Coulomb drag refers to the phenomenon in which a current driven through one conducting layer induces a voltage nearby,electrically isolated layer sorely through interlayer Coulomb interactions between charge carriers....Coulomb drag refers to the phenomenon in which a current driven through one conducting layer induces a voltage nearby,electrically isolated layer sorely through interlayer Coulomb interactions between charge carriers.It has been extensively studied in various systems,including parallel nanowires,double quantum wells,and double-layer graphene.Here,we report the observation of Coulomb drag in a novel system consisting of two graphene layers separated laterally by a 30 nm gap within the material plane,exhibiting behavior distinct from that in vertical graphene heterostructures.Our experiments reveal pronounced negative drag resistances under an out-of-plane magnetic field at the quantum Hall edges,reaching a maximum when the carrier densities in both graphene layers are tuned to the charge neutrality point via gate voltages.Our work establish two separate and spatially closed quantum Hall edge modes as a new platform to explore electronic interaction physics between one dimensional systems.展开更多
For hypersonic air-breathing vehicles,the V-shaped leading edges(VSLEs)of supersonic combustion ramjet(scramjet)inlets experience complex shock interactions and intense aerodynamic loads.This paper provides a comprehe...For hypersonic air-breathing vehicles,the V-shaped leading edges(VSLEs)of supersonic combustion ramjet(scramjet)inlets experience complex shock interactions and intense aerodynamic loads.This paper provides a comprehensive review of flow characteristics at the crotch of VSLEs,with particular focus on the transition of shock interaction types and the variation of wall heat flux under different freestream Mach numbers and geometric configurations.The mechanisms governing shock transition,unsteady oscillations,hysteresis,and three-dimensional effects in VSLE flows are first examined.Subsequently,thermal protection strategies aimed at mitigating extreme heating loads are reviewed,emphasizing their relevance to practical engineering applications.Special attention is given to recent studies addressing thermochemical nonequilibrium effects on VSLE shock interactions,and the limitations of current research are critically assessed.Finally,perspectives for future investigations into hypersonic VSLE shock interactions are outlined,highlighting opportunities for advancing design and thermal management strategies.展开更多
Few-layer graphene grown on Ni thin films has been studied by scanning tunneling microscopy. In most areas on the surfaces, moir6 patterns resulted from rotational stacking faults were observed. At a bias lower than 2...Few-layer graphene grown on Ni thin films has been studied by scanning tunneling microscopy. In most areas on the surfaces, moir6 patterns resulted from rotational stacking faults were observed. At a bias lower than 200 mV, only one sublattice shows up in regions without moir6 patterns while both sublattices are seen in regions with moir6 pattens. This phenomenon can be used to identify AB stacked regions. The scattering characteristics at various types of step edges are different from those of monolayer graphene edges, either armchair or zigzag.展开更多
Upgrading of abundant cellulosic biomass to isosorbide can reduce the dependence on limited fossil resources and provide a sustainable way to produce isosorbide,utilized for polymers,medicine and health care product s...Upgrading of abundant cellulosic biomass to isosorbide can reduce the dependence on limited fossil resources and provide a sustainable way to produce isosorbide,utilized for polymers,medicine and health care product synth-esis.This review comprehensively examines the key steps and catalytic systems involved in the conversion of cel-lulose to isosorbide.Initially,the reaction pathway from cellulose to isosorbide is elucidated,emphasizing three critical steps:cellulose hydrolysis,glucose hydrogenation,and the two-step dehydration of sorbitol to produce isosorbide.Additionally,the activation energy and acidic sites during cellulose hydrolysis,the impact of metal particle size and catalyst support on hydrogenation,and the effects of catalyst acidity,pore structure,and reaction conditions on sorbitol dehydration have been thoroughly examined.Finally,the progress made in cellulose con-version to isosorbide is summarized,current challenges are highlighted,and future development trends are pro-jected in this review.展开更多
Functional traits are characteristics associated with the growth,reproduction,and survival of individuals.Studying them helps us understand how species traits drive ecosystem functioning.Thus,we evaluated the differen...Functional traits are characteristics associated with the growth,reproduction,and survival of individuals.Studying them helps us understand how species traits drive ecosystem functioning.Thus,we evaluated the differences in traits and functional diversity between forest edges and interiors,and how the inclusion of intraspecific trait variation affects the assessment of functional diversity in these habitats.We sampled 10 representative forest patches,and,in each patch,we established five plots on the edge and five inside the forest,collecting leaf functional traits,allometric and wood density for all species.We assessed functional diversity using functional richness(FRic),divergence(FDiv),and dispersion(FDis).To assess the impact of incorporating intraspecific variation when comparing trait values and functional diversity indices,we established two scenarios:one that excludes intraspecific variation and another that includes it.We found that the edge and interior harbor individuals with distinct functional traits that alleviate the inherent stress of each habitat.The edge was also found to be more selective in terms of the range of functional traits,resulting in lower functional diversity.Our findings demonstrated that habitats play an important role in intraspecific trait variation(ITV)and that statistically significant differences between habitats,in relation to traits and functional diversity,were better observed with the inclusion of intraspecific variation.Our study highlights the potential of using natural forest patches to understand the edge effect,regardless of habitat loss.Additionally,we emphasize the importance of incorporating ITV into functional diversity studies,especially those on a smaller scale that incorporate quantitative variables,to better understand and predict ecological patterns.展开更多
