Purpose-Amidst an increasingly severe cybersecurity landscape,the widespread adoption of Xinchuang endpoints has become a strategic imperative.Governments and enterprises have established terminal localization as a cr...Purpose-Amidst an increasingly severe cybersecurity landscape,the widespread adoption of Xinchuang endpoints has become a strategic imperative.Governments and enterprises have established terminal localization as a critical objective,aiming for comprehensive indigenous replacement through rapid technological iteration.Consequently,Xinchuang systems and Windows platforms are expected to coexist over an extended period.This study seeks to establish an automated verification framework for multi-version operating systems and validate the efficacy of baseline hardening in mitigating security risks.Design/methodology/approach-Based on the Classified Protection 2.0 framework and relevant national standards for endpoint security,this study proposes an endpoint security baseline verification scheme applicable to multiple operating systems.The scheme addresses divergent security policies and implementation methodologies across heterogeneous environments.It automates the inspection of core baselines,including account password complexity,default shared service status and patch installation status.Furthermore,a comprehensive scoring model is established by incorporating differentiated weights for account security,patch management and log auditing,ultimately generating visualized risk reports to facilitate remediation prioritization.Findings-This study reveals that baseline configuration serves as the fundamental prerequisite in endpoint security practices.Through a scalable detection engine and quantitative scoring model,the system can promptly identify and remediate potential risks,thereby reducing the attack surface and mitigating intrusion risks.However,on certain domestic chip architectures,compatibility issues persist in detecting specific configuration items.Further improvement in hardware-software co-adaptation for domestic platforms is required to advance the development of localized security protection systems.Originality/value-Through in-depth research on security baseline configurations across multiple operating systems,this study implements an automated and visualized baseline verification methodology.This approach significantly strengthens the security posture of domestic operating systems and supports the establishment of a more robust,national-level cybersecurity defense framework.展开更多
The pursuit of simultaneously high wear resistance and excellent lubrication in multi‐principal element alloy(MPEA)composites is often hindered by a fundamental trade‐off,which is exacerbated by the agglomeration of...The pursuit of simultaneously high wear resistance and excellent lubrication in multi‐principal element alloy(MPEA)composites is often hindered by a fundamental trade‐off,which is exacerbated by the agglomeration of high‐content graphene reinforcements.This compromise becomes particularly severe in composites with high‐content graphene reinforcements,whose agglomeration leads to embrittlement and lubrication failure.Here,a flake powder-metallurgy strategy is developed to construct a self‐assembled lamellar structure in graphene/CoCrNi MPEA composites(Gr/MPEA_(AL)).This approach enables the uniform dispersion of a high graphene content(3.0 wt%),which is unattainable by conventional methods.The resulting composite exhibits a rare dual enhancement in performance:an order‐of‐magnitude improvement in wear resistance coupled with a low coefficient of friction.Intriguingly,the tribological behavior shows significant anisotropy,with optimal performance observed when sliding perpendicular to the lamellae.Through a multi‐scale methodology combining molecular dynamics simulations,finite element analysis,and systematic experiments,it is revealed that this exceptional performance stems from the synergy of high‐density deformation nanotwins,efficient strain delocalization,and abundant graphene‐derived lubricating sites.This work establishes a general paradigm for designing composite architectures that reconcile traditionally incompatible properties,offering broad implications for developing next‐generation structural materials with integrated mechanical robustness and surface functionality for safety‐critical applications.展开更多
Transformer-based neural-network quantum states(NNQS)have shown great promise in representing quantum manybody ground states,offering high flexibility and accuracy.However,the interpretability of such models remains l...Transformer-based neural-network quantum states(NNQS)have shown great promise in representing quantum manybody ground states,offering high flexibility and accuracy.However,the interpretability of such models remains limited,especially in terms of connecting network components to physically meaningful quantities.We propose that the attention mechanism—a central module in transformer architectures—explicitly models the conditional information flow between orbitals.Intuitively,as the transformer learns to predict orbital configurations by optimizing an energy functional,it approximates the conditional probability distribution p(xn|x_(1),...,x_(n-1)),implicitly encoding conditional mutual information(CMI)among orbitals.This suggests a natural correspondence between attention maps and CMI structures in quantum systems.To probe this idea,we compare weighted attention scores from trained transformer wavefunction ansatze with CMI matrices across several representative small molecules.In most cases,we observe a positive rank-level correlation(Kendall's tau)between attention and CMI,suggesting that the learned attention can reflect physically relevant orbital dependencies.This study provides a quantitative link between transformer attention and conditional mutual information in the NNQS setting.Our results provide a step toward explainable deep learning in quantum chemistry,pointing to opportunities in interpreting attention as a proxy for physical correlations.展开更多
As the frontier of multidimensional transportation systems,urban air mobility(UAM)is receiving increasing attention from international organizations,governments,and stakeholders in industry and academia owing to its h...As the frontier of multidimensional transportation systems,urban air mobility(UAM)is receiving increasing attention from international organizations,governments,and stakeholders in industry and academia owing to its high efficiency,low carbon footprint,and operational flexibility.Vertical take-off and landing(VTOL)infrastructure is the core facility that enables UAM and is therefore essential for its safe,efficient,and large-scale commercial implementation.However,the key technologies for establishing low-altitude VTOL infrastructure are still nascent,and government,industry,and academia have yet to harmonize the corresponding construction,management,and operation standards.To address this gap,we herein systematically review the related progress and trends,comprehensively surveying the key technologies of establishing VTOL infrastructure serving unmanned aerial vehicles(UAVs)and electric VTOL aircraft from three complementary perspectives of ground-side,airspace-side,and communication,navigation,surveillance,and information services.In the light of future UAM operations characterized by diverse vehicle types and dense air traffic,we propose a conceptual design for a public multioperator VTOL site to provide constructive insights into the sustainable growth of the low-altitude economy.展开更多
