Rechargeable alkali metal-sulfur(M-S)batteries,including Li/Na/K-S chemistries,have the potential to utilize abundant and low-cost sulfur cathodes yet offer high theoretical energy densities.However,their practical el...Rechargeable alkali metal-sulfur(M-S)batteries,including Li/Na/K-S chemistries,have the potential to utilize abundant and low-cost sulfur cathodes yet offer high theoretical energy densities.However,their practical electrochemical performance is fundamentally limited by the polysulfide shuttle effect.This challenge is particularly exacerbated in Na-S and K-S systems owing to larger metal-ion radii,weaker solvation energies,slower redox kinetics,and greater electrolyte-electrode incompatibilities compared to Li-S batteries.This review presents a comparative analysis of interface engineering strategies designed to suppress the shuttle effect across these three systems.Following a summary of sulfur cathode properties and reaction mechanisms,we systematically examine the origins of polysulfide shuttling.Our analysis progresses from functional separator design and interlayer enhancements to the implementation of solid-state electrolytes for root-cause inhibition.By evaluating interface engineering research specific to Na-S and K-S batteries,we elucidate both shared principles and unique challenges inherent to alkali M-S systems.Finally,we propose multifaceted solutions to achieve shuttlefree operation and enhance overall battery performance,thereby establishing a foundation for future advancements.展开更多
Owing to the harsh conditions,wind turbine bearings are prone to faults,and the resulting fault information is easily submerged by strong noise disturbance,making conventional diagnosis challenging.Therefore,this stud...Owing to the harsh conditions,wind turbine bearings are prone to faults,and the resulting fault information is easily submerged by strong noise disturbance,making conventional diagnosis challenging.Therefore,this study presents an innovative bearing fault diagnosis approach predicated on Parameter⁃Optimized Symplectic Geometry Mode Decomposition(POSGMD)and Improved Convolutional Neural Network(ICNN).Firstly,assisted by the relative entropy⁃based adaptive selection of embedding dimension,a POSGMD is presented to adaptively decompose the collected bearing vibration signals into various Symplectic Geometry Components(SGC),which can solve the problem of manual selection of the embedding dimension in the raw Symplectic Geometry Mode Decomposition(SGMD).Meanwhile,the signal reconstruction on the decomposed SGC is conducted based on kurtosis⁃weighted principle to obtain the reconstructed signals.Subsequently,the Continuous Wavelet Transform(CWT)of the reconstructed signals is calculated to generate the corresponding time⁃frequency images as sample set.Finally,an ICNN is introduced for model training and automatic recognition of bearing faults.Two case studies are used to validate the presented methods efficacy.Comparing the presented method with traditional fault diagnosis methods,experimental results show that it can achieve greater identification accuracy and superior anti⁃noise resilience.This work provides a practical and effective solution for fault diagnosis in wind turbine bearings,contributing to the timely detection of faults and the reliable operation of wind turbines or other rotational machinery in industrial applications.展开更多
Soft robotic manipulators represent a rapidly evolving field characterized by inherent compliance,adaptability,and safe interactions within unstructured environments.Over the past decade(2015-2025),significant advance...Soft robotic manipulators represent a rapidly evolving field characterized by inherent compliance,adaptability,and safe interactions within unstructured environments.Over the past decade(2015-2025),significant advancements have trans-formed their capabilities through novel designs inspired by biological systems,advanced modeling frameworks,sophisti-cated control strategies,and integration into diverse real-world applications.Recent innovations in multifunctional mate-rials and emerging actuation technologies have markedly expanded manipulator performance,reliability,and dexterity.Concurrently,developments in modeling have progressed from simplified geometric methods toward highly accurate physics-based and hybrid data-driven approaches,substantially improving real-time prediction and controllability.Coupled with these developments,adaptive and robust control strategies-including learning-based techniques-have enabled unprec-edented autonomy and precision in challenging application domains such as Minimally Invasive Surgery(MIS),precision agriculture,deep-sea exploration,disaster recovery,and space missions.Despite these remarkable strides,key challenges remain,notably regarding scalability,long-term material durability,robust integrated sensing,and standardized evaluation procedures.This review comprehensively synthesizes recent advances,critically evaluates state-of-the-art methodologies,and systematically identifies existing gaps to provide a clear roadmap and targeted research directions,guiding future developments toward the broader adoption and optimal utilization of soft robotic manipulators.展开更多
Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving,owing to its efficiency and applicability in both training and evaluating algorithms.Consequently,there has been i...Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving,owing to its efficiency and applicability in both training and evaluating algorithms.Consequently,there has been increasing attention on generating highly realistic and consistent driving videos,particularly those involving viewpoint changes guided by the control commands or trajectories of ego vehicles.However,current reconstruction approaches,such as Neural Radiance Fields and 3D Gaussian Splatting,frequently suffer from limited generalization and depend on substantial input data.Meanwhile,2D generative models,though capable of producing unknown scenes,still have room for improvement in terms of coherence and visual realism.To overcome these challenges,we introduce GenScene,a world model that synthesizes front-view driving videos conditioned on trajectories.A new temporal module is presented to improve video consistency by extracting the global context of each frame,calculating relationships of frames using these global representations,and fusing frame contexts accordingly.Moreover,we propose an innovative attention mechanism that computes relations of pixels within each frame and pixels in the corresponding window range of the initial frame.Extensive experiments show that our approach surpasses various state-of-the-art models in driving video generation,and the introduced modules contribute significantly to model performance.This work establishes a new paradigm for goal-oriented video synthesis in autonomous driving,which facilitates on-demand simulation to expedite algorithm development.展开更多