The primary Mach Reflection(MR)and pressure/heating loads on V-shaped Blunt Leading Edges(VBLEs)with variable elliptic cross-sections and conic crotches are theoretically investigated in this study.The simplified cont...The primary Mach Reflection(MR)and pressure/heating loads on V-shaped Blunt Leading Edges(VBLEs)with variable elliptic cross-sections and conic crotches are theoretically investigated in this study.The simplified continuity method is used to forecast the shock configurations.The theoretical predictions and the numerical simulations for the Mach stem and the triple point as well as the curved shock accord well.Based on the theoretical model,an analysis of the impact of the axial ratio a/b of the cross-sectional shape and the eccentricity e of the crotch sweep path on shock structures is carried out.The shock configurations obtained from the theoretical model enable the derivation of the transition boundaries between the primary MR and the same family Regular Reflection(sRR).It is found that the increase of a/b and e can both facilitate the primary MR to sRR transition.The resulting transition and the corresponding generation of the wall pressure and heat flux are then investigated.The results indicate that higher values of the ratio a/b can significantly reduce the wall pressure and heating loads by inducing the primary MR to sRR transition.Conversely,the increase in the eccentricity e results in increased loads,despite causing the same transition.展开更多
Reptile fauna should be considered a conservation objective,especially in respect of the impacts of climate change on their distribution and range’s dynamics.Investigating the environmental drivers of reptile species...Reptile fauna should be considered a conservation objective,especially in respect of the impacts of climate change on their distribution and range’s dynamics.Investigating the environmental drivers of reptile species richness and identifying their suitable habitats is a fundamental prerequisite to setting efficient long-term conservation measures.This study focused on geographical patterns and estimations of species richness for herpetofauna widely spread Z.vivipara,N.natrix,V.berus,A.colchica,and protected in Latvia C.austriaca,E.orbicularis,L.agilis inhabiting northern(model territory Latvia)and southern(model territory Ukraine)part of their European range.The ultimate goal was to designate a conservation network that will meet long-term goals for survival of the target species in the context of climate change.We used stacked species distribution models for creating maps depicting the distribution of species richness under current and future(by 2050)climates for marginal reptilepopulations.Using cluster analysis,we showed that this herpeto-complex can be divided into“widespread species”and“forest species”.For all forest species we predicted a climate-driven reduction in their distribution range both North(Latvia)and South(Ukraine).The most vulnerable populations of“forest species”tend to be located in the South of their range,as a consequence of northward shifts by 2050.By 2050 the greatest reduction in range is predicted for currently widely spread Z.vivipara(by 1.4 times)and V.berus(by 2.2 times).In terms of designing an effective protected-area network,these results permit to identify priority conservation areas where the full ensemble of selected reptile species can be found,and confirms the relevance of abioticmulti-factor GIS-modelling for achieving this goal.展开更多
Step ladder-structured nitrocellulose(LNC)is a novel energetic binder prepared by chemically modifying nitrocellulose(NC)with the introduction of flexible polyethylene glycol(PEG-400)chain segments,with a regular stru...Step ladder-structured nitrocellulose(LNC)is a novel energetic binder prepared by chemically modifying nitrocellulose(NC)with the introduction of flexible polyethylene glycol(PEG-400)chain segments,with a regular structure and good performance of bonding.The step ladder-structured addresses critical limitations of NC-based propellants,including low-temperature brittleness and high sensitivity,while enhancing process safety.Although the structural,thermal,and other properties of LNC have been investigated in our previous research,there is a lack of systematic studies on the rheological properties during solution and gelatinization.The study of the relationship between the structural features and rheological properties of LNC is a key factor in guiding its gelatinization and improving the properties of LNC-based propellants.Steady-state rheology flow experiments revealed that LNC exhibited shear thinning in different solutions,which decreased with increasing concentration.It has desirable solu-bility and dispersion in N,N-dimethylformamide(DMF)solvent.The effect of solvents on the entan-glement or orientation of LNC molecular chains may be reduced.These results can be quantitatively demonstrated using the Herschel-Bulkley model.Dynamic viscoelastic studies identified a critical point of concentration-frequency of 2.5 rad/s.This particular frequency point is a turning point in the law of the effect of concentration on the loss factor(tanδ).For gelatinized systems,increasing the solvent content reduces the temperature sensitivity of the gelatinized materials.The viscosity-temperature correlation based on the Arrhenius equation allowed the optimization of the solvent content through the derived equilibrium relationship.These structure-rheological performance relationships establish basic guidelines for the precision gelatinization of LNC-based propellant,provide theoretical support for the replacement of conventional NC by LNC,and guide the gelatinization process to improve the performance of gun propellants.展开更多
This work presents a systematic analysis of proton-induced total ionizing dose(TID)effects in 1.2 k V silicon carbide(SiC)power devices with various edge termination structures.Three edge terminations including ring-a...This work presents a systematic analysis of proton-induced total ionizing dose(TID)effects in 1.2 k V silicon carbide(SiC)power devices with various edge termination structures.Three edge terminations including ring-assisted junction termination extension(RA-JTE),multiple floating zone JTE(MFZ-JTE),and field limiting rings(FLR)were fabricated and irradiated with45 Me V protons at fluences ranging from 1×10^(12) to 1×10^(14) cm^(-2).Experimental results,supported by TCAD simulations,show that the RA-JTE structure maintained stable breakdown performance with less than 1%variation due to its effective electric field redistribution by multiple P+rings.In contrast,MFZ-JTE and FLR exhibit breakdown voltage shifts of 6.1%and 15.2%,respectively,under the highest fluence.These results demonstrate the superior radiation tolerance of the RA-JTE structure under TID conditions and provide practical design guidance for radiation-hardened Si C power devices in space and other highradiation environments.展开更多