With the proliferation of the Internet of Things(IoT),various services are emerging with totally different features and requirements,which cannot be supported by the current fifth generation of mobile cellular network...With the proliferation of the Internet of Things(IoT),various services are emerging with totally different features and requirements,which cannot be supported by the current fifth generation of mobile cellular networks(5G).The future sixth generation of mobile cellular networks(6G)is expected to have the capability to support new and unknown services with changing requirements.Hence,in addition to enhancing its capability by 10–100 times compared with 5G,6G should also be intelligent and open to adapt to the ever-changing services in the IoT,which requires a convergence of Communication,Computing and Caching(3C).Based on the analysis of the requirements of new services for 6G,this paper identifies key enabling technologies for an intelligent and open 6G network,all featured with 3C convergence.These technologies cover fundamental and emerging topics,including 3C-based spectrum management,radio channel construction,delay-aware transmission,wireless distributed computing,and network self-evolution.From the detailed analysis of these 3C-based technologies presented in this paper,we can see that although they are promising to enable an intelligent and open 6G,more efforts are needed to realize the expected 6G network.展开更多
With analysis of limitations Trusted Computing Group （TCG） has encountered, we argued that virtual machine monitor （VMM） is the appropriate architecture for implementing TCG specification. Putting together the VMM...With analysis of limitations Trusted Computing Group （TCG） has encountered, we argued that virtual machine monitor （VMM） is the appropriate architecture for implementing TCG specification. Putting together the VMM architecture, TCG hardware and application-oriented ＂thin＂ virtual machine （VM）, Trusted VMM-based security architecture is present in this paper with the character of reduced and distributed trusted computing base （TCB）. It provides isolation and integrity guarantees based on which general security requirements can be satisfied.展开更多
Dominance-based rough set approach(DRSA) permits representation and analysis of all phenomena involving monotonicity relationship between some measures or perceptions.DRSA has also some merits within granular computin...Dominance-based rough set approach(DRSA) permits representation and analysis of all phenomena involving monotonicity relationship between some measures or perceptions.DRSA has also some merits within granular computing,as it extends the paradigm of granular computing to ordered data,specifies a syntax and modality of information granules which are appropriate for dealing with ordered data,and enables computing with words and reasoning about ordered data.Granular computing with ordered data is a very general paradigm,because other modalities of information constraints,such as veristic,possibilistic and probabilistic modalities,have also to deal with ordered value sets(with qualifiers relative to grades of truth,possibility and probability),which gives DRSA a large area of applications.展开更多
1.Introduction The rapid expansion of satellite constellations in recent years has resulted in the generation of massive amounts of data.This surge in data,coupled with diverse application scenarios,underscores the es...1.Introduction The rapid expansion of satellite constellations in recent years has resulted in the generation of massive amounts of data.This surge in data,coupled with diverse application scenarios,underscores the escalating demand for high-performance computing over space.Computing over space entails the deployment of computational resources on platforms such as satellites to process large-scale data under constraints such as high radiation exposure,restricted power consumption,and minimized weight.展开更多
Wireless communication-enabled Cooperative Adaptive Cruise Control(CACC)is expected to improve the safety and traffic capacity of vehicle platoons.Existing CACC considers a conventional communication delay with fixed ...Wireless communication-enabled Cooperative Adaptive Cruise Control(CACC)is expected to improve the safety and traffic capacity of vehicle platoons.Existing CACC considers a conventional communication delay with fixed Vehicular Communication Network(VCN)topologies.However,when the network is under attack,the communication delay may be much higher,and the stability of the system may not be guaranteed.This paper proposes a novel communication Delay Aware CACC with Dynamic Network Topologies(DADNT).The main idea is that for various communication delays,in order to maximize the traffic capacity while guaranteeing stability and minimizing the following error,the CACC should dynamically adjust the VCN network topology to achieve the minimum inter-vehicle spacing.To this end,a multi-objective optimization problem is formulated,and a 3-step Divide-And-Conquer sub-optimal solution(3DAC)is proposed.Simulation results show that with 3DAC,the proposed DADNT with CACC can reduce the inter-vehicle spacing by 5%,10%,and 14%,respectively,compared with the traditional CACC with fixed one-vehicle,two-vehicle,and three-vehicle look-ahead network topologies,thereby improving the traffic efficiency.展开更多
The advent of Grover’s algorithm presents a significant threat to classical block cipher security,spurring research into post-quantum secure cipher design.This study engineers quantum circuit implementations for thre...The advent of Grover’s algorithm presents a significant threat to classical block cipher security,spurring research into post-quantum secure cipher design.This study engineers quantum circuit implementations for three versions of the Ballet family block ciphers.The Ballet‑p/k includes a modular-addition operation uncommon in lightweight block ciphers.Quantum ripple-carry adder is implemented for both“32+32”and“64+64”scale to support this operation.Subsequently,qubits,quantum gates count,and quantum circuit depth of three versions of Ballet algorithm are systematically evaluated under quantum computing model,and key recovery attack circuits are constructed based on Grover’s algorithm against each version.The comprehensive analysis shows:Ballet-128/128 fails to NIST Level 1 security,while when the resource accounting is restricted to the Clifford gates and T gates set for the Ballet-128/256 and Ballet-256/256 quantum circuits,the design attains Level 3.展开更多
Foreign object intrusion into railway lines poses a significant threat to train operational safety.Current intelligent identification systems encounter substantial challenges in addressing issues such as data scarcity...Foreign object intrusion into railway lines poses a significant threat to train operational safety.Current intelligent identification systems encounter substantial challenges in addressing issues such as data scarcity and foreign object diversity.To address the aforementioned issues,the paper proposes an intelligent method for detecting foreign objects on railway lines based on a large-scale AI model.The study takes into account the model's feature extraction capabilities and generalization performance during pre-training,expands its structural depth and width,and conducts model fine-tuning in line with relevant transfer learning strategies so as to effectively adapt the model to the task of identifying foreign objects on railway lines.The experimental results demonstrate that the detection algorithm,powered by a large-scale AI model,can significantly reduce reliance on annotated data.Even in the face of limited training data and a diverse array of unknown foreign object categories,the algorithm achieves high detection accuracy and real-time performance.This highlights its robust capability to handle unknown and varied foreign object intrusions in complex environments.展开更多
