In two-scale topology optimization,enhancing the connectivity between adjacent microstructures is crucial for achieving the collaborative optimization of micro-scale performance and macro-scale manufacturability.This ...In two-scale topology optimization,enhancing the connectivity between adjacent microstructures is crucial for achieving the collaborative optimization of micro-scale performance and macro-scale manufacturability.This paper proposes a two-scale concurrent topology optimization strategy aimed at improving the interface connection strength.This method employs a parametric approach to explicitly divide the micro-design domain into a“boundary connection region”and a“free design domain”at the initial stage of optimization.The boundary connection region is used to generate a connection layer that enhances the interface strength,while the free design domain is not constrained by this layer,thus fully exploiting the design potential of the material layout.During the optimization process,the solid isotropic material with penalization(SIMP)method is first used to optimize the material distribution in the free design domain,and filtering and projection techniques are employed to alleviate numerical instability and obtain a clear topological structure.Subsequently,the effective performance of the microstructure is calculated through homogenization and transferred to the macro-scale for global response analysis.Throughout the iterative process,the geometry of the connection layer remains unchanged,and only the free design domain is optimized,thereby achieving a balance between high performance and good manufacturability.The effectiveness of the proposed method is verified through numerical examples.展开更多
Let G=(V,E)be a connected graph.For an integer h≥0,a subset F■V(G)(resp.F■E(G))of G,if any,is called an h-restricted vertex cut(resp.h-restricted edge cut)of G,if G-F is disconnected and every vertex in G-F has at ...Let G=(V,E)be a connected graph.For an integer h≥0,a subset F■V(G)(resp.F■E(G))of G,if any,is called an h-restricted vertex cut(resp.h-restricted edge cut)of G,if G-F is disconnected and every vertex in G-F has at least h neighbors.The cardinality of a minimum h-restricted vertex-cut(resp.h-restricted edge cut)of G is the h-restricted connectivity(resp.h-restricted edge connectivity)of G,and denoted by κ^(h)(G)(resp.λ^(h)(G)).The enhanced hypercube Q_(n,κ)(1≤k≤n)is a variant of the hypercube Q_(n).In this paper,we consider the h-restricted connectivity of Q_(n,κ) for 2≤k≤n-1.Our main results are as follows:(1)κ^(h)(Q_(n,κ))=2^(h)(n-h+1)for 4≤k≤n-1 and 0≤h≤n-3,λ^(h)(Q_(n,κ))=2^(h)(n-h+1)for 2≤k≤n-1 and 0≤h≤n-2.(2)κ^(h)(Q_(n,3))=2^(h-1)(n-h+1)for n≥5 and 4≤h≤n-1,κ^(h)(Q_(n,2))=2^(h-1)(n-h+1)for n≥4 and 3≤h≤n-1.(3)κ^(3)(Q_(n,3))=6n-16 for n≥5,κ^(2)(Q_(n,3))=4n-8 for n≥4 and κ^(2)(Q_(n,2))=3n-5 for n≥3,κ^(1)(Q_(n,3))=2n and κ^(3)(Q_(n,2))=2n-2 for n≥3.展开更多
To achieve efficient connection and integrity in prefabricated frame structures,a novel beam-column connection between prefabricated steel-reinforced concrete(SRC)columns and cast-in-situ reinforced concrete(RC)beams ...To achieve efficient connection and integrity in prefabricated frame structures,a novel beam-column connection between prefabricated steel-reinforced concrete(SRC)columns and cast-in-situ reinforced concrete(RC)beams is proposed.Four connection specimens with varying thicknesses of connecting cover plates were designed and fabricated.Low-cycle reversed loading tests were conducted to obtain mechanical performance data,allowing for analysis of behavioral patterns,including load-displacement hysteresis curves,skeleton curves,load-carrying capacity,deformation performance,energy dissipation capacity,and stiffness degradation.Test results indicate that properly designed prefabricated connections exhibit mechanical properties comparable to those of cast-in-situ concrete connections.The hysteresis curves of the prefabricated connections are well-shaped,with nearly no reduction in load-carrying capacity and deformation performance,demonstrating excellent seismic behavior.The findings provide a theoretical basis and reference for the design of prefabricated concrete frame structures.展开更多
In the Kigongo area of Mwanza Region,northwest Tanzania,fishmonger Neema Aisha remembers how the morning’s fresh catch would sour while she queued for the ferry,putting her business at risk.
Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic ...Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic Graph(DAG)structure often suffer from performance limitations.The DAG lattice structure is a novel blockchain model in which each node maintains its own account chain,and only the node itself is allowed to update it.This feature makes the DAG lattice structure particularly suitable for addressing the challenges in dynamically connected IoV environment.In this paper,we propose a blockchain architecture based on the DAG lattice structure,specifically designed for dynamically connected IoV.In the proposed system,nodes must obtain authorization from a trusted authority before joining,forming a permissioned blockchain.Each node is assigned an individual account chain,allowing vehicles with limited storage capacity to participate in the blockchain by storing transactions only from nearby vehicles’account chains.Every transmitted message is treated as a transaction and added to the blockchain,enablingmore efficient data transmission in a dynamic network environment.Areputation-based incentivemechanism is introduced to encourage nodes to behave normally.Experimental results demonstrate that the proposed architecture achieves better performance compared with traditional single-chain and DAG-based approaches in terms of average transmission delay and storage cost.展开更多
BACKGROUND Suicide constitutes the second leading cause of death among adolescents globally and represents a critical public health concern.The neural mechanisms underlying suicidal behavior in adolescents with major ...BACKGROUND Suicide constitutes the second leading cause of death among adolescents globally and represents a critical public health concern.The neural mechanisms underlying suicidal behavior in adolescents with major depressive disorder(MDD)remain poorly understood.Aberrant resting-state functional connectivity(rsFC)in the amygdala,a key region implicated in emotional regulation and threat detection,is strongly implicated in depression and suicidal behavior.AIM To investigate rsFC alterations between amygdala subregions and whole-brain networks in adolescent patients with depression and suicide attempts.METHODS Resting-state functional magnetic resonance imaging data were acquired from 32 adolescents with MDD and suicide attempts(sMDD)group,33 adolescents with MDD but without suicide attempts(nsMDD)group,and 34 demographically matched healthy control(HC)group,with the lateral and medial amygdala(MeA)defined as regions of interest.The rsFC patterns of amygdala subregions were compared across the three groups,and associations between aberrant rsFC values and clinical symptom severity scores were examined.RESULTS Compared with the nsMDD group,the sMDD group exhibited reduced rsFC between the right lateral amygdala(LA)and the right inferior occipital gyrus as well as the left middle occipital gyrus.Compared with the HC group,the abnormal brain regions of rsFC in the sMDD group and nsMDD group involve the parahippocampal gyrus(PHG)and fusiform gyrus.In the sMDD group,right MeA and right temporal pole:Superior temporal gyrus rsFC value negatively correlated with the Rosenberg Self-Esteem Scale scores(r=-0.409,P=0.025),while left LA and right PHG rsFC value positively correlated with the Adolescent Self-Rating Life Events Checklist interpersonal relationship scores(r=0.372,P=0.043).CONCLUSION Aberrant rsFC changes between amygdala subregions and these brain regions provide novel insights into the underlying neural mechanisms of suicide attempts in adolescents with MDD.展开更多
This study proposes a new post-tensioned precast bridge column(PT-PBC)with a socket connection.Compared to conventional PBCs connected by PT tendons,the combination of the PT tendons with the socket connection can avo...This study proposes a new post-tensioned precast bridge column(PT-PBC)with a socket connection.Compared to conventional PBCs connected by PT tendons,the combination of the PT tendons with the socket connection can avoid tensioning the PT tendons on site,which further accelerates construction speed while improving construction quality and safety.In addition,compared to conventional PBCs with a socket connection,a rocking interface can avoid the formation of a plastic hinge in a column,which greatly alleviates seismic damage to that area.One specimen for quasi-static testing is used to validate the feasibility of this connection type.Subsequently,finite element models(FEM)are established to systematically predict the responses of the proposed columns under lateral cyclic loading.The accuracy of the FEM is verified through quasistatic testing.Next,the influences of the key design parameters of the PT-PBC,including the area ratio and prestress level of the PT tendons,the area ratio of energy dissipation(ED)steel rebars,and the total axial compression ratio on the seismic performances of PT-PBC are systematically investigated.The use of shape memory alloy(SMA)rods as energy dissipation devices and their performances also are investigated.The results show that increasing the area ratio and prestress level of PT tendons has an overall positive impact on the self-centering capacity of the column.The prestress level of PT tendons should be kept between 35%and 55%,depending on different conditions.The total compression axial ratio of the columns should be maintained between 0.3 and 0.4.Both ED steel rebars and SMA rods can boost the column’s energy dissipation capacity,while SMA rods can reduce residual deformation due to their inherent mechanical properties.展开更多
The sustainability of the Internet of Things(IoT)involves various issues,such as poor connectivity,scalability problems,interoperability issues,and energy inefficiency.Although the Sixth Generation of mobile networks(...The sustainability of the Internet of Things(IoT)involves various issues,such as poor connectivity,scalability problems,interoperability issues,and energy inefficiency.Although the Sixth Generation of mobile networks(6G)allows for Ultra-Reliable Low-Latency Communication(URLLC),enhanced Mobile Broadband(eMBB),and massive Machine-Type Communications(mMTC)services,it faces deployment challenges such as the short range of sub-THz and THz frequency bands,low capability to penetrate obstacles,and very high path loss.This paper presents a network architecture to enhance the connectivity of wireless IoT mesh networks that employ both 6G and Wi-Fi technologies.In this architecture,local communications are carried through the mesh network,which uses a virtual backbone to relay packets to local nodes,while remote communications are carried through the 6G network.The virtual backbone is created using a heuristic distributed ConnectedDominating Set(CDS)algorithm.In this algorithm,each node uses information collected from its one-and two-hop neighbors to determine its role and find the set of expansion nodes that are used to select the next CDS nodes.The proposed algorithm has O(n)message and O(K)time complexities,where n is the number of nodes in the network,and K is the depth of the cluster.The study proved that the approximation ratio of the algorithmhas an upper bound of 2.06748(3.4306MCDS+4.8185).Performance evaluations compared the size of the CDS against the theoretical limit and recent CDS clustering algorithms.Results indicate that the proposed algorithm has the smallest average slope for the size of the CDS as the number of nodes increases.展开更多
