A new species of the Macrophya malaisei group in the genus Macrophya Dahlbom,1835 from Zhejiang in China is described:M.alboclypea Li,Liu&Wei sp.nov.A revised key to the Chinese and Japanese species of the M.malai...A new species of the Macrophya malaisei group in the genus Macrophya Dahlbom,1835 from Zhejiang in China is described:M.alboclypea Li,Liu&Wei sp.nov.A revised key to the Chinese and Japanese species of the M.malaisei group is provided.展开更多
Mining and tailings deposition can cause serious heavy metal(loids)pollution to the surrounding soil environment.Soil microorganisms adapt their metabolism to such conditions,driving alterations in soil function.This ...Mining and tailings deposition can cause serious heavy metal(loids)pollution to the surrounding soil environment.Soil microorganisms adapt their metabolism to such conditions,driving alterations in soil function.This study aims to elucidate the response patterns of nitrogen-cycling microorganisms under long-term heavy metal(loids)exposure.The results showed that the diversity and abundance of nitrogen-cyclingmicroorganisms showed negative feedback to heavy metal(loids)concentrations.Denitrifying microorganisms were shown to be the dominant microorganisms with over 60%of relative abundance and a complex community structure including 27 phyla.Further,the key bacterial species in the denitrification process were calculated using a random forest model,where the top three key species(Pseudomonas stutzei,Sphingobium japonicum and Leifsonia rubra)were found to play a prominent role in nitrite reduction.Functional gene analysis and qPCR revealed that nirK,which is involved in nitrite reduction,significantly accumulated in the most metal-rich soil with the increase of absolute abundance of 63.86%.The experimental results confirmed that the activity of nitrite reductase(Nir)encoded by nirK in the soil was increased at high concentrations of heavy metal(loids).Partial least squares-path model identified three potential modes of nitrite reduction processes being stimulated by heavy metal(loids),the most prominent of which contributed to enhanced nirK abundance and soil Nir activity through positive stimulation of key species.The results provide new insights and preliminary evidence on the stimulation of nitrite reduction processes by heavy metal(loids).展开更多
The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology play...The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in vehicle localization and navigation. Traditional Simultaneous Localization and Mapping (SLAM) systems are designed for use in static environments, and they can result in poor performance in terms of accuracy and robustness when used in dynamic environments where objects are in constant movement. To address this issue, a new real-time visual SLAM system called MG-SLAM has been developed. Based on ORB-SLAM2, MG-SLAM incorporates a dynamic target detection process that enables the detection of both known and unknown moving objects. In this process, a separate semantic segmentation thread is required to segment dynamic target instances, and the Mask R-CNN algorithm is applied on the Graphics Processing Unit (GPU) to accelerate segmentation. To reduce computational cost, only key frames are segmented to identify known dynamic objects. Additionally, a multi-view geometry method is adopted to detect unknown moving objects. The results demonstrate that MG-SLAM achieves higher precision, with an improvement from 0.2730 m to 0.0135 m in precision. Moreover, the processing time required by MG-SLAM is significantly reduced compared to other dynamic scene SLAM algorithms, which illustrates its efficacy in locating objects in dynamic scenes.展开更多
