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
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
We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training ph...We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.展开更多
To address the significant degradation of Space-Time Adaptive Processing(STAP)performance when the array elements have mutual coupling and gain/phase errors,a STAP algorithm with adaptive calibration for the above two...To address the significant degradation of Space-Time Adaptive Processing(STAP)performance when the array elements have mutual coupling and gain/phase errors,a STAP algorithm with adaptive calibration for the above two array errors is proposed in this article.First,based on a defined error matrix that simultaneously considers both array mutual coupling and gain/phase errors,a STAP signal model including these errors is given.Then,utilizing the defined signal model,it is demonstrated that the estimation of the defined error matrix can be formulized as a standard convex optimization problem with the low-rank structure of the clutter covariance matrix and the subspace projection theory.Once the defined error matrix is estimated by solving the convex optimization problem,it is illustrated that a STAP method with adaptive calibration of the mutual coupling and gain/phase errors is coined.Analyses also show that the proposed adaptive calibration algorithm only needs one training sample to construct the adaptive weight vector.Therefore,it can achieve a good detection performance even with severe non-homogeneous clutter environments.Finally,the simulation experiments verify the effectiveness of the proposed algorithm and the correctness of the analytical results.展开更多
The development of the adaptive cycle engine is a crucial direction of advanced fighter power sources in the near future.However,this new technology brings more uncertainty to the design of the control system.To addre...The development of the adaptive cycle engine is a crucial direction of advanced fighter power sources in the near future.However,this new technology brings more uncertainty to the design of the control system.To address the versatile thrust demand under complex dynamic characteristics of the adaptive cycle engine,this paper proposes a direct thrust estimation and control method based on the Model-Free Adaptive Control(MFAC)algorithm.First,an improved Sliding Mode Control-MFAC(SMC-MFAC)algorithm has been developed by introducing a sliding mode variable structure into the standard Full Format Dynamic Linearization-MFAC(FFDL-MFAC)and designing self-adaptive weight coefficients.Then a trivariate double-loop direct thrust control structure with a controller-based thrust estimator and an outer command compensation loop has been established.Through thrust feedback and command correction,accurate control under multi-mode and operation conditions is achieved.The main contribution of this paper is the improved algorithm that combines the tracking capability of the MFAC and the robustness of the SMC,thus enhancing the dynamic performance.Considering the requirements of the online thrust feedback,the designed MFAC-based thrust estimator significantly speeds up the calculation.Additionally,the proposed command correction module can achieve the adaptive thrust control without affecting the operation of the inner loop.Simulations and Hardware-in-Loop(HIL)experiments have been performed on an adaptive cycle engine component-level model to investigate the estimation and control effect under different modes and health conditions.The results demonstrate that both the thrust estimation precision and operation speed are significantly improved compared with Extended Kalman Filter(EKF).Furthermore,the system can accelerate the response of the controlled plant,reduce the overshoot,and realize the thrust recovery within the safety range when the engine encounters the degradation.展开更多
On the basis of the theory of adaptive active noise control(AANC) in a duct, this article discusses the algorithms of the adaptive control, compares the algorithm characteristics using LMS, RLS and LSL algorithms in t...On the basis of the theory of adaptive active noise control(AANC) in a duct, this article discusses the algorithms of the adaptive control, compares the algorithm characteristics using LMS, RLS and LSL algorithms in the adaptive filter in the AANC system, derives the recursive formulas of LMS algorithm. and obtains the LMS algorithm in computer simulation using FIR and IIR filters in AANC system. By means of simulation, we compare the attenuation levels with various input signals in AANC system and discuss the effects of step factor, order of filters and sound delay on the algorithm's convergence rate and attenuation level.We also discuss the attenuation levels with sound feedback using are and IIR filters in AANC system.展开更多
Subtropical evergreen broad-leaved trees are usually vulnerable to freezing stress,while hexaploid wild Camellia oleifera shows strong freezing tolerance.As a valuable genetic resource of woody oil crop C.oleifera,wil...Subtropical evergreen broad-leaved trees are usually vulnerable to freezing stress,while hexaploid wild Camellia oleifera shows strong freezing tolerance.As a valuable genetic resource of woody oil crop C.oleifera,wild C.oleifera can serve as a case for studying the molecular bases of adaptive evolution to freezing stress.Here,47 wild C.oleifera from 11 natural distribution sites in China and 4 relative species of C.oleifera were selected for genome sequencing.“Min Temperature of Coldest Month”(BIO6)had the highest comprehensive contribution to wild C.oleifera distribution.The population genetic structure of wild C.oleifera could be divided into two groups:in cold winter(BIO6≤0℃)and warm winter(BIO6>0℃)areas.Wild C.oleifera in cold winter areas might have experienced stronger selection pressures and population bottlenecks with lower N_(e) than those in warm winter areas.155 singlenucleotide polymorphisms(SNPs)were significantly correlated with the key bioclimatic variables(106 SNPs significantly correlated with BIO6).Twenty key SNPs and 15 key copy number variation regions(CNVRs)were found with genotype differentiation>50%between the two groups of wild C.oleifera.Key SNPs in cis-regulatory elements might affect the expression of key genes associated with freezing tolerance,and they were also found within a CNVR suggesting interactions between them.Some key CNVRs in the exon regions were closely related to the differentially expressed genes under freezing stress.The findings suggest that rich SNPs and CNVRs in polyploid trees may contribute to the adaptive evolution to freezing stress.展开更多
