To analyze main factors affecting the separation reliability between a missile and adapters for the launching process, a six DOF underwater dynamic model for the missile and adapters is utilized to simulate the separa...To analyze main factors affecting the separation reliability between a missile and adapters for the launching process, a six DOF underwater dynamic model for the missile and adapters is utilized to simulate the separation process, considering elastic forces of separating springs, hydrodynamic forces, gravity and buoyancy. Moreover, a criterion based on the maximum separating distance is put forward to determine whether adapters separate with the missile reliably. The results show that the magnitude and position of elastic force, the wedge angle and mass of the adapter significantly affect the separating process. The local sensitivity analysis for the reference status of design parameters demonstrates that the wedge angle of adapters has the maximum influence about 70. 4% on the separating distance.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
1.Introduction The field of exercise science is experiencing a renaissance,with recent research illuminating the molecular,cellular,and systemic effects of physical activity.This is largely due to the now unequivocal ...1.Introduction The field of exercise science is experiencing a renaissance,with recent research illuminating the molecular,cellular,and systemic effects of physical activity.This is largely due to the now unequivocal evidence that a lack of physical activity,not only has direct effects on the prevalence of non-contagious diseases(NCDs)but has profound additive effects of other risk factors for NCD such as obesity and hypertension.1 The articles in this special topic of Journal of Sport and Health Science(JSHS)are dedicated to research on Exercise biochemistry&metabolism.展开更多
In 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
Background:Investigators from low-,middle-,and high-income countries representing 6 continents contributed to the development of the Global Adolescent and Child Physical Activity Questionnaire(GAC-PAQ).The GAC-PAQ is ...Background:Investigators from low-,middle-,and high-income countries representing 6 continents contributed to the development of the Global Adolescent and Child Physical Activity Questionnaire(GAC-PAQ).The GAC-PAQ is designed to assess physical activity(PA)across all key domains(i.e.,school,chores,work/volunteering,transport,free time,outdoor time).It aimed to address multiple gaps in global PA surveillance(e.g.,omission of important PA domains,insufficient cultural adaptation,underrepresentation of rural areas in questionnaire validation studies).The purpose of this study was to assess the content validity of the GAC-PAQ among PA experts,8-to 17-year-olds,and one of their parents/guardians,and to discuss changes made to the questionnaire based on participants'feedback.Methods:Sixty-two experts in PA measurement and/or surveillance from 24 countries completed an online survey that included both closed-and open-ended questions about the content validity of the GAC-PAQ.The proportion of experts who agreed or strongly agreed with the items was calculated.Child-parent/guardian dyads from 15 countries(n=250;10-40 per country)participated in a structured cognitive interview to assess the clarity of the questions and response options,and they were encouraged to provide suggestions to improve clarity and facilitate completion of the questionnaire.Participating countries are:Aotearoa New Zealand,Brazil,Canada,China,Colombia,Czech Republic,India,Malawi,Mexico,Nepal,Nigeria,Spain,Sweden,Thailand,and the United Arab Emirates.Interviews were conducted in 13 different languages and structured by PA domain.Generic images were included to help participants in answering questions about PA intensity.Results:Expert agreement with the items for each domain exceeded 75%,and their qualitative feedback was used to revise the questionnaire before cognitive interviews.In general,participants found the questionnaire to be comprehensive.Adolescents(12-17 years)found it easier than children(8-11 years)to answer the questions.Several children struggled to answer questions about the duration and intensity of activities and/or concepts related to travel modes,active trips,and organized activities.Many parents/guardians were unsure about the frequency,duration,and intensity of their children's or adolescents'PA at school and/or recommended using more culturally relevant and appropriate images.Some participants misunderstood the concept of activities that“make you stronger”(intended to assess resistance activities)and/or struggled to differentiate between work,volunteering,and chores.Conclusion:Participants'feedback was used to develop a revised,simplified,and culturally adapted GAC-PAQ,which will be pilot-tested in all15 countries in an App that will include country-specific images and narration in local languages.Further research is needed to assess the reliability and validity of the revised GAC-PAQ.展开更多
