Three-dimensional(3D)functional graphenebased architecture with superior electrical conductivity and good mechanical strength has promising applications in energy storage and electrics.Viscoelasticity-adjustable inks ...Three-dimensional(3D)functional graphenebased architecture with superior electrical conductivity and good mechanical strength has promising applications in energy storage and electrics.Viscoelasticity-adjustable inks make it possible to achieve desired 3D architectures with interconnected and continuous interior networks by microextrusion printing.In this work,ultra-low-concentration graphene oxide(GO)inks of~15 mg·ml-1 have been obtained and demonstrated in direct 3D printing with a facile cross-linking(direct ink writing).The rheological behavior of the GO strategy by cations,which is the lowest concentration to achieve direct ink writing inks,could be adjusted from 1×10^(4) to 1×10^(5) Pa·s^(-1) with different concentrations of cations due to strong cross-linking networks between GO sheets and cations.Meanwhile,the specific strength and electrical conductivity of 3D-printed graphene architecture are notably enhanced,reaching up to 51.7×10^(3) N·m·kg^(-1)and 119 S·m^(-1),which are superior to conventional graphene aerogels.Furthermore,3D printing graphene-based architecture assembled in micro-superc apacitor exhibits excellent electrochemical performance,which can be ascribed to the effective ion transportation through the interconnected networks.The strategy demonstrated is useful in the design of complex-shaped,graphene-based architectures for scalable manufacturing of practical energy storage applications.展开更多
The effect of the volume fraction of 3D-metal matrix composites(MMC)framework on the compressive properties of 3D interpenetrating hierarchical ZrO_(2)-toughened Al_(2)O_(3)particle(ZTAp)/40Cr steel composites was inv...The effect of the volume fraction of 3D-metal matrix composites(MMC)framework on the compressive properties of 3D interpenetrating hierarchical ZrO_(2)-toughened Al_(2)O_(3)particle(ZTAp)/40Cr steel composites was investigated.The results showed that the compressive properties of the material tended to decrease as the volume fraction of 3D-MMC framework increased.The composite with 35 vol.%3D-MMC had a yield strength of 1455.2 MPa and compressive strength of 1612.8 MPa,which occurred at a strain value of 5.6%.Compared to the homogeneously dispersed composite material,the composite with 35 vol.%3D-MMC had a 144.7%higher yield strength,which occurred at a 20%higher strain.An analysis of the cracks inside the material revealed that the crack was hindered and deflected by the matrix during propagation,which lengthened the crack propagation path and consumed more energy,thus leading to toughening.The results indicated that 3D interpenetrating hierarchical structure had a strengthening and toughening effect on ZTAp/40Cr composites.展开更多
The occurrence of high temperature(HT)in crop production is becoming more frequent and unpredictable with global warming,severely threatening food security.The state of an organ’s growth and development is largely de...The occurrence of high temperature(HT)in crop production is becoming more frequent and unpredictable with global warming,severely threatening food security.The state of an organ’s growth and development is largely determined by the temperature conditions it is exposed to over time.Maize is the main cereal crop,and its stem growth and plant architecture are closely related to lodging resistance,and especially sensitive to temperature.However,systematic research on the timing effect of HT on the sequentially developing internode and stem is currently lacking.To identify the timing effect of HT on the morphology and plasticity of the stem in maize,two hybrids(Zhengdan 958(ZD958),Xianyu 335(XY335))characterized by distinct morphological traits in the stem were exposed to a 7-day HT treatment from the V6 to V17 stages(Vn presents the vegetative stage with n leaves fully expanded)in 2019-2020.The results demonstrated that exposure to HT during V6-V12 accelerated the rapid elongation of stems.For instance,HT occurring at V7 and V12 specifically promoted the lengths and weights of the 3rd-5th and 9th-11th internodes,respectively.Meanwhile,HT slowed the growth of internodes adjacent to the promoted internodes.Interestingly,compared with control,the plant height was significantly increased soon after HT treatment,but the promotion effect became narrower at the subsequent flowering stage,demonstrating a self-adjusting mechanism in the maize plant in response to HT.Importantly,HT altered the plant architectures,including a rising of the ear position and increase in the ear position coefficient.XY335 exhibited greater sensitivity in stem development than ZD958 under HT treatment.These findings improve our systematic understanding of the plasticity of internode and plant architecture in response to the timing of HT exposure.展开更多
MXene has been the limelight for studies on electrode active materials,aiming at developing supercapacitors with boosted energy density to meet the emerging influx of wearable and portable electronic devices.Despite i...MXene has been the limelight for studies on electrode active materials,aiming at developing supercapacitors with boosted energy density to meet the emerging influx of wearable and portable electronic devices.Despite its various desirable properties including intrinsic flexibility,high specific surface area,excellent metallic conductivity and unique abundance of surface functionalities,its full potential for electrochemical performance is hindered by the notorious restacking phenomenon of MXene nanosheets.Ascribed to its two-dimensional(2D)nature and surface functional groups,inevitable Van der Waals interactions drive the agglomeration of nanosheets,ultimately reducing the exposure of electrochemically active sites to the electrolyte,as well as severely lengthening electrolyte ion transport pathways.As a result,energy and power density deteriorate,limiting the application versatility of MXene-based supercapacitors.Constructing 3D architectures using 2D nanosheets presents as a straightforward yet ingenious approach to mitigate the fatal flaws of MXene.However,the sheer number of distinct methodologies reported,thus far,calls for a systematic review that unravels the rationale behind such 3D MXene structural designs.Herein,this review aims to serve this purpose while also scrutinizing the structure–property relationship to correlate such structural modifications to their ensuing electrochemical performance enhancements.Besides,the physicochemical properties of MXene play fundamental roles in determining the effective charge storage capabilities of 3D MXene-based electrodes.This largely depends on different MXene synthesis techniques and synthesis condition variations,hence,elucidated in this review as well.Lastly,the challenges and perspectives for achieving viable commercialization of MXene-based supercapacitor electrodes are highlighted.展开更多
