Tunneling nanotubes are crucial structures for cellular communication and are observed in a variety of cell types.Glial cells,the most abundant cells in the nervous system,play a vital role in intercellular signaling ...Tunneling nanotubes are crucial structures for cellular communication and are observed in a variety of cell types.Glial cells,the most abundant cells in the nervous system,play a vital role in intercellular signaling and can show abnormal activation under pathological conditions.Our bibliometric analysis indicated a substantial increase in research on tunneling nanotubes over the past two decades,highlighting their important role in cellular communication.This review focuses on the formation of tunneling nanotubes in various types of glial cells,including astrocytes,microglia,glioma cells,and Schwann cells,as well as their roles in cellular communication and cargo transport.We found that glial cells influence the stability of the neural system and play a role in nerve regeneration through tunneling nanotubes.Tunneling nanotubes facilitate the transmission and progression of diseases by transporting pathogens and harmful substances.However,they are also involved in alleviating cellular stress by removing toxins and delivering essential nutrients.Understanding the interactions between glial cells through tunneling nanotubes could provide valuable insights into the complex neural networks that govern brain function and responses to injury.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 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
Stroke-induced alterations in cerebral blood flow trigger neurovascular remodeling,as manifested by the blood-brain barrier dysfunction and subs equent neurovascular repair activities such as angiogenesis.This process...Stroke-induced alterations in cerebral blood flow trigger neurovascular remodeling,as manifested by the blood-brain barrier dysfunction and subs equent neurovascular repair activities such as angiogenesis.This process involves neurovascular communication that facilitates the transport of mediators among cerebrovascular endothelial cells,pericytes,glial cells,and neurons,thereby transmitting signals from donor to recipient cells to elicit a collaborative response.展开更多
Introduction:One of the main events that regulate a cell’s well-being is cell-to-cell communication.This intercellular mechanism of information transfer is often mediated by vesicular trafficking.Mitochondrial-derive...Introduction:One of the main events that regulate a cell’s well-being is cell-to-cell communication.This intercellular mechanism of information transfer is often mediated by vesicular trafficking.Mitochondrial-derived vesicles(MDVs)are an emerging subpopulation of extracellular vesicle(EV)first discovered in 2008 that allow mitochondria to communicate with their surroundings.展开更多
In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network e...In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications.In scenarios where edge servers are sparsely deployed,the lack of coordination and information sharing often leads to load imbalance,thereby increasing system latency.Furthermore,in regions without edge server coverage,tasks must be processed locally,which further exacerbates latency issues.To address these challenges,we propose a novel and efficient Deep Reinforcement Learning(DRL)-based approach aimed at minimizing average task latency.The proposed method incorporates three offloading strategies:local computation,direct offloading to the edge server in local region,and device-to-device(D2D)-assisted offloading to edge servers in other regions.We formulate the task offloading process as a complex latency minimization optimization problem.To solve it,we propose an advanced algorithm based on the Dueling Double Deep Q-Network(D3QN)architecture and incorporating the Prioritized Experience Replay(PER)mechanism.Experimental results demonstrate that,compared with existing offloading algorithms,the proposed method significantly reduces average task latency,enhances user experience,and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads.展开更多
Drug development for Alzheimer’s disease is extremely challenging,as demonstrated by the repeated failures of amyloid-β-targeted therapeutics and the controversies surrounding the amyloid-βcascade hypothesis.More r...Drug development for Alzheimer’s disease is extremely challenging,as demonstrated by the repeated failures of amyloid-β-targeted therapeutics and the controversies surrounding the amyloid-βcascade hypothesis.More recently,advances in the development of Lecanemab,an anti-amyloid-βmonoclonal antibody,have shown positive results in reducing brain A burden and slowing cognitive decline in patients with early-stage Alzheimer’s disease in the Phase Ⅲ clinical trial(Clarity Alzheimer’s disease).Despite these promising results,side effects such as amyloid-related imaging abnormalities(ARIA)may limit its usage.ARIA can manifest as ARIA-E(cerebral edema or effusions)and ARIA-H(microhemorrhages or superficial siderosis)and is thought to be caused by increased vascular permeability due to inflammatory responses,leading to leakages of blood products and protein-rich fluid into brain parenchyma.Endothelial dysfunction is an early pathological feature of Alzheimer’s disease,and the blood-brain barrier becomes increasingly leaky as the disease progresses.In addition,APOE4,the strongest genetic risk factor for Alzheimer’s disease,is associated with higher vascular amyloid burden,increased ARIA incidence,and accelerated blood-brain barrier disruptions.These interconnected vascular abnormalities highlight the importance of vascular contributions to the pathophysiology of Alzheimer’s disease.Here,we will closely examine recent research evaluating the heterogeneity of brain endothelial cells in the microvasculature of different brain regions and their relationships with Alzheimer’s disease progression.展开更多