Stroke patients experience varying degrees of upper limb functional impairment.Although bilateral arm training can help stroke patients recover movement after stroke,little is known about the way in which the brain an...Stroke patients experience varying degrees of upper limb functional impairment.Although bilateral arm training can help stroke patients recover movement after stroke,little is known about the way in which the brain and muscles work together during this type of training.To address this,we conducted a cross-sectional study at The Seventh Affiliated Hospital,Sun Yat-sen University in China,where we observed the connection between brain and muscle activity during bilateral upper limb training in 21 stroke patients and 17 healthy controls.We used functional near-infrared spectroscopy and surface electromyography to measure changes in cerebral cortex oxygenation and upper limb muscle contraction signals,respectively.The results showed that,compared with the healthy control group,stroke patients had reduced functional connectivity and more irregular muscle activity in the affected flexor muscle during bilateral upper limb training.Moreover,we found a significant correlation between the surface electromyographic signal characteristics of upper limb muscles and cerebral oxygenation indicators of multiple brain regions in stroke patients.These findings indicate that bilateral upper limb training is an effective rehabilitation method that improves upper limb motor function in stroke patients by promoting brain functional connectivity and improving muscle activity patterns.展开更多
Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based...Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.展开更多
Collagen fibrillogenesis underlies the structural and mechanical properties of the extracellular matrix in connective and other tissues,yet its molecular mechanism remains poorly understood.Here,we show that a europi...Collagen fibrillogenesis underlies the structural and mechanical properties of the extracellular matrix in connective and other tissues,yet its molecular mechanism remains poorly understood.Here,we show that a europium(Ⅲ)dipicolinate complex(EuDPA)acts as a luminescent reporter of collagen aggregation.We combine Raman microscopy,circularly polarized luminescence(CPL),and molecular dynamics(MD)simulations to study this process.While Raman imaging directly visualizes the EuDPA-enhanced fibrillar architecture,CPL reveals enantioselective EuDPA-collagen interactions that accompany the fibrillogenesis.MD simulations indicate the presence of stabilizing interactions between hydroxyproline residues and the dipicolinate ligand.The results pave the way to monitoring of protein aggregation in general,and are relevant to fibrotic pathologies and biomimetic materials design.展开更多
The Qaidam Basin,a typical alpine arid inland basin on the northern Qinghai-Xizang Plateau,China,hosts wetland ecosystems that are strongly constrained by topography and extreme climate.These ecosystems exhibit pronou...The Qaidam Basin,a typical alpine arid inland basin on the northern Qinghai-Xizang Plateau,China,hosts wetland ecosystems that are strongly constrained by topography and extreme climate.These ecosystems exhibit pronounced spatiotemporal heterogeneity and fragmented distribution patterns,rendering them highly sensitive to environmental change.This study integrated Sentinel-2 remote sensing imagery with the SedInConnect model to delineate wetland patch distributions and calculate the Index of Connectivity(IC)values across the basin.Based on IC values,we stratified field sampling sites into high-,moderate-,and lowconnectivity gradient groups to analyze the relationships among plant community characteristics,vegetation spatial patterns,and wetland connectivity in the Qaidam Basin.Partial Least Squares Path Modeling(PLSPM)was further employed to quantify the driving mechanisms underlying wetland vegetation characteristics.The results revealed that wetland connectivity across the basin was generally low,with IC values up to 1.32 and displaying a west-to-east decreasing gradient.The west and northwest were characterized by relatively continuous high-connectivity wetland networks,while fragmented and low-connectivity wetlands predominated in the east and southeast.Connectivity regulated wetland vegetation patterns primarily by affecting patch size,fragmentation,and internal adjacency.High-connectivity areas had higher class area(CA),largest patch index(LPI),and area-weighted mean patch size(AREA_AM)than low-connectivity areas.Connectivity had the strongest effect on vegetation coverage,which declined sharply from 87.577%in highconnectivity areas to 12.152%in low-connectivity areas.Meanwhile,species diversity showed a moderately negative response to connectivity changes,whereas species evenness remained relatively unaffected.PLS-PM explained 78.300%and 67.500%of the variance in vegetation community and vegetation pattern,respectively.Climate played a dominant role in shaping vegetation characteristics,with significant negative effects on both vegetation community and pattern.Topography influenced vegetation indirectly through climate,and connectivity was influenced by both drivers and exerted positive effects on vegetation community and pattern.This study reveals the multi-pathway driving mechanisms underlying vegetation pattern formation in alpine wetlands,providing a theoretical foundation and decision-support framework for the scientific conservation and adaptive management of wetlands in the Qaidam Basin.展开更多
Background:Social connection is widely recognized as a protective determinant of health,yet its direct and indirect effects on mental health remain underexplored.This study examines the relationship between social con...Background:Social connection is widely recognized as a protective determinant of health,yet its direct and indirect effects on mental health remain underexplored.This study examines the relationship between social connection and mental health,focusing on the mediating role of quality of life(QoL)and the moderating effect of regional differences.Methods:We analyzed data from the 2019 Korean Community Health Survey,comprising 229,099 adults.Mental health was assessed through validated measures of depressive symptoms and psychological well-being.Social connection was measured using indicators of interpersonal ties and community participation,and QoL was assessed via self-reported health-related satisfaction across major life domains.Analytical procedures included mediation modeling and subgroup analyses by region,with significance levels set at p<0.05.Results:The results indicate that social connections are significantly associated with lower stress levels and reduced depressive symptoms,with QoL playing a critical mediating role.Notably,the indirect effect of social connection on mental health via QoL is stronger in rural areas compared to urban regions,highlighting the importance of social cohesion and community support in mental well-being.Among 203,567 adults,greater social participation was associated with lower subjective stress(total effect=−0.052,p<0.001)and fewer depressive symptoms(PHQ-9 total effect=−0.308,p<0.001).QoL significantly mediated these associations,with the strongest indirect pathways observed through usual activities(19.2%for stress;27.6%for depression)and mobility(24.4%for depression).Regional analysis showed stronger mediation in rural areas(up to 26.8%for stress and 32.6%for depression)than in urban areas(8–16%and 14.9–23%).Direct effects remained significant,indicating partial mediation.These findings highlight that social participation enhances mental health directly and indirectly through QoL,particularly in rural contexts.Conclusions:Social connection contributes to better mental health both directly and indirectly through improved QoL,with stronger effects observed in rural communities.These findings highlight the importance of fostering social cohesion and enhancing life quality as strategies for improving population mental health.Policy interventions should adopt context-sensitive approaches that account for regional differences in social resources and service availability.展开更多