Starting from the foundational static traits underlying the growth and development of flue-cured tobacco, this research conducts a systematic examination of the phenomena and theoretical principles associated with env...Starting from the foundational static traits underlying the growth and development of flue-cured tobacco, this research conducts a systematic examination of the phenomena and theoretical principles associated with environment-driven adaptive changes during its cultivation. It was found that environmental variables-including temperature, light, and moisture-elicit directional shifts in static traits ( e.g. , chemical composition, morphological architecture, and leaf tissue structure) toward enhanced environmental adaptation, characterized by graduality, juvenility, similarity, and correlativity. Upon alterations in ambient conditions, flue-cured tobacco modulates its static traits through integrated physical, chemical, and biological-genetic mechanisms, aiming to optimize resource utilization, mitigate environmental constraints, and preserve internal homeostasis alongside metabolic balance. The investigation further reveals that the adaptive scope of flue-cured tobacco to field environments is malleable and can be extended and elevated via adaptive conditioning commencing at the juvenile stage. In addition, the adaptive alignment between static traits and environmental parameters exerts a substantial impact on the plant s growth dynamics, yield performance, and quality attributes. Beyond its relevance to flue-cured tobacco, the proposed theory offers a meaningful framework for elucidating the pervasive adaptive strategies employed by plants and broader biological systems in response to environmental contingencies.展开更多
After billions of years of evolution,biological intelligence has converged on unrivalled energy efficiency and environmental adaptability.The human brain,for instance,is highly efficient in information transmission,co...After billions of years of evolution,biological intelligence has converged on unrivalled energy efficiency and environmental adaptability.The human brain,for instance,is highly efficient in information transmission,consuming only about 20 W onaverage in a resting state[1,2].A key to this efficiency is that biological signal transduction and processing rely significantly on multi-ions as the signal carriers.Inspired by this paradigm.展开更多
Accurate photovoltaic(PV)power generation forecasting is essential for the efficient integration of renewable energy into power grids.However,the nonlinear and non-stationary characteristics of PV power signals,driven...Accurate photovoltaic(PV)power generation forecasting is essential for the efficient integration of renewable energy into power grids.However,the nonlinear and non-stationary characteristics of PV power signals,driven by fluctuating weather conditions,pose significant challenges for reliable prediction.This study proposes a DOEP(Decomposition–Optimization–Error Correction–Prediction)framework,a hybrid forecasting approach that integrates adaptive signal decomposition,machine learning,metaheuristic optimization,and error correction.The PV power signal is first decomposed using CEEMDAN to extract multi-scale temporal features.Subsequently,the hyperparameters and window sizes of the LSSVM are optimized using a Segment-based EBQPSO strategy.The main novelty of the proposed DOEP framework lies in the incorporation of Segment-based EBQPSO as a structured optimization mechanism that balances elite exploitation and population diversity during LSSVM tuning within the CEEMDAN-based forecasting pipeline.This strategy effectively mitigates convergence instability and sensitivity to initialization,which are common limitations in existing hybrid PV forecasting models.Each IMF is then predicted individually and aggregated to generate an initial forecast.In the error-correction stage,the residual error series is modeled using LSTM,and the final prediction is obtained by combining the initial forecast with the predicted error component.The proposed framework is evaluated using two PV power plant datasets with different levels of complexity.The results demonstrate that DOEP consistently outperforms benchmark models across multiple error-based and goodness-of-fit metrics,achieving MSE reductions of approximately 15%–60%on the ResPV-BDG dataset and 37%–92%on the NREL dataset.Analyses of predicted vs.observed values and residual distributions further confirm the superior calibration and robustness of the proposed approach.Although the DOEP framework entails higher computational costs than single model methods,it delivers significantly improved accuracy and stability for PV power forecasting under complex operating conditions.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
Nursing education is undergoing a paradigm shift from skill training to clinical thinking cultivation.The integration of artificial intelligence technology offers technical possibilities for this transformation,but it...Nursing education is undergoing a paradigm shift from skill training to clinical thinking cultivation.The integration of artificial intelligence technology offers technical possibilities for this transformation,but it also brings about a deep tension between the cultivation of humanistic qualities and a standardized training.Based on the analysis of the practical forms of nursing smart education,this paper examines the cognitive gap between the deterministic feedback of virtual simulation systems and the complexity of real clinical scenarios,reveals the potential narrowing effect of data-driven ability profiling on the all-round development of nursing students,and then proposes the design logic of intelligent teaching resources centered on real clinical problems,a hierarchical teaching model with clear human-machine division of labor,and a dynamic assessment mechanism for technology application led by professional nursing teachers,in an attempt to find a balance between technological empowerment and humanistic commitment in smart nursing education.展开更多
