One approach to understand the importance of reproductive barriers to the speciation process is to study the break- down of barriers between formerly distinct species. One reproductive barrier, sexual isolation, reduc...One approach to understand the importance of reproductive barriers to the speciation process is to study the break- down of barriers between formerly distinct species. One reproductive barrier, sexual isolation, reduces gene flow between species through differences in mate preferences and mating signals and is likely important for species formation and maintenance. We measure sexual isolation in two limnetic-benthic threespine stickleback species pairs （Gasterosteus spp.）. One species pair main- tains strong reproductive isolation while the other species pair has recently collapsed into a hybrid swarm. We compare the strength of sexual isolation in the hybridizing pair to the currently isolated pair. We provide the first evidence that sexual isolation has been lost in the hybridizing pair and show furthermore that preferences females have for conspecific mates and the traits they use to distinguish conspecific and heterospecific males contribute to this loss. This work highlights the fragility of reproductive isolation between young species pairs and considers the role of sexual isolation in speciation [Current Zoology 59 （5）： 591-603, 2013].展开更多
Many studies on fiber reinforced polymer composite bars, as a substitute for reinforcing bars, have been conducted to solve corrosion of steel in reinforced concrete structures since 1960s’. However, FRP Bars have a ...Many studies on fiber reinforced polymer composite bars, as a substitute for reinforcing bars, have been conducted to solve corrosion of steel in reinforced concrete structures since 1960s’. However, FRP Bars have a lower elastic modulus than steel rebar as a structural component of concrete structures. Material properties with brittleness fracture and low elastic modulus can be improved by combining cheaper steel than carbon or aramid fibers. In this study, prototypes of FRP Bars with inserted steel wires (i.e., “FRP Hybrid Bars”) were developed and their tensile performance was compared depending on the proportion and diameter of steel. The FRP Hybrid Bars were made by dividing them into D13 and D16 according to the diameter and proportion of inserted wires: GFRPs were combined with wires having different diameters of 0.5 mm, 1.0 mm, and 2.0 mm in the proportion of 10%, 30%, 50%, and 70%, respectively. As a result of tensile tests, the elastic modulus of FRP Hybrid Bars were improved as 20% - 190% in comparison with the fully GFRP Bars.展开更多
Correlations among behavioral traits can generate trade-offs and constrain phenotypic evolution. Interspecific hybridization has the potential to alter behavioral trait correlations, but the effect of hybridization on...Correlations among behavioral traits can generate trade-offs and constrain phenotypic evolution. Interspecific hybridization has the potential to alter behavioral trait correlations, but the effect of hybridization on suites of behavioral traits has received relatively little attention. We evaluated how natural hybridization changes the relationship between boldness （time of emergence and proportion of time out of shelter） and response to a simulated predator threat in swordtails （Teleostei： Xiphophorus）. In poeciliid fishes, bold individuals have increased survival in the presence of predators. This non-intuitive observation may arise as a result of bold individuals being more likely to engage in anti-predator behaviors. Contrary to our prediction, bold indi- viduals were less likely to perform a fast-start response to a predator threat. This correlation was consistent among populations and species but was only significant in hybrids. The observed correlation between boldness and anti-predator behavior could im- pact hybrid fitness and the evolvability of hybrid lineages. More generally, our findings suggest that hybridization could influence the integration of behavioral phenotypes, as has been amply documented for morphology. Animal personality and behavioral syndromes could therefore play an important role in the evolutionary fate of natural hybrids [Current Zoology 61 （4）： 596-603, 2015].展开更多
The closely related Black-headed Bunting(Emberiza melanocephala,a western Palearctic lineage)and Red-headed Bunting(Emberiza bruniceps,an eastern Palearctic lineage)hybridize and replace each other south of the Caspia...The closely related Black-headed Bunting(Emberiza melanocephala,a western Palearctic lineage)and Red-headed Bunting(Emberiza bruniceps,an eastern Palearctic lineage)hybridize and replace each other south of the Caspian Sea.The parental species have distinct phenotypes and therefore morphology is useful for assessing hybridization in the contact zone.In the years of 1940 and 1977,quite a few hybrids were collected and studied morphologically.Since then,the hybrid zone appears to have expanded westwards,but there has been a time gap in the collection of morphological data.Here we reanalyze bunting specimens morphologically and compare the historical data with recent data.Morphometric and phenotypic traits from three time periods(1940,1977 and recent)were studied to assess phenotypic variation of hybrids,pattern of hybridization,and transgressive traits in the hybrid zone.Our results show that most of the birds in the hybrid zone exhibit intermediate phenotypes(both colors and morphometric characters),ranging from the pure phenotype of either of the parental species.However,hybridization has also produced novel phenotypes not seen in any of the parents.Using a canonical discriminant function analysis,the morphometric characters separated each parental species and the hybrids quite well.Our results showed morphometric intermediacy of hybrids in accordance with phenotypes.We observe a time trend in which recent hybrids are more similar to Red-headed Buntings phenotypically compared to historical samples.This pattern is likely a signature of a westward expansion of the Red-headed Bunting into the breeding range of the Black-headed Bunting.展开更多
