Photothermoelectric(PTE)photodetectors with selfpowered and uncooled advantages have attracted much interest due to the wide application prospects in the military and civilian fields.However,traditional PTE photodetec...Photothermoelectric(PTE)photodetectors with selfpowered and uncooled advantages have attracted much interest due to the wide application prospects in the military and civilian fields.However,traditional PTE photodetectors lack of mechanical flexibility and cannot operate independently without the test instrument.Herein,we present a flexible PTE photodetector capable of dual-mode output,combining electrical and optical signal generation for enhanced functionality.Using solution processing,high-quality MXene thin films are assembled on asymmetric electrodes as the photosensitive layer.The geometrically asymmetric electrode design significantly enhances the responsivity,achieving 0.33 m A W^(-1)under infrared illumination,twice that of the symmetrical configuration.This improvement stems from optimized photothermal conversion and an expanded temperature gradient.The PTE device maintains stable performance after 300 bending cycles,demonstrating excellent flexibility.A new energy conversion pathway has been established by coupling the photothermal conversion of MXene with thermochromic composite materials,leading to a real-time visualization of invisible infrared radiation.Leveraging this functionality,we demonstrate the first human-machine collaborative infrared imaging system,wherein the dual-mode photodetector arrays synchronously generate human-readable pattern and machine-readable pattern.Our study not only provides a new solution for functional integration of flexible photodetectors,but also sets a new benchmark for human-machine collaborative optoelectronics.展开更多
In the parallel steering coordination control strategy for path tracking,it is difficult to match the current driver steering model using the fixed parameters with the actual driver,and the designed steering coordinat...In the parallel steering coordination control strategy for path tracking,it is difficult to match the current driver steering model using the fixed parameters with the actual driver,and the designed steering coordination control strategy under a single objective and simple conditions is difficult to adapt to the multi-dimensional state variables’input.In this paper,we propose a deep reinforcement learning algorithm-based multi-objective parallel human-machine steering coordination strategy for path tracking considering driver misoperation and external disturbance.Firstly,the driver steering mathematical model is constructed based on the driver preview characteristics and steering delay response,and the driver characteristic parameters are fitted after collecting the actual driver driving data.Secondly,considering that the vehicle is susceptible to the influence of external disturbances during the driving process,the Tube MPC(Tube Model Predictive Control)based path tracking steering controller is designed based on the vehicle system dynamics error model.After verifying that the driver steering model meets the driver steering operation characteristics,DQN(Deep Q-network),DDPG(Deep Deterministic Policy Gradient)and TD3(Twin Delayed Deep Deterministic Policy Gradient)deep reinforcement learning algorithms are utilized to design a multi-objective parallel steering coordination strategy which satisfies the multi-dimensional state variables’input of the vehicle.Finally,the tracking accuracy,lateral safety,human-machine conflict and driver steering load evaluation index are designed in different driver operation states and different road environments,and the performance of the parallel steering coordination control strategies with different deep reinforcement learning algorithms and fuzzy algorithms are compared by simulations and hardware in the loop experiments.The results show that the parallel steering collaborative strategy based on a deep reinforcement learning algorithm can more effectively assist the driver in tracking the target path under lateral wind interference and driver misoperation,and the TD3-based coordination control strategy has better overall performance.展开更多
Rock discontinuities control rock mechanical behaviors and significantly influence the stability of rock masses.However,existing discontinuity mapping algorithms are susceptible to noise,and the calculation results ca...Rock discontinuities control rock mechanical behaviors and significantly influence the stability of rock masses.However,existing discontinuity mapping algorithms are susceptible to noise,and the calculation results cannot be fed back to users timely.To address this issue,we proposed a human-machine interaction(HMI)method for discontinuity mapping.Users can help the algorithm identify the noise and make real-time result judgments and parameter adjustments.For this,a regular cube was selected to illustrate the workflows:(1)point cloud was acquired using remote sensing;(2)the HMI method was employed to select reference points and angle thresholds to detect group discontinuity;(3)individual discontinuities were extracted from the group discontinuity using a density-based cluster algorithm;and(4)the orientation of each discontinuity was measured based on a plane fitting algorithm.The method was applied to a well-studied highway road cut and a complex natural slope.The consistency of the computational results with field measurements demonstrates its good accuracy,and the average error in the dip direction and dip angle for both cases was less than 3.Finally,the computational time of the proposed method was compared with two other popular algorithms,and the reduction in computational time by tens of times proves its high computational efficiency.This method provides geologists and geological engineers with a new idea to map rapidly and accurately rock structures under large amounts of noises or unclear features.展开更多