In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),dee...In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),deeply embedded in the perception layer architecture of the IoT,play a crucial role as“tactile nerve endings.”A vast number of micro sensor nodes are widely distributed in monitoring areas according to preset deployment strategies,continuously and accurately perceiving and collecting real-time data on environmental parameters such as temperature,humidity,light intensity,air pressure,and pollutant concentration.These data are transmitted to the IoT cloud platform through stable and reliable communication links,forming a massive and detailed basic data resource pool.By using cutting-edge big data processing algorithms,machine learning models,and artificial intelligence analysis tools,in-depth mining and intelligent analysis of these multi-source heterogeneous data are conducted to generate high-value-added decision-making bases.This precisely empowers multiple fields,including agriculture,medical and health care,smart home,environmental science,and industrial manufacturing,driving intelligent transformation and catalyzing society to move towards a new stage of high-quality development.This paper comprehensively analyzes the technical cores of the IoT and WSNs,systematically sorts out the advanced key technologies of WSNs and the evolution of their strategic significance in the IoT system,deeply explores the innovative application scenarios and practical effects of the two in specific vertical fields,and looks forward to the technological evolution trends.It provides a detailed and highly practical guiding reference for researchers,technical engineers,and industrial decision-makers.展开更多
This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolu...This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolution and bi-directional feature pyramid network(BiFPN)_Concat to improve the adaptability of the network.Secondly,the simple attention module(SimAm)is embedded to prioritize key features and reduce the complexity of the model after the C2f layer at the end of the backbone network.Next,the focal efficient intersection over union(EloU)is introduced to adjust the weights of challenging samples.Finally,we accomplish the design and deployment for the mobile app.The results demonstrate improvements,with the F1 score of 0.8987,mean average precision(mAP)@0.5 of 98.8%,mAP@0.5:0.95 of 75.6%,and the detection speed of 50 frames per second(FPS).展开更多
Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in speci...Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.展开更多
Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in c...Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in climate models.Previous studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC simulations.In this work,with a low-resolution earth system model,the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its collapse.Through a new optimization strategy,the extra freshwater flux(FWF)parameter is determined to be the dominant one affecting the AMOC’s variability.The traditional ensemble optimal interpolation(EnOI)data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO_(2) forcing experiment to improve the adaptability of model parameters and accelerate the recovery of AMOC.The results show that,under an abrupt 4×CO_(2) forcing in millennial simulations,the AMOC will first collapse and then re-establish by the default FWF parameter slowly.However,during the parameter adjustment process,the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC,according to their physical relationship with FWF on the interdecadal timescale.展开更多
The Type-2 generalized Feistel structure is widely used in block cipher design.This work conducts a quantum key recovery attack on TWINE-80,a lightweight block cipher based on the improved Type-2 generalized Feistel s...The Type-2 generalized Feistel structure is widely used in block cipher design.This work conducts a quantum key recovery attack on TWINE-80,a lightweight block cipher based on the improved Type-2 generalized Feistel structure.By constructing a round function,a new 7-round quantum distinguisher for TWINE-80 is identified.Leveraging the reuse characteristics of round keys in the algorithm,three pairs of repeated round keys are discovered during the 5-round transformation process.Using Grover’s algorithm to search for partial round keys,a 17-round quantum key recovery attack on TWINE-80 is successfully implemented,with a time complexity of 296 and requiring 327 qubits.Compared to similar studies,this work reduces the time complexity by 26 and slightly decreases the required quantum resources by 12 qubits.展开更多
State Key Laboratory of Baiyun Obo Rare Earth Resource Researches and Comprehensive Utilization was approved by the Ministry of Science and Technology to be one of the national key laboratories in November 2022.