FeSiBCuNb/FeNi composite magnetic powder cores(CMPCs)with outstanding high-frequency permeability and low core loss(P_(cv))have been developed by hybridizing HNO 3-passivated Fe_(73.5)Cu_(1)Nb_(3)Si_(13.5)B_(9) nanocr...FeSiBCuNb/FeNi composite magnetic powder cores(CMPCs)with outstanding high-frequency permeability and low core loss(P_(cv))have been developed by hybridizing HNO 3-passivated Fe_(73.5)Cu_(1)Nb_(3)Si_(13.5)B_(9) nanocrystalline powder with finer Fe 50Ni 50 powder.A 7 wt.%HNO 3 passivation treatment forms a Fe_(3)O_(4)-dominant oxidation layer on the nanocrystalline powder surface,which increases its electrical resistivity and reduces the P_(cv) of the power cores.As FeNi content increases from 0 to 40 wt.%,the porosity of the CMPCs decreases consistently,while saturation magnetization(M_(s)),effective permeability(μ_(e)),and P_(cv) gradually enhance.An appropriate increase in compaction pressure further decreases the porosity and leads to enhanced M s,μe,and lowered P_(cv).The CMPC with 40 wt.%FeNi,compacted at 1000 MPa,exhibits the best comprehensive soft magnetic properties with M s,μe,and P_(cv) at 50 mT/200 kHz of 136.3 emu/g,57.4,and 599 mW/cm^(3),respectively.The improved M_(s) andμe result from the reduced porosity combined with the FeNi’s inherently higher M_(s) and μ_(e).The increased high-frequency P_(cv) after FeNi addition mainly arises from the raised eddy loss due to decreased electrical resistivity.The reduced P_(cv) through the optimal compaction pressure is due to the further elimination of pore defects in the CMPCs.展开更多
In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honey...In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.展开更多
Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r...Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.展开更多
In order to address environmental pollution and resource depletion caused by traditional power generation,this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved D...In order to address environmental pollution and resource depletion caused by traditional power generation,this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved Dung Beetle Optimizer(IDBO)with VariationalMode Decomposition(VMD).The IDBO-VMD method is designed to enhance the accuracy and efficiency of wind-speed time-series decomposition and to effectively smooth photovoltaic power fluctuations.This study innovatively improves the traditional variational mode decomposition(VMD)algorithm,and significantly improves the accuracy and adaptive ability of signal decomposition by IDBO selfoptimization of key parameters K and a.On this basis,Fourier transform technology is used to define the boundary point between high frequency and low frequency signals,and a targeted energy distribution strategy is proposed:high frequency fluctuations are allocated to supercapacitors to quickly respond to transient power fluctuations;Lowfrequency components are distributed to lead-carbon batteries,optimizing long-term energy storage and scheduling efficiency.This strategy effectively improves the response speed and stability of the energy storage system.The experimental results demonstrate that the IDBO-VMD algorithm markedly outperforms traditional methods in both decomposition accuracy and computational efficiency.Specifically,it effectively reduces the charge–discharge frequency of the battery,prolongs battery life,and optimizes the operating ranges of the state-of-charge(SOC)for both leadcarbon batteries and supercapacitors.In addition,the energy management strategy based on the algorithm not only improves the overall energy utilization efficiency of the system,but also shows excellent performance in the dynamic management and intelligent scheduling of renewable energy generation.展开更多
Carbon superstructures with multiscale hierarchies and functional attributes represent an appealing cathode candidate for zinc hybrid capacitors,but their tailor-made design to optimize the capacitive activity remains...Carbon superstructures with multiscale hierarchies and functional attributes represent an appealing cathode candidate for zinc hybrid capacitors,but their tailor-made design to optimize the capacitive activity remains a confusing topic.Here we develop a hydrogen-bond-oriented interfacial super-assembly strategy to custom-tailor nanosheet-intertwined spherical carbon superstructures(SCSs)for Zn-ion storage with double-high capacitive activity and durability.Tetrachlorobenzoquinone(H-bond acceptor)and dimethylbenzidine(H-bond donator)can interact to form organic nanosheet modules,which are sequentially assembled,orientally compacted and densified into well-orchestrated superstructures through multiple H-bonds(N-H···O).Featured with rich surface-active heterodiatomic motifs,more exposed nanoporous channels,and successive charge migration paths,SCSs cathode promises high accessibility of built-in zincophilic sites and rapid ion diffusion with low energy barriers(3.3Ωs-0.5).Consequently,the assembled Zn||SCSs capacitor harvests all-round improvement in Zn-ion storage metrics,including high energy density(166 Wh kg-1),high-rate performance(172 m Ah g^(-1)at 20 A g^(-1)),and long-lasting cycling lifespan(95.5%capacity retention after 500,000 cycles).An opposite chargecarrier storage mechanism is rationalized for SCSs cathode to maximize spatial capacitive charge storage,involving high-kinetics physical Zn^(2+)/CF_(3)SO_(3)-adsorption and chemical Zn^(2+)redox with carbonyl/pyridine groups.This work gives insights into H-bond-guided interfacial superassembly design of superstructural carbons toward advanced energy storage.展开更多
