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Adaptive fixed-time fuzzy fault-tolerant cooperative spiral-diving guidance law with initial state-independent prescribed performance 认领 引用
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作者 Tianyi WU Zheng WANG +2 位作者 Caisheng WEI Xin NING Zhansheng CHEN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第5期483-503,共21页
This paper proposes a novel performance guaranteed fixed-time fuzzy adaptive faulttolerant cooperative spiral-diving guidance law for a group of flight vehicles subject to system internal uncertainties,actuator faults... This paper proposes a novel performance guaranteed fixed-time fuzzy adaptive faulttolerant cooperative spiral-diving guidance law for a group of flight vehicles subject to system internal uncertainties,actuator faults,and external disturbances simultaneously.Firstly,based on the analysis of the spiral-diving terminal guidance process,a novel set of guidance dynamic equations,which is different from the traditional line-of-sight angle equations but suitable for the design of a cooperative spiral guidance law,is established.Thereafter,by designing a novel Initial StateIndependent Fixed-Time Prescribed Performance Function(ISIFTPPF),the time-to-go free virtual guidance law that can ensure prescribed performance in spite of any initial condition is developed to generate spiral maneuvers.Subsequently,the fixed-time adaptive guidance law based on the auxiliary subsystem is designed,ensuring that actuator saturation constraints can be satisfied and the unknown disturbances as well as actuator faults can be effectively handled.It should be noted that the proposed method is a low-complexity hierarchical spiral guidance scheme that can remarkably reduce the consumption of computational resources.Theoretical analysis illustrates that the closedloop system is practically fixed-time stable,and the tracking errors will converge within the pregiven boundaries.Finally,the effectiveness,superiority,and practical application potential of the proposed cooperative guidance method are demonstrated by several numerical simulations. 展开更多
关键词 Adaptive fuzzy faulttolerant Cooperative guidance Multiple constraints Multiple flight vehicles Practical fixed-time stability Spiral trajectories
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Simultaneous wireless information and power transfer with fixed and adaptive modulation 认领 引用 被引量：1
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作者 Jie Hu Guangming Liang +2 位作者 Qin Yu Kun Yang Xiaofeng Lu 《Digital Communications and Networks》 SCIE CSCD 2022年第3期303-313,共11页
Activating Wireless Power Transfer (WPT) in Radio-Frequency (RF) to provide on-demand energy supply to widely deployed Internet of Everything devices is a key to the next-generation energy self-sustainable 6G network.... Activating Wireless Power Transfer (WPT) in Radio-Frequency (RF) to provide on-demand energy supply to widely deployed Internet of Everything devices is a key to the next-generation energy self-sustainable 6G network. However, Simultaneous Wireless Information and Power Transfer (SWIPT) in the same RF bands is challenging. The majority of previous studies compared SWIPT performance to Gaussian signaling with an infinite alphabet, which is impossible to implement in any realistic communication system. In contrast, we study the SWIPT system in a well-known Nakagami-m wireless fading channel using practical modulation techniques with finite alphabet. The attainable rate-energy-reliability tradeoff and the corresponding rationale are revealed for fixed modulation schemes. Furthermore, an adaptive modulation-based transceiver is provided for further expanding the attainable rate-energy-reliability region based on various SWIPT performances of different modulation schemes. The modulation switching thresholds and transmit power allocation at the SWIPT transmitter and the power splitting ratios at the SWIPT receiver are jointly optimized to maximize the attainable spectrum efficiency of wireless information transfer while satisfying the WPT requirement and the instantaneous and average BER constraints. Numerical results demonstrate the SWIPT performance of various fixed modulation schemes in different fading conditions. The advantage of the adaptive modulation-based SWIPT transceiver is validated. 展开更多
关键词 Simultaneous wireless information and power transfer(SWIPT) Fixed/adaptive modulation Rate-energy-reliability trade-off Transceiver design Energy self-sustainability
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Decoding the genomic basis of adaptive capacity and vulnerability in the high-altitude Saussurea obvallata complex 认领 引用 被引量：1
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作者 Hum Kala Rana Santosh Kumar Rana +2 位作者 Jacob B.Landis Hang Sun Dong Luo 《Plant Diversity》 SCIE CAS CSCD 2026年第3期474-486,共13页
