This article centers on The Call of the Wild,an English novel by American author Jack London,alongside two Chinese translations by Dajie Liu and Menglin Zhang,and Rongyue Liu.Seventy sentences containing motion events...This article centers on The Call of the Wild,an English novel by American author Jack London,alongside two Chinese translations by Dajie Liu and Menglin Zhang,and Rongyue Liu.Seventy sentences containing motion events and their corresponding translations were randomly selected for analysis.The study focuses on the primary elements of motion events-manner,path,and ground-and examines their Chinese translations through the lens of Skopos theory.Skopos theory emphasizes whether translators can adopt appropriate translation strategies according to various contextual factors during the translation process.Compared to verb-framed languages,satellite-framed languages possess a richer vocabulary for manner verbs,express more detailed manner information,use more satellite words to indicate paths,and incorporate more background information.Verb-framed languages,by contrast,typically express manner information only when necessary and tend to include less background information.The analysis reveals that both Chinese translations embody the core principle of Skopos theory:translation strategies are determined by their purpose.To fulfill the novel’s translation objectives,the translators adeptly adjust their strategies for motion event components based on different contextual needs.It is noted that the Chinese translations do not fully retain the characteristics of English as a typical satellite-framed language.This observation aligns with Skopos theory’s purpose-oriented approach,which prioritizes translation goals over strict adherence to source text characteristics.展开更多
Microrobots powered by an external magnetic field could be used for sophisticated medical applications such as cell treatment,micromanipulation,and noninvasive surgery inside the body.Untethered microrobot application...Microrobots powered by an external magnetic field could be used for sophisticated medical applications such as cell treatment,micromanipulation,and noninvasive surgery inside the body.Untethered microrobot applications can benefit from haptic technology and telecommunication,enabling telemedical micro-manipulation.Users can manipulate the microrobots with haptic feedback by interacting with the robot operating system remotely in such applications.Artificially created haptic forces based on wirelessly transmitted data and model-based guidance can aid human operators with haptic sensations while manipulating microrobots.The system presented here includes a haptic device and a magnetic tweezer system linked together using a network-based teleoperation method with motion models in fluids.The magnetic microrobots can be controlled remotely,and the haptic interactions with the remote environment can be felt in real time.A time-domain passivity controller is applied to overcome network delay and ensure stability of communication.This study develops and tests a motion model for microrobots and evaluates two image-based 3D tracking algorithms to improve tracking accuracy in various Newtonian fluids.Additionally,it demonstrates that microrobots can group together to transport multiple larger objects,move through microfluidic channels for detailed tasks,and use a novel method for disassembly,greatly expanding their range of use in microscale operations.Remote medical treatment in multiple locations,remote delivery of medication without the need for physical penetration of the skin,and remotely controlled cell manipulations are some of the possible uses of the proposed technology.展开更多
The flocculation behavior of carbon black (CB)-filled isoprene rubber (IR) nanocomposites was systematically investigated under both dynamic and static conditions to unravel the distinct mechanisms governing filler ne...The flocculation behavior of carbon black (CB)-filled isoprene rubber (IR) nanocomposites was systematically investigated under both dynamic and static conditions to unravel the distinct mechanisms governing filler network evolution.Under dynamic conditions,small oscillatory shear strains (0.1%) significantly enhanced filler particle motion,leading to pronounced agglomeration and a flocculation degree of about 4.3MPa at 145℃.In contrast,static flocculation exhibited a fundamentally different mechanism dominated by polymer chain dynamics,which is driven mainly by thermal activation.Radial distribution function (RDF) analysis of transmission electron microscopy (TEM) images revealed a slight decrease (2 nm) in the interparticle distance peak after static annealing at 100℃ for 7 h,indicating localized motion of CB particles.However,the overall filler network remained stable,with no significant agglomeration observed.The increase in bound rubber content from about 23% to 28% with rising temperature further confirmed the dominant role of polymer chain adsorption and interfacial reinforcement in static flocculation.These findings highlight the critical influence of external strain on filler network formation and provide new insights into the polymer-dominated mechanism of static flocculation.The results offer practical guidance for optimizing the storage and processing of rubber nanocomposites,particularly in applications where static flocculation during prolonged storage is a concern.展开更多