This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 20...This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 2025.The primary objective is to evaluate methodological advancements,model performance,dataset usage,and existing challenges in developing clinically robust AI systems.We included peer-reviewed journal articles and highimpact conference papers published between 2022 and 2025,written in English,that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification.Excluded were non-open-access publications,books,and non-English articles.A structured search was conducted across Scopus,Google Scholar,Wiley,and Taylor&Francis,with the last search performed in August 2025.Risk of bias was not formally quantified but considered during full-text screening based on dataset diversity,validation methods,and availability of performance metrics.We used narrative synthesis and tabular benchmarking to compare performance metrics(e.g.,accuracy,Dice score)across model types(CNN,Transformer,Hybrid),imaging modalities,and datasets.A total of 49 studies were included(43 journal articles and 6 conference papers).These studies spanned over 9 public datasets(e.g.,BraTS,Figshare,REMBRANDT,MOLAB)and utilized a range of imaging modalities,predominantly MRI.Hybrid models,especially ResViT and UNetFormer,consistently achieved high performance,with classification accuracy exceeding 98%and segmentation Dice scores above 0.90 across multiple studies.Transformers and hybrid architectures showed increasing adoption post2023.Many studies lacked external validation and were evaluated only on a few benchmark datasets,raising concerns about generalizability and dataset bias.Few studies addressed clinical interpretability or uncertainty quantification.Despite promising results,particularly for hybrid deep learning models,widespread clinical adoption remains limited due to lack of validation,interpretability concerns,and real-world deployment barriers.展开更多
In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail ...In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail to accurately characterize the complex influence of marine environments.To overcome these challenges,we propose an acoustic physics-informed intelligent path planning framework for underwater target search,integrating three core modules:The acoustic-physical modeling module adopts 3D ray-tracing theory and the active sonar equation to construct a physics-driven sonar detection model,explicitly accounting for environmental factors that influence sonar performance across heterogeneous spaces.The hybrid parallel computing module adopts a message passing interface(MPI)/open multi-processing(Open MP)hybrid strategy for large-scale acoustic simulations,combining computational domain decomposition and physics-intensive task acceleration.The search path optimization module adopts the covariance matrix adaptation evolution algorithm to solve continuous optimization problems of heading angles,which ensures maximum search coverage for targets.Largescale experiments conducted in the Pacific and Atlantic Oceans demonstrate the framework's effectiveness:(1)Precise capture of sonar detection range variations from 5.45 km to 50 km in heterogeneous marine environments.(2)Significant speedup of 453.43×for acoustic physics modeling through hybrid parallelization.(3)Notable improvements of 7.23%in detection coverage and 15.86%reduction in optimization time compared to the optimal baseline method.The framework provides a robust solution for underwater search missions in complex marine environments.展开更多
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal...Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.展开更多
Co_(3)S_(4)electrocatalysts with mixed valences of Co ions and excellent structural stability possess favorable oxygen evolution reaction(OER)activity,yet challenges remain in fabricating rechargeable lithiumoxygen ba...Co_(3)S_(4)electrocatalysts with mixed valences of Co ions and excellent structural stability possess favorable oxygen evolution reaction(OER)activity,yet challenges remain in fabricating rechargeable lithiumoxygen batteries(LOBs)due to their poor OER performance,resulting from poor electrical conductivity and overly strong intermediate adsorption.In this work,fancy double heterojunctions on 1T/2H-MoS_(2)@Co_(3)S_(4)(1T/2H-MCS)were constructed derived from the charge donation from Co to Mo ions,thus inducing the phase transformation of Mo S_(2)from 2H to 1T.The unique features of these double heterojunctions endow the1T/2H-MCS with complementary catalysis during charging and discharging processes.It is worth noting that 1T-Mo S2@Co3S4could provide fast Co-S-Mo electron transport channels to promote ORR/OER kinetics,and 2H-MoS_(2)@Co_(3)S_(4)contributed to enabling moderate egorbital occupancy when adsorbed with oxygen-containing intermediates.On the basis,the Li_(2)O_(2)nucleation route was changed to solution and surface dual pathways,improving reversible deposition and decomposition kinetics.As a result,1T/2H-MCS cathodes exhibit an improved electrocatalytic performance compared with those of Co_(3)S_(4)and Mo S2cathodes.This innovative heterostructure design provides a reliable strategy to construct efficient transition metal sulfide catalysts by improving electrical conductivity and modulating adsorption toward oxygenated intermediates for LOBs.展开更多
