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
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
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
In this paper, we present a new method for reducing seismic noise while preserving structural and stratigraphic discontinuities. Structure-oriented edge-preserving smoothing requires information such as the local orie...In this paper, we present a new method for reducing seismic noise while preserving structural and stratigraphic discontinuities. Structure-oriented edge-preserving smoothing requires information such as the local orientation and edge of the reflections. The information is usually estimated from seismic data with full frequency bandwidth. When the data has a very low signal to noise ratio （SNR）, the noise usually reduces the estimation accuracy. For seismic data with extremely low SNR, the dominant frequency has higher SNR than other frequencies, so it can provide orientation and edge information more reliably than other frequencies. Orientation and edge are usually described in terms of apparent reflection dips and coherence differences, respectively. When frequency changes, both dip and coherence difference change more slowly than the seismogram itself. For this reason, dip and coherence estimated from dominant frequency data can approximately represent those of other frequency data. Ricker wavelet are widely used in seismic modeling. The Marr wavelet has the same shape as Ricker wavelets in both time and frequency domains, so the Marr wavelet transform is selected to divide seismic data into several frequency bands. Reflection apparent dip as well as the edge information can be obtained by scanning the dominant frequency data. This information can be used to selectively smooth the frequency bands （dominant, low, and high frequencies） separately by structure-oriented edge-preserving smoothing technology. The ultimate noise-suppressed seismic data is the combination of the smoothed frequency band data. Application to synthetic and real data shows the method can effectively reduce noise, preserve edges, improve trackable reflection continuity, and maintain useful information in seismic data.展开更多
With positive integers r,t and n,where n≥rt and t≥2,the maximum number of edges of a simple graph of order n is estimated,which does not contain r disjoint copies of K_r for r=2 and 3.
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
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
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
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
In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network e...In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications.In scenarios where edge servers are sparsely deployed,the lack of coordination and information sharing often leads to load imbalance,thereby increasing system latency.Furthermore,in regions without edge server coverage,tasks must be processed locally,which further exacerbates latency issues.To address these challenges,we propose a novel and efficient Deep Reinforcement Learning(DRL)-based approach aimed at minimizing average task latency.The proposed method incorporates three offloading strategies:local computation,direct offloading to the edge server in local region,and device-to-device(D2D)-assisted offloading to edge servers in other regions.We formulate the task offloading process as a complex latency minimization optimization problem.To solve it,we propose an advanced algorithm based on the Dueling Double Deep Q-Network(D3QN)architecture and incorporating the Prioritized Experience Replay(PER)mechanism.Experimental results demonstrate that,compared with existing offloading algorithms,the proposed method significantly reduces average task latency,enhances user experience,and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads.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
The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often...The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.展开更多
The hybridization gap in strained-layer InAs/In_(x)Ga_(1−x) Sb quantum spin Hall insulators(QSHIs)is significantly enhanced compared to binary InAs/GaSb QSHI structures,where the typical indium composition,x,ranges be...The hybridization gap in strained-layer InAs/In_(x)Ga_(1−x) Sb quantum spin Hall insulators(QSHIs)is significantly enhanced compared to binary InAs/GaSb QSHI structures,where the typical indium composition,x,ranges between 0.2 and 0.4.This enhancement prompts a critical question:to what extent can quantum wells(QWs)be strained while still preserving the fundamental QSHI phase?In this study,we demonstrate the controlled molecular beam epitaxial growth of highly strained-layer QWs with an indium composition of x=0.5.These structures possess a substantial compressive strain within the In_(0.5)Ga_(0.5)Sb QW.Detailed crystal structure analyses confirm the exceptional quality of the resulting epitaxial films,indicating coherent lattice structures and the absence of visible dislocations.Transport measurements further reveal that the QSHI phase in InAs/In_(0.5)Ga_(0.5)Sb QWs is robust and protected by time-reversal symmetry.Notably,the edge states in these systems exhibit giant magnetoresistance when subjected to a modest perpendicular magnetic field.This behavior is in agreement with the𝑍2 topological property predicted by the Bernevig–Hughes–Zhang model,confirming the preservation of topologically protected edge transport in the presence of enhanced bulk strain.展开更多