Panicle architecture is an agronomic determinant of crop yield and a target for cereal crop improvement.To investigate its molecular mechanisms in rice,we performed map-based cloning and characterization of OPEN PANIC...Panicle architecture is an agronomic determinant of crop yield and a target for cereal crop improvement.To investigate its molecular mechanisms in rice,we performed map-based cloning and characterization of OPEN PANICLE 1(OP1),a gain-of-function allele of LIGULELESS 1(LG1),controlling the spread-panicle phenotype.This allele results from a 48-bp deletion in the LG1 upstream region and promotes pulvinus development at the base of the primary branch.Increased OP1 expression and altered panicle phenotype in chimeric transgenic plants and upstream-region knockout mutants indicated that the deletion regulates spread-panicle architecture in the mutant spread panicle 1(sp1).Knocking out BRASSINOSTEROID UPREGULATED1(BU1)gene in the background of OP1 complementary plants resulted in compact panicles,suggesting OP1 may regulate inflorescence architecture via the brassinosteroid signaling pathway.We regard that manipulating the upstream regulatory region of OP1 or genes involved in BR signal pathway could be an efficient way to improve rice inflorescence architecture.展开更多
Drought is an important abiotic stress factor in cotton production.The root system architecture(RSA)of cotton shows high plasticity which can alleviate drought-related stress under drought stress(DS)conditions;however...Drought is an important abiotic stress factor in cotton production.The root system architecture(RSA)of cotton shows high plasticity which can alleviate drought-related stress under drought stress(DS)conditions;however,this alleviation is cultivar dependent.Therefore,this study estimated the genetic variability of RSA in cotton under DS.Using the paper-based growth system,we assessed the RSA variability in 80 cotton cultivars at the seedling stage,with 0 and10%polyethylene glycol 6000(PEG6000)as the control(CK)and DS treatment,respectively.An analysis of 23 aboveground and root traits in the 80 cotton cultivars revealed different responses to DS.On the 10th day after DS treatment,the degree of variation in the RSA traits under DS(5–55%)was greater than that of CK(5–49%).The 80 cultivars were divided into drought-tolerant cultivars(group 1),intermediate drought-tolerant cultivars(group 2),and drought-sensitive cultivars(group 3)based on their comprehensive evaluation values of drought resistance.Under DS,the root lengthlower,root area-lower,root volume-lower,and root length density-lower were significantly reduced by 63,71,76,and 4%in the drought-sensitive cultivars compared to CK.Notably,the drought-tolerant cultivars maintained their root lengthlower,root area-lower,root volume-lower,and root length density–lower attributes.Compared to CK,the root diameter(0–2 mm)-lower increased by 21%in group 1 but decreased by 3 and 64%in groups 2 and 3,respectively,under DS.Additionally,the drought-tolerant cultivars displayed a plastic response under DS that was characterized by an increase in the root-lower characteristics.Drought resistance was positively correlated with the root area-lower and root length density-lower.Overall,the RSA of the different cotton cultivars varied greatly under DS.Therefore,important root traits,such as the root-lower traits,provide great insights for exploring whether drought-tolerant cotton cultivars can effectively withstand adverse environments.展开更多
Seafloor topography plays an important role in the evolution of submarine lobes.However,it is still not so clear how the shape of slope affects the three-dimensional(3-D)architecture of submarine lobes.In this study,w...Seafloor topography plays an important role in the evolution of submarine lobes.However,it is still not so clear how the shape of slope affects the three-dimensional(3-D)architecture of submarine lobes.In this study,we analyze the effect of topography factors on different hierarchical lobe architectures that formed during Pliocene to Quaternary in the Rovuma Basin offshore East Africa.We characterize the shape,size and growth pattern of different hierarchical lobe architectures using 3-D seismic data.We find that the relief of the topographic slope determines the location of preferential deposition of lobe complexes and single lobes.When the topography is irregular and presents topographic lows,lobe complexes first infill these depressions.Single lobes are deposited preferentially at positions with higher longitudinal(i.e.across-slope)slope gradients.As the longitudinal slope becomes higher,the aspect ratio of the single lobes increases.Lateral(i.e.along-slope)topography does not seem to have a strong influence on the shape of single lobe,but it seems to affect the overlap of single lobes.When the lateral slope gradient is relatively high,the single lobes tend to have a larger overlap surface.Furthermore,as the average of lateral slope and longitudinal slope gets greater,the width/thickness ratio of the single lobe is smaller,i.e.sediments tend to accumulate vertically.The results demonstrate that the shape of slopes more comprehensively influences the 3-D architecture of lobes in natural deep-sea systems than previously other lobe deposits and analogue experiments,which helps us better understand the development and evolution of the distal parts of turbidite systems.展开更多
By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-grow...By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this article.The proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for UEs.Firstly,we introduce the AGC-MEC architecture and elaborate three typical use cases.Then,we discuss four main challenges in the AGC-MEC as well as their potential solutions.Next,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy.Finally,we highlight several potential research directions of the AGC-MEC.展开更多
The impact of epigenetic modifications like DNA methylation on plant phenotypes has expanded the possibilities for crop development.DNA methylation plays a part in the regulation of both the chromatin structure and ge...The impact of epigenetic modifications like DNA methylation on plant phenotypes has expanded the possibilities for crop development.DNA methylation plays a part in the regulation of both the chromatin structure and gene expression,and the enzyme involved,DNA methyltransferase,executes the methylation process within the plant genome.By regulating crucial biological pathways,epigenetic changes actively contribute to the creation of the phenotype.Therefore,epigenome editing may assist in overcoming some of the drawbacks of genome editing,which can have minor off-target consequences and merely facilitate the loss of a gene’s function.These drawbacks include gene knockout,which can have such off-target effects.This review provides examples of several molecular characteristics of DNA methylation,as well as some plant physiological processes that are impacted by these epigenetic changes in the plants.We also discuss how DNA alterations might be used to improve crops and meet the demands of sustainable and environmentally-friendly farming.展开更多
