approach of greenly synthesizing novel MOF/COF stationary phases and extends their applications in the field of chromatography.展开更多
In multi-modal emotion recognition,excessive reliance on historical context often impedes the detection of emotional shifts,while modality heterogeneity and unimodal noise limit recognition performance.Existing method...In multi-modal emotion recognition,excessive reliance on historical context often impedes the detection of emotional shifts,while modality heterogeneity and unimodal noise limit recognition performance.Existing methods struggle to dynamically adjust cross-modal complementary strength to optimize fusion quality and lack effective mechanisms to model the dynamic evolution of emotions.To address these issues,we propose a multi-level dynamic gating and emotion transfer framework for multi-modal emotion recognition.A dynamic gating mechanism is applied across unimodal encoding,cross-modal alignment,and emotion transfer modeling,substantially improving noise robustness and feature alignment.First,we construct a unimodal encoder based on gated recurrent units and feature-selection gating to suppress intra-modal noise and enhance contextual representation.Second,we design a gated-attention crossmodal encoder that dynamically calibrates the complementary contributions of visual and audio modalities to the dominant textual features and eliminates redundant information.Finally,we introduce a gated enhanced emotion transfer module that explicitly models the temporal dependence of emotional evolution in dialogues via transfer gating and optimizes continuity modeling with a comparative learning loss.Experimental results demonstrate that the proposed method outperforms state-of-the-art models on the public MELD and IEMOCAP datasets.展开更多
Carbon dots(CDs)-based composites have shown impressive performance in fields of information encryption and sensing,however,a great challenge is to simultaneously implement multi-mode luminescence and room-temperature...Carbon dots(CDs)-based composites have shown impressive performance in fields of information encryption and sensing,however,a great challenge is to simultaneously implement multi-mode luminescence and room-temperature phosphorescence(RTP)detection in single system due to the formidable synthesis.Herein,a multifunctional composite of Eu&CDs@p RHO has been designed by co-assembly strategy and prepared via a facile calcination and impregnation treatment.Eu&CDs@p RHO exhibits intense fluorescence(FL)and RTP coming from two individual luminous centers,Eu3+in the free pores and CDs in the interrupted structure of RHO zeolite.Unique four-mode color outputs including pink(Eu^(3+),ex.254 nm),light violet(CDs,ex.365 nm),blue(CDs,254 nm off),and green(CDs,365 nm off)could be realized,on the basis of it,a preliminary application of advanced information encoding has been demonstrated.Given the free pores of matrix and stable RTP in water of confined CDs,a visual RTP detection of Fe^(3+)ions is achieved with the detection limit as low as 9.8μmol/L.This work has opened up a new perspective for the strategic amalgamation of luminous vips with porous zeolite to construct the advanced functional materials.展开更多
Prostate cancer(PCa)is characterized by high incidence and propensity for easy metastasis,presenting significant challenges in clinical diagnosis and treatment.Tumor microenvironment(TME)-responsive nanomaterials prov...Prostate cancer(PCa)is characterized by high incidence and propensity for easy metastasis,presenting significant challenges in clinical diagnosis and treatment.Tumor microenvironment(TME)-responsive nanomaterials provide a promising prospect for imaging-guided precision therapy.Considering that tumor-derived alkaline phosphatase(ALP)is over-expressed in metastatic PCa,it makes a great chance to develop a theranostics system with ALP responsive in the TME.Herein,an ALP-responsive aggregationinduced emission luminogens(AIEgens)nanoprobe AMNF self-assembly was designed for enhancing the diagnosis and treatment of metastatic PCa.The nanoprobe exhibited self-aggregation in the presence of ALP resulted in aggregation-induced fluorescence,and enhanced accumulation and prolonged retention period at the tumor site.In terms of detection,the fluorescence(FL)/computed tomography(CT)/magnetic resonance(MR)multi-mode imaging effect of nanoprobe was significantly improved post-aggregation,enabling precise diagnosis through the amalgamation of multiple imaging modes.Enhanced CT/MR imaging can achieve assist preoperative tumor diagnosis,and enhanced FL imaging technology can achieve“intraoperative visual navigation”,showing its potential application value in clinical tumor detection and surgical guidance.In terms of treatment,AMNF showed strong absorption in the near infrared region after aggregation,which improved the photothermal treatment effect.Overall,our work developed an effective aggregation-enhanced theranostic strategy for ALP-related cancers.展开更多
Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power li...Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power line communication(DC-PLC)enables real-time data transmission on DC power lines.With traffic adaptation,DC-PLC can be integrated with other complementary media such as 5G to reduce transmission delay and improve reliability.However,traffic adaptation for DC-PLC and 5G integration still faces the challenges such as coupling between traffic admission control and traffic partition,dimensionality curse,and the ignorance of extreme event occurrence.To address these challenges,we propose a deep reinforcement learning(DRL)-based delay sensitive and reliable traffic adaptation algorithm(DSRTA)to minimize the total queuing delay under the constraints of traffic admission control,queuing delay,and extreme events occurrence probability.DSRTA jointly optimizes traffic admission control and traffic partition,and enables learning-based intelligent traffic adaptation.The long-term constraints are incorporated into both state and bound of drift-pluspenalty to achieve delay awareness and enforce reliability guarantee.Simulation results show that DSRTA has lower queuing delay and more reliable quality of service(QoS)guarantee than other state-of-the-art algorithms.展开更多
Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frame...Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.展开更多
Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,...Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility,adhesion,self-healing,and environmental robustness with excellent sensing metrics.Herein,we report a multifunctional,anti-freezing,selfadhesive,and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes(CoN CNT)embedded in a polyvinyl alcohol-gelatin(PVA/GLE)matrix.Fabricated using a binary solvent system of water and ethylene glycol(EG),the CoN CNT/PVA/GLE organogel exhibits excellent flexibility,biocompatibility,and temperature tolerance with remarkable environmental stability.Electrochemical impedance spectroscopy confirms near-stable performance across a broad humidity range(40%-95%RH).Freeze-tolerant conductivity under sub-zero conditions(-20℃)is attributed to the synergistic role of CoN CNT and EG,preserving mobility and network integrity.The Co N CNT/PVA/GLE organogel sensor exhibits high sensitivity of 5.75 k Pa^(-1)in the detection range from 0 to 20 k Pa,ideal for subtle biomechanical motion detection.A smart human-machine interface for English letter recognition using deep learning achieved 98%accuracy.The organogel sensor utility was extended to detect human gestures like finger bending,wrist motion,and throat vibration during speech.展开更多