Objectives This study aimed to evaluate the effectiveness of the stepped self-care program on the self-care,self-efficacy,and quality of life of stroke survivors.Methods This quasi-experimental study allocated 110 str...Objectives This study aimed to evaluate the effectiveness of the stepped self-care program on the self-care,self-efficacy,and quality of life of stroke survivors.Methods This quasi-experimental study allocated 110 stroke survivors from two neurology wards into an intervention group(n=55)who received the stepped self-care program and a control group(n=55)who received usual care from June to December 2023.The Self-Care of Stroke Inventory,Stroke Self-Efficacy Questionnaire,and the short version of the Stroke Specific Quality of Life Scale were administered at baseline(T0),immediately post-intervention(T_(1)),and at 1-month(T_(2))and 3-month(T_(3))follow-ups.Data were analyzed using repeated measures analyses of variance,and generalized estimating equations.Results A total of 48 participants in the intervention group and 50 participants in the control group completed the study.No statistically significant differences were observed at T0 in any of the measured indicators(all P>0.05).The study showed significant group,time,and group×time interaction effects across the assessed outcomes(all P<0.05).Follow-up between-group comparisons at T_(1),T_(2),and T_(3) indicated that the intervention group had significantly higher scores in self-care maintenance,self-care monitoring,self-care management,self-efficacy,and quality of life than the control group(all P<0.001).Conclusions The stepped self-care program significantly improved self-care behaviors,self-efficacy,and quality of life among stroke survivors.These findings support the broader implementation of this approach in post-discharge home self-care.展开更多
Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approa...Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approach in current practices.However,in complex and dynamic traffic scenes,particularly with smaller traffic sign objects,challenges such as missed and false detections can lead to reduced overall detection accuracy.To address this issue,this paper proposes a detection algorithm that integrates edge and shape information.Recognizing that traffic signs have specific shapes and distinct edge contours,this paper introduces an edge feature extraction branch within the backbone network,enabling adaptive fusion with features of the same hierarchical level.Additionally,a shape prior convolution module is designed to replaces the first two convolutional modules of the backbone network,aimed at enhancing the model's perception ability for specific shape objects and reducing its sensitivity to background noise.The algorithm was evaluated on the CCTSDB and TT100k datasets,and compared to YOLOv8s,the mAP50 values increased by 3.0%and 10.4%,respectively,demonstrating the effectiveness of the proposed method in improving the accuracy of traffic sign detection.展开更多
Due to the growth of smart cities,many real-time systems have been developed to support smart cities using Internet of Things(IoT)and emerging technologies.They are formulated to collect the data for environment monit...Due to the growth of smart cities,many real-time systems have been developed to support smart cities using Internet of Things(IoT)and emerging technologies.They are formulated to collect the data for environment monitoring and automate the communication process.In recent decades,researchers have made many efforts to propose autonomous systems for manipulating network data and providing on-time responses in critical operations.However,the widespread use of IoT devices in resource-constrained applications and mobile sensor networks introduces significant research challenges for cybersecurity.These systems are vulnerable to a variety of cyberattacks,including unauthorized access,denial-of-service attacks,and data leakage,which compromise the network’s security.Additionally,uneven load balancing between mobile IoT devices,which frequently experience link interferences,compromises the trustworthiness of the system.This paper introduces a Multi-Agent secured framework using lightweight edge computing to enhance cybersecurity for sensor networks,aiming to leverage artificial intelligence for adaptive routing and multi-metric trust evaluation to achieve data privacy and mitigate potential threats.Moreover,it enhances the efficiency of distributed sensors for energy consumption through intelligent data analytics techniques,resulting in highly consistent and low-latency network communication.Using simulations,the proposed framework reveals its significant performance compared to state-of-the-art approaches for energy consumption by 43%,latency by 46%,network throughput by 51%,packet loss rate by 40%,and denial of service attacks by 42%.展开更多
With the large-scale deployment of the Internet ofThings(IoT)devices,their weak securitymechanisms make them prime targets for malware attacks.Attackers often use Domain Generation Algorithm(DGA)to generate random dom...With the large-scale deployment of the Internet ofThings(IoT)devices,their weak securitymechanisms make them prime targets for malware attacks.Attackers often use Domain Generation Algorithm(DGA)to generate random domain names,hiding the real IP of Command and Control(C&C)servers to build botnets.Due to the randomness and dynamics of DGA,traditional methods struggle to detect them accurately,increasing the difficulty of network defense.This paper proposes a lightweight DGA detection model based on knowledge distillation for resource-constrained IoT environments.Specifically,a teacher model combining CharacterBERT,a bidirectional long short-term memory(BiLSTM)network,and attention mechanism(ATT)is constructed:it extracts character-level semantic features viaCharacterBERT,captures sequence dependencieswith the BiLSTM,and integrates theATT for key feature weighting,formingmulti-granularity feature fusion.An improved knowledge distillation approach transfers the teacher model’s learned knowledge to the simplified DistilBERT student model.Experimental results show the teacher model achieves 98.68%detection accuracy.The student modelmaintains slightly improved accuracy while significantly compressing parameters to approximately 38.4%of the teacher model’s scale,greatly reducing computational overhead for IoT deployment.展开更多