Bone screws are devices used to fix implants or bones to bones.However,conventional screws are mechanically fixed with thread and often face long-term failure due to poor osseointegration.To improve osseointegration,s...Bone screws are devices used to fix implants or bones to bones.However,conventional screws are mechanically fixed with thread and often face long-term failure due to poor osseointegration.To improve osseointegration,screws are evolving from solid and smooth to porous and rough.Additive manufacturing(AM)offers a high degree of manufacturing freedom,enabling the preparation of predesigned screws that are porous and rough.This paper provides an overview of the problems currently faced by bone screws:long-term loosening and screw breakage.Next,advances in osseointegrated screws are summarized hierarchically(sub-micro,micro,and macro).At the sub-microscale level,we describe surface-modification techniques for enhancing osseointegration.At the micro level,we summarize the micro-design parameters that affect the mechanical and biological properties of porous osseointegrated screws,including porosity,pore size,and pore shape.In addition,we highlight three promising pore shapes:triply periodic minimal surface,auxetic structure with negative Poisson ratio,and the Voronoi structure.At the macro level,we outline the strategies of graded design,gradient design,and topology optimization design to improve the mechanical strength of porous osseointegrated screws.Simultaneously,this paper outlines advances in AM technology for enhancing the mechanical properties of porous osseointegrated screws.AM osseointegrated screws with hierarchical design are expected to provide excellent long-term fixation and the required mechanical strength.展开更多
Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human ...Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.展开更多
Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the system.This technology has been widely used and has developed rapidly in big data systems...Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the system.This technology has been widely used and has developed rapidly in big data systems across various fields.An increasing number of users are participating in application systems that use blockchain as their underlying architecture.As the number of transactions and the capital involved in blockchain grow,ensuring information security becomes imperative.Addressing the verification of transactional information security and privacy has emerged as a critical challenge.Blockchain-based verification methods can effectively eliminate the need for centralized third-party organizations.However,the efficiency of nodes in storing and verifying blockchain data faces unprecedented challenges.To address this issue,this paper introduces an efficient verification scheme for transaction security.Initially,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all transactions.Subsequently,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous trees.Theoretical analyses and simulation experiments conclusively demonstrate the superior performance of this scheme.When verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional methods.The findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of Things.This scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.展开更多
With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The networ...With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time requirements.Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats.This paper proposes a machine-learning security defense algorithm based on metadata association features.Emphasize control over unauthorized users through privacy,integrity,and availability.The user model is established and the mapping between the user model and the metadata of the data source is generated.By analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be realized.Define and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense system.The experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is good.This method can effectively resist data intrusion and has high air traffic control security.It can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.展开更多
Spin qubits and superconducting qubits are promising candidates for realizing solid-state quantum information processors.Designing a hybrid architecture that combines the advantages of different qubits on the same chi...Spin qubits and superconducting qubits are promising candidates for realizing solid-state quantum information processors.Designing a hybrid architecture that combines the advantages of different qubits on the same chip is a highly desirable but challenging goal.Here we propose a hybrid architecture that utilizes a high-impedance SQUID array resonator as a quantum bus,thereby coherently coupling different solid-state qubits.We employ a resonant exchange spin qubit hosted in a triple quantum dot and a superconducting transmon qubit.Since this hybrid system is highly tunable,it can operate in a dispersive regime,where the interaction between the different qubits is mediated by virtual photons.By utilizing such interactions,entangling gate operations between different qubits can be realized in a short time of 30 ns with a fidelity of up to 96.5%under realistic parameter conditions.Further utilizing this interaction,remote entangled state between different qubits can be prepared and is robust to perturbations of various parameters.These results pave the way for exploring efficient fault-tolerant quantum computation on hybrid quantum architecture platforms.展开更多
Deep learning has emerged in many practical applications,such as image classification,fault diagnosis,and object detection.More recently,convolutional neural networks(CNNs),representative models of deep learning,have ...Deep learning has emerged in many practical applications,such as image classification,fault diagnosis,and object detection.More recently,convolutional neural networks(CNNs),representative models of deep learning,have been used to solve fault detection.However,the current design of CNNs for fault detection of wind turbine blades is highly dependent on domain knowledge and requires a large amount of trial and error.For this reason,an evolutionary YOLOv8 network has been developed to automatically find the network architecture for wind turbine blade-based fault detection.YOLOv8 is a CNN-backed object detection model.Specifically,to reduce the parameter count,we first design an improved FasterNet module based on the Partial Convolution(PConv)operator.Then,to enhance convergence performance,we improve the loss function based on the efficient complete intersection over the union.Based on this,a flexible variable-length encoding is proposed,and the corresponding reproduction operators are designed.Related experimental results confirmthat the proposed approach can achieve better fault detection results and improve by 2.6%in mean precision at 50(mAP50)compared to the existing methods.Additionally,compared to training with the YOLOv8n model,the YOLOBFE model reduces the training parameters by 933,937 and decreases the GFLOPS(Giga Floating Point Operations Per Second)by 1.1.展开更多