The accurate assessment of cardiac motion is crucial for diagnosing and monitoring cardiovascular diseases.In this context,digital volume correlation(DVC)has emerged as a promising technique for tracking cardiac motio...The accurate assessment of cardiac motion is crucial for diagnosing and monitoring cardiovascular diseases.In this context,digital volume correlation(DVC)has emerged as a promising technique for tracking cardiac motion from cardiac computed tomography angiographic(CTA)images.This paper presents a comprehensive performance evaluation of the DVC method,specifically focusing on tracking the motion of the left atrium using cardiac CTA data.The study employed a comparative experimental approach while simultaneously optimizing the existing DVC algorithm.Multiple sets of controlled experiments were designed to conduct quantitative analyses on the parameters“radius”and“step”.The results revealed that the optimized DVC algorithm enhanced tracking accuracy within a reasonable computational time.These findings contributed to the understanding of the efficacy and limitations of the DVC algorithm in analyzing heart deformation.展开更多
The March 28,2025 Myanmar earthquake generated ground shaking that was perceptible throughout Myanmar and adjacent regions.This study simulated three-component ground motions across the affected region using an improv...The March 28,2025 Myanmar earthquake generated ground shaking that was perceptible throughout Myanmar and adjacent regions.This study simulated three-component ground motions across the affected region using an improved stochastic finite-fault method to systematically assess seismic impacts.Observed near-field recordings at MM.NGU station was used to determine the reliability of the theoretically derived stress drop as input for simulation.Far-field recordings constrained the frequency-dependent S-wave quality factors(Q(f)=283.305f^(0.588))for anelastic attenuation modeling.Comparisons of peak accelerations between simulation and empirical ground-motion models showed good agreement at moderate-to-large distances.However,lower near-fault simulations indicate a weaker-than-average source effect.Analysis of simulated instrumental seismic intensity revealed key patterns.Maximum intensity(Ⅹ)occurred in isolated patches within the ruptured fault projection,correlating with shallow high-slip areas.TheⅨ-intensity zone formed a north-south elongated band centered on fault projection.Significant asymmetry inⅧ-intensity distribution perpendicular to the fault strike was observed,with a wider western extension attributed to lower shear-wave velocities west of the fault.Supershear rupture behavior enhanced ground motions,expanding intensity ranges by~20%compared to sub-shear rupture.This study reveals the integrated effects of fault geometry,slip spatial distribution,rupture velocity,and site condition in governing ground motion patterns.展开更多
Although the Chinese new-generation Fengyun-4B(FY-4B) geostationary satellite Atmospheric Motion Vector(AMV) products became operational in June 2022, their accuracy and utility remain largely unexamined. This study c...Although the Chinese new-generation Fengyun-4B(FY-4B) geostationary satellite Atmospheric Motion Vector(AMV) products became operational in June 2022, their accuracy and utility remain largely unexamined. This study comprehensively evaluates FY-4B AMV products for August and October 2023, as well as January and April 2024,exploring their application in monitoring the South China Sea Summer Monsoon(SCSSM) onset. The results indicate that AMV products derived from the upper-level water vapor absorption channel(AMV_WV) and the infrared channel(AMV_IR) demonstrate high accuracy when compared with ERA5 reanalysis data. The root mean square error(RMSE) is mostly between 4.5 m s^(–1)and 6.4 m s^(–1), with coefficients of determination(R2) values ranging from 0.7 to 0.8, indicating the overall reliability of FY-4B AMVs. The observation errors of AMVs exhibit significant vertical structure characteristics. Specifically, the AMV_WV products demonstrate superior accuracy above 350 h Pa, while the AMV_IR products exhibit reduced errors in the layers between 200–500 h Pa and 700–950 h Pa. Spatially, most areas exhibit low observation errors for AMVs, while clear-sky weather and deep convective cloud systems can increase errors. A lack of clouds or water vapor may reduce the number of observation samples in some areas, leading to unstable RMSE performance, which is particularly evident for AMV_WV RMSE around 25°–30°N in January and near 25°S in August. Deep convective cloud systems can influence AMV retrieval results, leading to systematic observation errors, especially for the infrared channel.Additionally, AMV_WV is more reliable during the daytime, with a lower RMSE compared to nighttime, while AMV_IR exhibits a diverging diurnal variation pattern. Finally, the FY-4B AMV_WV products were applied to monitor the SCSSM event in 2024. Significant zonal wind direction reversal characteristics were observed in key regions around the onset date,indicating that AMVs can serve as effective indicators for monitoring the SCSSM onset.展开更多