The hybridization gap in strained-layer InAs/In_(x)Ga_(1−x) Sb quantum spin Hall insulators(QSHIs)is significantly enhanced compared to binary InAs/GaSb QSHI structures,where the typical indium composition,x,ranges be...The hybridization gap in strained-layer InAs/In_(x)Ga_(1−x) Sb quantum spin Hall insulators(QSHIs)is significantly enhanced compared to binary InAs/GaSb QSHI structures,where the typical indium composition,x,ranges between 0.2 and 0.4.This enhancement prompts a critical question:to what extent can quantum wells(QWs)be strained while still preserving the fundamental QSHI phase?In this study,we demonstrate the controlled molecular beam epitaxial growth of highly strained-layer QWs with an indium composition of x=0.5.These structures possess a substantial compressive strain within the In_(0.5)Ga_(0.5)Sb QW.Detailed crystal structure analyses confirm the exceptional quality of the resulting epitaxial films,indicating coherent lattice structures and the absence of visible dislocations.Transport measurements further reveal that the QSHI phase in InAs/In_(0.5)Ga_(0.5)Sb QWs is robust and protected by time-reversal symmetry.Notably,the edge states in these systems exhibit giant magnetoresistance when subjected to a modest perpendicular magnetic field.This behavior is in agreement with the𝑍2 topological property predicted by the Bernevig–Hughes–Zhang model,confirming the preservation of topologically protected edge transport in the presence of enhanced bulk strain.展开更多
To enhance power flow regulation in scenarios involving large-scale renewable energy transmission via high-voltage direct current(HVDC)links and multi-infeed DC systems in load-center regions,this paper proposes a hyb...To enhance power flow regulation in scenarios involving large-scale renewable energy transmission via high-voltage direct current(HVDC)links and multi-infeed DC systems in load-center regions,this paper proposes a hybrid modular multilevel converter–capacitor-commutated line-commutated converter(MMC-CLCC)HVDC transmission system and its corresponding control strategy.First,the system topology is constructed,and a submodule configuration method for the MMC—combining full-bridge submodules(FBSMs)and half-bridge submodules(HBSMs)—is proposed to enable direct power flow reversal.Second,a hierarchical control strategy is introduced,includingMMCvoltage control,CLCC current control,and a coordinationmechanism,along with the derivation of the hybrid system’s power flow reversal characteristics.Third,leveraging the CLCC’s fast current regulation and theMMC’s negative voltage control capability,a coordinated power flow reversal control strategy is developed.Finally,an 800 kV MMC-CLCC hybrid HVDC system is modeled in PSCAD/EMTDC to validate the power flow reversal performance under a high proportion of full-bridge submodule configuration.Results demonstrate that the proposed control strategy enables rapid(1-s transition)and smooth switching of bidirectional power flow without modifying the structure of primary equipment:the transient fluctuation ofDC voltage from the rated value(UdcN)to themaximumreverse voltage(-kUdcN)is less than 5%;the DC current strictly follows the preset characteristic curve with a deviation of≤3%;the active power reverses continuously,and the system maintains stable operation throughout the reversal process.展开更多
Supercapacitors are gaining popularity due to their high cycling stability,power density,and fast charge and discharge rates.Researchers are ex-ploring electrode materials,electrolytes,and separat-ors for cost-effecti...Supercapacitors are gaining popularity due to their high cycling stability,power density,and fast charge and discharge rates.Researchers are ex-ploring electrode materials,electrolytes,and separat-ors for cost-effective energy storage systems.Ad-vances in materials science have led to the develop-ment of hybrid nanomaterials,such as combining fil-amentous carbon forms with inorganic nanoparticles,to create new charge and energy transfer processes.Notable materials for electrochemical energy-stor-age applications include MXenes,2D transition met-al carbides,and nitrides,carbon black,carbon aerogels,activated carbon,carbon nanotubes,conducting polymers,carbon fibers,and nanofibers,and graphene,because of their thermal,electrical,and mechanical properties.Carbon materials mixed with conducting polymers,ceramics,metal oxides,transition metal oxides,metal hydroxides,transition metal sulfides,trans-ition metal dichalcogenide,metal sulfides,carbides,nitrides,and biomass materials have received widespread attention due to their remarkable performance,eco-friendliness,cost-effectiveness,and renewability.This article explores the development of carbon-based hybrid materials for future supercapacitors,including electric double-layer capacitors,pseudocapacitors,and hy-brid supercapacitors.It investigates the difficulties that influence structural design,manufacturing(electrospinning,hydro-thermal/solvothermal,template-assisted synthesis,electrodeposition,electrospray,3D printing)techniques and the latest car-bon-based hybrid materials research offer practical solutions for producing high-performance,next-generation supercapacitors.展开更多
基金Acknowledgements We thank C. Long and M. Rounds for help with data collection. Thanks to Tom Getty, Genevieve Kozak, Michael Jennions, several anonymous reviewers, and the Boughman lab for helping to improve this manuscript. Research was conducted under permits from the Ministry of the Environment, BC and approval from University of Wis- consin-Madison Institutional Animal Care and Use Committee. This work was supported by the Emlen Fund from the Zoolo- gy Department of the University of Wisconsin, Madison to ACRL and the National Science Foundation to JWB.