Rheumatoid arthritis(RA)patients face significant psychological challenges alongside physical symptoms,necessitating a comprehensive understanding of how psychological vulnerability and adaptation patterns evolve thro...Rheumatoid arthritis(RA)patients face significant psychological challenges alongside physical symptoms,necessitating a comprehensive understanding of how psychological vulnerability and adaptation patterns evolve throughout the disease course.This review examined 95 studies(2000-2025)from PubMed,Web of Science,and CNKI databases including longitudinal cohorts,randomized controlled trials,and mixed-methods research,to characterize the complex interplay between biological,psychological,and social factors affecting RA patients’mental health.Findings revealed three distinct vulnerability trajectories(45%persistently low,30%fluctuating improvement,25%persistently high)and four adaptation stages,with critical intervention periods occurring 3-6 months postdiagnosis and during disease flares.Multiple factors significantly influence psychological outcomes,including gender(females showing 1.8-fold increased risk),age(younger patients experiencing 42%higher vulnerability),pain intensity,inflammatory markers,and neuroendocrine dysregulation(48%showing cortisol rhythm disruption).Early psychological intervention(within 3 months of diagnosis)demonstrated robust benefits,reducing depression incidence by 42%with effects persisting 24-36 months,while different modalities showed complementary advantages:Cognitive behavioral therapy for depression(Cohen’s d=0.68),mindfulness for pain acceptance(38%improvement),and peer support for meaning reconstruction(25.6%increase).These findings underscore the importance of integrating routine psychological assessment into standard RA care,developing stage-appropriate interventions,and advancing research toward personalized biopsychosocial approaches that address the dynamic psychological dimensions of the disease.展开更多
Extreme climate events(e.g.,heatwaves and droughts)are becoming increasingly frequent due to global climate change,which inevitably affects tree growth and various other ecological processes.While the impacts of droug...Extreme climate events(e.g.,heatwaves and droughts)are becoming increasingly frequent due to global climate change,which inevitably affects tree growth and various other ecological processes.While the impacts of droughts on these processes have been widely evaluated,the effects of heatwaves on tree growth and soil water content(SWC)remain poorly understood,particularly those related to thinning treatment.In this study,we evaluated the impacts of the 2021 Pacific Northwest Heatwave and thinning on forest growth and SWC,as well as assessed how thinning might mitigate the heatwave's impacts in lodgepole pine forests in British Columbia,Canada.We measured meteorological data(air temperature,rainfall,solar radiation(SR),relative humidity(RH),and wind speed(W_(s)),sap flow,SWC,soil temperature(T_(s)),and tree diameters at the breast height(DBH)during the growing season(June–September)in the control(27,000 stems·ha^(-1)),lightly thinned(4,500 stems·ha^(-1)),and heavily thinned(1,100 stems·ha^(-1))experimental plots from 2018 to 2024.We found that thinning persistently and significantly(p<0.05)increased individual tree growth,with the most pronounced effects in the heavily thinned stands.The 2021 Pacific Northwest Heatwave led to an exceptionally hot growing season,significantly(p<0.05)reducing forest growth and SWC across all plots.Forest growth recovered in 2022 in the thinned plots but remained suppressed in the unthinned plots,suggesting that thinning effectively mitigated the impact of the heatwave on forest growth,while the heatwave's impacts were persistent in the unthinned plots.Our study highlights that thinning is a practical management strategy for improving tree growth and supporting climate change adaptation to extreme climate events.展开更多
Background:As an important indicator of subjective well-being(SWB),decent work is a key guarantee for the sustainable development of teachers and their psychological health and work quality.Faced with the rapid develo...Background:As an important indicator of subjective well-being(SWB),decent work is a key guarantee for the sustainable development of teachers and their psychological health and work quality.Faced with the rapid development of artificial intelligence and the global labor market,vocational college teachers are facing challenges such as workload pressure and limited career development,which may harm their well-being.This study aims to localize the measurement method of decent work in Chinese vocational education based on the theory of the Psychology of Working Theory,and explore the relationship mechanism between organizational support,career adaptability,decent work,and job satisfaction among vocational college teachers.Methods:A cross-sectional survey was conducted with 422 HVCU teachers in China(202 male,220 female)using the localized Perceived Organizational Support Scale,Career Adaptability Scale,Decent Work Scale,and Job Satisfaction Scale.Results:The overall level of HVCU teachers’decent work was above the median(Mean=4.09,SD=0.69),laying a foundation for their SWB.Decent work significantly and positively predicted job satisfaction(β=0.620,p<0.001).Organizational support(r=0.58,p<0.001)and career adaptability(r=0.82,p<0.001)can positively affect decent work,and further improve job satisfaction(collective R2 rising from 38.3%to 41.1%).Bootstrap analysis confirmed these mediating effects were robust.Conclusions:This study confirms that the combined effects of organizational support and career adaptability can enhance decent work,further improving teachers’job satisfaction and subsequent subjective well-being.Besides,this study provides an empirical basis for improving the well-being of higher vocational teachers and the sustainable development of vocational education,and has practical significance for improving the teacher incentive policy.展开更多