Motion intention recognition is considered the key technology for enhancing the training effectiveness of upper limb rehabilitation robots for stroke patients,but traditional recognition systems are difficult to simul...Motion intention recognition is considered the key technology for enhancing the training effectiveness of upper limb rehabilitation robots for stroke patients,but traditional recognition systems are difficult to simultaneously balance real-time performance and reliability.To achieve real-time and accurate upper limb motion intention recognition,a multi-modal fusion method based on surface electromyography(sEMG)signals and arrayed flexible thin-film pressure(AFTFP)sensors was proposed.Through experimental tests on 10 healthy subjects(5 males and 5 females,age 23±2 years),sEMG signals and human-machine interaction force(HMIF)signals were collected during elbow flexion,extension,and shoulder internal and external rotation.The AFTFP signals based on dynamic calibration compensation and the sEMG signals were processed for feature extraction and fusion,and the recognition performance of single signals and fused signals was compared using a support vector machine(SVM).The experimental results showed that the sEMG signals consistently appeared 175±25 ms earlier than the HMIF signals(p<0.01,paired t-test).In offline conditions,the recognition accuracy of the fused signals exceeded 99.77%across different time windows.Under a 0.1 s time window,the real-time recognition accuracy of the fused signals was 14.1%higher than that of the single sEMG signal,and the system’s end-to-end delay was reduced to less than 100 ms.The AFTFP sensor is applied to motion intention recognition for the first time.And its low-cost,high-density array design provided an innovative solution for rehabilitation robots.The findings demonstrate that the AFTFP sensor adopted in this study effectively enhances intention recognition performance.The fusion of its output HMIF signals with sEMG signals combines the advantages of both modalities,enabling real-time and accurate motion intention recognition.This provides efficient command output for human-machine interaction in scenarios such as stroke rehabilitation.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
To investigate the dispersion and deposition behavior of the nanoparticles(NPs)in the molten steel under the combined effects of turbulent flow and Brownian motion,a 3D model utilizing volume of fluid-discrete phase m...To investigate the dispersion and deposition behavior of the nanoparticles(NPs)in the molten steel under the combined effects of turbulent flow and Brownian motion,a 3D model utilizing volume of fluid-discrete phase model was developed based on a small-size ingot casting process.A modified Brownian motion model was implemented into the simulation using user-defined function to more accurately predict the motion behavior and distribution of the NPs in the molten steel.The results show that the NPs tend to deposit at the bottom or disperse toward the wall under the turbulent flow.The introduction of Brownian motion increases the horizontal dispersion rate(DH)to 21.3%and reduces the bottom deposition rate by 12.8%.A reduction in the particle size and density promotes higher particle mobility,characterized by increased velocity and DH,along with diminished deposition.As the particle size decreases to 1×10^(-7)m,Brownian motion becomes a significant factor influencing the particle dynamics.Additionally,increasing the initial velocity of the molten steel results in a lower DH of the particles.However,once the velocity exceeds 0.15 m s^(-1),its influence on the particle velocity becomes negligible.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
This research presents a Human Lower Limb Activity Recognition(HLLAR)system that identifies specific activities and predicts the angles of the knees simultaneously,based on the EMG signals.The HLLAR systems streamline...This research presents a Human Lower Limb Activity Recognition(HLLAR)system that identifies specific activities and predicts the angles of the knees simultaneously,based on the EMG signals.The HLLAR systems streamlines the research on the lower limb activities.The HILLAR model includes Discrete Hermite Wavelets Transform-based Synchrosqueezing(DHWTS),Deep Two-Layer Multiscale Convolutional Neural Network(DTLMCNN),and Generalized Regression Neural Network(GRNN)as feature extraction,activity recognition,and knee angle prediction respectively.Electromyography signal-based automatic lower limb activity detection is crucial to rehabilitation and human movement analysis.Yet several of these methods face issues in feature extraction in complex data,overlapping signals,extraction of crucial parameters,and adaptation constraints.This research aims classify lower limb activities and predict knee joint angles from electromy-ography signals using HILLAR model.The model is validated on two datasets,comprising 26 subjects performing three classes of activities:walking,standing,and sitting.The proposed model obtained a classification accuracy of 99.95%,along with significant achievements in precision(99.93%),recall(99.91%),and F1-score(99.93%).The generalized regression neural network predicted angles of the knee joint with a root mean squared error of 1.25%.Robustness is demonstrated through consistent results in five-fold cross-validation and statistical significance testing(p-value=0.004,McNemar's test).Additionally,the proposed model showed superior performance over baseline methods by reducing error rates by 18%and decreasing processing time to 0.98 s.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 1739 M8.0 Pingluo earthquake occurred around the Yinchuan Graben,bounded by the Helan Mountains to the west and the Ordos Block to the east.Seismological observations have shown that surface fault displacement rea...The 1739 M8.0 Pingluo earthquake occurred around the Yinchuan Graben,bounded by the Helan Mountains to the west and the Ordos Block to the east.Seismological observations have shown that surface fault displacement reaches about 2–3 m,mainly by dip-slip motion along the Helanshan Piedmont Fault.However,the documented seismic intensity is distributed predominantly within the basin area,exhibiting a sharp asymmetry across the Helanshan Piedmont Fault.Thus,the general pattern of earthquake faulting is still under debate.We built a three-dimensional elastodynamic finiteelement model to reappraise the fault mechanism.In the model,predictions from synthetic rupture models,based on available observations and the earthquake scaling law,were used as an input with the split-node technique,and the effect of basin sediment on elastic wave propagation was considered.The numerical results show that if an earthquake occurred on the Helanshan Piedmont Fault characterized by a high-angle(70°)normal fault,earthquake shaking,as predicted from the modeled peak ground velocity and peak ground acceleration,has difficulty fitting the observed result,even when the effect of sediment amplification is considered.To better fit the observed shaking pattern,the dip angle of the Helanshan Piedmont Fault must be less than about 35°between the depths of about 8–27 km,where the coseismic slip may reach about 6 m.This result leads us to conclude that the 1739 M8.0 great earthquake likely occurred on a listric normal fault at depth,in agreement with the geometry of the Helanshan Piedmont Fault,as recently evidenced by seismic reflection explorations.This conclusion means that in an intracontinental setting,a reduction in the fault dip angle along the subsurface could increase the width of the fault in the elastic crust,making misalignment between the surface rupture and the isoseismals and resulting in an increase in the upper bound of earthquake magnitude relative to simple high-angle faulting.展开更多
Cryptochromes are blue light photorecep-tors that mediate key biological processes through photoinduced electron transfer(ET).We present a theoretical study of the second ET step along the conserved trypto-phan triad ...Cryptochromes are blue light photorecep-tors that mediate key biological processes through photoinduced electron transfer(ET).We present a theoretical study of the second ET step along the conserved trypto-phan triad in Arabidopsis thaliana cryp-tochrome,focusing on how vibrational envi-ronments affect the ultrafast electron trans-fer.Using the numerically exact low temper-ature quantum Fokker-Planck equation(LT-QFPE)within the hierarchical equations of motion(HEOM)framework,we resolve the contributions of individual Brownian oscillator modes:low frequency modes with large reorganization energies markedly reduce the ET rate,whereas weakly damped high frequency modes generate oscillations linked to electronic-vibra-tional coherence.The LT-QFPE results are also used to benchmark three second order pertur-bative methods:the non-equilibrium Fermi’s golden rule(NE-FGR),non-interacting blip ap-proximation(NIBA),and the time local generalized quantum master equation.Although all approximate methods reproduce correct long-time ET rates,they fail to capture short time non-equilibrium dynamics.These findings clarify how spectral width and bath correlation time,in addition to total reorganization energy,govern the validity of perturbative treat-ments and highlight the need for numerically exact methods such as HEOM in modeling non-equilibrium cryptochrome ET dynamics.s.展开更多
基金Humanities and Social Sciences Research Project of China’s Ministry of Education(23YJCZH242)Major Humanities and Social Sciences Research Projects in Zhejiang Higher Education Institutions(2024QN069)Hangzhou Collaborative Innovation Institute of Language Services,Hangzhou City University,China。