文摘One approach to understand the importance of reproductive barriers to the speciation process is to study the break- down of barriers between formerly distinct species. One reproductive barrier, sexual isolation, reduces gene flow between species through differences in mate preferences and mating signals and is likely important for species formation and maintenance. We measure sexual isolation in two limnetic-benthic threespine stickleback species pairs （Gasterosteus spp.）. One species pair main- tains strong reproductive isolation while the other species pair has recently collapsed into a hybrid swarm. We compare the strength of sexual isolation in the hybridizing pair to the currently isolated pair. We provide the first evidence that sexual isolation has been lost in the hybridizing pair and show furthermore that preferences females have for conspecific mates and the traits they use to distinguish conspecific and heterospecific males contribute to this loss. This work highlights the fragility of reproductive isolation between young species pairs and considers the role of sexual isolation in speciation [Current Zoology 59 （5）： 591-603, 2013].
文摘Many studies on fiber reinforced polymer composite bars, as a substitute for reinforcing bars, have been conducted to solve corrosion of steel in reinforced concrete structures since 1960s’. However, FRP Bars have a lower elastic modulus than steel rebar as a structural component of concrete structures. Material properties with brittleness fracture and low elastic modulus can be improved by combining cheaper steel than carbon or aramid fibers. In this study, prototypes of FRP Bars with inserted steel wires (i.e., “FRP Hybrid Bars”) were developed and their tensile performance was compared depending on the proportion and diameter of steel. The FRP Hybrid Bars were made by dividing them into D13 and D16 according to the diameter and proportion of inserted wires: GFRPs were combined with wires having different diameters of 0.5 mm, 1.0 mm, and 2.0 mm in the proportion of 10%, 30%, 50%, and 70%, respectively. As a result of tensile tests, the elastic modulus of FRP Hybrid Bars were improved as 20% - 190% in comparison with the fully GFRP Bars.
基金We would like to thank the Mexican federal government and the state of Hidalgo for providing permits to collect fish and Nick Ratterman, Kirk Winemiller and Lee Fitzgerald for helpful comments on early versions of this manuscript. This work was supported by funding provided by a National Science Foundation grant （I0S-0923825） award- ed to G.G.R.R.E. was supported by an Undergraduate Program in Biological and Mathematical Sciences （UBM National Science Foundation grant （DBI-1029401） directed by Dr. Jay Walton. All experiments conducted in this study complied with current state, federal, and local laws in the United States and Mexico.
文摘Correlations among behavioral traits can generate trade-offs and constrain phenotypic evolution. Interspecific hybridization has the potential to alter behavioral trait correlations, but the effect of hybridization on suites of behavioral traits has received relatively little attention. We evaluated how natural hybridization changes the relationship between boldness （time of emergence and proportion of time out of shelter） and response to a simulated predator threat in swordtails （Teleostei： Xiphophorus）. In poeciliid fishes, bold individuals have increased survival in the presence of predators. This non-intuitive observation may arise as a result of bold individuals being more likely to engage in anti-predator behaviors. Contrary to our prediction, bold indi- viduals were less likely to perform a fast-start response to a predator threat. This correlation was consistent among populations and species but was only significant in hybrids. The observed correlation between boldness and anti-predator behavior could im- pact hybrid fitness and the evolvability of hybrid lineages. More generally, our findings suggest that hybridization could influence the integration of behavioral phenotypes, as has been amply documented for morphology. Animal personality and behavioral syndromes could therefore play an important role in the evolutionary fate of natural hybrids [Current Zoology 61 （4）： 596-603, 2015].
基金support from the Shiraz University(during 2015-2022)Ferdowsi University of Mashhad,Iran(during 2011-2013)the Department of Environment(DOE)for giving permission to AG for sampling(91.51843)。
文摘The closely related Black-headed Bunting(Emberiza melanocephala,a western Palearctic lineage)and Red-headed Bunting(Emberiza bruniceps,an eastern Palearctic lineage)hybridize and replace each other south of the Caspian Sea.The parental species have distinct phenotypes and therefore morphology is useful for assessing hybridization in the contact zone.In the years of 1940 and 1977,quite a few hybrids were collected and studied morphologically.Since then,the hybrid zone appears to have expanded westwards,but there has been a time gap in the collection of morphological data.Here we reanalyze bunting specimens morphologically and compare the historical data with recent data.Morphometric and phenotypic traits from three time periods(1940,1977 and recent)were studied to assess phenotypic variation of hybrids,pattern of hybridization,and transgressive traits in the hybrid zone.Our results show that most of the birds in the hybrid zone exhibit intermediate phenotypes(both colors and morphometric characters),ranging from the pure phenotype of either of the parental species.However,hybridization has also produced novel phenotypes not seen in any of the parents.Using a canonical discriminant function analysis,the morphometric characters separated each parental species and the hybrids quite well.Our results showed morphometric intermediacy of hybrids in accordance with phenotypes.We observe a time trend in which recent hybrids are more similar to Red-headed Buntings phenotypically compared to historical samples.This pattern is likely a signature of a westward expansion of the Red-headed Bunting into the breeding range of the Black-headed Bunting.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFB3804100)the National Natural Science Foundation of China(Grant Nos.52171153,and 52371149).
