1.Introduction The field of exercise science is experiencing a renaissance,with recent research illuminating the molecular,cellular,and systemic effects of physical activity.This is largely due to the now unequivocal ...1.Introduction The field of exercise science is experiencing a renaissance,with recent research illuminating the molecular,cellular,and systemic effects of physical activity.This is largely due to the now unequivocal evidence that a lack of physical activity,not only has direct effects on the prevalence of non-contagious diseases(NCDs)but has profound additive effects of other risk factors for NCD such as obesity and hypertension.1 The articles in this special topic of Journal of Sport and Health Science(JSHS)are dedicated to research on Exercise biochemistry&metabolism.展开更多
In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the op...In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.展开更多
In federated learning,backdoor attacks have become an important research topic with their wide application in processing sensitive datasets.Since federated learning detects or modifies local models through defense mec...In federated learning,backdoor attacks have become an important research topic with their wide application in processing sensitive datasets.Since federated learning detects or modifies local models through defense mechanisms during aggregation,it is difficult to conduct effective backdoor attacks.In addition,existing backdoor attack methods are faced with challenges,such as low backdoor accuracy,poor ability to evade anomaly detection,and unstable model training.To address these challenges,a method called adaptive simulation backdoor attack(ASBA)is proposed.Specifically,ASBA improves the stability of model training by manipulating the local training process and using an adaptive mechanism,the ability of the malicious model to evade anomaly detection by combing large simulation training and clipping,and the backdoor accuracy by introducing a stimulus model to amplify the impact of the backdoor in the global model.Extensive comparative experiments under five advanced defense scenarios show that ASBA can effectively evade anomaly detection and achieve high backdoor accuracy in the global model.Furthermore,it exhibits excellent stability and effectiveness after multiple rounds of attacks,outperforming state-of-the-art backdoor attack methods.展开更多
Qingke,a staple crop grown on the high-altitude Tibetan Plateau,has evolved a metabolomic profile providing both environmental stress resilience and human nutrition.We review the hypothesis that the metabolites that c...Qingke,a staple crop grown on the high-altitude Tibetan Plateau,has evolved a metabolomic profile providing both environmental stress resilience and human nutrition.We review the hypothesis that the metabolites that confer cold and UV resistance on the crop also facilitate human adaptation to high-altitude stresses.Specifically,β-glucans regulate blood glucose primarily via short-chain fatty acids(SCFAs)produced through gut microbiota fermentation,which directly mediate glucose homeostasis.Phenolamides accumulate via the phenylpropanoid pathway,with chalcone isomerase(CHI)serving as a key enzyme in flavonoid biosynthesis and enhancing UV-B resistance.Under low temperatures,β-glucans improve frost tolerance by modulating osmotic balance and inhibiting ice-nucleating proteins,while lipids maintain membrane fluidity to sustain cellular function during cold stress.Importantly,we explore the hypothesis that these same metabolites,upon consumption,may facilitate human adaptation to high-altitude stresses.This hypothesis is supported by preliminary epidemiological associations between Qingke consumption and favorable health outcomes in high-altitude populations,as well as established bioactivities of the implicated metabolites in vitro and in animal models.However,direct causal evidence in humans and a comprehensive understanding of the underlying molecular mechanisms remain key knowledge gaps that warrant future investigation.Qingke as a unique resource at the interface of agricultural resilience and human nutrition.Understanding its metabolic blueprint will inform the development of functional foods and climate-resilient crops.展开更多
Starting from the foundational static traits underlying the growth and development of flue-cured tobacco, this research conducts a systematic examination of the phenomena and theoretical principles associated with env...Starting from the foundational static traits underlying the growth and development of flue-cured tobacco, this research conducts a systematic examination of the phenomena and theoretical principles associated with environment-driven adaptive changes during its cultivation. It was found that environmental variables-including temperature, light, and moisture-elicit directional shifts in static traits ( e.g. , chemical composition, morphological architecture, and leaf tissue structure) toward enhanced environmental adaptation, characterized by graduality, juvenility, similarity, and correlativity. Upon alterations in ambient conditions, flue-cured tobacco modulates its static traits through integrated physical, chemical, and biological-genetic mechanisms, aiming to optimize resource utilization, mitigate environmental constraints, and preserve internal homeostasis alongside metabolic balance. The investigation further reveals that the adaptive scope of flue-cured tobacco to field environments is malleable and can be extended and elevated via adaptive conditioning commencing at the juvenile stage. In addition, the adaptive alignment between static traits and environmental parameters exerts a substantial impact on the plant s growth dynamics, yield performance, and quality attributes. Beyond its relevance to flue-cured tobacco, the proposed theory offers a meaningful framework for elucidating the pervasive adaptive strategies employed by plants and broader biological systems in response to environmental contingencies.展开更多
After billions of years of evolution,biological intelligence has converged on unrivalled energy efficiency and environmental adaptability.The human brain,for instance,is highly efficient in information transmission,co...After billions of years of evolution,biological intelligence has converged on unrivalled energy efficiency and environmental adaptability.The human brain,for instance,is highly efficient in information transmission,consuming only about 20 W onaverage in a resting state[1,2].A key to this efficiency is that biological signal transduction and processing rely significantly on multi-ions as the signal carriers.Inspired by this paradigm.展开更多