This study examines the adaptive boundary control problem of flexible marine riser with internal flow coupling.The dynamic model of the flexible marine riser system with internal flow coupling is derived using the Ham...This study examines the adaptive boundary control problem of flexible marine riser with internal flow coupling.The dynamic model of the flexible marine riser system with internal flow coupling is derived using the Hamiltonian principle.An analysis of internal flow’s influence on the vibration characteristics of flexible marine risers is conducted.Then,for the uncertain environmental disturbance,the adaptive fuzzy logic system is introduced to dynamically approximate the boundary disturbance,and a robust adaptive fuzzy boundary control is proposed.The uniform boundedness of the closed-loop system is proved based on Lyapunov theory.The well-posedness of the closed-loop system is proved by operator semigroup theory.The proposed control’s effectiveness is validated through comparison with existing control methods.展开更多
The stability of supersonic inlets faces challenges due to various changes in flight conditions,and flow control methods that address shock wave/boundary layer interactions under only one set of conditions cannot meet...The stability of supersonic inlets faces challenges due to various changes in flight conditions,and flow control methods that address shock wave/boundary layer interactions under only one set of conditions cannot meet developmental requirements.This paper proposes an adaptive bump control scheme and employs dynamic mesh technology for numerical simulation to investigate the unsteady control effects of adaptive bumps.The obtained results indicate that the use of moving bumps to control shock wave/boundary layer interactions is feasible.The adaptive control effects of five different bump speeds are evaluated.Within the range of bump speeds studied,the analysis of the flow field structure reveals the patterns of change in the separation zone area during the control process,as well as the relationship between the bump motion speed and the control effect on the separation zone.It is concluded that the moving bump endows the boundary layer with additional energy.展开更多
With the widespread adoption of electric vehicles and energy storage systems,predicting the remaining useful life(RUL)of lithium-ion batteries(LIBs)is critical for enhancing system reliability and enabling predictive ...With the widespread adoption of electric vehicles and energy storage systems,predicting the remaining useful life(RUL)of lithium-ion batteries(LIBs)is critical for enhancing system reliability and enabling predictive maintenance.Traditional RUL prediction methods often exhibit reduced accuracy during the nonlinear aging stages of batteries and struggle to accommodate complex degradation processes.This paper introduces a novel adaptive long short-term memory(LSTM)approach that dynamically adjusts observation and prediction horizons to optimize predictive performance across various aging stages.The proposed method employs principal component analysis(PCA)for dimensionality reduction on publicly available NASA and Mendeley battery datasets to extract health indicators(HIs)and applies K-means clustering to segment the battery lifecycle into three aging stages(run-in,linear aging,and nonlinear aging),providing aging-stage-based input features for the model.Experimental results show that,in the NASA dataset,the adaptive LSTM reduces the MAE and RMSE by 0.042 and 0.043,respectively,compared to the CNN,demonstrating its effectiveness in mitigating error accumulation during the nonlinear aging stage.However,in the Mendeley dataset,the average prediction accuracy of the adaptive LSTM is slightly lower than that of the CNN and Transformer.These findings indicate that defining aging-stage-based adaptive observation and prediction horizons for LSTM can effectively enhance its performance in predicting battery RUL across the entire lifecycle.展开更多
This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method...This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.展开更多
Recently,for developing neuromorphic visual systems,adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances a...Recently,for developing neuromorphic visual systems,adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances and flexible func-tionalities.In this review,based on a description of the biological adaptive functions that are favorable for dynamically perceiv-ing,filtering,and processing information in the varying environment,we summarize the representative strategies for achiev-ing these adaptabilities in optoelectronic transistors,including the adaptation for detecting information,adaptive synaptic weight change,and history-dependent plasticity.Moreover,the key points of the corresponding strategies are comprehen-sively discussed.And the applications of these adaptive optoelectronic transistors,including the adaptive color detection,sig-nal filtering,extending the response range of light intensity,and improve learning efficiency,are also illustrated separately.Lastly,the challenges faced in developing adaptive optoelectronic transistor for artificial vision system are discussed.The descrip-tion of biological adaptive functions and the corresponding inspired neuromorphic devices are expected to provide insights for the design and application of next-generation artificial visual systems.展开更多