This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temp...This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temperature distribution,suitable for control design due to its balance between physical fidelity and computational simplicity.The controller uses a wavelet-based neural proportional,integral,derivative(PID)controller with IIR filtering(infinite impulse response).The dynamic model captures the essential heat and mass transfer phenomena through a nonlinear energy balance,where the cooling capacity“Qevap”is expressed as a non-linear function of the compressor frequency and the temperature difference,specifically,Q_(evap)=k_(1)u(T_(in)−T_(e))with u as compressor frequency,Te evaporator temperature,and Tin inlet fluid temperature.The operating conditions of the system,in general terms,focus on the following variables,the overall thermal capacity is 1000 J/K,typical for small-capacity heat exchangers,The mass flow is 0.05 kg/s,typical for secondary liquid cooling circuits,the overall loss coefficient of 50 W/K that corresponds to small evaporators with partial insulation,the temperatures(inlet)of 10℃and the temperature of environment of 25℃,thermal load of 200 W that corresponds to a small-scaled air conditioning applications.To handle system nonlinearities and improve control performance,aMorlet wavelet-based neural network(Wavenet)is used to dynamically adjust the PID gains online.An IIR filter is incorporated to smooth the adaptive gains,improving stability and reducing oscillations.In contrast to prior wavelet-or neural-adaptive PID controllers in HVAC applications,which typically adjust gains without explicit filtering or not tailored to evaporator dynamics,this work introduces the first PID–Wavenet scheme augmented with an IIR-based stabilization layer,specifically designed to address the combined challenges of nonlinear evaporator behavior,gain oscillation,and real-time implementability.The proposed controller(PID-Wavenet+IIR)is implemented and validated inMATLAB/Simulink,demonstrating superior performance compared to a conventional PID tuned using Simulink’s auto-tuning function.Key results include a reduction in settling time from 13.3 to 8.2 s,a reduction in overshoot from 3.5%to 0.8%,a reduction in steady-state error from 0.12℃ to 0.02℃and a 13%reduction in energy overall consumption.The controller also exhibits greater robustness and adaptability under varying thermal loads.This explicit integration of wavelet-driven adaptation with IIR-filtered gain shaping constitutes the main methodological contribution and novelty of the work.These findings validate the effectiveness of the wavelet-based adaptive approach for advanced thermal management in refrigeration and HVAC systems,with potential applications in controlling variable-speed compressors,liquid chillers,and compact cooling units.展开更多
To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervis...To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.展开更多
Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional ...Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.展开更多
Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting incr...Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting increased interest in swarm intelligence algorithms.Among these,the Cuckoo Search(CS)algorithm stands out for its promising global search capabilities.However,it often suffers from premature convergence when tackling complex problems.To address this limitation,this paper proposes a Grouped Dynamic Adaptive CS(GDACS)algorithm.Theenhancements incorporated intoGDACS can be summarized into two key aspects.Firstly,a chaotic map is employed to generate initial solutions,leveraging the inherent randomness of chaotic sequences to ensure a more uniform distribution across the search space and enhance population diversity from the outset.Secondly,Cauchy and Levy strategies replace the standard CS population update.This strategy involves evaluating the fitness of candidate solutions to dynamically group the population based on performance.Different step-size adaptation strategies are then applied to distinct groups,enabling an adaptive search mechanism that balances exploration and exploitation.Experiments were conducted on six benchmark functions and four constrained engineering design problems,and the results indicate that the proposed GDACS achieves good search efficiency and produces more accurate optimization results compared with other state-of-the-art algorithms.展开更多
文摘To analyze main factors affecting the separation reliability between a missile and adapters for the launching process, a six DOF underwater dynamic model for the missile and adapters is utilized to simulate the separation process, considering elastic forces of separating springs, hydrodynamic forces, gravity and buoyancy. Moreover, a criterion based on the maximum separating distance is put forward to determine whether adapters separate with the missile reliably. The results show that the magnitude and position of elastic force, the wedge angle and mass of the adapter significantly affect the separating process. The local sensitivity analysis for the reference status of design parameters demonstrates that the wedge angle of adapters has the maximum influence about 70. 4% on the separating distance.
基金Supported by the National Natural Science Foundation of China(12071133)Natural Science Foundation of Henan Province(252300421993)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B110005)。
文摘In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.
文摘1.Introduction The field of exercise science is experiencing a renaissance,with recent research illuminating the molecular,cellular,and systemic effects of physical activity.This is largely due to the now unequivocal evidence that a lack of physical activity,not only has direct effects on the prevalence of non-contagious diseases(NCDs)but has profound additive effects of other risk factors for NCD such as obesity and hypertension.1 The articles in this special topic of Journal of Sport and Health Science(JSHS)are dedicated to research on Exercise biochemistry&metabolism.