文摘FeSiBCuNb/FeNi composite magnetic powder cores(CMPCs)with outstanding high-frequency permeability and low core loss(P_(cv))have been developed by hybridizing HNO 3-passivated Fe_(73.5)Cu_(1)Nb_(3)Si_(13.5)B_(9) nanocrystalline powder with finer Fe 50Ni 50 powder.A 7 wt.%HNO 3 passivation treatment forms a Fe_(3)O_(4)-dominant oxidation layer on the nanocrystalline powder surface,which increases its electrical resistivity and reduces the P_(cv) of the power cores.As FeNi content increases from 0 to 40 wt.%,the porosity of the CMPCs decreases consistently,while saturation magnetization(M_(s)),effective permeability(μ_(e)),and P_(cv) gradually enhance.An appropriate increase in compaction pressure further decreases the porosity and leads to enhanced M s,μe,and lowered P_(cv).The CMPC with 40 wt.%FeNi,compacted at 1000 MPa,exhibits the best comprehensive soft magnetic properties with M s,μe,and P_(cv) at 50 mT/200 kHz of 136.3 emu/g,57.4,and 599 mW/cm^(3),respectively.The improved M_(s) andμe result from the reduced porosity combined with the FeNi’s inherently higher M_(s) and μ_(e).The increased high-frequency P_(cv) after FeNi addition mainly arises from the raised eddy loss due to decreased electrical resistivity.The reduced P_(cv) through the optimal compaction pressure is due to the further elimination of pore defects in the CMPCs.
基金the financial supports from National Key R&D Program for Young Scientists of China(Grant No.2022YFC3080900)National Natural Science Foundation of China(Grant No.52374181)+1 种基金BIT Research and Innovation Promoting Project(Grant No.2024YCXZ017)supported by Science and Technology Innovation Program of Beijing institute of technology under Grant No.2022CX01025。
文摘In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.
文摘Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.
基金funded by the Institute of Smart Energy,Huaiyin Institute of Technology,under Grant No.HIT-ISE-2024-07.
文摘In order to address environmental pollution and resource depletion caused by traditional power generation,this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved Dung Beetle Optimizer(IDBO)with VariationalMode Decomposition(VMD).The IDBO-VMD method is designed to enhance the accuracy and efficiency of wind-speed time-series decomposition and to effectively smooth photovoltaic power fluctuations.This study innovatively improves the traditional variational mode decomposition(VMD)algorithm,and significantly improves the accuracy and adaptive ability of signal decomposition by IDBO selfoptimization of key parameters K and a.On this basis,Fourier transform technology is used to define the boundary point between high frequency and low frequency signals,and a targeted energy distribution strategy is proposed:high frequency fluctuations are allocated to supercapacitors to quickly respond to transient power fluctuations;Lowfrequency components are distributed to lead-carbon batteries,optimizing long-term energy storage and scheduling efficiency.This strategy effectively improves the response speed and stability of the energy storage system.The experimental results demonstrate that the IDBO-VMD algorithm markedly outperforms traditional methods in both decomposition accuracy and computational efficiency.Specifically,it effectively reduces the charge–discharge frequency of the battery,prolongs battery life,and optimizes the operating ranges of the state-of-charge(SOC)for both leadcarbon batteries and supercapacitors.In addition,the energy management strategy based on the algorithm not only improves the overall energy utilization efficiency of the system,but also shows excellent performance in the dynamic management and intelligent scheduling of renewable energy generation.
基金financially supported by the National Natural Science Foundation of China(Nos.22272118,22172111,and 22309134)the Science and Technology Commission of Shanghai Municipality,China(Nos.22ZR1464100,20ZR1460300,and 19DZ2271500)+2 种基金the China Postdoctoral Science Foundation(2022M712402),the Shanghai Rising-Star Program(23YF1449200)the Zhejiang Provincial Science and Technology Project(2022C01182)the Fundamental Research Funds for the Central Universities(2023-3-YB-07)。
文摘Carbon superstructures with multiscale hierarchies and functional attributes represent an appealing cathode candidate for zinc hybrid capacitors,but their tailor-made design to optimize the capacitive activity remains a confusing topic.Here we develop a hydrogen-bond-oriented interfacial super-assembly strategy to custom-tailor nanosheet-intertwined spherical carbon superstructures(SCSs)for Zn-ion storage with double-high capacitive activity and durability.Tetrachlorobenzoquinone(H-bond acceptor)and dimethylbenzidine(H-bond donator)can interact to form organic nanosheet modules,which are sequentially assembled,orientally compacted and densified into well-orchestrated superstructures through multiple H-bonds(N-H···O).Featured with rich surface-active heterodiatomic motifs,more exposed nanoporous channels,and successive charge migration paths,SCSs cathode promises high accessibility of built-in zincophilic sites and rapid ion diffusion with low energy barriers(3.3Ωs-0.5).Consequently,the assembled Zn||SCSs capacitor harvests all-round improvement in Zn-ion storage metrics,including high energy density(166 Wh kg-1),high-rate performance(172 m Ah g^(-1)at 20 A g^(-1)),and long-lasting cycling lifespan(95.5%capacity retention after 500,000 cycles).An opposite chargecarrier storage mechanism is rationalized for SCSs cathode to maximize spatial capacitive charge storage,involving high-kinetics physical Zn^(2+)/CF_(3)SO_(3)-adsorption and chemical Zn^(2+)redox with carbonyl/pyridine groups.This work gives insights into H-bond-guided interfacial superassembly design of superstructural carbons toward advanced energy storage.