Due to rapid development in the past decade, air transportation system has attracted considerable research attention from diverse communities. While most of the previous studies focused on airline networks, here we sy...Due to rapid development in the past decade, air transportation system has attracted considerable research attention from diverse communities. While most of the previous studies focused on airline networks, here we systematically explore the robustness of the Chinese air route network, and identify the vital edges which form the backbone of Chinese air transportation system.Specifically, we employ a memetic algorithm to minimize the network robustness after removing certain edges, and hence the solution of this model is the set of vital edges. Counterintuitively,our results show that the most vital edges are not necessarily the edges of the highest topological importance, for which we provide an extensive explanation from the microscope view. Our findings also offer new insights to understanding and optimizing other real-world network systems.展开更多
Graphical Electromagnetic Computing (GRECO) is one of the valuable methods for computing the radar cross section (RCS) of complex radar target in high frequency region. But there are some shortages of wedge detecting ...Graphical Electromagnetic Computing (GRECO) is one of the valuable methods for computing the radar cross section (RCS) of complex radar target in high frequency region. But there are some shortages of wedge detecting method in the original GRECO literature. A new method for collecting the edge pixels and wedge parameters is presented in this paper. An expression of edge diffraction field in the original GRECO literature is in error, the error-corrected formulas are derived by using method of equivalent edge currents (MEC) and physical theory of diffraction (PTD). Finally, the total RCS expression is given by physical optics (PO) and PTD method. The computing results are in close agreement with the measured data.展开更多
In this paper the method of the reciprocal theorem (MRT) is extended to solve the steady state responses of rectangular plater under harmonic disturbing forces. A series of the closed solutions of rectangular plates w...In this paper the method of the reciprocal theorem (MRT) is extended to solve the steady state responses of rectangular plater under harmonic disturbing forces. A series of the closed solutions of rectangular plates with various boundary conditions are given and the tables and figures which have practical value are provided.MRT is a simple, convenient and general method for solving the steady stale responses of rectangular plates under various harmonic disturbing forces.The paper contains three parts: (I) rectangular plates with four damped edges and with three clamped edges; (II) rectangular plates with two adjacent clamped edges; (III) cantilever plates.We arc going to publish them one after another.展开更多
Camouflage is ubiquitous in the natural world and benefits both predators and prey. Amongst the range of conceal- ment strategies, disruptive coloration is thought to visually fragment an animal＇s＇ outline, thereby ...Camouflage is ubiquitous in the natural world and benefits both predators and prey. Amongst the range of conceal- ment strategies, disruptive coloration is thought to visually fragment an animal＇s＇ outline, thereby reducing its rate of discovery. Here, I propose two non-mutually exclusive hypotheses for how disruptive camouflage functions, and describe the visual me- chanisms that might underlie them. （1） The local edge disruption hypothesis states that camouflage is achieved by breaking up edge information. （2） The global feature disruption hypothesis states camouflage is achieved by breaking up the characteristic features of an animal （e.g., overall shape or facial features）. Research clearly shows that putatively disruptive edge markings do increase concealment; however, few tests have been undertaken to determine whether this survival advantage is attributable to the distortion of features, so the global feature disruption hypothesis is under studied. In this review the evidence for global feature disruption is evaluated. Further, I address if object recognition processing provides a feasible mechanism for animals＇ features to influence concealment. This review concludes that additional studies are needed to test if disruptive camouflage operates through the global feature disruption and proposes future research directions [Current Zoology 61 （4）： 708-717, 2015].展开更多
Nanostructured carbon materials, including carbon nanotubes, graphene and nanoporous carbon, show promise for expanding renewable energy. In particular, the configuration and electronic properties of graphene edges in...Nanostructured carbon materials, including carbon nanotubes, graphene and nanoporous carbon, show promise for expanding renewable energy. In particular, the configuration and electronic properties of graphene edges in relation with their electrochemical activity have become a major issue in carbon-based energy storage devices. Here, we review recent results concerning the important roles of graphene edges as the gateway for lithium ion intercalation in the anode of lithium-ion batteries, as promoters of high capacitance in carbon-based supercapacitors, and as anchoring sites for Pt nanoparticles in fuel cells. We envisage that the controlled synthesis of a specific, clean, and stable edge configuration could be achieved to maximize the electrochemical performance of nanostructured carbon-based energy storage devices.展开更多