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 a Project Grant(Grant No.PJT183705)an Early Career Investigator Prize(Grant No.ECP 184184)from the Canadian Institutes of Health Research+7 种基金a Prentice Institute Research Affiliate Fund Grant from the Prentice Institute for Global Population and Economy(Grant No.G00004116)a Te Herenga Waka Victoria University of Wellington Division of Science Health Engineering Architecture and Design Innovation Faculty Strategic Research Grant(Grant No.FSRG-SHEADI-10724)The Thailand Physical Activity Knowledge Development Centre(TPAK)/Thai Health Promotion Foundation provided funding for the cognitive interviews and pilot study in Thailand(Grant No.66-P1-0473)The University Pablo de Olavide provided a scholarship for 2 undergraduate students working on the project(codes PPI2207 and PPI2308)In the Czech Republicthe study was supported by Palacky University IGA(Grant No.IGA_FTK_2023_017)supported by the Division of Intramural Research at the National Institute on Minority Health and Health Disparities of the National Institutes of Healthsupported by the Key Project of the National Philosophy and Social Science Foundation of China(23&ZD197)。
文摘Background:Investigators from low-,middle-,and high-income countries representing 6 continents contributed to the development of the Global Adolescent and Child Physical Activity Questionnaire(GAC-PAQ).The GAC-PAQ is designed to assess physical activity(PA)across all key domains(i.e.,school,chores,work/volunteering,transport,free time,outdoor time).It aimed to address multiple gaps in global PA surveillance(e.g.,omission of important PA domains,insufficient cultural adaptation,underrepresentation of rural areas in questionnaire validation studies).The purpose of this study was to assess the content validity of the GAC-PAQ among PA experts,8-to 17-year-olds,and one of their parents/guardians,and to discuss changes made to the questionnaire based on participants'feedback.Methods:Sixty-two experts in PA measurement and/or surveillance from 24 countries completed an online survey that included both closed-and open-ended questions about the content validity of the GAC-PAQ.The proportion of experts who agreed or strongly agreed with the items was calculated.Child-parent/guardian dyads from 15 countries(n=250;10-40 per country)participated in a structured cognitive interview to assess the clarity of the questions and response options,and they were encouraged to provide suggestions to improve clarity and facilitate completion of the questionnaire.Participating countries are:Aotearoa New Zealand,Brazil,Canada,China,Colombia,Czech Republic,India,Malawi,Mexico,Nepal,Nigeria,Spain,Sweden,Thailand,and the United Arab Emirates.Interviews were conducted in 13 different languages and structured by PA domain.Generic images were included to help participants in answering questions about PA intensity.Results:Expert agreement with the items for each domain exceeded 75%,and their qualitative feedback was used to revise the questionnaire before cognitive interviews.In general,participants found the questionnaire to be comprehensive.Adolescents(12-17 years)found it easier than children(8-11 years)to answer the questions.Several children struggled to answer questions about the duration and intensity of activities and/or concepts related to travel modes,active trips,and organized activities.Many parents/guardians were unsure about the frequency,duration,and intensity of their children's or adolescents'PA at school and/or recommended using more culturally relevant and appropriate images.Some participants misunderstood the concept of activities that“make you stronger”(intended to assess resistance activities)and/or struggled to differentiate between work,volunteering,and chores.Conclusion:Participants'feedback was used to develop a revised,simplified,and culturally adapted GAC-PAQ,which will be pilot-tested in all15 countries in an App that will include country-specific images and narration in local languages.Further research is needed to assess the reliability and validity of the revised GAC-PAQ.
基金Supported by Changsha Tobacco Company Science and Technology Project(2020-2024A04).
文摘Starting from the foundational static traits underlying the growth and development of flue-cured tobacco, this research conducts a systematic examination of the phenomena and theoretical principles associated with environment-driven adaptive changes during its cultivation. It was found that environmental variables-including temperature, light, and moisture-elicit directional shifts in static traits ( e.g. , chemical composition, morphological architecture, and leaf tissue structure) toward enhanced environmental adaptation, characterized by graduality, juvenility, similarity, and correlativity. Upon alterations in ambient conditions, flue-cured tobacco modulates its static traits through integrated physical, chemical, and biological-genetic mechanisms, aiming to optimize resource utilization, mitigate environmental constraints, and preserve internal homeostasis alongside metabolic balance. The investigation further reveals that the adaptive scope of flue-cured tobacco to field environments is malleable and can be extended and elevated via adaptive conditioning commencing at the juvenile stage. In addition, the adaptive alignment between static traits and environmental parameters exerts a substantial impact on the plant s growth dynamics, yield performance, and quality attributes. Beyond its relevance to flue-cured tobacco, the proposed theory offers a meaningful framework for elucidating the pervasive adaptive strategies employed by plants and broader biological systems in response to environmental contingencies.
文摘After billions of years of evolution,biological intelligence has converged on unrivalled energy efficiency and environmental adaptability.The human brain,for instance,is highly efficient in information transmission,consuming only about 20 W onaverage in a resting state[1,2].A key to this efficiency is that biological signal transduction and processing rely significantly on multi-ions as the signal carriers.Inspired by this paradigm.
基金support from the Ministry of Science and Technology of Taiwan(Contract Nos.113-2221-E-011-130-MY2 and 113-2218-E-011-002)the support from Intelligent Manufactur-ing Innovation Center(IMIC),National Taiwan University of Science and Technology(NTUST),Taipei,Taiwan.
