Dear Editor,This letter deals with the autonomous underwater vehicle(AUV)three dimensional(3D)trajectory tracking control chronically suffering from poor accuracy and efficiency in complex hydrodynamics.A state-of-the...Dear Editor,This letter deals with the autonomous underwater vehicle(AUV)three dimensional(3D)trajectory tracking control chronically suffering from poor accuracy and efficiency in complex hydrodynamics.A state-of-the-art predictive adaptive controller(PAC)is proposed with a distinct dual closed-loop structure.展开更多
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
Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning sc...Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning scenarios.In this work,we propose an Adaptive Meta-Loss Network(Adaptive-MLN)that learns to generate taskagnostic loss functions tailored to evolving classification problems.Unlike traditional methods that rely on static objectives,Adaptive-MLN treats the loss function itself as a trainable component,parameterized by a shallow neural network.To enable flexible,gradient-free optimization,we introduce a hybrid evolutionary approach that combines GeneticAlgorithms(GA)for global exploration and Evolution Strategies(ES)for local refinement.This co-evolutionary process dynamically adjusts the loss landscape,improvingmodel generalization without relying on analytic gradients or handcrafted heuristics.Experimental evaluations on synthetic tasks and the CIFAR-10 andMNIST datasets demonstrate that our approach consistently outperforms standard losses such as Cross-Entropy and Mean Squared Error in terms of accuracy,convergence,and adaptability.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
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
Biological sex is increasingly recognized as a crucial factor in evaluating the translational value of preclinical spinal cord injury(SCI)studies.The rising incidence of SCI in females challenges the historical preced...Biological sex is increasingly recognized as a crucial factor in evaluating the translational value of preclinical spinal cord injury(SCI)studies.The rising incidence of SCI in females challenges the historical precedent of SCI being a male-dominated condition.In contrast,most basic science researchers utilize single-sex studies to minimize complications associated with bladder care in males(Stewart et al.,2020).The findings of our recent publication identify sexually dimorphic immune responses to SCI in both mice and pigs(Kumari et al.,2025).Here,we will highlight these findings and discuss the impact of sex on SCI inflammation and recovery.展开更多
The outstanding growth in the applications of large language models(LLMs)demonstrates the significance of adaptive and efficient prompt engineering tactics.The existing methods may not be variable,vigorous and streaml...The outstanding growth in the applications of large language models(LLMs)demonstrates the significance of adaptive and efficient prompt engineering tactics.The existing methods may not be variable,vigorous and streamlined in different domains.The offered study introduces an immediate optimization outline,named PROMPTx-PE,that is going to yield a greater level of precision and strength when it comes to the assignments that are premised on LLM.The proposed systemfeatures a timely selection schemewhich is informed by reinforcement learning,a contextual layer and a dynamic weighting module which is regulated by Lyapunov-based stability guidelines.The PROMPTx-PE dynamically varies the exploration and exploitation of the prompt space,depending on real-time feedback and multi-objective reward development.Extensive testing on both benchmark(GLUE,SuperGLUE)and domain-specific data(Healthcare-QA and Industrial-NER)demonstrates a large best performance to be 89.4%and a strong robustness disconnect with under 3%computation expense.The results confirm the effectiveness,consistency,and scalability of PROMPTx-PE as a platform of adaptive prompt engineering based on recent uses of LLMs.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
In this paper,we propose a fuzzy logic-based coded event-triggered control with self-adjustable prescribed performance(FL-CEC-SPP)to address the trade-off between control performance and communication efficiency in re...In this paper,we propose a fuzzy logic-based coded event-triggered control with self-adjustable prescribed performance(FL-CEC-SPP)to address the trade-off between control performance and communication efficiency in resource-constrained networked control systems.The method integrates a fuzzy-coded event-triggered controller into a coded control framework to dynamically adjust the triggering threshold,thereby reducing unnecessary transmissions while maintaining system stability.A self-adjustable prescribed performance constraint is also incorporated to ensure that the tracking error remains within predefined bounds under arbitrary initial conditions.Theoretical analyses and simulation comparisons show that the method proposed in this paper maintains good tracking performance and stability while reducing the communication burden,and has wide applications in resource-constrained network control systems.展开更多
Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combini...Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combining silhouette and skeleton data is a promising direction,effectively fusing these heterogeneous modalities and adaptively weighting their contributions in response to diverse conditions remains a central problem.This paper introduces GaitMAFF,a novelMulti-modal Adaptive Feature Fusion Network,to address this challenge.Our approach first transforms discrete skeleton joints into a dense SkeletonMap representation to align with silhouettes,then employs an attention-based module to dynamically learn the fusion weights between the two modalities.These fused features are processed by a powerful spatio-temporal backbone withWeighted Global-Local Feature FusionModules(WFFM)to learn a discriminative representation.Extensive experiments on the challenging CCPG and Gait3D datasets show that GaitMAFF achieves state-of-the-art performance,with an average Rank-1 accuracy of 84.6%on CCPG and 58.7%on Gait3D.These results demonstrate that our adaptive fusion strategy effectively integrates complementary multimodal information,significantly enhancing gait recognition robustness and accuracy in complex scenes and providing a practical solution for real-world applications.展开更多
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
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
Lithium metal is a promising anode material for high-energy-density batteries;however,its practical applications are significantly hindered by unstable lithium deposition and dendrite growth at the solid electrolyte i...Lithium metal is a promising anode material for high-energy-density batteries;however,its practical applications are significantly hindered by unstable lithium deposition and dendrite growth at the solid electrolyte interface.Functional protective coatings on lithium metal surfaces offer a viable solution to these challenges.Herein,an innovative adaptive protective layer for lithium metal anodes based on a thiourea H-bonded supramolecular polymer is developed for the first time.With dense thiourea H-bonding,the lithium bis(trifluoromethanesulfonyl)imide(Li TFSI)incorporated poly(ether-thiourea)protective layer shows strong adhesion to the lithium metal surface and good adaptive properties.The unique viscoelastic and flow characteristics of the poly(ether-thiourea)coating facilitate uniform Li⁺flux,effectively suppressing dendrite formation at the solid electrolyte interface.Furthermore,this innovative polymer integrates in situ generated compounds,such as Li3N and Li_(2)O,significantly enhancing interfacial stability.A comprehensive analysis involving X-ray photoelectron spectroscopy,scanning electron microscopy,X-ray tomography,and COMSOL simulations elucidates the beneficial effects of the adaptive coating.Enhanced performances in Li||Cu,Li||Li,Li||LiFePO_(4),and Li||S cells demonstrate the effectiveness of the poly(ether-thiourea)coating and its undeniable capability to improve lithium deposition and cycling stability.This study highlights a promising new candidate for developing supramolecular materials capable of stabilizing lithium metal anodes.展开更多
The healthcare field is fraught with challenges associated with severe class imbalance,wherein such critical conditions like sepsis,cardiac arrest,and drug adverse reactions are rare but have dire clinical consequence...The healthcare field is fraught with challenges associated with severe class imbalance,wherein such critical conditions like sepsis,cardiac arrest,and drug adverse reactions are rare but have dire clinical consequences.This paper presents a new framework,Deep Reinforcement Adaptive Gradient Optimization Network to Mining Rare Events(DRAGON-MINE),to demonstrate how deep reinforcement learning can be used synergistically with adaptive gradient optimization and address the inherent weaknesses of current methods in the prediction of rare health events.The suggested architecture uses a dual-pathway consisting of a reinforcement learning agent to dynamically reweigh samples and an adaptive gradient optimizer to follow novel learning rates.With extensive experiments on the MIMIC-IV and eICU-CRD datasets,DRAGON-MINE consistently outperforms recent state-of-the-art methods for sepsis,cardiac arrest,and adverse drug reaction prediction,achieving AUROC values of 92.3%and 91.6%for sepsis prediction on MIMIC-IV and eICU-CRD,respectively,while consistently outperforming Transformer-,CNN-RNN-,and Fed-Ensemble-based methods across all evaluated tasks and datasets,with particularly strong gains observed in precision-recall performance under severe class imbalance.With its high sensitivity(88.4%)and specificity(90.2%),DRAGON-MINE enables reliable early warning of rare clinical events in critical care settings while minimizing false alarms,supporting safer clinical decision support systems,and demonstrating strong potential for scalable deployment across multi-institutional intensive care environments through federated learning.展开更多
