This paper focuses on the application of the adaptive mesh method in the study of underwater shock waves near the water surface.By integrating theoretical analysis with a five-equation model under axisymmetric coordin...This paper focuses on the application of the adaptive mesh method in the study of underwater shock waves near the water surface.By integrating theoretical analysis with a five-equation model under axisymmetric coordinates,we developed an optimized computational framework for multi-material fluid simulations.The moving mesh method(r-method)is used to accurately capture complex underwater shock wave systems.Multiple numerical experiments are conducted,including deep-water explosions,near-surface explosions for both spherical charge and cylindrical charge,and regular–irregular reflection interface calculations.The results show that compared to the fixed mesh method,the adaptive mesh method provides results closer to the theoretical values and achieves local high-resolution computation of multi-material fluids.By adjusting the adaptive function,different mesh refinement effects can be obtained.This method also has certain advantages in calculating the regular–irregular reflection interface in underwater explosions.This study establishes a validated computational framework for underwater explosion research,enhancing the predictive accuracy for underwater shock wave propagation in engineering assessments and providing new insights into the fundamental physics of multi-material explosion phenomena.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
Aberration-corrected focus scanning is crucial for high-precision optics,but the conventional optical systems rely on bulky and complicated dynamic correctors.Recently,Shiyi Xiao's group proposed a method using tw...Aberration-corrected focus scanning is crucial for high-precision optics,but the conventional optical systems rely on bulky and complicated dynamic correctors.Recently,Shiyi Xiao's group proposed a method using two rotating cascaded transmissive metasurfaces for adaptive aberration correction in focus scanning.The optimized phase profiles enable precise control of the focal position for scanning custom-curved surfaces.This concept was experimentally validated by two allsilicon meta-devices in the terahertz regime,paving the way for high-precision and compact optical devices in various applications.展开更多
Starting from the foundational static traits underlying the growth and development of flue-cured tobacco, this research conducts a systematic examination of the phenomena and theoretical principles associated with env...Starting from the foundational static traits underlying the growth and development of flue-cured tobacco, this research conducts a systematic examination of the phenomena and theoretical principles associated with environment-driven adaptive changes during its cultivation. It was found that environmental variables-including temperature, light, and moisture-elicit directional shifts in static traits ( e.g. , chemical composition, morphological architecture, and leaf tissue structure) toward enhanced environmental adaptation, characterized by graduality, juvenility, similarity, and correlativity. Upon alterations in ambient conditions, flue-cured tobacco modulates its static traits through integrated physical, chemical, and biological-genetic mechanisms, aiming to optimize resource utilization, mitigate environmental constraints, and preserve internal homeostasis alongside metabolic balance. The investigation further reveals that the adaptive scope of flue-cured tobacco to field environments is malleable and can be extended and elevated via adaptive conditioning commencing at the juvenile stage. In addition, the adaptive alignment between static traits and environmental parameters exerts a substantial impact on the plant s growth dynamics, yield performance, and quality attributes. Beyond its relevance to flue-cured tobacco, the proposed theory offers a meaningful framework for elucidating the pervasive adaptive strategies employed by plants and broader biological systems in response to environmental contingencies.展开更多
Background:The mechanisms underlying the beneficial effects of exercise on the human placenta are poorly understood.The objective of the current study was to ascertain the influence of a supervised concurrent exercise...Background:The mechanisms underlying the beneficial effects of exercise on the human placenta are poorly understood.The objective of the current study was to ascertain the influence of a supervised concurrent exercise intervention from gestational Week 17 until birth on key cytokines involved in placental development and function.Secondary aims were to explore:(a)the moderating effects of fetal sex and maternal weight status;and(b)whether gestational weight gain,lifestyle behaviors(diet,sleep patterns,and physical activity),and physical fitness(strength and cardiorespiratory fitness)mediated the effects of exercise on placental cytokines.Methods:Seventy-six pregnant women(33±4 years,mean±SD),divided into exercise(n=40)and control(n=36)groups,participated in this study.The exercise group followed a 60-min,3 days/week(aerobic+resistance)training program of moderate-to-vigorous