Current topology optimization methods for nonlinear continuum structures often suffer from low computational efficiency and limited applicability to complex nonlinear problems.To address these issues,this paper propos...Current topology optimization methods for nonlinear continuum structures often suffer from low computational efficiency and limited applicability to complex nonlinear problems.To address these issues,this paper proposes an improved bi-directional evolutionary structural optimization(BESO)method tailored for maximizing stiffness in nonlinear structures.The optimization program is developed in Python and can be combined with Abaqus software to facilitate finite element analysis(FEA).To accelerate the speed of optimization,a novel adaptive evolutionary ratio(ER)strategy based on the BESO method is introduced,with four distinct adaptive ER functions proposed.The Newton-Raphson method is utilized for iteratively solving nonlinear equilibrium equations,and the sensitivity information for updating design variables is derived using the adjoint method.Additionally,this study extends topology optimization to account for both material nonlinearity and geometric nonlinearity,analyzing the effects of various nonlinearities.A series of comparative studies are conducted using benchmark cases to validate the effectiveness of the proposed method.The results show that the BESO method with adaptive ER significantly improves the optimization efficiency.Compared to the BESO method with a fixed ER,the convergence speed of the four adaptive ER BESO methods is increased by 37.3%,26.7%,12%and 18.7%,respectively.Given that Abaqus is a powerful FEA platform,this method has the potential to be extended to large-scale engineering structures and to address more complex optimization problems.This research proposes an improved BESO method with novel adaptive ER,which significantly accelerates the optimization process and enables its application to topology optimization of nonlinear structures.展开更多
It would be well to note that in the absence of clear data about the formation of adaptation systems,or mechanisms of their occurrence,all that is recognized is the realization of the micro evolutionary processes.Ther...It would be well to note that in the absence of clear data about the formation of adaptation systems,or mechanisms of their occurrence,all that is recognized is the realization of the micro evolutionary processes.There is no well-defined connection between information exchange and formation展开更多
With the fast development of artificial intelligence,a lot of translation methods and search methods have been proposed to address molecular optimization problems in drug design,which enables this field to achieve rem...With the fast development of artificial intelligence,a lot of translation methods and search methods have been proposed to address molecular optimization problems in drug design,which enables this field to achieve remarkable progress.However,existing methods still encounter great difficulties in addressing problems involving more than three properties,since these problems pose stiff challenges to translation methods and search methods in terms of acquiring high-quality training data and balancing multiple properties,respectively.In this paper,we propose an adaptive evolutionary optimization framework to address the many-property molecular optimization problems(namely MaOMO).MaOMO adaptively identifies the property with the largest improvement potential in each iteration,which generates high-quality molecules as efficiently as possible by devoting more efforts to the property.Besides,MaOMO adopts a dynamic selection strategy to select molecules with large property improvement,good property diversity,and structure diversity.We investigate the performance of MaOMO framework on both benchmark and practical molecular optimization tasks,which involve the simultaneous optimization of four or more properties.Experimental results show that the proposed framework is superior to five state-of-the-art competitors,which achieves a success rate improvement of more than 20%on practical optimization tasks.展开更多
The convergence of the Metaverse and the Internet of Things(IoT)paves the way for extensive data interaction between connected devices and digital twins;however,this simultaneously introduces considerable cybersecurit...The convergence of the Metaverse and the Internet of Things(IoT)paves the way for extensive data interaction between connected devices and digital twins;however,this simultaneously introduces considerable cybersecurity threats,including data breaches,ransomware,and device tampering.Existing intrusion detection algorithms struggle to effectively defend against emerging cyberattacks in the rapidly evolving Metaverse environment.Designing effective neural networks for intrusion detection algorithms relies heavily on expert experience,making the manual process time-consuming and often yielding suboptimal results.This paper addresses a critical gap in cybersecurity for Metaverse devices,which are often overlooked in traditional detection methods,and proposes an adaptive multiobjective evolutionary generative adversarial network(AME-GAN)as a novel,scalable solution for optimizing network intrusion detection.An inversely proportional hybrid attention-based long