The learning and inferencing capabilities of large language models(LLMs)that underlie generative artificial intelligence(GenAI)can be exploited to optimize cancer treatments and improve patient outcomes.The ability of...The learning and inferencing capabilities of large language models(LLMs)that underlie generative artificial intelligence(GenAI)can be exploited to optimize cancer treatments and improve patient outcomes.The ability of these models to learn from large amounts of clinical,molecular,and radiomic data on cancer patients and their treatments is driving research interest in their application to treatment decision-making.The learning and predictive power of LLMs make them uniquely suitable for supporting adaptive cancer therapy.However,the clinical validation of GenAI support for clinical decisions in oncology needs to address the complexity and unique challenges of GenAI clinical interventions.The United States Food and Drug Administration(FDA)guidelines on the clinical evaluation of software as a medical device(SaMD)are explored as a basis for the clinical evaluation of GenAI-assisted adaptive cancer therapy.Metrics are proposed to address clinical associations and analytical validation along with an outlook on randomized clinical trials.This article provides a much-needed and timely perspective on the clinical evaluation of GenAI-assisted cancer treatments and provides insights into overcoming the inherent challenges of GenAI in its acceptance and adoption in real-world clinical 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
Scientific analysis of aeolian sand environments is fundamental for sustainable disaster mitigation along desert highways.However,significant regional variability in wind energy conditions complicates accurate charact...Scientific analysis of aeolian sand environments is fundamental for sustainable disaster mitigation along desert highways.However,significant regional variability in wind energy conditions complicates accurate characterization of wind regimes and introduces uncertainty in determining optimal monitoring timescales.Moreover,prevailing sand control measures often rely on standardized designs rather than site-specific adaptive strategies.To address these issues,this study proposes an integrated framework for aeolian environment analysis and develops targeted disaster mitigation strategies tailored for desert highways.The proposed framework employs wavelet transform to unravel the periodic characteristics of wind speed time series and integrates multi-source data(including ERA5 wind datasets,sand samples,ASTER GDEM,and multi-temporal remote sensing imagery)to enable a comprehensive aeolian environmental assessment.Concurrently,a suite of adaptive strategies is formulated to mitigate disaster risks along desert highways.Validated through a case study of the Tumushuk-Kunyu Desert Highway in Xinjiang,China,the framework exhibits high accuracy:predictions of annual aeolian sand transport activity show relative errors mostly below 7%against long-term reference sequences,and the calculated resultant drift direction exhibits a strong correlation with observed dune migration,yielding an R-squared value of 0.96.These findings confirm the framework’s reliability and provide a robust basis for designing adaptive,location-specific mitigation strategies,thereby enhancing the sustainability of desert highway infrastructure.展开更多
Small RNAs(sRNAs)are important non-coding RNAs that usually play crucial roles in gene expression at the post-transcriptional level.The sRNAs have mostly been investigated in model microorganisms such as Escherichia c...Small RNAs(sRNAs)are important non-coding RNAs that usually play crucial roles in gene expression at the post-transcriptional level.The sRNAs have mostly been investigated in model microorganisms such as Escherichia coli and some pathogens.Nevertheless,microbial sRNAs from extreme environments such as the polar regions and deep sea have recently been discovered and analyzed for their unique roles in stress response,metabolic regulation and adaptation to extreme environments.These sRNAs fine-tune gene expression during oxidative and radiation stress,and modulate temperature and osmotic pressure responses.Representative sRNAs and their functions in thermophilic,psychrophilic,halophilic and radiation-tolerant bacteria are summarized in this review.Despite challenges in sample collection,RNA isolation,and functional annotation,the study of sRNAs in extreme environments provides opportunities for discovering novel regulatory mechanisms,applying them to biotechnology,and advancing our understanding of evolutionary adaptations.Looking ahead,high-throughput sequencing,synthetic biology,and multi-omics integration will bring new breakthroughs in discovering novel sRNAs and their functions and regulatory mechanisms.Such advancements are poised to enable comprehensive characterization of sRNA-mediated regulatory networks in extremophiles and unlock their biotechnological potential through mechanism-driven applications.展开更多