Vision applications in the railway sector often face challenges such as complex and dynamic scenarios,coupled with a limited number of effective samples.Designing small models for individual scenarios is not only time...Vision applications in the railway sector often face challenges such as complex and dynamic scenarios,coupled with a limited number of effective samples.Designing small models for individual scenarios is not only time-consuming and resource-intensive but also fails to meet the diverse business needs.Therefore,developing large vision models specifically for the railway industry is of critical importance.This paper examines and explores potential application scenarios for large vision models within the railway sector,proposing a solution for their development.The research builds upon the UPerNet network,utilizing InternImage to replace the original backbone network,thereby enhancing the model's ability to capture details of image targets.To further improve model robustness,Semantic-Aware Normalization(SAN)and Semantic-Aware Whitening(SAW)attention mechanisms are introduced in place of the original pyramid pooling module.Additionally,the integration of spatial attention and channel attention replaces the original decoding part,allowing for dynamic adjustments to attention across various regions.Finally,datasets for railway scenarios were established through semi-automatic annotation.The experimental results indicate that the improved UPerNet_InternImage large vision model proposed for the railway industry has potential to enhance the segmentation accuracy and robustness.The model exhibits faster convergence speeds and improved effectiveness when tackling segmentation tasks in specific railway scenarios.It offers new insights and methodologies for addressing issues prevalent in railway vision scenarios.展开更多
To effectively address the new challenges in the operation and maintenance of track,communication and signaling,and power supply of intercity railways,it is imperative to conduct thorough analyses of these operation a...To effectively address the new challenges in the operation and maintenance of track,communication and signaling,and power supply of intercity railways,it is imperative to conduct thorough analyses of these operation and maintenance requirements,integrate advanced technologies,and meet the infrastructure maintenance requirements of metropolitan rail networks.In response to these needs,an integrated intelligent operation and maintenance platform for track,communication and signaling,and power supply of intercity railways has been developed,based on an assessment of the current status and needs of intercity railway operation and maintenance.This platform combines a comprehensive maintenance management information system,a PHM system for track,communication and signaling,and power supply,as well as an intelligent decision-making operation and maintenance management system.This platform plays a critical role in ensuring high stability and reliability throughout the full life cycle of infrastructure while effectively reducing operation and maintenance costs.展开更多
Artificial intelligence technologies are rapidly evolving,with generative AI advancements—particularly those driven by large models—drawing significant attention.Large model technologies will play a pivotal role in ...Artificial intelligence technologies are rapidly evolving,with generative AI advancements—particularly those driven by large models—drawing significant attention.Large model technologies will play a pivotal role in railway intelligent operation and maintenance(O&M)by leveraging natural language as the medium.Based on the multi-source and heterogeneous data characteristics of railway infrastructure,this study investigates data analysis methods and application scenarios for railway infrastructure O&M leveraging large natural language models.An overall architecture is proposed for intelligent O&M of railway infrastructure,centered on railway large natural language models and featuring multi-source model synergy.This architecture is developed through a detailed analysis of O&M knowledge sources and structures,as well as data analysis requirements spanning the entire life cycle of railway infrastructure.These railwayspecific models are employed to derive railway intelligent O&M scenario models,which are driven by intelligent agent technologies and integrate traditional models,knowledge graphs,and other technologies to empower railway intelligent O&M.Further research focuses on key technologies,including the fine-tuning of railway large natural language models,retrievalaugmented generation,and AI agent technologies.These technologies are combined with the capabilities inherent in large natural language models—such as logical reasoning,content generation,and intelligent decision-making—to explore applications of large natural language models in inspection,repair,and maintenance of railway infrastructure,management of equipment maintenance information,equipment condition inspection,fault handling and emergency response in accidents,and intelligent O&M decision-making.展开更多
Purpose–This paper conducts a joint analysis of monitoring data in the hidden danger areas of railway subgrade deformation using a data-driven method,thereby realizing the systematic risk identification of regional h...Purpose–This paper conducts a joint analysis of monitoring data in the hidden danger areas of railway subgrade deformation using a data-driven method,thereby realizing the systematic risk identification of regional hidden dangers.Design/methodology/approach–The paper proposes a regional systematic risk identification method based on Bayesian and independent component analysis(ICA)theories.Firstly,the Gray Wolf Optimization(GWO)algorithm is used to partition each group of monitoring data in the hidden danger area,so that the data distribution characteristics within each sub-block are similar.Then,a distributed ICA early warning model is constructed to obtain prior knowledge such as control limits and statistics of the area under normal conditions.For the online evaluation process,the input data is partitioned following the above-mentioned procedure and the ICA statistics of each sub-block are calculated.The Bayesian method is applied to fuse online parameters with offline parameters,yielding statistics under a specific confidence interval.These statistics are then compared with the control limits–specifically,checking whether they exceed the pre-set confidence parameters–thus realizing the systematic risk identification of the hidden danger area.Findings–Through simulation experiments,the proposed method can integrate prior knowledge such as control limits and statistics to effectively determine the overall stability status of the area,thereby realizing the systematic risk identification of the hidden danger area.Originality/value–The proposed method leverages Bayesian theory to fuse online process parameters with offline parameters and further compares them with confidence parameters,thereby effectively enhancing the utilization efficiency of monitoring data and the robustness of the analytical model.展开更多
Purpose-The indoor vibration compaction test(IVCT)was a key step in controlling the compaction quality for high-speed railway graded aggregate(HRGA),which currently had a research gap on the assessment indicators and ...Purpose-The indoor vibration compaction test(IVCT)was a key step in controlling the compaction quality for high-speed railway graded aggregate(HRGA),which currently had a research gap on the assessment indicators and compaction parameters.Design/methodology/approach-To address these issues,a novel multi-indicator IVCT method was proposed,including physical indicator dry density(ρd)and mechanical indicators dynamic stiffness(Krb)and bearing capacity coefficient(K20).Then,a series of IVCTs on HRGA under different compaction parameters were conducted with an improved vibration compactor,which could monitor the physical-mechanical indicators in real-time.Finally,the optimal vibration compaction parameters,including the moisture content(ω),the diameter-to-maximum particle size ratio(Rd),the thickness-to-maximum particle size ratio(Rh),the vibration frequency(f),the vibration mass(Mc)and the eccentric distance(re),were determined based on the evolution characteristics for the physical-mechanical indicators during compaction.Findings-All results indicated that theρd gradually increased and then stabilized,and the Krb initially increased and then decreased.Moreover,the inflection time of the Krb was present as the optimal compaction time(Tlp)during compaction.Additionally,optimal compaction was achieved whenωwas the water-holding content after mud pumping,Rd was 3.4,Rh was 3.5,f was the resonance frequency,and the ratio between the excitation force and the Mc was 1.8.Originality/value-The findings of this paper were significant for the quality control of HRGA compaction.展开更多