short-term memory GAN is proposed,combining GANs to generate minority class samples and alleviate the imbalance problem in training datasets,which has long hindered accurate intrusion detection.Additionally,an adaptive evolutionary neural architecture search algorithm for the supernet of the GAN is designed to guide the mutation direction of the supernet,enhancing the training stability.This paper further introduces a double mutation multiobjective evolutionary neural architecture search algorithm,integrating both the multiobjective evolutionary algorithm and the neural architecture search to optimize accuracy,real-time performance,and model diversity—a crucial aspect for Metaverse devices with diverse hardware constraints.Experiments conducted on 3 well-known datasets—NSL-KDD,UNSW-NB15,and CIC-IDS2017—demonstrate that AME-GAN outperforms state-of-the-art approaches,with improvements of 0.32%in accuracy,0.31%in F1 score,0.47%in precision,and 0.37%in recall.This paper offers a promising,adaptive framework to enhance cybersecurity in the Metaverse,improving detection performance and real-time applicability,and contributing to the future of network intrusion detection in next-generation digital environments.展开更多
The phenomenon of fear memory generalization can be defined as the expansion of an individual's originally specific fear responses to a similar yet genuinely harmless stimulus or situation subsequent to the occurr...The phenomenon of fear memory generalization can be defined as the expansion of an individual's originally specific fear responses to a similar yet genuinely harmless stimulus or situation subsequent to the occurrence of a traumatic event[1].Fear generalization within the normal range represents an adaptive evolutionary mechanism to facilitate prompt reactions to potential threats and to enhance the likelihood of survival.展开更多
Bacteriophages and archaeal viruses are the most abundant biological entities on Earth.Through a long-standing co-evolutionary arms race,they have driven the emergence of a diverse repertoire of prokaryotic defense sy...Bacteriophages and archaeal viruses are the most abundant biological entities on Earth.Through a long-standing co-evolutionary arms race,they have driven the emergence of a diverse repertoire of prokaryotic defense systems.This review summarizes these systems,highlighting their diverse antiviral mechanisms across distinct stages of viral infection,from surface barriers and inducible innate responses to specific adaptive defenses,and the intricate interplay between these defense strategies.By examining host-virus counter defense dynamics,the trade-off between survival benefit and adaptive cost,the co-evolution of RNA and protein components,and the comparison with eukaryotic immune systems,we underscore the intrinsic complexity and evolutionary plasticity of prokaryotic antiviral immunity.A deeper understanding of these processes and mechanisms will not only shed light on the origins and evolution of the immune system but also provide valuable opportunities for the development of biotechnological tools.展开更多
Dear Editor,Dichogamy is a temporal reproductive strategy in which male and female reproductive organs mature at different times,preventing self-fertilization and promoting outcrossing to maintain genetic diversity an...Dear Editor,Dichogamy is a temporal reproductive strategy in which male and female reproductive organs mature at different times,preventing self-fertilization and promoting outcrossing to maintain genetic diversity and support evolutionary adaptation(Goodwillie et al.,2005;Lee et al.,2018).Dichogamous species have evolved diverse and complex mating strategies,one of which involves the temporal separation of male and female reproductive phases within a single flower.Dichogamy has two main subtypes:protandry(PA)and protogyny(PG).In the context of a bisexual flower,PA occurs when the stamen matures and releases pollen before the stigma becomes receptive.PG is the reverse process,in which the pistil becomes receptive before the anther releases pollen.These phenomena,which were historically referred to as male-female and female-male sequences,have now been renamed PA and PG,respectively(Li et al.,2002;Li et al.,2001a;Li et al.,2001b).A number of Zingiberaceae species exhibit PA and PG morphs in bisexual flowers through stylar behavior(flexistyly)during flowering to encourage outcrossing.展开更多
Thellungiella salsuginea （halophila） is a close relative of Arabidopsis thaliana but, unlike A. thaliana, it grows well in extreme conditions of cold, salt, and drought as well as nitrogen limitation. Over the last ...Thellungiella salsuginea （halophila） is a close relative of Arabidopsis thaliana but, unlike A. thaliana, it grows well in extreme conditions of cold, salt, and drought as well as nitrogen limitation. Over the last decade, many laboratories have started to use Thellungiella to investigate the physiological, metabolic, and molecular mechanisms of abiotic stress tolerance in plants, and new knowledge has been gained in particular with respect to ion transport and gene expression. The advantage of Thellungiella over other extremophile model plants is that it can be directly compared with Arabidopsis, and therefore generate information on both essential and critical components of stress tolerance. Thellungiella research is supported by a growing body of technical resources comprising physiological and molecular protocols, ecotype collections, expressed sequence tags, cDNA-libraries, microarrays, and a pending genome sequence. This review summarizes the current state of knowledge on Thellungiella and re-evaluates its usefulness as a model for research into plant stress tolerance.展开更多