This paper is concerned with adaptive consensus tracking control of nonlinear multi-agent systems with actuator faults and unknown nonidentical control directions under double semi-Markovian switching topologies.Consi...This paper is concerned with adaptive consensus tracking control of nonlinear multi-agent systems with actuator faults and unknown nonidentical control directions under double semi-Markovian switching topologies.Considering the complex working environment and the stability differences in communication links between leaders and followers,a double semi-Markov process is first introduced to describe the random switching of communication topologies in the leader-follower structure.In order to address challenges from the unknown nonidentical control directions and partial loss of effectiveness actuator faults,a completely independent parameter is introduced into the Nussbaum function to overcome the inherent obstacle of mutual cancellation and avoid the rapid growth rate.Considering only the state information of agents is transmitted among the agents,an adaptive distributed fault-tolerant consensus tracking control is proposed based on the double semi-Markovian switching topologies using the designed Nussbaum function.Furthermore,the stability of the closed-loop nonlinear multi-agent systems is analyzed using contradiction argument and Lyapunov theorem,from which the asymptotic consensus tracking in mean square sense can be obtained.A numerical simulation example is provided to verify the effectiveness of the proposed algorithm.展开更多
To enhance speech emotion recognition capability,this study constructs a speech emotion recognition model integrating the adaptive acoustic mixup(AAM)and improved coordinate and shuffle attention(ICASA)methods.The AAM...To enhance speech emotion recognition capability,this study constructs a speech emotion recognition model integrating the adaptive acoustic mixup(AAM)and improved coordinate and shuffle attention(ICASA)methods.The AAM method optimizes data augmentation by combining a sample selection strategy and dynamic interpolation coefficients,thus enabling information fusion of speech data with different emotions at the acoustic level.The ICASA method enhances feature extraction capability through dynamic fusion of the improved coordinate attention(ICA)and shuffle attention(SA)techniques.The ICA technique reduces computational overhead by employing depth-separable convolution and an h-swish activation function and captures long-range dependencies of multi-scale time-frequency features using the attention weights.The SA technique promotes feature interaction through channel shuffling,which helps the model learn richer and more discriminative emotional features.Experimental results demonstrate that,compared to the baseline model,the proposed model improves the weighted accuracy by 5.42%and 4.54%,and the unweighted accuracy by 3.37%and 3.85%on the IEMOCAP and RAVDESS datasets,respectively.These improvements were confirmed to be statistically significant by independent samples t-tests,further supporting the practical reliability and applicability of the proposed model in real-world emotion-aware speech systems.展开更多
基金supported by the National Natural Science Foundation of China(U24B20183)the Pioneer Leading Goose+X Science and Technology Program of Zhejiang Province(2025C02018)。
文摘Dear Editor,This letter deals with the autonomous underwater vehicle(AUV)three dimensional(3D)trajectory tracking control chronically suffering from poor accuracy and efficiency in complex hydrodynamics.A state-of-the-art predictive adaptive controller(PAC)is proposed with a distinct dual closed-loop structure.
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.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant number:82171965.
文摘Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning scenarios.In this work,we propose an Adaptive Meta-Loss Network(Adaptive-MLN)that learns to generate taskagnostic loss functions tailored to evolving classification problems.Unlike traditional methods that rely on static objectives,Adaptive-MLN treats the loss function itself as a trainable component,parameterized by a shallow neural network.To enable flexible,gradient-free optimization,we introduce a hybrid evolutionary approach that combines GeneticAlgorithms(GA)for global exploration and Evolution Strategies(ES)for local refinement.This co-evolutionary process dynamically adjusts the loss landscape,improvingmodel generalization without relying on analytic gradients or handcrafted heuristics.Experimental evaluations on synthetic tasks and the CIFAR-10 andMNIST datasets demonstrate that our approach consistently outperforms standard losses such as Cross-Entropy and Mean Squared Error in terms of accuracy,convergence,and adaptability.
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.
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 University of Kentucky-SCoBIRC Endowed Chair#5Craig H.Neilsen 1173493 and NIH RF1NS135504(to JCG).
文摘Biological sex is increasingly recognized as a crucial factor in evaluating the translational value of preclinical spinal cord injury(SCI)studies.The rising incidence of SCI in females challenges the historical precedent of SCI being a male-dominated condition.In contrast,most basic science researchers utilize single-sex studies to minimize complications associated with bladder care in males(Stewart et al.,2020).The findings of our recent publication identify sexually dimorphic immune responses to SCI in both mice and pigs(Kumari et al.,2025).Here,we will highlight these findings and discuss the impact of sex on SCI inflammation and recovery.
基金supported by the National Science and Technology Council(NSTC),Taiwan,under grant number 114-2221-E-182-041-MY3by Chang Gung University and Chang Gung Memorial Hospital under project number NERPD4Q0021.