The decarbonization of transportation and environmental quality enhancement have become more and more reliant on eco-innovation,which incorporates both technological change and systemic coordination and governance.The...The decarbonization of transportation and environmental quality enhancement have become more and more reliant on eco-innovation,which incorporates both technological change and systemic coordination and governance.The review is a summary of the evidence that can be translated into environmental sustainability outcomes on how smart vehicle technologies,including electrified powertrains and vehicle-grid interfaces,connected and cooperative systems(Vehicleto-Everything,V2X),automation and advanced automation,and Artificial Intelligence(AI)-enabled optimization can be transformed.Using a structured analytical framework linking technology capability to eco-innovation mechanisms and sustainability impacts,we reconcile findings across operational,well-to-wheel,and life-cycle boundaries.The literature indicates that electrification delivers strong local air-quality benefits and,in most contexts,substantial climate gains,but net outcomes depend on grid carbon intensity,charging time profiles,battery production,and end-of-life pathways,making managed charging and circularity pivotal complements.Connectivity and cooperative control improve energy efficiency primarily through coordination effects such as traffic smoothing,eco-routing,and platooning,yet benefits are non-linear and sensitive to penetration rates and infrastructure interoperability.Automation offers efficiency and safety co-benefits but exhibits the widest uncertainty because induced demand,empty travel,and mode substitution can offset per-vehicle improvements.AI-driven fleet optimization can reduce empty miles and extend component life,although computational and hardware overhead and rapid obsolescence can introduce trade-offs.We identify persistent gaps in comparability,non-exhaust emissions assessment,causal evaluation at scale,and equity-aware impact metrics,and propose a research and policy agenda emphasizing integrated Life Cycle Assessment(LCA)system modeling,standardized reporting,interoperable data governance,and demand management to secure durable environmental gains.展开更多
Floodplain wetlands are invaluable ecosystems providing numerous ecological benefits,yet they face a global crisis necessitating sustainable preservation efforts.This study examines the depletion of floodplain wetland...Floodplain wetlands are invaluable ecosystems providing numerous ecological benefits,yet they face a global crisis necessitating sustainable preservation efforts.This study examines the depletion of floodplain wetlands within the Hastinapur Wildlife Sanctuary(HWLS)in Uttar Pradesh.Encroachment activities such as grazing,agriculture,and human settlements have fragmented and degraded critical wetland ecosystems.Additionally,irrigation projects,dam construction,and water diversion have disrupted natural water flow and availability.To assess wetland inundation in 2023,five classification techniques were employed:Random Forest(RF),Support Vector Machine(SVM),artificial neural network(ANN),Spectral Information Divergence(SID),and Maximum Likelihood Classifier(MLC).SVM emerged as the most precise method,as determined by kappa coefficient and index-based validation.Consequently,the SVM classifier was used to model wetland inundation areas from 1983 to 2023 and analyze spatiotemporal changes and fragmentation patterns.The findings revealed that the SVM clas-sifier accurately mapped 2023 wetland areas.The modeled time-series data demonstrated a 62.55%and 38.12%reduction in inundated wetland areas over the past 40 years in the pre-and post-monsoon periods,respectively.Fragmentation analysis indicated an 86.27%decrease in large core wetland areas in the pre-monsoon period,signifying severe habitat degradation.This rapid decline in wetlands within protected areas raises concerns about their ecological impacts.By linking wetland loss to global sustainability objectives,this study underscores the global urgency for strengthened wetland protection measures and highlights the need for integrating wetland conservation into broader sustainable development goals.Effective policies and adaptive management strategies are crucial for preserving these ecosystems and their vital services,which are essential for biodiversity,climate regulation,and human well-being.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 concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It ha...The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It has been suggested that the size of the brain(brain reserve) and the extent of neural connections acquired through life(neural reserve) set a threshold beyond which noticeable impairments occur.In contrast,cognitive reserve refers to the brain's ability to adapt and reo rganize stru cturally and functionally to resist damage and maintain function,including neural reserve and brain maintenance,resilience,and compensation(Verkhratsky and Zorec,2024).展开更多
The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener...The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.展开更多
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘In two-scale topology optimization,enhancing the connectivity between adjacent microstructures is crucial for achieving the collaborative optimization of micro-scale performance and macro-scale manufacturability.This paper proposes a two-scale concurrent topology optimization strategy aimed at improving the interface connection strength.This method employs a parametric approach to explicitly divide the micro-design domain into a“boundary connection region”and a“free design domain”at the initial stage of optimization.The boundary connection region is used to generate a connection layer that enhances the interface strength,while the free design domain is not constrained by this layer,thus fully exploiting the design potential of the material layout.During the optimization process,the solid isotropic material with penalization(SIMP)method is first used to optimize the material distribution in the free design domain,and filtering and projection techniques are employed to alleviate numerical instability and obtain a clear topological structure.Subsequently,the effective performance of the microstructure is calculated through homogenization and transferred to the macro-scale for global response analysis.Throughout the iterative process,the geometry of the connection layer remains unchanged,and only the free design domain is optimized,thereby achieving a balance between high performance and good manufacturability.The effectiveness of the proposed method is verified through numerical examples.