Decoding how adaptation and vulnerability are distributed across rugged landscape is essential for anticipating biodiversity responses to climatic change.We investigated the Saussurea obvallata complex,a group of clos... Decoding how adaptation and vulnerability are distributed across rugged landscape is essential for anticipating biodiversity responses to climatic change.We investigated the Saussurea obvallata complex,a group of closely related lineages distributed across the Himalayan–Hengduan Mountains(HHM),to ask how climatic heterogeneity and historical isolation shape genomic variation,ecological divergence,and the speciation continuum.To address these questions,we integrated plastome and RAD-seq based nuclear SNPs with genotype–environment association(GEA)analyses,gradient forest(GF),generalized dissimilarity modeling(GDM),and ensemble species distribution models(SDMs).Projected genomic offset under future climate scenarios(2070)and ensemble SDMs were used to map genomic vulnerability and forecast habitat shifts.Nuclear SNPs resolve shallow divergence and cytonuclear discordance consistent with incomplete lineages sorting and episodic introgression.After formally accounting for background population structure and spatial effects,the pure environmental fraction remains modest but significant,with within-population label–permutation nulls falling well below observed values.Concordant signals across partial redundancy analysis,latent factor mixed model(notably June cloud metrics),and GF/GDM(temperature seasonality,slope/elevation,cloud regime)indicate a genuine environment-linked component of allele-frequency turnover,while acknowledging that reduced-representation data may under detect polygenic architecture.Genomic offset maps highlight vulnerability hotspots along the southern Western and Eastern Himalayas and parts of the Hengduan Mountains,whereas the central HHM shows lower offset and potential refugial stability.Together,results support a speciation continuum shaped by both isolation and environmental selection and motivate a dual conservation strategy:safeguard diversity-rich,low-offset refugia while mitigating risk in high-offset peripheral regions through enhanced connectivity,microrefugia,and genomic monitoring. 展开更多
关键词 Genomic offset Genotype-environment association Himalaya-Hengduan mountains Local adaptation Conservation genomics
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Modeling adaptive growth in forest trees:Integrating individual variation to understand climate responses in widely-distributed species 认领 引用 被引量：1
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作者 Arne Buechling Charles D.Canham +9 位作者 Nataliya Korolyova Melanie Saulnier Patrick H.Martin Magnuz Engardt Martin Mikoláš Ondřej Vostarek Daniel Kozák Pavel Janda Jeňýk Hofmeister Miroslav Svoboda 《Forest Ecosystems》 SCIE CAS CSCD 2026年第3期797-811,共15页
An improved understanding of how forest trees may respond individually and differentially to climate across broad environmental gradients,due to adaptation or physiological acclimation,may facilitate more robust forec... An improved understanding of how forest trees may respond individually and differentially to climate across broad environmental gradients,due to adaptation or physiological acclimation,may facilitate more robust forecasts of forest resilience under climate change.We present a framework for modeling stem diameter growth in adult canopy trees that accounts for responses to climate that may be unique for individuals in different ecological settings.We used data from>10,000 tree cores from 888 forest inventory plots distributed across wide climatic gradients in two mountain ranges in Europe.We formulated a suite of nonlinear models for each of the four species to understand factors regulating annual radial growth.The models accounted for the effects of tree ontogeny,competition,nitrogen deposition(Nd),temperature,and precipitation.We compared two approaches to evaluate evidence for adaptation or acclimation in the growth-climate relations of trees.One method tested whether growth responses diverged for individual trees associated with distinct climate regimes.An alternate method fitted climate response functions with the deviation of climate in a given year from the prevailing average conditions at a tree location.We also tested whether the peak height of this function,representing the maximum growth capacity of a tree,depended on local average climate.For all taxa,models that incorporated within-species variation received stronger support relative to simpler models that assumed a consistent species-average growth response to climate.Growth in all but one species was best predicted by models fitted with climate deviations.Trees differed markedly in terms of their peak growth potential and climate optima,and in some cases,occupied suboptimal environments.Growth responses to nitrogen(N)inputs were also modulated by climate.Our framework offers a flexible approach for integrating individual-level climate sensitivity into tree demography models,which may allow for more rigorous investigations of forest dynamics,the outcomes of which may inform adaptive management strategies for mitigating climate change impacts. 展开更多
关键词 Acclimation Competition Differentiation Gaussian functions Local adaptation Nitrogen deposition(Nd) Nonlinear growth models
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Fixed-Time Three-Dimensional Formation Control for Underactuated Autonomous Underwater Vehicles via Event-Triggered Mechanism 认领 引用
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作者 DING Wen-jun ZHANG Guo-zong +4 位作者 HUANG Feng-tian-yi ZHANG Lei CHAI Ya-jun MAO Zhao-yong LI Bo 《China Ocean Engineering》 SCIE EI CSCD 2026年第3期590-603,共14页