Silicon(Si)is widely used as a lithium‐ion‐battery anode owing to its high capacity and abundant crustal reserves.However,large volume change upon cycling and poor conductivity of Si cause rapid capacity decay and p...Silicon(Si)is widely used as a lithium‐ion‐battery anode owing to its high capacity and abundant crustal reserves.However,large volume change upon cycling and poor conductivity of Si cause rapid capacity decay and poor fast‐charging capability limiting its commercial applications.Here,we propose a multilevel carbon architecture with vertical graphene sheets(VGSs)grown on surfaces of subnanoscopically and homogeneously dispersed Si–C composite nanospheres,which are subsequently embedded into a carbon matrix(C/VGSs@Si–C).Subnanoscopic C in the Si–C nanospheres,VGSs,and carbon matrix form a three‐dimensional conductive and robust network,which significantly improves the conductivity and suppresses the volume expansion of Si,thereby boosting charge transport and improving electrode stability.The VGSs with vast exposed edges considerably increase the contact area with the carbon matrix and supply directional transport channels through the entire material,which boosts charge transport.The carbon matrix encapsulates VGSs@Si–C to decrease the specific surface area and increase tap density,thus yielding high first Coulombic efficiency and electrode compaction density.Consequently,C/VGSs@Si–C delivers excellent Li‐ion storage performances under industrial electrode conditions.In particular,the full cells show high energy densities of 603.5 Wh kg^(−1)and 1685.5 Wh L^(−1)at 0.1 C and maintain 80.7%of the energy density at 3 C.展开更多
In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a p...In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling paradigm for semantics-aware speech recognition in air traffic control communications, which could contribute to the advancement of intelligent and efficient aviation safety management.展开更多
With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate...With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.展开更多
Precision engineering of catalytic sites to guide more favorable pathways for Li_(2)O_(2) nucleation and decom-position represents an enticing kinetic strategy for mitigating overpotential,enhancing discharge capac-it...Precision engineering of catalytic sites to guide more favorable pathways for Li_(2)O_(2) nucleation and decom-position represents an enticing kinetic strategy for mitigating overpotential,enhancing discharge capac-ity,and improving recycling stability of Li-O_(2) batteries.In this work,we employ metal-organic frameworks(MOFs)derivation and ion substitution strategies to construct atomically dispersed Mn-N_(4) moieties on hierarchical porous nitrogen-doped carbon(Mn SAs-NC)with the aim of reducing the over-potential and improving the cycling stability of Li-O_(2) batteries.The porous structure provides more chan-nels for mass transfer and exposes more highly active sites for electrocatalytic reactions,thus promoting the formation and decomposition of Li_(2)O_(2).The Li-O_(2) batteries with Mn SAs-NC cathode achieve lower overpotential,higher specific capacity(14290 mA h g^(-1) at 100 mAg^(-1)),and superior cycle stability(>100 cycles at 200 mA g^(-1))compared with the Mn NPs-NC and NC.Density functional theory(DFT)cal-culations reveal that the construction of Mn-N_(4) moiety tunes the charge distribution of the pyridinic N-rich vacancy and balances the affinity of the intermediates(LiO_(2) and Li_(2)O_(2)).The initial nucleation of Li_(2)O_(2) on Mn SAs-NC favors the O_(2)-→LiO_(2)→Li_(2)O_(2) surface-adsorption pathway,which mitigates the overpoten-tials of the oxygen reduction(ORR)and oxygen evolution reaction(OER).As a result,Mn SAs-NC with Mn-N_(4) moiety effectively facilitates the Li_(2)O_(2) nucleation and enables its reversible decomposition.This work establishes a methodology for constructing carbon-based electrocatalysts with high activity and selectivity for Li-O_(2)batteries.展开更多
Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data ana...Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data analysis.This paper presents a model based on these nanowire networks,with an improved conductance variation profile.We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses.The nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction analysis.Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction accuracy.Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing.展开更多
基金financially supported by the National Natural Science Foundation of China(No.51802195)Chen Guang Scholar Project of Shanghai Education Commission(No.19CG53)。
文摘Three-dimensional(3D)functional graphenebased architecture with superior electrical conductivity and good mechanical strength has promising applications in energy storage and electrics.Viscoelasticity-adjustable inks make it possible to achieve desired 3D architectures with interconnected and continuous interior networks by microextrusion printing.In this work,ultra-low-concentration graphene oxide(GO)inks of~15 mg·ml-1 have been obtained and demonstrated in direct 3D printing with a facile cross-linking(direct ink writing).The rheological behavior of the GO strategy by cations,which is the lowest concentration to achieve direct ink writing inks,could be adjusted from 1×10^(4) to 1×10^(5) Pa·s^(-1) with different concentrations of cations due to strong cross-linking networks between GO sheets and cations.Meanwhile,the specific strength and electrical conductivity of 3D-printed graphene architecture are notably enhanced,reaching up to 51.7×10^(3) N·m·kg^(-1)and 119 S·m^(-1),which are superior to conventional graphene aerogels.Furthermore,3D printing graphene-based architecture assembled in micro-superc apacitor exhibits excellent electrochemical performance,which can be ascribed to the effective ion transportation through the interconnected networks.The strategy demonstrated is useful in the design of complex-shaped,graphene-based architectures for scalable manufacturing of practical energy storage applications.
基金supported by the National Natural Science Foundation of China(No.51461025).
