目的探讨脑小血管病(CSVD)患者外周血G蛋白耦联雌激素受体30(GPER30)、神经元PAS结构域蛋白4(NPAS4)、FK506结合蛋白5(FKBP5)表达与认知功能障碍(CD)的相关性。方法前瞻性选取2022年1月至2023年12月郑州大学第二附属医院收治的227例CSV...目的探讨脑小血管病(CSVD)患者外周血G蛋白耦联雌激素受体30(GPER30)、神经元PAS结构域蛋白4(NPAS4)、FK506结合蛋白5(FKBP5)表达与认知功能障碍(CD)的相关性。方法前瞻性选取2022年1月至2023年12月郑州大学第二附属医院收治的227例CSVD患者,根据有无CD分为障碍组(n=66)与无障碍组(n=161)。比较两组患者的一般资料及外周血GPER30、NPAS4、FKBP5 m RNA表达水平,Logistic回归分析CSVD患者CD的影响因素,比较不同程度CD患者外周血GPER30、NPAS4、FKBP5 m RNA表达水平,采用Pearson法分析外周血GPER30、NPAS4、FKBP5 m RNA表达与蒙特利尔认知评估量表(Mo CA)评分的相关性。结果障碍组患者的年龄、病程分别为(72.49±5.68)岁、(2.69±0.78)年,明显高(长)于无障碍组的(67.51±7.04)岁、(2.31±0.62)年,差异均有统计学意义(P<0.05);障碍组患者的外周血GPER30 m RNA表达水平为1.02±0.17,明显低于无障碍组的1.66±0.31,NPAS4m RNA、FKBP5 m RNA表达水平分别为2.79±0.60、3.88±1.12,明显高于无障碍组的1.55±0.51、2.10±0.59,差异均有统计学意义(P<0.05);Logistic回归分析结果显示,年龄、病程、GPER30 m RNA、NPAS4 m RNA及FKBP5 m RNA均为CSVD患者CD的独立影响因素(P<0.05)。轻度组患者的外周血GPER30 m RNA表达水平为1.27±0.25,明显高于中重度组的0.70±0.12,NPAS4 m RNA、FKBP5 m RNA表达水平分别为2.31±0.58、3.19±1.07,明显低于中重度组的3.40±0.72、4.76±1.39,差异均有统计学意义(P<0.05);Pearson法分析结果显示,外周血GPER30 m RNA表达与CSVD患者Mo CA评分呈正相关(r=0.704,P<0.05),NPAS4 m RNA、FKBP5 m RNA与Mo CA评分呈负相关(r=-0.572、-0.542,P<0.05)。结论外周血GPER30、NPAS4、FKBP5是CSVD患者CD的独立相关因素,各指标表达水平与CD病情严重程度均具有一定相关性,可为临床判断CD、评估CD病情严重程度提供参考,以指导后续临床工作。展开更多
The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile networks.These networks are sufficiently scaled to interconnect billions of user...The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile networks.These networks are sufficiently scaled to interconnect billions of users and devices.Researchers in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous vehicles.NextG continuously improves its network functionality to support these applications.Multiple input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in conjunctionwithNextG.This article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial attacks.The technique aims to create long-lasting and secure NextG networks using this extended approach.The viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this research.Moreover,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.展开更多
The explosive growth of data traffic and heterogeneous service requirements of 5G networks—covering Enhanced Mobile Broadband(eMBB),Ultra-Reliable Low Latency Communication(URLLC),and Massive Machine Type Communicati...The explosive growth of data traffic and heterogeneous service requirements of 5G networks—covering Enhanced Mobile Broadband(eMBB),Ultra-Reliable Low Latency Communication(URLLC),and Massive Machine Type Communication(mMTC)—present tremendous challenges to conventional methods of bandwidth allocation.A new deep reinforcement learning-based(DRL-based)bandwidth allocation system for real-time,dynamic management of 5G radio access networks is proposed in this paper.Unlike rule-based and static strategies,the proposed system dynamically updates itself according to shifting network conditions such as traffic load and channel conditions to maximize the achievable throughput,fairness,and compliance with QoS requirements.By using extensive simulations mimicking real-world 5G scenarios,the proposed DRL model outperforms current baselines like Long Short-Term Memory(LSTM),linear regression,round-robin,and greedy algorithms.It attains 90%–95%of the maximum theoretical achievable throughput and nearly twice the conventional equal allocation.It is also shown to react well under delay and reliability constraints,outperforming round-robin(hindered by excessive delay and packet loss)and proving to be more efficient than greedy approaches.In conclusion,the efficiency of DRL in optimizing the allocation of bandwidth is highlighted,and its potential to realize self-optimizing,Artificial Intelligence-assisted(AI-assisted)resource management in 5G as well as upcoming 6G networks is revealed.展开更多