This research presents a fixed-time three-dimensional formation control strategy for underactuated autonomous underwater vehicles(AUVs)utilizing an event-triggered mechanism.The study introduces virtual AUVs to transf... This research presents a fixed-time three-dimensional formation control strategy for underactuated autonomous underwater vehicles(AUVs)utilizing an event-triggered mechanism.The study introduces virtual AUVs to transform the formation control problem into a trajectory tracking challenge.A virtual velocity regulation law is developed for virtual AUVs,enabling follower AUVs to track the reference position through virtual AUVs without requiring the leader autonomous underwater vehicle’s velocity information.To manage system uncertainties,the research implements a fixed-time disturbance observer based on AUV dynamic models,providing accurate estimations of parameter uncertainties and external disturbances.Through the backstepping approach,an expected velocity regulation law is formulated for underactuated AUVs,ensuring position tracking error convergence within a fixed time.Additionally,a fixed-time dynamic controller is implemented to facilitate rapid achievement of expected velocity by the follower AUV,while the event-triggered mechanism reduces control input triggering frequency.The stability analysis,based on Lyapunov theory,demonstrates that the closed-loop system’s tracking error converges to a compact residual set within a fixed time.Comparative simulation results confirm the proposed algorithm’s enhanced performance. 展开更多
关键词 underactuated autonomous underwater vehicles fixed time formation control event triggered mechanism fixed time disturbance observer
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LL-Refiner:Learning Adaptive Refinement for Ultra-High-Definition Low-Light Image Enhancement 认领 引用
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作者 Xunpeng Yi Qinglong Yan +4 位作者 Yibing Zhang Zongrong Li Hao Zhang Zhanchuan Cai Jiayi Ma 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1288-1300,共13页
Low-light image enhancement aims to address various degradations in low-light conditions,such as low illumination,noise pollution,color distortion,and missing scene content.With advances in digital imaging technology,... Low-light image enhancement aims to address various degradations in low-light conditions,such as low illumination,noise pollution,color distortion,and missing scene content.With advances in digital imaging technology,the resolution of captured images has seen substantial improvements.This poses new challenges in achieving good enhancement performance for multiscale details in ultra-high-definition images,as well as in managing the overhead for supporting the use of consumer-grade GPUs.In this paper,we proposed learning the adaptive refinement framework for ultra-high-definition image enhancement,termed LL-Refiner.It integrates the advantages of hierarchical adaptive refinement guided by coarse enhancement results to enhance the ultra-high-definition images.In detail,firstly,we conduct the coarse enhancement on the low resolution image by employing a Transformer-based coarse enhancement network.Secondly,the coarse enhancement output is fed into the adapted refinement injection.It assigns resolution-aware inputs as guidance to the corresponding adaptive aggregation module,which interacts with the backbone features of the adaptive refinement network.Ultimately,the adaptive refinement network incorporates a combination of hierarchical dense residual connection modules and lightweight convolutional modules at different resolution stages.Also,it integrates a multi-scale enhanced perceptual loss to progressively achieve ultra-high-definition image enhancement.Extensive experiments on ultra-high-definition image enhancement validate the effectiveness and superiority of the proposed method.Our code is publicly available at http://gffzz188fe103f8f1460asn5qpxbk9u05o6nou.ffgz.tsg.suse.edu.cn/XunpengYi/LL-Refiner. 展开更多
关键词 Adaptive refinement low-light image enhancement ultra-high-definition image
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Adaptive robust control of multi-DOF hydraulic manipulator with precise online load estimation 认领 引用
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作者 Qixian Wang Manzhi Qi +2 位作者 Yangxiu Xia Shizhao Zhou Zheng Chen 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第2期155-166,共12页
Hydraulic manipulator shows vast application potential in heavy load working conditions.However,achieving high control precision in these devices is notably more challenging compared to electric manipulators,due to th... Hydraulic manipulator shows vast application potential in heavy load working conditions.However,achieving high control precision in these devices is notably more challenging compared to electric manipulators,due to the complexities introduced by uncertainties and high-order nonlinear dynamics.The presence of heavy unknown payload further reduces control accuracy.In this paper,a new load estimation method based on direct/indirect adaptive robust controller(DIARC) is proposed to facilitate online payload estimation and compensate the impact of the unknown payload.To compensate for the high-order dynamics,backstepping strategy is utilized.A modified recursive least squares adaptive law is also developed to realize,precise,real-time payload estimation under dynamic conditions.By incorporating this accurate load mass estimation into the control strategy,an improvement in overall control performance can be achieved.The effectiveness of this enhanced controller with load mass estimation is initially verified through simulations conducted in MATLAB.The close-loop control performance is further analyzed and validated on a four-degree-of-freedom hydraulic manipulator.The experimental results indicate that,under dynamic scenarios,our proposed control method succeeds in achieving precise online load mass estimation,achieving an average estimation error of 2.8% for a 7.5 kg payload.Furthermore,enhanced control accuracy is achieved compared to traditional controllers,with the maximum tracking error being only 0.69° during the simulated operation scenario.This research provides a viable solution for precision control and load estimation in hydraulic manipulators,eliminating the need for costly forceorque sensors. 展开更多