intensity.Placental cytokines—including granulocyte-macrophage colony-stimulating factor(GM-CSF),granulocyte colony-stimulating factor(G-CSF),plateletderived growth factor AA(PDGF-AA),epidermal growth factor(EGF),monocyte chemoattractant protein-1(MCP-1),fractalkine,interleukin(IL)-8,IL-6,IL-1β,interleukin 1-receptor antagonist(IL-1ra),IL-10,tumor necrosis factor alpha(TNF-a),and interferon gamma(IFN-γ)were analyzed using Luminex multi-analyte profiling(x MAP)technology.Results:The exercise group presented higher placental levels of G-CSF and lower concentrations of EGF and IL-1ra than the control group(p<0.05).Significant effects of exercise on placental G-CSF and TNF-a(p<0.05)and a trend toward lower IL-6(p=0.08)were observed only in female placentas.Additionally,a reduction in weight gain partially mediated the effects of exercise on G-CSF(p<0.05).Conclusion:Maternal exercise during pregnancy is related to increased placental levels of G-CSF and lower EGF and IL-1ra levels.Some exercise-induced effects are observed exclusively in female placentas,including increased G-CSF and lower TNF-a and IL-6 concentrations.Notably,the increased levels of G-CSF observed with exercise might be due to a more adequate gestational weight gain.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
Epoxy resins are widely employed in wind turbine blades,drone rotors,and automotive interiors due to their excel-lent mechani-cal proper-ties and long service life.However,their insoluble and infusible cross-linked ne...Epoxy resins are widely employed in wind turbine blades,drone rotors,and automotive interiors due to their excel-lent mechani-cal proper-ties and long service life.However,their insoluble and infusible cross-linked networks pose a significant re-cycling challenge,particularly with the impending retirement of the first generation of wind turbine blades.In this work,we reported a fully bio-based epoxy Vitrimer(FEP)incorporat-ing a dual-dynamic covalent network design and systematically investigated the influence of the 1,5,7-triazabicyclo[4.4.0]dec-5-ene(TBD)catalyst on its curing kinetics,thermal/mechan-ical properties,dynamic exchange behavior,and degradation performance in a mild alkaline solution.Compared to conventional epoxy resins,FEP exhibited superior tensile strength and elongation at break at an optimal TBD concentration(2 wt%),achieving an excellent strength-toughness balance.The presence of TBD accelerated the exchange rates of both disulfide and ester bonds,endowing FEP with notable stress relaxation at elevated tempera-tures.Moreover,FEP demonstrated complete dissolution in 1 mol/L NaOH within 6 h at 25℃.These results underscored the exceptional strength,toughness,and recyclability of FEP,positioning it as a promising,environmentally friendly matrix resin for next-generation appli-cations in the new energy sector.展开更多
Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—esp...Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—especially critical in scenarios like sudden electronic warfare or degraded command,where static weights cannot reflect the operational value decay or surge of key indicators.To address this issue,this study proposes a dynamic adaptive weightingmethod for evaluation indicators based onG1-CRITIC-PIVW.First,theG1(Sequential Relationship Analysis Method)subjective weighting method—translates expert knowledge into indicator importance rankings—leverages expert knowledge to quantify the relative importance of indicators via sequential relationship ranking,while the CRITIC(Criteria Importance Through Intercriteria Correlation)objective weighting method—derives weights from data characteristics by integrating variability and inter-correlations—calculates weights by integrating indicator variability and inter-indicator correlations,ensuring data-driven objectivity.These two sets of weights are then fused using a deviation coefficient optimization model,minimizing the squared deviation from a reference weight and adjusting the fusion coefficient via Spearman’s rank correlation to resolve potential conflicts between subjective and objective judgments.Subsequently,the PIVW(Punishment-Incentive VariableWeight)theory—adapts weights to realtime indicator performance via penalty/incentive rules—is applied for dynamic adjustment.Scenario-specific penalty λ_(1) and incentive λ_(2) thresholds are set based on operational priorities and indicator volatility,penalizing indicators with values below λ_(1) and incentivizing those exceeding λ_(2) to reflect real-time indicator performance.Experimental validation was conducted using an Air Defense and Anti-Missile(ADAM)system effectiveness assessment framework,with data covering 7 indicators across 3 combat scenarios.Results show that compared to static weighting methods,the proposed method reduces MAE(Mean Absolute Error)by 15%-20% and weighted decision error rate by 84.2%,effectively reducing overestimation/underestimation of combat effectiveness in dynamic scenarios;compared to Entropy-TOPSIS,it lowers MAE by 12% while achieving a weighted Kendall’sτconsistency coefficient of 0.85,ensuring higher alignment with expert judgment.This method enhances the accuracy and scenario adaptability of effectiveness assessment,providing reliable decision support for dynamic battlefield environments.展开更多
The rapidly evolving cybersecurity threat landscape exposes a critical flaw in traditional educational programs where static curricula cannot adapt swiftly to novel attack vectors.This creates a significant gap betwee...The rapidly evolving cybersecurity threat landscape exposes a critical flaw in traditional educational programs where static curricula cannot adapt swiftly to novel attack vectors.This creates a significant gap between theoretical knowledge and the practical defensive capabilities needed in the field.To address this,we propose TeachSecure-CTI,a novel framework for adaptive cybersecurity curriculumgeneration that integrates real-time Cyber Threat Intelligence(CTI)with AI-driven personalization.Our framework employs a layered architecture featuring a CTI ingestion and clusteringmodule,natural language processing for semantic concept extraction,and a reinforcement learning agent for adaptive content sequencing.Bydynamically aligning learningmaterialswithboththe evolving threat environment and individual learner profiles,TeachSecure-CTI ensures content remains current,relevant,and tailored.A 12-week study with 150 students across three institutions demonstrated that the framework improves learning gains by 34%,significantly exceeding the 12%–21%reported in recent literature.The system achieved 84.8%personalization accuracy,85.9%recognition accuracy for MITRE ATT&CK tactics,and a 31%faster competency development rate compared to static curricula.These findings have implications beyond academia,extending to workforce development,cyber range training,and certification programs.By bridging the gap between dynamic threats and static educational materials,TeachSecure-CTI offers an empirically validated,scalable solution for cultivating cybersecurity professionals capable of responding to modern threats.展开更多
While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance re...While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance remains underexplored in field investigations.To evaluate the practical applicability of this emerging technique in adverse shallow sea channels,a field experiment was conducted using three communication modes:orthogonal frequency division multiplexing(OFDM),M-ary frequency-shift keying(MFSK),and direct sequence spread spectrum(DSSS)for reinforcement learning-driven adaptive modulation.Specifically,a Q-learning method is used to select the optimal modulation mode according to the channel quality quantified by signal-to-noise ratio,multipath spread length,and Doppler frequency offset.Experimental results demonstrate that the reinforcement learning-based adaptive modulation scheme outperformed fixed threshold detection in terms of total throughput and average bit error rate,surpassing conventional adaptive modulation strategies.展开更多
基金supported by the Overall Planning and Development Project of China Academy of Engineering Physics(CAEP)(Grant No.TCGH0909)the Science and Technology Development Fund of the Chinese Academy of Meteorological Sciences(CAMS)(Grant No.2023KJ048)the Basic Research Fund of the Chinese Academy of Meteorological Sciences(Grant No.2023Z001)。
文摘This paper focuses on the application of the adaptive mesh method in the study of underwater shock waves near the water surface.By integrating theoretical analysis with a five-equation model under axisymmetric coordinates,we developed an optimized computational framework for multi-material fluid simulations.The moving mesh method(r-method)is used to accurately capture complex underwater shock wave systems.Multiple numerical experiments are conducted,including deep-water explosions,near-surface explosions for both spherical charge and cylindrical charge,and regular–irregular reflection interface calculations.The results show that compared to the fixed mesh method,the adaptive mesh method provides results closer to the theoretical values and achieves local high-resolution computation of multi-material fluids.By adjusting the adaptive function,different mesh refinement effects can be obtained.This method also has certain advantages in calculating the regular–irregular reflection interface in underwater explosions.This study establishes a validated computational framework for underwater explosion research,enhancing the predictive accuracy for underwater shock wave propagation in engineering assessments and providing new insights into the fundamental physics of multi-material explosion phenomena.
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Aberration-corrected focus scanning is crucial for high-precision optics,but the conventional optical systems rely on bulky and complicated dynamic correctors.Recently,Shiyi Xiao's group proposed a method using two rotating cascaded transmissive metasurfaces for adaptive aberration correction in focus scanning.The optimized phase profiles enable precise control of the focal position for scanning custom-curved surfaces.This concept was experimentally validated by two allsilicon meta-devices in the terahertz regime,paving the way for high-precision and compact optical devices in various applications.
基金Supported by Changsha Tobacco Company Science and Technology Project(2020-2024A04).
文摘Starting from the foundational static traits underlying the growth and development of flue-cured tobacco, this research conducts a systematic examination of the phenomena and theoretical principles associated with environment-driven adaptive changes during its cultivation. It was found that environmental variables-including temperature, light, and moisture-elicit directional shifts in static traits ( e.g. , chemical composition, morphological architecture, and leaf tissue structure) toward enhanced environmental adaptation, characterized by graduality, juvenility, similarity, and correlativity. Upon alterations in ambient conditions, flue-cured tobacco modulates its static traits through integrated physical, chemical, and biological-genetic mechanisms, aiming to optimize resource utilization, mitigate environmental constraints, and preserve internal homeostasis alongside metabolic balance. The investigation further reveals that the adaptive scope of flue-cured tobacco to field environments is malleable and can be extended and elevated via adaptive conditioning commencing at the juvenile stage. In addition, the adaptive alignment between static traits and environmental parameters exerts a substantial impact on the plant s growth dynamics, yield performance, and quality attributes. Beyond its relevance to flue-cured tobacco, the proposed theory offers a meaningful framework for elucidating the pervasive adaptive strategies employed by plants and broader biological systems in response to environmental contingencies.
基金funded by the Regional Ministry of Health of the Junta de Andalucıa(PI-0395-2016)the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement(No.101027215)+1 种基金supported by the PLACENTRAINING project,funded through the FEDER-UGR23 funding call(European Regional Development Fund University of Granada programGrant No.C-EXP-336UGR23)。
文摘Background:The mechanisms underlying the beneficial effects of exercise on the human placenta are poorly understood.The objective of the current study was to ascertain the influence of a supervised concurrent exercise intervention from gestational Week 17 until birth on key cytokines involved in placental development and function.Secondary aims were to explore:(a)the moderating effects of fetal sex and maternal weight status;and(b)whether gestational weight gain,lifestyle behaviors(diet,sleep patterns,and physical activity),and physical fitness(strength and cardiorespiratory fitness)mediated the effects of exercise on placental cytokines.Methods:Seventy-six pregnant women(33±4 years,mean±SD),divided into exercise(n=40)and control(n=36)groups,participated in this study.The exercise group followed a 60-min,3 days/week(aerobic+resistance)training program of moderate-to-vigorous intensity.Placental cytokines—including granulocyte-macrophage colony-stimulating factor(GM-CSF),granulocyte colony-stimulating factor(G-CSF),plateletderived growth factor AA(PDGF-AA),epidermal growth factor(EGF),monocyte chemoattractant protein-1(MCP-1),fractalkine,interleukin(IL)-8,IL-6,IL-1β,interleukin 1-receptor antagonist(IL-1ra),IL-10,tumor necrosis factor alpha(TNF-a),and interferon gamma(IFN-γ)were analyzed using Luminex multi-analyte profiling(x MAP)technology.Results:The exercise group presented higher placental levels of G-CSF and lower concentrations of EGF and IL-1ra than the control group(p<0.05).Significant effects of exercise on placental G-CSF and TNF-a(p<0.05)and a trend toward lower IL-6(p=0.08)were observed only in female placentas.Additionally,a reduction in weight gain partially mediated the effects of exercise on G-CSF(p<0.05).Conclusion:Maternal exercise during pregnancy is related to increased placental levels of G-CSF and lower EGF and IL-1ra levels.Some exercise-induced effects are observed exclusively in female placentas,including increased G-CSF and lower TNF-a and IL-6 concentrations.Notably,the increased levels of G-CSF observed with exercise might be due to a more adequate gestational weight gain.
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support from the National Natural Science Foundation of China(Nos.22293011,T2341001)the Major Science and Technology Project of Anhui Province(202203a06020010).
文摘Epoxy resins are widely employed in wind turbine blades,drone rotors,and automotive interiors due to their excel-lent mechani-cal proper-ties and long service life.However,their insoluble and infusible cross-linked networks pose a significant re-cycling challenge,particularly with the impending retirement of the first generation of wind turbine blades.In this work,we reported a fully bio-based epoxy Vitrimer(FEP)incorporat-ing a dual-dynamic covalent network design and systematically investigated the influence of the 1,5,7-triazabicyclo[4.4.0]dec-5-ene(TBD)catalyst on its curing kinetics,thermal/mechan-ical properties,dynamic exchange behavior,and degradation performance in a mild alkaline solution.Compared to conventional epoxy resins,FEP exhibited superior tensile strength and elongation at break at an optimal TBD concentration(2 wt%),achieving an excellent strength-toughness balance.The presence of TBD accelerated the exchange rates of both disulfide and ester bonds,endowing FEP with notable stress relaxation at elevated tempera-tures.Moreover,FEP demonstrated complete dissolution in 1 mol/L NaOH within 6 h at 25℃.These results underscored the exceptional strength,toughness,and recyclability of FEP,positioning it as a promising,environmentally friendly matrix resin for next-generation appli-cations in the new energy sector.
基金funded by the National Natural Science Foundation of China(NSFC)under Grant Number 72071209.
文摘Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—especially critical in scenarios like sudden electronic warfare or degraded command,where static weights cannot reflect the operational value decay or surge of key indicators.To address this issue,this study proposes a dynamic adaptive weightingmethod for evaluation indicators based onG1-CRITIC-PIVW.First,theG1(Sequential Relationship Analysis Method)subjective weighting method—translates expert knowledge into indicator importance rankings—leverages expert knowledge to quantify the relative importance of indicators via sequential relationship ranking,while the CRITIC(Criteria Importance Through Intercriteria Correlation)objective weighting method—derives weights from data characteristics by integrating variability and inter-correlations—calculates weights by integrating indicator variability and inter-indicator correlations,ensuring data-driven objectivity.These two sets of weights are then fused using a deviation coefficient optimization model,minimizing the squared deviation from a reference weight and adjusting the fusion coefficient via Spearman’s rank correlation to resolve potential conflicts between subjective and objective judgments.Subsequently,the PIVW(Punishment-Incentive VariableWeight)theory—adapts weights to realtime indicator performance via penalty/incentive rules—is applied for dynamic adjustment.Scenario-specific penalty λ_(1) and incentive λ_(2) thresholds are set based on operational priorities and indicator volatility,penalizing indicators with values below λ_(1) and incentivizing those exceeding λ_(2) to reflect real-time indicator performance.Experimental validation was conducted using an Air Defense and Anti-Missile(ADAM)system effectiveness assessment framework,with data covering 7 indicators across 3 combat scenarios.Results show that compared to static weighting methods,the proposed method reduces MAE(Mean Absolute Error)by 15%-20% and weighted decision error rate by 84.2%,effectively reducing overestimation/underestimation of combat effectiveness in dynamic scenarios;compared to Entropy-TOPSIS,it lowers MAE by 12% while achieving a weighted Kendall’sτconsistency coefficient of 0.85,ensuring higher alignment with expert judgment.This method enhances the accuracy and scenario adaptability of effectiveness assessment,providing reliable decision support for dynamic battlefield environments.
文摘The rapidly evolving cybersecurity threat landscape exposes a critical flaw in traditional educational programs where static curricula cannot adapt swiftly to novel attack vectors.This creates a significant gap between theoretical knowledge and the practical defensive capabilities needed in the field.To address this,we propose TeachSecure-CTI,a novel framework for adaptive cybersecurity curriculumgeneration that integrates real-time Cyber Threat Intelligence(CTI)with AI-driven personalization.Our framework employs a layered architecture featuring a CTI ingestion and clusteringmodule,natural language processing for semantic concept extraction,and a reinforcement learning agent for adaptive content sequencing.Bydynamically aligning learningmaterialswithboththe evolving threat environment and individual learner profiles,TeachSecure-CTI ensures content remains current,relevant,and tailored.A 12-week study with 150 students across three institutions demonstrated that the framework improves learning gains by 34%,significantly exceeding the 12%–21%reported in recent literature.The system achieved 84.8%personalization accuracy,85.9%recognition accuracy for MITRE ATT&CK tactics,and a 31%faster competency development rate compared to static curricula.These findings have implications beyond academia,extending to workforce development,cyber range training,and certification programs.By bridging the gap between dynamic threats and static educational materials,TeachSecure-CTI offers an empirically validated,scalable solution for cultivating cybersecurity professionals capable of responding to modern threats.
基金funding from the National Key Research and Development Program of China(No.2018YFE0110000)the National Natural Science Foundation of China(No.11274259,No.11574258)the Science and Technology Commission Foundation of Shanghai(21DZ1205500)in support of the present research.
文摘While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance remains underexplored in field investigations.To evaluate the practical applicability of this emerging technique in adverse shallow sea channels,a field experiment was conducted using three communication modes:orthogonal frequency division multiplexing(OFDM),M-ary frequency-shift keying(MFSK),and direct sequence spread spectrum(DSSS)for reinforcement learning-driven adaptive modulation.Specifically,a Q-learning method is used to select the optimal modulation mode according to the channel quality quantified by signal-to-noise ratio,multipath spread length,and Doppler frequency offset.Experimental results demonstrate that the reinforcement learning-based adaptive modulation scheme outperformed fixed threshold detection in terms of total throughput and average bit error rate,surpassing conventional adaptive modulation strategies.