文摘Accurate photovoltaic(PV)power generation forecasting is essential for the efficient integration of renewable energy into power grids.However,the nonlinear and non-stationary characteristics of PV power signals,driven by fluctuating weather conditions,pose significant challenges for reliable prediction.This study proposes a DOEP(Decomposition–Optimization–Error Correction–Prediction)framework,a hybrid forecasting approach that integrates adaptive signal decomposition,machine learning,metaheuristic optimization,and error correction.The PV power signal is first decomposed using CEEMDAN to extract multi-scale temporal features.Subsequently,the hyperparameters and window sizes of the LSSVM are optimized using a Segment-based EBQPSO strategy.The main novelty of the proposed DOEP framework lies in the incorporation of Segment-based EBQPSO as a structured optimization mechanism that balances elite exploitation and population diversity during LSSVM tuning within the CEEMDAN-based forecasting pipeline.This strategy effectively mitigates convergence instability and sensitivity to initialization,which are common limitations in existing hybrid PV forecasting models.Each IMF is then predicted individually and aggregated to generate an initial forecast.In the error-correction stage,the residual error series is modeled using LSTM,and the final prediction is obtained by combining the initial forecast with the predicted error component.The proposed framework is evaluated using two PV power plant datasets with different levels of complexity.The results demonstrate that DOEP consistently outperforms benchmark models across multiple error-based and goodness-of-fit metrics,achieving MSE reductions of approximately 15%–60%on the ResPV-BDG dataset and 37%–92%on the NREL dataset.Analyses of predicted vs.observed values and residual distributions further confirm the superior calibration and robustness of the proposed approach.Although the DOEP framework entails higher computational costs than single model methods,it delivers significantly improved accuracy and stability for PV power forecasting under complex operating conditions.
文摘The rapidly evolving cybersecurity threat landscape exposes a critical flaw in traditional educational programs where static curricula cannot adapt swiftly to novel attack vectors.This creates a significant gap between theoretical knowledge and the practical defensive capabilities needed in the field.To address this,we propose TeachSecure-CTI,a novel framework for adaptive cybersecurity curriculumgeneration that integrates real-time Cyber Threat Intelligence(CTI)with AI-driven personalization.Our framework employs a layered architecture featuring a CTI ingestion and clusteringmodule,natural language processing for semantic concept extraction,and a reinforcement learning agent for adaptive content sequencing.Bydynamically aligning learningmaterialswithboththe evolving threat environment and individual learner profiles,TeachSecure-CTI ensures content remains current,relevant,and tailored.A 12-week study with 150 students across three institutions demonstrated that the framework improves learning gains by 34%,significantly exceeding the 12%–21%reported in recent literature.The system achieved 84.8%personalization accuracy,85.9%recognition accuracy for MITRE ATT&CK tactics,and a 31%faster competency development rate compared to static curricula.These findings have implications beyond academia,extending to workforce development,cyber range training,and certification programs.By bridging the gap between dynamic threats and static educational materials,TeachSecure-CTI offers an empirically validated,scalable solution for cultivating cybersecurity professionals capable of responding to modern threats.
基金Funding Project for Ideological and Political Model Courses of“Epidemic Fighting”Courses in Henan Province(Project No.:531,2020)University-level Curriculum Ideological and Political Demonstration Course Support Project of Zhengzhou Sias University(Project No.:34,2024)+2 种基金University-level Key Discipline Support Project of Zhengzhou Sias University(Project No.:1,2022)2025 Key Scientific Research Projects of Henan Universities(Project No.:25B360003)Henan Province Private Brand Professional Support Project(Project No.:527,2019)。
文摘Nursing education is undergoing a paradigm shift from skill training to clinical thinking cultivation.The integration of artificial intelligence technology offers technical possibilities for this transformation,but it also brings about a deep tension between the cultivation of humanistic qualities and a standardized training.Based on the analysis of the practical forms of nursing smart education,this paper examines the cognitive gap between the deterministic feedback of virtual simulation systems and the complexity of real clinical scenarios,reveals the potential narrowing effect of data-driven ability profiling on the all-round development of nursing students,and then proposes the design logic of intelligent teaching resources centered on real clinical problems,a hierarchical teaching model with clear human-machine division of labor,and a dynamic assessment mechanism for technology application led by professional nursing teachers,in an attempt to find a balance between technological empowerment and humanistic commitment in smart nursing education.