In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantl...In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantly degrades image quality.Traditional denoising methods,typically based on filter techniques,often face challenges related to inefficiency and limited adaptability.To address these limitations,this study proposes a novel SAR image denoising algorithm based on an enhanced residual network architecture,with the objective of enhancing the utility of SAR imagery in complex electromagnetic environments.The proposed algorithm integrates residual network modules,which directly process the noisy input images to generate denoised outputs.This approach not only reduces computational complexity but also mitigates the difficulties associated with model training.By combining the Transformer module with the residual block,the algorithm enhances the network's ability to extract global features,offering superior feature extraction capabilities compared to CNN-based residual modules.Additionally,the algorithm employs the adaptive activation function Meta-ACON,which dynamically adjusts the activation patterns of neurons,thereby improving the network's feature extraction efficiency.The effectiveness of the proposed denoising method is empirically validated using real SAR images from the RSOD dataset.The proposed algorithm exhibits remarkable performance in terms of EPI,SSIM,and ENL,while achieving a substantial enhancement in PSNR when compared to traditional and deep learning-based algorithms.The PSNR performance is enhanced by over twofold.Moreover,the evaluation of the MSTAR SAR dataset substantiates the algorithm's robustness and applicability in SAR denoising tasks,with a PSNR of 25.2021 being attained.These findings underscore the efficacy of the proposed algorithm in mitigating speckle noise while preserving critical features in SAR imagery,thereby enhancing its quality and usability in practical scenarios.展开更多
Agriculture,significantly impacted by climate change and climate variability,serves as the primary livelihood for smallholder farmers in South Asia.This study aims to examine and evaluate the factors influencing small...Agriculture,significantly impacted by climate change and climate variability,serves as the primary livelihood for smallholder farmers in South Asia.This study aims to examine and evaluate the factors influencing smallholder farmers'adaptive capacity(AC)in addressing these risks through surveys from 633 households across Nepal,India,and Bangladesh.The findings reveal that AC is influenced by various indicators categorized under eight principal factors.The first three factors,which explain about one-third of the variance in each country,include distinct significant indicators for each nation:in Nepal,these indicators are landholding size,skill-development training,knowledge of improved seed varieties,number of income sources,access to markets,and access to financial institutions;in India,they encompass ac-cess to agricultural-input information,knowledge of seed varieties,access to markets,access to crop insurance,changing the sowing/harvesting times of crops,and access to financial ser-vices;in Bangladesh,the key factors are access to financial institutions,community coopera-tion,changing the sowing/harvesting times of crops,knowledge of improved seed varieties,and access to agricultural-input information.Notably,indicators such as trust in weather in-formation,changing sowing/harvesting times of crops,and crop insurance were identified as important determinants of AC,which have been overlooked in previous studies.展开更多
Managing sensitive data in dynamic and high-stakes environments,such as healthcare,requires access control frameworks that offer real-time adaptability,scalability,and regulatory compliance.BIG-ABAC introduces a trans...Managing sensitive data in dynamic and high-stakes environments,such as healthcare,requires access control frameworks that offer real-time adaptability,scalability,and regulatory compliance.BIG-ABAC introduces a transformative approach to Attribute-Based Access Control(ABAC)by integrating real-time policy evaluation and contextual adaptation.Unlike traditional ABAC systems that rely on static policies,BIG-ABAC dynamically updates policies in response to evolving rules and real-time contextual attributes,ensuring precise and efficient access control.Leveraging decision trees evaluated in real-time,BIG-ABAC overcomes the limitations of conventional access control models,enabling seamless adaptation to complex,high-demand scenarios.The framework adheres to the NIST ABAC standard while incorporating modern distributed streaming technologies to enhance scalability and traceability.Its flexible policy enforcement mechanisms facilitate the implementation of regulatory requirements such as HIPAA and GDPR,allowing organizations to align access control policies with compliance needs dynamically.Performance evaluations demonstrate that BIG-ABAC processes 95% of access requests within 50 ms and updates policies dynamically with a latency of 30 ms,significantly outperforming traditional ABAC models.These results establish BIG-ABAC as a benchmark for adaptive,scalable,and context-aware access control,making it an ideal solution for dynamic,high-risk domains such as healthcare,smart cities,and Industrial IoT(IIoT).展开更多
Dynamic adaptability is a key feature in biological macromolecules,enabling selective binding and catalysis[1].From DNA supercoiling to enzyme conformational changes,biological systems have evolved intricate ways to d...Dynamic adaptability is a key feature in biological macromolecules,enabling selective binding and catalysis[1].From DNA supercoiling to enzyme conformational changes,biological systems have evolved intricate ways to dynamically adjust their structures to accommodate functional needs.Mimicking this adaptability in synthetic systems is an ongoing challenge in supramolecular chemistry.展开更多