The rapidly evolving cybersecurity threat landscape exposes a critical flaw in traditional educational programs where static curricula cannot adapt swiftly to novel attack vectors.This creates a significant gap betwee...The rapidly evolving cybersecurity threat landscape exposes a critical flaw in traditional educational programs where static curricula cannot adapt swiftly to novel attack vectors.This creates a significant gap between theoretical knowledge and the practical defensive capabilities needed in the field.To address this,we propose TeachSecure-CTI,a novel framework for adaptive cybersecurity curriculumgeneration that integrates real-time Cyber Threat Intelligence(CTI)with AI-driven personalization.Our framework employs a layered architecture featuring a CTI ingestion and clusteringmodule,natural language processing for semantic concept extraction,and a reinforcement learning agent for adaptive content sequencing.Bydynamically aligning learningmaterialswithboththe evolving threat environment and individual learner profiles,TeachSecure-CTI ensures content remains current,relevant,and tailored.A 12-week study with 150 students across three institutions demonstrated that the framework improves learning gains by 34%,significantly exceeding the 12%–21%reported in recent literature.The system achieved 84.8%personalization accuracy,85.9%recognition accuracy for MITRE ATT&CK tactics,and a 31%faster competency development rate compared to static curricula.These findings have implications beyond academia,extending to workforce development,cyber range training,and certification programs.By bridging the gap between dynamic threats and static educational materials,TeachSecure-CTI offers an empirically validated,scalable solution for cultivating cybersecurity professionals capable of responding to modern threats.展开更多
Rheumatoid arthritis(RA)patients face significant psychological challenges alongside physical symptoms,necessitating a comprehensive understanding of how psychological vulnerability and adaptation patterns evolve thro...Rheumatoid arthritis(RA)patients face significant psychological challenges alongside physical symptoms,necessitating a comprehensive understanding of how psychological vulnerability and adaptation patterns evolve throughout the disease course.This review examined 95 studies(2000-2025)from PubMed,Web of Science,and CNKI databases including longitudinal cohorts,randomized controlled trials,and mixed-methods research,to characterize the complex interplay between biological,psychological,and social factors affecting RA patients’mental health.Findings revealed three distinct vulnerability trajectories(45%persistently low,30%fluctuating improvement,25%persistently high)and four adaptation stages,with critical intervention periods occurring 3-6 months postdiagnosis and during disease flares.Multiple factors significantly influence psychological outcomes,including gender(females showing 1.8-fold increased risk),age(younger patients experiencing 42%higher vulnerability),pain intensity,inflammatory markers,and neuroendocrine dysregulation(48%showing cortisol rhythm disruption).Early psychological intervention(within 3 months of diagnosis)demonstrated robust benefits,reducing depression incidence by 42%with effects persisting 24-36 months,while different modalities showed complementary advantages:Cognitive behavioral therapy for depression(Cohen’s d=0.68),mindfulness for pain acceptance(38%improvement),and peer support for meaning reconstruction(25.6%increase).These findings underscore the importance of integrating routine psychological assessment into standard RA care,developing stage-appropriate interventions,and advancing research toward personalized biopsychosocial approaches that address the dynamic psychological dimensions of the disease.展开更多
Background:As an important indicator of subjective well-being(SWB),decent work is a key guarantee for the sustainable development of teachers and their psychological health and work quality.Faced with the rapid develo...Background:As an important indicator of subjective well-being(SWB),decent work is a key guarantee for the sustainable development of teachers and their psychological health and work quality.Faced with the rapid development of artificial intelligence and the global labor market,vocational college teachers are facing challenges such as workload pressure and limited career development,which may harm their well-being.This study aims to localize the measurement method of decent work in Chinese vocational education based on the theory of the Psychology of Working Theory,and explore the relationship mechanism between organizational support,career adaptability,decent work,and job satisfaction among vocational college teachers.Methods:A cross-sectional survey was conducted with 422 HVCU teachers in China(202 male,220 female)using the localized Perceived Organizational Support Scale,Career Adaptability Scale,Decent Work Scale,and Job Satisfaction Scale.Results:The overall level of HVCU teachers’decent work was above the median(Mean=4.09,SD=0.69),laying a foundation for their SWB.Decent work significantly and positively predicted job satisfaction(β=0.620,p<0.001).Organizational support(r=0.58,p<0.001)and career adaptability(r=0.82,p<0.001)can positively affect decent work,and further improve job satisfaction(collective R2 rising from 38.3%to 41.1%).Bootstrap analysis confirmed these mediating effects were robust.Conclusions:This study confirms that the combined effects of organizational support and career adaptability can enhance decent work,further improving teachers’job satisfaction and subsequent subjective well-being.Besides,this study provides an empirical basis for improving the well-being of higher vocational teachers and the sustainable development of vocational education,and has practical significance for improving the teacher incentive policy.展开更多
This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temp...This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temperature distribution,suitable for control design due to its balance between physical fidelity and computational simplicity.The controller uses a wavelet-based neural proportional,integral,derivative(PID)controller with IIR filtering(infinite impulse response).The dynamic model captures the essential heat and mass transfer phenomena through a nonlinear energy balance,where the cooling capacity“Qevap”is expressed as a non-linear function of the compressor frequency and the temperature difference,specifically,Q_(evap)=k_(1)u(T_(in)−T_(e))with u as compressor frequency,Te evaporator temperature,and Tin inlet fluid temperature.The operating conditions of the system,in general terms,focus on the following variables,the overall thermal capacity is 1000 J/K,typical for small-capacity heat exchangers,The mass flow is 0.05 kg/s,typical for secondary liquid cooling circuits,the overall loss coefficient of 50 W/K that corresponds to small evaporators with partial insulation,the temperatures(inlet)of 10℃and the temperature of environment of 25℃,thermal load of 200 W that corresponds to a small-scaled air conditioning applications.To handle system nonlinearities and improve control performance,aMorlet wavelet-based neural network(Wavenet)is used to dynamically adjust the PID gains online.An IIR filter is incorporated to smooth the adaptive gains,improving stability and reducing oscillations.In contrast to prior wavelet-or neural-adaptive PID controllers in HVAC applications,which typically adjust gains without explicit filtering or not tailored to evaporator dynamics,this work introduces