BACKGROUND： The peripheral morphologic characteristics of hepatocellular carcinoma （HCC） reflect tumor growth patterns. Computed tomography （CT） perfusion is a new method to analyze hemodynamic changes in tissues...BACKGROUND： The peripheral morphologic characteristics of hepatocellular carcinoma （HCC） reflect tumor growth patterns. Computed tomography （CT） perfusion is a new method to analyze hemodynamic changes in tissues. We assessed the relationship between CT perfusion and histopathologic findings in the periphery of HCC lesions. METHODS： Non-contrast CT, enhanced dual-phase CT, and CT perfusion were performed on 77 subjects （47 patients and 30 controls）. Based on the imaging findings of enhanced dual- phase CT, the tumor edges were classified into three types： type Ⅰ （sharp）; type Ⅱ （blurry）; and type Ⅲ （mixed）. The CT perfusion parameters included hepatic blood flow, hepatic arterial fraction, hepatic arterial perfusion, and hepatic portal perfusion. The tissue sections from resected specimens were subjected to routine hematoxylin and eosin staining and immunohistochemical staining for CD34. The correlations between microvessel density （MVD） and the CT perfusion parameters were analyzed using Pearson＇s product-moment correlation coefficient. Changes in the perfusion parameters in tumor edges of different tumor types were evaluated. RESULTS： Type Ⅰ （sharp）： the pathologic findings showed fibrous connective tissue capsules in the tumor edges, and an MVD 〈30/ram2. Type Ⅱ （blurry）： the histology showed that the edges were clear with no capsules and an MVD 〉30/ram2. Type Ⅲ （mixed）： the pathology was similar to that of types I and II, and an MVD 〉30/mm~. Hepatic blood flow, hepatic arterial fraction, hepatic arterial perfusion, and hepatic portal perfusion were significantly increased in the tumor edges of HCC patients compared to those of the controls （P〈0.05）. The correlation between CT perfusion parameters and MVD was higher in blurry tumor edges of type II than in those of types Ⅰ or Ⅲ. CONCLUSION： CT perfusion imaging of tumor edges may be helpful in revealing histopathological features, and indirectly reflect angiogenic changes of HCCs.展开更多
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.
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.
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 China National Petroleum Corporation (CNPC) Innovation Fund (Grant No.07E1019)Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP) (Grant No.200804251502)
文摘In this paper, we present a new method for reducing seismic noise while preserving structural and stratigraphic discontinuities. Structure-oriented edge-preserving smoothing requires information such as the local orientation and edge of the reflections. The information is usually estimated from seismic data with full frequency bandwidth. When the data has a very low signal to noise ratio （SNR）, the noise usually reduces the estimation accuracy. For seismic data with extremely low SNR, the dominant frequency has higher SNR than other frequencies, so it can provide orientation and edge information more reliably than other frequencies. Orientation and edge are usually described in terms of apparent reflection dips and coherence differences, respectively. When frequency changes, both dip and coherence difference change more slowly than the seismogram itself. For this reason, dip and coherence estimated from dominant frequency data can approximately represent those of other frequency data. Ricker wavelet are widely used in seismic modeling. The Marr wavelet has the same shape as Ricker wavelets in both time and frequency domains, so the Marr wavelet transform is selected to divide seismic data into several frequency bands. Reflection apparent dip as well as the edge information can be obtained by scanning the dominant frequency data. This information can be used to selectively smooth the frequency bands （dominant, low, and high frequencies） separately by structure-oriented edge-preserving smoothing technology. The ultimate noise-suppressed seismic data is the combination of the smoothed frequency band data. Application to synthetic and real data shows the method can effectively reduce noise, preserve edges, improve trackable reflection continuity, and maintain useful information in seismic data.
文摘With positive integers r,t and n,where n≥rt and t≥2,the maximum number of edges of a simple graph of order n is estimated,which does not contain r disjoint copies of K_r for r=2 and 3.
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 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 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 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.
基金supported by the National Natural Science Foundation of China(62202215)Liaoning Province Applied Basic Research Program(Youth Special Project,2023JH2/101600038)+4 种基金Shenyang Youth Science and Technology Innovation Talent Support Program(RC220458)Guangxuan Program of Shenyang Ligong University(SYLUGXRC202216)the Basic Research Special Funds for Undergraduate Universities in Liaoning Province(LJ212410144067)the Natural Science Foundation of Liaoning Province(2024-MS-113)the science and technology funds from Liaoning Education Department(LJKZ0242).
