The headquarters of Plutus Financial Group Ltd,based in Hong Kong of China,stands as a silent yet razorsharp marker—rooted deeply in the heart of traditional finance,while piercing boundaries,exploring the vast nebul...The headquarters of Plutus Financial Group Ltd,based in Hong Kong of China,stands as a silent yet razorsharp marker—rooted deeply in the heart of traditional finance,while piercing boundaries,exploring the vast nebulae of blockchain and artificial intelligence.This integrated financial services group,newly listed on Nasdaq this February,is moving through the cut-and-thrust of the capital market with the postureof a"white knight."展开更多
Dr.He＇s bloodletting therapy utilizing three edged needles is one of his ＂three adjusting methods of acupuncture＂. During his 70 years of clinical practice, he developed the theory that most diseases are caused by ...Dr.He＇s bloodletting therapy utilizing three edged needles is one of his ＂three adjusting methods of acupuncture＂. During his 70 years of clinical practice, he developed the theory that most diseases are caused by qi stagnation, and in order to restore qi circulation one needs to improve the blood circulation first. Based on this theory, in combination with empirical insights from clinical practice, he has developed a unique technique for using three-edged needles. He has also categorized and extended the application of bloodletting therapy with three edged needles to cover over 150 diseases. In addition, Dr. He＇s bloodletting therapy with three edged needles is an innovation that may inspire other physicians to develope and expand the use of acupuncture-related therapies to treat disease.展开更多
This study involved numerical simulations of a double tube heat exchanger using the ANSYS FLUENT programversion 22.The study aims to examine methods for minimizing pressure loss and consequently enhancing the thermal ...This study involved numerical simulations of a double tube heat exchanger using the ANSYS FLUENT programversion 22.The study aims to examine methods for minimizing pressure loss and consequently enhancing the thermal performance index(TPI)of a heat exchanger fitted with wavy edge tape that is a heat recovery system(the hot air in simulation instead of t heat from the exhaust gases of the brick factory furnaces and return it to warm the heavy fuel oil by substituting the electrical heater with a heat exchanger to recuperate waste heat from the flue gases,so elevating the temperature of Heavy fuel oil(HFO)to inject from the roof nozzles of combustion chamber of the furnace furthermore reducing cost(by finding the optimal design of wavy edge tape))and energy consumption.Air was selected as the hot gas in the inner pipe instead of furnace exhaust gases due to their similar thermal characteristics.A numerical analysis was conducted to create a novel wavy edge tape with varying widths(50%Di,75%Di,and 95%Di),lengths(1000,1200,1400)mm,amplitudes(5,10,15)mm,and periods of wavy length(5,10,15)mm.The flow rate of the outer pipe fluid(oil)ranges from(0.06 to 0.1)kg/s,while the velocity of the hot fluid(air)varies from(1 to 27)m/s,Re_(air)(6957 to 187,837).The entrance temperature of the hot fluid can be either(200,225,and 250)℃.The study finds that wavy edge tape tubes are more effective than smooth tubes in terms of oil outlet temperature;results revealed that an increase in the oil mass flow rate leads to a decrease in the oil outlet temperature and an increase in the heat transfer rate,at the air temperature 250℃.Additionally,the results indicate that increasing the width,length,and amplitude also leads to an increase in the oil outlet temperature of(94-94.12)℃,the pressure drop of(568.3)Pa,and the Nusselt number(65.7-66.5)respectively on the oil side.Finally,the heat exchanger’s best thermal performance index was found by investigating temperature contour at amplitude(A=5),period(p=15),width(w=75%Di),and length(L=1200 mm).The values for these parameters are,in order(1.02,1.025,1.02,and 1.0077).展开更多
A hardwale demodulation method for 2-D edge detection is proposed. The filtering step and the differential step are implemented by using the hardware circuit. This demodulation circuit simplifies the edgefinder and re...A hardwale demodulation method for 2-D edge detection is proposed. The filtering step and the differential step are implemented by using the hardware circuit. This demodulation circuit simplifies the edgefinder and reduces the measuring cycle. The calibration method of scale setting is also presented,and bymeasuring some calibrated objects,the demodulation errors and the error correction table is obtained.展开更多