Quantum key distribution(QKD)is recognized as an unconditionally secure method of communication encryption,relying solely on the principles of quantum mechanics.A key performance metric for QKD systems is secure key r...Quantum key distribution(QKD)is recognized as an unconditionally secure method of communication encryption,relying solely on the principles of quantum mechanics.A key performance metric for QKD systems is secure key rate(SKR),which is a critical factor for real-world applications.Herein,we report a practical QKD system,equipped with compact gated InGaAs/InP single-photon detectors(SPDs),that can generate a high SKR of 15.2 Mb/s with a channel loss of 2 dB.This exceptional performance stems from the ultra-low afterpulsing probability of the SPDs,which significantly reduces the bit error rate in the QKD system.The typical quantum bit error rate is 1.3%.The results validate the feasibility of an integrated,practical QKD system and offer a reliable solution for the future development of real-world QKD networks.展开更多
5G technology has endowed mobile communication terminals with features such as ultrawideband access,low latency,and high reliability transmission,which can complete the network access and interconnection of a large nu...5G technology has endowed mobile communication terminals with features such as ultrawideband access,low latency,and high reliability transmission,which can complete the network access and interconnection of a large number of devices,thus realizing richer application scenarios and constructing 5G-enabled vehicular networks.However,due to the vulnerability of wireless communication,vehicle privacy and communication security have become the key problems to be solved in vehicular networks.Moreover,the large-scale communication in the vehicular networks also makes the higher communication efficiency an inevitable requirement.In order to achieve efficient and secure communication while protecting vehicle privacy,this paper proposes a lightweight key agreement and key update scheme for 5G vehicular networks based on blockchain.Firstly,the key agreement is accomplished using certificateless public key cryptography,and based on the aggregate signature and the cooperation between the vehicle and the trusted authority,an efficient key updating method is proposed,which reduces the overhead and protects the privacy of the vehicle while ensuring the communication security.Secondly,by introducing blockchain and using smart contracts to load the vehicle public key table for key management,this meets the requirements of vehicle traceability and can dynamically track and revoke misbehaving vehicles.Finally,the formal security proof under the eck security model and the informal security analysis is conducted,it turns out that our scheme is more secure than other authentication schemes in the vehicular networks.Performance analysis shows that our scheme has lower overhead than existing schemes in terms of communication and computation.展开更多
State Key Laboratory of Baiyun Obo Rare Earth Resource Researches and Comprehensive Utilization was approved by the Ministry of Science and Technology to be one of the national key laboratories in November 2022.The la...State Key Laboratory of Baiyun Obo Rare Earth Resource Researches and Comprehensive Utilization was approved by the Ministry of Science and Technology to be one of the national key laboratories in November 2022.The laboratory was reconstructed based on former State Key Laboratory of Baiyun Obo Rare Earth Resources Researches and Comprehensive Utilization.展开更多
State Key Laboratory of Baiyun Obo Rare Earth Resource Researches and Comprehensive Utilization was approved by the Ministry of Science and Technology to be one of the national key laboratories in November 2022.The la...State Key Laboratory of Baiyun Obo Rare Earth Resource Researches and Comprehensive Utilization was approved by the Ministry of Science and Technology to be one of the national key laboratories in November 2022.The laboratory was reconstructed based on former State Key Laboratory of Baiyun Obo Rare Earth Resources Researches and Comprehensive Utilization.展开更多
Sometimes you lose something.Itmaybeakeyoryourphone.Thenyou haveto look for it.Youcan lookinmanyplaces.Itmaybeinapocketor in adrawer.Youcan also look onthings.Itmay beonadeskora table.You may also need to look underth...Sometimes you lose something.Itmaybeakeyoryourphone.Thenyou haveto look for it.Youcan lookinmanyplaces.Itmaybeinapocketor in adrawer.Youcan also look onthings.Itmay beonadeskora table.You may also need to look underthings.Look between things,too.Itmay be between two books ortwosheets of paper.Or it may be nextto yourbed or your backpack.Lookcarefully.Youcan find it!展开更多
Two new species of Dolerinae from Mt.Liupan,Ningxia of China are described:Dolerus liupanicus Wei sp.nov.and D.xiai Wei sp.nov.Dolerus liupanicus is similar to D.purus Jakovlev,1891 but differs from it by the pronotum...Two new species of Dolerinae from Mt.Liupan,Ningxia of China are described:Dolerus liupanicus Wei sp.nov.and D.xiai Wei sp.nov.Dolerus liupanicus is similar to D.purus Jakovlev,1891 but differs from it by the pronotum,tegula,mesoscutal middle lobe and the first abdominal tergum entirely black,the setae of apical sheath almost straight and extending outward at an angle of about 100 degrees,the alaspiculae of middle annuli very large,the middle serrulae each with about 14 to 15 small subbasal teeth.Dolerus xiai is similar to D.japonicus Kirby,1882 but differs from the latter by the fore wing deeply infuscate,the mesepisternum and legs entirely black,the setae on apical sheath blackish brown,the apical sheath in lateral view narrow and strongly tapering toward apex,the middle carina on mesoscutellar appendage not concave,the male abdomen with broad reddish brown rings,and the dorsal apical corner of penis valve convex.A key to species of Dolerinae from Mt.Liupan and nearby area is also provided.展开更多
基金supported by the Scientific Research Project of Baishanzu National Park(2023JBGS07)the National Natural Science Foundation of China(31970447,32370500)the Special Funds for Scientific Research of Postdoctoral Work Station Assessment in Zhejiang Province,China(2023).