To address the issue that hybrid flow shop production struggles to handle order disturbance events,a dynamic scheduling model was constructed.The model takes minimizing the maximum makespan,delivery time deviation,and...To address the issue that hybrid flow shop production struggles to handle order disturbance events,a dynamic scheduling model was constructed.The model takes minimizing the maximum makespan,delivery time deviation,and scheme deviation degree as the optimization objectives.An adaptive dynamic scheduling strategy based on the degree of order disturbance is proposed.An improved multi-objective Grey Wolf(IMOGWO)optimization algorithm is designed by combining the“job-machine”two-layer encoding strategy,the timing-driven two-stage decoding strategy,the opposition-based learning initialization population strategy,the POX crossover strategy,the dualoperation dynamic mutation strategy,and the variable neighborhood search strategy for problem solving.A variety of test cases with different scales were designed,and ablation experiments were conducted to verify the effectiveness of the improved strategies.The results show that each improved strategy can effectively enhance the performance of the IMOGWO.Additionally,performance analysis was conducted by comparing the proposed algorithm with three mature and classical algorithms.The results demonstrate that the proposed algorithm exhibits superior performance in solving the hybrid flow-shop scheduling problem(HFSP).Case validations were conducted for different types of order disturbance scenarios.The results demonstrate that the proposed adaptive dynamic scheduling strategy and the IMOGWO algorithm can effectively address order disturbance events.They enable rapid response to order disturbance while ensuring the stability of the production system.展开更多
Hypersonic morphing vehicle(HMV)can reconfigure aerodynamic geometries in real time,adapting to diverse needs like multi-mission profiles and wide-speed-range flight,spanwise morphing and sweep angle variation are rep...Hypersonic morphing vehicle(HMV)can reconfigure aerodynamic geometries in real time,adapting to diverse needs like multi-mission profiles and wide-speed-range flight,spanwise morphing and sweep angle variation are representative large-scale wing reconfiguration modes.To meet the HMV's need for an increased lift and a lift to drag ratio during hypersonic maneuverability and cruise or reentry equilibrium glide,this paper proposes an innovative single-DOF coupled morphing-wing system.We then systematically analyze its open-loop kinematics and closed-loop connectivity constraints,and the proposed system integrates three functional modules:the preset locking/release mechanism,the coupled morphing-wing mechanism,and the integrated wing locking with active stiffness control mechanism.Experimental validation confirms stable,continuous morphing under simulated aerodynamic loads.The experimental results indicate:(i)SMA actuators exhibit response times ranging from 18 s to 160 s,providing sufficient force output for wing unlocking;(ii)The integrated wing locking with active stiffness control mechanism effectively secures wing positions while eliminating airframe clearance via SMA actuation,improving the first-order natural frequency by more than 17%;(iii)The distributed aerodynamic loading system enables precise multi-stage follow-up loading during morphing,with the coupled morphing wing maintaining stable,continuous operation under 0-3500 N normal loads and 110-140 N axial force.The proposed single-DOF coupled morphing mechanism not only simplifies and improves structural efficiency but also demonstrates superior performance in locking control,stiffness enhancement,and aerodynamic responsiveness.This establishes a foundational framework for the design of future intelligent morphing configurations and the implementation of flight control systems.展开更多
Conventional locking/release mechanisms often face challenges in aircraft wing separation processes,such as excessive impact loads and insufficient synchronization.These may cause structural damage to the airframe or ...Conventional locking/release mechanisms often face challenges in aircraft wing separation processes,such as excessive impact loads and insufficient synchronization.These may cause structural damage to the airframe or attitude instability,seriously compromising mission reliability.To address this engineering challenge,this paper proposes a multi-point low-impact locking/release mechanism based on the mobility model and energy conversion strategy.Through establishing a DOF constraint framework system,this paper systematically analyzes the energy transfer and conversion characteristics during the wing separation process,reveals the generation mechanism of impact loads,and conducts research on low-impact design based on energy conversion strategy.Building on this foundation,a single-point locking/release mechanism employing parallel trapezoidal key shaft structure was designed,which increases frictional contact time and reduces the energy release rate,thereby achieving low-impact characteristics.The mechanism's performance was validated through physical prototype development and systematic functional testing(including unlocking force,synchronization,and impact tests).Experimental results demonstrate:(1)Under 14 kN preload condition,the maximum unlocking force was only 92.54 N,showing a linear relationship with preload that satisfies the"strong-connection/weak-unlock"design requirement;(2)Wing separation was completed within 46 ms,with synchronization time difference among three separation mechanisms stably controlled within 12-14 ms,proving rapid and reliable operation;(3)The unlocking impact acceleration ranged between 26 and 73 g,below the 100 g design limit,confirming the effectiveness of the energy conversion strategy.The proposed low-impact locking/release mechanism design method based on energy conversion strategy resolves the traditional challenges of high impact and synchronization deficiencies.The synergistic optimization mechanism of"structural load reduction and performance improvement"provides a highly reliable technical solution for wing separable mechanisms while offering novel design insights for wing connection/separation systems engineering.展开更多
The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is m...The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.展开更多
To explore the distribution law of the temperature field in the motor pump and the influence of the fanshaped DC channel with spoiler in the pump housing on its heat dissipation performance.This study takes the arc-ge...To explore the distribution law of the temperature field in the motor pump and the influence of the fanshaped DC channel with spoiler in the pump housing on its heat dissipation performance.This study takes the arc-gear type hydraulicmotor pump as the research object.In COMSOL,a coupled heat transfer simulationmodel of themotor pump’s fluid-solid coupling is established,and the internal temperature field characteristics are analyzed.To improve the heat dissipation effect of the motor pump,it is proposed to arrange spoiler in the fan-shaped DC channel of the pump housing to enhance heat dissipation.Three types of spoilers,namely,wing-shaped,inclined rectangle-shaped,and wave-shaped,are designed.The simulation results show that when the motor pump operates under rated conditions,due to the poor heat dissipation environment inside the motor pump,the high-temperature areas of the motor pump are concentrated in the rotor and permanent magnet parts.After arranging the spoiler,the turbulent kinetic energy and vorticity in the fan-shaped DC channel of the pump housing are significantly enhanced.All three spoiler structures can reduce the maximum temperature of each component of the motor.According to the comprehensive performance evaluation criterion(PEC),the inclined rectangle-shaped structure has the best comprehensive heat transfer performance(PEC=1.114),while the wave-shaped structure has higher heat transfer efficiency but greater pressure loss.The wing-shaped structure has relatively limited enhancement effect on heat dissipation.This study systematically quantifies the influence of different spoiler structures on heat dissipation performance and flowresistance characteristics,providing a solution for enhancing the heat dissipation of the motor pump.展开更多