关键词 Hydraulic manipulator Adaptive control Load estimation
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Electrospinning-Driven IGZO Nanofiber Phototransistors Bridging Adaptive Processing and Recognition Learning 认领 引用
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作者 Shanshan Jiang Shuo Cheng +3 位作者 Jiawei Yang Xiaohui Guo Bo He Gang He 《Rare Metals》 SCIE EI CAS CSCD 2026年第4期537-548,共12页
The development of neuromorphic electronics with visual perception and adaptive capability is highly desirable for advancing artificial vision systems.Herein,we have demonstrated a dual-plasticity adaptive photo trans... The development of neuromorphic electronics with visual perception and adaptive capability is highly desirable for advancing artificial vision systems.Herein,we have demonstrated a dual-plasticity adaptive photo transistor based on IGZO nanofibers that exhibits the perception and dynamic adaptation behavior of rod and cone cells to varying light environments.Benefiting from the coexistence of oxygen vacancies and trap states in nanofibers,the separation and injection of photogenerated carriers are significantly improved,thereby enabling the light-intensity-dependent dynamic adaptability and the gate-modulated photosensitivity with a narrower adaptation timescale than the bio-systems(<2 min).Moreover,the phototransistor array replicates both photopic and scotopic adaptation,and achieves adaptive contrast enhancement for the overexposed images.Finally,the system enables real-time neuromorphic encoding and recognition of digital signals under varying adaptive processes,and the pattern recognition accuracy is significantly improved from 10%to 95.8%.These results not only demonstrate a facile route for bridging visual sensing,adaptive processing,and neuromorphic computing in a single transistor device but also lay the groundwork for future applications in machine vision and next-generation neuromorphic sensory systems. 展开更多
关键词 adaptive capability IGZO nanofibers neuromorphic computing phototransistor visual perception
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Performance degradation and maintenance optimization strategy of rolling bearings based on data fusion and adaptive partition 认领 引用
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作者 Hong-Chuan Cheng Xin-Hai Li +3 位作者 Guo-Hui Ma Yu Cui Zhi-Wu Shang Xia-Fei Shi 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期178-194,共17页
The traditional method of performance degradation prediction and maintenance of rolling bearings only considers a single sensor signal,which makes it difficult to automatically partition degradation stages and prone t... The traditional method of performance degradation prediction and maintenance of rolling bearings only considers a single sensor signal,which makes it difficult to automatically partition degradation stages and prone to over-detection.A new method of performance degradation evaluation and maintenance of rolling bearings based on data-level fusion,adaptive health state partitioning,and state maintenance is proposed.Firstly,considering the degradation and impact in the process of bearing deterioration,the multi-sensor signals are dynamically weighted to achieve data-level fusion.Secondly,a bearing health index was established based on fast spectral correlation,Wasserstein distance,and linear rectification techniques.On this basis,by combining the Bayesian information criterion and the elbow rule,the precise division of rolling bearing health state is realized through hidden Markov model regression.Then,random forest was used to classify and predict the data to verify the validity of the proposed data fusion method and health indicator.Finally,condition-based maintenance strategy based on the fourth moment,stress-strength interference model,and Gamma process is proposed to avoid excessive detection and reduce maintenance costs.Through accelerated degradation experiments and field validation tests on the rolling bearing test data set of Xi’an Jiaotong University and FEMTO(PRONOSTIA),the accuracy and superiority of the proposed method in the prediction and maintenance of bearing health state are verified. 展开更多
关键词 Rolling bearing Data fusion Gamma process Adaptive partition Maintenance strategy
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Analysis of a Mirror-based Assist-as-needed Strategy via Task-performance-based Adaptive Impedance Control 认领 引用
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作者 Qing Sun Qingfeng Li +2 位作者 Xiaolong Yang Shuai Guo Jianwei Niu 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第2期771-782,共12页