Accurate evaluation of elec-tron correlations is essential for the reliable quantitative de-scription of electronic struc-tures in strongly correlated sys-tems,including bond-dissociat-ing molecules,polyradicals,large...Accurate evaluation of elec-tron correlations is essential for the reliable quantitative de-scription of electronic struc-tures in strongly correlated sys-tems,including bond-dissociat-ing molecules,polyradicals,large conjugated molecules,and transition metal complex-es.To provide a user-friendly tool for studying such challeng-ing systems,our team developed Kylin 1.0[J.Comput.Chem.44,1316(2023)],an ab initio quantum chemistry program designed for efficient density matrix renormalization group(DMRG)and post-DMRG methods,enabling high-accuracy calculations with large active spaces.We have now further advanced the software with the release of Kylin 1.3,featuring optimized DMRG algorithms and an improved tensor contraction scheme in the diagonaliza-tion step.Benchmark calculations on the Mn_(4)CaO_(5)cluster demonstrate a remarkable speed-up of up to 16 fater than Kylin 1.0.Moreover,a more user-friendly and efficient algorithm[J.Chem.Theory Comput.17,3414(2021)]for sampling configurations from DMRG wavefunc-tion is implemented as well.Additionally,we have also implemented a spin-adapted version of the externally contracted multi-reference configuration interaction(EC-MRCI)method[J.Phys.Chem.A 128,958(2024)],further enhancing the program’s efficiency and accuracy for electron correlation calculations.展开更多
Behavioral scoring based on clinical observations remains the gold standard for screening,diagnosing,and evaluating infantile epileptic spasm syndrome(IESS).The accurate identification of seizures is crucial for clini...Behavioral scoring based on clinical observations remains the gold standard for screening,diagnosing,and evaluating infantile epileptic spasm syndrome(IESS).The accurate identification of seizures is crucial for clinical diagnosis and assessment.In this study,we propose an innovative seizure detection method based on video feature recognition of patient spasms.To capture the temporal characteristics of the spasm behavior presented in the videos effectively,we incorporate asymmetric convolutions and convolution–batch normalization–ReLU(CBR)modules.Specifically within the 3D-ResNet residual blocks,we split the larger convolutional kernels into two asymmetric 3D convolutional kernels.These kernels are connected in series to enhance the ability of the convolutional layers to extract key local features,both horizontally and vertically.In addition,we introduce a 3D convolutional block attention module to enhance the spatial correlations between video frame channels efficiently.To improve the generalization ability,we design a composite loss function that combines cross-entropy loss with triplet loss to balance the classification and similarity requirements.We train and evaluate our method using the PLA IESS-VIDEO dataset,achieving an average seizure recognition accuracy of 90.59%,precision of 90.94%,and recall of 87.64%.To validate its generalization capability further,we conducted external validation using six different patient monitoring videos compared with assessments by six human experts from various medical centers.The final test results demonstrate that our method achieved a recall of 0.6476,surpassing the average level achieved by human experts(0.5595),while attaining a high F1-score of 0.7219.These findings have substantial significance for the long-term assessment of patients with IESS.展开更多
LEO satellite communication systems have the characteristics of high-speed and periodic movement.The handover of user link occurs frequently,which has a serious impact on user terminal application and system capacity....LEO satellite communication systems have the characteristics of high-speed and periodic movement.The handover of user link occurs frequently,which has a serious impact on user terminal application and system capacity.To address this issue,we propose a handover strategy of LEO satellite user terminal based on multi-attribute and multi-point(MAMP)cooperation.Firstly,the satellite-user-time matrix is established by using the satellite constellation coverage and handover model.Then,combined with the visual time and signal quality,the user access matrix and satellite load matrix are extracted to determine the weight equation of the handover strategy with the channel reservation.According to the system modeling simulation,the algorithm improves the handover success rate by 2.5%,the lasted call access success rate by 3.2%,the load balancing degree by 20%,and the robustness by two orders of magnitude.展开更多
Knowledge graphs,which combine structured representation with semantic modeling,have shown great potential in knowledge expression,causal inference,and automated reasoning,and are widely used in fields such as intelli...Knowledge graphs,which combine structured representation with semantic modeling,have shown great potential in knowledge expression,causal inference,and automated reasoning,and are widely used in fields such as intelligent question answering,decision support,and fault diagnosis.As high-speed train systems become increasingly intelligent and interconnected,fault patterns have grown more complex and dynamic.Knowledge graphs offer a promising solution to support the structured management and real-time reasoning of fault knowledge,addressing key requirements such as interpretability,accuracy,and continuous evolution in intelligent diagnostic systems.However,conventional knowledge graph construction relies heavily on domain expertise and specialized tools,resulting in high entry barriers for non-experts and limiting their practical application in frontline maintenance scenarios.To address this limitation,this paper proposes a fault knowledge modeling approach for high-speed trains that integrates structured logic diagrams with knowledge graphs.The method employs a seven-layer logic structure—comprising fault name,applicable vehicles,diagnostic logic,signal parameters,verification conditions,fault causes,and emergency measures—to transform unstructured knowledge into a visual and hierarchical representation.A semantic mapping mechanism is then used to automatically convert logic diagrams into machine-interpretable knowledge graphs,enabling dynamic reasoning and knowledge reuse.Furthermore,the proposed method establishes a three-layer architecture—logic structuring,knowledge graph transformation,and dynamic inference—to bridge human-expert logic with machinebased reasoning.Experimental validation and system implementation demonstrate that this approach not only improves knowledge interpretability and inference precision but also significantly enhances modeling efficiency and system maintainability.It provides a scalable and adaptable solution for intelligent operation and maintenance platforms in the high-speed rail domain.展开更多
基金supported by scientific research projects of China Academy of Railway Sciences Co.,Ltd.(grant no.2024YJ117).