Malware continues to pose a significant threat to cybersecurity,with new advanced infections that go beyond traditional detection.Limitations in existing systems include high false-positive rates,slow system response ...Malware continues to pose a significant threat to cybersecurity,with new advanced infections that go beyond traditional detection.Limitations in existing systems include high false-positive rates,slow system response times,and inability to respond quickly to new malware forms.To overcome these challenges,this paper proposes OMD-RAS:Implementing Malware Detection in an Optimized Way through Real-Time and Adaptive Security as an extensive approach,hoping to get good results towards better malware threat detection and remediation.The significant steps in the model are data collection followed by comprehensive preprocessing consisting of feature engineering and normalization.Static analysis,along with dynamic analysis,is done to capture the whole spectrum of malware behavior for the feature extraction process.The extracted processed features are given with a continuous learning mechanism to the Extreme Learning Machine model of real-time detection.This OMD-RAS trains quickly and has great accuracy,providing elite,advanced real-time detection capabilities.This approach uses continuous learning to adapt to new threats—ensuring the effectiveness of detection even as strategies used by malware may change over time.The experimental results showed that OMD-RAS performs better than the traditional approaches.For instance,the OMD-RAS model has been able to achieve an accuracy of 96.23%and massively reduce the rate of false positives across all datasets while eliciting a consistently high rate of precision and recall.The model’s adaptive learning reflected enhancements on other performance measures-for example,Matthews Correlation Coefficients and Log Loss.展开更多
In adaptive immunity,antigens are presented to T cells,which then become effector T cells(CD4+)or cytotoxic T cells(CD8+).These are called adaptive immune T cells.Cancer immunotherapy based on anti-programmed death re...In adaptive immunity,antigens are presented to T cells,which then become effector T cells(CD4+)or cytotoxic T cells(CD8+).These are called adaptive immune T cells.Cancer immunotherapy based on anti-programmed death receptor-1(PD-1)/programmed cell death 1 ligand 1(PD-L1)antibodies is a new way to treat cancer.Chinese herbal medicines are often used with cancer treatments in clinical practice.Recent studies have shown that Chinese herbal medicines affect the immune system and have an effect on PD-1/PD-L1.Baicalin,the main ingredient of Scutellaria baicalensis,can stop Tregs from working,increase the number of CD8+T cells in the tumour microenvironment and avoid PD-1 resistance.Solamargine has anti-cancer activity in a variety of tumours,including stopping tumour growth,stopping PD-L1 expression and blocking immune escape in combination with Immune checkpoint inhibitors.Taraxasterol,found in dandelion,can regulate anti-tumour T cells.It affects CD4+T cells by inhibiting STAT3.Platycodonis Radix can reduce the expression of PD-1 on the surface of CD8+T cells and increase their ability to kill tumour cells.Licorice compounds can regulate the cell cycle and PD-L1 expression,which can lead to tumour cell cycle blockade and increase the level of PD-L1 expression,thereby exerting anti-tumour effects.Marsdenia tenacissima extracts weakened the immunosuppressive effect of IL-10,improved T-cell function,stopped tumour cells escaping the immune system and reduced TGF-β1 and PD-L1.Strobilanthes crispus F3 extract increases lymphocyte infiltration,improves T-cell-mediated cytotoxicity,modulates immune cell expression,stops tumour-associated macrophage activity and slows tumour progression.The last five years of research on herbs with purgative and detoxifying effects were reviewed.This review will investigate how herbs can affect adaptive immune T cells in the immune system to improve cancer treatment.展开更多
The globin superfamily,central to oxygen(O_(2))cascade dynamics,exemplifies how canalization—evolutionary stabilization of phenotypic traits—enables vertebrates to thrive in extreme environments.In birds,hemoglobins...The globin superfamily,central to oxygen(O_(2))cascade dynamics,exemplifies how canalization—evolutionary stabilization of phenotypic traits—enables vertebrates to thrive in extreme environments.In birds,hemoglobins(Hbs)serve as a paradigm of this process,with structural and functional canalization underpinning their exceptional aerobic capacity and elevational diversification.Despite significant advances of globins in our understanding of avian aerobic adaptation,a comprehensive synthesis of functional diversity,molecular evolution,and structural innovation is essential to fully elucidate their canalized roles in O_(2)homeostasis.Integrating perspectives on globin functional diversity and structural evolution,this review examines how chance(mutation/fixation biases)and contingency(historical genetic/epistatic constraints)shape Hb divergence and parallelism,thereby bridging molecular mechanisms with physiological adaptation in birds.We highlight how avian Hbs,canalized through compensatory substitutions and allosteric regulation,achieves a balance between evolutionary robustness and adaptive plasticity.However,critical gaps remain persist:the roles of understudied globins(e.g.,neuroglobin,globin E)and the mechanisms of genetic assimilation in migratory taxa.We propose an integrative framework that incorporates ecological divergence(elevation,flight endurance),phylogenetic timescales,and systems biology to unravel how canalization directs adaptive compromise.By focusing on birds within the amniotes,this synthesis advances a cohesive model for vertebrate evolution,wherein canalized globins reconcile metabolic precision with ecological innovation.Ultimately,this review refines hypotheses of O_(2)cascade evolution and calls for cross-disciplinary studies to decode the genetic and physiological architecture underlying adaptive canalization in extreme environments.展开更多
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 by the Natural Science Research Project of Colleges and Universities in Anhui Province (No.KJ2021A0479)the Science Research Program of Anhui University of Finance and Economics (No.ACKYC22082)。
文摘We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.