Smart edge computing(SEC)is a novel paradigm for computing that could transfer cloud-based applications to the edge network,supporting computation-intensive services like face detection and natural language processing...Smart edge computing(SEC)is a novel paradigm for computing that could transfer cloud-based applications to the edge network,supporting computation-intensive services like face detection and natural language processing.A core feature of mobile edge computing,SEC improves user experience and device performance by offloading local activities to edge processors.In this framework,blockchain technology is utilized to ensure secure and trustworthy communication between edge devices and servers,protecting against potential security threats.Additionally,Deep Learning algorithms are employed to analyze resource availability and optimize computation offloading decisions dynamically.IoT applications that require significant resources can benefit from SEC,which has better coverage.Although access is constantly changing and network devices have heterogeneous resources,it is not easy to create consistent,dependable,and instantaneous communication between edge devices and their processors,specifically in 5G Heterogeneous Network(HN)situations.Thus,an Intelligent Management of Resources for Smart Edge Computing(IMRSEC)framework,which combines blockchain,edge computing,and Artificial Intelligence(AI)into 5G HNs,has been proposed in this paper.As a result,a unique dual schedule deep reinforcement learning(DS-DRL)technique has been developed,consisting of a rapid schedule learning process and a slow schedule learning process.The primary objective is to minimize overall unloading latency and system resource usage by optimizing computation offloading,resource allocation,and application caching.Simulation results demonstrate that the DS-DRL approach reduces task execution time by 32%,validating the method’s effectiveness within the IMRSEC framework.展开更多
The Fifth Generation of Mobile Communications for Railways(5G-R)brings significant opportunities for the rail industry.However,alongside the potential and benefits of the railway 5G network are complex security challe...The Fifth Generation of Mobile Communications for Railways(5G-R)brings significant opportunities for the rail industry.However,alongside the potential and benefits of the railway 5G network are complex security challenges.Ensuring the security and reliability of railway 5G networks is therefore essential.This paper presents a detailed examination of security assessment techniques for railway 5G networks,focusing on addressing the unique security challenges in this field.In this paper,various security requirements in railway 5G networks are analyzed,and specific processes and methods for conducting comprehensive security risk assessments are presented.This study provides a framework for securing railway 5G network development and ensuring its long-term sustainability.展开更多
随着4G/5G网络的快速发展,4G/5G网络覆盖的深度和广度有待完善,部分区域存在弱覆盖或盲区,针对网络覆盖建设周期长、成本高的问题。本文首先分析快速解决4G/5G网络弱覆盖存在的问题,其次围绕怎样实现弱覆盖或盲点区域快速覆盖4G/5G网络...随着4G/5G网络的快速发展,4G/5G网络覆盖的深度和广度有待完善,部分区域存在弱覆盖或盲区,针对网络覆盖建设周期长、成本高的问题。本文首先分析快速解决4G/5G网络弱覆盖存在的问题,其次围绕怎样实现弱覆盖或盲点区域快速覆盖4G/5G网络开展研究,提出利旧小区宽带空闲的传输网络资源连接微功率射频拉远单元(pico remote radio unit,PRRU)的方案,该方案可实现4G/5G网络弱覆盖快速处理。最后对方案的实用性及有效性进行了阐述。本研究为快速解决4G/5G网络弱覆盖问题提供了新思路和新方法。展开更多
The adoption of 5G for Railways(5G-R)is expanding,particularly in high-speed trains,due to the benefits offered by 5G technology.High-speed trains must provide seamless connectivity and Quality of Service(QoS)to ensur...The adoption of 5G for Railways(5G-R)is expanding,particularly in high-speed trains,due to the benefits offered by 5G technology.High-speed trains must provide seamless connectivity and Quality of Service(QoS)to ensure passengers have a satisfactory experience throughout their journey.Installing base stations along urban environments can improve coverage but can dramatically reduce the experience of users due to interference.In particular,when a user with a mobile phone is a passenger in a high speed train traversing between urban centres,the coverage and the 5G resources in general need to be adequate not to diminish her experience of the service.The utilization of macro,pico,and femto cells may optimize the utilization of 5G resources.In this paper,a Genetic Algorithm(GA)-based approach to address the challenges of 5G network planning for 5G-R services is presented.The network is divided into three cell types,macro,pico,and femto cells—and the optimization process is designed to achieve a balance between key objectives:providing comprehensive coverage,minimizing