In the sixth generation mobile communication(6G) system,Non-Terrestrial Networks(NTN),as a supplement to terrestrial network,can meet the requirements of wide area intelligent connection and global ubiquitous seamless...In the sixth generation mobile communication(6G) system,Non-Terrestrial Networks(NTN),as a supplement to terrestrial network,can meet the requirements of wide area intelligent connection and global ubiquitous seamless access,establish intelligent connection for wide area objects,and provide intelligent services.Due to issues such as massive access,doppler shift,and limited spectrum resources in NTN,research on resource management is crucial for optimizing NTN performance.In this paper,a comprehensive survey of multi-pattern heterogeneous NTN resource management is provided.Firstly,the key technologies involved in NTN resource management is summarized.Secondly,NTN resource management is discussed from network pattern and resource pattern.The network pattern focuses on the application of different optimization methods to different network dimension communication resource management,and the resource type pattern focuses on the research and application of multi-domain resource management such as computation,cache,communication and sensing.Finally,future research directions and challenges of 6G NTN resource management are discussed.展开更多
From fifth-generation(5G)communication technology onward,non-terrestrial networks(NTNs)have emerged as a key component of future network architectures.Especially through the rise of low-Earth-orbit satellite constella...From fifth-generation(5G)communication technology onward,non-terrestrial networks(NTNs)have emerged as a key component of future network architectures.Especially through the rise of low-Earth-orbit satellite constellations,NTNs enable a space Internet and present a paradigm shift in delivering reliable services to even the most remote regions on Earth.However,the extensive coverage and rapid movement of satellites pose unique challenges in user equipment access and inter-satellite transmission,impacting the quality of service and service continuity.This paper offers an in-depth review of NTN networking technologies in the context of six-generation(6G)mobile networks evolution,focusing on access management,satellite mobility,and hetero-network slicing.Building on this foundation and considering the latest trends in NTN development,we then present innovative perspectives on emerging challenges,including satellite beamforming,handover mechanisms,and service delivery.Lastly,we identify key open research areas and propose future directions to improve NTN performance and accelerate satellite Internet deployment.展开更多
With the widespread application of com-munication technology in the non-terrestrial network(NTN),the issue of the insecure communication due to the inherent openness of the NTN is increasingly being recognized.Consequ...With the widespread application of com-munication technology in the non-terrestrial network(NTN),the issue of the insecure communication due to the inherent openness of the NTN is increasingly being recognized.Consequently,safeguarding com-munication information in the NTN has emerged as a critical challenge.To address this issue,we pro-pose a beamforming and horizontal trajectory joint op-timization method for unmanned aerial vehicle(UAV)covert communications in the NTN.First,we formu-late an optimization problem that considers constraints such as the transmitting power and the distance.More-over,we employ the integrated communication and jamming(ICAJ)signal as Alice’s transmitting signal,further protecting the content of communication in-formation.Next,we construct two subproblems,and we propose an alternate optimization(AO)algorithm based on quadratic transform and penalty term method to solve the proposed two subproblems.Simulation re-sults demonstrate that the proposed method is effective and has better performance than benchmarks.展开更多
With the coming of digital era,profound changes are happening in communication field.Within these,non-terrestrial network(NTN)is considered as a leading-edge technology.NTN not only represents an innovation,but also s...With the coming of digital era,profound changes are happening in communication field.Within these,non-terrestrial network(NTN)is considered as a leading-edge technology.NTN not only represents an innovation,but also signifies the main development trend of future global communication.As a layered heterogeneous network,NTN will integrate multiple communication platforms,including satellites,high altitude platform systems(HAPS),and unmanned aerial systems(UAS),these provide flexible and composable solutions for achieving ubiquitous global communication coverage.展开更多
With the advent of the digital era,the field of communications is undergoing profound transformation.Among the most groundbreaking developments is the emergence of non-terrestrial networks(NTN),which represent not onl...With the advent of the digital era,the field of communications is undergoing profound transformation.Among the most groundbreaking developments is the emergence of non-terrestrial networks(NTN),which represent not only a technological leap forward but also a major trend shaping the future of global connectivity.As a layered heterogeneous network,NTN integrates multiple aerial platforms—including satellites,high-altitude platform systems(HAPS),and unmanned aerial systems(UAS)—to provide flexible and composable solutions aimed at achieving seamless worldwide communication coverage.展开更多