Virtual reality(VR)is an emerging communication means and creates extensive opportunities in interacting scenarios such as remote collaboration and metaverse.Human-machine interfaces(HMIs)play important roles in VR as...Virtual reality(VR)is an emerging communication means and creates extensive opportunities in interacting scenarios such as remote collaboration and metaverse.Human-machine interfaces(HMIs)play important roles in VR as they provide interaction platforms between users and virtual environments.However,traditional VR HMIs based on handheld devices or keyboards cannot recognize diverse three-dimensional(3D)gestures,which results in limited freedom of VR interactions.Here,we report a noncontact VR HMI enabled by an electret-nanofiber-based triboelectric sensor(ETS),which is fabricated by the electrospun polylactic acid/thermoplastic polyurethane(PLA/TPU)electret nanofiber film.The nanofiber structure of PLA/TPU electret enhanced the charge retention ability of triboelectric sensor and thus significantly improved its signal strength and stability.Integrated with a deep learning-based multilayer perceptron neural network,the ETS realizes the recognition of 18 different types of 3D gestures with a high average accuracy of 97.3%.An intelligent noncontact VR interactive system based on the ETS is further developed,which is used to manipulate game characters for performing different actions by 3D gestures.Compared with traditional VR HMIs,the proposed VR HMI based on PLA/TPU electret nanofiber film can detect various 3D gestures and offers a superior interaction freedom.This work for the first time introduces the triboelectric 3D gesture recognition method to the VR HMIs,and could make the interaction between human and virtual environments become more efficient and fascinating.展开更多
Hydrogel-based triboelectric nanoge nerator(TENG)has a promising applied prospect in wearable electronic devices.However,its low performance,poor stability,insufficient recyclability and inferior self-healing seriousl...Hydrogel-based triboelectric nanoge nerator(TENG)has a promising applied prospect in wearable electronic devices.However,its low performance,poor stability,insufficient recyclability and inferior self-healing seriously hinder its development.Herein,we report a robust route to a liquid metal(LM)/polyvinyl alcohol(PVA)hydrogel-based TENG(LP-TENG).Owing to the intrinsically liquid feature of conductive LM within the flexible PVA hydrogel,the as-prepared LP-TENG exhibited comprehensiye advantages of adaptability,biocompatibility,outstanding electrical performance,superior stability,recyclability and diverse applications,which were unattainable by traditional systems.Concretely,the LP-TENG delivered appealing open circuit voltage of 250 V,short circuit current of 4μA and transferred charge of 120 nC with high stability,outperforming most advanced TENG systems.The LP-TENG was successfully employed for versatile applications with multifunctionality,including human motion detection,handwriting recognition,energy collection,message transmission and human-machine interaction.This work presents significant prospects for crafting advanced materials and devices in the fields of wearable electronics,flexible skin and smart robots.展开更多
The Chinese express delivery industry processes nearly 110 billion items in 2022,averaging an annual growth rate of 200%.Among the various types of sorting systems used for handling express items,cross-belt sorting sy...The Chinese express delivery industry processes nearly 110 billion items in 2022,averaging an annual growth rate of 200%.Among the various types of sorting systems used for handling express items,cross-belt sorting systems stand out as the most crucial.However,despite their high degree of automation,the workload for operators has intensified owing to the surging volume of express items.In the era of Industry 5.0,it is imperative to adopt new technologies that not only enhance worker welfare but also improve the efficiency of cross-belt systems.Striking a balance between efficiency in handling express items and operator well-being is challenging.Digital twin technology offers a promising solution in this respect.A realization method of a human-machine integrated digital twin is proposed in this study,enabling the interaction of biological human bodies,virtual human bodies,virtual equipment,and logistics equipment in a closed loop,thus setting an operating framework.Key technologies in the proposed framework include a collection of heterogeneous data from multiple sources,construction of the relationship between operator fatigue and operation efficiency based on physiological measurements,virtual model construction,and an online optimization module based on real-time simulation.The feasibility of the proposed method was verified in an express distribution center.展开更多
As the Internet of Things advances,gesture recognition emerges as a prominent domain in human-machine interaction(HMI).However,interactive wearables based on conductive hydrogels for individuals with single-arm functi...As the Internet of Things advances,gesture recognition emerges as a prominent domain in human-machine interaction(HMI).However,interactive wearables based on conductive hydrogels for individuals with single-arm functionality or disabilities remain underexplored.Here,we devised a wearable one-handed keyboard with gesture recognition,employing machine learning algorithms and hydrogel-based mechanical sensors to boost productivity.PCG(PAM/CMC/rGO)hydrogels are composed of polyacrylamide(PAM),sodium carboxymethyl cellulose(CMC),and reduced graphene oxide(rGO),which function as a strain,pressure sensor,and electrode material.The PAM chains offer the gel’s elasticity by covalent cross-linking,while the biocompatible CMC improves the dispersion of rGO and promotes electromechanical properties.Integrating rGO sheets into the polymer matrix facilitates cross-linking and generates supple-mentary conductive pathways,thereby augmenting the gel system’s elasticity,sensitivity,and durability.Our hydrogel sensors include high sensitivity(gage factor(GF)=8.18,395.6%-551.96%)and superior pressure sensing capabilities(Sensitivity(S)=0.3116 kPa^(-1),0-9.82 kPa).Furthermore,we developed a wearable keyboard with up to 98.13%accuracy using convolutional neural networks and a custom data acquisition system.This study establishes the groundwork for creating multifunctional gel sensors for intelligent machines,wearable devices,and brain-computer interfaces.展开更多