文摘Purpose-Amidst an increasingly severe cybersecurity landscape,the widespread adoption of Xinchuang endpoints has become a strategic imperative.Governments and enterprises have established terminal localization as a critical objective,aiming for comprehensive indigenous replacement through rapid technological iteration.Consequently,Xinchuang systems and Windows platforms are expected to coexist over an extended period.This study seeks to establish an automated verification framework for multi-version operating systems and validate the efficacy of baseline hardening in mitigating security risks.Design/methodology/approach-Based on the Classified Protection 2.0 framework and relevant national standards for endpoint security,this study proposes an endpoint security baseline verification scheme applicable to multiple operating systems.The scheme addresses divergent security policies and implementation methodologies across heterogeneous environments.It automates the inspection of core baselines,including account password complexity,default shared service status and patch installation status.Furthermore,a comprehensive scoring model is established by incorporating differentiated weights for account security,patch management and log auditing,ultimately generating visualized risk reports to facilitate remediation prioritization.Findings-This study reveals that baseline configuration serves as the fundamental prerequisite in endpoint security practices.Through a scalable detection engine and quantitative scoring model,the system can promptly identify and remediate potential risks,thereby reducing the attack surface and mitigating intrusion risks.However,on certain domestic chip architectures,compatibility issues persist in detecting specific configuration items.Further improvement in hardware-software co-adaptation for domestic platforms is required to advance the development of localized security protection systems.Originality/value-Through in-depth research on security baseline configurations across multiple operating systems,this study implements an automated and visualized baseline verification methodology.This approach significantly strengthens the security posture of domestic operating systems and supports the establishment of a more robust,national-level cybersecurity defense framework.
基金supported by Guangdong Basic and Applied Basic Research Foundation(No.2024A1515012378)Natural Science Foundation of China(Nos.52471093,52274367)+3 种基金fund of the State Key Laboratory of Solidification Processing in NPU(No.2025‐QZ‐03)the Practice and Innovation Funds for Graduate Students of Northwestern Polytechnical University(No.PF2025041)Fundamental Research Projects of Science&Technology Innovation and development Plan in Yantai City(No.2024JCYJ099)project(No.ZR2024QE213)supported by Shandong Provincial Natural Science Foundation.
文摘The pursuit of simultaneously high wear resistance and excellent lubrication in multi‐principal element alloy(MPEA)composites is often hindered by a fundamental trade‐off,which is exacerbated by the agglomeration of high‐content graphene reinforcements.This compromise becomes particularly severe in composites with high‐content graphene reinforcements,whose agglomeration leads to embrittlement and lubrication failure.Here,a flake powder-metallurgy strategy is developed to construct a self‐assembled lamellar structure in graphene/CoCrNi MPEA composites(Gr/MPEA_(AL)).This approach enables the uniform dispersion of a high graphene content(3.0 wt%),which is unattainable by conventional methods.The resulting composite exhibits a rare dual enhancement in performance:an order‐of‐magnitude improvement in wear resistance coupled with a low coefficient of friction.Intriguingly,the tribological behavior shows significant anisotropy,with optimal performance observed when sliding perpendicular to the lamellae.Through a multi‐scale methodology combining molecular dynamics simulations,finite element analysis,and systematic experiments,it is revealed that this exceptional performance stems from the synergy of high‐density deformation nanotwins,efficient strain delocalization,and abundant graphene‐derived lubricating sites.This work establishes a general paradigm for designing composite architectures that reconcile traditionally incompatible properties,offering broad implications for developing next‐generation structural materials with integrated mechanical robustness and surface functionality for safety‐critical applications.
基金supported by the National Natural Science Foundation of China(Grant No.T2222026)the CAS Project for Young Scientists in Basic Research(Grant No.YSBR-034)the Robotic AIScientist Platform of the Chinese Academy of Sciences。
文摘Transformer-based neural-network quantum states(NNQS)have shown great promise in representing quantum manybody ground states,offering high flexibility and accuracy.However,the interpretability of such models remains limited,especially in terms of connecting network components to physically meaningful quantities.We propose that the attention mechanism—a central module in transformer architectures—explicitly models the conditional information flow between orbitals.Intuitively,as the transformer learns to predict orbital configurations by optimizing an energy functional,it approximates the conditional probability distribution p(xn|x_(1),...,x_(n-1)),implicitly encoding conditional mutual information(CMI)among orbitals.This suggests a natural correspondence between attention maps and CMI structures in quantum systems.To probe this idea,we compare weighted attention scores from trained transformer wavefunction ansatze with CMI matrices across several representative small molecules.In most cases,we observe a positive rank-level correlation(Kendall's tau)between attention and CMI,suggesting that the learned attention can reflect physically relevant orbital dependencies.This study provides a quantitative link between transformer attention and conditional mutual information in the NNQS setting.Our results provide a step toward explainable deep learning in quantum chemistry,pointing to opportunities in interpreting attention as a proxy for physical correlations.
基金supported by the National Natural Science Foundation of China(No.U2333214).
文摘As the frontier of multidimensional transportation systems,urban air mobility(UAM)is receiving increasing attention from international organizations,governments,and stakeholders in industry and academia owing to its high efficiency,low carbon footprint,and operational flexibility.Vertical take-off and landing(VTOL)infrastructure is the core facility that enables UAM and is therefore essential for its safe,efficient,and large-scale commercial implementation.However,the key technologies for establishing low-altitude VTOL infrastructure are still nascent,and government,industry,and academia have yet to harmonize the corresponding construction,management,and operation standards.To address this gap,we herein systematically review the related progress and trends,comprehensively surveying the key technologies of establishing VTOL infrastructure serving unmanned aerial vehicles(UAVs)and electric VTOL aircraft from three complementary perspectives of ground-side,airspace-side,and communication,navigation,surveillance,and information services.In the light of future UAM operations characterized by diverse vehicle types and dense air traffic,we propose a conceptual design for a public multioperator VTOL site to provide constructive insights into the sustainable growth of the low-altitude economy.