基金Supported by Chongqing Health Commission and Chongqing Science and Technology Bureau,No.2023MSXM182。
文摘Rheumatoid arthritis(RA)patients face significant psychological challenges alongside physical symptoms,necessitating a comprehensive understanding of how psychological vulnerability and adaptation patterns evolve throughout the disease course.This review examined 95 studies(2000-2025)from PubMed,Web of Science,and CNKI databases including longitudinal cohorts,randomized controlled trials,and mixed-methods research,to characterize the complex interplay between biological,psychological,and social factors affecting RA patients’mental health.Findings revealed three distinct vulnerability trajectories(45%persistently low,30%fluctuating improvement,25%persistently high)and four adaptation stages,with critical intervention periods occurring 3-6 months postdiagnosis and during disease flares.Multiple factors significantly influence psychological outcomes,including gender(females showing 1.8-fold increased risk),age(younger patients experiencing 42%higher vulnerability),pain intensity,inflammatory markers,and neuroendocrine dysregulation(48%showing cortisol rhythm disruption).Early psychological intervention(within 3 months of diagnosis)demonstrated robust benefits,reducing depression incidence by 42%with effects persisting 24-36 months,while different modalities showed complementary advantages:Cognitive behavioral therapy for depression(Cohen’s d=0.68),mindfulness for pain acceptance(38%improvement),and peer support for meaning reconstruction(25.6%increase).These findings underscore the importance of integrating routine psychological assessment into standard RA care,developing stage-appropriate interventions,and advancing research toward personalized biopsychosocial approaches that address the dynamic psychological dimensions of the disease.
基金supported by the British Columbia Ministry of Forces through long-term annual contracts with University of British Columbia(Okanagan)(No.RE25SIR242)the Natural Sciences and Engineering Research Council of Canada(NSERC),Discovery Grants Program(No.RGPIN-2021-02628)+1 种基金supported by the China Postdoctoral Science Foundation(No.2024M760387)Heilongjiang Postdoctoral Financial Assistance(No.LBH-Z24062)。
文摘Extreme climate events(e.g.,heatwaves and droughts)are becoming increasingly frequent due to global climate change,which inevitably affects tree growth and various other ecological processes.While the impacts of droughts on these processes have been widely evaluated,the effects of heatwaves on tree growth and soil water content(SWC)remain poorly understood,particularly those related to thinning treatment.In this study,we evaluated the impacts of the 2021 Pacific Northwest Heatwave and thinning on forest growth and SWC,as well as assessed how thinning might mitigate the heatwave's impacts in lodgepole pine forests in British Columbia,Canada.We measured meteorological data(air temperature,rainfall,solar radiation(SR),relative humidity(RH),and wind speed(W_(s)),sap flow,SWC,soil temperature(T_(s)),and tree diameters at the breast height(DBH)during the growing season(June–September)in the control(27,000 stems·ha^(-1)),lightly thinned(4,500 stems·ha^(-1)),and heavily thinned(1,100 stems·ha^(-1))experimental plots from 2018 to 2024.We found that thinning persistently and significantly(p<0.05)increased individual tree growth,with the most pronounced effects in the heavily thinned stands.The 2021 Pacific Northwest Heatwave led to an exceptionally hot growing season,significantly(p<0.05)reducing forest growth and SWC across all plots.Forest growth recovered in 2022 in the thinned plots but remained suppressed in the unthinned plots,suggesting that thinning effectively mitigated the impact of the heatwave on forest growth,while the heatwave's impacts were persistent in the unthinned plots.Our study highlights that thinning is a practical management strategy for improving tree growth and supporting climate change adaptation to extreme climate events.
基金funded by Nanjing University of Posts and Telecommunications Humanities and Social Sciences Research Fund Project(NYY222055)Special research project on teaching reform of innovation and entrepreneurship education in Nanjing University of Posts and Telecommunications(GCSJG202528)+2 种基金General Subject of Educational Science Planning in Jiangsu Province(C/2024/01/76)General project of educational science research in Shanghai(C24288)Key funded project of Shandong Vocational Education Teaching Reform Research in 2022(2022052).
文摘Background:As an important indicator of subjective well-being(SWB),decent work is a key guarantee for the sustainable development of teachers and their psychological health and work quality.Faced with the rapid development of artificial intelligence and the global labor market,vocational college teachers are facing challenges such as workload pressure and limited career development,which may harm their well-being.This study aims to localize the measurement method of decent work in Chinese vocational education based on the theory of the Psychology of Working Theory,and explore the relationship mechanism between organizational support,career adaptability,decent work,and job satisfaction among vocational college teachers.Methods:A cross-sectional survey was conducted with 422 HVCU teachers in China(202 male,220 female)using the localized Perceived Organizational Support Scale,Career Adaptability Scale,Decent Work Scale,and Job Satisfaction Scale.Results:The overall level of HVCU teachers’decent work was above the median(Mean=4.09,SD=0.69),laying a foundation for their SWB.Decent work significantly and positively predicted job satisfaction(β=0.620,p<0.001).Organizational support(r=0.58,p<0.001)and career adaptability(r=0.82,p<0.001)can positively affect decent work,and further improve job satisfaction(collective R2 rising from 38.3%to 41.1%).Bootstrap analysis confirmed these mediating effects were robust.Conclusions:This study confirms that the combined effects of organizational support and career adaptability can enhance decent work,further improving teachers’job satisfaction and subsequent subjective well-being.Besides,this study provides an empirical basis for improving the well-being of higher vocational teachers and the sustainable development of vocational education,and has practical significance for improving the teacher incentive policy.