The personalized fine-tuning of large languagemodels(LLMs)on edge devices is severely constrained by limited computation resources.Although split federated learning alleviates on-device burdens,its effectiveness dimin...The personalized fine-tuning of large languagemodels(LLMs)on edge devices is severely constrained by limited computation resources.Although split federated learning alleviates on-device burdens,its effectiveness diminishes in few-shot reasoning scenarios due to the low data efficiency of conventional supervised fine-tuning,which leads to excessive communication overhead.To address this,we propose Language-Empowered Split Fine-Tuning(LESFT),a framework that integrates split architectures with a contrastive-inspired fine-tuning paradigm.LESFT simultaneously learns frommultiple logically equivalent but linguistically diverse reasoning chains,providing richer supervisory signals and improving data efficiency.This process-oriented training allows more effective reasoning adaptation with fewer samples.Extensive experiments demonstrate that LESFT consistently outperforms strong baselines such as SplitLoRA in task accuracy.LESFT consistently outperforms strong baselines on GSM8K,CommonsenseQA,and AQUA_RAT,with the largest gains observed on Qwen2.5-3B.These results indicate that LESFT can effectively adapt large language models for reasoning tasks under the computational and communication constraints of edge environments.展开更多
In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to...In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to dangerous situations.Furthermore,autonomous vehicles may operate abnormally in bad weather due to limitations of their sensors and GPS.Driving simulators,which replicate driving conditions nearly identical to those in the real world,can drastically reduce the time and cost required for market entry validation;consequently,they have become widely used.In this paper,we design a virtual driving test environment capable of collecting and verifying SiLS data under adverse weather conditions using multi-source images.The proposed method generates a virtual testing environment that incorporates various events,including weather,time of day,and moving objects,that cannot be easily verified in real-world autonomous driving tests.By setting up scenario-based virtual environment events,multi-source image analysis and verification using real-world DCUs(Data Concentrator Units)with V2X-Car edge cloud can effectively address risk factors that may arise in real-world situations.We tested and validated the proposed method with scenarios employing V2X communication and multi-source image analysis.展开更多
Nowadays,advances in communication technology and cloud computing have spawned a variety of smart mobile devices,which will generate a great amount of computing-intensive businesses,and require corresponding resources...Nowadays,advances in communication technology and cloud computing have spawned a variety of smart mobile devices,which will generate a great amount of computing-intensive businesses,and require corresponding resources of computation and communication.Multiaccess edge computing(MEC)can offload computing-intensive tasks to the nearby edge servers,which alleviates the pressure of devices.Ultra-dense network(UDN)can provide effective spectrum resources by deploying a large number of micro base stations.Furthermore,network slicing can support various applications in different communication scenarios.Therefore,this paper integrates the ultra-dense network slicing and the MEC technology,and introduces a hybrid computing offloading strategy in order to satisfy various quality of service(QoS)of edge devices.In order to dynamically allocate limited resources,the above problem is formulated as multiagent distributed deep reinforcement learning(DRL),which will achieve low overhead computation offloading strategy and real-time resource allocation decisions.In this context,federated learning is added to train DRL agents in a distributed manner,where each agent is dedicated to exploring actions composed of offloading decisions and allocating resources,so as to jointly optimize system delay and energy consumption.Simulation results show that the proposed learning algorithm has better performance compared with other strategies in literature.展开更多
This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagno...This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency.To this end,a total of 3234 high-resolution images(2400×1080)were collected from three major rice diseases Rice Blast,Bacterial Blight,and Brown Spot—frequently found in actual rice cultivation fields.These images served as the training dataset.The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone,thereby resulting in both model compression and improved inference speed.Additionally,YOLOv5-P,based on PP-PicoDet,was configured as a comparative model to quantitatively evaluate performance.Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance,with an mAP 0.5 of 89.6%,mAP 0.5–0.95 of 66.7%,precision of 91.3%,and recall of 85.6%,while maintaining a lightweight model size of 6.45 MB.In contrast,YOLOv5-P exhibited a smaller model size of 4.03 MB,but showed lower performance with an mAP 0.5 of 70.3%,mAP 0.5–0.95 of 35.2%,precision of 62.3%,and recall of 74.1%.This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.展开更多
This paper investigates the edge-based dynamic event-triggered inverse optimal formation control problem for multiple quadrotor unmanned aerial vehicles(QUAVs) with attitude constraints. To improve communication effic...This paper investigates the edge-based dynamic event-triggered inverse optimal formation control problem for multiple quadrotor unmanned aerial vehicles(QUAVs) with attitude constraints. To improve communication efficiency, an edge-based dynamic event-triggered mechanism is developed for the communication channels between neighboring QUAVs. However, this edge-based dynamic event-triggered communication(DETC) may cause discontinuities in the reference signals. To solve this problem, a distributed estimator is designed for each QUAV to obtain the leader's output signals. Considering the safety of QUAV formation flying, this paper designs a function transformation method that constrains the attitudes of the QUAVs to a strictly safe region. Furthermore, an inverse optimal control strategy is proposed based on the backstepping methodology. This scheme not only minimizes the cost function but also avoids the necessity of solving the Hamilton-Jacobi-Bellman equation. Finally, the stability of the QUAV systems is proven using Lyapunov theory, and the effectiveness of the proposed control method is verified through simulation.展开更多
With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ...With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.展开更多
基金support from the National Key Projects for Research and Development of China(Grant Nos.2022YFA1204700,2021YFA1400400)National Natural Science Foundation of China(Grant No.12525403)+3 种基金Natural Science Foundation of Jiangsu Province(Grant Nos.BK20220066,BK20233001)Program for Innovative Talents and Entrepreneur in Jiangsu(Grant No.JSSCTD202101)support from the JSPS KAKENHI(Grant Numbers 21H05233 and 23H02052)World Premier International Research Center Initiative(WPI),MEXT,Japan.