To solve the challenges of connecting and coordinating multiple platforms in the automotive industry and to enhance collaboration among different participants,this research focuses on addressing the complex supply rel...To solve the challenges of connecting and coordinating multiple platforms in the automotive industry and to enhance collaboration among different participants,this research focuses on addressing the complex supply relationships in the automotive market,improving data sharing and interactions across various platforms,and achieving more detailed integration of data and operations.We propose a trust evaluation permission delegation method based on the automotive industry chain.The proposed method combines smart contracts with trust evaluation mechanisms,dynamically calculating the trust value of users based on the historical behavior of the delegated entity,network environment,and other factors to avoid malicious node attacks during the permission delegation process.We also introduce strict control over the cross-domain permission granting and revocation mechanisms to manage the delegation path,prevent information leakage caused by malicious node interception,and effectively protect data integrity and privacy.Experimental analysis shows that this method meets the realtime requirements of collaborative interaction in the automotive industry chain and provides a feasible solution to permission delegation issues in the automotive industry chain,offering dynamic flexibility in authorization and scalability compared to most existing solutions.展开更多
This study investigates the effects of AI-mediated communication (AMC) on trust-building and negotiation outcomes in professional remote collaboration settings. Through a mixed-methods approach combining experimental ...This study investigates the effects of AI-mediated communication (AMC) on trust-building and negotiation outcomes in professional remote collaboration settings. Through a mixed-methods approach combining experimental design and qualitative analysis (N = 120), we examine how AI intermediaries influence communication dynamics, relationship building, and decision-making processes. Results indicate that while AMC initially creates barriers to trust formation, it ultimately leads to enhanced communication outcomes and stronger professional relationships when implemented with appropriate transparency and support. The study revealed a 31% improvement in cross-cultural understanding and a 24% increase in negotiation satisfaction rates when using AI-mediated channels with proper transparency measures. These findings contribute to the theoretical understanding of technology-mediated communication and practical applications for organizations implementing AI communication tools.展开更多
Border Gateway Protocol(BGP),as the standard inter-domain routing protocol,is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous Systems(AS).BGP nodes,com...Border Gateway Protocol(BGP),as the standard inter-domain routing protocol,is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous Systems(AS).BGP nodes,communicating in a distributed dynamic environment,face several security challenges,with trust being one of the most important issues in inter-domain routing.Existing research,which performs trust evaluation when exchanging routing information to suppress malicious routing behavior,cannot meet the scalability requirements of BGP nodes.In this paper,we propose a blockchain-based trust model for inter-domain routing.Our model achieves scalability by allowing the master node of an AS alliance to transmit the trust evaluation data of its member nodes to the blockchain.The BGP nodes can expedite the trust evaluation process by accessing a global view of other BGP nodes through the master node of their respective alliance.We incorporate security service evaluation before direct evaluation and indirect recommendations to assess the security services that BGP nodes provide for themselves and prioritize to guarantee their security of routing service.We forward the trust evaluation for neighbor discovery and prioritize the nodes with high trust as neighbor nodes to reduce the malicious exchange routing behavior.We use simulation software to simulate a real BGP environments and employ a comparative experimental research approach to demonstrate the performance evaluation of our trust model.Compared with the classical trust model,our trust model not only saves more storage overhead,but also provides higher security,especially reducing the impact of collusion attacks.展开更多
目的评估不同孕周应用苄星青霉素G对妊娠梅毒患者的疗效,及其对母婴结局和甲苯胺红不加热血清试验(TRUST)滴度的干预效果。方法回顾性选取2021年6月至2024年6月西北妇女儿童医院收治的98例妊娠梅毒患者,按不同孕周应用苄星青霉素G分为3...目的评估不同孕周应用苄星青霉素G对妊娠梅毒患者的疗效,及其对母婴结局和甲苯胺红不加热血清试验(TRUST)滴度的干预效果。方法回顾性选取2021年6月至2024年6月西北妇女儿童医院收治的98例妊娠梅毒患者,按不同孕周应用苄星青霉素G分为3组:孕早期组(≤13+6周,n=32)、孕中期组(14~27+6周,n=33)和孕晚期组(≥28周,n=33)。比较3组产妇治疗前、治疗2个疗程后的氧化应激指标[超氧化物歧化酶(SOD)、丙二醛、晚期氧化蛋白产物(AOPP)]水平、新生儿及产妇TRUST滴度比、产妇情况(产后出血率、早产率)及新生儿情况(先天梅毒儿、出生后5 min Apgar评分、体质量)。结果(1)较治疗前,3组产妇治疗2个疗程后血清SOD水平均更高,丙二醛、AOPP水平均更低,差异均有统计学意义(P<0.05);较孕早期组,孕中期组、孕晚期组产妇治疗2个疗程后SOD水平更低,丙二醛、AOPP水平更高,差异均有统计学意义(P<0.05)。(2)孕中期组、孕晚期组新生儿治疗后TRUST阴性率分别为30.30%、24.24%,较孕早期组(56.25%)更低,差异有统计学意义(P<0.05)。(3)孕中期组、孕晚期组产妇治疗后TRUST阴性率分别为9.09%、3.03%,较孕早期组(28.13%)更低,差异有统计学意义(P<0.05)。(4)较孕早期组,孕中期组、孕晚期组新生儿先天梅毒儿占比更高,出生后5 min Apgar评分、体质量更低,差异均有统计学意义(P<0.05)。结论妊娠梅毒患者孕早期应用苄星青霉素G疗效优于孕中期和孕晚期,对氧化应激改善更为显著,能进一步提升母婴TRUST阴性率及改善母婴结局。展开更多
GS1 is an international standards organization,which focuses on product identification and product data,helping businesses and governments to improve commerce and supply chain.Why trusted data is essential to high-qua...GS1 is an international standards organization,which focuses on product identification and product data,helping businesses and governments to improve commerce and supply chain.Why trusted data is essential to high-quality development?More than 50 years ago,GS1 was initiated with the bar code,a profound transformation of the way we work and live.From then on,a simple scan connected a physical product to its digital identity.It transformed commerce,improving supply chains and enabling safer healthcare.Collaboration between industry and governments,and a strong partnership with ISO and IEC laid the foundations for the global adoption of a common product identification over the past 50 years and all around the world.展开更多
Nowadays,we are witnessing the tremendous changes brought by AI technologies.What role can standards play in this process?How can we build global trust and enable responsible innovation?