Realistic assessments of the impacts of global warming on population extinction risk are likely to require an integrated analysis of the roles of standing genetic variation,microhabitat thermal complexity,and the inte...Realistic assessments of the impacts of global warming on population extinction risk are likely to require an integrated analysis of the roles of standing genetic variation,microhabitat thermal complexity,and the inter-individual variation of heat tolerance due to both genetic differences and seasonal acclimatization effects.Here,we examine whether balancing selection and microhabitat temperature heterogeneity can interact to enhance the population persistence to thermal stress for the black mussel Septifer virgatus.We deployed biomimetic data loggers on the shore to measure the microhabitat-specific thermal variation from June 2014 to April 2016.Thermal tolerance of specimens was indexed by measuring effects of temperature on heart rate.Genotyping of specimens was performed using double digestion restriction association RADSeq(ddRADseq).Our results show that inter-individual variations in thermal tolerance correlate significantly with genetic differences at some specific gene loci,and that heterozygotes have higher thermal tolerances than homozygotes.The observed seasonal changes in genotype frequency suggest that these loci are under balancing selection.The ability of thermally resistant heterozygotes to survive in sun-exposed microhabitats acts to balance the loss of homozygotes during summer and enable the persistence of genetic polymorphisms.Population persistence of the mussel is also facilitated by the micro-scale variation in temperature,which provides refugia from thermal stress.Our results emphasize that inter-individual variation in thermal tolerance and in microhabitat heterogeneity in temperature are important for the persistence of populations in rocky shore habitats.展开更多
“Carpe diem”(enjoy the day)is a famous quotation of the Roman poet Horace,and these two words in a complementary way refer both to the logistics and to the semantics of human time(Zhao et al.,2022).Every day is an e...“Carpe diem”(enjoy the day)is a famous quotation of the Roman poet Horace,and these two words in a complementary way refer both to the logistics and to the semantics of human time(Zhao et al.,2022).Every day is an evolutionary adaptation to the geophysical cycle of 24 hours;this circadian rhythm(Aschoff,1965)is an example of the logistics of human time together with other temporal phenomena having shorter durations,as reflected in“time windows”(Bao et al.,2015).The logistic basis of temporal processing in human behavior is the necessary but not sufficient basis of how we(hopefully)enjoy the day,how we use our time efficiently,how we give meaning to our personal time,and how we experience temporal phenomena.The semantics of human time is reflected in temporal satisfaction or dissatisfaction,in the joy of having achieved something or the frustration of failure,and also in procrastination,in the experienced scarcity of time,or in boredom.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.52105271).
文摘Current topology optimization methods for nonlinear continuum structures often suffer from low computational efficiency and limited applicability to complex nonlinear problems.To address these issues,this paper proposes an improved bi-directional evolutionary structural optimization(BESO)method tailored for maximizing stiffness in nonlinear structures.The optimization program is developed in Python and can be combined with Abaqus software to facilitate finite element analysis(FEA).To accelerate the speed of optimization,a novel adaptive evolutionary ratio(ER)strategy based on the BESO method is introduced,with four distinct adaptive ER functions proposed.The Newton-Raphson method is utilized for iteratively solving nonlinear equilibrium equations,and the sensitivity information for updating design variables is derived using the adjoint method.Additionally,this study extends topology optimization to account for both material nonlinearity and geometric nonlinearity,analyzing the effects of various nonlinearities.A series of comparative studies are conducted using benchmark cases to validate the effectiveness of the proposed method.The results show that the BESO method with adaptive ER significantly improves the optimization efficiency.Compared to the BESO method with a fixed ER,the convergence speed of the four adaptive ER BESO methods is increased by 37.3%,26.7%,12%and 18.7%,respectively.Given that Abaqus is a powerful FEA platform,this method has the potential to be extended to large-scale engineering structures and to address more complex optimization problems.This research proposes an improved BESO method with novel adaptive ER,which significantly accelerates the optimization process and enables its application to topology optimization of nonlinear structures.