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
Interplay between topology and magnetism can give rise to exotic properties in topological materials.Two-dimensional bismuth has been extensively studied owing to its topological states with a strong spin-orbit coupli...Interplay between topology and magnetism can give rise to exotic properties in topological materials.Two-dimensional bismuth has been extensively studied owing to its topological states with a strong spin-orbit coupling,and 1T-VTe_(2)monolayer theoretically predicted to host an intrinsic magnetism as experimentally suggested.In this work,we successfully constructed a vertical heterostructure composed of the two-dimensional Bi(110)monolayer and 1T-VTe_(2)monolayer by using molecular beam epitaxy(MBE).Scanning tunneling microscopy(STM)measurements revealed that the growth of Bi preferably occurs along the step edges of the VTe_(2)monolayer,forming a Bi(110)monolayer on top of the VTe_(2)monolayer next to a peripheral Bi bilayer.The Bi(100)/VTe_(2)heterostructure exhibits a specific lattice registry with a well-defined moiréperiodicity.Scanning tunneling spectroscopy(STS)measurements further unveiled an universal suppression in the local density-of-states at the boundary of the Bi(110)/VTe_(2)bilayer.By examining the atomic structures of Bi(110)boundaries,we found this effect does not originate from the previously proposed atomic reconstruction at the step edge of Bi(110),but is likely related to the magnetic properties of the VTe_(2)monolayer.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
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
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
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 growing developments in 5G and 6G wireless communications have revolutionized communications technologies,providing faster speeds with reduced latency and improved connectivity to users.However,it raises significa...The growing developments in 5G and 6G wireless communications have revolutionized communications technologies,providing faster speeds with reduced latency and improved connectivity to users.However,it raises significant security challenges,including impersonation threats,data manipulation,distributed denial of service(DDoS)attacks,and privacy breaches.Traditional security measures are inadequate due to the decentralized and dynamic nature of next-generation networks.This survey provides a comprehensive review of how Federated Learning(FL),Blockchain,and Digital Twin(DT)technologies can collectively enhance the security of 5G and 6G systems.Blockchain offers decentralized,immutable,and transparent mechanisms for securing network transactions,while FL enables privacy-preserving collaborative learning without sharing raw data.Digital Twins create virtual replicas of network components,enabling real-time monitoring,anomaly detection,and predictive threat analysis.The survey examines major security issues in emerging wireless architectures and analyzes recent advancements that integrate FL,Blockchain,and DT to mitigate these threats.Additionally,it presents practical use cases,synthesizes key lessons learned,and identifies ongoing research challenges.Finally,the survey outlines future research directions to support the development of scalable,intelligent,and robust security frameworks for next-generation wireless networks.展开更多
In scenarios where ground-based cloud computing infrastructure is unavailable,unmanned aerial vehicles(UAVs)act as mobile edge computing(MEC)servers to provide on-demand computation services for ground terminals.To ad...In scenarios where ground-based cloud computing infrastructure is unavailable,unmanned aerial vehicles(UAVs)act as mobile edge computing(MEC)servers to provide on-demand computation services for ground terminals.To address the challenge of jointly optimizing task scheduling and UAV trajectory under limited resources and high mobility of UAVs,this paper presents PER-MATD3,a multi-agent deep reinforcement learning algorithm with prioritized experience replay(PER)into the Centralized Training with Decentralized Execution(CTDE)framework.Specifically,PER-MATD3 enables each agent to learn a decentralized policy using only local observations during execution,while leveraging a shared replay buffer with prioritized sampling and centralized critic during training to accelerate convergence and improve sample efficiency.Simulation results show that PER-MATD3 reduces average task latency by up to 23%,improves energy efficiency by 21%,and enhances service coverage compared to state-of-the-art baselines,demonstrating its effectiveness and practicality in scenarios without terrestrial networks.展开更多
Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination syst...Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.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
Purpose-With the deepening integration of rail transit systems-encompassing urban rail,regional railways,trunk lines and medium-low capacity transportation-the four-network integration imposes higher demands on operat...Purpose-With the deepening integration of rail transit systems-encompassing urban rail,regional railways,trunk lines and medium-low capacity transportation-the four-network integration imposes higher demands on operation and maintenance systems regarding cross-modal coordination,full-element interconnectivity and dynamic responsiveness.Design/methodology/approach-This paper,based on policy directives and engineering practices,analyzes the operational maintenance characteristics of urban rail traction systems from perspectives including device interconnectivity and fault data mining.A non-intrusive high-frequency diagnostic device independent of vehicle control is proposed,informed by practical onboard operation experience.This innovation significantly enhances diagnostic accuracy for components requiring high sampling frequency,while integrating“Flash”storage with far greater capacity than conventional control chips.Findings-This article will systematically introduces the key points and diagnostic methods for typical faults in urban rail traction systems.Through rational diagnostic algorithms combined with high-precision,highstorage diagnostic instrumentation,the overall safety and reliability of urban rail traction systems have been improved.The proposed non-intrusive high-frequency diagnostic solution has been validated across multiple rail lines.Originality/value-This paper introduces an innovative non-intrusive diagnostic device with a dual-channel design for multi-system compatibility and a high-speed acquisition architecture enabling 400 kHz sampling.Its originality stems from the independent,high-fidelity capture of microsecond-level transient faults like IGBT shoot-through and pantograph arcing;Validated in operational environments,this approach provides a significant leap in diagnostic precision,directly enhancing traction system availability and operational safety by enabling precise fault localization and intelligent,adaptive protection strategies.展开更多
As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays...As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies,conflicting objectives,and limited resources.This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC.We jointly consider task heterogeneity,high-dimensional objectives,and flexible resource scheduling,modeling the problem as a Many-objective optimization.To solve it,we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on decomposition(MOCC/D)and a flexible scheduling strategy.Experimental results on benchmark functions and simulation scenarios show that the proposed method outperforms existing approaches in both convergence and solution quality.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
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
文摘The headquarters of Plutus Financial Group Ltd,based in Hong Kong of China,stands as a silent yet razorsharp marker—rooted deeply in the heart of traditional finance,while piercing boundaries,exploring the vast nebulae of blockchain and artificial intelligence.This integrated financial services group,newly listed on Nasdaq this February,is moving through the cut-and-thrust of the capital market with the postureof a"white knight."
文摘Dr.He＇s bloodletting therapy utilizing three edged needles is one of his ＂three adjusting methods of acupuncture＂. During his 70 years of clinical practice, he developed the theory that most diseases are caused by qi stagnation, and in order to restore qi circulation one needs to improve the blood circulation first. Based on this theory, in combination with empirical insights from clinical practice, he has developed a unique technique for using three-edged needles. He has also categorized and extended the application of bloodletting therapy with three edged needles to cover over 150 diseases. In addition, Dr. He＇s bloodletting therapy with three edged needles is an innovation that may inspire other physicians to develope and expand the use of acupuncture-related therapies to treat disease.