Founded in September 2020,the International SparkLink Alliance(iSLA)now has approximately 1,200 members in diverse sectors including terminals,homes,vehicles,manufacturing,transportation,finance and healthcare.The iSL...Founded in September 2020,the International SparkLink Alliance(iSLA)now has approximately 1,200 members in diverse sectors including terminals,homes,vehicles,manufacturing,transportation,finance and healthcare.The iSLA has established a technical standards system for wireless short-range communication covering full-stack standards such as the end-to-end protocol system.展开更多
Pain is often comorbid with emotional disorders such as anxiety and depression.Hyperexcitability of the anterior cingulate cortex has been implicated in pain and pain-related negative emotions that arise from impairme...Pain is often comorbid with emotional disorders such as anxiety and depression.Hyperexcitability of the anterior cingulate cortex has been implicated in pain and pain-related negative emotions that arise from impairments in inhibitory gamma-aminobutyric acid neurotransmission.This review primarily aims to outline the main circuitry(including the input and output connectivity)of the anterior cingulate cortex and classification and functions of different gamma-aminobutyric acidergic neurons;it also describes the neurotransmitters/neuromodulators affecting these neurons,their intercommunication with other neurons,and their importance in mental comorbidities associated with chronic pain disorders.Improving understanding on their role in pain-related mental comorbidities may facilitate the development of more effective treatments for these conditions.However,the mechanisms that regulate gamma-aminobutyric acidergic systems remain elusive.It is also unclear as to whether the mechanisms are presynaptic or postsynaptic.Further exploration of the complexities of this system may reveal new pathways for research and drug development.展开更多
Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BC...Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BCIs,including their fundamental principles,technical advancements,and applications in specific domains.However,these reviews often focus on signal processing,hardware development,or limited applications such as motor rehabilitation or communication.This paper aims to offer a comprehensive review of recent electroencephalogram(EEG)-based BCI applications in the medical field across 8 critical areas,encompassing rehabilitation,daily communication,epilepsy,cerebral resuscitation,sleep,neurodegenerative diseases,anesthesiology,and emotion recognition.Moreover,the current challenges and future trends of BCIs were also discussed,including personal privacy and ethical concerns,network security vulnerabilities,safety issues,and biocompatibility.展开更多
This paper proposes a novel modified uni-traveling-carrier photodiode(MUTC-PD)featuring an electric field regulation layer:a p-type doped thin layer inserted behind the PD’s n-doped cliff layer.This electric field re...This paper proposes a novel modified uni-traveling-carrier photodiode(MUTC-PD)featuring an electric field regulation layer:a p-type doped thin layer inserted behind the PD’s n-doped cliff layer.This electric field regulation layer enhances the PD’s performance by not only reducing and smoothing the electric field intensity in the collector layer,allowing photo-generated electrons to transit at peak drift velocity,but also improving the electric field intensity in the depleted absorber layer and optimizing the photo-generated carriers’saturated transit performance.Additionally,the transport characteristics of the peak drift velocity of photogenerated electrons in the device’s collection layer can be used to optimize its parasitic characteristics.The electron’s peak drift velocity compensates for the lost transit time.Thus improving the 3 dB bandwidth of the PD’s photo response.Finally obtains a MUTC-PD with a 3 dB bandwidth of 68 GHz at a responsivity of 0.502 A/W,making it suitable for 100 Gbit/s optical receivers.展开更多
The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,s...The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.展开更多
The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport ...The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport industry must innovate in key technologies to ensure high-quality transmissions for passengers and railway operations.These systems must function effectively under high mobility conditions while prioritizing safety,ecofriendliness,comfort,transparency,predictability,and reliability.On the other hand,the proposal of 6 G wireless technology introduces new possibilities for innovation in communication technologies,which may truly realize the current vision of HSR.Therefore,this article gives a review of the current advanced 6 G wireless communication technologies for HSR,including random access and switching,channel estimation and beamforming,integrated sensing and communication,and edge computing.The main application scenarios of these technologies are reviewed,as well as their current research status and challenges,followed by an outlook on future development directions.展开更多
