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
The Yangtze finless porpoise(Neophocaena asiaeorientalis asiaeorientalis,Pilleri&Gihr,1972;YFP)is an exclusively freshwater cetacean species inhabiting the Yangtze River and its connecting large lakes.As the prima...The Yangtze finless porpoise(Neophocaena asiaeorientalis asiaeorientalis,Pilleri&Gihr,1972;YFP)is an exclusively freshwater cetacean species inhabiting the Yangtze River and its connecting large lakes.As the primary line of defense in maintaining physiological equilibrium,the epidermis of the porpoise is expected to have undergone structural adaptations due to the shift from the marine to the freshwater environment.This study compared the microstructural and ultrastructural features of the epidermis of YFP and its marine counterpart,the East Asian finless porpoise(Neophocaena asiaeorientalis sunameri,Pilleri&Gihr,1975;EAFP).Microscopic structural observations and statistical analyses of the epidermal thickness demonstrated no significant differences in the cell structure or distribution between the two porpoise species.However,the epidermis of the YFP contained more abundant stratum basale cells.The outermost lipid stratum corneum exhibited a thinner cell layer with wider neutral lipid droplets to resist the passive entry of water molecules in the hypotonic environment.In contrast,for the EAFp,a more uniformly arranged stratum basale in the epidermis led to denser keratin fibers and robust desmosomes within each epidermal layer at the ultrastructural level.This tight arrangement of cells can reduce transepidermal water loss(TEWL)in an environment with higher osmotic pressure.In conclusion,the 2 finless porpoise species appear to employ different epidermal mechanisms to adapt to their distinct osmotic environments.The YFP appears to possess a"lipid waterproofing"epidermal structure,while the EAFP possesses a"thick and compact water-retaining"epidermal structure to copewithpotentialwaterloss.展开更多
Patient-reported outcome measures(PROMs)are essential in orthopedic research and clinical practice,promoting standardized assessments of treatment efficacy and patient well-being.However,their validity across diverse ...Patient-reported outcome measures(PROMs)are essential in orthopedic research and clinical practice,promoting standardized assessments of treatment efficacy and patient well-being.However,their validity across diverse populations depends on rigorous processes of translation,cultural adaptation,and validation.AlMousa et al successfully validated the Arabic version of the American Academy of Orthopedic Surgeons Foot and Ankle Outcomes Questionnaire,demonstrating strong reliability and clinical applicability for Arabic-speaking patients with traumatic foot and ankle injuries.This article highlights the vital role of culturally adapted PROMs in expanding the global scope of orthopedic research,enhancing patient-centered care,and promoting international collaboration.By addressing linguistic and cultural barriers,such adaptations support a more comprehensive and inclusive approach to evidence-based medicine.Future research should aim to optimize the integration of adapted PROMs into clinical practice,evaluate their influences on treatment decision-making,and investigate their functions in largescale multicenter studies to further globally advance orthopedic research.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
[Objectives]This study was conducted to achieve rapid and accurate detection of protein content in rice with a particle size of 1.0 mm.[Methods]A multi-model fusion strategy was proposed on the basis of Stacking ensem...[Objectives]This study was conducted to achieve rapid and accurate detection of protein content in rice with a particle size of 1.0 mm.[Methods]A multi-model fusion strategy was proposed on the basis of Stacking ensemble learning.A base learner pool was constructed,containing Partial Least Squares(PLS),Support Vector Machine(SVM),Deep Extreme Learning Machine(DELM),Random Forest(RF),Gradient Boosting Decision Tree(GBDT),and Multilayer Perceptron(MLP).PLS,DELM,and Linear Regression(LR)were used as meta-learner candidates.Employing integer coding technology,systematic dynamic combinations of base learners and meta-learners were generated,resulting in a total of 40 non-repetitive fusion models.The optimal combination was selected through a comprehensive evaluation based on multiple assessment indicators.[Results]The combination"PLS-DELM-MLP-LR"(code 1367)achieved coefficients of determination of 0.9732 and 0.9780 on the validation set and independent test set,respectively,with relative root mean square errors of 2.35%and 2.36%,and residual predictive deviations of 6.1075 and 6.7479,respectively.[Conclusions]The Stacking fusion model significantly enhances the predictive accuracy and robustness of spectral quantitative analysis,providing an efficient and feasible solution for modeling complex agricultural product spectral data.展开更多
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
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