文摘This study involved numerical simulations of a double tube heat exchanger using the ANSYS FLUENT programversion 22.The study aims to examine methods for minimizing pressure loss and consequently enhancing the thermal performance index(TPI)of a heat exchanger fitted with wavy edge tape that is a heat recovery system(the hot air in simulation instead of t heat from the exhaust gases of the brick factory furnaces and return it to warm the heavy fuel oil by substituting the electrical heater with a heat exchanger to recuperate waste heat from the flue gases,so elevating the temperature of Heavy fuel oil(HFO)to inject from the roof nozzles of combustion chamber of the furnace furthermore reducing cost(by finding the optimal design of wavy edge tape))and energy consumption.Air was selected as the hot gas in the inner pipe instead of furnace exhaust gases due to their similar thermal characteristics.A numerical analysis was conducted to create a novel wavy edge tape with varying widths(50%Di,75%Di,and 95%Di),lengths(1000,1200,1400)mm,amplitudes(5,10,15)mm,and periods of wavy length(5,10,15)mm.The flow rate of the outer pipe fluid(oil)ranges from(0.06 to 0.1)kg/s,while the velocity of the hot fluid(air)varies from(1 to 27)m/s,Re_(air)(6957 to 187,837).The entrance temperature of the hot fluid can be either(200,225,and 250)℃.The study finds that wavy edge tape tubes are more effective than smooth tubes in terms of oil outlet temperature;results revealed that an increase in the oil mass flow rate leads to a decrease in the oil outlet temperature and an increase in the heat transfer rate,at the air temperature 250℃.Additionally,the results indicate that increasing the width,length,and amplitude also leads to an increase in the oil outlet temperature of(94-94.12)℃,the pressure drop of(568.3)Pa,and the Nusselt number(65.7-66.5)respectively on the oil side.Finally,the heat exchanger’s best thermal performance index was found by investigating temperature contour at amplitude(A=5),period(p=15),width(w=75%Di),and length(L=1200 mm).The values for these parameters are,in order(1.02,1.025,1.02,and 1.0077).
文摘A hardwale demodulation method for 2-D edge detection is proposed. The filtering step and the differential step are implemented by using the hardware circuit. This demodulation circuit simplifies the edgefinder and reduces the measuring cycle. The calibration method of scale setting is also presented,and bymeasuring some calibrated objects,the demodulation errors and the error correction table is obtained.
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financially supported by the National Key Research and Development Program of China(Grant No.2021YFA1400403)the National Natural Science Foundation of China(Grant Nos.12374183,92165205)+2 种基金the Natural Science Foundation of Jiangsu Province(Grant No.BK20233001)the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0302800)the Fundamental Research Funds for the Central Universities(Grant No.020414380207).
文摘Interplay between topology and magnetism can give rise to exotic properties in topological materials.Two-dimensional bismuth has been extensively studied owing to its topological states with a strong spin-orbit coupling,and 1T-VTe_(2)monolayer theoretically predicted to host an intrinsic magnetism as experimentally suggested.In this work,we successfully constructed a vertical heterostructure composed of the two-dimensional Bi(110)monolayer and 1T-VTe_(2)monolayer by using molecular beam epitaxy(MBE).Scanning tunneling microscopy(STM)measurements revealed that the growth of Bi preferably occurs along the step edges of the VTe_(2)monolayer,forming a Bi(110)monolayer on top of the VTe_(2)monolayer next to a peripheral Bi bilayer.The Bi(100)/VTe_(2)heterostructure exhibits a specific lattice registry with a well-defined moiréperiodicity.Scanning tunneling spectroscopy(STS)measurements further unveiled an universal suppression in the local density-of-states at the boundary of the Bi(110)/VTe_(2)bilayer.By examining the atomic structures of Bi(110)boundaries,we found this effect does not originate from the previously proposed atomic reconstruction at the step edge of Bi(110),but is likely related to the magnetic properties of the VTe_(2)monolayer.
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 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derived from a research grant“Cybersecurity Research and Innovation Pioneers Grants Initiative”funded by The National Program for RDI in Cybersecurity(National Cybersecurity Authority)-Kingdom of Saudi Arabia-with grant number(CRPG-25-3168)supported by EIAS Data Science and Blockchain Lab,CCIS,Prince Sultan University.