Mirror-assisted strategies are commonly used in the rehabilitation training of patients with hemiparesis in the upper limbs following a stroke.Traditional robotic mirror assistance focuses on achieving high-precision ... Mirror-assisted strategies are commonly used in the rehabilitation training of patients with hemiparesis in the upper limbs following a stroke.Traditional robotic mirror assistance focuses on achieving high-precision mirror trajectory tracking,often neglecting the issue of active movement in the affected side.This paper proposes a task performance-based adaptive impedance control,where the robot assists the affected side in an assist-as-needed manner,thereby encouraging the patient to perform active movements.To account for inter-individual variability,a method for assessing the affected side’s motor performance,based on the healthy side’s movement level,is introduced.Adaptive impedance control is then constructed based on the motor performance of the affected side,enabling the robot to provide adaptive assistance force.Eight healthy participants were recruited for experimental testing.Experimental results show that when the robot provides mirror-based assist-as-needed to the affected side,the robot’s stiffness coefficient and assistance force are positively correlated with the motor assessment coefficient of the affected side,thereby verifying the feasibility of the proposed strategy.This study offers a robotic-assisted rehabilitation strategy for stroke patients that balances active participation and individual adaptability,with the potential to enhance rehabilitation outcomes and enable precise rehabilitation interventions. 展开更多
关键词 Bimanual robot Adaptive impedance control Task performance Mirror assistance Assist-as-needed
An Adaptive Intrusion Detection Framework for IoT: Balancing Accuracy and Computational Efficiency 认领 引用
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作者 Abdulaziz A.Alsulami Badraddin Alturki +2 位作者 Ahmad J.Tayeb Rayan A.Alsemmeari Raed Alsini 《Computers, Materials & Continua》 SCIE EI 2026年第6期1214-1241,共28页
Intrusion Detection Systems(IDS)play a critical role in protecting networked environments from cyberattacks.They have become increasingly important in smart environments such as the Internet of Things(IoT)systems.Howe... Intrusion Detection Systems(IDS)play a critical role in protecting networked environments from cyberattacks.They have become increasingly important in smart environments such as the Internet of Things(IoT)systems.However,IDS for IoT networks face critical challenges due to hardware constraints,including limited computational resources and storage capacity,which lead to high feature dimensionality,prediction uncertainty,and increased processing cost.These factors make many conventional detection approaches unsuitable for real-time IoT deployment.To address these challenges,this paper proposes an adaptive intrusion detection framework that intelligently balances detection accuracy and computational efficiency.The proposed framework integrates mutual information(MI)feature selection model,deep contextual embeddings,and an adaptive decision mechanism.The MI model identifies and retains the most informative features,which reduces dimensionality while maintaining high detection accuracy.The adaptive decision dynamically selects between multiple inference paths to ensure that additional computation is needed only when the uncertainty level is high.Experimental evaluations on benchmark IoT datasets namely RT-IoT-2022,CIC-IoT-2023 and CIC-IoMT-2024 show that the proposed framework achieves F1-score of 99.92%,96.66%,and 99.84%,respectively,with an average inference time of approximately 0.105 ms per sample.These results demonstrate that the framework effectively adapts inference complexity to data uncertainty,which provides an intelligent,interpretable and efficient solution for real-world IoT intrusion detection. 展开更多
关键词 Adaptive IDS cyberattack detection computational cost IoT security
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A residual-based adaptive refinement physics-informed neural networks(RAR-PINNs)method for fifth-order KdV equation 认领 引用
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作者 Shi-Fang Tian Ya-Xuan Yu Biao Li 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第5期284-292,共9页
In the field of nonlinear partial differential equations(PDEs),the fifth-order Korteweg-De Vries(KdV)equation serves as a fundamental model with significant physical implications,extending the classical KdV framework ... In the field of nonlinear partial differential equations(PDEs),the fifth-order Korteweg-De Vries(KdV)equation serves as a fundamental model with significant physical implications,extending the classical KdV framework through the incorporation of high-order spatial derivatives to capture strong dispersion effects.However,the inherent nonlinearity and complexity of this PDE present substantial challenges for obtaining accurate numerical solutions.To address these issues,this paper proposes a residual-based adaptive refinement physics-informed neural networks(RAR-PINNs)method.This approach synergizes the nonlinear approximation capability of PINNs with a residual-driven adaptive sampling strategy.By dynamically redistributing training points according to the magnitude of the PDE residuals,RAR-PINNs effectively concentrate computational resources on“critical regions”,such as soliton peaks and high-gradient zones,where errors are predominant.Furthermore,we construct a composite physics-informed loss function that incorporates initial and boundary conditions,PDE residuals,and,in an enhanced variant,energy conservation laws,to further improve solution fidelity.Numerical experiments on two variants of the fifth-order KdV equation demonstrate that RAR-PINNs significantly outperform conventional PINNs in terms of both accuracy(reducing relative errors by one to two orders of magnitude)and computational efficiency.The conservation-law-enhanced version of the model yields even higher precision,underscoring the efficacy and robustness of the proposed method.This study establishes a powerful deep learning framework for tackling complex PDEs with sharp or singular solution structures. 展开更多