文摘The effect of the volume fraction of 3D-metal matrix composites(MMC)framework on the compressive properties of 3D interpenetrating hierarchical ZrO_(2)-toughened Al_(2)O_(3)particle(ZTAp)/40Cr steel composites was investigated.The results showed that the compressive properties of the material tended to decrease as the volume fraction of 3D-MMC framework increased.The composite with 35 vol.%3D-MMC had a yield strength of 1455.2 MPa and compressive strength of 1612.8 MPa,which occurred at a strain value of 5.6%.Compared to the homogeneously dispersed composite material,the composite with 35 vol.%3D-MMC had a 144.7%higher yield strength,which occurred at a 20%higher strain.An analysis of the cracks inside the material revealed that the crack was hindered and deflected by the matrix during propagation,which lengthened the crack propagation path and consumed more energy,thus leading to toughening.The results indicated that 3D interpenetrating hierarchical structure had a strengthening and toughening effect on ZTAp/40Cr composites.
基金This work was supported by the earmarked fund for China Agriculture Research System(CARS-02-16).
文摘The occurrence of high temperature(HT)in crop production is becoming more frequent and unpredictable with global warming,severely threatening food security.The state of an organ’s growth and development is largely determined by the temperature conditions it is exposed to over time.Maize is the main cereal crop,and its stem growth and plant architecture are closely related to lodging resistance,and especially sensitive to temperature.However,systematic research on the timing effect of HT on the sequentially developing internode and stem is currently lacking.To identify the timing effect of HT on the morphology and plasticity of the stem in maize,two hybrids(Zhengdan 958(ZD958),Xianyu 335(XY335))characterized by distinct morphological traits in the stem were exposed to a 7-day HT treatment from the V6 to V17 stages(Vn presents the vegetative stage with n leaves fully expanded)in 2019-2020.The results demonstrated that exposure to HT during V6-V12 accelerated the rapid elongation of stems.For instance,HT occurring at V7 and V12 specifically promoted the lengths and weights of the 3rd-5th and 9th-11th internodes,respectively.Meanwhile,HT slowed the growth of internodes adjacent to the promoted internodes.Interestingly,compared with control,the plant height was significantly increased soon after HT treatment,but the promotion effect became narrower at the subsequent flowering stage,demonstrating a self-adjusting mechanism in the maize plant in response to HT.Importantly,HT altered the plant architectures,including a rising of the ear position and increase in the ear position coefficient.XY335 exhibited greater sensitivity in stem development than ZD958 under HT treatment.These findings improve our systematic understanding of the plasticity of internode and plant architecture in response to the timing of HT exposure.
基金supported by the Fundamental Research Grant Scheme by Ministry of Higher Education Malaysia(FRGS/1/2021/STG04/XMU/02/1 and FRGS/1/2022/TK09/XMU/03/2)the Xiamen University Malaysia Research Fund(XMUMRF/2023-C11/IENG/0056)。
文摘MXene has been the limelight for studies on electrode active materials,aiming at developing supercapacitors with boosted energy density to meet the emerging influx of wearable and portable electronic devices.Despite its various desirable properties including intrinsic flexibility,high specific surface area,excellent metallic conductivity and unique abundance of surface functionalities,its full potential for electrochemical performance is hindered by the notorious restacking phenomenon of MXene nanosheets.Ascribed to its two-dimensional(2D)nature and surface functional groups,inevitable Van der Waals interactions drive the agglomeration of nanosheets,ultimately reducing the exposure of electrochemically active sites to the electrolyte,as well as severely lengthening electrolyte ion transport pathways.As a result,energy and power density deteriorate,limiting the application versatility of MXene-based supercapacitors.Constructing 3D architectures using 2D nanosheets presents as a straightforward yet ingenious approach to mitigate the fatal flaws of MXene.However,the sheer number of distinct methodologies reported,thus far,calls for a systematic review that unravels the rationale behind such 3D MXene structural designs.Herein,this review aims to serve this purpose while also scrutinizing the structure–property relationship to correlate such structural modifications to their ensuing electrochemical performance enhancements.Besides,the physicochemical properties of MXene play fundamental roles in determining the effective charge storage capabilities of 3D MXene-based electrodes.This largely depends on different MXene synthesis techniques and synthesis condition variations,hence,elucidated in this review as well.Lastly,the challenges and perspectives for achieving viable commercialization of MXene-based supercapacitor electrodes are highlighted.
基金supported by the National Natural Science Foundation of China(31925029,31471457)the National Key Research and Development Project of China(2021YFD120010105)Guangdong Key Laboratory of New Technology in Rice Breeding(2020B1212060047)。
文摘Panicle architecture is an agronomic determinant of crop yield and a target for cereal crop improvement.To investigate its molecular mechanisms in rice,we performed map-based cloning and characterization of OPEN PANICLE 1(OP1),a gain-of-function allele of LIGULELESS 1(LG1),controlling the spread-panicle phenotype.This allele results from a 48-bp deletion in the LG1 upstream region and promotes pulvinus development at the base of the primary branch.Increased OP1 expression and altered panicle phenotype in chimeric transgenic plants and upstream-region knockout mutants indicated that the deletion regulates spread-panicle architecture in the mutant spread panicle 1(sp1).Knocking out BRASSINOSTEROID UPREGULATED1(BU1)gene in the background of OP1 complementary plants resulted in compact panicles,suggesting OP1 may regulate inflorescence architecture via the brassinosteroid signaling pathway.We regard that manipulating the upstream regulatory region of OP1 or genes involved in BR signal pathway could be an efficient way to improve rice inflorescence architecture.