文摘This article centers on The Call of the Wild,an English novel by American author Jack London,alongside two Chinese translations by Dajie Liu and Menglin Zhang,and Rongyue Liu.Seventy sentences containing motion events and their corresponding translations were randomly selected for analysis.The study focuses on the primary elements of motion events-manner,path,and ground-and examines their Chinese translations through the lens of Skopos theory.Skopos theory emphasizes whether translators can adopt appropriate translation strategies according to various contextual factors during the translation process.Compared to verb-framed languages,satellite-framed languages possess a richer vocabulary for manner verbs,express more detailed manner information,use more satellite words to indicate paths,and incorporate more background information.Verb-framed languages,by contrast,typically express manner information only when necessary and tend to include less background information.The analysis reveals that both Chinese translations embody the core principle of Skopos theory:translation strategies are determined by their purpose.To fulfill the novel’s translation objectives,the translators adeptly adjust their strategies for motion event components based on different contextual needs.It is noted that the Chinese translations do not fully retain the characteristics of English as a typical satellite-framed language.This observation aligns with Skopos theory’s purpose-oriented approach,which prioritizes translation goals over strict adherence to source text characteristics.
基金supported by National Science Foundation Grant No.2123824.
文摘Microrobots powered by an external magnetic field could be used for sophisticated medical applications such as cell treatment,micromanipulation,and noninvasive surgery inside the body.Untethered microrobot applications can benefit from haptic technology and telecommunication,enabling telemedical micro-manipulation.Users can manipulate the microrobots with haptic feedback by interacting with the robot operating system remotely in such applications.Artificially created haptic forces based on wirelessly transmitted data and model-based guidance can aid human operators with haptic sensations while manipulating microrobots.The system presented here includes a haptic device and a magnetic tweezer system linked together using a network-based teleoperation method with motion models in fluids.The magnetic microrobots can be controlled remotely,and the haptic interactions with the remote environment can be felt in real time.A time-domain passivity controller is applied to overcome network delay and ensure stability of communication.This study develops and tests a motion model for microrobots and evaluates two image-based 3D tracking algorithms to improve tracking accuracy in various Newtonian fluids.Additionally,it demonstrates that microrobots can group together to transport multiple larger objects,move through microfluidic channels for detailed tasks,and use a novel method for disassembly,greatly expanding their range of use in microscale operations.Remote medical treatment in multiple locations,remote delivery of medication without the need for physical penetration of the skin,and remotely controlled cell manipulations are some of the possible uses of the proposed technology.
基金supported by the National Natural Science Foundation of China(No.52293471)National Key R&D Program of China(No.2022YFB3707303).
文摘The flocculation behavior of carbon black (CB)-filled isoprene rubber (IR) nanocomposites was systematically investigated under both dynamic and static conditions to unravel the distinct mechanisms governing filler network evolution.Under dynamic conditions,small oscillatory shear strains (0.1%) significantly enhanced filler particle motion,leading to pronounced agglomeration and a flocculation degree of about 4.3MPa at 145℃.In contrast,static flocculation exhibited a fundamentally different mechanism dominated by polymer chain dynamics,which is driven mainly by thermal activation.Radial distribution function (RDF) analysis of transmission electron microscopy (TEM) images revealed a slight decrease (2 nm) in the interparticle distance peak after static annealing at 100℃ for 7 h,indicating localized motion of CB particles.However,the overall filler network remained stable,with no significant agglomeration observed.The increase in bound rubber content from about 23% to 28% with rising temperature further confirmed the dominant role of polymer chain adsorption and interfacial reinforcement in static flocculation.These findings highlight the critical influence of external strain on filler network formation and provide new insights into the polymer-dominated mechanism of static flocculation.The results offer practical guidance for optimizing the storage and processing of rubber nanocomposites,particularly in applications where static flocculation during prolonged storage is a concern.