关键词 data-driven PINNs residual-based adaptive refinement fifth-order KdV equation
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Relative Motion Based Predictive Adaptive Control:A Case Study of AUV 3D Trajectory Tracking 认领 引用
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作者 Daxiong Ji Xinwei Wang Yuanchang Liu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期492-494,共3页
Dear Editor,This letter deals with the autonomous underwater vehicle(AUV)three dimensional(3D)trajectory tracking control chronically suffering from poor accuracy and efficiency in complex hydrodynamics.A state-of-the... Dear Editor,This letter deals with the autonomous underwater vehicle(AUV)three dimensional(3D)trajectory tracking control chronically suffering from poor accuracy and efficiency in complex hydrodynamics.A state-of-the-art predictive adaptive controller(PAC)is proposed with a distinct dual closed-loop structure. 展开更多
关键词 adaptive controller pac autonomous underwater vehicle auv three predictive adaptive control relative motion D trajectory tracking hydrodynamics closed loop structure complex hydrodynamicsa
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Effect of Catalyst Concentration on the Properties of Bio-based Epoxy Vitrimer with Dynamically Adaptive Networks 认领 引用
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作者 Wenyan Zhang Yuting Chu +1 位作者 Chuang Li Yao Fu 《Chinese Journal of Chemical Physics》 SCIE EI CAS CSCD 2026年第1期136-144,I0043,共9页
Epoxy resins are widely employed in wind turbine blades,drone rotors,and automotive interiors due to their excel-lent mechani-cal proper-ties and long service life.However,their insoluble and infusible cross-linked ne... Epoxy resins are widely employed in wind turbine blades,drone rotors,and automotive interiors due to their excel-lent mechani-cal proper-ties and long service life.However,their insoluble and infusible cross-linked networks pose a significant re-cycling challenge,particularly with the impending retirement of the first generation of wind turbine blades.In this work,we reported a fully bio-based epoxy Vitrimer(FEP)incorporat-ing a dual-dynamic covalent network design and systematically investigated the influence of the 1,5,7-triazabicyclo[4.4.0]dec-5-ene(TBD)catalyst on its curing kinetics,thermal/mechan-ical properties,dynamic exchange behavior,and degradation performance in a mild alkaline solution.Compared to conventional epoxy resins,FEP exhibited superior tensile strength and elongation at break at an optimal TBD concentration(2 wt%),achieving an excellent strength-toughness balance.The presence of TBD accelerated the exchange rates of both disulfide and ester bonds,endowing FEP with notable stress relaxation at elevated tempera-tures.Moreover,FEP demonstrated complete dissolution in 1 mol/L NaOH within 6 h at 25℃.These results underscored the exceptional strength,toughness,and recyclability of FEP,positioning it as a promising,environmentally friendly matrix resin for next-generation appli-cations in the new energy sector. 展开更多
关键词 Bio-based materials Epoxy Vitrimer Catalyst concentration Dynamically adaptive networks
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Interfacial Modulation of Lithium Deposition via an Adaptive Poly(Ether-Thiourea)Protective Layer 认领 引用
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作者 Yongsheng Zhang Xiaolong He +6 位作者 Yinyu Xiang Lieke M.H.Germain Marco Di Michiel Pierre-Olivier Autran Yutao Pei Petra Rudolf Giuseppe Portale 《Carbon Energy》 SCIE EI CAS CSCD 2026年第2期44-58,共15页
Lithium metal is a promising anode material for high-energy-density batteries;however,its practical applications are significantly hindered by unstable lithium deposition and dendrite growth at the solid electrolyte i... Lithium metal is a promising anode material for high-energy-density batteries;however,its practical applications are significantly hindered by unstable lithium deposition and dendrite growth at the solid electrolyte interface.Functional protective coatings on lithium metal surfaces offer a viable solution to these challenges.Herein,an innovative adaptive protective layer for lithium metal anodes based on a thiourea H-bonded supramolecular polymer is developed for the first time.With dense thiourea H-bonding,the lithium bis(trifluoromethanesulfonyl)imide(Li TFSI)incorporated poly(ether-thiourea)protective layer shows strong adhesion to the lithium metal surface and good adaptive properties.The unique viscoelastic and flow characteristics of the poly(ether-thiourea)coating facilitate uniform Li⁺flux,effectively suppressing dendrite formation at the solid electrolyte interface.Furthermore,this innovative polymer integrates in situ generated compounds,such as Li3N and Li2O,significantly enhancing interfacial stability.A comprehensive analysis involving X-ray photoelectron spectroscopy,scanning electron microscopy,X-ray tomography,and COMSOL simulations elucidates the beneficial effects of the adaptive coating.Enhanced performances in Li||Cu,Li||Li,Li||LiFePO4,and Li||S cells demonstrate the effectiveness of the poly(ether-thiourea)coating and its undeniable capability to improve lithium deposition and cycling stability.This study highlights a promising new candidate for developing supramolecular materials capable of stabilizing lithium metal anodes. 展开更多