文摘In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications.In scenarios where edge servers are sparsely deployed,the lack of coordination and information sharing often leads to load imbalance,thereby increasing system latency.Furthermore,in regions without edge server coverage,tasks must be processed locally,which further exacerbates latency issues.To address these challenges,we propose a novel and efficient Deep Reinforcement Learning(DRL)-based approach aimed at minimizing average task latency.The proposed method incorporates three offloading strategies:local computation,direct offloading to the edge server in local region,and device-to-device(D2D)-assisted offloading to edge servers in other regions.We formulate the task offloading process as a complex latency minimization optimization problem.To solve it,we propose an advanced algorithm based on the Dueling Double Deep Q-Network(D3QN)architecture and incorporating the Prioritized Experience Replay(PER)mechanism.Experimental results demonstrate that,compared with existing offloading algorithms,the proposed method significantly reduces average task latency,enhances user experience,and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads.
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The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025)。
文摘The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant Nos.XDB28000000 and XDB0460000)the Quantum Science and Technology-National Science and Technology Major Project (Grant No.2021ZD0302600)the National Key Research and Development Program of China(Grant No.2024YFA1409002)。
文摘The hybridization gap in strained-layer InAs/In_(x)Ga_(1−x) Sb quantum spin Hall insulators(QSHIs)is significantly enhanced compared to binary InAs/GaSb QSHI structures,where the typical indium composition,x,ranges between 0.2 and 0.4.This enhancement prompts a critical question:to what extent can quantum wells(QWs)be strained while still preserving the fundamental QSHI phase?In this study,we demonstrate the controlled molecular beam epitaxial growth of highly strained-layer QWs with an indium composition of x=0.5.These structures possess a substantial compressive strain within the In_(0.5)Ga_(0.5)Sb QW.Detailed crystal structure analyses confirm the exceptional quality of the resulting epitaxial films,indicating coherent lattice structures and the absence of visible dislocations.Transport measurements further reveal that the QSHI phase in InAs/In_(0.5)Ga_(0.5)Sb QWs is robust and protected by time-reversal symmetry.Notably,the edge states in these systems exhibit giant magnetoresistance when subjected to a modest perpendicular magnetic field.This behavior is in agreement with the𝑍2 topological property predicted by the Bernevig–Hughes–Zhang model,confirming the preservation of topologically protected edge transport in the presence of enhanced bulk strain.
基金supported by the National Natural Science Foundation of China (Nos. 91538204, 61425014, 61521091)National Key Research and Development Program of China (No. 2016YFB1200100)National Key Technology R&D Program of China (No. 2015BAG15B01)
文摘Due to rapid development in the past decade, air transportation system has attracted considerable research attention from diverse communities. While most of the previous studies focused on airline networks, here we systematically explore the robustness of the Chinese air route network, and identify the vital edges which form the backbone of Chinese air transportation system.Specifically, we employ a memetic algorithm to minimize the network robustness after removing certain edges, and hence the solution of this model is the set of vital edges. Counterintuitively,our results show that the most vital edges are not necessarily the edges of the highest topological importance, for which we provide an extensive explanation from the microscope view. Our findings also offer new insights to understanding and optimizing other real-world network systems.
文摘Graphical Electromagnetic Computing (GRECO) is one of the valuable methods for computing the radar cross section (RCS) of complex radar target in high frequency region. But there are some shortages of wedge detecting method in the original GRECO literature. A new method for collecting the edge pixels and wedge parameters is presented in this paper. An expression of edge diffraction field in the original GRECO literature is in error, the error-corrected formulas are derived by using method of equivalent edge currents (MEC) and physical theory of diffraction (PTD). Finally, the total RCS expression is given by physical optics (PO) and PTD method. The computing results are in close agreement with the measured data.
文摘In this paper the method of the reciprocal theorem (MRT) is extended to solve the steady state responses of rectangular plater under harmonic disturbing forces. A series of the closed solutions of rectangular plates with various boundary conditions are given and the tables and figures which have practical value are provided.MRT is a simple, convenient and general method for solving the steady stale responses of rectangular plates under various harmonic disturbing forces.The paper contains three parts: (I) rectangular plates with four damped edges and with three clamped edges; (II) rectangular plates with two adjacent clamped edges; (III) cantilever plates.We arc going to publish them one after another.
