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 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
本研究旨在分析序列相似性家族135成员A(Family With Sequence Similarity 135 Member A, FAM135A)基因和丝/酪氨酸蛋白激酶3(Serine/Threonine Protein Kinase 3, STK3)基因的多态性位点与山羊产羔数的关联。基于Sequenom MassARRAY SN...本研究旨在分析序列相似性家族135成员A(Family With Sequence Similarity 135 Member A, FAM135A)基因和丝/酪氨酸蛋白激酶3(Serine/Threonine Protein Kinase 3, STK3)基因的多态性位点与山羊产羔数的关联。基于Sequenom MassARRAY SNP技术对FAM135A和STK3基因的4个候选位点进行分型,并分析了云上黑山羊(n=544)、济宁青山羊(n=133)和辽宁绒山羊(n=91)3个群体中4个候选位点的遗传学特征,同时将这些突变位点与云上黑山羊的繁殖性能(包括产羔数、初生窝重和断奶窝重)作了关联分析。结果显示,在不同繁殖力品种(济宁青山羊和辽宁绒山羊)中,FAM135A基因的g.91260230 G>T和g.91261141 G>A位点以及STK3基因的g.16434985 C>T位点基因型的分布存在着极显著差异(P <0.01);而不同繁殖力品种中STK3基因的g.16648187 C>T位点的基因型分布却没有明显差异(P>0.05)。关联分析结果表明,g.91260230 G>T位点的各基因型在云上黑山羊的产羔数、初生窝重和断奶窝重上都没有明显差异(P>0.05);但g.91261141 G>A位点与产羔数和初生窝重显著相关,GG型产羔数和窝重均显著高于AG型(P <0.05);g.16434985 C>T位点TT型的初生窝重显著高于CT型(P <0.05),但该位点的各基因型对山羊的产羔数和断奶窝重并没有显著影响(P>0.05);g.16648187 C>T位点与山羊的产羔数和初生窝重之间都没有显著关联(P>0.05)。综上,本研究发现FAM135A基因的g.91261141 G>A位点是云上黑山羊产羔数与初生窝重选择的潜在遗传标记。STK3基因的g.16434985 C>T位点适合初生窝重的选择。展开更多
Spartina alterniflora invasions seriously threaten the structure and functions of coastal wetlands in China.In this study,the Suaeda salsa community in the Yellow River Estuary wetland was monitored using long-term La...Spartina alterniflora invasions seriously threaten the structure and functions of coastal wetlands in China.In this study,the Suaeda salsa community in the Yellow River Estuary wetland was monitored using long-term Landsat satellite images acquired from 1997 to 2020 to quantify the impact of changes in hydrological connectivity induced by S.alterniflora on neighboring vegetation com-munities.The results showed that S.alterniflora rapidly expanded in the estuary area at a rate of 4.91 km^(2)/yr from 2010 to 2020.At the same time,the hydrological connectivity of the area and the distribution of S.salsa changed significantly.Small tidal creeks dominated the S.alterniflora landscape.The number of tidal creeks increased significantly,but their average length decreased and they tended to develop in a horizontal tree-like pattern.Affected by the changes in hydrological connectivity due to the S.alterniflora invasion,the area of S.salsa decreased by 41.1%,and the degree of landscape fragmentation increased from 1997 to 2020.Variations in the Largest Patch Index(LPI)indicated that the S.alterniflora landscape had become the dominant landscape type in the Yellow River Estuary.The res-ults of standard deviation ellipse(SDE)and Pearson’s correlation analyses indicated that a well-developed hydrological connectivity could promote the maintenance of the S.salsa landscape.The degradation of most S.salsa communities is caused by the influence of S.alterniflora on the morphological characteristics of the hydrological connectivity of tidal creek systems.展开更多
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 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.
基金Under the auspices of Key Program of the National Natural Science Foundation of China(No.U2006215,U1806218)the National Key R&D Program of China(No.2017YFC0505902)。
文摘Spartina alterniflora invasions seriously threaten the structure and functions of coastal wetlands in China.In this study,the Suaeda salsa community in the Yellow River Estuary wetland was monitored using long-term Landsat satellite images acquired from 1997 to 2020 to quantify the impact of changes in hydrological connectivity induced by S.alterniflora on neighboring vegetation com-munities.The results showed that S.alterniflora rapidly expanded in the estuary area at a rate of 4.91 km^(2)/yr from 2010 to 2020.At the same time,the hydrological connectivity of the area and the distribution of S.salsa changed significantly.Small tidal creeks dominated the S.alterniflora landscape.The number of tidal creeks increased significantly,but their average length decreased and they tended to develop in a horizontal tree-like pattern.Affected by the changes in hydrological connectivity due to the S.alterniflora invasion,the area of S.salsa decreased by 41.1%,and the degree of landscape fragmentation increased from 1997 to 2020.Variations in the Largest Patch Index(LPI)indicated that the S.alterniflora landscape had become the dominant landscape type in the Yellow River Estuary.The res-ults of standard deviation ellipse(SDE)and Pearson’s correlation analyses indicated that a well-developed hydrological connectivity could promote the maintenance of the S.salsa landscape.The degradation of most S.salsa communities is caused by the influence of S.alterniflora on the morphological characteristics of the hydrological connectivity of tidal creek systems.