Background Computed tomography(CT) and cone-beam computed tomography(CBCT) image registration play pivotal roles in computer-assisted navigation for orthopedic surgery. Traditional methods often apply uniform deformat...Background Computed tomography(CT) and cone-beam computed tomography(CBCT) image registration play pivotal roles in computer-assisted navigation for orthopedic surgery. Traditional methods often apply uniform deformation models, neglecting the biomechanical differences between rigid structures and soft tissues, which compromises registration accuracy, especially during significant bone displacements. Method To address this issue, we introduce RE-Reg, a rigid-elastic CT-CBCT image registration framework that jointly learns rigid bone motion and soft tissue deformation. RE-Reg incorporates a rigid alignment(RA) module to estimate global bone motion and an elastic deformation(ED) module to model soft tissue deformation, preserving bony structures through bone shape preservation(BSP) loss. Result Our comprehensive evaluation on publicly available datasets demonstrates that RE-Reg significantly outperforms existing methods in terms of registration accuracy and rigid bone structure preservation, achieving a 1.3% improvement in Dice similarity coefficient(DSC) and a 23% reduction in rigid bone deformation(%Δvol) compared with the best baseline. Conclusion This framework not only enhances anatomical fidelity but also ensures biomechanical plausibility and provides a valuable tool for image-guided orthopedic surgery. This code is available athttps://github.com/Zq-Huang/RE-Reg.展开更多
In order to optimize the reaming process of the type IV composite hydrogen storage cylinder,the netting theory was employed for the design of stacking sequences,and the thickness in the head section was predicted.A fi...In order to optimize the reaming process of the type IV composite hydrogen storage cylinder,the netting theory was employed for the design of stacking sequences,and the thickness in the head section was predicted.A finite element model of the plastic-lined composite hydrogen storage cylinder,designed to withstand a working pressure of 70.0 MPa,was established by using the wound composite modeler(WCM)in the Abaqus software to analyze the forces acting on the winding layer.The Hashin failure criterion was utilized as the standard for assessing composite failure,and a progressive failure analysis of the cylinder was conducted to predict both the bursting pressure and the failure location of the composite hydrogen storage cylinder.The results indicate that the reaming process can effectively reduce the maximum filament winding thickness in the head section and promote a more uniform transition.At the bursting pressure,the stress within the head liner decreases,thereby enhancing the ultimate bearing capacity of the cylinder.A control system for a four-axis winding machine was designed by utilizing an industrial computer and a programmable multi-axis controller(PMAC).The winding line pattern is designed and the G-code trajectory is generated by the industrial computer.The numerical control system,composed of the PMAC and servo motor,executes the four-axis interpolation motion.展开更多
Next-generation fire safety systems demand precise detection and motion recognition of flames.In-sensor computing,which integrates sensing,memory,and processing capabilities,has emerged as a key technology in flame de...Next-generation fire safety systems demand precise detection and motion recognition of flames.In-sensor computing,which integrates sensing,memory,and processing capabilities,has emerged as a key technology in flame detection.However,the implementation of hardware-level functional demonstrations based on artificial vision systems in the solar-blind ultraviolet(UV)band(200-280 nm)is hindered by the weak detection capability.Here,we propose Ga_(2)O_(3)/In_(2)Se_(3) heterojunctions for the ferroelectric(abbreviation:Fe)optoelectronic sensor(abbreviation:OES)array(5×5 pixels),which is capable of ultraweak UV light detection with an ultrahigh detectivity through ferroelectric regulation and features in configurable multimode functionality.The Fe-OES array can directly sense different flame motions and simulate the non-spiking gradient neurons of insect visual system.Moreover,the flame signal can be effectively amplified in combination with leaky integration-and-fire neuron hardware.Using this Fe-OES system and neuromorphic hardware,we successfully demonstrate three flame processing tasks:achieving efficient flame detection across all time periods with terminal and cloud-based alarms;flame motion recognition with a lightweight convolutional neural network achieving 96.47%accuracy;and flame light recognition with 90.51%accuracy by means of a photosensitive artificial neural system.This work provides effective tools and approaches for addressing a variety of complex flame detection tasks.展开更多
Soft-tissue motion introduces significant challenges in robotic teleoperation,especially in medical scenarios where precise target tracking is critical.Latency across sensing,computation,and actuation chains leads to ...Soft-tissue motion introduces significant challenges in robotic teleoperation,especially in medical scenarios where precise target tracking is critical.Latency across sensing,computation,and actuation chains leads to degraded tracking performance,particularly around high-acceleration segments and trajectory inflection points.This study investigates machine learning-based predictive compensation for latency mitigation in soft-tissue tracking.Three models—autoregressive(AR),long short-term memory(LSTM),and temporal convolutional network(TCN)—were implemented and evaluated on both synthetic and real datasets.By aligning the prediction horizon with the end-to-end system delay,we demonstrate that prediction-based compensation significantly reduces tracking errors.Among the models,TCN achieved superior robustness and accuracy on complex motion patterns,particularly in multi-step prediction tasks,and exhibited better latency–horizon compatibility.The results suggest that TCN is a promising candidate for real-time latency compensation in teleoperated robotic systems involving dynamic soft-tissue interaction.展开更多