基金co-supported by the National Natural Science Foundation of China(No.12374431)。
文摘To address the significant degradation of Space-Time Adaptive Processing(STAP)performance when the array elements have mutual coupling and gain/phase errors,a STAP algorithm with adaptive calibration for the above two array errors is proposed in this article.First,based on a defined error matrix that simultaneously considers both array mutual coupling and gain/phase errors,a STAP signal model including these errors is given.Then,utilizing the defined signal model,it is demonstrated that the estimation of the defined error matrix can be formulized as a standard convex optimization problem with the low-rank structure of the clutter covariance matrix and the subspace projection theory.Once the defined error matrix is estimated by solving the convex optimization problem,it is illustrated that a STAP method with adaptive calibration of the mutual coupling and gain/phase errors is coined.Analyses also show that the proposed adaptive calibration algorithm only needs one training sample to construct the adaptive weight vector.Therefore,it can achieve a good detection performance even with severe non-homogeneous clutter environments.Finally,the simulation experiments verify the effectiveness of the proposed algorithm and the correctness of the analytical results.
基金supported by National Natural Science Foundation of China(No.52302472)。
文摘The development of the adaptive cycle engine is a crucial direction of advanced fighter power sources in the near future.However,this new technology brings more uncertainty to the design of the control system.To address the versatile thrust demand under complex dynamic characteristics of the adaptive cycle engine,this paper proposes a direct thrust estimation and control method based on the Model-Free Adaptive Control(MFAC)algorithm.First,an improved Sliding Mode Control-MFAC(SMC-MFAC)algorithm has been developed by introducing a sliding mode variable structure into the standard Full Format Dynamic Linearization-MFAC(FFDL-MFAC)and designing self-adaptive weight coefficients.Then a trivariate double-loop direct thrust control structure with a controller-based thrust estimator and an outer command compensation loop has been established.Through thrust feedback and command correction,accurate control under multi-mode and operation conditions is achieved.The main contribution of this paper is the improved algorithm that combines the tracking capability of the MFAC and the robustness of the SMC,thus enhancing the dynamic performance.Considering the requirements of the online thrust feedback,the designed MFAC-based thrust estimator significantly speeds up the calculation.Additionally,the proposed command correction module can achieve the adaptive thrust control without affecting the operation of the inner loop.Simulations and Hardware-in-Loop(HIL)experiments have been performed on an adaptive cycle engine component-level model to investigate the estimation and control effect under different modes and health conditions.The results demonstrate that both the thrust estimation precision and operation speed are significantly improved compared with Extended Kalman Filter(EKF).Furthermore,the system can accelerate the response of the controlled plant,reduce the overshoot,and realize the thrust recovery within the safety range when the engine encounters the degradation.
文摘On the basis of the theory of adaptive active noise control(AANC) in a duct, this article discusses the algorithms of the adaptive control, compares the algorithm characteristics using LMS, RLS and LSL algorithms in the adaptive filter in the AANC system, derives the recursive formulas of LMS algorithm. and obtains the LMS algorithm in computer simulation using FIR and IIR filters in AANC system. By means of simulation, we compare the attenuation levels with various input signals in AANC system and discuss the effects of step factor, order of filters and sound delay on the algorithm's convergence rate and attenuation level.We also discuss the attenuation levels with sound feedback using are and IIR filters in AANC system.
基金funded by the National Natural Science Foundation of China(grant no.32270238 and 31870311).
文摘Subtropical evergreen broad-leaved trees are usually vulnerable to freezing stress,while hexaploid wild Camellia oleifera shows strong freezing tolerance.As a valuable genetic resource of woody oil crop C.oleifera,wild C.oleifera can serve as a case for studying the molecular bases of adaptive evolution to freezing stress.Here,47 wild C.oleifera from 11 natural distribution sites in China and 4 relative species of C.oleifera were selected for genome sequencing.“Min Temperature of Coldest Month”(BIO6)had the highest comprehensive contribution to wild C.oleifera distribution.The population genetic structure of wild C.oleifera could be divided into two groups:in cold winter(BIO6≤0℃)and warm winter(BIO6>0℃)areas.Wild C.oleifera in cold winter areas might have experienced stronger selection pressures and population bottlenecks with lower N_(e) than those in warm winter areas.155 singlenucleotide polymorphisms(SNPs)were significantly correlated with the key bioclimatic variables(106 SNPs significantly correlated with BIO6).Twenty key SNPs and 15 key copy number variation regions(CNVRs)were found with genotype differentiation>50%between the two groups of wild C.oleifera.Key SNPs in cis-regulatory elements might affect the expression of key genes associated with freezing tolerance,and they were also found within a CNVR suggesting interactions between them.Some key CNVRs in the exon regions were closely related to the differentially expressed genes under freezing stress.The findings suggest that rich SNPs and CNVRs in polyploid trees may contribute to the adaptive evolution to freezing stress.