Conventional ignition methods are proving to be ineffective for low-sensitivity energetic materials,highlighting the need to investigate alternative ignition systems,such as laser-based techniques.Over the past decade...Conventional ignition methods are proving to be ineffective for low-sensitivity energetic materials,highlighting the need to investigate alternative ignition systems,such as laser-based techniques.Over the past decade,lasers have emerged as a promising solution,providing focused energy beams for controllable,efficient,and reliable ignition in the field of energetic materials.This study presents a comparative analysis of two state-of-the-art ignition approaches:direct laser ignition and laser-driven flyer ignition.Experiments were performed using a Neodymium-doped Yttrium Aluminum Garnet(Nd:YAG)laser at different energy beam levels to systematically evaluate ignition onset.In the direct laser ignition test setup,the laser beam was applied directly to the energetic tested material,while laserdriven flyer ignition utilized 40 and 100μm aluminum foils,propelled at velocities ranging from 300 to 1250 m/s.Comparative analysis with the Lawrence and Trott model substantiated the velocity data and provided insight into the ignition mechanisms.Experimental results indicate that the ignition time for the laser-driven flyer method was significantly shorter,with the pyrotechnic composition achieving complete combustion faster compared to direct laser ignition.Moreover,precise ignition thresholds were determined for both methods,providing critical parameters for optimizing ignition systems in energetic materials.This work elucidates the advantages and limitations of each technique while advancing next-generation ignition technology,enhancing the reliability and safety of propulsion systems.展开更多
In this paper,a hierarchical reinforcement learning(HRL)based real-time formation control approach is proposed for heterogeneous aerial-ground agents(HAGAs).Initially,to address the issue of imprecise modeling of HAGA...In this paper,a hierarchical reinforcement learning(HRL)based real-time formation control approach is proposed for heterogeneous aerial-ground agents(HAGAs).Initially,to address the issue of imprecise modeling of HAGAs,a unified heterogeneous chained system model is constructed using the hand-position method.Subsequently,a hierarchical framework is designed:(1)To decouple multi-agent collaborative interactions and individual dynamic rules through hierarchical resolution,which enables controller design to be independent of direct reliance on neighborhood collaborative errors.(2)By adopting a dual-layer framework that separates collaborative topology management from individual control strategies,seamless switching between multiple task scenarios can be achieved simply by reconstructing the collaborative topology of the first layer.Moreover,to overcome the issue of non-asymptotic stability of tracking errors caused by the discount factor in traditional optimal control,a cost function based on the derivative of the tracking error is introduced.This not only addresses the error issue caused by the discount factor but also effectively resolves the problem of the unboundedness of the quadratic cost function.Finally,the efficacy of the proposed algorithm is substantiated through simulation experiments.展开更多
The continuous improvement of solar thermal technologies is essential to meet the growing demand for sustainable heat generation and to support global decarbonization efforts.This study presents the design,implementat...The continuous improvement of solar thermal technologies is essential to meet the growing demand for sustainable heat generation and to support global decarbonization efforts.This study presents the design,implementation,and validation of a real-time monitoring framework based on the Internet ofThings(IoT)and cloud computing to enhance the thermal performance of evacuated tube solar water heaters(ETSWHs).A commercial system and a custom-built prototype were instrumented with Industry 4.0 technologies,including platinum resistance temperature detectors(PT100),solar irradiance and wind speed sensors,a programmable logic controller(PLC),a SCADAinterface,and a cloud-connected IoT gateway.Data were processed locally and transmitted to cloud storage for continuous analysis and visualization via amobile application.Experimental results demonstrated the prototype’s superior thermal energy storage capacity−47.4 vs.36.2 MJ for the commercial system,representing a 31%—achieved through the novel integration of Industry 4.0 architecture with an optimized collector design.This improvement is attributed to optimized geometric design parameters,including a reduced tilt angle,increased inter-tube spacing,and the incorporation of an aluminum reflective surface.These modifications collectively enhanced solar heat absorption and reduced optical losses.The framework effectively identified thermal stratification,monitored environmental effects on heat transfer,and enabled real-time system diagnostics.By integrating automation,IoT,and cloud computing,the proposed architecture establishes a scalable and replicable model for the intelligent management of solar thermal systems,facilitating predictive maintenance and future integration with artificial intelligence for performance forecasting.This work provides a practical,data-driven approach to digitizing and optimizing heat transfer systems,promoting more efficient and sustainable solar thermal energy applications.展开更多
Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approach...Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.展开更多
Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection mo...Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.展开更多
The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic,creating significant challenges for network energy consumption.Also,with the extraordinary gro...The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic,creating significant challenges for network energy consumption.Also,with the extraordinary growth of mobile communications,the data traffic has dramatically expanded,which has led to massive grid power consumption and incurred high operating expenditure(OPEX).However,the majority of current network designs struggle to efficientlymanage a massive amount of data using little power,which degrades energy efficiency performance.Thereby,it is necessary to have an efficient mechanism to reduce power consumption when processing large amounts of data in network data centers.Utilizing renewable energy sources to power the Cloud Radio Access Network(C-RAN)greatly reduces the need to purchase energy from the utility grid.In this paper,we propose a bandwidth-aware hybrid energypowered C-RAN that focuses on throughput and energy efficiency(EE)by lowering grid usage,aiming to enhance the EE.This paper examines the energy efficiency,spectral efficiency(SE),and average on-grid energy consumption,dealing with the major challenges of the temporal and spatial nature of traffic and renewable energy generation across various network setups.To assess the effectiveness of the suggested network by changing the transmission bandwidth,a comprehensive simulation has been conducted.The numerical findings support the efficacy of the suggested approach.展开更多