Gibbons are small,arboreal apes that play a critical role in tropical biodiversity and ecosystem ecology.However,nearly all species of gibbons are threatened by habitat loss,illegal trade,hunting,and other human activ...Gibbons are small,arboreal apes that play a critical role in tropical biodiversity and ecosystem ecology.However,nearly all species of gibbons are threatened by habitat loss,illegal trade,hunting,and other human activities.Long-term poor understanding of their genetics and evolution undermines effective conservation efforts.In this study,we analyse comparative population genomic data of four Nomascus species.Our results reveal strong genetic differentiation and gene flow among Nomascus species.Additionally,we identify genomic features that are potentially related to natural selection linked to vocalization,fructose metabolism,motor balance,and body size,consistent with the unique phenotype and adaptability of gibbons.Inbreeding,coupled with population declines due to climate change and historical human activities,leads to reduced genetic diversity and the accumulation of deleterious variations that likely affect cardiovascular disease and the reproductive potential of gibbons and further reduce their fitness,highlighting the urgent need for effective conservation strategies.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
Nitric oxide(NO)is a key vasodilator that regulates vascular pressure and blood flow.Tibetans have developed a"blunted"mechanism for regulating NO levels at high altitude,with GTP cyclohydrolase 1(GCH1)ident...Nitric oxide(NO)is a key vasodilator that regulates vascular pressure and blood flow.Tibetans have developed a"blunted"mechanism for regulating NO levels at high altitude,with GTP cyclohydrolase 1(GCH1)identified as a key candidate gene.Here,we present comprehensive genetic and functional analyses of GCH1,which exhibits strong Darwinian positive selection in Tibetans.We show that Tibetan-enriched GCH1 variants down-regulate its expression in the blood of Tibetans.Based on this observation,we generate the heterozygous Gch1 knockout(Gch1^(+/-))mouse model to simulate its downregulation in Tibetans.We find that under prolonged hypoxia,the Gch1^(+/-)mice have relatively higher blood NO and blood oxygen saturation levels compared with the wild-type(WT)controls,providing better oxygen supplies to the cardiovascular and pulmonary systems.Markedly,hypoxia-induced cardiac hypertrophy and pulmonary remodeling are significantly attenuated in the Gch1^(^(+/-))mice compared with the WT controls,likely due to the adaptive changes in molecular regulations related to metabolism,inflammation,circadian rhythm,extracellular matrix,and oxidative stress.This study sheds light on the role of GCH1 in regulating blood NO,contributing to the physiological adaptation of the cardiovascular and pulmonary systems in Tibetans at high altitude.展开更多
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
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 λ_(1) and incentive λ_(2) thresholds are set based on operational priorities and indicator volatility,penalizing indicators with values below λ_(1) and incentivizing those exceeding λ_(2) 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
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
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
This paper explores the adaptive exponentially designated-time stabilization issue via event-triggered feedback for a kind of uncertain high-order nonlinear systems.The motivation mainly comes from the following two c...This paper explores the adaptive exponentially designated-time stabilization issue via event-triggered feedback for a kind of uncertain high-order nonlinear systems.The motivation mainly comes from the following two challenges:the undesired singularity problem arising from infinite control gains at the prescribed-time instant,the effective trade-off between the control amplitude and the triggering duration.The goal is to build an event-triggered mechanism comprising a skillful triggered rule alongside a time-dependent threshold.Utilizing the designed control strategy,the solutions' existence and the prevention of Zeno phenomenon are successfully guaranteed by using a new transformation equipped with a time-varying function and redesigning the continuous state-feedback dominance approach with an array of integral functions involving embedded sign functions.Better than existing prescribed-time methods,our approach not only ensures that state variables converge to a small compact set before a designated time and stay there henceforth,and converge to the origin exponentially,but also ensures that the controller continuously works on the whole-time horizon.Two illustrative examples are given to show the effectiveness of the devised scheme.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
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
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
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 the National Key R&D Program of China(2021YFD1200304).