文摘This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temperature distribution,suitable for control design due to its balance between physical fidelity and computational simplicity.The controller uses a wavelet-based neural proportional,integral,derivative(PID)controller with IIR filtering(infinite impulse response).The dynamic model captures the essential heat and mass transfer phenomena through a nonlinear energy balance,where the cooling capacity“Qevap”is expressed as a non-linear function of the compressor frequency and the temperature difference,specifically,Q_(evap)=k_(1)u(T_(in)−T_(e))with u as compressor frequency,Te evaporator temperature,and Tin inlet fluid temperature.The operating conditions of the system,in general terms,focus on the following variables,the overall thermal capacity is 1000 J/K,typical for small-capacity heat exchangers,The mass flow is 0.05 kg/s,typical for secondary liquid cooling circuits,the overall loss coefficient of 50 W/K that corresponds to small evaporators with partial insulation,the temperatures(inlet)of 10℃and the temperature of environment of 25℃,thermal load of 200 W that corresponds to a small-scaled air conditioning applications.To handle system nonlinearities and improve control performance,aMorlet wavelet-based neural network(Wavenet)is used to dynamically adjust the PID gains online.An IIR filter is incorporated to smooth the adaptive gains,improving stability and reducing oscillations.In contrast to prior wavelet-or neural-adaptive PID controllers in HVAC applications,which typically adjust gains without explicit filtering or not tailored to evaporator dynamics,this work introduces the first PID–Wavenet scheme augmented with an IIR-based stabilization layer,specifically designed to address the combined challenges of nonlinear evaporator behavior,gain oscillation,and real-time implementability.The proposed controller(PID-Wavenet+IIR)is implemented and validated inMATLAB/Simulink,demonstrating superior performance compared to a conventional PID tuned using Simulink’s auto-tuning function.Key results include a reduction in settling time from 13.3 to 8.2 s,a reduction in overshoot from 3.5%to 0.8%,a reduction in steady-state error from 0.12℃ to 0.02℃and a 13%reduction in energy overall consumption.The controller also exhibits greater robustness and adaptability under varying thermal loads.This explicit integration of wavelet-driven adaptation with IIR-filtered gain shaping constitutes the main methodological contribution and novelty of the work.These findings validate the effectiveness of the wavelet-based adaptive approach for advanced thermal management in refrigeration and HVAC systems,with potential applications in controlling variable-speed compressors,liquid chillers,and compact cooling units.
基金supported by the National Natural Science Foundation of China Funded Project(Project Name:Research on Robust Adaptive Allocation Mechanism of Human Machine Co-Driving System Based on NMS Features,Project Approval Number:52172381).
文摘To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00406320)the Institute of Information&Communica-tions Technology Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization Program Grant funded by the Korea government(MSIT)(IITP-2026-RS-2023-00259678).
文摘Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.
基金supported in part by the Ministry of Higher Education Malaysia(MOHE)through Fundamental Research Grant Scheme(FRGS)Ref:FRGS/1/2024/ICT02/UTM/02/10,Vot.No:R.J130000.7828.5F748the Scientific Research Project of Education Department of Hunan Province(Nos.22B1046 and 24A0771).
文摘Engineering optimization problems are often characterized by high dimensionality,constraints,and complex,multimodal landscapes.Traditional deterministic methods frequently struggle under such conditions,prompting increased interest in swarm intelligence algorithms.Among these,the Cuckoo Search(CS)algorithm stands out for its promising global search capabilities.However,it often suffers from premature convergence when tackling complex problems.To address this limitation,this paper proposes a Grouped Dynamic Adaptive CS(GDACS)algorithm.Theenhancements incorporated intoGDACS can be summarized into two key aspects.Firstly,a chaotic map is employed to generate initial solutions,leveraging the inherent randomness of chaotic sequences to ensure a more uniform distribution across the search space and enhance population diversity from the outset.Secondly,Cauchy and Levy strategies replace the standard CS population update.This strategy involves evaluating the fitness of candidate solutions to dynamically group the population based on performance.Different step-size adaptation strategies are then applied to distinct groups,enabling an adaptive search mechanism that balances exploration and exploitation.Experiments were conducted on six benchmark functions and four constrained engineering design problems,and the results indicate that the proposed GDACS achieves good search efficiency and produces more accurate optimization results compared with other state-of-the-art algorithms.