With the rapid advancements in biomedical engineering,bioprinting has emerged as a pivotal solution to address the shortage of organ transplants and advance disease model research.The evolution of bioprinting has prog...With the rapid advancements in biomedical engineering,bioprinting has emerged as a pivotal solution to address the shortage of organ transplants and advance disease model research.The evolution of bioprinting has progressed from the fabrication of simple models(1.0)to the fabrication of permanent implants(2.0),tissue engineering scaffolds(3.0),and complex biostructures utilizing living cells(4.0).Nevertheless,significant challenges remain,particularly in accurately replicating the structure and function of host tissues,selecting appropriate materials,and optimizing printing parameters.The integration of artificial intelligence(AI),especially machine learning,provides promising novel opportunities in bioprinting(5.0).This review systematically summarizes the current applications of AI in bioprinting,discussing both construction strategies and application scenarios.It also explores the potential of AI to improve bioprinting in the preparation of complex functional tissues and in situ tissue repair.Overall,the synergy between AI and bioprinting is poised to drive the development of personalized medicine,facilitate high-throughput preparation of in vitro models,and provide robust tools for regenerative medicine and precision healthcare.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
Tissue engineering is an emerging means for resolving the problems of tissue repair and organ replacement in regenerative medicine.Insufficient supply of nutrients and oxygen to cells in large-scale tissues has led to...Tissue engineering is an emerging means for resolving the problems of tissue repair and organ replacement in regenerative medicine.Insufficient supply of nutrients and oxygen to cells in large-scale tissues has led to the demand to prepare blood vessels.Scaffold-based tissue engineering approaches are effective methods to form new blood vessel tissues.The demand for blood vessels prompts systematic research on fabrication strategies of vascular scaffolds for tissue engineering.Recent advances in 3D printing have facilitated fabrication of vascular scaffolds,contributing to broad prospects for tissue vascularization.This review presents state of the art on modeling methods,print materials and preparation processes for fabrication of vascular scaffolds,and discusses the advantages and application fields of each method.Specially,significance and importance of scaffold-based tissue engineering for vascular regeneration are emphasized.Print materials and preparation processes are discussed in detail.And a focus is placed on preparation processes based on 3D printing technologies and traditional manufacturing technologies including casting,electrospinning,and Lego-like construction.And related studies are exemplified.Transformation of vascular scaffolds to clinical application is discussed.Also,four trends of 3D printing of tissue engineering vascular scaffolds are presented,including machine learning,near-infrared photopolymerization,4D printing,and combination of self-assembly and 3D printing-based methods.展开更多
LuGre model has been widely used in friction modeling and compensation.However,the new friction regime,named prestiction regime,cannot be accurately characterized by LuGre model in the latest research.With the extensi...LuGre model has been widely used in friction modeling and compensation.However,the new friction regime,named prestiction regime,cannot be accurately characterized by LuGre model in the latest research.With the extensive experimental observations of friction behaviors in the prestiction,some variables were abstracted to depict the rules in the prestiction regime.Based upon the knowledge of friction modeling,a novel friction model including the presliding regime,the gross sliding regime and the prestiction regime was then presented to overcome the shortcomings of the LuGre model.The reason that LuGre model cannot estimate the prestiction friction was analyzed in theory.Feasibility analysis of the proposed model in modeling the prestiction friction was also addressed.A parameter identification method for the proposed model based on multilevel coordinate search algorithm was presented.The proposed friction compensation strategy was composed of a nonlinear friction observer and a feedforward mechanism.The friction observer was designed to estimate the friction force in the presliding and the gross sliding regimes.And the friction force was estimated based on the model in the prestiction regime.The comparative trajectory tracking experiments were conducted on a simulator of inertially stabilization platforms among three control schemes:the single proportional–derivative(PD)control,the PD with LuGre model-based compensation and the PD with compensator based on the presented model.The experimental results reveal that the control scheme based on the proposed model has the best tracking performance.It reduces the peak-to-peak value(PPV)of tracking error to 0.2 mrad,which is improved almost 50%compared with the PD with LuGre model-based compensation.Compared to the single PD control,it reduces the PPV of error by 66.7%.展开更多
The sensitivity engineering model of the coupling capacitance detector is built to provide the theoretic foundation for designing its circuits and electrodes scientifically. The sensitivity concept model of the capaci...The sensitivity engineering model of the coupling capacitance detector is built to provide the theoretic foundation for designing its circuits and electrodes scientifically. The sensitivity concept model of the capacitance proximity detector is discussed, and the detecting sensitivity of the coupling capacitance detector is analyzed theoretically. Then the sensitivity engineering model, which can reflect the main parameters relationship of the detecting circuit is set up based on the foregoing analyses. It is concluded that: ① the sensitivity is mainly correlative with some parameters including the voltage transmission factor of the demodulator, the oscillating voltage amplitude and the amplitude variation constant of the oscillator; ② the sensitivity is also influenced by the areas of electrodes and the distance between electrodes of the detector.展开更多
基金supported by the National Natural Science Foundation of China(52371131)the 10th Youth Talent Lifting Project of the China Association for Science and Technology.