文摘The outstanding growth in the applications of large language models(LLMs)demonstrates the significance of adaptive and efficient prompt engineering tactics.The existing methods may not be variable,vigorous and streamlined in different domains.The offered study introduces an immediate optimization outline,named PROMPTx-PE,that is going to yield a greater level of precision and strength when it comes to the assignments that are premised on LLM.The proposed systemfeatures a timely selection schemewhich is informed by reinforcement learning,a contextual layer and a dynamic weighting module which is regulated by Lyapunov-based stability guidelines.The PROMPTx-PE dynamically varies the exploration and exploitation of the prompt space,depending on real-time feedback and multi-objective reward development.Extensive testing on both benchmark(GLUE,SuperGLUE)and domain-specific data(Healthcare-QA and Industrial-NER)demonstrates a large best performance to be 89.4%and a strong robustness disconnect with under 3%computation expense.The results confirm the effectiveness,consistency,and scalability of PROMPTx-PE as a platform of adaptive prompt engineering based on recent uses of LLMs.
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 Singapore RIE2025 Manufacturing,Trade and Connectivity Industry Alignment Fund-Pre-Positioning(IAF-PP)under Grant M24N2a0039 through WP2-Intelligent Switching Controlthe National Research Foundation Singapore under its AI Singapore Programme under Grant AISG4-GC-2023-007-1B.
文摘In this paper,we propose a fuzzy logic-based coded event-triggered control with self-adjustable prescribed performance(FL-CEC-SPP)to address the trade-off between control performance and communication efficiency in resource-constrained networked control systems.The method integrates a fuzzy-coded event-triggered controller into a coded control framework to dynamically adjust the triggering threshold,thereby reducing unnecessary transmissions while maintaining system stability.A self-adjustable prescribed performance constraint is also incorporated to ensure that the tracking error remains within predefined bounds under arbitrary initial conditions.Theoretical analyses and simulation comparisons show that the method proposed in this paper maintains good tracking performance and stability while reducing the communication burden,and has wide applications in resource-constrained network control systems.
基金funded by the Natural Science Foundation of Chongqing Municipality,grant number CSTB2022NSCQ-MSX0503.
文摘Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combining silhouette and skeleton data is a promising direction,effectively fusing these heterogeneous modalities and adaptively weighting their contributions in response to diverse conditions remains a central problem.This paper introduces GaitMAFF,a novelMulti-modal Adaptive Feature Fusion Network,to address this challenge.Our approach first transforms discrete skeleton joints into a dense SkeletonMap representation to align with silhouettes,then employs an attention-based module to dynamically learn the fusion weights between the two modalities.These fused features are processed by a powerful spatio-temporal backbone withWeighted Global-Local Feature FusionModules(WFFM)to learn a discriminative representation.Extensive experiments on the challenging CCPG and Gait3D datasets show that GaitMAFF achieves state-of-the-art performance,with an average Rank-1 accuracy of 84.6%on CCPG and 58.7%on Gait3D.These results demonstrate that our adaptive fusion strategy effectively integrates complementary multimodal information,significantly enhancing gait recognition robustness and accuracy in complex scenes and providing a practical solution for real-world applications.
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 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Yongsheng Zhang(CSC no.202106050027),Xiaolong He(CSC no.202106340039),Yinyu Xiang(CSC no.201806950083)acknowledge the financial support from the Chinese Scholarship Council(CSC)the Advanced Materials Research program of the Zernike Institute under the Bonus Incentive Scheme of the Dutch Ministry for Education,Culture and Science(OCW)the Battery NL-Next Generation Battery based on Understanding Materials Interfaces project(with project number NWA 1389.20.089)of the NWA research program“Research on Routes by Consortia(ORC)”funded by the Dutch Research Council(NWO)。
文摘Lithium metal is a promising anode material for high-energy-density batteries;however,its practical applications are significantly hindered by unstable lithium deposition and dendrite growth at the solid electrolyte interface.Functional protective coatings on lithium metal surfaces offer a viable solution to these challenges.Herein,an innovative adaptive protective layer for lithium metal anodes based on a thiourea H-bonded supramolecular polymer is developed for the first time.With dense thiourea H-bonding,the lithium bis(trifluoromethanesulfonyl)imide(Li TFSI)incorporated poly(ether-thiourea)protective layer shows strong adhesion to the lithium metal surface and good adaptive properties.The unique viscoelastic and flow characteristics of the poly(ether-thiourea)coating facilitate uniform Li⁺flux,effectively suppressing dendrite formation at the solid electrolyte interface.Furthermore,this innovative polymer integrates in situ generated compounds,such as Li3N and Li_(2)O,significantly enhancing interfacial stability.A comprehensive analysis involving X-ray photoelectron spectroscopy,scanning electron microscopy,X-ray tomography,and COMSOL simulations elucidates the beneficial effects of the adaptive coating.Enhanced performances in Li||Cu,Li||Li,Li||LiFePO_(4),and Li||S cells demonstrate the effectiveness of the poly(ether-thiourea)coating and its undeniable capability to improve lithium deposition and cycling stability.This study highlights a promising new candidate for developing supramolecular materials capable of stabilizing lithium metal anodes.