文摘This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 2025.The primary objective is to evaluate methodological advancements,model performance,dataset usage,and existing challenges in developing clinically robust AI systems.We included peer-reviewed journal articles and highimpact conference papers published between 2022 and 2025,written in English,that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification.Excluded were non-open-access publications,books,and non-English articles.A structured search was conducted across Scopus,Google Scholar,Wiley,and Taylor&Francis,with the last search performed in August 2025.Risk of bias was not formally quantified but considered during full-text screening based on dataset diversity,validation methods,and availability of performance metrics.We used narrative synthesis and tabular benchmarking to compare performance metrics(e.g.,accuracy,Dice score)across model types(CNN,Transformer,Hybrid),imaging modalities,and datasets.A total of 49 studies were included(43 journal articles and 6 conference papers).These studies spanned over 9 public datasets(e.g.,BraTS,Figshare,REMBRANDT,MOLAB)and utilized a range of imaging modalities,predominantly MRI.Hybrid models,especially ResViT and UNetFormer,consistently achieved high performance,with classification accuracy exceeding 98%and segmentation Dice scores above 0.90 across multiple studies.Transformers and hybrid architectures showed increasing adoption post2023.Many studies lacked external validation and were evaluated only on a few benchmark datasets,raising concerns about generalizability and dataset bias.Few studies addressed clinical interpretability or uncertainty quantification.Despite promising results,particularly for hybrid deep learning models,widespread clinical adoption remains limited due to lack of validation,interpretability concerns,and real-world deployment barriers.
基金supported by Natural Science Foundation of Hu'nan Province(2024JJ5409)。
文摘In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail to accurately characterize the complex influence of marine environments.To overcome these challenges,we propose an acoustic physics-informed intelligent path planning framework for underwater target search,integrating three core modules:The acoustic-physical modeling module adopts 3D ray-tracing theory and the active sonar equation to construct a physics-driven sonar detection model,explicitly accounting for environmental factors that influence sonar performance across heterogeneous spaces.The hybrid parallel computing module adopts a message passing interface(MPI)/open multi-processing(Open MP)hybrid strategy for large-scale acoustic simulations,combining computational domain decomposition and physics-intensive task acceleration.The search path optimization module adopts the covariance matrix adaptation evolution algorithm to solve continuous optimization problems of heading angles,which ensures maximum search coverage for targets.Largescale experiments conducted in the Pacific and Atlantic Oceans demonstrate the framework's effectiveness:(1)Precise capture of sonar detection range variations from 5.45 km to 50 km in heterogeneous marine environments.(2)Significant speedup of 453.43×for acoustic physics modeling through hybrid parallelization.(3)Notable improvements of 7.23%in detection coverage and 15.86%reduction in optimization time compared to the optimal baseline method.The framework provides a robust solution for underwater search missions in complex marine environments.
文摘Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.