基金financially supported by Sichuan Science and Technology Program(Grant No.2023NSFSC1980).
文摘This study examines the adaptive boundary control problem of flexible marine riser with internal flow coupling.The dynamic model of the flexible marine riser system with internal flow coupling is derived using the Hamiltonian principle.An analysis of internal flow’s influence on the vibration characteristics of flexible marine risers is conducted.Then,for the uncertain environmental disturbance,the adaptive fuzzy logic system is introduced to dynamically approximate the boundary disturbance,and a robust adaptive fuzzy boundary control is proposed.The uniform boundedness of the closed-loop system is proved based on Lyapunov theory.The well-posedness of the closed-loop system is proved by operator semigroup theory.The proposed control’s effectiveness is validated through comparison with existing control methods.
基金supported by the National Key R&D Program of China(Grant No.2019YFA0405300)the National Natural Science Foundation of China(Grant No.11972368)the Natural Science Foundation of Hunan Province(Grant No.2021JJ10045).
文摘The stability of supersonic inlets faces challenges due to various changes in flight conditions,and flow control methods that address shock wave/boundary layer interactions under only one set of conditions cannot meet developmental requirements.This paper proposes an adaptive bump control scheme and employs dynamic mesh technology for numerical simulation to investigate the unsteady control effects of adaptive bumps.The obtained results indicate that the use of moving bumps to control shock wave/boundary layer interactions is feasible.The adaptive control effects of five different bump speeds are evaluated.Within the range of bump speeds studied,the analysis of the flow field structure reveals the patterns of change in the separation zone area during the control process,as well as the relationship between the bump motion speed and the control effect on the separation zone.It is concluded that the moving bump endows the boundary layer with additional energy.
基金supported by National Natural Science Foundation of China(Grant No.62403475).
文摘With the widespread adoption of electric vehicles and energy storage systems,predicting the remaining useful life(RUL)of lithium-ion batteries(LIBs)is critical for enhancing system reliability and enabling predictive maintenance.Traditional RUL prediction methods often exhibit reduced accuracy during the nonlinear aging stages of batteries and struggle to accommodate complex degradation processes.This paper introduces a novel adaptive long short-term memory(LSTM)approach that dynamically adjusts observation and prediction horizons to optimize predictive performance across various aging stages.The proposed method employs principal component analysis(PCA)for dimensionality reduction on publicly available NASA and Mendeley battery datasets to extract health indicators(HIs)and applies K-means clustering to segment the battery lifecycle into three aging stages(run-in,linear aging,and nonlinear aging),providing aging-stage-based input features for the model.Experimental results show that,in the NASA dataset,the adaptive LSTM reduces the MAE and RMSE by 0.042 and 0.043,respectively,compared to the CNN,demonstrating its effectiveness in mitigating error accumulation during the nonlinear aging stage.However,in the Mendeley dataset,the average prediction accuracy of the adaptive LSTM is slightly lower than that of the CNN and Transformer.These findings indicate that defining aging-stage-based adaptive observation and prediction horizons for LSTM can effectively enhance its performance in predicting battery RUL across the entire lifecycle.
基金The National Natural Science Foundation of China(W2431048)The Science and Technology Research Program of Chongqing Municipal Education Commission,China(KJZDK202300807)The Chongqing Natural Science Foundation,China(CSTB2024NSCQQCXMX0052).
文摘This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.
基金the National Key Research and Development Program of China(2021YFA0717900)National Natural Science Foundation of China(62471251,62405144,62288102,22275098,and 62174089)+1 种基金Basic Research Program of Jiangsu(BK20240033,BK20243057)Jiangsu Funding Program for Excellent Postdoctoral Talent(2022ZB402).
文摘Recently,for developing neuromorphic visual systems,adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances and flexible func-tionalities.In this review,based on a description of the biological adaptive functions that are favorable for dynamically perceiv-ing,filtering,and processing information in the varying environment,we summarize the representative strategies for achiev-ing these adaptabilities in optoelectronic transistors,including the adaptation for detecting information,adaptive synaptic weight change,and history-dependent plasticity.Moreover,the key points of the corresponding strategies are comprehen-sively discussed.And the applications of these adaptive optoelectronic transistors,including the adaptive color detection,sig-nal filtering,extending the response range of light intensity,and improve learning efficiency,are also illustrated separately.Lastly,the challenges faced in developing adaptive optoelectronic transistor for artificial vision system are discussed.The descrip-tion of biological adaptive functions and the corresponding inspired neuromorphic devices are expected to provide insights for the design and application of next-generation artificial visual systems.