interference,and maximizing energy efficiency.The study focuses on environments with high user density,such as high-speed trains,where reliable and high-quality connectivity is critical.Through simulations,the effectiveness of the GA-driven framework in optimizing coverage and performance in such scenarios is demonstrated.The algorithm is compared with the Particle Swarm Optimisation(PSO)and the Simulated Annealing(SA)methods and interesting insights emerged.The GA offers a strong balance between coverage and efficiency,achieving significantly higher coverage than PSO while maintaining competitive energy efficiency and interference levels.Its steady fitness improvement and adaptability make it well-suited for scenarios where wide coverage is a priority alongside acceptable performance trade-offs.展开更多
文摘目的探讨脑小血管病(CSVD)患者外周血G蛋白耦联雌激素受体30(GPER30)、神经元PAS结构域蛋白4(NPAS4)、FK506结合蛋白5(FKBP5)表达与认知功能障碍(CD)的相关性。方法前瞻性选取2022年1月至2023年12月郑州大学第二附属医院收治的227例CSVD患者,根据有无CD分为障碍组(n=66)与无障碍组(n=161)。比较两组患者的一般资料及外周血GPER30、NPAS4、FKBP5 m RNA表达水平,Logistic回归分析CSVD患者CD的影响因素,比较不同程度CD患者外周血GPER30、NPAS4、FKBP5 m RNA表达水平,采用Pearson法分析外周血GPER30、NPAS4、FKBP5 m RNA表达与蒙特利尔认知评估量表(Mo CA)评分的相关性。结果障碍组患者的年龄、病程分别为(72.49±5.68)岁、(2.69±0.78)年,明显高(长)于无障碍组的(67.51±7.04)岁、(2.31±0.62)年,差异均有统计学意义(P<0.05);障碍组患者的外周血GPER30 m RNA表达水平为1.02±0.17,明显低于无障碍组的1.66±0.31,NPAS4m RNA、FKBP5 m RNA表达水平分别为2.79±0.60、3.88±1.12,明显高于无障碍组的1.55±0.51、2.10±0.59,差异均有统计学意义(P<0.05);Logistic回归分析结果显示,年龄、病程、GPER30 m RNA、NPAS4 m RNA及FKBP5 m RNA均为CSVD患者CD的独立影响因素(P<0.05)。轻度组患者的外周血GPER30 m RNA表达水平为1.27±0.25,明显高于中重度组的0.70±0.12,NPAS4 m RNA、FKBP5 m RNA表达水平分别为2.31±0.58、3.19±1.07,明显低于中重度组的3.40±0.72、4.76±1.39,差异均有统计学意义(P<0.05);Pearson法分析结果显示,外周血GPER30 m RNA表达与CSVD患者Mo CA评分呈正相关(r=0.704,P<0.05),NPAS4 m RNA、FKBP5 m RNA与Mo CA评分呈负相关(r=-0.572、-0.542,P<0.05)。结论外周血GPER30、NPAS4、FKBP5是CSVD患者CD的独立相关因素,各指标表达水平与CD病情严重程度均具有一定相关性,可为临床判断CD、评估CD病情严重程度提供参考,以指导后续临床工作。
基金funding from King Saud University through Researchers Supporting Project number(RSP2024R387),King Saud University,Riyadh,Saudi Arabia.
文摘The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile networks.These networks are sufficiently scaled to interconnect billions of users and devices.Researchers in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous vehicles.NextG continuously improves its network functionality to support these applications.Multiple input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in conjunctionwithNextG.This article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial attacks.The technique aims to create long-lasting and secure NextG networks using this extended approach.The viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this research.Moreover,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.
文摘The explosive growth of data traffic and heterogeneous service requirements of 5G networks—covering Enhanced Mobile Broadband(eMBB),Ultra-Reliable Low Latency Communication(URLLC),and Massive Machine Type Communication(mMTC)—present tremendous challenges to conventional methods of bandwidth allocation.A new deep reinforcement learning-based(DRL-based)bandwidth allocation system for real-time,dynamic management of 5G radio access networks is proposed in this paper.Unlike rule-based and static strategies,the proposed system dynamically updates itself according to shifting network conditions such as traffic load and channel conditions to maximize the achievable throughput,fairness,and compliance with QoS requirements.By using extensive simulations mimicking real-world 5G scenarios,the proposed DRL model outperforms current baselines like Long Short-Term Memory(LSTM),linear regression,round-robin,and greedy algorithms.It attains 90%–95%of the maximum theoretical achievable throughput and nearly twice the conventional equal allocation.It is also shown to react well under delay and reliability constraints,outperforming round-robin(hindered by excessive delay and packet loss)and proving to be more efficient than greedy approaches.In conclusion,the efficiency of DRL in optimizing the allocation of bandwidth is highlighted,and its potential to realize self-optimizing,Artificial Intelligence-assisted(AI-assisted)resource management in 5G as well as upcoming 6G networks is revealed.