基金supported by the Australian Research Council(ARC)(Grant No.DP200103492)the National Natural Science Foundation of China(Grant Nos.12172089,12372307,and 61821002)+2 种基金Medical Research Future Fund(Grant Nos.2016165 and 2023977)the CBT Early Career Researcher Grant funded and the Roland Bishop Biomedical Engineering Research Award by Queensland University of Technologythe Springboard Funding and the Global Collaboration Funding by London South Bank University.Computational resources and services used in this work were provided by the High-Performance Computing and Research Support Group,Queensland University of Technology,Brisbane,Australia.
文摘The accurate assessment of cardiac motion is crucial for diagnosing and monitoring cardiovascular diseases.In this context,digital volume correlation(DVC)has emerged as a promising technique for tracking cardiac motion from cardiac computed tomography angiographic(CTA)images.This paper presents a comprehensive performance evaluation of the DVC method,specifically focusing on tracking the motion of the left atrium using cardiac CTA data.The study employed a comparative experimental approach while simultaneously optimizing the existing DVC algorithm.Multiple sets of controlled experiments were designed to conduct quantitative analyses on the parameters“radius”and“step”.The results revealed that the optimized DVC algorithm enhanced tracking accuracy within a reasonable computational time.These findings contributed to the understanding of the efficacy and limitations of the DVC algorithm in analyzing heart deformation.
基金National Key R&D Program of China under Grant No.2022YFC3003601。
文摘The March 28,2025 Myanmar earthquake generated ground shaking that was perceptible throughout Myanmar and adjacent regions.This study simulated three-component ground motions across the affected region using an improved stochastic finite-fault method to systematically assess seismic impacts.Observed near-field recordings at MM.NGU station was used to determine the reliability of the theoretically derived stress drop as input for simulation.Far-field recordings constrained the frequency-dependent S-wave quality factors(Q(f)=283.305f^(0.588))for anelastic attenuation modeling.Comparisons of peak accelerations between simulation and empirical ground-motion models showed good agreement at moderate-to-large distances.However,lower near-fault simulations indicate a weaker-than-average source effect.Analysis of simulated instrumental seismic intensity revealed key patterns.Maximum intensity(Ⅹ)occurred in isolated patches within the ruptured fault projection,correlating with shallow high-slip areas.TheⅨ-intensity zone formed a north-south elongated band centered on fault projection.Significant asymmetry inⅧ-intensity distribution perpendicular to the fault strike was observed,with a wider western extension attributed to lower shear-wave velocities west of the fault.Supershear rupture behavior enhanced ground motions,expanding intensity ranges by~20%compared to sub-shear rupture.This study reveals the integrated effects of fault geometry,slip spatial distribution,rupture velocity,and site condition in governing ground motion patterns.
基金Science and Technology Planning Project of Guangdong Province (2023B1212060019)Natural Science Foundation of China (42175086)Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)(SML2023SP208)。
文摘Although the Chinese new-generation Fengyun-4B(FY-4B) geostationary satellite Atmospheric Motion Vector(AMV) products became operational in June 2022, their accuracy and utility remain largely unexamined. This study comprehensively evaluates FY-4B AMV products for August and October 2023, as well as January and April 2024,exploring their application in monitoring the South China Sea Summer Monsoon(SCSSM) onset. The results indicate that AMV products derived from the upper-level water vapor absorption channel(AMV_WV) and the infrared channel(AMV_IR) demonstrate high accuracy when compared with ERA5 reanalysis data. The root mean square error(RMSE) is mostly between 4.5 m s^(–1)and 6.4 m s^(–1), with coefficients of determination(R2) values ranging from 0.7 to 0.8, indicating the overall reliability of FY-4B AMVs. The observation errors of AMVs exhibit significant vertical structure characteristics. Specifically, the AMV_WV products demonstrate superior accuracy above 350 h Pa, while the AMV_IR products exhibit reduced errors in the layers between 200–500 h Pa and 700–950 h Pa. Spatially, most areas exhibit low observation errors for AMVs, while clear-sky weather and deep convective cloud systems can increase errors. A lack of clouds or water vapor may reduce the number of observation samples in some areas, leading to unstable RMSE performance, which is particularly evident for AMV_WV RMSE around 25°–30°N in January and near 25°S in August. Deep convective cloud systems can influence AMV retrieval results, leading to systematic observation errors, especially for the infrared channel.Additionally, AMV_WV is more reliable during the daytime, with a lower RMSE compared to nighttime, while AMV_IR exhibits a diverging diurnal variation pattern. Finally, the FY-4B AMV_WV products were applied to monitor the SCSSM event in 2024. Significant zonal wind direction reversal characteristics were observed in key regions around the onset date,indicating that AMVs can serve as effective indicators for monitoring the SCSSM onset.