基金the National Natural Science Foundation of China(31871569 and 32172120)the Natural Science Foundation of Hebei Province,China(C2020204066)。
文摘Drought is an important abiotic stress factor in cotton production.The root system architecture(RSA)of cotton shows high plasticity which can alleviate drought-related stress under drought stress(DS)conditions;however,this alleviation is cultivar dependent.Therefore,this study estimated the genetic variability of RSA in cotton under DS.Using the paper-based growth system,we assessed the RSA variability in 80 cotton cultivars at the seedling stage,with 0 and10%polyethylene glycol 6000(PEG6000)as the control(CK)and DS treatment,respectively.An analysis of 23 aboveground and root traits in the 80 cotton cultivars revealed different responses to DS.On the 10th day after DS treatment,the degree of variation in the RSA traits under DS(5–55%)was greater than that of CK(5–49%).The 80 cultivars were divided into drought-tolerant cultivars(group 1),intermediate drought-tolerant cultivars(group 2),and drought-sensitive cultivars(group 3)based on their comprehensive evaluation values of drought resistance.Under DS,the root lengthlower,root area-lower,root volume-lower,and root length density-lower were significantly reduced by 63,71,76,and 4%in the drought-sensitive cultivars compared to CK.Notably,the drought-tolerant cultivars maintained their root lengthlower,root area-lower,root volume-lower,and root length density–lower attributes.Compared to CK,the root diameter(0–2 mm)-lower increased by 21%in group 1 but decreased by 3 and 64%in groups 2 and 3,respectively,under DS.Additionally,the drought-tolerant cultivars displayed a plastic response under DS that was characterized by an increase in the root-lower characteristics.Drought resistance was positively correlated with the root area-lower and root length density-lower.Overall,the RSA of the different cotton cultivars varied greatly under DS.Therefore,important root traits,such as the root-lower traits,provide great insights for exploring whether drought-tolerant cotton cultivars can effectively withstand adverse environments.
基金The study is funded by the Cooperation Project of China National Petroleum Company(CNPC)and China University of Petroleum-Beijing(CUPB)(No.RIPED-2021-JS-552)the National Natural Science Foundation of China(Nos.42002112,42272110)+2 种基金the Strategic Cooperation Technology Projects of CNPC and CUPB(No.ZLZX2020-02)the Science Foundation for Youth Scholars of CUPB(No.24620222BJRC006)We thank the China Scholarship Council(CSC)(No.202106440048)for having funded the research stay of Mei Chen at MARUM,University of Bremen.We thank Elda Miramontes for her constructive comments and suggestions that helped us improve our manuscript.
文摘Seafloor topography plays an important role in the evolution of submarine lobes.However,it is still not so clear how the shape of slope affects the three-dimensional(3-D)architecture of submarine lobes.In this study,we analyze the effect of topography factors on different hierarchical lobe architectures that formed during Pliocene to Quaternary in the Rovuma Basin offshore East Africa.We characterize the shape,size and growth pattern of different hierarchical lobe architectures using 3-D seismic data.We find that the relief of the topographic slope determines the location of preferential deposition of lobe complexes and single lobes.When the topography is irregular and presents topographic lows,lobe complexes first infill these depressions.Single lobes are deposited preferentially at positions with higher longitudinal(i.e.across-slope)slope gradients.As the longitudinal slope becomes higher,the aspect ratio of the single lobes increases.Lateral(i.e.along-slope)topography does not seem to have a strong influence on the shape of single lobe,but it seems to affect the overlap of single lobes.When the lateral slope gradient is relatively high,the single lobes tend to have a larger overlap surface.Furthermore,as the average of lateral slope and longitudinal slope gets greater,the width/thickness ratio of the single lobe is smaller,i.e.sediments tend to accumulate vertically.The results demonstrate that the shape of slopes more comprehensively influences the 3-D architecture of lobes in natural deep-sea systems than previously other lobe deposits and analogue experiments,which helps us better understand the development and evolution of the distal parts of turbidite systems.
基金supported in part by the National Natural Science Foundation of China under Grant 62171465,62072303,62272223,U22A2031。
文摘By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this article.The proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for UEs.Firstly,we introduce the AGC-MEC architecture and elaborate three typical use cases.Then,we discuss four main challenges in the AGC-MEC as well as their potential solutions.Next,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy.Finally,we highlight several potential research directions of the AGC-MEC.
文摘The impact of epigenetic modifications like DNA methylation on plant phenotypes has expanded the possibilities for crop development.DNA methylation plays a part in the regulation of both the chromatin structure and gene expression,and the enzyme involved,DNA methyltransferase,executes the methylation process within the plant genome.By regulating crucial biological pathways,epigenetic changes actively contribute to the creation of the phenotype.Therefore,epigenome editing may assist in overcoming some of the drawbacks of genome editing,which can have minor off-target consequences and merely facilitate the loss of a gene’s function.These drawbacks include gene knockout,which can have such off-target effects.This review provides examples of several molecular characteristics of DNA methylation,as well as some plant physiological processes that are impacted by these epigenetic changes in the plants.We also discuss how DNA alterations might be used to improve crops and meet the demands of sustainable and environmentally-friendly farming.
基金supported by the National Natural Science Foundation of China(Nos.82272504 and 82072456)the National Key R&D Program of China(No.2018YFB1105100)+4 种基金the Department of Science and Technology of Jilin Province,China(Nos.20200404202YY,20200403086SF,20210101321JC,20210204104YY,20200201453JC,20220204119YY,202201ZYTS131,202201ZYTS129,20220401084YY,202201ZYTS505,and YDZJ202301ZYTS076)the Department of Finance of Jilin Province,China(No.2020SCZT037)the Jilin Provincial Development and Reform Commission,China(Nos.2018C010 and 2022C043-5)the Interdisciplinary Integration and Cultivation Project of Jilin University(No.JLUXKJC2020307)the Central University Basic Scientific Research Fund(No.2023-JCXK-04).