文摘In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantly degrades image quality.Traditional denoising methods,typically based on filter techniques,often face challenges related to inefficiency and limited adaptability.To address these limitations,this study proposes a novel SAR image denoising algorithm based on an enhanced residual network architecture,with the objective of enhancing the utility of SAR imagery in complex electromagnetic environments.The proposed algorithm integrates residual network modules,which directly process the noisy input images to generate denoised outputs.This approach not only reduces computational complexity but also mitigates the difficulties associated with model training.By combining the Transformer module with the residual block,the algorithm enhances the network's ability to extract global features,offering superior feature extraction capabilities compared to CNN-based residual modules.Additionally,the algorithm employs the adaptive activation function Meta-ACON,which dynamically adjusts the activation patterns of neurons,thereby improving the network's feature extraction efficiency.The effectiveness of the proposed denoising method is empirically validated using real SAR images from the RSOD dataset.The proposed algorithm exhibits remarkable performance in terms of EPI,SSIM,and ENL,while achieving a substantial enhancement in PSNR when compared to traditional and deep learning-based algorithms.The PSNR performance is enhanced by over twofold.Moreover,the evaluation of the MSTAR SAR dataset substantiates the algorithm's robustness and applicability in SAR denoising tasks,with a PSNR of 25.2021 being attained.These findings underscore the efficacy of the proposed algorithm in mitigating speckle noise while preserving critical features in SAR imagery,thereby enhancing its quality and usability in practical scenarios.
基金The Alliance of International Science Organizations(ANSO),No.ANSO-CR-PP-2021-06The Second Tibetan Plateau Scientific Expedition and Research,No.2019QZKK0603。
文摘Agriculture,significantly impacted by climate change and climate variability,serves as the primary livelihood for smallholder farmers in South Asia.This study aims to examine and evaluate the factors influencing smallholder farmers'adaptive capacity(AC)in addressing these risks through surveys from 633 households across Nepal,India,and Bangladesh.The findings reveal that AC is influenced by various indicators categorized under eight principal factors.The first three factors,which explain about one-third of the variance in each country,include distinct significant indicators for each nation:in Nepal,these indicators are landholding size,skill-development training,knowledge of improved seed varieties,number of income sources,access to markets,and access to financial institutions;in India,they encompass ac-cess to agricultural-input information,knowledge of seed varieties,access to markets,access to crop insurance,changing the sowing/harvesting times of crops,and access to financial ser-vices;in Bangladesh,the key factors are access to financial institutions,community coopera-tion,changing the sowing/harvesting times of crops,knowledge of improved seed varieties,and access to agricultural-input information.Notably,indicators such as trust in weather in-formation,changing sowing/harvesting times of crops,and crop insurance were identified as important determinants of AC,which have been overlooked in previous studies.
文摘Managing sensitive data in dynamic and high-stakes environments,such as healthcare,requires access control frameworks that offer real-time adaptability,scalability,and regulatory compliance.BIG-ABAC introduces a transformative approach to Attribute-Based Access Control(ABAC)by integrating real-time policy evaluation and contextual adaptation.Unlike traditional ABAC systems that rely on static policies,BIG-ABAC dynamically updates policies in response to evolving rules and real-time contextual attributes,ensuring precise and efficient access control.Leveraging decision trees evaluated in real-time,BIG-ABAC overcomes the limitations of conventional access control models,enabling seamless adaptation to complex,high-demand scenarios.The framework adheres to the NIST ABAC standard while incorporating modern distributed streaming technologies to enhance scalability and traceability.Its flexible policy enforcement mechanisms facilitate the implementation of regulatory requirements such as HIPAA and GDPR,allowing organizations to align access control policies with compliance needs dynamically.Performance evaluations demonstrate that BIG-ABAC processes 95% of access requests within 50 ms and updates policies dynamically with a latency of 30 ms,significantly outperforming traditional ABAC models.These results establish BIG-ABAC as a benchmark for adaptive,scalable,and context-aware access control,making it an ideal solution for dynamic,high-risk domains such as healthcare,smart cities,and Industrial IoT(IIoT).
基金the Natural Science Foundation of China(No.22301131)the Natural Science Foundation of Jiangsu Province(Nos.BK20220781,BK20240679)the National Key Research and Development Program of China(No.2024YFB3815700)are greatly acknowledged.
文摘Dynamic adaptability is a key feature in biological macromolecules,enabling selective binding and catalysis[1].From DNA supercoiling to enzyme conformational changes,biological systems have evolved intricate ways to dynamically adjust their structures to accommodate functional needs.Mimicking this adaptability in synthetic systems is an ongoing challenge in supramolecular chemistry.
基金supported by a grant from the Center of Excellence in Information Assurance(CoEIA),King Saud University(KSU).