文摘Rechargeable alkali metal-sulfur(M-S)batteries,including Li/Na/K-S chemistries,have the potential to utilize abundant and low-cost sulfur cathodes yet offer high theoretical energy densities.However,their practical electrochemical performance is fundamentally limited by the polysulfide shuttle effect.This challenge is particularly exacerbated in Na-S and K-S systems owing to larger metal-ion radii,weaker solvation energies,slower redox kinetics,and greater electrolyte-electrode incompatibilities compared to Li-S batteries.This review presents a comparative analysis of interface engineering strategies designed to suppress the shuttle effect across these three systems.Following a summary of sulfur cathode properties and reaction mechanisms,we systematically examine the origins of polysulfide shuttling.Our analysis progresses from functional separator design and interlayer enhancements to the implementation of solid-state electrolytes for root-cause inhibition.By evaluating interface engineering research specific to Na-S and K-S batteries,we elucidate both shared principles and unique challenges inherent to alkali M-S systems.Finally,we propose multifaceted solutions to achieve shuttlefree operation and enhance overall battery performance,thereby establishing a foundation for future advancements.
基金Jiangsu Association for Science and Technology Youth Talent Support Project(Grant No.JSTJ-2024-031)National Natural Science Foundation of China(Grant No.52005265)Natural Science Fund for Colleges and Universities in Jiangsu Province(Grant No.20KJB460002)。
文摘Owing to the harsh conditions,wind turbine bearings are prone to faults,and the resulting fault information is easily submerged by strong noise disturbance,making conventional diagnosis challenging.Therefore,this study presents an innovative bearing fault diagnosis approach predicated on Parameter⁃Optimized Symplectic Geometry Mode Decomposition(POSGMD)and Improved Convolutional Neural Network(ICNN).Firstly,assisted by the relative entropy⁃based adaptive selection of embedding dimension,a POSGMD is presented to adaptively decompose the collected bearing vibration signals into various Symplectic Geometry Components(SGC),which can solve the problem of manual selection of the embedding dimension in the raw Symplectic Geometry Mode Decomposition(SGMD).Meanwhile,the signal reconstruction on the decomposed SGC is conducted based on kurtosis⁃weighted principle to obtain the reconstructed signals.Subsequently,the Continuous Wavelet Transform(CWT)of the reconstructed signals is calculated to generate the corresponding time⁃frequency images as sample set.Finally,an ICNN is introduced for model training and automatic recognition of bearing faults.Two case studies are used to validate the presented methods efficacy.Comparing the presented method with traditional fault diagnosis methods,experimental results show that it can achieve greater identification accuracy and superior anti⁃noise resilience.This work provides a practical and effective solution for fault diagnosis in wind turbine bearings,contributing to the timely detection of faults and the reliable operation of wind turbines or other rotational machinery in industrial applications.
基金Open access funding provided by The Science,Technology&Innovation Funding Authority(STDF)in cooperation with The Egyptian Knowledge Bank(EKB).
文摘Soft robotic manipulators represent a rapidly evolving field characterized by inherent compliance,adaptability,and safe interactions within unstructured environments.Over the past decade(2015-2025),significant advancements have trans-formed their capabilities through novel designs inspired by biological systems,advanced modeling frameworks,sophisti-cated control strategies,and integration into diverse real-world applications.Recent innovations in multifunctional mate-rials and emerging actuation technologies have markedly expanded manipulator performance,reliability,and dexterity.Concurrently,developments in modeling have progressed from simplified geometric methods toward highly accurate physics-based and hybrid data-driven approaches,substantially improving real-time prediction and controllability.Coupled with these developments,adaptive and robust control strategies-including learning-based techniques-have enabled unprec-edented autonomy and precision in challenging application domains such as Minimally Invasive Surgery(MIS),precision agriculture,deep-sea exploration,disaster recovery,and space missions.Despite these remarkable strides,key challenges remain,notably regarding scalability,long-term material durability,robust integrated sensing,and standardized evaluation procedures.This review comprehensively synthesizes recent advances,critically evaluates state-of-the-art methodologies,and systematically identifies existing gaps to provide a clear roadmap and targeted research directions,guiding future developments toward the broader adoption and optimal utilization of soft robotic manipulators.
基金supported by the Cultivation Program for Major Scientific Research Projects of Harbin Institute of Technology(ZDXMPY20180109).
文摘Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving,owing to its efficiency and applicability in both training and evaluating algorithms.Consequently,there has been increasing attention on generating highly realistic and consistent driving videos,particularly those involving viewpoint changes guided by the control commands or trajectories of ego vehicles.However,current reconstruction approaches,such as Neural Radiance Fields and 3D Gaussian Splatting,frequently suffer from limited generalization and depend on substantial input data.Meanwhile,2D generative models,though capable of producing unknown scenes,still have room for improvement in terms of coherence and visual realism.To overcome these challenges,we introduce GenScene,a world model that synthesizes front-view driving videos conditioned on trajectories.A new temporal module is presented to improve video consistency by extracting the global context of each frame,calculating relationships of frames using these global representations,and fusing frame contexts accordingly.Moreover,we propose an innovative attention mechanism that computes relations of pixels within each frame and pixels in the corresponding window range of the initial frame.Extensive experiments show that our approach surpasses various state-of-the-art models in driving video generation,and the introduced modules contribute significantly to model performance.This work establishes a new paradigm for goal-oriented video synthesis in autonomous driving,which facilitates on-demand simulation to expedite algorithm development.