The core missions of IoT are to sense data,transmit data and give feedback to the real world based on the calculation of the sensed data.The trust of sensing source data and transmission network is extremely important...The core missions of IoT are to sense data,transmit data and give feedback to the real world based on the calculation of the sensed data.The trust of sensing source data and transmission network is extremely important to IoT security.5G-IoT with its low latency,wide connectivity and high-speed transmission extends the business scenarios of IoT,yet it also brings new challenges to trust proof solutions of IoT.Currently,there is a lack of efficient and reliable trust proof solutions for massive dynamically connected nodes,while the existing solutions have high computational complexity and can't adapt to time-sensitive services in 5G-IoT scenarios.In order to solve the above problems,this paper proposes an adaptive multi-dimensional trust proof solution.Firstly,the static and dynamic attributes of sensing nodes are metricized,and the historical interaction as well as the recommendation information are combined with the comprehensive metric of sensing nodes,and a multi-dimensional fine-grained trusted metric model is established in this paper.Then,based on the comprehensive metrics,the sensing nodes are logically grouped and assigned with service levels to achieve the screening and isolation of malicious nodes.At the same time,the proposed solution reduces the energy consumption of the metric process and optimizes the impact of real-time metrics on the interaction latency.Simulation experiments show that the solution can accurately and efficiently identify malicious nodes and effectively guarantee the safe and trustworthy operation of 5G-IoT nodes,while having a small impact on the latency of the 5G network.展开更多
With the introduction of 5G,users and devices can access the industrial network from anywhere in the world.Therefore,traditional perimeter-based security technologies for industrial networks can no longer work well.To...With the introduction of 5G,users and devices can access the industrial network from anywhere in the world.Therefore,traditional perimeter-based security technologies for industrial networks can no longer work well.To solve this problem,a new security model called Zero Trust(ZT)is desired,which believes in“never trust and always verify”.Every time the asset in the industrial network is accessed,the subject is authenticated and its trustworthiness is assessed.In this way,the asset in industrial network can be well protected,whether the subject is in the internal network or the external network.However,in order to construct the zero trust model in the 5G Industrial Internet collaboration system,there are still many problems to be solved.In this paper,we first introduce the security issues in the 5G Industrial Internet collaboration system,and illustrate the zero trust architecture.Then,we analyze the gap between existing security techniques and the zero trust architecture.Finally,we discuss several potential security techniques that can be used to implement the zero trust model.The purpose of this paper is to point out the further direction for the realization of the Zero Trust Architecture(ZTA)in the 5G Industrial Internet collaboration system.展开更多
In the complex environment of Wireless Sensor Networks(WSNs),various malicious attacks have emerged,among which internal attacks pose particularly severe security risks.These attacks seriously threaten network stabili...In the complex environment of Wireless Sensor Networks(WSNs),various malicious attacks have emerged,among which internal attacks pose particularly severe security risks.These attacks seriously threaten network stability,data transmission reliability,and overall performance.To effectively address this issue and significantly improve intrusion detection speed,accuracy,and resistance to malicious attacks,this research designs a Three-level Intrusion Detection Model based on Dynamic Trust Evaluation(TIDM-DTE).This study conducts a detailed analysis of how different attack types impact node trust and establishes node models for data trust,communication trust,and energy consumption trust by focusing on characteristics such as continuous packet loss and energy consumption changes.By dynamically predicting node trust values using the grey Markov model,the model accurately and sensitively reflects changes in node trust levels during attacks.Additionally,DBSCAN(Density-Based Spatial Clustering of Applications with Noise)data noise monitoring technology is employed to quickly identify attacked nodes,while a trust recovery mechanism restores the trust of temporarily faulty nodes to reduce False Alarm Rate.Simulation results demonstrate that TIDM-DTE achieves high detection rates,fast detection speed,and low False Alarm Rate when identifying various network attacks,including selective forwarding attacks,Sybil attacks,switch attacks,and black hole attacks.TIDM-DTE significantly enhances network security,ensures secure and reliable data transmission,moderately improves network energy efficiency,reduces unnecessary energy consumption,and provides strong support for the stable operation of WSNs.Meanwhile,the research findings offer new ideas and methods for WSN security protection,possessing important theoretical significance and practical application value.展开更多
Today,I want to share how international standards can forge trust and fuel innovation,laying the foundation for a future where AI benefits everyone,everywhere.First,AI standards,developed jointly by ISO and IEC-the In...Today,I want to share how international standards can forge trust and fuel innovation,laying the foundation for a future where AI benefits everyone,everywhere.First,AI standards,developed jointly by ISO and IEC-the International Electrotechnical Commission-help build global trust and enable responsible innovation by bringing clarity and coherence to an ever-changing AI landscape.As developments in AI continue to emerge at speed,regulation is struggling to keep up and the proliferation of competing standards has created confusion rather than clarity.ISO and our partner IEC are addressing this challenge through the work of our expert committee on AI,SC 42,which takes a holistic,cohesive approach to AI standardization.展开更多
基金supported by the Fundamental Research Funds for the Central Universities(xxj022019009)。
文摘Photothermoelectric(PTE)photodetectors with selfpowered and uncooled advantages have attracted much interest due to the wide application prospects in the military and civilian fields.However,traditional PTE photodetectors lack of mechanical flexibility and cannot operate independently without the test instrument.Herein,we present a flexible PTE photodetector capable of dual-mode output,combining electrical and optical signal generation for enhanced functionality.Using solution processing,high-quality MXene thin films are assembled on asymmetric electrodes as the photosensitive layer.The geometrically asymmetric electrode design significantly enhances the responsivity,achieving 0.33 m A W^(-1)under infrared illumination,twice that of the symmetrical configuration.This improvement stems from optimized photothermal conversion and an expanded temperature gradient.The PTE device maintains stable performance after 300 bending cycles,demonstrating excellent flexibility.A new energy conversion pathway has been established by coupling the photothermal conversion of MXene with thermochromic composite materials,leading to a real-time visualization of invisible infrared radiation.Leveraging this functionality,we demonstrate the first human-machine collaborative infrared imaging system,wherein the dual-mode photodetector arrays synchronously generate human-readable pattern and machine-readable pattern.Our study not only provides a new solution for functional integration of flexible photodetectors,but also sets a new benchmark for human-machine collaborative optoelectronics.