文摘Malware continues to pose a significant threat to cybersecurity,with new advanced infections that go beyond traditional detection.Limitations in existing systems include high false-positive rates,slow system response times,and inability to respond quickly to new malware forms.To overcome these challenges,this paper proposes OMD-RAS:Implementing Malware Detection in an Optimized Way through Real-Time and Adaptive Security as an extensive approach,hoping to get good results towards better malware threat detection and remediation.The significant steps in the model are data collection followed by comprehensive preprocessing consisting of feature engineering and normalization.Static analysis,along with dynamic analysis,is done to capture the whole spectrum of malware behavior for the feature extraction process.The extracted processed features are given with a continuous learning mechanism to the Extreme Learning Machine model of real-time detection.This OMD-RAS trains quickly and has great accuracy,providing elite,advanced real-time detection capabilities.This approach uses continuous learning to adapt to new threats—ensuring the effectiveness of detection even as strategies used by malware may change over time.The experimental results showed that OMD-RAS performs better than the traditional approaches.For instance,the OMD-RAS model has been able to achieve an accuracy of 96.23%and massively reduce the rate of false positives across all datasets while eliciting a consistently high rate of precision and recall.The model’s adaptive learning reflected enhancements on other performance measures-for example,Matthews Correlation Coefficients and Log Loss.
文摘In adaptive immunity,antigens are presented to T cells,which then become effector T cells(CD4+)or cytotoxic T cells(CD8+).These are called adaptive immune T cells.Cancer immunotherapy based on anti-programmed death receptor-1(PD-1)/programmed cell death 1 ligand 1(PD-L1)antibodies is a new way to treat cancer.Chinese herbal medicines are often used with cancer treatments in clinical practice.Recent studies have shown that Chinese herbal medicines affect the immune system and have an effect on PD-1/PD-L1.Baicalin,the main ingredient of Scutellaria baicalensis,can stop Tregs from working,increase the number of CD8+T cells in the tumour microenvironment and avoid PD-1 resistance.Solamargine has anti-cancer activity in a variety of tumours,including stopping tumour growth,stopping PD-L1 expression and blocking immune escape in combination with Immune checkpoint inhibitors.Taraxasterol,found in dandelion,can regulate anti-tumour T cells.It affects CD4+T cells by inhibiting STAT3.Platycodonis Radix can reduce the expression of PD-1 on the surface of CD8+T cells and increase their ability to kill tumour cells.Licorice compounds can regulate the cell cycle and PD-L1 expression,which can lead to tumour cell cycle blockade and increase the level of PD-L1 expression,thereby exerting anti-tumour effects.Marsdenia tenacissima extracts weakened the immunosuppressive effect of IL-10,improved T-cell function,stopped tumour cells escaping the immune system and reduced TGF-β1 and PD-L1.Strobilanthes crispus F3 extract increases lymphocyte infiltration,improves T-cell-mediated cytotoxicity,modulates immune cell expression,stops tumour-associated macrophage activity and slows tumour progression.The last five years of research on herbs with purgative and detoxifying effects were reviewed.This review will investigate how herbs can affect adaptive immune T cells in the immune system to improve cancer treatment.
基金supported by the National Natural Science Foundation of China(NSFC 31900313)to X.Z.the Central guide local science and technology development funds(XZ202301YD0007C)to L.Y.+1 种基金NSFC(32471572)National Key Research and Development Program of China(2024YFC2310303)to D.L。
文摘The globin superfamily,central to oxygen(O_(2))cascade dynamics,exemplifies how canalization—evolutionary stabilization of phenotypic traits—enables vertebrates to thrive in extreme environments.In birds,hemoglobins(Hbs)serve as a paradigm of this process,with structural and functional canalization underpinning their exceptional aerobic capacity and elevational diversification.Despite significant advances of globins in our understanding of avian aerobic adaptation,a comprehensive synthesis of functional diversity,molecular evolution,and structural innovation is essential to fully elucidate their canalized roles in O_(2)homeostasis.Integrating perspectives on globin functional diversity and structural evolution,this review examines how chance(mutation/fixation biases)and contingency(historical genetic/epistatic constraints)shape Hb divergence and parallelism,thereby bridging molecular mechanisms with physiological adaptation in birds.We highlight how avian Hbs,canalized through compensatory substitutions and allosteric regulation,achieves a balance between evolutionary robustness and adaptive plasticity.However,critical gaps remain persist:the roles of understudied globins(e.g.,neuroglobin,globin E)and the mechanisms of genetic assimilation in migratory taxa.We propose an integrative framework that incorporates ecological divergence(elevation,flight endurance),phylogenetic timescales,and systems biology to unravel how canalization directs adaptive compromise.By focusing on birds within the amniotes,this synthesis advances a cohesive model for vertebrate evolution,wherein canalized globins reconcile metabolic precision with ecological innovation.Ultimately,this review refines hypotheses of O_(2)cascade evolution and calls for cross-disciplinary studies to decode the genetic and physiological architecture underlying adaptive canalization in extreme environments.