[Objective]This study aims to investigate the multi-body hydrodynamic interaction mechanisms during offshore lifting operations of aquaculture net cages in wind-fishery integration systems.By integrating numerical sim...[Objective]This study aims to investigate the multi-body hydrodynamic interaction mechanisms during offshore lifting operations of aquaculture net cages in wind-fishery integration systems.By integrating numerical simulations and dynamic analysis methods,this study systematically investigates the coupled dynamic response characteristics during the cage-carrier vessel separation process to reveal its dynamic evolution patterns and key influence mechanisms.[Method]Based on potential flow theory,a fully coupled dynamic analysis model of crane vessel-net cage-semi-submersible barge was established for a marine ranch project in Guangdong.The complete lifting process was dynamically simulated using SESAM software.Five typical operating sea states were configured to investigate the influence of wave parameters on the system's motion response under combined wave-current-wind actions.[Result]The results demonstrate that wave period dominates the system stability.Under short-period conditions,the system maintains stable motion with relatively small horizontal relative displacements,while long-period conditions excite low-frequency resonance,leading to significant slow-drift motions.Vertical response analysis reveals that long-period waves cause severe relative displacement fluctuations between the cage and semi-submersible vessel,with actual displacement amplitudes doubling the preset safety target of 2.045 m.Quantitative analysis further indicates that when significant wave height increases from 1.0 m to 1.5 m,the actual displacement amplitude increases by approximately 20%relative to the target displacement of 2.045 m,demonstrating that its influence is significantly weaker than the displacement variations induced by wave period changes.The complete dynamic simulation successfully captures the continuous dynamic response characteristics during the lifting process.[Conclusion]This research clarifies the influence mechanisms of wave parameters on the cage lifting process,identifying wave period as the crucial factor for operational safety.An operation window assessment method incorporating multi-body coupling effects is established,proposing a safety criterion with peak period not exceeding six seconds as the core requirement.The findings provide theoretical foundation for safe installation of marine ranch net cages and offer valuable references for similar offshore lifting operations.展开更多
In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we devel...In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics.展开更多
The widely distributed loess deposits in the Yellow River Basin exhibit unique engineering geological characteristics.The variations in their thickness and stratigraphic structure significantly amplify ground motion p...The widely distributed loess deposits in the Yellow River Basin exhibit unique engineering geological characteristics.The variations in their thickness and stratigraphic structure significantly amplify ground motion parameters,directly influencing the regional seismic hazard risk level.This study methodically conducted on-site studies and observations of building collapses and damages resulting from seismic amplification effects,using the Wenchuan M_(S)8.0 earthquake as a case study.Comprehensive experimental and numerical simulation studies were carried out.A large-scale shaking table test was performed,and numerical models for 14 different loess sites types were established.Various types of seismic waves were incorporated into these models for systematic numerical simulation calculations.The research reveals the mechanisms by which loess deposit thickness and stratigraphic structure in the Yellow River Basin affect seismic ground motion amplification.The results indicate that as the epicentral distance increases,the peak ground motion shows a marked attenuation trend,with the horizontal component attenuating substantially faster than the vertical component.As the overlying loess layer thickness increases from 50 to 100 m,the seismic intensity may escalate by 3−4 degrees,and the peak acceleration may amplify by 1.5−2.2 times.With the augmentation of loess deposit thickness and the proliferation of soil layers,both the peak acceleration response spectrum and the characteristic period demonstrate an upward tendency,exhibiting slight fluctuations contingent upon the seismic wave type.展开更多
The research findings on the ground motion and liquefaction potential analyses during the 2018 Great Indonesia Earthquake(M_(w)7.5)are significant and crucial.The earthquake triggered soil-structure damage due to liqu...The research findings on the ground motion and liquefaction potential analyses during the 2018 Great Indonesia Earthquake(M_(w)7.5)are significant and crucial.The earthquake triggered soil-structure damage due to liquefaction.This study,which thoroughly investigated four sites at Palu,was conducted by performing a comprehensive ground motion parameter analysis.The ground motion characteristics were presented and justified,particularly for the most impacted direction.Ground motion predictions were analysed to define the spectral accelerations,and matching spectral accelerations were conducted to produce ground motions for each site.Non-linear seismic ground response analysis based on the hyperbolic model of pressure pressure-dependent was performed to investigate cyclic soil behaviour.The results revealed that ground motion is crucial in significant soil damage,and the earthquake energy could trigger deep liquefaction.As the most significant ground motion,the vertical ground motion is essential in determining deep liquefaction.The discussion on the impact of liquefaction based on the results of the numerical analysis is presented.Significant ground motion with a longer duration could have a substantial impact on deep liquefaction in the study area.These findings depict how the 2018 Indonesia Earthquake(M_(w)7.5)triggered a mega-liquefaction in Palu City.The results could enhance the understanding of the importance of seismic hazard assessment.It is recommended that site investigation and soil improvement should be planned to counteract liquefaction damage before construction.This study also suggests conducting seismic hazard assessments for city development to minimise the potential disaster impact in the study area.展开更多
Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the clou...Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the cloud and inference can be obtained on real-world data.In most applications,it is important to compress the vision data due to the enormous bandwidth and memory requirements.Video codecs exploit spatial and temporal correlations to achieve high compression ratios,but they are computationally expensive.This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos.However,contrary to the normal practice of reconstructing the full-resolution frames through motion compensation,this work proposes to infer the class label from the block-based computed motion fields directly.Motion fields are a richer and more complex representation of motion vectors,where each motion vector carries the magnitude and direction information.This approach has two advantages:the cost of motion compensation and video decoding is avoided,and the dimensions of the input signal are highly reduced.This results in a shallower network for classification.The neural network can be trained using motion vectors in two ways:complex representations and magnitude-direction pairs.The proposed work trains a convolutional neural network on the direction and magnitude tensors of the motion fields.Our experimental results show 20×faster convergence during training,reduced overfitting,and accelerated inference on a hand gesture recognition dataset compared to full-resolution and downsampled frames.We validate the proposed methodology on the HGds dataset,achieving a testing accuracy of 99.21%,on the HMDB51 dataset,achieving 82.54%accuracy,and on the UCF101 dataset,achieving 97.13%accuracy,outperforming state-of-the-art methods in computational efficiency.展开更多