基金funded by National Key Research and Development Program Projects of China under Grant No.2020YFB1713500.
文摘To address the issue that hybrid flow shop production struggles to handle order disturbance events,a dynamic scheduling model was constructed.The model takes minimizing the maximum makespan,delivery time deviation,and scheme deviation degree as the optimization objectives.An adaptive dynamic scheduling strategy based on the degree of order disturbance is proposed.An improved multi-objective Grey Wolf(IMOGWO)optimization algorithm is designed by combining the“job-machine”two-layer encoding strategy,the timing-driven two-stage decoding strategy,the opposition-based learning initialization population strategy,the POX crossover strategy,the dualoperation dynamic mutation strategy,and the variable neighborhood search strategy for problem solving.A variety of test cases with different scales were designed,and ablation experiments were conducted to verify the effectiveness of the improved strategies.The results show that each improved strategy can effectively enhance the performance of the IMOGWO.Additionally,performance analysis was conducted by comparing the proposed algorithm with three mature and classical algorithms.The results demonstrate that the proposed algorithm exhibits superior performance in solving the hybrid flow-shop scheduling problem(HFSP).Case validations were conducted for different types of order disturbance scenarios.The results demonstrate that the proposed adaptive dynamic scheduling strategy and the IMOGWO algorithm can effectively address order disturbance events.They enable rapid response to order disturbance while ensuring the stability of the production system.
基金supported by the National Natural Science Foundation of China(Grant No.52405257)the China Postdoctoral Science Foundation(Grant No.2024M764201).
文摘Hypersonic morphing vehicle(HMV)can reconfigure aerodynamic geometries in real time,adapting to diverse needs like multi-mission profiles and wide-speed-range flight,spanwise morphing and sweep angle variation are representative large-scale wing reconfiguration modes.To meet the HMV's need for an increased lift and a lift to drag ratio during hypersonic maneuverability and cruise or reentry equilibrium glide,this paper proposes an innovative single-DOF coupled morphing-wing system.We then systematically analyze its open-loop kinematics and closed-loop connectivity constraints,and the proposed system integrates three functional modules:the preset locking/release mechanism,the coupled morphing-wing mechanism,and the integrated wing locking with active stiffness control mechanism.Experimental validation confirms stable,continuous morphing under simulated aerodynamic loads.The experimental results indicate:(i)SMA actuators exhibit response times ranging from 18 s to 160 s,providing sufficient force output for wing unlocking;(ii)The integrated wing locking with active stiffness control mechanism effectively secures wing positions while eliminating airframe clearance via SMA actuation,improving the first-order natural frequency by more than 17%;(iii)The distributed aerodynamic loading system enables precise multi-stage follow-up loading during morphing,with the coupled morphing wing maintaining stable,continuous operation under 0-3500 N normal loads and 110-140 N axial force.The proposed single-DOF coupled morphing mechanism not only simplifies and improves structural efficiency but also demonstrates superior performance in locking control,stiffness enhancement,and aerodynamic responsiveness.This establishes a foundational framework for the design of future intelligent morphing configurations and the implementation of flight control systems.
文摘Conventional locking/release mechanisms often face challenges in aircraft wing separation processes,such as excessive impact loads and insufficient synchronization.These may cause structural damage to the airframe or attitude instability,seriously compromising mission reliability.To address this engineering challenge,this paper proposes a multi-point low-impact locking/release mechanism based on the mobility model and energy conversion strategy.Through establishing a DOF constraint framework system,this paper systematically analyzes the energy transfer and conversion characteristics during the wing separation process,reveals the generation mechanism of impact loads,and conducts research on low-impact design based on energy conversion strategy.Building on this foundation,a single-point locking/release mechanism employing parallel trapezoidal key shaft structure was designed,which increases frictional contact time and reduces the energy release rate,thereby achieving low-impact characteristics.The mechanism's performance was validated through physical prototype development and systematic functional testing(including unlocking force,synchronization,and impact tests).Experimental results demonstrate:(1)Under 14 kN preload condition,the maximum unlocking force was only 92.54 N,showing a linear relationship with preload that satisfies the"strong-connection/weak-unlock"design requirement;(2)Wing separation was completed within 46 ms,with synchronization time difference among three separation mechanisms stably controlled within 12-14 ms,proving rapid and reliable operation;(3)The unlocking impact acceleration ranged between 26 and 73 g,below the 100 g design limit,confirming the effectiveness of the energy conversion strategy.The proposed low-impact locking/release mechanism design method based on energy conversion strategy resolves the traditional challenges of high impact and synchronization deficiencies.The synergistic optimization mechanism of"structural load reduction and performance improvement"provides a highly reliable technical solution for wing separable mechanisms while offering novel design insights for wing connection/separation systems engineering.
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.
基金supported by the Henan Provincial Key Research and Development Special Project(251111220200)Natural Science Foundation of Henan Province Project(252300420446).
文摘To explore the distribution law of the temperature field in the motor pump and the influence of the fanshaped DC channel with spoiler in the pump housing on its heat dissipation performance.This study takes the arc-gear type hydraulicmotor pump as the research object.In COMSOL,a coupled heat transfer simulationmodel of themotor pump’s fluid-solid coupling is established,and the internal temperature field characteristics are analyzed.To improve the heat dissipation effect of the motor pump,it is proposed to arrange spoiler in the fan-shaped DC channel of the pump housing to enhance heat dissipation.Three types of spoilers,namely,wing-shaped,inclined rectangle-shaped,and wave-shaped,are designed.The simulation results show that when the motor pump operates under rated conditions,due to the poor heat dissipation environment inside the motor pump,the high-temperature areas of the motor pump are concentrated in the rotor and permanent magnet parts.After arranging the spoiler,the turbulent kinetic energy and vorticity in the fan-shaped DC channel of the pump housing are significantly enhanced.All three spoiler structures can reduce the maximum temperature of each component of the motor.According to the comprehensive performance evaluation criterion(PEC),the inclined rectangle-shaped structure has the best comprehensive heat transfer performance(PEC=1.114),while the wave-shaped structure has higher heat transfer efficiency but greater pressure loss.The wing-shaped structure has relatively limited enhancement effect on heat dissipation.This study systematically quantifies the influence of different spoiler structures on heat dissipation performance and flowresistance characteristics,providing a solution for enhancing the heat dissipation of the motor pump.