文摘The healthcare field is fraught with challenges associated with severe class imbalance,wherein such critical conditions like sepsis,cardiac arrest,and drug adverse reactions are rare but have dire clinical consequences.This paper presents a new framework,Deep Reinforcement Adaptive Gradient Optimization Network to Mining Rare Events(DRAGON-MINE),to demonstrate how deep reinforcement learning can be used synergistically with adaptive gradient optimization and address the inherent weaknesses of current methods in the prediction of rare health events.The suggested architecture uses a dual-pathway consisting of a reinforcement learning agent to dynamically reweigh samples and an adaptive gradient optimizer to follow novel learning rates.With extensive experiments on the MIMIC-IV and eICU-CRD datasets,DRAGON-MINE consistently outperforms recent state-of-the-art methods for sepsis,cardiac arrest,and adverse drug reaction prediction,achieving AUROC values of 92.3%and 91.6%for sepsis prediction on MIMIC-IV and eICU-CRD,respectively,while consistently outperforming Transformer-,CNN-RNN-,and Fed-Ensemble-based methods across all evaluated tasks and datasets,with particularly strong gains observed in precision-recall performance under severe class imbalance.With its high sensitivity(88.4%)and specificity(90.2%),DRAGON-MINE enables reliable early warning of rare clinical events in critical care settings while minimizing false alarms,supporting safer clinical decision support systems,and demonstrating strong potential for scalable deployment across multi-institutional intensive care environments through federated learning.
文摘The learning and inferencing capabilities of large language models(LLMs)that underlie generative artificial intelligence(GenAI)can be exploited to optimize cancer treatments and improve patient outcomes.The ability of these models to learn from large amounts of clinical,molecular,and radiomic data on cancer patients and their treatments is driving research interest in their application to treatment decision-making.The learning and predictive power of LLMs make them uniquely suitable for supporting adaptive cancer therapy.However,the clinical validation of GenAI support for clinical decisions in oncology needs to address the complexity and unique challenges of GenAI clinical interventions.The United States Food and Drug Administration(FDA)guidelines on the clinical evaluation of software as a medical device(SaMD)are explored as a basis for the clinical evaluation of GenAI-assisted adaptive cancer therapy.Metrics are proposed to address clinical associations and analytical validation along with an outlook on randomized clinical trials.This article provides a much-needed and timely perspective on the clinical evaluation of GenAI-assisted cancer treatments and provides insights into overcoming the inherent challenges of GenAI in its acceptance and adoption in real-world clinical 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jointly funded by the Joint Funds of the National Natural Science Foundation of China(Grant No.U2568210)the Interdisciplinary Research Program of Shihezi University(Grant No.JCYJ202317)the National Natural Science Foundation of China(Grant No.12362035)。
文摘Scientific analysis of aeolian sand environments is fundamental for sustainable disaster mitigation along desert highways.However,significant regional variability in wind energy conditions complicates accurate characterization of wind regimes and introduces uncertainty in determining optimal monitoring timescales.Moreover,prevailing sand control measures often rely on standardized designs rather than site-specific adaptive strategies.To address these issues,this study proposes an integrated framework for aeolian environment analysis and develops targeted disaster mitigation strategies tailored for desert highways.The proposed framework employs wavelet transform to unravel the periodic characteristics of wind speed time series and integrates multi-source data(including ERA5 wind datasets,sand samples,ASTER GDEM,and multi-temporal remote sensing imagery)to enable a comprehensive aeolian environmental assessment.Concurrently,a suite of adaptive strategies is formulated to mitigate disaster risks along desert highways.Validated through a case study of the Tumushuk-Kunyu Desert Highway in Xinjiang,China,the framework exhibits high accuracy:predictions of annual aeolian sand transport activity show relative errors mostly below 7%against long-term reference sequences,and the calculated resultant drift direction exhibits a strong correlation with observed dune migration,yielding an R-squared value of 0.96.These findings confirm the framework’s reliability and provide a robust basis for designing adaptive,location-specific mitigation strategies,thereby enhancing the sustainability of desert highway infrastructure.