The soft actuator is characterized by high safety,flexibility,and adaptability.It is capable of both active and passive defor-mations.This paper presents a discrete degree of freedom(DOF)method for soft actuators to r...The soft actuator is characterized by high safety,flexibility,and adaptability.It is capable of both active and passive defor-mations.This paper presents a discrete degree of freedom(DOF)method for soft actuators to reveal DOF characteristics.The method draws on the superposition mechanism of the deformation characteristics of the sarcomere in the skeletal muscles of living organisms.Firstly,the multi-DOF deformation characteristics of the soft actuator are discretized into superimposed combinations of single-DOF micro-units.Then,the soft actuator was determined to contain deformation characteristics such as extension-contraction,bending,and twisting.Eighteen types of micro-units with basic deforma-tion characteristics were obtained depending on the axis and orientation.Further,the mapping relationship between the combination of micro-units and the motion characteristics of the soft actuator based on the GF set theory was established.Finally,an active-passive DOF co-structured soft actuator(APCSA)was developed.The graphical approach analyzes the experimental results,and it can be concluded that active and passive DOFs can coexist in the composite deformation of the soft actuator.展开更多
This study presents an effective hybrid simulation approach for simulating broadband ground motion in complex near-fault locations.The approach utilizes a deterministic approach based on the spectral element method(SE...This study presents an effective hybrid simulation approach for simulating broadband ground motion in complex near-fault locations.The approach utilizes a deterministic approach based on the spectral element method(SEM),which is used to simulate low-frequency ground motion(f<1 Hz)by incorporating an innovative efficient discontinuous Galerkin(DG)method for grid division to accurately model basin sedimentary layers at reduced costs.It also introduces a comprehensive hybrid source model for high-frequency random scattering and a nonlinear analysis module for basin sedimentary layers.Deterministic outcomes are combined with modified three-dimensional stochastic finite fault method(3D-EXSIM)simulations of high-frequency ground motion(f>1 Hz).A fourth-order Butterworth filter with zero phase shift is employed for time-domain filtering of low-and high-frequency time series at a crossover frequency of 1 Hz,merging the low and high-frequency ground motions into a broadband time series.Taking an Ms 6.8 Luding earthquake,as an example,this hybrid method was used for a rapid and efficient simulation analysis of broadband ground motion in the region.The accuracy and efficiency of this hybrid method were verified through comparisons with actually observed station data and empirical attenuation curves.Deterministic method simulation results revealed the effects of mountainous topography,basin effects,nonlinear effects within the basin’s sedimentary layers,and a coupling interaction between the basin and the mountains.The findings are consistent with similar studies,showing that near-fault sedimentary basins significantly focus and amplify strong ground motion,and the soil’s nonlinear behavior in the basin influences ground motion to varying extents at different distances from the fault.The mountainous topography impacts the basin’s response to ground motion,leading to barrier effects.This research provides a scientific foundation for seismic zoning,urban planning,and seismic design in nearfault mountain basin regions.展开更多
This paper considers the problem of time varying congestion pricing to determine optimal time-varying tolls at peak periods for a queuing network with the interactions between buses and private cars.Through the combin...This paper considers the problem of time varying congestion pricing to determine optimal time-varying tolls at peak periods for a queuing network with the interactions between buses and private cars.Through the combined applications of the space-time expanded network（STEN） and the conventional network equilibrium modeling techniques,a multi-class,multi-mode and multi-criteria traffic network equilibrium model is developed.Travelers of different classes have distinctive value of times（VOTs）,and travelers from the same class perceive their travel disutility or generalized costs on a route according to different weights of travel time and travel costs.Moreover,the symmetric cost function model is extended to deal with the interactions between buses and private cars.It is found that there exists a uniform（anonymous） link toll pattern which can drive a multi-class,multi-mode and multi-criteria user equilibrium flow pattern to a system optimum when the system＇s objective function is measured in terms of money.It is also found that the marginal cost pricing models with a symmetric travel cost function do not reflect the interactions between traffic flows of different road sections,and the obtained congestion pricing toll is smaller than the real value.展开更多
基金supported by National Natural Science Foundation of China (Nos.21906124,32302202)Natural Science Foundation of Hubei Province (No.2017CFB220)Natural Science Foundation of Shandong Province (No.ZR2023MH278)。
文摘Metal organic framework(MOF) assembled with coordination bonds has the disadvantage of poor stability that limits its application in the field of stationary phase,while covalent organic framework(COF)assembled through covalent bonds exhibits excellent structural stability.It has been shown that the stationary phases prepared by combining MOF and COF can make up for the poor stability of MOF@SiO_(2),and the MOF/COF composites have superior chromatographic separation performance.However,the traditional methods for preparing COF/MOF based stationary phases are generally solvent thermal synthesis.In this study,a green and low-cost synthesis method was proposed for the preparation of MOF/COF@SiO_(2) stationary phase.Firstly,COF@SiO_(2) was prepared in a choline chloride/ethylene glycol based deep eutectic solvent(DES).Secondly,another acid-base tunable DES prepared by mixing p-toluenesulfonic acid(PTSA)and 2-methylimidazole in different proportions was introduced as the reaction solvent and reactant for rapid synthesis of MOF/COF@SiO_(2).Compared with the toxic transition metal-based MOFs selected in most previous studies,a lightweight and non-toxic S-zone metal(calcium) based MOF was employed in this study.PTSA and calcium will form the calcium/oxygen-containing organic acid framework in acidic DES,which assembles with terephthalic acid dissolved in basic DES to form MOF.The strong hydrogen bonding effect of DES can facilitate rapid assembly of Ca-MOF.The obtained Ca-MOF/COF@SiO_(2) can be used for multi-mode chromatography to efficiently separate multiple isomeric/hydrophilic/hydrophobic analytes.The synthesis method of Ca-MOF/COF@SiO_(2) is green and mild,especially the use of acid-base tunable DES promotes the rapid synthesis of non-toxic Ca-MOF/COF@silica composites,which offers an innovative approach of greenly synthesizing novel MOF/COF stationary phases and extends their applications in the field of chromatography.