Wireless networks support numerous terminals,manage large data volumes,and provide diverse services,but the vulnerability to environmental changes leads to increased complexity and costs.Situational awareness has been...Wireless networks support numerous terminals,manage large data volumes,and provide diverse services,but the vulnerability to environmental changes leads to increased complexity and costs.Situational awareness has been widely applied in network management,but existing methods fail to find optimal solutions due to the high heterogeneity of base stations,numerous metrics,and complex intercell dependencies.To address this gap,this paper proposes a specialized framework for wireless networks,integrating an evaluation model and control approach.The framework expands the indicator set into four key areas,introduces an evaluation method,and proposes the indicator perturbation greedy(IPG)algorithm and the adjustment scheme selection method based on damping coefficient(DCSS)for effective network optimization.A case study in an urban area demonstrates the framework’s ability to balance and improve network performance,enhancing situational awareness and operational efficiency under dynamic conditions.展开更多
The nonlinearity and fear hypothesis predicts that highly aroused vocal mammals and birds produce vocalizations(notably alarm calls and screams)which contain a variety of nonlinear phenomena(NLP).Such vocalizations of...The nonlinearity and fear hypothesis predicts that highly aroused vocal mammals and birds produce vocalizations(notably alarm calls and screams)which contain a variety of nonlinear phenomena(NLP).Such vocalizations often sound“noisy”because vocal production systems are over-blown when animals are highly aroused.While much is known about the conditions under which animals produce vocalizations containing NLP and how species respond to them,there is little research about the heritability of such behavioral traits.Using the quantitative genetic animal model,we estimated the genetic basis of“noise”in alarm calls produced by females and found significant heritability in call entropy-our measure of the noisiness.About 9%of the variance in noisiness can be accounted for by genetic differences.Taken together,these findings suggest that the degree to which marmots produce noisy calls is modestly heritable and can be thus subject to further evolution via natural selection.展开更多
Along with the proliferating research interest in semantic communication(Sem Com),joint source channel coding(JSCC)has dominated the attention due to the widely assumed existence in efficiently delivering information ...Along with the proliferating research interest in semantic communication(Sem Com),joint source channel coding(JSCC)has dominated the attention due to the widely assumed existence in efficiently delivering information semantics.Nevertheless,this paper challenges the conventional JSCC paradigm and advocates for adopting separate source channel coding(SSCC)to enjoy a more underlying degree of freedom for optimization.We demonstrate that SSCC,after leveraging the strengths of the Large Language Model(LLM)for source coding and Error Correction Code Transformer(ECCT)complemented for channel coding,offers superior performance over JSCC.Our proposed framework also effectively highlights the compatibility challenges between Sem Com approaches and digital communication systems,particularly concerning the resource costs associated with the transmission of high-precision floating point numbers.Through comprehensive evaluations,we establish that assisted by LLM-based compression and ECCT-enhanced error correction,SSCC remains a viable and effective solution for modern communication systems.In other words,separate source channel coding is still what we need.展开更多
In the applications such as food production,the environmental temperature should be measured continuously dur-ing the entire process,which requires an ultra-low-power temperature sensor for long-termly monitoring.Conv...In the applications such as food production,the environmental temperature should be measured continuously dur-ing the entire process,which requires an ultra-low-power temperature sensor for long-termly monitoring.Conventional tempera-ture sensors trade the measurement accuracy with power consumption.In this work,we present a battery-free wireless tempera-ture sensing chip for long-termly monitoring during food production.A calibrated oscillator-based CMOS temperature sensor is proposed instead of the ADC-based power-hungry circuits in conventional works.In addition,the sensor chip can harvest the power transferred by a remote reader to eliminate the use of battery.Meanwhile,the system conducts wireless bidirectional communication between the sensor chip and reader.In this way,the temperature sensor can realize both a high precision and battery-free operation.The temperature sensing chip is fabricated in 55 nm CMOS process,and the reader chip is imple-mented in 65 nm CMOS technology.Experimental results show that the temperature measurement error achieves±1.6℃ from 25 to 50℃,with battery-free readout by a remote reader.展开更多