基金supported by the National Natural Science Foundation of China(Grant nos.42476264,41976224).
文摘Small RNAs(sRNAs)are important non-coding RNAs that usually play crucial roles in gene expression at the post-transcriptional level.The sRNAs have mostly been investigated in model microorganisms such as Escherichia coli and some pathogens.Nevertheless,microbial sRNAs from extreme environments such as the polar regions and deep sea have recently been discovered and analyzed for their unique roles in stress response,metabolic regulation and adaptation to extreme environments.These sRNAs fine-tune gene expression during oxidative and radiation stress,and modulate temperature and osmotic pressure responses.Representative sRNAs and their functions in thermophilic,psychrophilic,halophilic and radiation-tolerant bacteria are summarized in this review.Despite challenges in sample collection,RNA isolation,and functional annotation,the study of sRNAs in extreme environments provides opportunities for discovering novel regulatory mechanisms,applying them to biotechnology,and advancing our understanding of evolutionary adaptations.Looking ahead,high-throughput sequencing,synthetic biology,and multi-omics integration will bring new breakthroughs in discovering novel sRNAs and their functions and regulatory mechanisms.Such advancements are poised to enable comprehensive characterization of sRNA-mediated regulatory networks in extremophiles and unlock their biotechnological potential through mechanism-driven applications.
基金supported by the National Natural Science Foundation of China(62333011,62020106003)the Natural Science Foundation of Jiangsu Province of China(BK20222012)+1 种基金the Fundamental Research Funds for the Central Universities(NE2024005)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX24_0594)。
文摘This paper is concerned with adaptive consensus tracking control of nonlinear multi-agent systems with actuator faults and unknown nonidentical control directions under double semi-Markovian switching topologies.Considering the complex working environment and the stability differences in communication links between leaders and followers,a double semi-Markov process is first introduced to describe the random switching of communication topologies in the leader-follower structure.In order to address challenges from the unknown nonidentical control directions and partial loss of effectiveness actuator faults,a completely independent parameter is introduced into the Nussbaum function to overcome the inherent obstacle of mutual cancellation and avoid the rapid growth rate.Considering only the state information of agents is transmitted among the agents,an adaptive distributed fault-tolerant consensus tracking control is proposed based on the double semi-Markovian switching topologies using the designed Nussbaum function.Furthermore,the stability of the closed-loop nonlinear multi-agent systems is analyzed using contradiction argument and Lyapunov theorem,from which the asymptotic consensus tracking in mean square sense can be obtained.A numerical simulation example is provided to verify the effectiveness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China under Grant No.12204062the Natural Science Foundation of Shandong Province under Grant No.ZR2022MF330。
文摘To enhance speech emotion recognition capability,this study constructs a speech emotion recognition model integrating the adaptive acoustic mixup(AAM)and improved coordinate and shuffle attention(ICASA)methods.The AAM method optimizes data augmentation by combining a sample selection strategy and dynamic interpolation coefficients,thus enabling information fusion of speech data with different emotions at the acoustic level.The ICASA method enhances feature extraction capability through dynamic fusion of the improved coordinate attention(ICA)and shuffle attention(SA)techniques.The ICA technique reduces computational overhead by employing depth-separable convolution and an h-swish activation function and captures long-range dependencies of multi-scale time-frequency features using the attention weights.The SA technique promotes feature interaction through channel shuffling,which helps the model learn richer and more discriminative emotional features.Experimental results demonstrate that,compared to the baseline model,the proposed model improves the weighted accuracy by 5.42%and 4.54%,and the unweighted accuracy by 3.37%and 3.85%on the IEMOCAP and RAVDESS datasets,respectively.These improvements were confirmed to be statistically significant by independent samples t-tests,further supporting the practical reliability and applicability of the proposed model in real-world emotion-aware speech systems.