基金Supported by National Natural Science Foundation of China(Grant Nos.U22A20246,52372382)Hefei Municipal Natural Science Foundation(Grant No.2022008)+1 种基金the Open Fund of State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures(Grant No.KF2023-06)S&T Program of Hebei(Grant No.225676162GH).
文摘In the parallel steering coordination control strategy for path tracking,it is difficult to match the current driver steering model using the fixed parameters with the actual driver,and the designed steering coordination control strategy under a single objective and simple conditions is difficult to adapt to the multi-dimensional state variables’input.In this paper,we propose a deep reinforcement learning algorithm-based multi-objective parallel human-machine steering coordination strategy for path tracking considering driver misoperation and external disturbance.Firstly,the driver steering mathematical model is constructed based on the driver preview characteristics and steering delay response,and the driver characteristic parameters are fitted after collecting the actual driver driving data.Secondly,considering that the vehicle is susceptible to the influence of external disturbances during the driving process,the Tube MPC(Tube Model Predictive Control)based path tracking steering controller is designed based on the vehicle system dynamics error model.After verifying that the driver steering model meets the driver steering operation characteristics,DQN(Deep Q-network),DDPG(Deep Deterministic Policy Gradient)and TD3(Twin Delayed Deep Deterministic Policy Gradient)deep reinforcement learning algorithms are utilized to design a multi-objective parallel steering coordination strategy which satisfies the multi-dimensional state variables’input of the vehicle.Finally,the tracking accuracy,lateral safety,human-machine conflict and driver steering load evaluation index are designed in different driver operation states and different road environments,and the performance of the parallel steering coordination control strategies with different deep reinforcement learning algorithms and fuzzy algorithms are compared by simulations and hardware in the loop experiments.The results show that the parallel steering collaborative strategy based on a deep reinforcement learning algorithm can more effectively assist the driver in tracking the target path under lateral wind interference and driver misoperation,and the TD3-based coordination control strategy has better overall performance.
基金supported by the National Key R&D Program of China(No.2023YFC3081200)the National Natural Science Foundation of China(No.42077264)the Scientific Research Project of PowerChina Huadong Engineering Corporation Limited(HDEC-2022-0301).
文摘Rock discontinuities control rock mechanical behaviors and significantly influence the stability of rock masses.However,existing discontinuity mapping algorithms are susceptible to noise,and the calculation results cannot be fed back to users timely.To address this issue,we proposed a human-machine interaction(HMI)method for discontinuity mapping.Users can help the algorithm identify the noise and make real-time result judgments and parameter adjustments.For this,a regular cube was selected to illustrate the workflows:(1)point cloud was acquired using remote sensing;(2)the HMI method was employed to select reference points and angle thresholds to detect group discontinuity;(3)individual discontinuities were extracted from the group discontinuity using a density-based cluster algorithm;and(4)the orientation of each discontinuity was measured based on a plane fitting algorithm.The method was applied to a well-studied highway road cut and a complex natural slope.The consistency of the computational results with field measurements demonstrates its good accuracy,and the average error in the dip direction and dip angle for both cases was less than 3.Finally,the computational time of the proposed method was compared with two other popular algorithms,and the reduction in computational time by tens of times proves its high computational efficiency.This method provides geologists and geological engineers with a new idea to map rapidly and accurately rock structures under large amounts of noises or unclear features.
基金supported by the National Natural Science Foundation of China(No.52303112)the Henan Province Science and Technology Research and Development Program Joint Fund Advantageous Discipline Cultivation Project(No.232301420033)+1 种基金the China Postdoctoral Science Foundation(Nos.2022TQ0281 and 2023M733213)the Key R&D and Promotion Special(Scientific Problem Tackling)Project of Henan Province(No.242102231014).