the first PID–Wavenet scheme augmented with an IIR-based stabilization layer,specifically designed to address the combined challenges of nonlinear evaporator behavior,gain oscillation,and real-time implementability.The proposed controller(PID-Wavenet+IIR)is implemented and validated inMATLAB/Simulink,demonstrating superior performance compared to a conventional PID tuned using Simulink’s auto-tuning function.Key results include a reduction in settling time from 13.3 to 8.2 s,a reduction in overshoot from 3.5%to 0.8%,a reduction in steady-state error from 0.12℃ to 0.02℃and a 13%reduction in energy overall consumption.The controller also exhibits greater robustness and adaptability under varying thermal loads.This explicit integration of wavelet-driven adaptation with IIR-filtered gain shaping constitutes the main methodological contribution and novelty of the work.These findings validate the effectiveness of the wavelet-based adaptive approach for advanced thermal management in refrigeration and HVAC systems,with potential applications in controlling variable-speed compressors,liquid chillers,and compact cooling units.展开更多
Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional ...Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.展开更多
End-to-end Temporal Action Detection(TAD)has achieved remarkable progress in recent years,driven by innovations in model architectures and the emergence of Video Foundation Models(VFMs).However,existing TAD methods th...End-to-end Temporal Action Detection(TAD)has achieved remarkable progress in recent years,driven by innovations in model architectures and the emergence of Video Foundation Models(VFMs).However,existing TAD methods that perform full fine-tuning of pretrained video models often incur substantial computational costs,which become particularly pronounced when processing long video sequences.Moreover,the need for precise temporal boundary annotations makes data labeling extremely expensive.In low-resource settings where annotated samples are scarce,direct fine-tuning tends to cause overfitting.To address these challenges,we introduce Dynamic LowRank Adapter(DyLoRA),a lightweight fine-tuning framework tailored specifically for the TAD task.Built upon the Low-Rank Adaptation(LoRA)architecture,DyLoRA adapts only the key layers of the pretrained model via low-rank decomposition,reducing the number of trainable parameters to less than 5%of full fine-tuning methods.This significantly lowers memory consumption and mitigates overfitting in low-resource settings.Notably,DyLoRA enhances the temporal modeling capability of pretrained models by optimizing temporal dimension weights,thereby alleviating the representation misalignment of temporal features.Experimental results demonstrate that DyLoRA-TAD achieves impressive performance,with 73.9%mAP on THUMOS14,39.52%on ActivityNet-1.3,and 28.2%on Charades,substantially surpassing the best traditional feature-based methods.展开更多
To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervis...To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.展开更多
This study constructs a dual-capacitor neuron circuit(connected via a memristor)integrated with a phototube and a thermistor to simulate the ability of biological neurons to simultaneously perceive light and thermal s...This study constructs a dual-capacitor neuron circuit(connected via a memristor)integrated with a phototube and a thermistor to simulate the ability of biological neurons to simultaneously perceive light and thermal stimuli.The circuit model converts photothermal signals into electrical signals,and its dynamic behavior is described using dimensionless equations derived from Kirchhoff's laws.Based on Helmholtz's theorem,a pseudo-Hamiltonian energy function is introduced to characterize the system's energy metabolism.Furthermore,an adaptive control function is proposed to elucidate temperature-dependent firing mechanisms,in which temperature dynamics are regulated by pseudo-Hamiltonian energy.Numerical simulations using the fourth-order Runge-Kutta method,combined with bifurcation diagrams,Lyapunov exponent spectra,and phase portraits,reveal that parameters such as capacitance ratio,phototube voltage amplitude/frequency,temperature,and thermistor reference resistance significantly modulate neuronal firing patterns,inducing transitions between periodic and chaotic states.Periodic states typically exhibit higher average pseudo-Hamiltonian energy than chaotic states.Two-parameter analysis demonstrates that phototube voltage amplitude and temperature jointly govern firing modes,with chaotic behavior emerging within specific parameter ranges.Adaptive control studies show that gain/attenuation factors,energy thresholds,ceiling temperatures,and initial temperatures regulate the timing and magnitude of system temperature saturation.During both heating and cooling phases,temperature dynamics are tightly coupled with pseudoHamiltonian energy and neuronal firing activity.These findings validate the circuit's ability to simulate photothermal perception and adaptive temperature regulation,contributing to a deeper understanding of neuronal encoding mechanisms and multimodal sensory processing.展开更多
In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic per...In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.展开更多
The construction of spot electricity markets plays a pivotal role in power system reforms,where market clearing systems profoundly influence market efficiency and security.Current clearing systems predominantly adopt ...The construction of spot electricity markets plays a pivotal role in power system reforms,where market clearing systems profoundly influence market efficiency and security.Current clearing systems predominantly adopt a single-system architecture,with research focusing primarily on accelerating solution algorithms through techniques such as high-efficiency parallel solvers and staggered decomposition of mixed-integer programming models.Notably absent are systematic studies evaluating the adaptability of primary-backup clearing systems incontingency scenarios—a critical gap given redundant systems’expanding applications in operational environments.This paper proposes a comprehensive evaluation framework for analyzing dual-system adaptability,demonstrated through an in-depth case study of the Inner Mongolia power market.First,we establish the innovative“Dual-Active Heterogeneous”architecture that enables independent parallelized operation and fault-isolated redundancy.Subsequently,key performance indices are quantitatively evaluated across four critical dimensions:unit commitment decisions,generator output constraints,transmission section congestion patterns,and clearing price formation mechanisms.An integrated fuzzy evaluation methodology incorporating grey relational analysis is employed for objective indicator weighting,enabling systematic quantification of system superiority under specific grid operating states.Empirical results based on actual operational data from 200 generation units demonstrate the framework’s efficacy in guiding optimal system selection,with particularly strong performance observed during peak load periods.The proposed approach shows high generalization potential for other regional markets employing redundant clearing mechanisms—particularly those with increasing renewable penetration and associated uncertainty.展开更多