基金This work is supported by the National Natural Science Youth Fund of China granted by No.61901452 and Innovative Project of ICT/CAS granted by No.20196110
文摘With the proliferation of the Internet of Things(IoT),various services are emerging with totally different features and requirements,which cannot be supported by the current fifth generation of mobile cellular networks(5G).The future sixth generation of mobile cellular networks(6G)is expected to have the capability to support new and unknown services with changing requirements.Hence,in addition to enhancing its capability by 10–100 times compared with 5G,6G should also be intelligent and open to adapt to the ever-changing services in the IoT,which requires a convergence of Communication,Computing and Caching(3C).Based on the analysis of the requirements of new services for 6G,this paper identifies key enabling technologies for an intelligent and open 6G network,all featured with 3C convergence.These technologies cover fundamental and emerging topics,including 3C-based spectrum management,radio channel construction,delay-aware transmission,wireless distributed computing,and network self-evolution.From the detailed analysis of these 3C-based technologies presented in this paper,we can see that although they are promising to enable an intelligent and open 6G,more efforts are needed to realize the expected 6G network.
基金Supported by the National Program on Key Basic Re-search Project of China (G1999035801)
文摘With analysis of limitations Trusted Computing Group （TCG） has encountered, we argued that virtual machine monitor （VMM） is the appropriate architecture for implementing TCG specification. Putting together the VMM architecture, TCG hardware and application-oriented ＂thin＂ virtual machine （VM）, Trusted VMM-based security architecture is present in this paper with the character of reduced and distributed trusted computing base （TCB）. It provides isolation and integrity guarantees based on which general security requirements can be satisfied.
文摘Dominance-based rough set approach(DRSA) permits representation and analysis of all phenomena involving monotonicity relationship between some measures or perceptions.DRSA has also some merits within granular computing,as it extends the paradigm of granular computing to ordered data,specifies a syntax and modality of information granules which are appropriate for dealing with ordered data,and enables computing with words and reasoning about ordered data.Granular computing with ordered data is a very general paradigm,because other modalities of information constraints,such as veristic,possibilistic and probabilistic modalities,have also to deal with ordered value sets(with qualifiers relative to grades of truth,possibility and probability),which gives DRSA a large area of applications.
基金supported in part by the National Natural Science Foundation of China(62025404)in part by the National Key Research and Development Program of China(2022YFB3902802)+1 种基金in part by the Beijing Natural Science Foundation(L241013)in part by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA000000).
文摘1.Introduction The rapid expansion of satellite constellations in recent years has resulted in the generation of massive amounts of data.This surge in data,coupled with diverse application scenarios,underscores the escalating demand for high-performance computing over space.Computing over space entails the deployment of computational resources on platforms such as satellites to process large-scale data under constraints such as high radiation exposure,restricted power consumption,and minimized weight.
基金supported by the National Natural Science Foundation of China under Grant U21A20449in part by Jiangsu Provincial Key Research and Development Program under Grant BE2021013-2。
文摘Wireless communication-enabled Cooperative Adaptive Cruise Control(CACC)is expected to improve the safety and traffic capacity of vehicle platoons.Existing CACC considers a conventional communication delay with fixed Vehicular Communication Network(VCN)topologies.However,when the network is under attack,the communication delay may be much higher,and the stability of the system may not be guaranteed.This paper proposes a novel communication Delay Aware CACC with Dynamic Network Topologies(DADNT).The main idea is that for various communication delays,in order to maximize the traffic capacity while guaranteeing stability and minimizing the following error,the CACC should dynamically adjust the VCN network topology to achieve the minimum inter-vehicle spacing.To this end,a multi-objective optimization problem is formulated,and a 3-step Divide-And-Conquer sub-optimal solution(3DAC)is proposed.Simulation results show that with 3DAC,the proposed DADNT with CACC can reduce the inter-vehicle spacing by 5%,10%,and 14%,respectively,compared with the traditional CACC with fixed one-vehicle,two-vehicle,and three-vehicle look-ahead network topologies,thereby improving the traffic efficiency.
基金State Key Lab of Processors,Institute of Computing Technology,Chinese Academy of Sciences(CLQ202516)the Fundamental Research Funds for the Central Universities of China(3282025047,3282024051,3282024009)。
文摘The advent of Grover’s algorithm presents a significant threat to classical block cipher security,spurring research into post-quantum secure cipher design.This study engineers quantum circuit implementations for three versions of the Ballet family block ciphers.The Ballet‑p/k includes a modular-addition operation uncommon in lightweight block ciphers.Quantum ripple-carry adder is implemented for both“32+32”and“64+64”scale to support this operation.Subsequently,qubits,quantum gates count,and quantum circuit depth of three versions of Ballet algorithm are systematically evaluated under quantum computing model,and key recovery attack circuits are constructed based on Grover’s algorithm against each version.The comprehensive analysis shows:Ballet-128/128 fails to NIST Level 1 security,while when the resource accounting is restricted to the Clifford gates and T gates set for the Ballet-128/256 and Ballet-256/256 quantum circuits,the design attains Level 3.
文摘Foreign object intrusion into railway lines poses a significant threat to train operational safety.Current intelligent identification systems encounter substantial challenges in addressing issues such as data scarcity and foreign object diversity.To address the aforementioned issues,the paper proposes an intelligent method for detecting foreign objects on railway lines based on a large-scale AI model.The study takes into account the model's feature extraction capabilities and generalization performance during pre-training,expands its structural depth and width,and conducts model fine-tuning in line with relevant transfer learning strategies so as to effectively adapt the model to the task of identifying foreign objects on railway lines.The experimental results demonstrate that the detection algorithm,powered by a large-scale AI model,can significantly reduce reliance on annotated data.Even in the face of limited training data and a diverse array of unknown foreign object categories,the algorithm achieves high detection accuracy and real-time performance.This highlights its robust capability to handle unknown and varied foreign object intrusions in complex environments.