文摘Conventional ignition methods are proving to be ineffective for low-sensitivity energetic materials,highlighting the need to investigate alternative ignition systems,such as laser-based techniques.Over the past decade,lasers have emerged as a promising solution,providing focused energy beams for controllable,efficient,and reliable ignition in the field of energetic materials.This study presents a comparative analysis of two state-of-the-art ignition approaches:direct laser ignition and laser-driven flyer ignition.Experiments were performed using a Neodymium-doped Yttrium Aluminum Garnet(Nd:YAG)laser at different energy beam levels to systematically evaluate ignition onset.In the direct laser ignition test setup,the laser beam was applied directly to the energetic tested material,while laserdriven flyer ignition utilized 40 and 100μm aluminum foils,propelled at velocities ranging from 300 to 1250 m/s.Comparative analysis with the Lawrence and Trott model substantiated the velocity data and provided insight into the ignition mechanisms.Experimental results indicate that the ignition time for the laser-driven flyer method was significantly shorter,with the pyrotechnic composition achieving complete combustion faster compared to direct laser ignition.Moreover,precise ignition thresholds were determined for both methods,providing critical parameters for optimizing ignition systems in energetic materials.This work elucidates the advantages and limitations of each technique while advancing next-generation ignition technology,enhancing the reliability and safety of propulsion systems.
基金supported by the National Natural Science Foundation of China(Grant Nos.T2421001,61922053,62403298)the Natural Science Foundation of Shanghai(Grant No.25ZR1401119)+1 种基金the China Postdoctoral Science Foundation(Grant No.2024M751933)the Shanghai Post-doctoral Excellence Program(Grant No.2023316)。
文摘In this paper,a hierarchical reinforcement learning(HRL)based real-time formation control approach is proposed for heterogeneous aerial-ground agents(HAGAs).Initially,to address the issue of imprecise modeling of HAGAs,a unified heterogeneous chained system model is constructed using the hand-position method.Subsequently,a hierarchical framework is designed:(1)To decouple multi-agent collaborative interactions and individual dynamic rules through hierarchical resolution,which enables controller design to be independent of direct reliance on neighborhood collaborative errors.(2)By adopting a dual-layer framework that separates collaborative topology management from individual control strategies,seamless switching between multiple task scenarios can be achieved simply by reconstructing the collaborative topology of the first layer.Moreover,to overcome the issue of non-asymptotic stability of tracking errors caused by the discount factor in traditional optimal control,a cost function based on the derivative of the tracking error is introduced.This not only addresses the error issue caused by the discount factor but also effectively resolves the problem of the unboundedness of the quadratic cost function.Finally,the efficacy of the proposed algorithm is substantiated through simulation experiments.
基金funded by the National Council of Science,Technology,and Technological Innovation(CONCYTEC)the National Program of Scientific Research and Advanced Studies(PROCIENCIA)under the E041-2022-“Applied Research Projects”competition.Contract number:PE501078609-2022-PROCIENCIA.
文摘The continuous improvement of solar thermal technologies is essential to meet the growing demand for sustainable heat generation and to support global decarbonization efforts.This study presents the design,implementation,and validation of a real-time monitoring framework based on the Internet ofThings(IoT)and cloud computing to enhance the thermal performance of evacuated tube solar water heaters(ETSWHs).A commercial system and a custom-built prototype were instrumented with Industry 4.0 technologies,including platinum resistance temperature detectors(PT100),solar irradiance and wind speed sensors,a programmable logic controller(PLC),a SCADAinterface,and a cloud-connected IoT gateway.Data were processed locally and transmitted to cloud storage for continuous analysis and visualization via amobile application.Experimental results demonstrated the prototype’s superior thermal energy storage capacity−47.4 vs.36.2 MJ for the commercial system,representing a 31%—achieved through the novel integration of Industry 4.0 architecture with an optimized collector design.This improvement is attributed to optimized geometric design parameters,including a reduced tilt angle,increased inter-tube spacing,and the incorporation of an aluminum reflective surface.These modifications collectively enhanced solar heat absorption and reduced optical losses.The framework effectively identified thermal stratification,monitored environmental effects on heat transfer,and enabled real-time system diagnostics.By integrating automation,IoT,and cloud computing,the proposed architecture establishes a scalable and replicable model for the intelligent management of solar thermal systems,facilitating predictive maintenance and future integration with artificial intelligence for performance forecasting.This work provides a practical,data-driven approach to digitizing and optimizing heat transfer systems,promoting more efficient and sustainable solar thermal energy applications.
基金funded by the National Natural Science Foundation of China,grant numbers 52374156 and 62476005。
文摘Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.IPP:172-830-2025.
文摘Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.