文摘Camouflage is ubiquitous in the natural world and benefits both predators and prey. Amongst the range of conceal- ment strategies, disruptive coloration is thought to visually fragment an animal＇s＇ outline, thereby reducing its rate of discovery. Here, I propose two non-mutually exclusive hypotheses for how disruptive camouflage functions, and describe the visual me- chanisms that might underlie them. （1） The local edge disruption hypothesis states that camouflage is achieved by breaking up edge information. （2） The global feature disruption hypothesis states camouflage is achieved by breaking up the characteristic features of an animal （e.g., overall shape or facial features）. Research clearly shows that putatively disruptive edge markings do increase concealment; however, few tests have been undertaken to determine whether this survival advantage is attributable to the distortion of features, so the global feature disruption hypothesis is under studied. In this review the evidence for global feature disruption is evaluated. Further, I address if object recognition processing provides a feasible mechanism for animals＇ features to influence concealment. This review concludes that additional studies are needed to test if disruptive camouflage operates through the global feature disruption and proposes future research directions [Current Zoology 61 （4）： 708-717, 2015].
基金supported by the grant No. 24310088 from the Ministry of Education, Culture, Sports, Science and Technology, Japansupport from the Research Center for Exotic NanoCarbon Project, Japan Regional Innovation Strategy Program by the Excellence
文摘Nanostructured carbon materials, including carbon nanotubes, graphene and nanoporous carbon, show promise for expanding renewable energy. In particular, the configuration and electronic properties of graphene edges in relation with their electrochemical activity have become a major issue in carbon-based energy storage devices. Here, we review recent results concerning the important roles of graphene edges as the gateway for lithium ion intercalation in the anode of lithium-ion batteries, as promoters of high capacitance in carbon-based supercapacitors, and as anchoring sites for Pt nanoparticles in fuel cells. We envisage that the controlled synthesis of a specific, clean, and stable edge configuration could be achieved to maximize the electrochemical performance of nanostructured carbon-based energy storage devices.
基金supported by grants from the National Nature Science Foundation of China (81471736)Heilongjiang Province Foundation for Returness (LC2013C38)
文摘BACKGROUND： The peripheral morphologic characteristics of hepatocellular carcinoma （HCC） reflect tumor growth patterns. Computed tomography （CT） perfusion is a new method to analyze hemodynamic changes in tissues. We assessed the relationship between CT perfusion and histopathologic findings in the periphery of HCC lesions. METHODS： Non-contrast CT, enhanced dual-phase CT, and CT perfusion were performed on 77 subjects （47 patients and 30 controls）. Based on the imaging findings of enhanced dual- phase CT, the tumor edges were classified into three types： type Ⅰ （sharp）; type Ⅱ （blurry）; and type Ⅲ （mixed）. The CT perfusion parameters included hepatic blood flow, hepatic arterial fraction, hepatic arterial perfusion, and hepatic portal perfusion. The tissue sections from resected specimens were subjected to routine hematoxylin and eosin staining and immunohistochemical staining for CD34. The correlations between microvessel density （MVD） and the CT perfusion parameters were analyzed using Pearson＇s product-moment correlation coefficient. Changes in the perfusion parameters in tumor edges of different tumor types were evaluated. RESULTS： Type Ⅰ （sharp）： the pathologic findings showed fibrous connective tissue capsules in the tumor edges, and an MVD 〈30/ram2. Type Ⅱ （blurry）： the histology showed that the edges were clear with no capsules and an MVD 〉30/ram2. Type Ⅲ （mixed）： the pathology was similar to that of types I and II, and an MVD 〉30/mm~. Hepatic blood flow, hepatic arterial fraction, hepatic arterial perfusion, and hepatic portal perfusion were significantly increased in the tumor edges of HCC patients compared to those of the controls （P〈0.05）. The correlation between CT perfusion parameters and MVD was higher in blurry tumor edges of type II than in those of types Ⅰ or Ⅲ. CONCLUSION： CT perfusion imaging of tumor edges may be helpful in revealing histopathological features, and indirectly reflect angiogenic changes of HCCs.