文摘Let G=(V,E)be a connected graph.For an integer h≥0,a subset F■V(G)(resp.F■E(G))of G,if any,is called an h-restricted vertex cut(resp.h-restricted edge cut)of G,if G-F is disconnected and every vertex in G-F has at least h neighbors.The cardinality of a minimum h-restricted vertex-cut(resp.h-restricted edge cut)of G is the h-restricted connectivity(resp.h-restricted edge connectivity)of G,and denoted by κ^(h)(G)(resp.λ^(h)(G)).The enhanced hypercube Q_(n,κ)(1≤k≤n)is a variant of the hypercube Q_(n).In this paper,we consider the h-restricted connectivity of Q_(n,κ) for 2≤k≤n-1.Our main results are as follows:(1)κ^(h)(Q_(n,κ))=2^(h)(n-h+1)for 4≤k≤n-1 and 0≤h≤n-3,λ^(h)(Q_(n,κ))=2^(h)(n-h+1)for 2≤k≤n-1 and 0≤h≤n-2.(2)κ^(h)(Q_(n,3))=2^(h-1)(n-h+1)for n≥5 and 4≤h≤n-1,κ^(h)(Q_(n,2))=2^(h-1)(n-h+1)for n≥4 and 3≤h≤n-1.(3)κ^(3)(Q_(n,3))=6n-16 for n≥5,κ^(2)(Q_(n,3))=4n-8 for n≥4 and κ^(2)(Q_(n,2))=3n-5 for n≥3,κ^(1)(Q_(n,3))=2n and κ^(3)(Q_(n,2))=2n-2 for n≥3.
基金supported by High-level Talents Program of Hainan Basic and Applied Basic Research Program of China(520RC543)Crosswise Tasks of Enterprise Entrusted(JG-ZH-A-202411-003).
文摘To achieve efficient connection and integrity in prefabricated frame structures,a novel beam-column connection between prefabricated steel-reinforced concrete(SRC)columns and cast-in-situ reinforced concrete(RC)beams is proposed.Four connection specimens with varying thicknesses of connecting cover plates were designed and fabricated.Low-cycle reversed loading tests were conducted to obtain mechanical performance data,allowing for analysis of behavioral patterns,including load-displacement hysteresis curves,skeleton curves,load-carrying capacity,deformation performance,energy dissipation capacity,and stiffness degradation.Test results indicate that properly designed prefabricated connections exhibit mechanical properties comparable to those of cast-in-situ concrete connections.The hysteresis curves of the prefabricated connections are well-shaped,with nearly no reduction in load-carrying capacity and deformation performance,demonstrating excellent seismic behavior.The findings provide a theoretical basis and reference for the design of prefabricated concrete frame structures.
文摘In the Kigongo area of Mwanza Region,northwest Tanzania,fishmonger Neema Aisha remembers how the morning’s fresh catch would sour while she queued for the ferry,putting her business at risk.
基金funded in part by the Supported by Natural Science Foundation of Inner Mongolia Autonomous Region of China under Grants 2024QN06022 and 2023QN06008in part by the First-Class Discipline Research Special Project under Grant YLXKZX-NGD-015in part by the Inner Mongolia University of Technology Scientific Research Start-Up Project under Grant BS2024067.
文摘Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic Graph(DAG)structure often suffer from performance limitations.The DAG lattice structure is a novel blockchain model in which each node maintains its own account chain,and only the node itself is allowed to update it.This feature makes the DAG lattice structure particularly suitable for addressing the challenges in dynamically connected IoV environment.In this paper,we propose a blockchain architecture based on the DAG lattice structure,specifically designed for dynamically connected IoV.In the proposed system,nodes must obtain authorization from a trusted authority before joining,forming a permissioned blockchain.Each node is assigned an individual account chain,allowing vehicles with limited storage capacity to participate in the blockchain by storing transactions only from nearby vehicles’account chains.Every transmitted message is treated as a transaction and added to the blockchain,enablingmore efficient data transmission in a dynamic network environment.Areputation-based incentivemechanism is introduced to encourage nodes to behave normally.Experimental results demonstrate that the proposed architecture achieves better performance compared with traditional single-chain and DAG-based approaches in terms of average transmission delay and storage cost.