文摘Coulomb drag refers to the phenomenon in which a current driven through one conducting layer induces a voltage nearby,electrically isolated layer sorely through interlayer Coulomb interactions between charge carriers.It has been extensively studied in various systems,including parallel nanowires,double quantum wells,and double-layer graphene.Here,we report the observation of Coulomb drag in a novel system consisting of two graphene layers separated laterally by a 30 nm gap within the material plane,exhibiting behavior distinct from that in vertical graphene heterostructures.Our experiments reveal pronounced negative drag resistances under an out-of-plane magnetic field at the quantum Hall edges,reaching a maximum when the carrier densities in both graphene layers are tuned to the charge neutrality point via gate voltages.Our work establish two separate and spatially closed quantum Hall edge modes as a new platform to explore electronic interaction physics between one dimensional systems.
基金funded by the Research Fund of National Key Laboratory of Aerospace Physics in Fluids,grant number 2024-APF-KFZD-01Guangdong Basic and Applied Basic Research Foundation,grant number 2025A1515012081+1 种基金National Natural Science Foundation of China,grant number 12002193Shandong Provincial Natural Science Foundation,China,grant number ZR2019QA018.
文摘For hypersonic air-breathing vehicles,the V-shaped leading edges(VSLEs)of supersonic combustion ramjet(scramjet)inlets experience complex shock interactions and intense aerodynamic loads.This paper provides a comprehensive review of flow characteristics at the crotch of VSLEs,with particular focus on the transition of shock interaction types and the variation of wall heat flux under different freestream Mach numbers and geometric configurations.The mechanisms governing shock transition,unsteady oscillations,hysteresis,and three-dimensional effects in VSLE flows are first examined.Subsequently,thermal protection strategies aimed at mitigating extreme heating loads are reviewed,emphasizing their relevance to practical engineering applications.Special attention is given to recent studies addressing thermochemical nonequilibrium effects on VSLE shock interactions,and the limitations of current research are critically assessed.Finally,perspectives for future investigations into hypersonic VSLE shock interactions are outlined,highlighting opportunities for advancing design and thermal management strategies.
基金supported by the National Basic Research Program of China(Grant No.2012CB921300)the National Natural Science Foundation of China(Grant Nos.11074005 and 91021007)the Chinese Ministry of Education
文摘Few-layer graphene grown on Ni thin films has been studied by scanning tunneling microscopy. In most areas on the surfaces, moir6 patterns resulted from rotational stacking faults were observed. At a bias lower than 200 mV, only one sublattice shows up in regions without moir6 patterns while both sublattices are seen in regions with moir6 pattens. This phenomenon can be used to identify AB stacked regions. The scattering characteristics at various types of step edges are different from those of monolayer graphene edges, either armchair or zigzag.
文摘Upgrading of abundant cellulosic biomass to isosorbide can reduce the dependence on limited fossil resources and provide a sustainable way to produce isosorbide,utilized for polymers,medicine and health care product synth-esis.This review comprehensively examines the key steps and catalytic systems involved in the conversion of cel-lulose to isosorbide.Initially,the reaction pathway from cellulose to isosorbide is elucidated,emphasizing three critical steps:cellulose hydrolysis,glucose hydrogenation,and the two-step dehydration of sorbitol to produce isosorbide.Additionally,the activation energy and acidic sites during cellulose hydrolysis,the impact of metal particle size and catalyst support on hydrogenation,and the effects of catalyst acidity,pore structure,and reaction conditions on sorbitol dehydration have been thoroughly examined.Finally,the progress made in cellulose con-version to isosorbide is summarized,current challenges are highlighted,and future development trends are pro-jected in this review.
基金the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES) by granting the scholarship (Finance Code 001)supported by the Brazilian National Council for Scientific and Technological Development (CNPq, project number 433828/2018-8,435598/2018-0)+1 种基金the Minas Gerais Research Funding Foundation (FAPEMIG, project number CRA APQ 00929-15)CNPq productivity fellowships
文摘Functional traits are characteristics associated with the growth,reproduction,and survival of individuals.Studying them helps us understand how species traits drive ecosystem functioning.Thus,we evaluated the differences in traits and functional diversity between forest edges and interiors,and how the inclusion of intraspecific trait variation affects the assessment of functional diversity in these habitats.We sampled 10 representative forest patches,and,in each patch,we established five plots on the edge and five inside the forest,collecting leaf functional traits,allometric and wood density for all species.We assessed functional diversity using functional richness(FRic),divergence(FDiv),and dispersion(FDis).To assess the impact of incorporating intraspecific variation when comparing trait values and functional diversity indices,we established two scenarios:one that excludes intraspecific variation and another that includes it.We found that the edge and interior harbor individuals with distinct functional traits that alleviate the inherent stress of each habitat.The edge was also found to be more selective in terms of the range of functional traits,resulting in lower functional diversity.Our findings demonstrated that habitats play an important role in intraspecific trait variation(ITV)and that statistically significant differences between habitats,in relation to traits and functional diversity,were better observed with the inclusion of intraspecific variation.Our study highlights the potential of using natural forest patches to understand the edge effect,regardless of habitat loss.Additionally,we emphasize the importance of incorporating ITV into functional diversity studies,especially those on a smaller scale that incorporate quantitative variables,to better understand and predict ecological patterns.