The present study investigates the quest for a fully distributed Nash equilibrium(NE) in networked non-cooperative games, with particular emphasis on actuator limitations. Existing distributed NE seeking approaches of...The present study investigates the quest for a fully distributed Nash equilibrium(NE) in networked non-cooperative games, with particular emphasis on actuator limitations. Existing distributed NE seeking approaches often overlook practical input constraints or rely on centralized information. To address these issues, a novel edge-based double-layer adaptive control framework is proposed. Specifically, adaptive scaling parameters are embedded into the edge weights of the communication graph, enabling a fully distributed scheme that avoids dependence on centralized or global knowledge. Every participant modifies its strategy by exclusively utilizing local information and communicating with its neighbors to iteratively approach the NE. By incorporating damping terms into the design of the adaptive parameters, the proposed approach effectively suppresses unbounded parameter growth and consequently guarantees the boundedness of the adaptive gains. In addition, to account for actuator saturation, the proposed distributed NE seeking approach incorporates a saturation function, which ensures that control inputs do not exceed allowable ranges. A rigorous Lyapunov-based analysis guarantees the convergence and boundedness of all system variables. Finally, the presentation of simulation results aims to validate the efficacy and theoretical soundness of the proposed approach.展开更多
Existing elevator fault diagnosis algorithms have limited engineering applicability due to variations in working conditions and differences in equipment structures.To address this limitation,this study proposes an uns...Existing elevator fault diagnosis algorithms have limited engineering applicability due to variations in working conditions and differences in equipment structures.To address this limitation,this study proposes an unsupervised subdomain adaptation method based on a time-frequency feature attention mechanism,LMMD-based subdomain alignment,and contrastive local alignment.This enables the application of the diagnosis model across different working conditions and equipment types.First,a novel time-frequency feature attention mechanism assigns weights to vibration signals of varying dimensions.Second,the time series is transformed to obtain a three-channel time-frequency diagram.This diagram is input into the proposed dimension-segmentation cross-channel multihead self-attention framework to extract high-dimensional frequencydomain fault features.These features are concatenated with the time-domain features to obtain a global feature representation.Then,the extracted high-dimensional features are sent to the classification module to obtain the predicted labels for the source and target domains.Finally,after confidence filtering,the true labels from the source domain and the prediction labels from the target domain are fed into a dynamically weighted multilevel feature alignment module to promote proximity between similar fault features across domains while enhancing separation among different fault types.The validity and superiority of the proposed method were demonstrated through simulation experiments conducted on two types of manned escalator systems under multiple working conditions.For the most challenging transfer task,the proposed method achieved higher accuracy on the target domain test set than DANN,ADDA,C-CLCN,TFA-CCN,and TFA-LCN by 26.87%,24.72%,11.44%,28.94%,and 16.85%,respectively.展开更多
Federated Learning(FL)protects data privacy through a distributed training mechanism,yet its decentralized nature also introduces new security vulnerabilities.Backdoor attacks inject malicious triggers into the global...Federated Learning(FL)protects data privacy through a distributed training mechanism,yet its decentralized nature also introduces new security vulnerabilities.Backdoor attacks inject malicious triggers into the global model through compromised updates,posing significant threats to model integrity and becoming a key focus in FL security.Existing backdoor attack methods typically embed triggers directly into original images and consider only data heterogeneity,resulting in limited stealth and adaptability.To address the heterogeneity of malicious client devices,this paper proposes a novel backdoor attack method named Capability-Adaptive Shadow Backdoor Attack(CASBA).By incorporating measurements of clients’computational and communication capabilities,CASBA employs a dynamic hierarchical attack strategy that adaptively aligns attack intensity with available resources.Furthermore,an improved deep convolutional generative adversarial network(DCGAN)is integrated into the attack pipeline to embed triggers without modifying original data,significantly enhancing stealthiness.Comparative experiments with Shadow Backdoor Attack(SBA)across multiple scenarios demonstrate that CASBA dynamically adjusts resource consumption based on device capabilities,reducing average memory usage per iteration by 5.8%.CASBA improves resource efficiency while keeping the drop in attack success rate within 3%.Additionally,the effectiveness of CASBA against three robust FL algorithms is also validated.展开更多
While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance re...While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance remains underexplored in field investigations.To evaluate the practical applicability of this emerging technique in adverse shallow sea channels,a field experiment was conducted using three communication modes:orthogonal frequency division multiplexing(OFDM),M-ary frequency-shift keying(MFSK),and direct sequence spread spectrum(DSSS)for reinforcement learning-driven adaptive modulation.Specifically,a Q-learning method is used to select the optimal modulation mode according to the channel quality quantified by signal-to-noise ratio,multipath spread length,and Doppler frequency offset.Experimental results demonstrate that the reinforcement learning-based adaptive modulation scheme outperformed fixed threshold detection in terms of total throughput and average bit error rate,surpassing conventional adaptive modulation strategies.展开更多
文摘1.Introduction The field of exercise science is experiencing a renaissance,with recent research illuminating the molecular,cellular,and systemic effects of physical activity.This is largely due to the now unequivocal evidence that a lack of physical activity,not only has direct effects on the prevalence of non-contagious diseases(NCDs)but has profound additive effects of other risk factors for NCD such as obesity and hypertension.1 The articles in this special topic of Journal of Sport and Health Science(JSHS)are dedicated to research on Exercise biochemistry&metabolism.