文摘A new species of the Macrophya malaisei group in the genus Macrophya Dahlbom,1835 from Zhejiang in China is described:M.alboclypea Li,Liu&Wei sp.nov.A revised key to the Chinese and Japanese species of the M.malaisei group is provided.
基金supported by the National Natural Science Foundation of China(No.41977029).
文摘Mining and tailings deposition can cause serious heavy metal(loids)pollution to the surrounding soil environment.Soil microorganisms adapt their metabolism to such conditions,driving alterations in soil function.This study aims to elucidate the response patterns of nitrogen-cycling microorganisms under long-term heavy metal(loids)exposure.The results showed that the diversity and abundance of nitrogen-cyclingmicroorganisms showed negative feedback to heavy metal(loids)concentrations.Denitrifying microorganisms were shown to be the dominant microorganisms with over 60%of relative abundance and a complex community structure including 27 phyla.Further,the key bacterial species in the denitrification process were calculated using a random forest model,where the top three key species(Pseudomonas stutzei,Sphingobium japonicum and Leifsonia rubra)were found to play a prominent role in nitrite reduction.Functional gene analysis and qPCR revealed that nirK,which is involved in nitrite reduction,significantly accumulated in the most metal-rich soil with the increase of absolute abundance of 63.86%.The experimental results confirmed that the activity of nitrite reductase(Nir)encoded by nirK in the soil was increased at high concentrations of heavy metal(loids).Partial least squares-path model identified three potential modes of nitrite reduction processes being stimulated by heavy metal(loids),the most prominent of which contributed to enhanced nirK abundance and soil Nir activity through positive stimulation of key species.The results provide new insights and preliminary evidence on the stimulation of nitrite reduction processes by heavy metal(loids).
基金funded by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(grant number 22KJD440001)Changzhou Science&Technology Program(grant number CJ20220232).
文摘The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in vehicle localization and navigation. Traditional Simultaneous Localization and Mapping (SLAM) systems are designed for use in static environments, and they can result in poor performance in terms of accuracy and robustness when used in dynamic environments where objects are in constant movement. To address this issue, a new real-time visual SLAM system called MG-SLAM has been developed. Based on ORB-SLAM2, MG-SLAM incorporates a dynamic target detection process that enables the detection of both known and unknown moving objects. In this process, a separate semantic segmentation thread is required to segment dynamic target instances, and the Mask R-CNN algorithm is applied on the Graphics Processing Unit (GPU) to accelerate segmentation. To reduce computational cost, only key frames are segmented to identify known dynamic objects. Additionally, a multi-view geometry method is adopted to detect unknown moving objects. The results demonstrate that MG-SLAM achieves higher precision, with an improvement from 0.2730 m to 0.0135 m in precision. Moreover, the processing time required by MG-SLAM is significantly reduced compared to other dynamic scene SLAM algorithms, which illustrates its efficacy in locating objects in dynamic scenes.