The rapid evolution of Fifth-Generation(5G)networks and the strategic development of Sixth-Generation(6G)technologies have significantly advanced the implementation of air-ground integrated networks with seamless cove...The rapid evolution of Fifth-Generation(5G)networks and the strategic development of Sixth-Generation(6G)technologies have significantly advanced the implementation of air-ground integrated networks with seamless coverage.Unmanned Aerial Vehicles(UAVs),serving as high-mobility aerial platforms,are extensively utilized to enhance coverage in long-distance emergency communication scenarios.The resource-constrained communication environments in emergencies by classifying UAVs into swarm UAVs and relay UAVs as aerial communication nodes is inversitgated.A horizontal deployment strategy for swarm UAVs is formulated through K-means clustering algorithm optimization,while a vertical deployment scheme is established using convex optimization methods.The minimum-path trajectory planning for relay UAVs is optimized via the Particle Swarm Optimization(PSO)algorithm,enhancing communication reliability between UAV swarms and terrestrial base stations.A three-dimensional heterogeneous network architecture is realized by modeling spatial multi-hop relay links.Experimental results demonstrate that the proposed joint UAV relay optimization framework outperforms conventional algorithms in both coverage performance and relay capability during video stream transmission,achieving significant improvements in coverage enhancement and relay efficiency.This work provides technical foundations for constructing high-reliability air-ground cooperative systems in emergency communications.展开更多
Advancements in mode-division multiplexing(MDM)techniques,aimed at surpassing the Shannon limit and augmenting transmission capacity,have garnered significant attention in optical fiber communica-tion,propelling the d...Advancements in mode-division multiplexing(MDM)techniques,aimed at surpassing the Shannon limit and augmenting transmission capacity,have garnered significant attention in optical fiber communica-tion,propelling the demand for high-quality multiplexers and demultiplexers.However,the criteria for ideal-mode multiplexers/demultiplexers,such as performance,scalability,compatibility,and ultra-compactness,have only partially been achieved using conventional bulky devices(e.g.,waveguides,grat-ings,and free space optics)—an issue that will substantially restrict the application of MDM techniques.Here,we present a neuro-meta-router(NMR)optimized through deep learning that achieves spatial multi-mode division and supports multi-channel communication,potentially offering scalability,com-patibility,and ultra-compactness.An MDM communication system based on an NMR is theoretically designed and experimentally demonstrated to enable simultaneous and independent multi-dataset transmission,showcasing a capacity of up to 100 gigabits per second(Gbps)and a symbol error rate down to the order of 104,all achieved without any compensation technologies or correlation devices.Our work presents a paradigm that merges metasurfaces,fiber communications,and deep learning,with potential applications in intelligent metasurface-aided optical interconnection,as well as all-optical pat-tern recognition and classification.展开更多
As an effective strategy to address urban traffic congestion,traffic flow prediction has gained attention from Federated-Learning(FL)researchers due FL’s ability to preserving data privacy.However,existing methods fa...As an effective strategy to address urban traffic congestion,traffic flow prediction has gained attention from Federated-Learning(FL)researchers due FL’s ability to preserving data privacy.However,existing methods face challenges:some are too simplistic to capture complex traffic patterns effectively,and others are overly complex,leading to excessive communication overhead between cloud and edge devices.Moreover,the problem of single point failure limits their robustness and reliability in real-world applications.To tackle these challenges,this paper proposes a new method,CMBA-FL,a Communication-Mitigated and Blockchain-Assisted Federated Learning model.First,CMBA-FL improves the client model’s ability to capture temporal traffic patterns by employing the Encoder-Decoder framework for each edge device.Second,to reduce the communication overhead during federated learning,we introduce a verification method based on parameter update consistency,avoiding unnecessary parameter updates.Third,to mitigate the risk of a single point of failure,we integrate consensus mechanisms from blockchain technology.To validate the effectiveness of CMBA-FL,we assess its performance on two widely used traffic datasets.Our experimental results show that CMBA-FL reduces prediction error by 11.46%,significantly lowers communication overhead,and improves security.展开更多
基金supported by the National Natural Science Foundation of China,No.82101115(to JY)the Wuhan University Independent Innovation Fund Youth Project,No.2042021kf0094(to JY).