基金Supported by the National Natural Science Foundation of China(12071133)Natural Science Foundation of Henan Province(252300421993)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B110005)。
文摘In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.
文摘In federated learning,backdoor attacks have become an important research topic with their wide application in processing sensitive datasets.Since federated learning detects or modifies local models through defense mechanisms during aggregation,it is difficult to conduct effective backdoor attacks.In addition,existing backdoor attack methods are faced with challenges,such as low backdoor accuracy,poor ability to evade anomaly detection,and unstable model training.To address these challenges,a method called adaptive simulation backdoor attack(ASBA)is proposed.Specifically,ASBA improves the stability of model training by manipulating the local training process and using an adaptive mechanism,the ability of the malicious model to evade anomaly detection by combing large simulation training and clipping,and the backdoor accuracy by introducing a stimulus model to amplify the impact of the backdoor in the global model.Extensive comparative experiments under five advanced defense scenarios show that ASBA can effectively evade anomaly detection and achieve high backdoor accuracy in the global model.Furthermore,it exhibits excellent stability and effectiveness after multiple rounds of attacks,outperforming state-of-the-art backdoor attack methods.
基金supported by the Financial Special Fund,grant number XZ202401JD0027National Barley Industry Technology System(CARS-05-01A-08)+3 种基金the Xizang Agri-Tech Innovation Project(XZNKY-2025-CXGC-T01)the Joint Funds of the National Natural Science Foundation of China(No.U20A2026)the Financial Special Fund,grant number(32401784,2017CZZX001/2,XZNKY-2018-C-021 and NYSTC202401)the China Agriculture Research System of Barley(CARS-05).
文摘Qingke,a staple crop grown on the high-altitude Tibetan Plateau,has evolved a metabolomic profile providing both environmental stress resilience and human nutrition.We review the hypothesis that the metabolites that confer cold and UV resistance on the crop also facilitate human adaptation to high-altitude stresses.Specifically,β-glucans regulate blood glucose primarily via short-chain fatty acids(SCFAs)produced through gut microbiota fermentation,which directly mediate glucose homeostasis.Phenolamides accumulate via the phenylpropanoid pathway,with chalcone isomerase(CHI)serving as a key enzyme in flavonoid biosynthesis and enhancing UV-B resistance.Under low temperatures,β-glucans improve frost tolerance by modulating osmotic balance and inhibiting ice-nucleating proteins,while lipids maintain membrane fluidity to sustain cellular function during cold stress.Importantly,we explore the hypothesis that these same metabolites,upon consumption,may facilitate human adaptation to high-altitude stresses.This hypothesis is supported by preliminary epidemiological associations between Qingke consumption and favorable health outcomes in high-altitude populations,as well as established bioactivities of the implicated metabolites in vitro and in animal models.However,direct causal evidence in humans and a comprehensive understanding of the underlying molecular mechanisms remain key knowledge gaps that warrant future investigation.Qingke as a unique resource at the interface of agricultural resilience and human nutrition.Understanding its metabolic blueprint will inform the development of functional foods and climate-resilient crops.
基金Supported by Changsha Tobacco Company Science and Technology Project(2020-2024A04).
文摘Starting from the foundational static traits underlying the growth and development of flue-cured tobacco, this research conducts a systematic examination of the phenomena and theoretical principles associated with environment-driven adaptive changes during its cultivation. It was found that environmental variables-including temperature, light, and moisture-elicit directional shifts in static traits ( e.g. , chemical composition, morphological architecture, and leaf tissue structure) toward enhanced environmental adaptation, characterized by graduality, juvenility, similarity, and correlativity. Upon alterations in ambient conditions, flue-cured tobacco modulates its static traits through integrated physical, chemical, and biological-genetic mechanisms, aiming to optimize resource utilization, mitigate environmental constraints, and preserve internal homeostasis alongside metabolic balance. The investigation further reveals that the adaptive scope of flue-cured tobacco to field environments is malleable and can be extended and elevated via adaptive conditioning commencing at the juvenile stage. In addition, the adaptive alignment between static traits and environmental parameters exerts a substantial impact on the plant s growth dynamics, yield performance, and quality attributes. Beyond its relevance to flue-cured tobacco, the proposed theory offers a meaningful framework for elucidating the pervasive adaptive strategies employed by plants and broader biological systems in response to environmental contingencies.
文摘After billions of years of evolution,biological intelligence has converged on unrivalled energy efficiency and environmental adaptability.The human brain,for instance,is highly efficient in information transmission,consuming only about 20 W onaverage in a resting state[1,2].A key to this efficiency is that biological signal transduction and processing rely significantly on multi-ions as the signal carriers.Inspired by this paradigm.
文摘The rapidly evolving cybersecurity threat landscape exposes a critical flaw in traditional educational programs where static curricula cannot adapt swiftly to novel attack vectors.This creates a significant gap between theoretical knowledge and the practical defensive capabilities needed in the field.To address this,we propose TeachSecure-CTI,a novel framework for adaptive cybersecurity curriculumgeneration that integrates real-time Cyber Threat Intelligence(CTI)with AI-driven personalization.Our framework employs a layered architecture featuring a CTI ingestion and clusteringmodule,natural language processing for semantic concept extraction,and a reinforcement learning agent for adaptive content sequencing.Bydynamically aligning learningmaterialswithboththe evolving threat environment and individual learner profiles,TeachSecure-CTI ensures content remains current,relevant,and tailored.A 12-week study with 150 students across three institutions demonstrated that the framework improves learning gains by 34%,significantly exceeding the 12%–21%reported in recent literature.The system achieved 84.8%personalization accuracy,85.9%recognition accuracy for MITRE ATT&CK tactics,and a 31%faster competency development rate compared to static curricula.These findings have implications beyond academia,extending to workforce development,cyber range training,and certification programs.By bridging the gap between dynamic threats and static educational materials,TeachSecure-CTI offers an empirically validated,scalable solution for cultivating cybersecurity professionals capable of responding to modern threats.