文摘Smart edge computing(SEC)is a novel paradigm for computing that could transfer cloud-based applications to the edge network,supporting computation-intensive services like face detection and natural language processing.A core feature of mobile edge computing,SEC improves user experience and device performance by offloading local activities to edge processors.In this framework,blockchain technology is utilized to ensure secure and trustworthy communication between edge devices and servers,protecting against potential security threats.Additionally,Deep Learning algorithms are employed to analyze resource availability and optimize computation offloading decisions dynamically.IoT applications that require significant resources can benefit from SEC,which has better coverage.Although access is constantly changing and network devices have heterogeneous resources,it is not easy to create consistent,dependable,and instantaneous communication between edge devices and their processors,specifically in 5G Heterogeneous Network(HN)situations.Thus,an Intelligent Management of Resources for Smart Edge Computing(IMRSEC)framework,which combines blockchain,edge computing,and Artificial Intelligence(AI)into 5G HNs,has been proposed in this paper.As a result,a unique dual schedule deep reinforcement learning(DS-DRL)technique has been developed,consisting of a rapid schedule learning process and a slow schedule learning process.The primary objective is to minimize overall unloading latency and system resource usage by optimizing computation offloading,resource allocation,and application caching.Simulation results demonstrate that the DS-DRL approach reduces task execution time by 32%,validating the method’s effectiveness within the IMRSEC framework.
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant No.2025JBXT010in part by NSFC under Grant No.62171021,in part by the Project of China State Railway Group under Grant No.N2024B004in part by ZTE IndustryUniversityInstitute Cooperation Funds under Grant No.l23L00010.
文摘The Fifth Generation of Mobile Communications for Railways(5G-R)brings significant opportunities for the rail industry.However,alongside the potential and benefits of the railway 5G network are complex security challenges.Ensuring the security and reliability of railway 5G networks is therefore essential.This paper presents a detailed examination of security assessment techniques for railway 5G networks,focusing on addressing the unique security challenges in this field.In this paper,various security requirements in railway 5G networks are analyzed,and specific processes and methods for conducting comprehensive security risk assessments are presented.This study provides a framework for securing railway 5G network development and ensuring its long-term sustainability.
文摘随着4G/5G网络的快速发展,4G/5G网络覆盖的深度和广度有待完善,部分区域存在弱覆盖或盲区,针对网络覆盖建设周期长、成本高的问题。本文首先分析快速解决4G/5G网络弱覆盖存在的问题,其次围绕怎样实现弱覆盖或盲点区域快速覆盖4G/5G网络开展研究,提出利旧小区宽带空闲的传输网络资源连接微功率射频拉远单元(pico remote radio unit,PRRU)的方案,该方案可实现4G/5G网络弱覆盖快速处理。最后对方案的实用性及有效性进行了阐述。本研究为快速解决4G/5G网络弱覆盖问题提供了新思路和新方法。
文摘The adoption of 5G for Railways(5G-R)is expanding,particularly in high-speed trains,due to the benefits offered by 5G technology.High-speed trains must provide seamless connectivity and Quality of Service(QoS)to ensure passengers have a satisfactory experience throughout their journey.Installing base stations along urban environments can improve coverage but can dramatically reduce the experience of users due to interference.In particular,when a user with a mobile phone is a passenger in a high speed train traversing between urban centres,the coverage and the 5G resources in general need to be adequate not to diminish her experience of the service.The utilization of macro,pico,and femto cells may optimize the utilization of 5G resources.In this paper,a Genetic Algorithm(GA)-based approach to address the challenges of 5G network planning for 5G-R services is presented.The network is divided into three cell types,macro,pico,and femto cells—and the optimization process is designed to achieve a balance between key objectives:providing comprehensive coverage,minimizing interference,and maximizing energy efficiency.The study focuses on environments with high user density,such as high-speed trains,where reliable and high-quality connectivity is critical.Through simulations,the effectiveness of the GA-driven framework in optimizing coverage and performance in such scenarios is demonstrated.The algorithm is compared with the Particle Swarm Optimisation(PSO)and the Simulated Annealing(SA)methods and interesting insights emerged.The GA offers a strong balance between coverage and efficiency,achieving significantly higher coverage than PSO while maintaining competitive energy efficiency and interference levels.Its steady fitness improvement and adaptability make it well-suited for scenarios where wide coverage is a priority alongside acceptable performance trade-offs.