文摘Virtual reality(VR)is an emerging communication means and creates extensive opportunities in interacting scenarios such as remote collaboration and metaverse.Human-machine interfaces(HMIs)play important roles in VR as they provide interaction platforms between users and virtual environments.However,traditional VR HMIs based on handheld devices or keyboards cannot recognize diverse three-dimensional(3D)gestures,which results in limited freedom of VR interactions.Here,we report a noncontact VR HMI enabled by an electret-nanofiber-based triboelectric sensor(ETS),which is fabricated by the electrospun polylactic acid/thermoplastic polyurethane(PLA/TPU)electret nanofiber film.The nanofiber structure of PLA/TPU electret enhanced the charge retention ability of triboelectric sensor and thus significantly improved its signal strength and stability.Integrated with a deep learning-based multilayer perceptron neural network,the ETS realizes the recognition of 18 different types of 3D gestures with a high average accuracy of 97.3%.An intelligent noncontact VR interactive system based on the ETS is further developed,which is used to manipulate game characters for performing different actions by 3D gestures.Compared with traditional VR HMIs,the proposed VR HMI based on PLA/TPU electret nanofiber film can detect various 3D gestures and offers a superior interaction freedom.This work for the first time introduces the triboelectric 3D gesture recognition method to the VR HMIs,and could make the interaction between human and virtual environments become more efficient and fascinating.
基金financially supported by the Natural Science Foundation of China(Nos.22109120,62104170 and 82202757)Zhejiang Provincial Natural Science Foundation of China(Nos.LQ21B030002 and LY23F040001)。
文摘Hydrogel-based triboelectric nanoge nerator(TENG)has a promising applied prospect in wearable electronic devices.However,its low performance,poor stability,insufficient recyclability and inferior self-healing seriously hinder its development.Herein,we report a robust route to a liquid metal(LM)/polyvinyl alcohol(PVA)hydrogel-based TENG(LP-TENG).Owing to the intrinsically liquid feature of conductive LM within the flexible PVA hydrogel,the as-prepared LP-TENG exhibited comprehensiye advantages of adaptability,biocompatibility,outstanding electrical performance,superior stability,recyclability and diverse applications,which were unattainable by traditional systems.Concretely,the LP-TENG delivered appealing open circuit voltage of 250 V,short circuit current of 4μA and transferred charge of 120 nC with high stability,outperforming most advanced TENG systems.The LP-TENG was successfully employed for versatile applications with multifunctionality,including human motion detection,handwriting recognition,energy collection,message transmission and human-machine interaction.This work presents significant prospects for crafting advanced materials and devices in the fields of wearable electronics,flexible skin and smart robots.
基金Supported by National Natural Science Foundation of China(Grant No.52075036)Key Technologies Research and Development Program of China(Grant No.2022YFC3302204).
文摘The Chinese express delivery industry processes nearly 110 billion items in 2022,averaging an annual growth rate of 200%.Among the various types of sorting systems used for handling express items,cross-belt sorting systems stand out as the most crucial.However,despite their high degree of automation,the workload for operators has intensified owing to the surging volume of express items.In the era of Industry 5.0,it is imperative to adopt new technologies that not only enhance worker welfare but also improve the efficiency of cross-belt systems.Striking a balance between efficiency in handling express items and operator well-being is challenging.Digital twin technology offers a promising solution in this respect.A realization method of a human-machine integrated digital twin is proposed in this study,enabling the interaction of biological human bodies,virtual human bodies,virtual equipment,and logistics equipment in a closed loop,thus setting an operating framework.Key technologies in the proposed framework include a collection of heterogeneous data from multiple sources,construction of the relationship between operator fatigue and operation efficiency based on physiological measurements,virtual model construction,and an online optimization module based on real-time simulation.The feasibility of the proposed method was verified in an express distribution center.
基金supported by the China Postdoctoral Science Foundation(No.2022BG011)the Fundamental Research Funds for Central Universities(No.2020CDJ-LHZZ-077)+1 种基金the Natural Science Foundation of Chongqing,China(No.c stc2020jcyj-msxmX0397)the Fundamental Research Funds for Central Universities(No.00007717).
文摘As the Internet of Things advances,gesture recognition emerges as a prominent domain in human-machine interaction(HMI).However,interactive wearables based on conductive hydrogels for individuals with single-arm functionality or disabilities remain underexplored.Here,we devised a wearable one-handed keyboard with gesture recognition,employing machine learning algorithms and hydrogel-based mechanical sensors to boost productivity.PCG(PAM/CMC/rGO)hydrogels are composed of polyacrylamide(PAM),sodium carboxymethyl cellulose(CMC),and reduced graphene oxide(rGO),which function as a strain,pressure sensor,and electrode material.The PAM chains offer the gel’s elasticity by covalent cross-linking,while the biocompatible CMC improves the dispersion of rGO and promotes electromechanical properties.Integrating rGO sheets into the polymer matrix facilitates cross-linking and generates supple-mentary conductive pathways,thereby augmenting the gel system’s elasticity,sensitivity,and durability.Our hydrogel sensors include high sensitivity(gage factor(GF)=8.18,395.6%-551.96%)and superior pressure sensing capabilities(Sensitivity(S)=0.3116 kPa^(-1),0-9.82 kPa).Furthermore,we developed a wearable keyboard with up to 98.13%accuracy using convolutional neural networks and a custom data acquisition system.This study establishes the groundwork for creating multifunctional gel sensors for intelligent machines,wearable devices,and brain-computer interfaces.
基金funded by the Sichuan Science and Technology Program,Grant Nos.2024NSFSC0515,2024ZHCG0182 and MZGC20230013.