文摘Tunneling nanotubes are crucial structures for cellular communication and are observed in a variety of cell types.Glial cells,the most abundant cells in the nervous system,play a vital role in intercellular signaling and can show abnormal activation under pathological conditions.Our bibliometric analysis indicated a substantial increase in research on tunneling nanotubes over the past two decades,highlighting their important role in cellular communication.This review focuses on the formation of tunneling nanotubes in various types of glial cells,including astrocytes,microglia,glioma cells,and Schwann cells,as well as their roles in cellular communication and cargo transport.We found that glial cells influence the stability of the neural system and play a role in nerve regeneration through tunneling nanotubes.Tunneling nanotubes facilitate the transmission and progression of diseases by transporting pathogens and harmful substances.However,they are also involved in alleviating cellular stress by removing toxins and delivering essential nutrients.Understanding the interactions between glial cells through tunneling nanotubes could provide valuable insights into the complex neural networks that govern brain function and responses to injury.
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supported by the National Natural Science Foundation of China,Nos.82171344(to ZY),82471313(to CKT)the Guangdong Basic and Applied Basic Research Foundation,China,Nos.2023B1515120035,2024A1515012035(to CKT)The Science and Technology Projects in Guangzhou Nos.2025A03J4169(to ZY)。
文摘Stroke-induced alterations in cerebral blood flow trigger neurovascular remodeling,as manifested by the blood-brain barrier dysfunction and subs equent neurovascular repair activities such as angiogenesis.This process involves neurovascular communication that facilitates the transport of mediators among cerebrovascular endothelial cells,pericytes,glial cells,and neurons,thereby transmitting signals from donor to recipient cells to elicit a collaborative response.
基金supported by project Emerging Infectious Diseases One Health Basic and Translational Research Actions addressing Unmet Needs on Emerging Infectious Diseases,INF-ACT,Spoke 1 and Spoke 5,Project number PE00000007,CUP B53C20040570005(to PP and DN).
文摘Introduction:One of the main events that regulate a cell’s well-being is cell-to-cell communication.This intercellular mechanism of information transfer is often mediated by vesicular trafficking.Mitochondrial-derived vesicles(MDVs)are an emerging subpopulation of extracellular vesicle(EV)first discovered in 2008 that allow mitochondria to communicate with their surroundings.
基金supported by the National Natural Science Foundation of China(62202215)Liaoning Province Applied Basic Research Program(Youth Special Project,2023JH2/101600038)+4 种基金Shenyang Youth Science and Technology Innovation Talent Support Program(RC220458)Guangxuan Program of Shenyang Ligong University(SYLUGXRC202216)the Basic Research Special Funds for Undergraduate Universities in Liaoning Province(LJ212410144067)the Natural Science Foundation of Liaoning Province(2024-MS-113)the science and technology funds from Liaoning Education Department(LJKZ0242).
文摘In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications.In scenarios where edge servers are sparsely deployed,the lack of coordination and information sharing often leads to load imbalance,thereby increasing system latency.Furthermore,in regions without edge server coverage,tasks must be processed locally,which further exacerbates latency issues.To address these challenges,we propose a novel and efficient Deep Reinforcement Learning(DRL)-based approach aimed at minimizing average task latency.The proposed method incorporates three offloading strategies:local computation,direct offloading to the edge server in local region,and device-to-device(D2D)-assisted offloading to edge servers in other regions.We formulate the task offloading process as a complex latency minimization optimization problem.To solve it,we propose an advanced algorithm based on the Dueling Double Deep Q-Network(D3QN)architecture and incorporating the Prioritized Experience Replay(PER)mechanism.Experimental results demonstrate that,compared with existing offloading algorithms,the proposed method significantly reduces average task latency,enhances user experience,and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads.
基金supported by the National Natural Science Foundation of China,Nos.82404892(to QY),82061160374(to ZZ)the Science and Technology Development Fund,Macao Special Administrative Region,China,Nos.0023/2020/AFJ,0035/2020/AGJ+2 种基金the University of Macao Research Grant,Nos.MYRG2022-00248-ICMS,MYRG-CRG2022-00010-ICMS(to MPMH)the Natural Science Foundation of Guangdong Province,No.2024A1515012818(to ZZ)the Fundamental Research Funds for the Central Universities,No.21623114(to ZZ).