基金Supported by Chongqing Health Commission and Chongqing Science and Technology Bureau,No.2023MSXM182。
文摘Rheumatoid arthritis(RA)patients face significant psychological challenges alongside physical symptoms,necessitating a comprehensive understanding of how psychological vulnerability and adaptation patterns evolve throughout the disease course.This review examined 95 studies(2000-2025)from PubMed,Web of Science,and CNKI databases including longitudinal cohorts,randomized controlled trials,and mixed-methods research,to characterize the complex interplay between biological,psychological,and social factors affecting RA patients’mental health.Findings revealed three distinct vulnerability trajectories(45%persistently low,30%fluctuating improvement,25%persistently high)and four adaptation stages,with critical intervention periods occurring 3-6 months postdiagnosis and during disease flares.Multiple factors significantly influence psychological outcomes,including gender(females showing 1.8-fold increased risk),age(younger patients experiencing 42%higher vulnerability),pain intensity,inflammatory markers,and neuroendocrine dysregulation(48%showing cortisol rhythm disruption).Early psychological intervention(within 3 months of diagnosis)demonstrated robust benefits,reducing depression incidence by 42%with effects persisting 24-36 months,while different modalities showed complementary advantages:Cognitive behavioral therapy for depression(Cohen’s d=0.68),mindfulness for pain acceptance(38%improvement),and peer support for meaning reconstruction(25.6%increase).These findings underscore the importance of integrating routine psychological assessment into standard RA care,developing stage-appropriate interventions,and advancing research toward personalized biopsychosocial approaches that address the dynamic psychological dimensions of the disease.
基金funded by Nanjing University of Posts and Telecommunications Humanities and Social Sciences Research Fund Project(NYY222055)Special research project on teaching reform of innovation and entrepreneurship education in Nanjing University of Posts and Telecommunications(GCSJG202528)+2 种基金General Subject of Educational Science Planning in Jiangsu Province(C/2024/01/76)General project of educational science research in Shanghai(C24288)Key funded project of Shandong Vocational Education Teaching Reform Research in 2022(2022052).
文摘Background:As an important indicator of subjective well-being(SWB),decent work is a key guarantee for the sustainable development of teachers and their psychological health and work quality.Faced with the rapid development of artificial intelligence and the global labor market,vocational college teachers are facing challenges such as workload pressure and limited career development,which may harm their well-being.This study aims to localize the measurement method of decent work in Chinese vocational education based on the theory of the Psychology of Working Theory,and explore the relationship mechanism between organizational support,career adaptability,decent work,and job satisfaction among vocational college teachers.Methods:A cross-sectional survey was conducted with 422 HVCU teachers in China(202 male,220 female)using the localized Perceived Organizational Support Scale,Career Adaptability Scale,Decent Work Scale,and Job Satisfaction Scale.Results:The overall level of HVCU teachers’decent work was above the median(Mean=4.09,SD=0.69),laying a foundation for their SWB.Decent work significantly and positively predicted job satisfaction(β=0.620,p<0.001).Organizational support(r=0.58,p<0.001)and career adaptability(r=0.82,p<0.001)can positively affect decent work,and further improve job satisfaction(collective R2 rising from 38.3%to 41.1%).Bootstrap analysis confirmed these mediating effects were robust.Conclusions:This study confirms that the combined effects of organizational support and career adaptability can enhance decent work,further improving teachers’job satisfaction and subsequent subjective well-being.Besides,this study provides an empirical basis for improving the well-being of higher vocational teachers and the sustainable development of vocational education,and has practical significance for improving the teacher incentive policy.
文摘This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temperature distribution,suitable for control design due to its balance between physical fidelity and computational simplicity.The controller uses a wavelet-based neural proportional,integral,derivative(PID)controller with IIR filtering(infinite impulse response).The dynamic model captures the essential heat and mass transfer phenomena through a nonlinear energy balance,where the cooling capacity“Qevap”is expressed as a non-linear function of the compressor frequency and the temperature difference,specifically,Q_(evap)=k_(1)u(T_(in)−T_(e))with u as compressor frequency,Te evaporator temperature,and Tin inlet fluid temperature.The operating conditions of the system,in general terms,focus on the following variables,the overall thermal capacity is 1000 J/K,typical for small-capacity heat exchangers,The mass flow is 0.05 kg/s,typical for secondary liquid cooling circuits,the overall loss coefficient of 50 W/K that corresponds to small evaporators with partial insulation,the temperatures(inlet)of 10℃and the temperature of environment of 25℃,thermal load of 200 W that corresponds to a small-scaled air conditioning applications.To handle system nonlinearities and improve control performance,aMorlet wavelet-based neural network(Wavenet)is used to dynamically adjust the PID gains online.An IIR filter is incorporated to smooth the adaptive gains,improving stability and reducing oscillations.In contrast to prior wavelet-or neural-adaptive PID controllers in HVAC applications,which typically adjust gains without explicit filtering or not tailored to evaporator dynamics,this work introduces the first PID–Wavenet scheme augmented with an IIR-based stabilization layer,specifically designed to address the combined challenges of nonlinear evaporator behavior,gain oscillation,and real-time implementability.The proposed controller(PID-Wavenet+IIR)is implemented and validated inMATLAB/Simulink,demonstrating superior performance compared to a conventional PID tuned using Simulink’s auto-tuning function.Key results include a reduction in settling time from 13.3 to 8.2 s,a reduction in overshoot from 3.5%to 0.8%,a reduction in steady-state error from 0.12℃ to 0.02℃and a 13%reduction in energy overall consumption.The controller also exhibits greater robustness and adaptability under varying thermal loads.This explicit integration of wavelet-driven adaptation with IIR-filtered gain shaping constitutes the main methodological contribution and novelty of the work.These findings validate the effectiveness of the wavelet-based adaptive approach for advanced thermal management in refrigeration and HVAC systems,with potential applications in controlling variable-speed compressors,liquid chillers,and compact cooling units.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00406320)the Institute of Information&Communica-tions Technology Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization Program Grant funded by the Korea government(MSIT)(IITP-2026-RS-2023-00259678).