文摘In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),deeply embedded in the perception layer architecture of the IoT,play a crucial role as“tactile nerve endings.”A vast number of micro sensor nodes are widely distributed in monitoring areas according to preset deployment strategies,continuously and accurately perceiving and collecting real-time data on environmental parameters such as temperature,humidity,light intensity,air pressure,and pollutant concentration.These data are transmitted to the IoT cloud platform through stable and reliable communication links,forming a massive and detailed basic data resource pool.By using cutting-edge big data processing algorithms,machine learning models,and artificial intelligence analysis tools,in-depth mining and intelligent analysis of these multi-source heterogeneous data are conducted to generate high-value-added decision-making bases.This precisely empowers multiple fields,including agriculture,medical and health care,smart home,environmental science,and industrial manufacturing,driving intelligent transformation and catalyzing society to move towards a new stage of high-quality development.This paper comprehensively analyzes the technical cores of the IoT and WSNs,systematically sorts out the advanced key technologies of WSNs and the evolution of their strategic significance in the IoT system,deeply explores the innovative application scenarios and practical effects of the two in specific vertical fields,and looks forward to the technological evolution trends.It provides a detailed and highly practical guiding reference for researchers,technical engineers,and industrial decision-makers.
基金supported by the Shanxi Agricultural University Science and Technology Innovation Enhancement Project。
文摘This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolution and bi-directional feature pyramid network(BiFPN)_Concat to improve the adaptability of the network.Secondly,the simple attention module(SimAm)is embedded to prioritize key features and reduce the complexity of the model after the C2f layer at the end of the backbone network.Next,the focal efficient intersection over union(EloU)is introduced to adjust the weights of challenging samples.Finally,we accomplish the design and deployment for the mobile app.The results demonstrate improvements,with the F1 score of 0.8987,mean average precision(mAP)@0.5 of 98.8%,mAP@0.5:0.95 of 75.6%,and the detection speed of 50 frames per second(FPS).
基金supported by the National Key R&D Program of China(No.2021YFB0301200)National Natural Science Foundation of China(No.62025208).
文摘Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.
基金supported by the National Key R&D Program of China [grant number 2023YFF0805202]the National Natural Science Foun-dation of China [grant number 42175045]the Strategic Priority Research Program of the Chinese Academy of Sciences [grant number XDB42000000]。
文摘Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in climate models.Previous studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC simulations.In this work,with a low-resolution earth system model,the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its collapse.Through a new optimization strategy,the extra freshwater flux(FWF)parameter is determined to be the dominant one affecting the AMOC’s variability.The traditional ensemble optimal interpolation(EnOI)data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO_(2) forcing experiment to improve the adaptability of model parameters and accelerate the recovery of AMOC.The results show that,under an abrupt 4×CO_(2) forcing in millennial simulations,the AMOC will first collapse and then re-establish by the default FWF parameter slowly.However,during the parameter adjustment process,the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC,according to their physical relationship with FWF on the interdecadal timescale.
文摘The Type-2 generalized Feistel structure is widely used in block cipher design.This work conducts a quantum key recovery attack on TWINE-80,a lightweight block cipher based on the improved Type-2 generalized Feistel structure.By constructing a round function,a new 7-round quantum distinguisher for TWINE-80 is identified.Leveraging the reuse characteristics of round keys in the algorithm,three pairs of repeated round keys are discovered during the 5-round transformation process.Using Grover’s algorithm to search for partial round keys,a 17-round quantum key recovery attack on TWINE-80 is successfully implemented,with a time complexity of 296 and requiring 327 qubits.Compared to similar studies,this work reduces the time complexity by 26 and slightly decreases the required quantum resources by 12 qubits.
文摘State Key Laboratory of Baiyun Obo Rare Earth Resource Researches and Comprehensive Utilization was approved by the Ministry of Science and Technology to be one of the national key laboratories in November 2022.
基金supported by the Innovation Program for Quantum Science and Technology(Grant No.2024ZD0302500)the National Natural Science Foundation of China(Grant No.62250710162)the Beijing Natural Science Foundation(Grant No.Z230005)。
文摘Quantum key distribution(QKD)is recognized as an unconditionally secure method of communication encryption,relying solely on the principles of quantum mechanics.A key performance metric for QKD systems is secure key rate(SKR),which is a critical factor for real-world applications.Herein,we report a practical QKD system,equipped with compact gated InGaAs/InP single-photon detectors(SPDs),that can generate a high SKR of 15.2 Mb/s with a channel loss of 2 dB.This exceptional performance stems from the ultra-low afterpulsing probability of the SPDs,which significantly reduces the bit error rate in the QKD system.The typical quantum bit error rate is 1.3%.The results validate the feasibility of an integrated,practical QKD system and offer a reliable solution for the future development of real-world QKD networks.