文摘Bone screws are devices used to fix implants or bones to bones.However,conventional screws are mechanically fixed with thread and often face long-term failure due to poor osseointegration.To improve osseointegration,screws are evolving from solid and smooth to porous and rough.Additive manufacturing(AM)offers a high degree of manufacturing freedom,enabling the preparation of predesigned screws that are porous and rough.This paper provides an overview of the problems currently faced by bone screws:long-term loosening and screw breakage.Next,advances in osseointegrated screws are summarized hierarchically(sub-micro,micro,and macro).At the sub-microscale level,we describe surface-modification techniques for enhancing osseointegration.At the micro level,we summarize the micro-design parameters that affect the mechanical and biological properties of porous osseointegrated screws,including porosity,pore size,and pore shape.In addition,we highlight three promising pore shapes:triply periodic minimal surface,auxetic structure with negative Poisson ratio,and the Voronoi structure.At the macro level,we outline the strategies of graded design,gradient design,and topology optimization design to improve the mechanical strength of porous osseointegrated screws.Simultaneously,this paper outlines advances in AM technology for enhancing the mechanical properties of porous osseointegrated screws.AM osseointegrated screws with hierarchical design are expected to provide excellent long-term fixation and the required mechanical strength.
基金supported in part by the National Natural Science Foundation of China (NSFC) under Grant No.61976242in part by the Natural Science Fund of Hebei Province for Distinguished Young Scholars under Grant No.F2021202010+2 种基金in part by the Fundamental Scientific Research Funds for Interdisciplinary Team of Hebei University of Technology under Grant No.JBKYTD2002funded by Science and Technology Project of Hebei Education Department under Grant No.JZX2023007supported by 2022 Interdisciplinary Postgraduate Training Program of Hebei University of Technology under Grant No.HEBUT-YXKJC-2022122.
文摘Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.
基金funded by the National Natural Science Foundation of China(62072056,62172058)the Researchers Supporting Project Number(RSP2023R102)King Saud University,Riyadh,Saudi Arabia+4 种基金funded by the Hunan Provincial Key Research and Development Program(2022SK2107,2022GK2019)the Natural Science Foundation of Hunan Province(2023JJ30054)the Foundation of State Key Laboratory of Public Big Data(PBD2021-15)the Young Doctor Innovation Program of Zhejiang Shuren University(2019QC30)Postgraduate Scientific Research Innovation Project of Hunan Province(CX20220940,CX20220941).
文摘Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the system.This technology has been widely used and has developed rapidly in big data systems across various fields.An increasing number of users are participating in application systems that use blockchain as their underlying architecture.As the number of transactions and the capital involved in blockchain grow,ensuring information security becomes imperative.Addressing the verification of transactional information security and privacy has emerged as a critical challenge.Blockchain-based verification methods can effectively eliminate the need for centralized third-party organizations.However,the efficiency of nodes in storing and verifying blockchain data faces unprecedented challenges.To address this issue,this paper introduces an efficient verification scheme for transaction security.Initially,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all transactions.Subsequently,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous trees.Theoretical analyses and simulation experiments conclusively demonstrate the superior performance of this scheme.When verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional methods.The findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of Things.This scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.
基金This work was supported by the National Natural Science Foundation of China(U2133208,U20A20161).
文摘With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time requirements.Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats.This paper proposes a machine-learning security defense algorithm based on metadata association features.Emphasize control over unauthorized users through privacy,integrity,and availability.The user model is established and the mapping between the user model and the metadata of the data source is generated.By analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be realized.Define and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense system.The experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is good.This method can effectively resist data intrusion and has high air traffic control security.It can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11974336 and 12304401)the National Key R&D Program of China(Grant No.2017YFA0304100)+1 种基金the Key Project of Natural Science Research in Universities of Anhui Province(Grant No.KJ2021A1107)the Scientific Research Foundation of Suzhou University(Grant Nos.2020BS006 and 2021XJPT18).
文摘Spin qubits and superconducting qubits are promising candidates for realizing solid-state quantum information processors.Designing a hybrid architecture that combines the advantages of different qubits on the same chip is a highly desirable but challenging goal.Here we propose a hybrid architecture that utilizes a high-impedance SQUID array resonator as a quantum bus,thereby coherently coupling different solid-state qubits.We employ a resonant exchange spin qubit hosted in a triple quantum dot and a superconducting transmon qubit.Since this hybrid system is highly tunable,it can operate in a dispersive regime,where the interaction between the different qubits is mediated by virtual photons.By utilizing such interactions,entangling gate operations between different qubits can be realized in a short time of 30 ns with a fidelity of up to 96.5%under realistic parameter conditions.Further utilizing this interaction,remote entangled state between different qubits can be prepared and is robust to perturbations of various parameters.These results pave the way for exploring efficient fault-tolerant quantum computation on hybrid quantum architecture platforms.
基金supported by the Liaoning Province Applied Basic Research Program Project of China(Grant:2023JH2/101300065)the Liaoning Province Science and Technology Plan Joint Fund(2023-MSLH-221).
文摘Deep learning has emerged in many practical applications,such as image classification,fault diagnosis,and object detection.More recently,convolutional neural networks(CNNs),representative models of deep learning,have been used to solve fault detection.However,the current design of CNNs for fault detection of wind turbine blades is highly dependent on domain knowledge and requires a large amount of trial and error.For this reason,an evolutionary YOLOv8 network has been developed to automatically find the network architecture for wind turbine blade-based fault detection.YOLOv8 is a CNN-backed object detection model.Specifically,to reduce the parameter count,we first design an improved FasterNet module based on the Partial Convolution(PConv)operator.Then,to enhance convergence performance,we improve the loss function based on the efficient complete intersection over the union.Based on this,a flexible variable-length encoding is proposed,and the corresponding reproduction operators are designed.Related experimental results confirmthat the proposed approach can achieve better fault detection results and improve by 2.6%in mean precision at 50(mAP50)compared to the existing methods.Additionally,compared to training with the YOLOv8n model,the YOLOBFE model reduces the training parameters by 933,937 and decreases the GFLOPS(Giga Floating Point Operations Per Second)by 1.1.