文摘Vision applications in the railway sector often face challenges such as complex and dynamic scenarios,coupled with a limited number of effective samples.Designing small models for individual scenarios is not only time-consuming and resource-intensive but also fails to meet the diverse business needs.Therefore,developing large vision models specifically for the railway industry is of critical importance.This paper examines and explores potential application scenarios for large vision models within the railway sector,proposing a solution for their development.The research builds upon the UPerNet network,utilizing InternImage to replace the original backbone network,thereby enhancing the model's ability to capture details of image targets.To further improve model robustness,Semantic-Aware Normalization(SAN)and Semantic-Aware Whitening(SAW)attention mechanisms are introduced in place of the original pyramid pooling module.Additionally,the integration of spatial attention and channel attention replaces the original decoding part,allowing for dynamic adjustments to attention across various regions.Finally,datasets for railway scenarios were established through semi-automatic annotation.The experimental results indicate that the improved UPerNet_InternImage large vision model proposed for the railway industry has potential to enhance the segmentation accuracy and robustness.The model exhibits faster convergence speeds and improved effectiveness when tackling segmentation tasks in specific railway scenarios.It offers new insights and methodologies for addressing issues prevalent in railway vision scenarios.
文摘To effectively address the new challenges in the operation and maintenance of track,communication and signaling,and power supply of intercity railways,it is imperative to conduct thorough analyses of these operation and maintenance requirements,integrate advanced technologies,and meet the infrastructure maintenance requirements of metropolitan rail networks.In response to these needs,an integrated intelligent operation and maintenance platform for track,communication and signaling,and power supply of intercity railways has been developed,based on an assessment of the current status and needs of intercity railway operation and maintenance.This platform combines a comprehensive maintenance management information system,a PHM system for track,communication and signaling,and power supply,as well as an intelligent decision-making operation and maintenance management system.This platform plays a critical role in ensuring high stability and reliability throughout the full life cycle of infrastructure while effectively reducing operation and maintenance costs.
文摘Artificial intelligence technologies are rapidly evolving,with generative AI advancements—particularly those driven by large models—drawing significant attention.Large model technologies will play a pivotal role in railway intelligent operation and maintenance(O&M)by leveraging natural language as the medium.Based on the multi-source and heterogeneous data characteristics of railway infrastructure,this study investigates data analysis methods and application scenarios for railway infrastructure O&M leveraging large natural language models.An overall architecture is proposed for intelligent O&M of railway infrastructure,centered on railway large natural language models and featuring multi-source model synergy.This architecture is developed through a detailed analysis of O&M knowledge sources and structures,as well as data analysis requirements spanning the entire life cycle of railway infrastructure.These railwayspecific models are employed to derive railway intelligent O&M scenario models,which are driven by intelligent agent technologies and integrate traditional models,knowledge graphs,and other technologies to empower railway intelligent O&M.Further research focuses on key technologies,including the fine-tuning of railway large natural language models,retrievalaugmented generation,and AI agent technologies.These technologies are combined with the capabilities inherent in large natural language models—such as logical reasoning,content generation,and intelligent decision-making—to explore applications of large natural language models in inspection,repair,and maintenance of railway infrastructure,management of equipment maintenance information,equipment condition inspection,fault handling and emergency response in accidents,and intelligent O&M decision-making.
基金supported by Science and Technology Research and Development Program Project of China State Railway Group Co.,Ltd.(award number:K2024X010).
文摘Purpose–This paper conducts a joint analysis of monitoring data in the hidden danger areas of railway subgrade deformation using a data-driven method,thereby realizing the systematic risk identification of regional hidden dangers.Design/methodology/approach–The paper proposes a regional systematic risk identification method based on Bayesian and independent component analysis(ICA)theories.Firstly,the Gray Wolf Optimization(GWO)algorithm is used to partition each group of monitoring data in the hidden danger area,so that the data distribution characteristics within each sub-block are similar.Then,a distributed ICA early warning model is constructed to obtain prior knowledge such as control limits and statistics of the area under normal conditions.For the online evaluation process,the input data is partitioned following the above-mentioned procedure and the ICA statistics of each sub-block are calculated.The Bayesian method is applied to fuse online parameters with offline parameters,yielding statistics under a specific confidence interval.These statistics are then compared with the control limits–specifically,checking whether they exceed the pre-set confidence parameters–thus realizing the systematic risk identification of the hidden danger area.Findings–Through simulation experiments,the proposed method can integrate prior knowledge such as control limits and statistics to effectively determine the overall stability status of the area,thereby realizing the systematic risk identification of the hidden danger area.Originality/value–The proposed method leverages Bayesian theory to fuse online process parameters with offline parameters and further compares them with confidence parameters,thereby effectively enhancing the utilization efficiency of monitoring data and the robustness of the analytical model.
基金funded by the National Key R&D Program“Transportation Infrastructure”project(No.2022YFB2603400)the Technology Research and Development Plan Program of China State Railway Group Co.,Ltd.(No.Q2024T001)the National project pre research project of Suzhou City University(No.2023SGY019).
文摘Purpose-The indoor vibration compaction test(IVCT)was a key step in controlling the compaction quality for high-speed railway graded aggregate(HRGA),which currently had a research gap on the assessment indicators and compaction parameters.Design/methodology/approach-To address these issues,a novel multi-indicator IVCT method was proposed,including physical indicator dry density(ρd)and mechanical indicators dynamic stiffness(Krb)and bearing capacity coefficient(K20).Then,a series of IVCTs on HRGA under different compaction parameters were conducted with an improved vibration compactor,which could monitor the physical-mechanical indicators in real-time.Finally,the optimal vibration compaction parameters,including the moisture content(ω),the diameter-to-maximum particle size ratio(Rd),the thickness-to-maximum particle size ratio(Rh),the vibration frequency(f),the vibration mass(Mc)and the eccentric distance(re),were determined based on the evolution characteristics for the physical-mechanical indicators during compaction.Findings-All results indicated that theρd gradually increased and then stabilized,and the Krb initially increased and then decreased.Moreover,the inflection time of the Krb was present as the optimal compaction time(Tlp)during compaction.Additionally,optimal compaction was achieved whenωwas the water-holding content after mud pumping,Rd was 3.4,Rh was 3.5,f was the resonance frequency,and the ratio between the excitation force and the Mc was 1.8.Originality/value-The findings of this paper were significant for the quality control of HRGA compaction.