文摘It would be well to note that in the absence of clear data about the formation of adaptation systems,or mechanisms of their occurrence,all that is recognized is the realization of the micro evolutionary processes.There is no well-defined connection between information exchange and formation
基金supported by funds from the National Natural Science Foundation of China(Nos.62322301,62172002,62202004,and 62403002)the University Synergy Innovation Program of Anhui Province(Nos.GXXT-2022-035 and GXXT-2021-039)+3 种基金the Anhui Provincial Natural Science Foundation(Nos.2108085QF267 and 2008085QF294)the University Outstanding Youth Research Project of Education Commission of Anhui Province(No.2022AH020010)the Project of Key Laboratory of Intelligent Computing&Signal Processing(Anhui University)of Ministry of Education(No.2020A005)the State Key Laboratory of Pathogenesis,Prevention and Treatment of High Incidence Diseases in Central Asia Fund(No.SKL-HIDCA-2024-AH1).
文摘With the fast development of artificial intelligence,a lot of translation methods and search methods have been proposed to address molecular optimization problems in drug design,which enables this field to achieve remarkable progress.However,existing methods still encounter great difficulties in addressing problems involving more than three properties,since these problems pose stiff challenges to translation methods and search methods in terms of acquiring high-quality training data and balancing multiple properties,respectively.In this paper,we propose an adaptive evolutionary optimization framework to address the many-property molecular optimization problems(namely MaOMO).MaOMO adaptively identifies the property with the largest improvement potential in each iteration,which generates high-quality molecules as efficiently as possible by devoting more efforts to the property.Besides,MaOMO adopts a dynamic selection strategy to select molecules with large property improvement,good property diversity,and structure diversity.We investigate the performance of MaOMO framework on both benchmark and practical molecular optimization tasks,which involve the simultaneous optimization of four or more properties.Experimental results show that the proposed framework is superior to five state-of-the-art competitors,which achieves a success rate improvement of more than 20%on practical optimization tasks.
基金supported by the National Key R&D Program of China under grant no.2023YFB4503000supported in part by the National Natural Science Foundation of China(NSFC)under grant no.62473129+3 种基金the Natural Science Fund of Hebei Province for Distinguished Young Scholars under grant no.F2021202010the Science and Technology Project of Hebei Education Department under grant no.JZX2023007the S&T Program of Hebei under grant no.225676163GHthe Interdisciplinary Postgraduate Training Program of Hebei University of Technology under grant no.HEBUT-Y-XKJC-2022116.
文摘The convergence of the Metaverse and the Internet of Things(IoT)paves the way for extensive data interaction between connected devices and digital twins;however,this simultaneously introduces considerable cybersecurity threats,including data breaches,ransomware,and device tampering.Existing intrusion detection algorithms struggle to effectively defend against emerging cyberattacks in the rapidly evolving Metaverse environment.Designing effective neural networks for intrusion detection algorithms relies heavily on expert experience,making the manual process time-consuming and often yielding suboptimal results.This paper addresses a critical gap in cybersecurity for Metaverse devices,which are often overlooked in traditional detection methods,and proposes an adaptive multiobjective evolutionary generative adversarial network(AME-GAN)as a novel,scalable solution for optimizing network intrusion detection.An inversely proportional hybrid attention-based long short-term memory GAN is proposed,combining GANs to generate minority class samples and alleviate the imbalance problem in training datasets,which has long hindered accurate intrusion detection.Additionally,an adaptive evolutionary neural architecture search algorithm for the supernet of the GAN is designed to guide the mutation direction of the supernet,enhancing the training stability.This paper further introduces a double mutation multiobjective evolutionary neural architecture search algorithm,integrating both the multiobjective evolutionary algorithm and the neural architecture search to optimize accuracy,real-time performance,and model diversity—a crucial aspect for Metaverse devices with diverse hardware constraints.Experiments conducted on 3 well-known datasets—NSL-KDD,UNSW-NB15,and CIC-IDS2017—demonstrate that AME-GAN outperforms state-of-the-art approaches,with improvements of 0.32%in accuracy,0.31%in F1 score,0.47%in precision,and 0.37%in recall.This paper offers a promising,adaptive framework to enhance cybersecurity in the Metaverse,improving detection performance and real-time applicability,and contributing to the future of network intrusion detection in next-generation digital environments.