文摘The Yangtze finless porpoise(Neophocaena asiaeorientalis asiaeorientalis,Pilleri&Gihr,1972;YFP)is an exclusively freshwater cetacean species inhabiting the Yangtze River and its connecting large lakes.As the primary line of defense in maintaining physiological equilibrium,the epidermis of the porpoise is expected to have undergone structural adaptations due to the shift from the marine to the freshwater environment.This study compared the microstructural and ultrastructural features of the epidermis of YFP and its marine counterpart,the East Asian finless porpoise(Neophocaena asiaeorientalis sunameri,Pilleri&Gihr,1975;EAFP).Microscopic structural observations and statistical analyses of the epidermal thickness demonstrated no significant differences in the cell structure or distribution between the two porpoise species.However,the epidermis of the YFP contained more abundant stratum basale cells.The outermost lipid stratum corneum exhibited a thinner cell layer with wider neutral lipid droplets to resist the passive entry of water molecules in the hypotonic environment.In contrast,for the EAFp,a more uniformly arranged stratum basale in the epidermis led to denser keratin fibers and robust desmosomes within each epidermal layer at the ultrastructural level.This tight arrangement of cells can reduce transepidermal water loss(TEWL)in an environment with higher osmotic pressure.In conclusion,the 2 finless porpoise species appear to employ different epidermal mechanisms to adapt to their distinct osmotic environments.The YFP appears to possess a"lipid waterproofing"epidermal structure,while the EAFP possesses a"thick and compact water-retaining"epidermal structure to copewithpotentialwaterloss.
基金Supported by the Hubei Provincial Natural Science Foundation,No.2025AFB845Graduate Innovation and Entrepreneurship Fund of Wuhan University of Science and Technology,No.JCX2024044.
文摘Patient-reported outcome measures(PROMs)are essential in orthopedic research and clinical practice,promoting standardized assessments of treatment efficacy and patient well-being.However,their validity across diverse populations depends on rigorous processes of translation,cultural adaptation,and validation.AlMousa et al successfully validated the Arabic version of the American Academy of Orthopedic Surgeons Foot and Ankle Outcomes Questionnaire,demonstrating strong reliability and clinical applicability for Arabic-speaking patients with traumatic foot and ankle injuries.This article highlights the vital role of culturally adapted PROMs in expanding the global scope of orthopedic research,enhancing patient-centered care,and promoting international collaboration.By addressing linguistic and cultural barriers,such adaptations support a more comprehensive and inclusive approach to evidence-based medicine.Future research should aim to optimize the integration of adapted PROMs into clinical practice,evaluate their influences on treatment decision-making,and investigate their functions in largescale multicenter studies to further globally advance orthopedic research.
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[Objectives]This study was conducted to achieve rapid and accurate detection of protein content in rice with a particle size of 1.0 mm.[Methods]A multi-model fusion strategy was proposed on the basis of Stacking ensemble learning.A base learner pool was constructed,containing Partial Least Squares(PLS),Support Vector Machine(SVM),Deep Extreme Learning Machine(DELM),Random Forest(RF),Gradient Boosting Decision Tree(GBDT),and Multilayer Perceptron(MLP).PLS,DELM,and Linear Regression(LR)were used as meta-learner candidates.Employing integer coding technology,systematic dynamic combinations of base learners and meta-learners were generated,resulting in a total of 40 non-repetitive fusion models.The optimal combination was selected through a comprehensive evaluation based on multiple assessment indicators.[Results]The combination"PLS-DELM-MLP-LR"(code 1367)achieved coefficients of determination of 0.9732 and 0.9780 on the validation set and independent test set,respectively,with relative root mean square errors of 2.35%and 2.36%,and residual predictive deviations of 6.1075 and 6.7479,respectively.[Conclusions]The Stacking fusion model significantly enhances the predictive accuracy and robustness of spectral quantitative analysis,providing an efficient and feasible solution for modeling complex agricultural product spectral data.
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 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.
基金supported by Science and Technology Program from the Forestry Administration of Guangdong Province(2024KJQT0012)the Guangdong Provincial Key R&D Program(2022B1111040001)+2 种基金the National Forestry Administration rare and endangered species field rescue and breeding project(Gui lin hu yu O10)the National Natural Science Foundation of China(32200337)a fellowship from the China Postdoctoral Science Foundation(2022M712003).