基金funded by“the Fanying Special Program of the National Natural Science Foundation of China,grant number 62341307”“the Scientific research project of Jiangxi Provincial Department of Education,grant number GJJ200839”“theDoctoral startup fund of JiangxiUniversity of Technology,grant number 205200100402”.
文摘In multi-modal emotion recognition,excessive reliance on historical context often impedes the detection of emotional shifts,while modality heterogeneity and unimodal noise limit recognition performance.Existing methods struggle to dynamically adjust cross-modal complementary strength to optimize fusion quality and lack effective mechanisms to model the dynamic evolution of emotions.To address these issues,we propose a multi-level dynamic gating and emotion transfer framework for multi-modal emotion recognition.A dynamic gating mechanism is applied across unimodal encoding,cross-modal alignment,and emotion transfer modeling,substantially improving noise robustness and feature alignment.First,we construct a unimodal encoder based on gated recurrent units and feature-selection gating to suppress intra-modal noise and enhance contextual representation.Second,we design a gated-attention crossmodal encoder that dynamically calibrates the complementary contributions of visual and audio modalities to the dominant textual features and eliminates redundant information.Finally,we introduce a gated enhanced emotion transfer module that explicitly models the temporal dependence of emotional evolution in dialogues via transfer gating and optimizes continuity modeling with a comparative learning loss.Experimental results demonstrate that the proposed method outperforms state-of-the-art models on the public MELD and IEMOCAP datasets.
基金supported by the National Natural Science Foundation of China(No.22288101)the 111 Project(No.B17020)。
文摘Carbon dots(CDs)-based composites have shown impressive performance in fields of information encryption and sensing,however,a great challenge is to simultaneously implement multi-mode luminescence and room-temperature phosphorescence(RTP)detection in single system due to the formidable synthesis.Herein,a multifunctional composite of Eu&CDs@p RHO has been designed by co-assembly strategy and prepared via a facile calcination and impregnation treatment.Eu&CDs@p RHO exhibits intense fluorescence(FL)and RTP coming from two individual luminous centers,Eu3+in the free pores and CDs in the interrupted structure of RHO zeolite.Unique four-mode color outputs including pink(Eu^(3+),ex.254 nm),light violet(CDs,ex.365 nm),blue(CDs,254 nm off),and green(CDs,365 nm off)could be realized,on the basis of it,a preliminary application of advanced information encoding has been demonstrated.Given the free pores of matrix and stable RTP in water of confined CDs,a visual RTP detection of Fe^(3+)ions is achieved with the detection limit as low as 9.8μmol/L.This work has opened up a new perspective for the strategic amalgamation of luminous vips with porous zeolite to construct the advanced functional materials.
基金supported by Natural Science Foundation of Jilin Province(No.SKL202302002)Key Research and Development project of Jilin Provincial Science and Technology Department(No.20210204142YY)+2 种基金The Science and Technology Development Program of Jilin Province(No.2020122256JC)Beijing Kechuang Medical Development Foundation Fund of China(No.KC2023-JX-0186BQ079)Talent Reserve Program(TRP),the First Hospital of Jilin University(No.JDYY-TRP-2024007)。
文摘Prostate cancer(PCa)is characterized by high incidence and propensity for easy metastasis,presenting significant challenges in clinical diagnosis and treatment.Tumor microenvironment(TME)-responsive nanomaterials provide a promising prospect for imaging-guided precision therapy.Considering that tumor-derived alkaline phosphatase(ALP)is over-expressed in metastatic PCa,it makes a great chance to develop a theranostics system with ALP responsive in the TME.Herein,an ALP-responsive aggregationinduced emission luminogens(AIEgens)nanoprobe AMNF self-assembly was designed for enhancing the diagnosis and treatment of metastatic PCa.The nanoprobe exhibited self-aggregation in the presence of ALP resulted in aggregation-induced fluorescence,and enhanced accumulation and prolonged retention period at the tumor site.In terms of detection,the fluorescence(FL)/computed tomography(CT)/magnetic resonance(MR)multi-mode imaging effect of nanoprobe was significantly improved post-aggregation,enabling precise diagnosis through the amalgamation of multiple imaging modes.Enhanced CT/MR imaging can achieve assist preoperative tumor diagnosis,and enhanced FL imaging technology can achieve“intraoperative visual navigation”,showing its potential application value in clinical tumor detection and surgical guidance.In terms of treatment,AMNF showed strong absorption in the near infrared region after aggregation,which improved the photothermal treatment effect.Overall,our work developed an effective aggregation-enhanced theranostic strategy for ALP-related cancers.
基金supported by the Science and Technology Project of State Grid Corporation of China under grant 52094021N010(5400-202199534A-0-5-ZN)。
文摘Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power line communication(DC-PLC)enables real-time data transmission on DC power lines.With traffic adaptation,DC-PLC can be integrated with other complementary media such as 5G to reduce transmission delay and improve reliability.However,traffic adaptation for DC-PLC and 5G integration still faces the challenges such as coupling between traffic admission control and traffic partition,dimensionality curse,and the ignorance of extreme event occurrence.To address these challenges,we propose a deep reinforcement learning(DRL)-based delay sensitive and reliable traffic adaptation algorithm(DSRTA)to minimize the total queuing delay under the constraints of traffic admission control,queuing delay,and extreme events occurrence probability.DSRTA jointly optimizes traffic admission control and traffic partition,and enables learning-based intelligent traffic adaptation.The long-term constraints are incorporated into both state and bound of drift-pluspenalty to achieve delay awareness and enforce reliability guarantee.Simulation results show that DSRTA has lower queuing delay and more reliable quality of service(QoS)guarantee than other state-of-the-art algorithms.