基金Supported by Suzhou Clinical Medical Center for Mood Disorders,No.Szlcyxzx202109Suzhou Key Laboratory,No.SZS2024016Multicenter Clinical Research on Major Diseases in Suzhou,No.DZXYJ202413.
文摘BACKGROUND Suicide constitutes the second leading cause of death among adolescents globally and represents a critical public health concern.The neural mechanisms underlying suicidal behavior in adolescents with major depressive disorder(MDD)remain poorly understood.Aberrant resting-state functional connectivity(rsFC)in the amygdala,a key region implicated in emotional regulation and threat detection,is strongly implicated in depression and suicidal behavior.AIM To investigate rsFC alterations between amygdala subregions and whole-brain networks in adolescent patients with depression and suicide attempts.METHODS Resting-state functional magnetic resonance imaging data were acquired from 32 adolescents with MDD and suicide attempts(sMDD)group,33 adolescents with MDD but without suicide attempts(nsMDD)group,and 34 demographically matched healthy control(HC)group,with the lateral and medial amygdala(MeA)defined as regions of interest.The rsFC patterns of amygdala subregions were compared across the three groups,and associations between aberrant rsFC values and clinical symptom severity scores were examined.RESULTS Compared with the nsMDD group,the sMDD group exhibited reduced rsFC between the right lateral amygdala(LA)and the right inferior occipital gyrus as well as the left middle occipital gyrus.Compared with the HC group,the abnormal brain regions of rsFC in the sMDD group and nsMDD group involve the parahippocampal gyrus(PHG)and fusiform gyrus.In the sMDD group,right MeA and right temporal pole:Superior temporal gyrus rsFC value negatively correlated with the Rosenberg Self-Esteem Scale scores(r=-0.409,P=0.025),while left LA and right PHG rsFC value positively correlated with the Adolescent Self-Rating Life Events Checklist interpersonal relationship scores(r=0.372,P=0.043).CONCLUSION Aberrant rsFC changes between amygdala subregions and these brain regions provide novel insights into the underlying neural mechanisms of suicide attempts in adolescents with MDD.
基金Natural Science Foundation of China under Grant No.52178449,the Beijing Natural Science Foundation under Grant No.8234060the Innovation Center of Beijing Association for Science and Technology。
文摘This study proposes a new post-tensioned precast bridge column(PT-PBC)with a socket connection.Compared to conventional PBCs connected by PT tendons,the combination of the PT tendons with the socket connection can avoid tensioning the PT tendons on site,which further accelerates construction speed while improving construction quality and safety.In addition,compared to conventional PBCs with a socket connection,a rocking interface can avoid the formation of a plastic hinge in a column,which greatly alleviates seismic damage to that area.One specimen for quasi-static testing is used to validate the feasibility of this connection type.Subsequently,finite element models(FEM)are established to systematically predict the responses of the proposed columns under lateral cyclic loading.The accuracy of the FEM is verified through quasistatic testing.Next,the influences of the key design parameters of the PT-PBC,including the area ratio and prestress level of the PT tendons,the area ratio of energy dissipation(ED)steel rebars,and the total axial compression ratio on the seismic performances of PT-PBC are systematically investigated.The use of shape memory alloy(SMA)rods as energy dissipation devices and their performances also are investigated.The results show that increasing the area ratio and prestress level of PT tendons has an overall positive impact on the self-centering capacity of the column.The prestress level of PT tendons should be kept between 35%and 55%,depending on different conditions.The total compression axial ratio of the columns should be maintained between 0.3 and 0.4.Both ED steel rebars and SMA rods can boost the column’s energy dissipation capacity,while SMA rods can reduce residual deformation due to their inherent mechanical properties.
基金Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number RI-44-0028.
文摘The sustainability of the Internet of Things(IoT)involves various issues,such as poor connectivity,scalability problems,interoperability issues,and energy inefficiency.Although the Sixth Generation of mobile networks(6G)allows for Ultra-Reliable Low-Latency Communication(URLLC),enhanced Mobile Broadband(eMBB),and massive Machine-Type Communications(mMTC)services,it faces deployment challenges such as the short range of sub-THz and THz frequency bands,low capability to penetrate obstacles,and very high path loss.This paper presents a network architecture to enhance the connectivity of wireless IoT mesh networks that employ both 6G and Wi-Fi technologies.In this architecture,local communications are carried through the mesh network,which uses a virtual backbone to relay packets to local nodes,while remote communications are carried through the 6G network.The virtual backbone is created using a heuristic distributed ConnectedDominating Set(CDS)algorithm.In this algorithm,each node uses information collected from its one-and two-hop neighbors to determine its role and find the set of expansion nodes that are used to select the next CDS nodes.The proposed algorithm has O(n)message and O(K)time complexities,where n is the number of nodes in the network,and K is the depth of the cluster.The study proved that the approximation ratio of the algorithmhas an upper bound of 2.06748(3.4306MCDS+4.8185).Performance evaluations compared the size of the CDS against the theoretical limit and recent CDS clustering algorithms.Results indicate that the proposed algorithm has the smallest average slope for the size of the CDS as the number of nodes increases.