基金support of the National Natural Science Foundation of China(Nos.U20A2069,12302389,12372295)the Natural Science Foundation of Fujian Province,China(No.2023J01046)。
文摘The primary Mach Reflection(MR)and pressure/heating loads on V-shaped Blunt Leading Edges(VBLEs)with variable elliptic cross-sections and conic crotches are theoretically investigated in this study.The simplified continuity method is used to forecast the shock configurations.The theoretical predictions and the numerical simulations for the Mach stem and the triple point as well as the curved shock accord well.Based on the theoretical model,an analysis of the impact of the axial ratio a/b of the cross-sectional shape and the eccentricity e of the crotch sweep path on shock structures is carried out.The shock configurations obtained from the theoretical model enable the derivation of the transition boundaries between the primary MR and the same family Regular Reflection(sRR).It is found that the increase of a/b and e can both facilitate the primary MR to sRR transition.The resulting transition and the corresponding generation of the wall pressure and heat flux are then investigated.The results indicate that higher values of the ratio a/b can significantly reduce the wall pressure and heating loads by inducing the primary MR to sRR transition.Conversely,the increase in the eccentricity e results in increased loads,despite causing the same transition.
文摘Reptile fauna should be considered a conservation objective,especially in respect of the impacts of climate change on their distribution and range’s dynamics.Investigating the environmental drivers of reptile species richness and identifying their suitable habitats is a fundamental prerequisite to setting efficient long-term conservation measures.This study focused on geographical patterns and estimations of species richness for herpetofauna widely spread Z.vivipara,N.natrix,V.berus,A.colchica,and protected in Latvia C.austriaca,E.orbicularis,L.agilis inhabiting northern(model territory Latvia)and southern(model territory Ukraine)part of their European range.The ultimate goal was to designate a conservation network that will meet long-term goals for survival of the target species in the context of climate change.We used stacked species distribution models for creating maps depicting the distribution of species richness under current and future(by 2050)climates for marginal reptilepopulations.Using cluster analysis,we showed that this herpeto-complex can be divided into“widespread species”and“forest species”.For all forest species we predicted a climate-driven reduction in their distribution range both North(Latvia)and South(Ukraine).The most vulnerable populations of“forest species”tend to be located in the South of their range,as a consequence of northward shifts by 2050.By 2050 the greatest reduction in range is predicted for currently widely spread Z.vivipara(by 1.4 times)and V.berus(by 2.2 times).In terms of designing an effective protected-area network,these results permit to identify priority conservation areas where the full ensemble of selected reptile species can be found,and confirms the relevance of abioticmulti-factor GIS-modelling for achieving this goal.
基金supported by the National Natural Science Foundation of China(No.22475100 and 22075146).
文摘Step ladder-structured nitrocellulose(LNC)is a novel energetic binder prepared by chemically modifying nitrocellulose(NC)with the introduction of flexible polyethylene glycol(PEG-400)chain segments,with a regular structure and good performance of bonding.The step ladder-structured addresses critical limitations of NC-based propellants,including low-temperature brittleness and high sensitivity,while enhancing process safety.Although the structural,thermal,and other properties of LNC have been investigated in our previous research,there is a lack of systematic studies on the rheological properties during solution and gelatinization.The study of the relationship between the structural features and rheological properties of LNC is a key factor in guiding its gelatinization and improving the properties of LNC-based propellants.Steady-state rheology flow experiments revealed that LNC exhibited shear thinning in different solutions,which decreased with increasing concentration.It has desirable solu-bility and dispersion in N,N-dimethylformamide(DMF)solvent.The effect of solvents on the entan-glement or orientation of LNC molecular chains may be reduced.These results can be quantitatively demonstrated using the Herschel-Bulkley model.Dynamic viscoelastic studies identified a critical point of concentration-frequency of 2.5 rad/s.This particular frequency point is a turning point in the law of the effect of concentration on the loss factor(tanδ).For gelatinized systems,increasing the solvent content reduces the temperature sensitivity of the gelatinized materials.The viscosity-temperature correlation based on the Arrhenius equation allowed the optimization of the solvent content through the derived equilibrium relationship.These structure-rheological performance relationships establish basic guidelines for the precision gelatinization of LNC-based propellant,provide theoretical support for the replacement of conventional NC by LNC,and guide the gelatinization process to improve the performance of gun propellants.
基金supported by the IITP(Institute for Information&Communications Technology Planning&Evaluation)under the ITRC(Information Technology Research Center)support program(IITP-2025-RS-2024-00438288)grant funded by the Korea government(MSIT)+1 种基金National Research Council of Science&Technology(NST)grant by the MSIT(Aerospace Semiconductor Strategy Research Project No.GTL25051-000)supported by the IC Design Education Center(IDEC),Korea。
文摘This work presents a systematic analysis of proton-induced total ionizing dose(TID)effects in 1.2 k V silicon carbide(SiC)power devices with various edge termination structures.Three edge terminations including ring-assisted junction termination extension(RA-JTE),multiple floating zone JTE(MFZ-JTE),and field limiting rings(FLR)were fabricated and irradiated with45 Me V protons at fluences ranging from 1×10^(12) to 1×10^(14) cm^(-2).Experimental results,supported by TCAD simulations,show that the RA-JTE structure maintained stable breakdown performance with less than 1%variation due to its effective electric field redistribution by multiple P+rings.In contrast,MFZ-JTE and FLR exhibit breakdown voltage shifts of 6.1%and 15.2%,respectively,under the highest fluence.These results demonstrate the superior radiation tolerance of the RA-JTE structure under TID conditions and provide practical design guidance for radiation-hardened Si C power devices in space and other highradiation environments.
基金The National Natural Science Foundation of China[72174184]provided policy and financialsupport for this research.
文摘Objectives This study aimed to evaluate the effectiveness of the stepped self-care program on the self-care,self-efficacy,and quality of life of stroke survivors.Methods This quasi-experimental study allocated 110 stroke survivors from two neurology wards into an intervention group(n=55)who received the stepped self-care program and a control group(n=55)who received usual care from June to December 2023.The Self-Care of Stroke Inventory,Stroke Self-Efficacy Questionnaire,and the short version of the Stroke Specific Quality of Life Scale were administered at baseline(T0),immediately post-intervention(T_(1)),and at 1-month(T_(2))and 3-month(T_(3))follow-ups.Data were analyzed using repeated measures analyses of variance,and generalized estimating equations.Results A total of 48 participants in the intervention group and 50 participants in the control group completed the study.No statistically significant differences were observed at T0 in any of the measured indicators(all P>0.05).The study showed significant group,time,and group×time interaction effects across the assessed outcomes(all P<0.05).Follow-up between-group comparisons at T_(1),T_(2),and T_(3) indicated that the intervention group had significantly higher scores in self-care maintenance,self-care monitoring,self-care management,self-efficacy,and quality of life than the control group(all P<0.001).Conclusions The stepped self-care program significantly improved self-care behaviors,self-efficacy,and quality of life among stroke survivors.These findings support the broader implementation of this approach in post-discharge home self-care.
基金supported by the National Natural Science Foundation of China(Grant Nos.62572057,62272049,U24A20331)Beijing Natural Science Foundation(Grant Nos.4232026,4242020)Academic Research Projects of Beijing Union University(Grant No.ZK10202404).
文摘Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approach in current practices.However,in complex and dynamic traffic scenes,particularly with smaller traffic sign objects,challenges such as missed and false detections can lead to reduced overall detection accuracy.To address this issue,this paper proposes a detection algorithm that integrates edge and shape information.Recognizing that traffic signs have specific shapes and distinct edge contours,this paper introduces an edge feature extraction branch within the backbone network,enabling adaptive fusion with features of the same hierarchical level.Additionally,a shape prior convolution module is designed to replaces the first two convolutional modules of the backbone network,aimed at enhancing the model's perception ability for specific shape objects and reducing its sensitivity to background noise.The algorithm was evaluated on the CCTSDB and TT100k datasets,and compared to YOLOv8s,the mAP50 values increased by 3.0%and 10.4%,respectively,demonstrating the effectiveness of the proposed method in improving the accuracy of traffic sign detection.
基金supported by the Deanship of Graduate Studies and Scientific Research at Jouf University.
文摘Due to the growth of smart cities,many real-time systems have been developed to support smart cities using Internet of Things(IoT)and emerging technologies.They are formulated to collect the data for environment monitoring and automate the communication process.In recent decades,researchers have made many efforts to propose autonomous systems for manipulating network data and providing on-time responses in critical operations.However,the widespread use of IoT devices in resource-constrained applications and mobile sensor networks introduces significant research challenges for cybersecurity.These systems are vulnerable to a variety of cyberattacks,including unauthorized access,denial-of-service attacks,and data leakage,which compromise the network’s security.Additionally,uneven load balancing between mobile IoT devices,which frequently experience link interferences,compromises the trustworthiness of the system.This paper introduces a Multi-Agent secured framework using lightweight edge computing to enhance cybersecurity for sensor networks,aiming to leverage artificial intelligence for adaptive routing and multi-metric trust evaluation to achieve data privacy and mitigate potential threats.Moreover,it enhances the efficiency of distributed sensors for energy consumption through intelligent data analytics techniques,resulting in highly consistent and low-latency network communication.Using simulations,the proposed framework reveals its significant performance compared to state-of-the-art approaches for energy consumption by 43%,latency by 46%,network throughput by 51%,packet loss rate by 40%,and denial of service attacks by 42%.
基金supported by the following projects:National Natural Science Foundation of China(62461041)Natural Science Foundation of Jiangxi Province China(20242BAB25068).
文摘With the large-scale deployment of the Internet ofThings(IoT)devices,their weak securitymechanisms make them prime targets for malware attacks.Attackers often use Domain Generation Algorithm(DGA)to generate random domain names,hiding the real IP of Command and Control(C&C)servers to build botnets.Due to the randomness and dynamics of DGA,traditional methods struggle to detect them accurately,increasing the difficulty of network defense.This paper proposes a lightweight DGA detection model based on knowledge distillation for resource-constrained IoT environments.Specifically,a teacher model combining CharacterBERT,a bidirectional long short-term memory(BiLSTM)network,and attention mechanism(ATT)is constructed:it extracts character-level semantic features viaCharacterBERT,captures sequence dependencieswith the BiLSTM,and integrates theATT for key feature weighting,formingmulti-granularity feature fusion.An improved knowledge distillation approach transfers the teacher model’s learned knowledge to the simplified DistilBERT student model.Experimental results show the teacher model achieves 98.68%detection accuracy.The student modelmaintains slightly improved accuracy while significantly compressing parameters to approximately 38.4%of the teacher model’s scale,greatly reducing computational overhead for IoT deployment.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant 62276109The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through the Research Group Project number(ORF-2025-585).
文摘The personalized fine-tuning of large languagemodels(LLMs)on edge devices is severely constrained by limited computation resources.Although split federated learning alleviates on-device burdens,its effectiveness diminishes in few-shot reasoning scenarios due to the low data efficiency of conventional supervised fine-tuning,which leads to excessive communication overhead.To address this,we propose Language-Empowered Split Fine-Tuning(LESFT),a framework that integrates split architectures with a contrastive-inspired fine-tuning paradigm.LESFT simultaneously learns frommultiple logically equivalent but linguistically diverse reasoning chains,providing richer supervisory signals and improving data efficiency.This process-oriented training allows more effective reasoning adaptation with fewer samples.Extensive experiments demonstrate that LESFT consistently outperforms strong baselines such as SplitLoRA in task accuracy.LESFT consistently outperforms strong baselines on GSM8K,CommonsenseQA,and AQUA_RAT,with the largest gains observed on Qwen2.5-3B.These results indicate that LESFT can effectively adapt large language models for reasoning tasks under the computational and communication constraints of edge environments.
基金supported by Institute of Information and Communications Technology Planning and Evaluation(IITP)grant funded by the Korean government(MSIT)(No.2019-0-01842,Artificial Intelligence Graduate School Program(GIST))supported by Korea Planning&Evaluation Institute of Industrial Technology(KEIT)grant funded by the Ministry of Trade,Industry&Energy(MOTIE,Republic of Korea)(RS-2025-25448249+1 种基金Automotive Industry Technology Development(R&D)Program)supported by the Regional Innovation System&Education(RISE)programthrough the(Gwangju RISE Center),funded by the Ministry of Education(MOE)and the Gwangju Metropolitan City,Republic of Korea(2025-RISE-05-001).
文摘In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to dangerous situations.Furthermore,autonomous vehicles may operate abnormally in bad weather due to limitations of their sensors and GPS.Driving simulators,which replicate driving conditions nearly identical to those in the real world,can drastically reduce the time and cost required for market entry validation;consequently,they have become widely used.In this paper,we design a virtual driving test environment capable of collecting and verifying SiLS data under adverse weather conditions using multi-source images.The proposed method generates a virtual testing environment that incorporates various events,including weather,time of day,and moving objects,that cannot be easily verified in real-world autonomous driving tests.By setting up scenario-based virtual environment events,multi-source image analysis and verification using real-world DCUs(Data Concentrator Units)with V2X-Car edge cloud can effectively address risk factors that may arise in real-world situations.We tested and validated the proposed method with scenarios employing V2X communication and multi-source image analysis.
文摘Nowadays,advances in communication technology and cloud computing have spawned a variety of smart mobile devices,which will generate a great amount of computing-intensive businesses,and require corresponding resources of computation and communication.Multiaccess edge computing(MEC)can offload computing-intensive tasks to the nearby edge servers,which alleviates the pressure of devices.Ultra-dense network(UDN)can provide effective spectrum resources by deploying a large number of micro base stations.Furthermore,network slicing can support various applications in different communication scenarios.Therefore,this paper integrates the ultra-dense network slicing and the MEC technology,and introduces a hybrid computing offloading strategy in order to satisfy various quality of service(QoS)of edge devices.In order to dynamically allocate limited resources,the above problem is formulated as multiagent distributed deep reinforcement learning(DRL),which will achieve low overhead computation offloading strategy and real-time resource allocation decisions.In this context,federated learning is added to train DRL agents in a distributed manner,where each agent is dedicated to exploring actions composed of offloading decisions and allocating resources,so as to jointly optimize system delay and energy consumption.Simulation results show that the proposed learning algorithm has better performance compared with other strategies in literature.
文摘This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency.To this end,a total of 3234 high-resolution images(2400×1080)were collected from three major rice diseases Rice Blast,Bacterial Blight,and Brown Spot—frequently found in actual rice cultivation fields.These images served as the training dataset.The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone,thereby resulting in both model compression and improved inference speed.Additionally,YOLOv5-P,based on PP-PicoDet,was configured as a comparative model to quantitatively evaluate performance.Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance,with an mAP 0.5 of 89.6%,mAP 0.5–0.95 of 66.7%,precision of 91.3%,and recall of 85.6%,while maintaining a lightweight model size of 6.45 MB.In contrast,YOLOv5-P exhibited a smaller model size of 4.03 MB,but showed lower performance with an mAP 0.5 of 70.3%,mAP 0.5–0.95 of 35.2%,precision of 62.3%,and recall of 74.1%.This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.
基金supported by the National Natural Science Foundation of China (Grant Nos.62573134,62473100,62433018)the Guangdong Basic and Applied Basic Research Foundation(Grant Nos.2025A1515060017,2025A1515011436,2025B1515020065,2025A1515011789)the Guangzhou Basic and Applied Basic Research Project (Grant No.2025A04J3534)。
文摘This paper investigates the edge-based dynamic event-triggered inverse optimal formation control problem for multiple quadrotor unmanned aerial vehicles(QUAVs) with attitude constraints. To improve communication efficiency, an edge-based dynamic event-triggered mechanism is developed for the communication channels between neighboring QUAVs. However, this edge-based dynamic event-triggered communication(DETC) may cause discontinuities in the reference signals. To solve this problem, a distributed estimator is designed for each QUAV to obtain the leader's output signals. Considering the safety of QUAV formation flying, this paper designs a function transformation method that constrains the attitudes of the QUAVs to a strictly safe region. Furthermore, an inverse optimal control strategy is proposed based on the backstepping methodology. This scheme not only minimizes the cost function but also avoids the necessity of solving the Hamilton-Jacobi-Bellman equation. Finally, the stability of the QUAV systems is proven using Lyapunov theory, and the effectiveness of the proposed control method is verified through simulation.
文摘With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.