文摘To solve the challenges of connecting and coordinating multiple platforms in the automotive industry and to enhance collaboration among different participants,this research focuses on addressing the complex supply relationships in the automotive market,improving data sharing and interactions across various platforms,and achieving more detailed integration of data and operations.We propose a trust evaluation permission delegation method based on the automotive industry chain.The proposed method combines smart contracts with trust evaluation mechanisms,dynamically calculating the trust value of users based on the historical behavior of the delegated entity,network environment,and other factors to avoid malicious node attacks during the permission delegation process.We also introduce strict control over the cross-domain permission granting and revocation mechanisms to manage the delegation path,prevent information leakage caused by malicious node interception,and effectively protect data integrity and privacy.Experimental analysis shows that this method meets the realtime requirements of collaborative interaction in the automotive industry chain and provides a feasible solution to permission delegation issues in the automotive industry chain,offering dynamic flexibility in authorization and scalability compared to most existing solutions.
文摘This study investigates the effects of AI-mediated communication (AMC) on trust-building and negotiation outcomes in professional remote collaboration settings. Through a mixed-methods approach combining experimental design and qualitative analysis (N = 120), we examine how AI intermediaries influence communication dynamics, relationship building, and decision-making processes. Results indicate that while AMC initially creates barriers to trust formation, it ultimately leads to enhanced communication outcomes and stronger professional relationships when implemented with appropriate transparency and support. The study revealed a 31% improvement in cross-cultural understanding and a 24% increase in negotiation satisfaction rates when using AI-mediated channels with proper transparency measures. These findings contribute to the theoretical understanding of technology-mediated communication and practical applications for organizations implementing AI communication tools.
基金funded by the National Natural Science Foundation of China,grant numbers(62272007,62001007)the Natural Science Foundation of Beijing,grant numbers(4234083,4212018)The authors also extend their appreciation to King Khalid University for funding this work through the Large Group Project under grant number RGP.2/373/45.
文摘Border Gateway Protocol(BGP),as the standard inter-domain routing protocol,is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous Systems(AS).BGP nodes,communicating in a distributed dynamic environment,face several security challenges,with trust being one of the most important issues in inter-domain routing.Existing research,which performs trust evaluation when exchanging routing information to suppress malicious routing behavior,cannot meet the scalability requirements of BGP nodes.In this paper,we propose a blockchain-based trust model for inter-domain routing.Our model achieves scalability by allowing the master node of an AS alliance to transmit the trust evaluation data of its member nodes to the blockchain.The BGP nodes can expedite the trust evaluation process by accessing a global view of other BGP nodes through the master node of their respective alliance.We incorporate security service evaluation before direct evaluation and indirect recommendations to assess the security services that BGP nodes provide for themselves and prioritize to guarantee their security of routing service.We forward the trust evaluation for neighbor discovery and prioritize the nodes with high trust as neighbor nodes to reduce the malicious exchange routing behavior.We use simulation software to simulate a real BGP environments and employ a comparative experimental research approach to demonstrate the performance evaluation of our trust model.Compared with the classical trust model,our trust model not only saves more storage overhead,but also provides higher security,especially reducing the impact of collusion attacks.
文摘目的评估不同孕周应用苄星青霉素G对妊娠梅毒患者的疗效,及其对母婴结局和甲苯胺红不加热血清试验(TRUST)滴度的干预效果。方法回顾性选取2021年6月至2024年6月西北妇女儿童医院收治的98例妊娠梅毒患者,按不同孕周应用苄星青霉素G分为3组:孕早期组(≤13+6周,n=32)、孕中期组(14~27+6周,n=33)和孕晚期组(≥28周,n=33)。比较3组产妇治疗前、治疗2个疗程后的氧化应激指标[超氧化物歧化酶(SOD)、丙二醛、晚期氧化蛋白产物(AOPP)]水平、新生儿及产妇TRUST滴度比、产妇情况(产后出血率、早产率)及新生儿情况(先天梅毒儿、出生后5 min Apgar评分、体质量)。结果(1)较治疗前,3组产妇治疗2个疗程后血清SOD水平均更高,丙二醛、AOPP水平均更低,差异均有统计学意义(P<0.05);较孕早期组,孕中期组、孕晚期组产妇治疗2个疗程后SOD水平更低,丙二醛、AOPP水平更高,差异均有统计学意义(P<0.05)。(2)孕中期组、孕晚期组新生儿治疗后TRUST阴性率分别为30.30%、24.24%,较孕早期组(56.25%)更低,差异有统计学意义(P<0.05)。(3)孕中期组、孕晚期组产妇治疗后TRUST阴性率分别为9.09%、3.03%,较孕早期组(28.13%)更低,差异有统计学意义(P<0.05)。(4)较孕早期组,孕中期组、孕晚期组新生儿先天梅毒儿占比更高,出生后5 min Apgar评分、体质量更低,差异均有统计学意义(P<0.05)。结论妊娠梅毒患者孕早期应用苄星青霉素G疗效优于孕中期和孕晚期,对氧化应激改善更为显著,能进一步提升母婴TRUST阴性率及改善母婴结局。
文摘GS1 is an international standards organization,which focuses on product identification and product data,helping businesses and governments to improve commerce and supply chain.Why trusted data is essential to high-quality development?More than 50 years ago,GS1 was initiated with the bar code,a profound transformation of the way we work and live.From then on,a simple scan connected a physical product to its digital identity.It transformed commerce,improving supply chains and enabling safer healthcare.Collaboration between industry and governments,and a strong partnership with ISO and IEC laid the foundations for the global adoption of a common product identification over the past 50 years and all around the world.