基金supported in part by the National Natural Science Foundation of China under Grant 61941113,Grant 61971033,and Grant 61671057by the Henan Provincial Department of Science and Technology Project(No.212102210408)by the Henan Provincial Key Scientific Research Project(No.22A520041).
文摘5G technology has endowed mobile communication terminals with features such as ultrawideband access,low latency,and high reliability transmission,which can complete the network access and interconnection of a large number of devices,thus realizing richer application scenarios and constructing 5G-enabled vehicular networks.However,due to the vulnerability of wireless communication,vehicle privacy and communication security have become the key problems to be solved in vehicular networks.Moreover,the large-scale communication in the vehicular networks also makes the higher communication efficiency an inevitable requirement.In order to achieve efficient and secure communication while protecting vehicle privacy,this paper proposes a lightweight key agreement and key update scheme for 5G vehicular networks based on blockchain.Firstly,the key agreement is accomplished using certificateless public key cryptography,and based on the aggregate signature and the cooperation between the vehicle and the trusted authority,an efficient key updating method is proposed,which reduces the overhead and protects the privacy of the vehicle while ensuring the communication security.Secondly,by introducing blockchain and using smart contracts to load the vehicle public key table for key management,this meets the requirements of vehicle traceability and can dynamically track and revoke misbehaving vehicles.Finally,the formal security proof under the eck security model and the informal security analysis is conducted,it turns out that our scheme is more secure than other authentication schemes in the vehicular networks.Performance analysis shows that our scheme has lower overhead than existing schemes in terms of communication and computation.
文摘State Key Laboratory of Baiyun Obo Rare Earth Resource Researches and Comprehensive Utilization was approved by the Ministry of Science and Technology to be one of the national key laboratories in November 2022.The laboratory was reconstructed based on former State Key Laboratory of Baiyun Obo Rare Earth Resources Researches and Comprehensive Utilization.
文摘State Key Laboratory of Baiyun Obo Rare Earth Resource Researches and Comprehensive Utilization was approved by the Ministry of Science and Technology to be one of the national key laboratories in November 2022.The laboratory was reconstructed based on former State Key Laboratory of Baiyun Obo Rare Earth Resources Researches and Comprehensive Utilization.
文摘Sometimes you lose something.Itmaybeakeyoryourphone.Thenyou haveto look for it.Youcan lookinmanyplaces.Itmaybeinapocketor in adrawer.Youcan also look onthings.Itmay beonadeskora table.You may also need to look underthings.Look between things,too.Itmay be between two books ortwosheets of paper.Or it may be nextto yourbed or your backpack.Lookcarefully.Youcan find it!
基金supported by the National Natural Science Foundation of China(32370500,31970447).
文摘Two new species of Dolerinae from Mt.Liupan,Ningxia of China are described:Dolerus liupanicus Wei sp.nov.and D.xiai Wei sp.nov.Dolerus liupanicus is similar to D.purus Jakovlev,1891 but differs from it by the pronotum,tegula,mesoscutal middle lobe and the first abdominal tergum entirely black,the setae of apical sheath almost straight and extending outward at an angle of about 100 degrees,the alaspiculae of middle annuli very large,the middle serrulae each with about 14 to 15 small subbasal teeth.Dolerus xiai is similar to D.japonicus Kirby,1882 but differs from the latter by the fore wing deeply infuscate,the mesepisternum and legs entirely black,the setae on apical sheath blackish brown,the apical sheath in lateral view narrow and strongly tapering toward apex,the middle carina on mesoscutellar appendage not concave,the male abdomen with broad reddish brown rings,and the dorsal apical corner of penis valve convex.A key to species of Dolerinae from Mt.Liupan and nearby area is also provided.