基金supported by Shandong Provincial Nat-ural Science Foundation(ZR2024ZD30)the National Natural Science Foundation of China(Nos.22325302 and 22403100).
文摘Accurate evaluation of elec-tron correlations is essential for the reliable quantitative de-scription of electronic struc-tures in strongly correlated sys-tems,including bond-dissociat-ing molecules,polyradicals,large conjugated molecules,and transition metal complex-es.To provide a user-friendly tool for studying such challeng-ing systems,our team developed Kylin 1.0[J.Comput.Chem.44,1316(2023)],an ab initio quantum chemistry program designed for efficient density matrix renormalization group(DMRG)and post-DMRG methods,enabling high-accuracy calculations with large active spaces.We have now further advanced the software with the release of Kylin 1.3,featuring optimized DMRG algorithms and an improved tensor contraction scheme in the diagonaliza-tion step.Benchmark calculations on the Mn_(4)CaO_(5)cluster demonstrate a remarkable speed-up of up to 16 fater than Kylin 1.0.Moreover,a more user-friendly and efficient algorithm[J.Chem.Theory Comput.17,3414(2021)]for sampling configurations from DMRG wavefunc-tion is implemented as well.Additionally,we have also implemented a spin-adapted version of the externally contracted multi-reference configuration interaction(EC-MRCI)method[J.Phys.Chem.A 128,958(2024)],further enhancing the program’s efficiency and accuracy for electron correlation calculations.
基金the National Social Science Foundation of China(No.21BTQ106),the Natural Science Foundation of Beijing(No.7222187),and the Key Project of Innovation Cultivation Fund of the Seventh Medical Center of PLA General Hospital(No.qzx-2023-1)。
文摘Behavioral scoring based on clinical observations remains the gold standard for screening,diagnosing,and evaluating infantile epileptic spasm syndrome(IESS).The accurate identification of seizures is crucial for clinical diagnosis and assessment.In this study,we propose an innovative seizure detection method based on video feature recognition of patient spasms.To capture the temporal characteristics of the spasm behavior presented in the videos effectively,we incorporate asymmetric convolutions and convolution–batch normalization–ReLU(CBR)modules.Specifically within the 3D-ResNet residual blocks,we split the larger convolutional kernels into two asymmetric 3D convolutional kernels.These kernels are connected in series to enhance the ability of the convolutional layers to extract key local features,both horizontally and vertically.In addition,we introduce a 3D convolutional block attention module to enhance the spatial correlations between video frame channels efficiently.To improve the generalization ability,we design a composite loss function that combines cross-entropy loss with triplet loss to balance the classification and similarity requirements.We train and evaluate our method using the PLA IESS-VIDEO dataset,achieving an average seizure recognition accuracy of 90.59%,precision of 90.94%,and recall of 87.64%.To validate its generalization capability further,we conducted external validation using six different patient monitoring videos compared with assessments by six human experts from various medical centers.The final test results demonstrate that our method achieved a recall of 0.6476,surpassing the average level achieved by human experts(0.5595),while attaining a high F1-score of 0.7219.These findings have substantial significance for the long-term assessment of patients with IESS.
基金supported by the Innovation Funding of ICT,CAS under Grant(No.E261020)Jiangsu Key Research and Development Program of China(No.BE2021013-2)Zhejiang Key Research and Development Program(No.2021C01040).
文摘LEO satellite communication systems have the characteristics of high-speed and periodic movement.The handover of user link occurs frequently,which has a serious impact on user terminal application and system capacity.To address this issue,we propose a handover strategy of LEO satellite user terminal based on multi-attribute and multi-point(MAMP)cooperation.Firstly,the satellite-user-time matrix is established by using the satellite constellation coverage and handover model.Then,combined with the visual time and signal quality,the user access matrix and satellite load matrix are extracted to determine the weight equation of the handover strategy with the channel reservation.According to the system modeling simulation,the algorithm improves the handover success rate by 2.5%,the lasted call access success rate by 3.2%,the load balancing degree by 20%,and the robustness by two orders of magnitude.
基金support from the Scientific Funding for the Center of National Railway Intelligent Transportation System Engineering and Technology,China Academy of Railway Sciences Corporation Limited(Grant No.2023YJ354)。
文摘Knowledge graphs,which combine structured representation with semantic modeling,have shown great potential in knowledge expression,causal inference,and automated reasoning,and are widely used in fields such as intelligent question answering,decision support,and fault diagnosis.As high-speed train systems become increasingly intelligent and interconnected,fault patterns have grown more complex and dynamic.Knowledge graphs offer a promising solution to support the structured management and real-time reasoning of fault knowledge,addressing key requirements such as interpretability,accuracy,and continuous evolution in intelligent diagnostic systems.However,conventional knowledge graph construction relies heavily on domain expertise and specialized tools,resulting in high entry barriers for non-experts and limiting their practical application in frontline maintenance scenarios.To address this limitation,this paper proposes a fault knowledge modeling approach for high-speed trains that integrates structured logic diagrams with knowledge graphs.The method employs a seven-layer logic structure—comprising fault name,applicable vehicles,diagnostic logic,signal parameters,verification conditions,fault causes,and emergency measures—to transform unstructured knowledge into a visual and hierarchical representation.A semantic mapping mechanism is then used to automatically convert logic diagrams into machine-interpretable knowledge graphs,enabling dynamic reasoning and knowledge reuse.Furthermore,the proposed method establishes a three-layer architecture—logic structuring,knowledge graph transformation,and dynamic inference—to bridge human-expert logic with machinebased reasoning.Experimental validation and system implementation demonstrate that this approach not only improves knowledge interpretability and inference precision but also significantly enhances modeling efficiency and system maintainability.It provides a scalable and adaptable solution for intelligent operation and maintenance platforms in the high-speed rail domain.