关键词 adaptive coating cycling stability dendrite growth lithium metal anode protective layer
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PASSIVITY-BASED SYNCHRONIZATION OF MARKOVIAN JUMP INERTIAL NEURAL NETWORKS VIA ADAPTIVE EVENT-DRIVEN PROTOCOL AND ITS APPLICATION TO IMAGE ENCRYPTION 认领 引用
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作者 A.CHANDRASEKAR Quanxin ZHU +2 位作者 T.RADHIKA M.KAMALI A.R.SUBHASHRI 《Acta Mathematica Scientia》 SCIE CSCD 2026年第3期1555-1580,共26页
This paper concentrates on the study of passivity-based synchronization of inertial neural networks including Markov jump parameters.The second-order differential equations are converted into first-order differential ... This paper concentrates on the study of passivity-based synchronization of inertial neural networks including Markov jump parameters.The second-order differential equations are converted into first-order differential equations using the variable transformation method.To make effective use of network bandwidth resources and to optimize the Markov jump inertial neural networks(MJINNs)performance,an adaptive event-driven protocol controller is studied.To achieve synchronization,an appropriate Lyapunov-Krasovskii functional(LKF)is constructed,which includes double integral terms that capture the information of time-varying delay terms.Some sufficient conditions are obtained in terms of linear matrix inequalities(LMIs)using Reciprocal convex combination lemma(RCCL).Then,a numerical simulation and an application of image encryption are carried out to illustrate the effectiveness of the proposed method. 展开更多
关键词 inertial neural networks adaptive event-driven protocol Markovian jump process synchronization
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Full-waveform inversion constrained by adaptive sonic logging data within a Bayesian framework 认领 引用
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作者 Jing Wang Qing-Qing Li +2 位作者 Li-Jun Gao Zong-Jie Li Li-Yun Fu 《Petroleum Science》 SCIE EI CAS CSCD 2026年第1期205-219,共15页
The full waveform inversion(FWI)utilizes full wave field data to invert subsurface parameters and is considered one of the most promising data-driven tools for obtaining high precision velocity models.However,the succ... The full waveform inversion(FWI)utilizes full wave field data to invert subsurface parameters and is considered one of the most promising data-driven tools for obtaining high precision velocity models.However,the successful application of FWI in geophysical explo ration remains limited,primarily due to the cycle-skipping issue caused by the absence of low-frequency data,which is one of the main reasons for FWI failures.Incorporating prior regularization constraints FWI can effectively compensate for the lacking low-frequency components and constrain the iterative updates of FWI toward the desired direction,offering a natural advantage in addressing this challenge.However,the weights of the prior information terms are still determined empirically,which introduces significant subjectivity and randomness to the inversion results.To solve this issue,we propose an adaptive method to determine the weight factor based on posterior probability distribution within the Bayesian theoretical framework.This factor adaptively adjusts during each iteration to balance the contributions of the data error term and the prior information term in FWI,which can effectively mitigate the cycle-skipping problem and alleviating the nonlinearity of the inversion process.Numerical examples from the Overthrust model and the Marmousi model show that our method not only enhance the accuracy of FWI,but also demonstrate strong noise resistance. 展开更多
关键词 Full waveform inversion Low-frequency data Sonic logging data Adaptive prior regularization
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Dynamic Adaptive Weighting of Effectiveness Assessment Indicators:Integrating G1,CRITIC and PIVW 认领 引用
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作者 Longyue Li Guoqing Zhang +2 位作者 Bo Cao Shuqi Wang Ye Tian 《Computers, Materials & Continua》 SCIE EI 2026年第2期1127-1152,共26页
Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—esp... Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—especially critical in scenarios like sudden electronic warfare or degraded command,where static weights cannot reflect the operational value decay or surge of key indicators.To address this issue,this study proposes a dynamic adaptive weightingmethod for evaluation indicators based onG1-CRITIC-PIVW.First,theG1(Sequential Relationship Analysis Method)subjective weighting method—translates expert knowledge into indicator importance rankings—leverages expert knowledge to quantify the relative importance of indicators via sequential relationship ranking,while the CRITIC(Criteria Importance Through Intercriteria Correlation)objective weighting method—derives weights from data characteristics by integrating variability and inter-correlations—calculates weights by integrating indicator variability and inter-indicator correlations,ensuring data-driven objectivity.These two sets of weights are then fused using a deviation coefficient optimization model,minimizing the squared deviation from a reference weight and adjusting the fusion