基金Guangdong Basic and Applied Basic Research Foundation,Grant/Award Number:2020A1515110762Research Grants Council of the Hong Kong Special Administrative Region,China,Grant/Award Number:R6005‐20Shenzhen Key Laboratory of Advanced Energy Storage,Grant/Award Number:ZDSYS20220401141000001。
文摘Silicon(Si)is widely used as a lithium‐ion‐battery anode owing to its high capacity and abundant crustal reserves.However,large volume change upon cycling and poor conductivity of Si cause rapid capacity decay and poor fast‐charging capability limiting its commercial applications.Here,we propose a multilevel carbon architecture with vertical graphene sheets(VGSs)grown on surfaces of subnanoscopically and homogeneously dispersed Si–C composite nanospheres,which are subsequently embedded into a carbon matrix(C/VGSs@Si–C).Subnanoscopic C in the Si–C nanospheres,VGSs,and carbon matrix form a three‐dimensional conductive and robust network,which significantly improves the conductivity and suppresses the volume expansion of Si,thereby boosting charge transport and improving electrode stability.The VGSs with vast exposed edges considerably increase the contact area with the carbon matrix and supply directional transport channels through the entire material,which boosts charge transport.The carbon matrix encapsulates VGSs@Si–C to decrease the specific surface area and increase tap density,thus yielding high first Coulombic efficiency and electrode compaction density.Consequently,C/VGSs@Si–C delivers excellent Li‐ion storage performances under industrial electrode conditions.In particular,the full cells show high energy densities of 603.5 Wh kg^(−1)and 1685.5 Wh L^(−1)at 0.1 C and maintain 80.7%of the energy density at 3 C.
基金This research was funded by Shenzhen Science and Technology Program(Grant No.RCBS20221008093121051)the General Higher Education Project of Guangdong Provincial Education Department(Grant No.2020ZDZX3085)+1 种基金China Postdoctoral Science Foundation(Grant No.2021M703371)the Post-Doctoral Foundation Project of Shenzhen Polytechnic(Grant No.6021330002K).
文摘In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling paradigm for semantics-aware speech recognition in air traffic control communications, which could contribute to the advancement of intelligent and efficient aviation safety management.
基金supported by the Beijing Academy of Quantum Information Sciencessupported by the National Natural Science Foundation of China(Grant No.92365206)+2 种基金the support of the China Postdoctoral Science Foundation(Certificate Number:2023M740272)supported by the National Natural Science Foundation of China(Grant No.12247168)China Postdoctoral Science Foundation(Certificate Number:2022TQ0036)。
文摘With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.
基金supported by the National Natural Science Foundation of China (21878340)supported in part by the High-Performance Computing Center of Central South University
文摘Precision engineering of catalytic sites to guide more favorable pathways for Li_(2)O_(2) nucleation and decom-position represents an enticing kinetic strategy for mitigating overpotential,enhancing discharge capac-ity,and improving recycling stability of Li-O_(2) batteries.In this work,we employ metal-organic frameworks(MOFs)derivation and ion substitution strategies to construct atomically dispersed Mn-N_(4) moieties on hierarchical porous nitrogen-doped carbon(Mn SAs-NC)with the aim of reducing the over-potential and improving the cycling stability of Li-O_(2) batteries.The porous structure provides more chan-nels for mass transfer and exposes more highly active sites for electrocatalytic reactions,thus promoting the formation and decomposition of Li_(2)O_(2).The Li-O_(2) batteries with Mn SAs-NC cathode achieve lower overpotential,higher specific capacity(14290 mA h g^(-1) at 100 mAg^(-1)),and superior cycle stability(>100 cycles at 200 mA g^(-1))compared with the Mn NPs-NC and NC.Density functional theory(DFT)cal-culations reveal that the construction of Mn-N_(4) moiety tunes the charge distribution of the pyridinic N-rich vacancy and balances the affinity of the intermediates(LiO_(2) and Li_(2)O_(2)).The initial nucleation of Li_(2)O_(2) on Mn SAs-NC favors the O_(2)-→LiO_(2)→Li_(2)O_(2) surface-adsorption pathway,which mitigates the overpoten-tials of the oxygen reduction(ORR)and oxygen evolution reaction(OER).As a result,Mn SAs-NC with Mn-N_(4) moiety effectively facilitates the Li_(2)O_(2) nucleation and enables its reversible decomposition.This work establishes a methodology for constructing carbon-based electrocatalysts with high activity and selectivity for Li-O_(2)batteries.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. U20A20227,62076208, and 62076207)Chongqing Talent Plan “Contract System” Project (Grant No. CQYC20210302257)+3 种基金National Key Laboratory of Smart Vehicle Safety Technology Open Fund Project (Grant No. IVSTSKL-202309)the Chongqing Technology Innovation and Application Development Special Major Project (Grant No. CSTB2023TIAD-STX0020)College of Artificial Intelligence, Southwest UniversityState Key Laboratory of Intelligent Vehicle Safety Technology
文摘Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data analysis.This paper presents a model based on these nanowire networks,with an improved conductance variation profile.We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses.The nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction analysis.Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction accuracy.Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing.