Based on our proposed adaptivity strategy for the vibration of Reissner-Mindlin plate,we develop it to apply for the vibration of Kirchhoff plate.The adaptive algorithm is based on the Geometry-Independent Field appro...Based on our proposed adaptivity strategy for the vibration of Reissner-Mindlin plate,we develop it to apply for the vibration of Kirchhoff plate.The adaptive algorithm is based on the Geometry-Independent Field approximaTion(GIFT),generalized from Iso-Geometric Analysis(IGA),and it can characterize the geometry of the structure with NURBS(Non-Uniform Rational B-Splines),and independently apply PHT-splines(Polynomial splines over Hierarchical T-meshes)to achieve local refinement in the solution field.TheMAC(Modal AssuranceCriterion)is improved to locate unique,as well as multiple,modal correspondence between different meshes,in order to deal with error estimation.Local adaptivity is carried out by sweeping modes from low to high frequency.Numerical examples showthat a proper choice of the spline space in solution field(with GIFT)can deliver better accuracy than using NURBS solution field.In addition,for vibration of heterogeneous Kirchhoff plates,our proposed method indicates that the adaptive local h-refinement achieves a better solution accuracy than the uniform h-refinement.展开更多
An h-adaptivity analysis scheme based on multiple scale reproducing kernel particle method was proposed, and two node refinement strategies were constructed using searching-neighbor-nodes（SNN） and local-Delaunay-tri...An h-adaptivity analysis scheme based on multiple scale reproducing kernel particle method was proposed, and two node refinement strategies were constructed using searching-neighbor-nodes（SNN） and local-Delaunay-triangulation（LDT） techniques, which were suitable and effective for h-adaptivity analysis on 2-D problems with the regular or irregular distribution of the nodes. The results of multiresolution and h- adaptivity analyses on 2-D linear elastostatics and bending plate problems demonstrate that the improper high-gradient indicator will reduce the convergence property of the h- adaptivity analysis, and that the efficiency of the LDT node refinement strategy is better than SNN, and that the presented h-adaptivity analysis scheme is provided with the validity, stability and good convergence property.展开更多
Currently,many mobile devices provide various interaction styles and modes which create complexity in the usage of interfaces.The context offers the information base for the development of Adaptive user interface(AUI)...Currently,many mobile devices provide various interaction styles and modes which create complexity in the usage of interfaces.The context offers the information base for the development of Adaptive user interface(AUI)frameworks to overcome the heterogeneity.For this purpose,the ontological modeling has been made for specific context and environment.This type of philosophy states to the relationship among elements(e.g.,classes,relations,or capacities etc.)with understandable satisfied representation.The contextmechanisms can be examined and understood by anymachine or computational framework with these formal definitions expressed in Web ontology language(WOL)/Resource description frame work(RDF).The Protégéis used to create taxonomy in which system is framed based on four contexts such as user,device,task and environment.Some competency questions and use-cases are utilized for knowledge obtaining while the information is refined through the instances of concerned parts of context tree.The consistency of the model has been verified through the reasoning software while SPARQL querying ensured the data availability in the models for defined use-cases.The semantic context model is focused to bring in the usage of adaptive environment.This exploration has finished up with a versatile,scalable and semantically verified context learning system.This model can be mapped to individual User interface(UI)display through smart calculations for versatile UIs.展开更多
The deferred correction(DeC)is an iterative procedure,characterized by increasing the accuracy at each iteration,which can be used to design numerical methods for systems of ODEs.The main advantage of such framework i...The deferred correction(DeC)is an iterative procedure,characterized by increasing the accuracy at each iteration,which can be used to design numerical methods for systems of ODEs.The main advantage of such framework is the automatic way of getting arbitrarily high order methods,which can be put in the Runge-Kutta(RK)form.The drawback is the larger computational cost with respect to the most used RK methods.To reduce such cost,in an explicit setting,we propose an efcient modifcation:we introduce interpolation processes between the DeC iterations,decreasing the computational cost associated to the low order ones.We provide the Butcher tableaux of the new modifed methods and we study their stability,showing that in some cases the computational advantage does not afect the stability.The fexibility of the novel modifcation allows nontrivial applications to PDEs and construction of adaptive methods.The good performances of the introduced methods are broadly tested on several benchmarks both in ODE and PDE contexts.展开更多
Metamaterials hold great potential to enhance the imaging performance of magnetic resonance imaging(MRI)as auxiliary devices,due to their unique ability to confine and enhance electromagnetic fields.Despite their prom...Metamaterials hold great potential to enhance the imaging performance of magnetic resonance imaging(MRI)as auxiliary devices,due to their unique ability to confine and enhance electromagnetic fields.Despite their promise,the current implementation of metamaterials faces obstacles for practical clinical adoption due to several notable limitations,including their bulky and rigid structures,deviations from optimal resonance frequency,and inevitable interference with the radiofrequency(RF)transmission field in MRI.Herein,we address these restrictions by introducing a flexible and smart metamaterial that enhances sensitivity by conforming to patient anatomies while ensuring comfort during MRI procedures.The proposed metamaterial selectively amplifies the magnetic field during the RF reception phase by passively sensing the excitation signal strength,remaining“off”during the RF transmission phase.Additionally,the metamaterial can be readily tuned to achieve a precise frequency match with the MRI system through a controlling circuit.The metamaterial presented here paves the way for the widespread utilization of metamaterials in clinical MRI,thereby translating this promising technology to the MRI bedside.展开更多
This paper addresses fully space-time adaptive magnetic field computations. We describe an adaptive Whitney finite element method for solving the magnetoquasistatic formulation of Maxwell＇s equations on unstructured ...This paper addresses fully space-time adaptive magnetic field computations. We describe an adaptive Whitney finite element method for solving the magnetoquasistatic formulation of Maxwell＇s equations on unstructured 3D tetrahedral grids. Spatial mesh re- finement and coarsening are based on hierarchical error estimators especially designed for combining tetrahedral H（curl）-conforming edge elements in space with linearly implicit Rosenbrock methods in time. An embedding technique is applied to get efficiency in time through variable time steps. Finally, we present numerical results for the magnetic recording write head benchmark problem proposed by the Storage Research Consortium in Japan.展开更多
This article is concerned with the numerical detection of bifurcation points of nonlinear partial differential equations as some parameter of interest is varied.In particular,we study in detail the numerical approxima...This article is concerned with the numerical detection of bifurcation points of nonlinear partial differential equations as some parameter of interest is varied.In particular,we study in detail the numerical approximation of the Bratu problem,based on exploiting the symmetric version of the interior penalty discontinuous Galerkin finite element method.A framework for a posteriori control of the discretization error in the computed critical parameter value is developed based upon the application of the dual weighted residual(DWR)approach.Numerical experiments are presented to highlight the practical performance of the proposed a posteriori error estimator.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
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
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
Qingke,a staple crop grown on the high-altitude Tibetan Plateau,has evolved a metabolomic profile providing both environmental stress resilience and human nutrition.We review the hypothesis that the metabolites that c...Qingke,a staple crop grown on the high-altitude Tibetan Plateau,has evolved a metabolomic profile providing both environmental stress resilience and human nutrition.We review the hypothesis that the metabolites that confer cold and UV resistance on the crop also facilitate human adaptation to high-altitude stresses.Specifically,β-glucans regulate blood glucose primarily via short-chain fatty acids(SCFAs)produced through gut microbiota fermentation,which directly mediate glucose homeostasis.Phenolamides accumulate via the phenylpropanoid pathway,with chalcone isomerase(CHI)serving as a key enzyme in flavonoid biosynthesis and enhancing UV-B resistance.Under low temperatures,β-glucans improve frost tolerance by modulating osmotic balance and inhibiting ice-nucleating proteins,while lipids maintain membrane fluidity to sustain cellular function during cold stress.Importantly,we explore the hypothesis that these same metabolites,upon consumption,may facilitate human adaptation to high-altitude stresses.This hypothesis is supported by preliminary epidemiological associations between Qingke consumption and favorable health outcomes in high-altitude populations,as well as established bioactivities of the implicated metabolites in vitro and in animal models.However,direct causal evidence in humans and a comprehensive understanding of the underlying molecular mechanisms remain key knowledge gaps that warrant future investigation.Qingke as a unique resource at the interface of agricultural resilience and human nutrition.Understanding its metabolic blueprint will inform the development of functional foods and climate-resilient crops.展开更多
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
基金This study was funded by Natural Science Foundation of China(Grant No.12102095)Research grant for 100 Talents of Guangxi Plan,The Starting Research Grant for High-Level Talents from Guangxi University,Generalized Isogeometric Analysis with Homogeniztion Theory for Soft Acoustic Metamaterials(AD20159080)+2 种基金Science and Technology Major Project of Guangxi Province(AA18118055)Guangxi Natural Science Foundation(2018JJB160052)Application of Key Technology in Building Construction of Prefabricated Steel Structure(BB30300105).
文摘Based on our proposed adaptivity strategy for the vibration of Reissner-Mindlin plate,we develop it to apply for the vibration of Kirchhoff plate.The adaptive algorithm is based on the Geometry-Independent Field approximaTion(GIFT),generalized from Iso-Geometric Analysis(IGA),and it can characterize the geometry of the structure with NURBS(Non-Uniform Rational B-Splines),and independently apply PHT-splines(Polynomial splines over Hierarchical T-meshes)to achieve local refinement in the solution field.TheMAC(Modal AssuranceCriterion)is improved to locate unique,as well as multiple,modal correspondence between different meshes,in order to deal with error estimation.Local adaptivity is carried out by sweeping modes from low to high frequency.Numerical examples showthat a proper choice of the spline space in solution field(with GIFT)can deliver better accuracy than using NURBS solution field.In addition,for vibration of heterogeneous Kirchhoff plates,our proposed method indicates that the adaptive local h-refinement achieves a better solution accuracy than the uniform h-refinement.
文摘An h-adaptivity analysis scheme based on multiple scale reproducing kernel particle method was proposed, and two node refinement strategies were constructed using searching-neighbor-nodes（SNN） and local-Delaunay-triangulation（LDT） techniques, which were suitable and effective for h-adaptivity analysis on 2-D problems with the regular or irregular distribution of the nodes. The results of multiresolution and h- adaptivity analyses on 2-D linear elastostatics and bending plate problems demonstrate that the improper high-gradient indicator will reduce the convergence property of the h- adaptivity analysis, and that the efficiency of the LDT node refinement strategy is better than SNN, and that the presented h-adaptivity analysis scheme is provided with the validity, stability and good convergence property.
基金This research is supported by the Ministry of Culture,Sports and Tourism and Korean Creative Content Agency(Project No:2020040243).
文摘Currently,many mobile devices provide various interaction styles and modes which create complexity in the usage of interfaces.The context offers the information base for the development of Adaptive user interface(AUI)frameworks to overcome the heterogeneity.For this purpose,the ontological modeling has been made for specific context and environment.This type of philosophy states to the relationship among elements(e.g.,classes,relations,or capacities etc.)with understandable satisfied representation.The contextmechanisms can be examined and understood by anymachine or computational framework with these formal definitions expressed in Web ontology language(WOL)/Resource description frame work(RDF).The Protégéis used to create taxonomy in which system is framed based on four contexts such as user,device,task and environment.Some competency questions and use-cases are utilized for knowledge obtaining while the information is refined through the instances of concerned parts of context tree.The consistency of the model has been verified through the reasoning software while SPARQL querying ensured the data availability in the models for defined use-cases.The semantic context model is focused to bring in the usage of adaptive environment.This exploration has finished up with a versatile,scalable and semantically verified context learning system.This model can be mapped to individual User interface(UI)display through smart calculations for versatile UIs.
文摘The deferred correction(DeC)is an iterative procedure,characterized by increasing the accuracy at each iteration,which can be used to design numerical methods for systems of ODEs.The main advantage of such framework is the automatic way of getting arbitrarily high order methods,which can be put in the Runge-Kutta(RK)form.The drawback is the larger computational cost with respect to the most used RK methods.To reduce such cost,in an explicit setting,we propose an efcient modifcation:we introduce interpolation processes between the DeC iterations,decreasing the computational cost associated to the low order ones.We provide the Butcher tableaux of the new modifed methods and we study their stability,showing that in some cases the computational advantage does not afect the stability.The fexibility of the novel modifcation allows nontrivial applications to PDEs and construction of adaptive methods.The good performances of the introduced methods are broadly tested on several benchmarks both in ODE and PDE contexts.
基金supported by the National Institutes of Health(NIH)of Biomedical Imaging and Bioengineering grant no.5R21EB024673-03the Rajen Kilachand Fund for Integrated Life Science and Engineering.
文摘Metamaterials hold great potential to enhance the imaging performance of magnetic resonance imaging(MRI)as auxiliary devices,due to their unique ability to confine and enhance electromagnetic fields.Despite their promise,the current implementation of metamaterials faces obstacles for practical clinical adoption due to several notable limitations,including their bulky and rigid structures,deviations from optimal resonance frequency,and inevitable interference with the radiofrequency(RF)transmission field in MRI.Herein,we address these restrictions by introducing a flexible and smart metamaterial that enhances sensitivity by conforming to patient anatomies while ensuring comfort during MRI procedures.The proposed metamaterial selectively amplifies the magnetic field during the RF reception phase by passively sensing the excitation signal strength,remaining“off”during the RF transmission phase.Additionally,the metamaterial can be readily tuned to achieve a precise frequency match with the MRI system through a controlling circuit.The metamaterial presented here paves the way for the widespread utilization of metamaterials in clinical MRI,thereby translating this promising technology to the MRI bedside.
基金supported by the Deutsche Forschungsgemeinschaft(DFG)within the project"Space-time adaptive magnetic field computation"(grants CL143/3-1,CL143/3-2,LA1372/3-1,LA1372/3-2)
文摘This paper addresses fully space-time adaptive magnetic field computations. We describe an adaptive Whitney finite element method for solving the magnetoquasistatic formulation of Maxwell＇s equations on unstructured 3D tetrahedral grids. Spatial mesh re- finement and coarsening are based on hierarchical error estimators especially designed for combining tetrahedral H（curl）-conforming edge elements in space with linearly implicit Rosenbrock methods in time. An embedding technique is applied to get efficiency in time through variable time steps. Finally, we present numerical results for the magnetic recording write head benchmark problem proposed by the Storage Research Consortium in Japan.
基金the financial support of the EPSRC under the grant EP/E013724the support of the EPSRC under the grant EP/F01340X.
文摘This article is concerned with the numerical detection of bifurcation points of nonlinear partial differential equations as some parameter of interest is varied.In particular,we study in detail the numerical approximation of the Bratu problem,based on exploiting the symmetric version of the interior penalty discontinuous Galerkin finite element method.A framework for a posteriori control of the discretization error in the computed critical parameter value is developed based upon the application of the dual weighted residual(DWR)approach.Numerical experiments are presented to highlight the practical performance of the proposed a posteriori error estimator.
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.
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 the Financial Special Fund,grant number XZ202401JD0027National Barley Industry Technology System(CARS-05-01A-08)+3 种基金the Xizang Agri-Tech Innovation Project(XZNKY-2025-CXGC-T01)the Joint Funds of the National Natural Science Foundation of China(No.U20A2026)the Financial Special Fund,grant number(32401784,2017CZZX001/2,XZNKY-2018-C-021 and NYSTC202401)the China Agriculture Research System of Barley(CARS-05).
文摘Qingke,a staple crop grown on the high-altitude Tibetan Plateau,has evolved a metabolomic profile providing both environmental stress resilience and human nutrition.We review the hypothesis that the metabolites that confer cold and UV resistance on the crop also facilitate human adaptation to high-altitude stresses.Specifically,β-glucans regulate blood glucose primarily via short-chain fatty acids(SCFAs)produced through gut microbiota fermentation,which directly mediate glucose homeostasis.Phenolamides accumulate via the phenylpropanoid pathway,with chalcone isomerase(CHI)serving as a key enzyme in flavonoid biosynthesis and enhancing UV-B resistance.Under low temperatures,β-glucans improve frost tolerance by modulating osmotic balance and inhibiting ice-nucleating proteins,while lipids maintain membrane fluidity to sustain cellular function during cold stress.Importantly,we explore the hypothesis that these same metabolites,upon consumption,may facilitate human adaptation to high-altitude stresses.This hypothesis is supported by preliminary epidemiological associations between Qingke consumption and favorable health outcomes in high-altitude populations,as well as established bioactivities of the implicated metabolites in vitro and in animal models.However,direct causal evidence in humans and a comprehensive understanding of the underlying molecular mechanisms remain key knowledge gaps that warrant future investigation.Qingke as a unique resource at the interface of agricultural resilience and human nutrition.Understanding its metabolic blueprint will inform the development of functional foods and climate-resilient crops.
基金Supported by 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.