文摘The growing developments in 5G and 6G wireless communications have revolutionized communications technologies,providing faster speeds with reduced latency and improved connectivity to users.However,it raises significant security challenges,including impersonation threats,data manipulation,distributed denial of service(DDoS)attacks,and privacy breaches.Traditional security measures are inadequate due to the decentralized and dynamic nature of next-generation networks.This survey provides a comprehensive review of how Federated Learning(FL),Blockchain,and Digital Twin(DT)technologies can collectively enhance the security of 5G and 6G systems.Blockchain offers decentralized,immutable,and transparent mechanisms for securing network transactions,while FL enables privacy-preserving collaborative learning without sharing raw data.Digital Twins create virtual replicas of network components,enabling real-time monitoring,anomaly detection,and predictive threat analysis.The survey examines major security issues in emerging wireless architectures and analyzes recent advancements that integrate FL,Blockchain,and DT to mitigate these threats.Additionally,it presents practical use cases,synthesizes key lessons learned,and identifies ongoing research challenges.Finally,the survey outlines future research directions to support the development of scalable,intelligent,and robust security frameworks for next-generation wireless networks.
基金supported by the National Natural Science Foundation of China under Grant No.61701100.
文摘In scenarios where ground-based cloud computing infrastructure is unavailable,unmanned aerial vehicles(UAVs)act as mobile edge computing(MEC)servers to provide on-demand computation services for ground terminals.To address the challenge of jointly optimizing task scheduling and UAV trajectory under limited resources and high mobility of UAVs,this paper presents PER-MATD3,a multi-agent deep reinforcement learning algorithm with prioritized experience replay(PER)into the Centralized Training with Decentralized Execution(CTDE)framework.Specifically,PER-MATD3 enables each agent to learn a decentralized policy using only local observations during execution,while leveraging a shared replay buffer with prioritized sampling and centralized critic during training to accelerate convergence and improve sample efficiency.Simulation results show that PER-MATD3 reduces average task latency by up to 23%,improves energy efficiency by 21%,and enhances service coverage compared to state-of-the-art baselines,demonstrating its effectiveness and practicality in scenarios without terrestrial networks.
基金supported by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(C)23K03898.
文摘Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.
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.
基金supported by the Fund of China Academy of Railway Sciences Corporation Limited(2023YJ342).
文摘Purpose-With the deepening integration of rail transit systems-encompassing urban rail,regional railways,trunk lines and medium-low capacity transportation-the four-network integration imposes higher demands on operation and maintenance systems regarding cross-modal coordination,full-element interconnectivity and dynamic responsiveness.Design/methodology/approach-This paper,based on policy directives and engineering practices,analyzes the operational maintenance characteristics of urban rail traction systems from perspectives including device interconnectivity and fault data mining.A non-intrusive high-frequency diagnostic device independent of vehicle control is proposed,informed by practical onboard operation experience.This innovation significantly enhances diagnostic accuracy for components requiring high sampling frequency,while integrating“Flash”storage with far greater capacity than conventional control chips.Findings-This article will systematically introduces the key points and diagnostic methods for typical faults in urban rail traction systems.Through rational diagnostic algorithms combined with high-precision,highstorage diagnostic instrumentation,the overall safety and reliability of urban rail traction systems have been improved.The proposed non-intrusive high-frequency diagnostic solution has been validated across multiple rail lines.Originality/value-This paper introduces an innovative non-intrusive diagnostic device with a dual-channel design for multi-system compatibility and a high-speed acquisition architecture enabling 400 kHz sampling.Its originality stems from the independent,high-fidelity capture of microsecond-level transient faults like IGBT shoot-through and pantograph arcing;Validated in operational environments,this approach provides a significant leap in diagnostic precision,directly enhancing traction system availability and operational safety by enabling precise fault localization and intelligent,adaptive protection strategies.
基金supported by Youth Talent Project of Scientific Research Program of Hubei Provincial Department of Education under Grant Q20241809Doctoral Scientific Research Foundation of Hubei University of Automotive Technology under Grant 202404.
文摘As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies,conflicting objectives,and limited resources.This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC.We jointly consider task heterogeneity,high-dimensional objectives,and flexible resource scheduling,modeling the problem as a Many-objective optimization.To solve it,we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on decomposition(MOCC/D)and a flexible scheduling strategy.Experimental results on benchmark functions and simulation scenarios show that the proposed method outperforms existing approaches in both convergence and solution quality.
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.
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.