文摘The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic,creating significant challenges for network energy consumption.Also,with the extraordinary growth of mobile communications,the data traffic has dramatically expanded,which has led to massive grid power consumption and incurred high operating expenditure(OPEX).However,the majority of current network designs struggle to efficientlymanage a massive amount of data using little power,which degrades energy efficiency performance.Thereby,it is necessary to have an efficient mechanism to reduce power consumption when processing large amounts of data in network data centers.Utilizing renewable energy sources to power the Cloud Radio Access Network(C-RAN)greatly reduces the need to purchase energy from the utility grid.In this paper,we propose a bandwidth-aware hybrid energypowered C-RAN that focuses on throughput and energy efficiency(EE)by lowering grid usage,aiming to enhance the EE.This paper examines the energy efficiency,spectral efficiency(SE),and average on-grid energy consumption,dealing with the major challenges of the temporal and spatial nature of traffic and renewable energy generation across various network setups.To assess the effectiveness of the suggested network by changing the transmission bandwidth,a comprehensive simulation has been conducted.The numerical findings support the efficacy of the suggested approach.
基金financially supported by the National Natural Science Foundation of China(Nos.32471396,82230071,82172098,82201716,and 61973206)the National Key R&D Program of China(No.2023YFC2411303)+4 种基金the Integrated Project of Major Research Plan of the National Natural Science Foundation of China(No.92249303)the Shanghai Committee of Science and Technology(No.23141900600,Laboratory Animal Research Project)the Shanghai Clinical Research Plan of SHDC2023CRT01the Young Elite Scientist Sponsorship Program by the China Association for Science and Technology(No.YESS20230049)the Baoshan District Health Commission Talents(Excellent Academic Leaders)Program(No.BSWSYX-2024-05)。
文摘With the rapid advancements in biomedical engineering,bioprinting has emerged as a pivotal solution to address the shortage of organ transplants and advance disease model research.The evolution of bioprinting has progressed from the fabrication of simple models(1.0)to the fabrication of permanent implants(2.0),tissue engineering scaffolds(3.0),and complex biostructures utilizing living cells(4.0).Nevertheless,significant challenges remain,particularly in accurately replicating the structure and function of host tissues,selecting appropriate materials,and optimizing printing parameters.The integration of artificial intelligence(AI),especially machine learning,provides promising novel opportunities in bioprinting(5.0).This review systematically summarizes the current applications of AI in bioprinting,discussing both construction strategies and application scenarios.It also explores the potential of AI to improve bioprinting in the preparation of complex functional tissues and in situ tissue repair.Overall,the synergy between AI and bioprinting is poised to drive the development of personalized medicine,facilitate high-throughput preparation of in vitro models,and provide robust tools for regenerative medicine and precision healthcare.
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.
文摘Tissue engineering is an emerging means for resolving the problems of tissue repair and organ replacement in regenerative medicine.Insufficient supply of nutrients and oxygen to cells in large-scale tissues has led to the demand to prepare blood vessels.Scaffold-based tissue engineering approaches are effective methods to form new blood vessel tissues.The demand for blood vessels prompts systematic research on fabrication strategies of vascular scaffolds for tissue engineering.Recent advances in 3D printing have facilitated fabrication of vascular scaffolds,contributing to broad prospects for tissue vascularization.This review presents state of the art on modeling methods,print materials and preparation processes for fabrication of vascular scaffolds,and discusses the advantages and application fields of each method.Specially,significance and importance of scaffold-based tissue engineering for vascular regeneration are emphasized.Print materials and preparation processes are discussed in detail.And a focus is placed on preparation processes based on 3D printing technologies and traditional manufacturing technologies including casting,electrospinning,and Lego-like construction.And related studies are exemplified.Transformation of vascular scaffolds to clinical application is discussed.Also,four trends of 3D printing of tissue engineering vascular scaffolds are presented,including machine learning,near-infrared photopolymerization,4D printing,and combination of self-assembly and 3D printing-based methods.
基金Projects(51135009,51105371) supported by the National Natural Science Foundation of China
文摘LuGre model has been widely used in friction modeling and compensation.However,the new friction regime,named prestiction regime,cannot be accurately characterized by LuGre model in the latest research.With the extensive experimental observations of friction behaviors in the prestiction,some variables were abstracted to depict the rules in the prestiction regime.Based upon the knowledge of friction modeling,a novel friction model including the presliding regime,the gross sliding regime and the prestiction regime was then presented to overcome the shortcomings of the LuGre model.The reason that LuGre model cannot estimate the prestiction friction was analyzed in theory.Feasibility analysis of the proposed model in modeling the prestiction friction was also addressed.A parameter identification method for the proposed model based on multilevel coordinate search algorithm was presented.The proposed friction compensation strategy was composed of a nonlinear friction observer and a feedforward mechanism.The friction observer was designed to estimate the friction force in the presliding and the gross sliding regimes.And the friction force was estimated based on the model in the prestiction regime.The comparative trajectory tracking experiments were conducted on a simulator of inertially stabilization platforms among three control schemes:the single proportional–derivative(PD)control,the PD with LuGre model-based compensation and the PD with compensator based on the presented model.The experimental results reveal that the control scheme based on the proposed model has the best tracking performance.It reduces the peak-to-peak value(PPV)of tracking error to 0.2 mrad,which is improved almost 50%compared with the PD with LuGre model-based compensation.Compared to the single PD control,it reduces the PPV of error by 66.7%.
文摘The sensitivity engineering model of the coupling capacitance detector is built to provide the theoretic foundation for designing its circuits and electrodes scientifically. The sensitivity concept model of the capacitance proximity detector is discussed, and the detecting sensitivity of the coupling capacitance detector is analyzed theoretically. Then the sensitivity engineering model, which can reflect the main parameters relationship of the detecting circuit is set up based on the foregoing analyses. It is concluded that: ① the sensitivity is mainly correlative with some parameters including the voltage transmission factor of the demodulator, the oscillating voltage amplitude and the amplitude variation constant of the oscillator; ② the sensitivity is also influenced by the areas of electrodes and the distance between electrodes of the detector.