文摘Stroke patients experience varying degrees of upper limb functional impairment.Although bilateral arm training can help stroke patients recover movement after stroke,little is known about the way in which the brain and muscles work together during this type of training.To address this,we conducted a cross-sectional study at The Seventh Affiliated Hospital,Sun Yat-sen University in China,where we observed the connection between brain and muscle activity during bilateral upper limb training in 21 stroke patients and 17 healthy controls.We used functional near-infrared spectroscopy and surface electromyography to measure changes in cerebral cortex oxygenation and upper limb muscle contraction signals,respectively.The results showed that,compared with the healthy control group,stroke patients had reduced functional connectivity and more irregular muscle activity in the affected flexor muscle during bilateral upper limb training.Moreover,we found a significant correlation between the surface electromyographic signal characteristics of upper limb muscles and cerebral oxygenation indicators of multiple brain regions in stroke patients.These findings indicate that bilateral upper limb training is an effective rehabilitation method that improves upper limb motor function in stroke patients by promoting brain functional connectivity and improving muscle activity patterns.
基金the Hebei Province Science and Technology Plan Project(19221909D)rincess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R308),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.
基金supported by the Czech Science Foundation(23-05378S to TW and 2515726S to PB)the Ministry of Education,Youth and Sports of the Czech Republic through the e-INFRA CZ(ID:90140).
文摘Collagen fibrillogenesis underlies the structural and mechanical properties of the extracellular matrix in connective and other tissues,yet its molecular mechanism remains poorly understood.Here,we show that a europium(Ⅲ)dipicolinate complex(EuDPA)acts as a luminescent reporter of collagen aggregation.We combine Raman microscopy,circularly polarized luminescence(CPL),and molecular dynamics(MD)simulations to study this process.While Raman imaging directly visualizes the EuDPA-enhanced fibrillar architecture,CPL reveals enantioselective EuDPA-collagen interactions that accompany the fibrillogenesis.MD simulations indicate the presence of stabilizing interactions between hydroxyproline residues and the dipicolinate ligand.The results pave the way to monitoring of protein aggregation in general,and are relevant to fibrotic pathologies and biomimetic materials design.
基金funded by the National Natural Science Foundation of China(42230720)the 2023 Annual Qinghai Province"Kunlun Talents-High-end Innovation and Entrepreneurship Talent"Program Project.
文摘The Qaidam Basin,a typical alpine arid inland basin on the northern Qinghai-Xizang Plateau,China,hosts wetland ecosystems that are strongly constrained by topography and extreme climate.These ecosystems exhibit pronounced spatiotemporal heterogeneity and fragmented distribution patterns,rendering them highly sensitive to environmental change.This study integrated Sentinel-2 remote sensing imagery with the SedInConnect model to delineate wetland patch distributions and calculate the Index of Connectivity(IC)values across the basin.Based on IC values,we stratified field sampling sites into high-,moderate-,and lowconnectivity gradient groups to analyze the relationships among plant community characteristics,vegetation spatial patterns,and wetland connectivity in the Qaidam Basin.Partial Least Squares Path Modeling(PLSPM)was further employed to quantify the driving mechanisms underlying wetland vegetation characteristics.The results revealed that wetland connectivity across the basin was generally low,with IC values up to 1.32 and displaying a west-to-east decreasing gradient.The west and northwest were characterized by relatively continuous high-connectivity wetland networks,while fragmented and low-connectivity wetlands predominated in the east and southeast.Connectivity regulated wetland vegetation patterns primarily by affecting patch size,fragmentation,and internal adjacency.High-connectivity areas had higher class area(CA),largest patch index(LPI),and area-weighted mean patch size(AREA_AM)than low-connectivity areas.Connectivity had the strongest effect on vegetation coverage,which declined sharply from 87.577%in highconnectivity areas to 12.152%in low-connectivity areas.Meanwhile,species diversity showed a moderately negative response to connectivity changes,whereas species evenness remained relatively unaffected.PLS-PM explained 78.300%and 67.500%of the variance in vegetation community and vegetation pattern,respectively.Climate played a dominant role in shaping vegetation characteristics,with significant negative effects on both vegetation community and pattern.Topography influenced vegetation indirectly through climate,and connectivity was influenced by both drivers and exerted positive effects on vegetation community and pattern.This study reveals the multi-pathway driving mechanisms underlying vegetation pattern formation in alpine wetlands,providing a theoretical foundation and decision-support framework for the scientific conservation and adaptive management of wetlands in the Qaidam Basin.
基金supported by the“Regional Innovation System&Education(RISE)”through the Seoul RISE Center,funded by the Ministry of Education(MOE)and the Seoul Metropolitan Government.(2025-RISE-01-005-07).