文摘Gibbons are small,arboreal apes that play a critical role in tropical biodiversity and ecosystem ecology.However,nearly all species of gibbons are threatened by habitat loss,illegal trade,hunting,and other human activities.Long-term poor understanding of their genetics and evolution undermines effective conservation efforts.In this study,we analyse comparative population genomic data of four Nomascus species.Our results reveal strong genetic differentiation and gene flow among Nomascus species.Additionally,we identify genomic features that are potentially related to natural selection linked to vocalization,fructose metabolism,motor balance,and body size,consistent with the unique phenotype and adaptability of gibbons.Inbreeding,coupled with population declines due to climate change and historical human activities,leads to reduced genetic diversity and the accumulation of deleterious variations that likely affect cardiovascular disease and the reproductive potential of gibbons and further reduce their fitness,highlighting the urgent need for effective conservation strategies.
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funded by grants from the National Natural Science Foundation of China(32288101 and 91631306 to B.S32170632 and 32000390 to Y.H.32400503 to Y.G.)Major Scientific Project of Yunnan Province(202305AH340007 to B.S.)+4 种基金Yunnan Revitalization Talent Support Program Science&Technology Champion Project(202005AB160004 to B.S.)Yunnan Revitalization Talent Support Program Innovation Team(202405AS350008)Yunnan Scientist Workshops(to B.S.)the Youth Innovation Promotion Association of CAS(to Y.H.),the Science and Technology General Program of Yunnan Province(202301AW070010 and 202001AT070110 to Y.H.)and the Provincial Key Research,Development,and Translational Program(XZ202101ZY0009G to Baima.).
文摘Nitric oxide(NO)is a key vasodilator that regulates vascular pressure and blood flow.Tibetans have developed a"blunted"mechanism for regulating NO levels at high altitude,with GTP cyclohydrolase 1(GCH1)identified as a key candidate gene.Here,we present comprehensive genetic and functional analyses of GCH1,which exhibits strong Darwinian positive selection in Tibetans.We show that Tibetan-enriched GCH1 variants down-regulate its expression in the blood of Tibetans.Based on this observation,we generate the heterozygous Gch1 knockout(Gch1^(+/-))mouse model to simulate its downregulation in Tibetans.We find that under prolonged hypoxia,the Gch1^(+/-)mice have relatively higher blood NO and blood oxygen saturation levels compared with the wild-type(WT)controls,providing better oxygen supplies to the cardiovascular and pulmonary systems.Markedly,hypoxia-induced cardiac hypertrophy and pulmonary remodeling are significantly attenuated in the Gch1^(^(+/-))mice compared with the WT controls,likely due to the adaptive changes in molecular regulations related to metabolism,inflammation,circadian rhythm,extracellular matrix,and oxidative stress.This study sheds light on the role of GCH1 in regulating blood NO,contributing to the physiological adaptation of the cardiovascular and pulmonary systems in Tibetans at high altitude.
基金support from the National Natural Science Foundation of China(Nos.22293011,T2341001)the Major Science and Technology Project of Anhui Province(202203a06020010).
文摘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.
基金funded by the National Natural Science Foundation of China(NSFC)under Grant Number 72071209.
文摘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 λ_(1) and incentive λ_(2) thresholds are set based on operational priorities and indicator volatility,penalizing indicators with values below λ_(1) and incentivizing those exceeding λ_(2) 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.
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.
基金supported in part by the National Natural Science Foundation of China(62173208)Taishan Scholar Project of Shandong Province of China(tsqn202103061)the National Science and Technology Council(NSTC),Taiwan,China(NSTC 113-2221-E-006-145-MY2)。
文摘This paper explores the adaptive exponentially designated-time stabilization issue via event-triggered feedback for a kind of uncertain high-order nonlinear systems.The motivation mainly comes from the following two challenges:the undesired singularity problem arising from infinite control gains at the prescribed-time instant,the effective trade-off between the control amplitude and the triggering duration.The goal is to build an event-triggered mechanism comprising a skillful triggered rule alongside a time-dependent threshold.Utilizing the designed control strategy,the solutions' existence and the prevention of Zeno phenomenon are successfully guaranteed by using a new transformation equipped with a time-varying function and redesigning the continuous state-feedback dominance approach with an array of integral functions involving embedded sign functions.Better than existing prescribed-time methods,our approach not only ensures that state variables converge to a small compact set before a designated time and stay there henceforth,and converge to the origin exponentially,but also ensures that the controller continuously works on the whole-time horizon.Two illustrative examples are given to show the effectiveness of the devised scheme.
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 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.