基金supported by Guangdong Basic and Applied Basic Research Foundation(No.2024A1515012810).
文摘Motion intention recognition is considered the key technology for enhancing the training effectiveness of upper limb rehabilitation robots for stroke patients,but traditional recognition systems are difficult to simultaneously balance real-time performance and reliability.To achieve real-time and accurate upper limb motion intention recognition,a multi-modal fusion method based on surface electromyography(sEMG)signals and arrayed flexible thin-film pressure(AFTFP)sensors was proposed.Through experimental tests on 10 healthy subjects(5 males and 5 females,age 23±2 years),sEMG signals and human-machine interaction force(HMIF)signals were collected during elbow flexion,extension,and shoulder internal and external rotation.The AFTFP signals based on dynamic calibration compensation and the sEMG signals were processed for feature extraction and fusion,and the recognition performance of single signals and fused signals was compared using a support vector machine(SVM).The experimental results showed that the sEMG signals consistently appeared 175±25 ms earlier than the HMIF signals(p<0.01,paired t-test).In offline conditions,the recognition accuracy of the fused signals exceeded 99.77%across different time windows.Under a 0.1 s time window,the real-time recognition accuracy of the fused signals was 14.1%higher than that of the single sEMG signal,and the system’s end-to-end delay was reduced to less than 100 ms.The AFTFP sensor is applied to motion intention recognition for the first time.And its low-cost,high-density array design provided an innovative solution for rehabilitation robots.The findings demonstrate that the AFTFP sensor adopted in this study effectively enhances intention recognition performance.The fusion of its output HMIF signals with sEMG signals combines the advantages of both modalities,enabling real-time and accurate motion intention recognition.This provides efficient command output for human-machine interaction in scenarios such as stroke rehabilitation.
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 111 Project(2.0)of China(No.BP0719037)the National Natural Science Foundation of China(No.51474065).
文摘To investigate the dispersion and deposition behavior of the nanoparticles(NPs)in the molten steel under the combined effects of turbulent flow and Brownian motion,a 3D model utilizing volume of fluid-discrete phase model was developed based on a small-size ingot casting process.A modified Brownian motion model was implemented into the simulation using user-defined function to more accurately predict the motion behavior and distribution of the NPs in the molten steel.The results show that the NPs tend to deposit at the bottom or disperse toward the wall under the turbulent flow.The introduction of Brownian motion increases the horizontal dispersion rate(DH)to 21.3%and reduces the bottom deposition rate by 12.8%.A reduction in the particle size and density promotes higher particle mobility,characterized by increased velocity and DH,along with diminished deposition.As the particle size decreases to 1×10^(-7)m,Brownian motion becomes a significant factor influencing the particle dynamics.Additionally,increasing the initial velocity of the molten steel results in a lower DH of the particles.However,once the velocity exceeds 0.15 m s^(-1),its influence on the particle velocity becomes negligible.
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.