文摘Drug development for Alzheimer’s disease is extremely challenging,as demonstrated by the repeated failures of amyloid-β-targeted therapeutics and the controversies surrounding the amyloid-βcascade hypothesis.More recently,advances in the development of Lecanemab,an anti-amyloid-βmonoclonal antibody,have shown positive results in reducing brain A burden and slowing cognitive decline in patients with early-stage Alzheimer’s disease in the Phase Ⅲ clinical trial(Clarity Alzheimer’s disease).Despite these promising results,side effects such as amyloid-related imaging abnormalities(ARIA)may limit its usage.ARIA can manifest as ARIA-E(cerebral edema or effusions)and ARIA-H(microhemorrhages or superficial siderosis)and is thought to be caused by increased vascular permeability due to inflammatory responses,leading to leakages of blood products and protein-rich fluid into brain parenchyma.Endothelial dysfunction is an early pathological feature of Alzheimer’s disease,and the blood-brain barrier becomes increasingly leaky as the disease progresses.In addition,APOE4,the strongest genetic risk factor for Alzheimer’s disease,is associated with higher vascular amyloid burden,increased ARIA incidence,and accelerated blood-brain barrier disruptions.These interconnected vascular abnormalities highlight the importance of vascular contributions to the pathophysiology of Alzheimer’s disease.Here,we will closely examine recent research evaluating the heterogeneity of brain endothelial cells in the microvasculature of different brain regions and their relationships with Alzheimer’s disease progression.
文摘Founded in September 2020,the International SparkLink Alliance(iSLA)now has approximately 1,200 members in diverse sectors including terminals,homes,vehicles,manufacturing,transportation,finance and healthcare.The iSLA has established a technical standards system for wireless short-range communication covering full-stack standards such as the end-to-end protocol system.
基金supported by the National Natural Science Foundation of China,Nos.82374561(to JD),82174490(to JF)the Medical and Health Science and Technology Program of Zhejiang Province,No.2021RC098(to JD)the Research Project of Zhejiang Chinese Medical University,Nos.2022JKZKTS44(to JD),2022FSYYZZ07(to JF).
文摘Pain is often comorbid with emotional disorders such as anxiety and depression.Hyperexcitability of the anterior cingulate cortex has been implicated in pain and pain-related negative emotions that arise from impairments in inhibitory gamma-aminobutyric acid neurotransmission.This review primarily aims to outline the main circuitry(including the input and output connectivity)of the anterior cingulate cortex and classification and functions of different gamma-aminobutyric acidergic neurons;it also describes the neurotransmitters/neuromodulators affecting these neurons,their intercommunication with other neurons,and their importance in mental comorbidities associated with chronic pain disorders.Improving understanding on their role in pain-related mental comorbidities may facilitate the development of more effective treatments for these conditions.However,the mechanisms that regulate gamma-aminobutyric acidergic systems remain elusive.It is also unclear as to whether the mechanisms are presynaptic or postsynaptic.Further exploration of the complexities of this system may reveal new pathways for research and drug development.
基金supported by the National Key R&D Program of China(2021YFF1200602)the National Science Fund for Excellent Overseas Scholars(0401260011)+3 种基金the National Defense Science and Technology Innovation Fund of Chinese Academy of Sciences(c02022088)the Tianjin Science and Technology Program(20JCZDJC00810)the National Natural Science Foundation of China(82202798)the Shanghai Sailing Program(22YF1404200).
文摘Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BCIs,including their fundamental principles,technical advancements,and applications in specific domains.However,these reviews often focus on signal processing,hardware development,or limited applications such as motor rehabilitation or communication.This paper aims to offer a comprehensive review of recent electroencephalogram(EEG)-based BCI applications in the medical field across 8 critical areas,encompassing rehabilitation,daily communication,epilepsy,cerebral resuscitation,sleep,neurodegenerative diseases,anesthesiology,and emotion recognition.Moreover,the current challenges and future trends of BCIs were also discussed,including personal privacy and ethical concerns,network security vulnerabilities,safety issues,and biocompatibility.
文摘This paper proposes a novel modified uni-traveling-carrier photodiode(MUTC-PD)featuring an electric field regulation layer:a p-type doped thin layer inserted behind the PD’s n-doped cliff layer.This electric field regulation layer enhances the PD’s performance by not only reducing and smoothing the electric field intensity in the collector layer,allowing photo-generated electrons to transit at peak drift velocity,but also improving the electric field intensity in the depleted absorber layer and optimizing the photo-generated carriers’saturated transit performance.Additionally,the transport characteristics of the peak drift velocity of photogenerated electrons in the device’s collection layer can be used to optimize its parasitic characteristics.The electron’s peak drift velocity compensates for the lost transit time.Thus improving the 3 dB bandwidth of the PD’s photo response.Finally obtains a MUTC-PD with a 3 dB bandwidth of 68 GHz at a responsivity of 0.502 A/W,making it suitable for 100 Gbit/s optical receivers.