基金supported by the National Natural Science Foundation of China(Grant No.72161034).
文摘Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.
基金supported by the Basic Science Research Program(2023R1A2C3004336,RS-202300243807)&Regional Leading Research Center(RS-202400405278)through the National Research Foundation of Korea(NRF)grant funded by the Korea Government(MSIT)。
文摘Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility,adhesion,self-healing,and environmental robustness with excellent sensing metrics.Herein,we report a multifunctional,anti-freezing,selfadhesive,and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes(CoN CNT)embedded in a polyvinyl alcohol-gelatin(PVA/GLE)matrix.Fabricated using a binary solvent system of water and ethylene glycol(EG),the CoN CNT/PVA/GLE organogel exhibits excellent flexibility,biocompatibility,and temperature tolerance with remarkable environmental stability.Electrochemical impedance spectroscopy confirms near-stable performance across a broad humidity range(40%-95%RH).Freeze-tolerant conductivity under sub-zero conditions(-20℃)is attributed to the synergistic role of CoN CNT and EG,preserving mobility and network integrity.The Co N CNT/PVA/GLE organogel sensor exhibits high sensitivity of 5.75 k Pa^(-1)in the detection range from 0 to 20 k Pa,ideal for subtle biomechanical motion detection.A smart human-machine interface for English letter recognition using deep learning achieved 98%accuracy.The organogel sensor utility was extended to detect human gestures like finger bending,wrist motion,and throat vibration during speech.
基金Supported by the National Natural Science Foundation of China(Grant Nos.62025104,62331005,and U22A2052)the Beijing Natural Science Foundation(Grant No.L242100).
文摘Background Computed tomography(CT) and cone-beam computed tomography(CBCT) image registration play pivotal roles in computer-assisted navigation for orthopedic surgery. Traditional methods often apply uniform deformation models, neglecting the biomechanical differences between rigid structures and soft tissues, which compromises registration accuracy, especially during significant bone displacements. Method To address this issue, we introduce RE-Reg, a rigid-elastic CT-CBCT image registration framework that jointly learns rigid bone motion and soft tissue deformation. RE-Reg incorporates a rigid alignment(RA) module to estimate global bone motion and an elastic deformation(ED) module to model soft tissue deformation, preserving bony structures through bone shape preservation(BSP) loss. Result Our comprehensive evaluation on publicly available datasets demonstrates that RE-Reg significantly outperforms existing methods in terms of registration accuracy and rigid bone structure preservation, achieving a 1.3% improvement in Dice similarity coefficient(DSC) and a 23% reduction in rigid bone deformation(%Δvol) compared with the best baseline. Conclusion This framework not only enhances anatomical fidelity but also ensures biomechanical plausibility and provides a valuable tool for image-guided orthopedic surgery. This code is available athttps://github.com/Zq-Huang/RE-Reg.
文摘In order to optimize the reaming process of the type IV composite hydrogen storage cylinder,the netting theory was employed for the design of stacking sequences,and the thickness in the head section was predicted.A finite element model of the plastic-lined composite hydrogen storage cylinder,designed to withstand a working pressure of 70.0 MPa,was established by using the wound composite modeler(WCM)in the Abaqus software to analyze the forces acting on the winding layer.The Hashin failure criterion was utilized as the standard for assessing composite failure,and a progressive failure analysis of the cylinder was conducted to predict both the bursting pressure and the failure location of the composite hydrogen storage cylinder.The results indicate that the reaming process can effectively reduce the maximum filament winding thickness in the head section and promote a more uniform transition.At the bursting pressure,the stress within the head liner decreases,thereby enhancing the ultimate bearing capacity of the cylinder.A control system for a four-axis winding machine was designed by utilizing an industrial computer and a programmable multi-axis controller(PMAC).The winding line pattern is designed and the G-code trajectory is generated by the industrial computer.The numerical control system,composed of the PMAC and servo motor,executes the four-axis interpolation motion.
基金supported by the Major Program(JD)of Hubei Province under Grant No.2023BAA009the National Natural Science Foundation of China(Grant No.22105162)+1 种基金the Natural Science Foundation of Hubei Province(Grant No.2023AFB623)the Original Exploration Seed Fund of Hubei University。
文摘Next-generation fire safety systems demand precise detection and motion recognition of flames.In-sensor computing,which integrates sensing,memory,and processing capabilities,has emerged as a key technology in flame detection.However,the implementation of hardware-level functional demonstrations based on artificial vision systems in the solar-blind ultraviolet(UV)band(200-280 nm)is hindered by the weak detection capability.Here,we propose Ga_(2)O_(3)/In_(2)Se_(3) heterojunctions for the ferroelectric(abbreviation:Fe)optoelectronic sensor(abbreviation:OES)array(5×5 pixels),which is capable of ultraweak UV light detection with an ultrahigh detectivity through ferroelectric regulation and features in configurable multimode functionality.The Fe-OES array can directly sense different flame motions and simulate the non-spiking gradient neurons of insect visual system.Moreover,the flame signal can be effectively amplified in combination with leaky integration-and-fire neuron hardware.Using this Fe-OES system and neuromorphic hardware,we successfully demonstrate three flame processing tasks:achieving efficient flame detection across all time periods with terminal and cloud-based alarms;flame motion recognition with a lightweight convolutional neural network achieving 96.47%accuracy;and flame light recognition with 90.51%accuracy by means of a photosensitive artificial neural system.This work provides effective tools and approaches for addressing a variety of complex flame detection tasks.
基金Support by Sichuan Science and Technology Program[2023YFSY0026,2023YFH0004]Guangzhou Huashang University[2024HSZD01,HS2023JYSZH01].