基金supported by the Shandong Provincial Natural Science Foundation(ZR2022QH144).
文摘The phenomenon of fear memory generalization can be defined as the expansion of an individual's originally specific fear responses to a similar yet genuinely harmless stimulus or situation subsequent to the occurrence of a traumatic event[1].Fear generalization within the normal range represents an adaptive evolutionary mechanism to facilitate prompt reactions to potential threats and to enhance the likelihood of survival.
基金supported by the grants from the National Natural Science Foundation of China(32230061 to H.X.)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0810000 to H.X.).
文摘Bacteriophages and archaeal viruses are the most abundant biological entities on Earth.Through a long-standing co-evolutionary arms race,they have driven the emergence of a diverse repertoire of prokaryotic defense systems.This review summarizes these systems,highlighting their diverse antiviral mechanisms across distinct stages of viral infection,from surface barriers and inducible innate responses to specific adaptive defenses,and the intricate interplay between these defense strategies.By examining host-virus counter defense dynamics,the trade-off between survival benefit and adaptive cost,the co-evolution of RNA and protein components,and the comparison with eukaryotic immune systems,we underscore the intrinsic complexity and evolutionary plasticity of prokaryotic antiviral immunity.A deeper understanding of these processes and mechanisms will not only shed light on the origins and evolution of the immune system but also provide valuable opportunities for the development of biotechnological tools.
基金supported by the Scientific and Technological Innovation Project of the China Academy of Chinese Medical Sciences(CI2023E002)the YEFICRC project of Yunnan provincial key programs(2019ZG009)the National Science and Technology Resource Sharing Service Platform Project(NTPGRC2023-01).
文摘Dear Editor,Dichogamy is a temporal reproductive strategy in which male and female reproductive organs mature at different times,preventing self-fertilization and promoting outcrossing to maintain genetic diversity and support evolutionary adaptation(Goodwillie et al.,2005;Lee et al.,2018).Dichogamous species have evolved diverse and complex mating strategies,one of which involves the temporal separation of male and female reproductive phases within a single flower.Dichogamy has two main subtypes:protandry(PA)and protogyny(PG).In the context of a bisexual flower,PA occurs when the stamen matures and releases pollen before the stigma becomes receptive.PG is the reverse process,in which the pistil becomes receptive before the anther releases pollen.These phenomena,which were historically referred to as male-female and female-male sequences,have now been renamed PA and PG,respectively(Li et al.,2002;Li et al.,2001a;Li et al.,2001b).A number of Zingiberaceae species exhibit PA and PG morphs in bisexual flowers through stylar behavior(flexistyly)during flowering to encourage outcrossing.
文摘Thellungiella salsuginea （halophila） is a close relative of Arabidopsis thaliana but, unlike A. thaliana, it grows well in extreme conditions of cold, salt, and drought as well as nitrogen limitation. Over the last decade, many laboratories have started to use Thellungiella to investigate the physiological, metabolic, and molecular mechanisms of abiotic stress tolerance in plants, and new knowledge has been gained in particular with respect to ion transport and gene expression. The advantage of Thellungiella over other extremophile model plants is that it can be directly compared with Arabidopsis, and therefore generate information on both essential and critical components of stress tolerance. Thellungiella research is supported by a growing body of technical resources comprising physiological and molecular protocols, ecotype collections, expressed sequence tags, cDNA-libraries, microarrays, and a pending genome sequence. This review summarizes the current state of knowledge on Thellungiella and re-evaluates its usefulness as a model for research into plant stress tolerance.