文摘This research presents a Human Lower Limb Activity Recognition(HLLAR)system that identifies specific activities and predicts the angles of the knees simultaneously,based on the EMG signals.The HLLAR systems streamlines the research on the lower limb activities.The HILLAR model includes Discrete Hermite Wavelets Transform-based Synchrosqueezing(DHWTS),Deep Two-Layer Multiscale Convolutional Neural Network(DTLMCNN),and Generalized Regression Neural Network(GRNN)as feature extraction,activity recognition,and knee angle prediction respectively.Electromyography signal-based automatic lower limb activity detection is crucial to rehabilitation and human movement analysis.Yet several of these methods face issues in feature extraction in complex data,overlapping signals,extraction of crucial parameters,and adaptation constraints.This research aims classify lower limb activities and predict knee joint angles from electromy-ography signals using HILLAR model.The model is validated on two datasets,comprising 26 subjects performing three classes of activities:walking,standing,and sitting.The proposed model obtained a classification accuracy of 99.95%,along with significant achievements in precision(99.93%),recall(99.91%),and F1-score(99.93%).The generalized regression neural network predicted angles of the knee joint with a root mean squared error of 1.25%.Robustness is demonstrated through consistent results in five-fold cross-validation and statistical significance testing(p-value=0.004,McNemar's test).Additionally,the proposed model showed superior performance over baseline methods by reducing error rates by 18%and decreasing processing time to 0.98 s.
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Natural Science Foundation of China(No.42120104004)。
文摘The 1739 M8.0 Pingluo earthquake occurred around the Yinchuan Graben,bounded by the Helan Mountains to the west and the Ordos Block to the east.Seismological observations have shown that surface fault displacement reaches about 2–3 m,mainly by dip-slip motion along the Helanshan Piedmont Fault.However,the documented seismic intensity is distributed predominantly within the basin area,exhibiting a sharp asymmetry across the Helanshan Piedmont Fault.Thus,the general pattern of earthquake faulting is still under debate.We built a three-dimensional elastodynamic finiteelement model to reappraise the fault mechanism.In the model,predictions from synthetic rupture models,based on available observations and the earthquake scaling law,were used as an input with the split-node technique,and the effect of basin sediment on elastic wave propagation was considered.The numerical results show that if an earthquake occurred on the Helanshan Piedmont Fault characterized by a high-angle(70°)normal fault,earthquake shaking,as predicted from the modeled peak ground velocity and peak ground acceleration,has difficulty fitting the observed result,even when the effect of sediment amplification is considered.To better fit the observed shaking pattern,the dip angle of the Helanshan Piedmont Fault must be less than about 35°between the depths of about 8–27 km,where the coseismic slip may reach about 6 m.This result leads us to conclude that the 1739 M8.0 great earthquake likely occurred on a listric normal fault at depth,in agreement with the geometry of the Helanshan Piedmont Fault,as recently evidenced by seismic reflection explorations.This conclusion means that in an intracontinental setting,a reduction in the fault dip angle along the subsurface could increase the width of the fault in the elastic crust,making misalignment between the surface rupture and the isoseismals and resulting in an increase in the upper bound of earthquake magnitude relative to simple high-angle faulting.
基金supported by the National Natural Science Foundation of China(No.22433006).
文摘Cryptochromes are blue light photorecep-tors that mediate key biological processes through photoinduced electron transfer(ET).We present a theoretical study of the second ET step along the conserved trypto-phan triad in Arabidopsis thaliana cryp-tochrome,focusing on how vibrational envi-ronments affect the ultrafast electron trans-fer.Using the numerically exact low temper-ature quantum Fokker-Planck equation(LT-QFPE)within the hierarchical equations of motion(HEOM)framework,we resolve the contributions of individual Brownian oscillator modes:low frequency modes with large reorganization energies markedly reduce the ET rate,whereas weakly damped high frequency modes generate oscillations linked to electronic-vibra-tional coherence.The LT-QFPE results are also used to benchmark three second order pertur-bative methods:the non-equilibrium Fermi’s golden rule(NE-FGR),non-interacting blip ap-proximation(NIBA),and the time local generalized quantum master equation.Although all approximate methods reproduce correct long-time ET rates,they fail to capture short time non-equilibrium dynamics.These findings clarify how spectral width and bath correlation time,in addition to total reorganization energy,govern the validity of perturbative treat-ments and highlight the need for numerically exact methods such as HEOM in modeling non-equilibrium cryptochrome ET dynamics.s.