文摘Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.
基金supported by the National Natural Science Foundation of China(Grant No.62266054)the Major Science and Technology Project of Yunnan Province(Grant No.202402AD080002)the Scientific Research Fund of the Yunnan Provincial Department of Education(Grant No.2025Y0302).
文摘End-to-end Temporal Action Detection(TAD)has achieved remarkable progress in recent years,driven by innovations in model architectures and the emergence of Video Foundation Models(VFMs).However,existing TAD methods that perform full fine-tuning of pretrained video models often incur substantial computational costs,which become particularly pronounced when processing long video sequences.Moreover,the need for precise temporal boundary annotations makes data labeling extremely expensive.In low-resource settings where annotated samples are scarce,direct fine-tuning tends to cause overfitting.To address these challenges,we introduce Dynamic LowRank Adapter(DyLoRA),a lightweight fine-tuning framework tailored specifically for the TAD task.Built upon the Low-Rank Adaptation(LoRA)architecture,DyLoRA adapts only the key layers of the pretrained model via low-rank decomposition,reducing the number of trainable parameters to less than 5%of full fine-tuning methods.This significantly lowers memory consumption and mitigates overfitting in low-resource settings.Notably,DyLoRA enhances the temporal modeling capability of pretrained models by optimizing temporal dimension weights,thereby alleviating the representation misalignment of temporal features.Experimental results demonstrate that DyLoRA-TAD achieves impressive performance,with 73.9%mAP on THUMOS14,39.52%on ActivityNet-1.3,and 28.2%on Charades,substantially surpassing the best traditional feature-based methods.
基金supported by the National Natural Science Foundation of China Funded Project(Project Name:Research on Robust Adaptive Allocation Mechanism of Human Machine Co-Driving System Based on NMS Features,Project Approval Number:52172381).
文摘To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.
基金supported by the Natural Science Founda tion of Chongqing(Grant No.CSTB2024NSCQ-MSX0944)。
文摘This study constructs a dual-capacitor neuron circuit(connected via a memristor)integrated with a phototube and a thermistor to simulate the ability of biological neurons to simultaneously perceive light and thermal stimuli.The circuit model converts photothermal signals into electrical signals,and its dynamic behavior is described using dimensionless equations derived from Kirchhoff's laws.Based on Helmholtz's theorem,a pseudo-Hamiltonian energy function is introduced to characterize the system's energy metabolism.Furthermore,an adaptive control function is proposed to elucidate temperature-dependent firing mechanisms,in which temperature dynamics are regulated by pseudo-Hamiltonian energy.Numerical simulations using the fourth-order Runge-Kutta method,combined with bifurcation diagrams,Lyapunov exponent spectra,and phase portraits,reveal that parameters such as capacitance ratio,phototube voltage amplitude/frequency,temperature,and thermistor reference resistance significantly modulate neuronal firing patterns,inducing transitions between periodic and chaotic states.Periodic states typically exhibit higher average pseudo-Hamiltonian energy than chaotic states.Two-parameter analysis demonstrates that phototube voltage amplitude and temperature jointly govern firing modes,with chaotic behavior emerging within specific parameter ranges.Adaptive control studies show that gain/attenuation factors,energy thresholds,ceiling temperatures,and initial temperatures regulate the timing and magnitude of system temperature saturation.During both heating and cooling phases,temperature dynamics are tightly coupled with pseudoHamiltonian energy and neuronal firing activity.These findings validate the circuit's ability to simulate photothermal perception and adaptive temperature regulation,contributing to a deeper understanding of neuronal encoding mechanisms and multimodal sensory processing.
基金supported by the Funds for Central-Guided Local Science and Technology Development(Grant No.202407AC110005)Key Technologies for the Construction of a Whole-Process Intelligent Service System for Neuroendocrine Neoplasm.Supported by 2023 Opening Research Fund of Yunnan Key Laboratory of Digital Communications(YNJTKFB-20230686,YNKLDC-KFKT-202304).
文摘In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.
基金supported by NARI Relays Electric Co.,Ltd.under the Project“Research on Evaluation of Clearing Results and Switching Criteria for Primary-Backup Systems in Electricity SpotMarkets”(Project No.CGSQ240800443).
文摘The construction of spot electricity markets plays a pivotal role in power system reforms,where market clearing systems profoundly influence market efficiency and security.Current clearing systems predominantly adopt a single-system architecture,with research focusing primarily on accelerating solution algorithms through techniques such as high-efficiency parallel solvers and staggered decomposition of mixed-integer programming models.Notably absent are systematic studies evaluating the adaptability of primary-backup clearing systems incontingency scenarios—a critical gap given redundant systems’expanding applications in operational environments.This paper proposes a comprehensive evaluation framework for analyzing dual-system adaptability,demonstrated through an in-depth case study of the Inner Mongolia power market.First,we establish the innovative“Dual-Active Heterogeneous”architecture that enables independent parallelized operation and fault-isolated redundancy.Subsequently,key performance indices are quantitatively evaluated across four critical dimensions:unit commitment decisions,generator output constraints,transmission section congestion patterns,and clearing price formation mechanisms.An integrated fuzzy evaluation methodology incorporating grey relational analysis is employed for objective indicator weighting,enabling systematic quantification of system superiority under specific grid operating states.Empirical results based on actual operational data from 200 generation units demonstrate the framework’s efficacy in guiding optimal system selection,with particularly strong performance observed during peak load periods.The proposed approach shows high generalization potential for other regional markets employing redundant clearing mechanisms—particularly those with increasing renewable penetration and associated uncertainty.