基金supported in part by the National Natural Science Foundation of China under Grant 62371181in part by the Changzhou Science and Technology International Cooperation Program under Grant CZ20230029+1 种基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2021R1A2B5B02087169)supported under the framework of international cooperation program managed by the National Research Foundation of Korea(2022K2A9A1A01098051)。
文摘The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.
基金National Natural Science Foundation of China(U2468201,62122012,62221001).
文摘The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport industry must innovate in key technologies to ensure high-quality transmissions for passengers and railway operations.These systems must function effectively under high mobility conditions while prioritizing safety,ecofriendliness,comfort,transparency,predictability,and reliability.On the other hand,the proposal of 6 G wireless technology introduces new possibilities for innovation in communication technologies,which may truly realize the current vision of HSR.Therefore,this article gives a review of the current advanced 6 G wireless communication technologies for HSR,including random access and switching,channel estimation and beamforming,integrated sensing and communication,and edge computing.The main application scenarios of these technologies are reviewed,as well as their current research status and challenges,followed by an outlook on future development directions.
文摘Wireless networks support numerous terminals,manage large data volumes,and provide diverse services,but the vulnerability to environmental changes leads to increased complexity and costs.Situational awareness has been widely applied in network management,but existing methods fail to find optimal solutions due to the high heterogeneity of base stations,numerous metrics,and complex intercell dependencies.To address this gap,this paper proposes a specialized framework for wireless networks,integrating an evaluation model and control approach.The framework expands the indicator set into four key areas,introduces an evaluation method,and proposes the indicator perturbation greedy(IPG)algorithm and the adjustment scheme selection method based on damping coefficient(DCSS)for effective network optimization.A case study in an urban area demonstrates the framework’s ability to balance and improve network performance,enhancing situational awareness and operational efficiency under dynamic conditions.
基金supported by the National Geographic Society,the University of California Los Angeles(Faculty Senate and Division of Life Sciences)an RMBL research fellowship and the U.S.National Science Foundation(NSF IDBR-0754247 and DEB-1119660 and 1557130 to D.T.B.,as well as DBI 0242960,07211346 and 1226713 to RMBL).
文摘The nonlinearity and fear hypothesis predicts that highly aroused vocal mammals and birds produce vocalizations(notably alarm calls and screams)which contain a variety of nonlinear phenomena(NLP).Such vocalizations often sound“noisy”because vocal production systems are over-blown when animals are highly aroused.While much is known about the conditions under which animals produce vocalizations containing NLP and how species respond to them,there is little research about the heritability of such behavioral traits.Using the quantitative genetic animal model,we estimated the genetic basis of“noise”in alarm calls produced by females and found significant heritability in call entropy-our measure of the noisiness.About 9%of the variance in noisiness can be accounted for by genetic differences.Taken together,these findings suggest that the degree to which marmots produce noisy calls is modestly heritable and can be thus subject to further evolution via natural selection.
基金supported in part by the National Key Research and Development Program of China under Grant No.2024YFE0200600the Zhejiang Provincial Natural Science Foundation of China under Grant No.LR23F010005the Huawei Cooperation Project under Grant No.TC20240829036。
文摘Along with the proliferating research interest in semantic communication(Sem Com),joint source channel coding(JSCC)has dominated the attention due to the widely assumed existence in efficiently delivering information semantics.Nevertheless,this paper challenges the conventional JSCC paradigm and advocates for adopting separate source channel coding(SSCC)to enjoy a more underlying degree of freedom for optimization.We demonstrate that SSCC,after leveraging the strengths of the Large Language Model(LLM)for source coding and Error Correction Code Transformer(ECCT)complemented for channel coding,offers superior performance over JSCC.Our proposed framework also effectively highlights the compatibility challenges between Sem Com approaches and digital communication systems,particularly concerning the resource costs associated with the transmission of high-precision floating point numbers.Through comprehensive evaluations,we establish that assisted by LLM-based compression and ECCT-enhanced error correction,SSCC remains a viable and effective solution for modern communication systems.In other words,separate source channel coding is still what we need.