文摘Soft-tissue motion introduces significant challenges in robotic teleoperation,especially in medical scenarios where precise target tracking is critical.Latency across sensing,computation,and actuation chains leads to degraded tracking performance,particularly around high-acceleration segments and trajectory inflection points.This study investigates machine learning-based predictive compensation for latency mitigation in soft-tissue tracking.Three models—autoregressive(AR),long short-term memory(LSTM),and temporal convolutional network(TCN)—were implemented and evaluated on both synthetic and real datasets.By aligning the prediction horizon with the end-to-end system delay,we demonstrate that prediction-based compensation significantly reduces tracking errors.Among the models,TCN achieved superior robustness and accuracy on complex motion patterns,particularly in multi-step prediction tasks,and exhibited better latency–horizon compatibility.The results suggest that TCN is a promising candidate for real-time latency compensation in teleoperated robotic systems involving dynamic soft-tissue interaction.
文摘[Objective]This study aims to investigate the multi-body hydrodynamic interaction mechanisms during offshore lifting operations of aquaculture net cages in wind-fishery integration systems.By integrating numerical simulations and dynamic analysis methods,this study systematically investigates the coupled dynamic response characteristics during the cage-carrier vessel separation process to reveal its dynamic evolution patterns and key influence mechanisms.[Method]Based on potential flow theory,a fully coupled dynamic analysis model of crane vessel-net cage-semi-submersible barge was established for a marine ranch project in Guangdong.The complete lifting process was dynamically simulated using SESAM software.Five typical operating sea states were configured to investigate the influence of wave parameters on the system's motion response under combined wave-current-wind actions.[Result]The results demonstrate that wave period dominates the system stability.Under short-period conditions,the system maintains stable motion with relatively small horizontal relative displacements,while long-period conditions excite low-frequency resonance,leading to significant slow-drift motions.Vertical response analysis reveals that long-period waves cause severe relative displacement fluctuations between the cage and semi-submersible vessel,with actual displacement amplitudes doubling the preset safety target of 2.045 m.Quantitative analysis further indicates that when significant wave height increases from 1.0 m to 1.5 m,the actual displacement amplitude increases by approximately 20%relative to the target displacement of 2.045 m,demonstrating that its influence is significantly weaker than the displacement variations induced by wave period changes.The complete dynamic simulation successfully captures the continuous dynamic response characteristics during the lifting process.[Conclusion]This research clarifies the influence mechanisms of wave parameters on the cage lifting process,identifying wave period as the crucial factor for operational safety.An operation window assessment method incorporating multi-body coupling effects is established,proposing a safety criterion with peak period not exceeding six seconds as the core requirement.The findings provide theoretical foundation for safe installation of marine ranch net cages and offer valuable references for similar offshore lifting operations.
基金The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number(PSAU/2024/01/32082).
文摘In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics.
基金supported by the Earthquake Science and Technology Spark Plan Project(No.XH23041C)The Natural Science Foundation of Gansu Province(No.22JR11RA090)Gansu Lanzhou Geophysics National Observation and Research Station(No.2021Y14).
文摘The widely distributed loess deposits in the Yellow River Basin exhibit unique engineering geological characteristics.The variations in their thickness and stratigraphic structure significantly amplify ground motion parameters,directly influencing the regional seismic hazard risk level.This study methodically conducted on-site studies and observations of building collapses and damages resulting from seismic amplification effects,using the Wenchuan M_(S)8.0 earthquake as a case study.Comprehensive experimental and numerical simulation studies were carried out.A large-scale shaking table test was performed,and numerical models for 14 different loess sites types were established.Various types of seismic waves were incorporated into these models for systematic numerical simulation calculations.The research reveals the mechanisms by which loess deposit thickness and stratigraphic structure in the Yellow River Basin affect seismic ground motion amplification.The results indicate that as the epicentral distance increases,the peak ground motion shows a marked attenuation trend,with the horizontal component attenuating substantially faster than the vertical component.As the overlying loess layer thickness increases from 50 to 100 m,the seismic intensity may escalate by 3−4 degrees,and the peak acceleration may amplify by 1.5−2.2 times.With the augmentation of loess deposit thickness and the proliferation of soil layers,both the peak acceleration response spectrum and the characteristic period demonstrate an upward tendency,exhibiting slight fluctuations contingent upon the seismic wave type.
基金The World Class Professor(WCP)Program of the Directorate of Resources,Directorate General of Higher Education,Ministry of Education and Culture in 2023 supports this studythe JAPAN-ASEAN Science and Technology Innovation Platform(JASTIP-WP4)+3 种基金the University of Bengkulu's International Collaboration Research Fund(2183/UN30.15/LT/2019)for partial fundingthe C2F Fund for Postdoctoral Fellowship from Chulalongkorn Universitythe Thailand Science Research and Innovation Fund Chulalongkorn University(DISF68210001)the National Research Council of Thailand(N42A670572)。
文摘The research findings on the ground motion and liquefaction potential analyses during the 2018 Great Indonesia Earthquake(M_(w)7.5)are significant and crucial.The earthquake triggered soil-structure damage due to liquefaction.This study,which thoroughly investigated four sites at Palu,was conducted by performing a comprehensive ground motion parameter analysis.The ground motion characteristics were presented and justified,particularly for the most impacted direction.Ground motion predictions were analysed to define the spectral accelerations,and matching spectral accelerations were conducted to produce ground motions for each site.Non-linear seismic ground response analysis based on the hyperbolic model of pressure pressure-dependent was performed to investigate cyclic soil behaviour.The results revealed that ground motion is crucial in significant soil damage,and the earthquake energy could trigger deep liquefaction.As the most significant ground motion,the vertical ground motion is essential in determining deep liquefaction.The discussion on the impact of liquefaction based on the results of the numerical analysis is presented.Significant ground motion with a longer duration could have a substantial impact on deep liquefaction in the study area.These findings depict how the 2018 Indonesia Earthquake(M_(w)7.5)triggered a mega-liquefaction in Palu City.The results could enhance the understanding of the importance of seismic hazard assessment.It is recommended that site investigation and soil improvement should be planned to counteract liquefaction damage before construction.This study also suggests conducting seismic hazard assessments for city development to minimise the potential disaster impact in the study area.