文摘Nowadays,we are witnessing the tremendous changes brought by AI technologies.What role can standards play in this process?How can we build global trust and enable responsible innovation?
基金supported by National Key R&D Program of China (2019YFB2102303)National Natural Science Foundation of China (NSFC61971014,NSFC11675199)+2 种基金Beijing Postdoctoral Research Foundation (2021-ZZ-079)Young Backbone Teacher Training Program of Henan Colleges and Universities (2021GGJS170)Henan Province Higher Education Key Research Project (23B520014)。
文摘The core missions of IoT are to sense data,transmit data and give feedback to the real world based on the calculation of the sensed data.The trust of sensing source data and transmission network is extremely important to IoT security.5G-IoT with its low latency,wide connectivity and high-speed transmission extends the business scenarios of IoT,yet it also brings new challenges to trust proof solutions of IoT.Currently,there is a lack of efficient and reliable trust proof solutions for massive dynamically connected nodes,while the existing solutions have high computational complexity and can't adapt to time-sensitive services in 5G-IoT scenarios.In order to solve the above problems,this paper proposes an adaptive multi-dimensional trust proof solution.Firstly,the static and dynamic attributes of sensing nodes are metricized,and the historical interaction as well as the recommendation information are combined with the comprehensive metric of sensing nodes,and a multi-dimensional fine-grained trusted metric model is established in this paper.Then,based on the comprehensive metrics,the sensing nodes are logically grouped and assigned with service levels to achieve the screening and isolation of malicious nodes.At the same time,the proposed solution reduces the energy consumption of the metric process and optimizes the impact of real-time metrics on the interaction latency.Simulation experiments show that the solution can accurately and efficiently identify malicious nodes and effectively guarantee the safe and trustworthy operation of 5G-IoT nodes,while having a small impact on the latency of the 5G network.
基金supported by the National Natural Science Foundation of China(U22B2026)the ZTE Industry-Academia-Research Project(HC-CN-20221029003,IA20230628015)。
文摘With the introduction of 5G,users and devices can access the industrial network from anywhere in the world.Therefore,traditional perimeter-based security technologies for industrial networks can no longer work well.To solve this problem,a new security model called Zero Trust(ZT)is desired,which believes in“never trust and always verify”.Every time the asset in the industrial network is accessed,the subject is authenticated and its trustworthiness is assessed.In this way,the asset in industrial network can be well protected,whether the subject is in the internal network or the external network.However,in order to construct the zero trust model in the 5G Industrial Internet collaboration system,there are still many problems to be solved.In this paper,we first introduce the security issues in the 5G Industrial Internet collaboration system,and illustrate the zero trust architecture.Then,we analyze the gap between existing security techniques and the zero trust architecture.Finally,we discuss several potential security techniques that can be used to implement the zero trust model.The purpose of this paper is to point out the further direction for the realization of the Zero Trust Architecture(ZTA)in the 5G Industrial Internet collaboration system.
基金supported by Gansu Provincial Higher Education Teachers’Innovation Fund under Grant 2025A-124Key Research Project of Gansu University of Political Science and Law under Grant No.GZF2022XZD08Soft Science Special Project of Gansu Basic Research Plan under Grant No.22JR11RA106.
文摘In the complex environment of Wireless Sensor Networks(WSNs),various malicious attacks have emerged,among which internal attacks pose particularly severe security risks.These attacks seriously threaten network stability,data transmission reliability,and overall performance.To effectively address this issue and significantly improve intrusion detection speed,accuracy,and resistance to malicious attacks,this research designs a Three-level Intrusion Detection Model based on Dynamic Trust Evaluation(TIDM-DTE).This study conducts a detailed analysis of how different attack types impact node trust and establishes node models for data trust,communication trust,and energy consumption trust by focusing on characteristics such as continuous packet loss and energy consumption changes.By dynamically predicting node trust values using the grey Markov model,the model accurately and sensitively reflects changes in node trust levels during attacks.Additionally,DBSCAN(Density-Based Spatial Clustering of Applications with Noise)data noise monitoring technology is employed to quickly identify attacked nodes,while a trust recovery mechanism restores the trust of temporarily faulty nodes to reduce False Alarm Rate.Simulation results demonstrate that TIDM-DTE achieves high detection rates,fast detection speed,and low False Alarm Rate when identifying various network attacks,including selective forwarding attacks,Sybil attacks,switch attacks,and black hole attacks.TIDM-DTE significantly enhances network security,ensures secure and reliable data transmission,moderately improves network energy efficiency,reduces unnecessary energy consumption,and provides strong support for the stable operation of WSNs.Meanwhile,the research findings offer new ideas and methods for WSN security protection,possessing important theoretical significance and practical application value.
文摘Today,I want to share how international standards can forge trust and fuel innovation,laying the foundation for a future where AI benefits everyone,everywhere.First,AI standards,developed jointly by ISO and IEC-the International Electrotechnical Commission-help build global trust and enable responsible innovation by bringing clarity and coherence to an ever-changing AI landscape.As developments in AI continue to emerge at speed,regulation is struggling to keep up and the proliferation of competing standards has created confusion rather than clarity.ISO and our partner IEC are addressing this challenge through the work of our expert committee on AI,SC 42,which takes a holistic,cohesive approach to AI standardization.