基金supported by the National Key R&D Program of China under Grant 2024YFE0203500Xiaomi Young Talents Program。
文摘In the applications such as food production,the environmental temperature should be measured continuously dur-ing the entire process,which requires an ultra-low-power temperature sensor for long-termly monitoring.Conventional tempera-ture sensors trade the measurement accuracy with power consumption.In this work,we present a battery-free wireless tempera-ture sensing chip for long-termly monitoring during food production.A calibrated oscillator-based CMOS temperature sensor is proposed instead of the ADC-based power-hungry circuits in conventional works.In addition,the sensor chip can harvest the power transferred by a remote reader to eliminate the use of battery.Meanwhile,the system conducts wireless bidirectional communication between the sensor chip and reader.In this way,the temperature sensor can realize both a high precision and battery-free operation.The temperature sensing chip is fabricated in 55 nm CMOS process,and the reader chip is imple-mented in 65 nm CMOS technology.Experimental results show that the temperature measurement error achieves±1.6℃ from 25 to 50℃,with battery-free readout by a remote reader.
文摘The rapid evolution of Fifth-Generation(5G)networks and the strategic development of Sixth-Generation(6G)technologies have significantly advanced the implementation of air-ground integrated networks with seamless coverage.Unmanned Aerial Vehicles(UAVs),serving as high-mobility aerial platforms,are extensively utilized to enhance coverage in long-distance emergency communication scenarios.The resource-constrained communication environments in emergencies by classifying UAVs into swarm UAVs and relay UAVs as aerial communication nodes is inversitgated.A horizontal deployment strategy for swarm UAVs is formulated through K-means clustering algorithm optimization,while a vertical deployment scheme is established using convex optimization methods.The minimum-path trajectory planning for relay UAVs is optimized via the Particle Swarm Optimization(PSO)algorithm,enhancing communication reliability between UAV swarms and terrestrial base stations.A three-dimensional heterogeneous network architecture is realized by modeling spatial multi-hop relay links.Experimental results demonstrate that the proposed joint UAV relay optimization framework outperforms conventional algorithms in both coverage performance and relay capability during video stream transmission,achieving significant improvements in coverage enhancement and relay efficiency.This work provides technical foundations for constructing high-reliability air-ground cooperative systems in emergency communications.
基金supported by the National Key Research and Development Program of China(2023YFB2804704)the National Natural Science Foundation of China(12174292,12374278,and 62105250).
文摘Advancements in mode-division multiplexing(MDM)techniques,aimed at surpassing the Shannon limit and augmenting transmission capacity,have garnered significant attention in optical fiber communica-tion,propelling the demand for high-quality multiplexers and demultiplexers.However,the criteria for ideal-mode multiplexers/demultiplexers,such as performance,scalability,compatibility,and ultra-compactness,have only partially been achieved using conventional bulky devices(e.g.,waveguides,grat-ings,and free space optics)—an issue that will substantially restrict the application of MDM techniques.Here,we present a neuro-meta-router(NMR)optimized through deep learning that achieves spatial multi-mode division and supports multi-channel communication,potentially offering scalability,com-patibility,and ultra-compactness.An MDM communication system based on an NMR is theoretically designed and experimentally demonstrated to enable simultaneous and independent multi-dataset transmission,showcasing a capacity of up to 100 gigabits per second(Gbps)and a symbol error rate down to the order of 104,all achieved without any compensation technologies or correlation devices.Our work presents a paradigm that merges metasurfaces,fiber communications,and deep learning,with potential applications in intelligent metasurface-aided optical interconnection,as well as all-optical pat-tern recognition and classification.
基金supported by the National Natural Science Foundation of China under Grant No.U20A20182.
文摘As an effective strategy to address urban traffic congestion,traffic flow prediction has gained attention from Federated-Learning(FL)researchers due FL’s ability to preserving data privacy.However,existing methods face challenges:some are too simplistic to capture complex traffic patterns effectively,and others are overly complex,leading to excessive communication overhead between cloud and edge devices.Moreover,the problem of single point failure limits their robustness and reliability in real-world applications.To tackle these challenges,this paper proposes a new method,CMBA-FL,a Communication-Mitigated and Blockchain-Assisted Federated Learning model.First,CMBA-FL improves the client model’s ability to capture temporal traffic patterns by employing the Encoder-Decoder framework for each edge device.Second,to reduce the communication overhead during federated learning,we introduce a verification method based on parameter update consistency,avoiding unnecessary parameter updates.Third,to mitigate the risk of a single point of failure,we integrate consensus mechanisms from blockchain technology.To validate the effectiveness of CMBA-FL,we assess its performance on two widely used traffic datasets.Our experimental results show that CMBA-FL reduces prediction error by 11.46%,significantly lowers communication overhead,and improves security.