coefficient via Spearman’s rank correlation to resolve potential conflicts between subjective and objective judgments.Subsequently,the PIVW(Punishment-Incentive VariableWeight)theory—adapts weights to realtime indicator performance via penalty/incentive rules—is applied for dynamic adjustment.Scenario-specific penalty λ1 and incentive λ2 thresholds are set based on operational priorities and indicator volatility,penalizing indicators with values below λ1 and incentivizing those exceeding λ2 to reflect real-time indicator performance.Experimental validation was conducted using an Air Defense and Anti-Missile(ADAM)system effectiveness assessment framework,with data covering 7 indicators across 3 combat scenarios.Results show that compared to static weighting methods,the proposed method reduces MAE(Mean Absolute Error)by 15%-20% and weighted decision error rate by 84.2%,effectively reducing overestimation/underestimation of combat effectiveness in dynamic scenarios;compared to Entropy-TOPSIS,it lowers MAE by 12% while achieving a weighted Kendall’sτconsistency coefficient of 0.85,ensuring higher alignment with expert judgment.This method enhances the accuracy and scenario adaptability of effectiveness assessment,providing reliable decision support for dynamic battlefield environments. 展开更多
关键词 Adaptive weighting combined weighting model G1-CRITIC-PIVW effectiveness assessment
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GaitMAFF:Adaptive Multi-Modal Fusion of Skeleton Maps and Silhouettes for Robust Gait Recognition in Complex Scenarios 认领 引用
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作者 Zhongbin Luo Zhaoyang Guan +2 位作者 Wenxing You Yunteng Wang Yanqiu Bi 《Computers, Materials & Continua》 SCIE EI 2026年第5期540-558,共19页
Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combini... Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combining silhouette and skeleton data is a promising direction,effectively fusing these heterogeneous modalities and adaptively weighting their contributions in response to diverse conditions remains a central problem.This paper introduces GaitMAFF,a novelMulti-modal Adaptive Feature Fusion Network,to address this challenge.Our approach first transforms discrete skeleton joints into a dense SkeletonMap representation to align with silhouettes,then employs an attention-based module to dynamically learn the fusion weights between the two modalities.These fused features are processed by a powerful spatio-temporal backbone withWeighted Global-Local Feature FusionModules(WFFM)to learn a discriminative representation.Extensive experiments on the challenging CCPG and Gait3D datasets show that GaitMAFF achieves state-of-the-art performance,with an average Rank-1 accuracy of 84.6%on CCPG and 58.7%on Gait3D.These results demonstrate that our adaptive fusion strategy effectively integrates complementary multimodal information,significantly enhancing gait recognition robustness and accuracy in complex scenes and providing a practical solution for real-world applications. 展开更多
关键词 Gait recognition multi-modal fusion adaptive feature fusion skeleton map silhouette
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Wavelet-based analysis of aeolian sand dynamics and adaptive mitigation strategies for desert highways 认领 引用
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作者 YANG Lingxiang CHENG Jianjun +3 位作者 YAO Bin WANG Yaqiang GAO Li WU Xiao 《Journal of Mountain Science》 SCIE CSCD 2026年第3期1182-1200,共19页
Scientific analysis of aeolian sand environments is fundamental for sustainable disaster mitigation along desert highways.However,significant regional variability in wind energy conditions complicates accurate charact... Scientific analysis of aeolian sand environments is fundamental for sustainable disaster mitigation along desert highways.However,significant regional variability in wind energy conditions complicates accurate characterization of wind regimes and introduces uncertainty in determining optimal monitoring timescales.Moreover,prevailing sand control measures often rely on standardized designs rather than site-specific adaptive strategies.To address these issues,this study proposes an integrated framework for aeolian environment analysis and develops targeted disaster mitigation strategies tailored for desert highways.The proposed framework employs wavelet transform to unravel the periodic characteristics of wind speed time series and integrates multi-source data(including ERA5 wind datasets,sand samples,ASTER GDEM,and multi-temporal remote sensing imagery)to enable a comprehensive aeolian environmental assessment.Concurrently,a suite of adaptive strategies is formulated to mitigate disaster risks along desert highways.Validated through a case study of the Tumushuk-Kunyu Desert Highway in Xinjiang,China,the framework exhibits high accuracy:predictions of annual aeolian sand transport activity show relative errors mostly below 7%against long-term reference sequences,and the calculated resultant drift direction exhibits a strong correlation with observed dune migration,yielding an R-squared value of 0.96.These findings confirm the framework’s reliability and provide a robust basis for designing adaptive,location-specific mitigation strategies,thereby enhancing the sustainability of desert highway infrastructure. 展开更多
关键词 Desert highway Aeolian sand environment Wavelet transform Drift potential Adaptive mitigation measures
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