基金financially supported by the National Natural Science Foundation of China(U21A20311,U24A2040,52171141,52272117)the Natural Science Foundation of Shandong Province(ZR2022JQ19)+3 种基金the Key Technology Research Project of Shandong Province(2023CXGC010202)the Taishan Industrial Experts Program(TSCX202306142)the Core Facility Sharing Platform of Shandong Universitythe Foundation of Key Laboratory of Advanced Energy Materials Chemistry(Ministry of Education),Nankai University。
文摘Co_(3)S_(4)electrocatalysts with mixed valences of Co ions and excellent structural stability possess favorable oxygen evolution reaction(OER)activity,yet challenges remain in fabricating rechargeable lithiumoxygen batteries(LOBs)due to their poor OER performance,resulting from poor electrical conductivity and overly strong intermediate adsorption.In this work,fancy double heterojunctions on 1T/2H-MoS_(2)@Co_(3)S_(4)(1T/2H-MCS)were constructed derived from the charge donation from Co to Mo ions,thus inducing the phase transformation of Mo S_(2)from 2H to 1T.The unique features of these double heterojunctions endow the1T/2H-MCS with complementary catalysis during charging and discharging processes.It is worth noting that 1T-Mo S2@Co3S4could provide fast Co-S-Mo electron transport channels to promote ORR/OER kinetics,and 2H-MoS_(2)@Co_(3)S_(4)contributed to enabling moderate egorbital occupancy when adsorbed with oxygen-containing intermediates.On the basis,the Li_(2)O_(2)nucleation route was changed to solution and surface dual pathways,improving reversible deposition and decomposition kinetics.As a result,1T/2H-MCS cathodes exhibit an improved electrocatalytic performance compared with those of Co_(3)S_(4)and Mo S2cathodes.This innovative heterostructure design provides a reliable strategy to construct efficient transition metal sulfide catalysts by improving electrical conductivity and modulating adsorption toward oxygenated intermediates for LOBs.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant Nos.XDB28000000 and XDB0460000)the Quantum Science and Technology-National Science and Technology Major Project (Grant No.2021ZD0302600)the National Key Research and Development Program of China(Grant No.2024YFA1409002)。
文摘The hybridization gap in strained-layer InAs/In_(x)Ga_(1−x) Sb quantum spin Hall insulators(QSHIs)is significantly enhanced compared to binary InAs/GaSb QSHI structures,where the typical indium composition,x,ranges between 0.2 and 0.4.This enhancement prompts a critical question:to what extent can quantum wells(QWs)be strained while still preserving the fundamental QSHI phase?In this study,we demonstrate the controlled molecular beam epitaxial growth of highly strained-layer QWs with an indium composition of x=0.5.These structures possess a substantial compressive strain within the In_(0.5)Ga_(0.5)Sb QW.Detailed crystal structure analyses confirm the exceptional quality of the resulting epitaxial films,indicating coherent lattice structures and the absence of visible dislocations.Transport measurements further reveal that the QSHI phase in InAs/In_(0.5)Ga_(0.5)Sb QWs is robust and protected by time-reversal symmetry.Notably,the edge states in these systems exhibit giant magnetoresistance when subjected to a modest perpendicular magnetic field.This behavior is in agreement with the𝑍2 topological property predicted by the Bernevig–Hughes–Zhang model,confirming the preservation of topologically protected edge transport in the presence of enhanced bulk strain.
基金supported by Science and Technology Project of the headquarters of the State Grid Corporation of China(No.5500-202324492A-3-2-ZN).
文摘To enhance power flow regulation in scenarios involving large-scale renewable energy transmission via high-voltage direct current(HVDC)links and multi-infeed DC systems in load-center regions,this paper proposes a hybrid modular multilevel converter–capacitor-commutated line-commutated converter(MMC-CLCC)HVDC transmission system and its corresponding control strategy.First,the system topology is constructed,and a submodule configuration method for the MMC—combining full-bridge submodules(FBSMs)and half-bridge submodules(HBSMs)—is proposed to enable direct power flow reversal.Second,a hierarchical control strategy is introduced,includingMMCvoltage control,CLCC current control,and a coordinationmechanism,along with the derivation of the hybrid system’s power flow reversal characteristics.Third,leveraging the CLCC’s fast current regulation and theMMC’s negative voltage control capability,a coordinated power flow reversal control strategy is developed.Finally,an 800 kV MMC-CLCC hybrid HVDC system is modeled in PSCAD/EMTDC to validate the power flow reversal performance under a high proportion of full-bridge submodule configuration.Results demonstrate that the proposed control strategy enables rapid(1-s transition)and smooth switching of bidirectional power flow without modifying the structure of primary equipment:the transient fluctuation ofDC voltage from the rated value(UdcN)to themaximumreverse voltage(-kUdcN)is less than 5%;the DC current strictly follows the preset characteristic curve with a deviation of≤3%;the active power reverses continuously,and the system maintains stable operation throughout the reversal process.
文摘Supercapacitors are gaining popularity due to their high cycling stability,power density,and fast charge and discharge rates.Researchers are ex-ploring electrode materials,electrolytes,and separat-ors for cost-effective energy storage systems.Ad-vances in materials science have led to the develop-ment of hybrid nanomaterials,such as combining fil-amentous carbon forms with inorganic nanoparticles,to create new charge and energy transfer processes.Notable materials for electrochemical energy-stor-age applications include MXenes,2D transition met-al carbides,and nitrides,carbon black,carbon aerogels,activated carbon,carbon nanotubes,conducting polymers,carbon fibers,and nanofibers,and graphene,because of their thermal,electrical,and mechanical properties.Carbon materials mixed with conducting polymers,ceramics,metal oxides,transition metal oxides,metal hydroxides,transition metal sulfides,trans-ition metal dichalcogenide,metal sulfides,carbides,nitrides,and biomass materials have received widespread attention due to their remarkable performance,eco-friendliness,cost-effectiveness,and renewability.This article explores the development of carbon-based hybrid materials for future supercapacitors,including electric double-layer capacitors,pseudocapacitors,and hy-brid supercapacitors.It investigates the difficulties that influence structural design,manufacturing(electrospinning,hydro-thermal/solvothermal,template-assisted synthesis,electrodeposition,electrospray,3D printing)techniques and the latest car-bon-based hybrid materials research offer practical solutions for producing high-performance,next-generation supercapacitors.