基金Supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R896).
文摘Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the cloud and inference can be obtained on real-world data.In most applications,it is important to compress the vision data due to the enormous bandwidth and memory requirements.Video codecs exploit spatial and temporal correlations to achieve high compression ratios,but they are computationally expensive.This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos.However,contrary to the normal practice of reconstructing the full-resolution frames through motion compensation,this work proposes to infer the class label from the block-based computed motion fields directly.Motion fields are a richer and more complex representation of motion vectors,where each motion vector carries the magnitude and direction information.This approach has two advantages:the cost of motion compensation and video decoding is avoided,and the dimensions of the input signal are highly reduced.This results in a shallower network for classification.The neural network can be trained using motion vectors in two ways:complex representations and magnitude-direction pairs.The proposed work trains a convolutional neural network on the direction and magnitude tensors of the motion fields.Our experimental results show 20×faster convergence during training,reduced overfitting,and accelerated inference on a hand gesture recognition dataset compared to full-resolution and downsampled frames.We validate the proposed methodology on the HGds dataset,achieving a testing accuracy of 99.21%,on the HMDB51 dataset,achieving 82.54%accuracy,and on the UCF101 dataset,achieving 97.13%accuracy,outperforming state-of-the-art methods in computational efficiency.
基金The Central Government Guides Local Foundation for Science and Technology Development(Grant No.YDZJSX2024B004).
文摘The soft actuator is characterized by high safety,flexibility,and adaptability.It is capable of both active and passive defor-mations.This paper presents a discrete degree of freedom(DOF)method for soft actuators to reveal DOF characteristics.The method draws on the superposition mechanism of the deformation characteristics of the sarcomere in the skeletal muscles of living organisms.Firstly,the multi-DOF deformation characteristics of the soft actuator are discretized into superimposed combinations of single-DOF micro-units.Then,the soft actuator was determined to contain deformation characteristics such as extension-contraction,bending,and twisting.Eighteen types of micro-units with basic deforma-tion characteristics were obtained depending on the axis and orientation.Further,the mapping relationship between the combination of micro-units and the motion characteristics of the soft actuator based on the GF set theory was established.Finally,an active-passive DOF co-structured soft actuator(APCSA)was developed.The graphical approach analyzes the experimental results,and it can be concluded that active and passive DOFs can coexist in the composite deformation of the soft actuator.
基金National Natural Science Foundation of China under Grant Nos.U2139208 and 52278516Key Laboratory of Earthquake Engineering and Engineering Vibration,China Earthquake Administration under Grant No.2024D15Key Laboratory of Soft Soil Characteristic and Engineering Environment,Tianjin Chengjian University under Grant No.2022SCEEKL003。
文摘This study presents an effective hybrid simulation approach for simulating broadband ground motion in complex near-fault locations.The approach utilizes a deterministic approach based on the spectral element method(SEM),which is used to simulate low-frequency ground motion(f<1 Hz)by incorporating an innovative efficient discontinuous Galerkin(DG)method for grid division to accurately model basin sedimentary layers at reduced costs.It also introduces a comprehensive hybrid source model for high-frequency random scattering and a nonlinear analysis module for basin sedimentary layers.Deterministic outcomes are combined with modified three-dimensional stochastic finite fault method(3D-EXSIM)simulations of high-frequency ground motion(f>1 Hz).A fourth-order Butterworth filter with zero phase shift is employed for time-domain filtering of low-and high-frequency time series at a crossover frequency of 1 Hz,merging the low and high-frequency ground motions into a broadband time series.Taking an Ms 6.8 Luding earthquake,as an example,this hybrid method was used for a rapid and efficient simulation analysis of broadband ground motion in the region.The accuracy and efficiency of this hybrid method were verified through comparisons with actually observed station data and empirical attenuation curves.Deterministic method simulation results revealed the effects of mountainous topography,basin effects,nonlinear effects within the basin’s sedimentary layers,and a coupling interaction between the basin and the mountains.The findings are consistent with similar studies,showing that near-fault sedimentary basins significantly focus and amplify strong ground motion,and the soil’s nonlinear behavior in the basin influences ground motion to varying extents at different distances from the fault.The mountainous topography impacts the basin’s response to ground motion,leading to barrier effects.This research provides a scientific foundation for seismic zoning,urban planning,and seismic design in nearfault mountain basin regions.
基金The National High Technology Research and Development Program of China (863 Program) (No. 2007AA11Z202)the National Key Technology R & D Program of China during the 11th Five-Year Plan Period(No. 2006BAJ18B03)the Fundamental Research Funds for the Central Universities (No. DUT10RC(3) 112)
文摘This paper considers the problem of time varying congestion pricing to determine optimal time-varying tolls at peak periods for a queuing network with the interactions between buses and private cars.Through the combined applications of the space-time expanded network（STEN） and the conventional network equilibrium modeling techniques,a multi-class,multi-mode and multi-criteria traffic network equilibrium model is developed.Travelers of different classes have distinctive value of times（VOTs）,and travelers from the same class perceive their travel disutility or generalized costs on a route according to different weights of travel time and travel costs.Moreover,the symmetric cost function model is extended to deal with the interactions between buses and private cars.It is found that there exists a uniform（anonymous） link toll pattern which can drive a multi-class,multi-mode and multi-criteria user equilibrium flow pattern to a system optimum when the system＇s objective function is measured in terms of money.It is also found that the marginal cost pricing models with a symmetric travel cost function do not reflect the interactions between traffic flows of different road sections,and the obtained congestion pricing toll is smaller than the real value.