基金from National Natural Science Foundation of China(41776135,41976142)Nature Science funds for Distinguished Young Scholars of Fujian Province,China(2017J07003)to YWD.
文摘Realistic assessments of the impacts of global warming on population extinction risk are likely to require an integrated analysis of the roles of standing genetic variation,microhabitat thermal complexity,and the inter-individual variation of heat tolerance due to both genetic differences and seasonal acclimatization effects.Here,we examine whether balancing selection and microhabitat temperature heterogeneity can interact to enhance the population persistence to thermal stress for the black mussel Septifer virgatus.We deployed biomimetic data loggers on the shore to measure the microhabitat-specific thermal variation from June 2014 to April 2016.Thermal tolerance of specimens was indexed by measuring effects of temperature on heart rate.Genotyping of specimens was performed using double digestion restriction association RADSeq(ddRADseq).Our results show that inter-individual variations in thermal tolerance correlate significantly with genetic differences at some specific gene loci,and that heterozygotes have higher thermal tolerances than homozygotes.The observed seasonal changes in genotype frequency suggest that these loci are under balancing selection.The ability of thermally resistant heterozygotes to survive in sun-exposed microhabitats acts to balance the loss of homozygotes during summer and enable the persistence of genetic polymorphisms.Population persistence of the mussel is also facilitated by the micro-scale variation in temperature,which provides refugia from thermal stress.Our results emphasize that inter-individual variation in thermal tolerance and in microhabitat heterogeneity in temperature are important for the persistence of populations in rocky shore habitats.
文摘“Carpe diem”(enjoy the day)is a famous quotation of the Roman poet Horace,and these two words in a complementary way refer both to the logistics and to the semantics of human time(Zhao et al.,2022).Every day is an evolutionary adaptation to the geophysical cycle of 24 hours;this circadian rhythm(Aschoff,1965)is an example of the logistics of human time together with other temporal phenomena having shorter durations,as reflected in“time windows”(Bao et al.,2015).The logistic basis of temporal processing in human behavior is the necessary but not sufficient basis of how we(hopefully)enjoy the day,how we use our time efficiently,how we give meaning to our personal time,and how we experience temporal phenomena.The semantics of human time is reflected in temporal satisfaction or dissatisfaction,in the joy of having achieved something or the frustration of failure,and also in procrastination,in the experienced scarcity of time,or in boredom.
基金supported by grants from the Taishan Scholar Project Special Funds of China(Grant No.tsqnz20231206)the National Key Research and Development Program of China(2023YFD2301000,2022YFD2100100)Natural Science Foundation of Shandong Province(ZR2024QC255,2023CXGC010709).
文摘Malate metabolism bridges plant evolutionary adaptation and fruit quality regulation,serving dual roles in energy metabolism(tricarboxylic acid cycle/glycolysis)and environmental stress responses(stomatal control,pH balance).In horticulture,apple malate content dictates flavor profiles,driving divergent consumer preferences(high-sugar in Asia vs.tartness in the West),necessitating precision breeding targeting vacuolar accumulation mechanisms.Recent bioinformatic studies and transporter biology(e.g.,Ma1,ALMT)have revealed genetic regulators of malate homeostasis,yet transcriptional regulation and post-translational modifications(PTMs)of transporters remain poorly understood.Notably,cultivated varieties exhibit distinct malate-related traits compared to their wild relatives,a divergence attributable to artificial selection during domestication.Additionally,agroecological factors including light,temperature,and soil conditions,dynamically regulate malate biosynthesis and storage.This metabolic plasticity reflects evolutionary adaptations influenced by domestication.This review integrates molecular physiology and domestication genetics to dissect cross-scale regulation of malate networks.We propose a transporter-engineering framework for developing market-tailored varieties and highlight unresolved questions,including PTM-mediated transporter regulation and metabolic plasticity modeling for climate-resilient crops.Bridging evolutionary adaptation with quality-driven breeding targeting malate,this synthesis advances strategies for sustainable horticulture in shifting agroclimatic landscapes.