基金supported by the National Natural Science Foundation of China (Grant No.62173009)the National Key Research and Development Program of China (Grant No.2021ZD0112302)。
文摘The present study investigates the quest for a fully distributed Nash equilibrium(NE) in networked non-cooperative games, with particular emphasis on actuator limitations. Existing distributed NE seeking approaches often overlook practical input constraints or rely on centralized information. To address these issues, a novel edge-based double-layer adaptive control framework is proposed. Specifically, adaptive scaling parameters are embedded into the edge weights of the communication graph, enabling a fully distributed scheme that avoids dependence on centralized or global knowledge. Every participant modifies its strategy by exclusively utilizing local information and communicating with its neighbors to iteratively approach the NE. By incorporating damping terms into the design of the adaptive parameters, the proposed approach effectively suppresses unbounded parameter growth and consequently guarantees the boundedness of the adaptive gains. In addition, to account for actuator saturation, the proposed distributed NE seeking approach incorporates a saturation function, which ensures that control inputs do not exceed allowable ranges. A rigorous Lyapunov-based analysis guarantees the convergence and boundedness of all system variables. Finally, the presentation of simulation results aims to validate the efficacy and theoretical soundness of the proposed approach.
基金supported by the National Natural Science Foundation of China(Grant Nos.52375255,51935007)the Shanghai Rising-Star Program(Grant No.24QB2705000)。
文摘Existing elevator fault diagnosis algorithms have limited engineering applicability due to variations in working conditions and differences in equipment structures.To address this limitation,this study proposes an unsupervised subdomain adaptation method based on a time-frequency feature attention mechanism,LMMD-based subdomain alignment,and contrastive local alignment.This enables the application of the diagnosis model across different working conditions and equipment types.First,a novel time-frequency feature attention mechanism assigns weights to vibration signals of varying dimensions.Second,the time series is transformed to obtain a three-channel time-frequency diagram.This diagram is input into the proposed dimension-segmentation cross-channel multihead self-attention framework to extract high-dimensional frequencydomain fault features.These features are concatenated with the time-domain features to obtain a global feature representation.Then,the extracted high-dimensional features are sent to the classification module to obtain the predicted labels for the source and target domains.Finally,after confidence filtering,the true labels from the source domain and the prediction labels from the target domain are fed into a dynamically weighted multilevel feature alignment module to promote proximity between similar fault features across domains while enhancing separation among different fault types.The validity and superiority of the proposed method were demonstrated through simulation experiments conducted on two types of manned escalator systems under multiple working conditions.For the most challenging transfer task,the proposed method achieved higher accuracy on the target domain test set than DANN,ADDA,C-CLCN,TFA-CCN,and TFA-LCN by 26.87%,24.72%,11.44%,28.94%,and 16.85%,respectively.
基金supported by the National Natural Science Foundation of China(Grant No.62172123)the Key Research and Development Program of Heilongjiang Province,China(GrantNo.2022ZX01A36).
文摘Federated Learning(FL)protects data privacy through a distributed training mechanism,yet its decentralized nature also introduces new security vulnerabilities.Backdoor attacks inject malicious triggers into the global model through compromised updates,posing significant threats to model integrity and becoming a key focus in FL security.Existing backdoor attack methods typically embed triggers directly into original images and consider only data heterogeneity,resulting in limited stealth and adaptability.To address the heterogeneity of malicious client devices,this paper proposes a novel backdoor attack method named Capability-Adaptive Shadow Backdoor Attack(CASBA).By incorporating measurements of clients’computational and communication capabilities,CASBA employs a dynamic hierarchical attack strategy that adaptively aligns attack intensity with available resources.Furthermore,an improved deep convolutional generative adversarial network(DCGAN)is integrated into the attack pipeline to embed triggers without modifying original data,significantly enhancing stealthiness.Comparative experiments with Shadow Backdoor Attack(SBA)across multiple scenarios demonstrate that CASBA dynamically adjusts resource consumption based on device capabilities,reducing average memory usage per iteration by 5.8%.CASBA improves resource efficiency while keeping the drop in attack success rate within 3%.Additionally,the effectiveness of CASBA against three robust FL algorithms is also validated.
基金funding from the National Key Research and Development Program of China(No.2018YFE0110000)the National Natural Science Foundation of China(No.11274259,No.11574258)the Science and Technology Commission Foundation of Shanghai(21DZ1205500)in support of the present research.
文摘While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance remains underexplored in field investigations.To evaluate the practical applicability of this emerging technique in adverse shallow sea channels,a field experiment was conducted using three communication modes:orthogonal frequency division multiplexing(OFDM),M-ary frequency-shift keying(MFSK),and direct sequence spread spectrum(DSSS)for reinforcement learning-driven adaptive modulation.Specifically,a Q-learning method is used to select the optimal modulation mode according to the channel quality quantified by signal-to-noise ratio,multipath spread length,and Doppler frequency offset.Experimental results demonstrate that the reinforcement learning-based adaptive modulation scheme outperformed fixed threshold detection in terms of total throughput and average bit error rate,surpassing conventional adaptive modulation strategies.