文摘Background:Social connection is widely recognized as a protective determinant of health,yet its direct and indirect effects on mental health remain underexplored.This study examines the relationship between social connection and mental health,focusing on the mediating role of quality of life(QoL)and the moderating effect of regional differences.Methods:We analyzed data from the 2019 Korean Community Health Survey,comprising 229,099 adults.Mental health was assessed through validated measures of depressive symptoms and psychological well-being.Social connection was measured using indicators of interpersonal ties and community participation,and QoL was assessed via self-reported health-related satisfaction across major life domains.Analytical procedures included mediation modeling and subgroup analyses by region,with significance levels set at p<0.05.Results:The results indicate that social connections are significantly associated with lower stress levels and reduced depressive symptoms,with QoL playing a critical mediating role.Notably,the indirect effect of social connection on mental health via QoL is stronger in rural areas compared to urban regions,highlighting the importance of social cohesion and community support in mental well-being.Among 203,567 adults,greater social participation was associated with lower subjective stress(total effect=−0.052,p<0.001)and fewer depressive symptoms(PHQ-9 total effect=−0.308,p<0.001).QoL significantly mediated these associations,with the strongest indirect pathways observed through usual activities(19.2%for stress;27.6%for depression)and mobility(24.4%for depression).Regional analysis showed stronger mediation in rural areas(up to 26.8%for stress and 32.6%for depression)than in urban areas(8–16%and 14.9–23%).Direct effects remained significant,indicating partial mediation.These findings highlight that social participation enhances mental health directly and indirectly through QoL,particularly in rural contexts.Conclusions:Social connection contributes to better mental health both directly and indirectly through improved QoL,with stronger effects observed in rural communities.These findings highlight the importance of fostering social cohesion and enhancing life quality as strategies for improving population mental health.Policy interventions should adopt context-sensitive approaches that account for regional differences in social resources and service availability.
文摘The decarbonization of transportation and environmental quality enhancement have become more and more reliant on eco-innovation,which incorporates both technological change and systemic coordination and governance.The review is a summary of the evidence that can be translated into environmental sustainability outcomes on how smart vehicle technologies,including electrified powertrains and vehicle-grid interfaces,connected and cooperative systems(Vehicleto-Everything,V2X),automation and advanced automation,and Artificial Intelligence(AI)-enabled optimization can be transformed.Using a structured analytical framework linking technology capability to eco-innovation mechanisms and sustainability impacts,we reconcile findings across operational,well-to-wheel,and life-cycle boundaries.The literature indicates that electrification delivers strong local air-quality benefits and,in most contexts,substantial climate gains,but net outcomes depend on grid carbon intensity,charging time profiles,battery production,and end-of-life pathways,making managed charging and circularity pivotal complements.Connectivity and cooperative control improve energy efficiency primarily through coordination effects such as traffic smoothing,eco-routing,and platooning,yet benefits are non-linear and sensitive to penetration rates and infrastructure interoperability.Automation offers efficiency and safety co-benefits but exhibits the widest uncertainty because induced demand,empty travel,and mode substitution can offset per-vehicle improvements.AI-driven fleet optimization can reduce empty miles and extend component life,although computational and hardware overhead and rapid obsolescence can introduce trade-offs.We identify persistent gaps in comparability,non-exhaust emissions assessment,causal evaluation at scale,and equity-aware impact metrics,and propose a research and policy agenda emphasizing integrated Life Cycle Assessment(LCA)system modeling,standardized reporting,interoperable data governance,and demand management to secure durable environmental gains.
基金support through the“Trans-Disciplinary Research”Grant(No.R/Dev/IoE/TDRProjects/2023-24/61658),which played a crucial role in enabling this research endeavor.
文摘Floodplain wetlands are invaluable ecosystems providing numerous ecological benefits,yet they face a global crisis necessitating sustainable preservation efforts.This study examines the depletion of floodplain wetlands within the Hastinapur Wildlife Sanctuary(HWLS)in Uttar Pradesh.Encroachment activities such as grazing,agriculture,and human settlements have fragmented and degraded critical wetland ecosystems.Additionally,irrigation projects,dam construction,and water diversion have disrupted natural water flow and availability.To assess wetland inundation in 2023,five classification techniques were employed:Random Forest(RF),Support Vector Machine(SVM),artificial neural network(ANN),Spectral Information Divergence(SID),and Maximum Likelihood Classifier(MLC).SVM emerged as the most precise method,as determined by kappa coefficient and index-based validation.Consequently,the SVM classifier was used to model wetland inundation areas from 1983 to 2023 and analyze spatiotemporal changes and fragmentation patterns.The findings revealed that the SVM clas-sifier accurately mapped 2023 wetland areas.The modeled time-series data demonstrated a 62.55%and 38.12%reduction in inundated wetland areas over the past 40 years in the pre-and post-monsoon periods,respectively.Fragmentation analysis indicated an 86.27%decrease in large core wetland areas in the pre-monsoon period,signifying severe habitat degradation.This rapid decline in wetlands within protected areas raises concerns about their ecological impacts.By linking wetland loss to global sustainability objectives,this study underscores the global urgency for strengthened wetland protection measures and highlights the need for integrating wetland conservation into broader sustainable development goals.Effective policies and adaptive management strategies are crucial for preserving these ecosystems and their vital services,which are essential for biodiversity,climate regulation,and human well-being.
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.
文摘The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It has been suggested that the size of the brain(brain reserve) and the extent of neural connections acquired through life(neural reserve) set a threshold beyond which noticeable impairments occur.In contrast,cognitive reserve refers to the brain's ability to adapt and reo rganize stru cturally and functionally to resist damage and maintain function,including neural reserve and brain maintenance,resilience,and compensation(Verkhratsky and Zorec,2024).
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00559546)supported by the IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259004).
文摘The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.