In the upcoming sixth-generation(6G)era,the demand for constructing a wide-area time-sensitive Internet of Things(IoT)continues to increase.As conventional cellular technologies are difficult to directly use for wide-...In the upcoming sixth-generation(6G)era,the demand for constructing a wide-area time-sensitive Internet of Things(IoT)continues to increase.As conventional cellular technologies are difficult to directly use for wide-area time-sensitive IoT,it is beneficial to use non-terrestrial infrastructures,including satellites and unmanned aerial vehicles(UAVs).Thus,we can build a non-terrestrial network(NTN)using a cell-free architecture.Driven by the time-sensitive requirements and uneven distribution of IoT devices,the NTN must be empowered using mobile edge computing(MEC)while providing oasisoriented on-demand coverage for devices.Nevertheless,communication and MEC systems are coupled with each other under the influence of a complex propagation environment in the MEC-empowered NTN,which makes it difficult to coordinate the resources.In this study,we propose a process-oriented framework to design communication and MEC systems in a time-division manner.In this framework,large-scale channel state information(CSI)is used to characterize the complex propagation environment at an affordable cost,where a nonconvex latency minimization problem is formulated.Subsequently,the approximated problem is provided,and it can be decomposed into sub-problems.These sub-problems are then solved iteratively.The simulation results demonstrated the superiority of the proposed process-oriented scheme over other algorithms,implied that the payload deployments of UAVs should be appropriately predesigned to improve the efficiency of using resources,and confirmed that it is advantageous to integrate NTN with MEC for wide-area time-sensitive IoT.展开更多
Complicated radio resource management,e.g.,handover condition,will trouble the user in non-terrestrial networks due to the impact of high mobility and hierarchical layouts which co-exist with terrestrial networks or v...Complicated radio resource management,e.g.,handover condition,will trouble the user in non-terrestrial networks due to the impact of high mobility and hierarchical layouts which co-exist with terrestrial networks or various platforms at different altitudes.It is necessary to optimize the handover strategy to reduce the signaling overhead and im⁃prove the service continuity.In this paper,a new handover strategy is proposed based on the convolutional neural network.Firstly,the handover process is modeled as a directed graph.Suppose a user knows its future signal strength,then he/she can search for the best handover strategy based on the graph.Secondly,a convolutional neural network is used to extract the underlying regularity of the best handover strategies of different users,based on which any user can make near-optimal handover decisions according to its historical signal strength.Numerical simulation shows that the proposed handover strategy can effi⁃ciently reduce the handover number while ensuring the signal strength.展开更多
In the advent of the 6G era, non-terrestrial networks (NTN) with expansive coverage are being increasingly recognized as a vital supplement to cellular networks for facilitating seamless communication. The intricate i...In the advent of the 6G era, non-terrestrial networks (NTN) with expansive coverage are being increasingly recognized as a vital supplement to cellular networks for facilitating seamless communication. The intricate interplay between network performance and service quality necessitates a thorough investigation into the modeling and analysis of services for efficient construction of NTN.Previous studies on service analysis, predominantly focused on terrestrial networks,fall short in addressing the unique challenges posed by NTN,particularly those related to platform distribution and antenna gain modeling. This deficiency in research,coupled with the varying preferences of users for different network types, forms the basis of this study. This paper explores the spatio-temporal characteristics of services within a multi-layered NTN framework.In this context,the spatial distribution of the platforms is modeled using a binomial point process, and the antennas are characterized by a sectorized beam pattern. We derive the closed-form expressions for the association probability,the number of accessed users, and the arrival rate of services with certain delay requirements towards different types of NTN. Simulation results are provided to evaluate the influence of various parameters on the association probability, the number of accessed users, and the total arrival rate of services. The number of satellites can be determined to achieve the optimal system utility,balancing the accessed services, offloading effects, and launching costs. This initial investigation lays the groundwork for further theoretical progress in the service analysis and platform deployment of NTN.展开更多
BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes tha...BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes that may relate to NSSI through distinct psychological mechanisms.However,how these subtypes interact with specific NSSI behaviors remains unclear.AIM To examine associations between rumination subtypes and specific NSSI behaviors in adolescents.METHODS We conducted a cross-sectional study with 305 hospitalized adolescents diagnosed with depressive disorders.The subjects ranged from 12-18 years in age.Rumi-nation subtypes were assessed using the Ruminative Response Scale,and 12 NSSI behaviors were evaluated using a validated questionnaire.Network analysis was applied to explore symptom-level associations and identify central symptoms.RESULTS The network analysis revealed close connections between rumination subtypes and NSSI behaviors.Brooding was linked to behaviors such as hitting objects and burning.Scratching emerged as the most influential NSSI symptom.Symptomfocused rumination served as a key bridge connecting rumination and NSSI.CONCLUSION Symptom-focused rumination and scratching were identified as potential intervention targets.These findings highlight the psychological significance of specific cognitive-behavioral links in adolescent depression and suggest directions for tailored prevention and treatment.However,the cross-sectional,single-site design limits causal inference and generalizability.Future longitudinal and multi-center studies are needed to confirm causal pathways and verify the generalizability of the findings to broader adolescent populations.展开更多
Background:Wenqing Yin(WQY)is a classic prescription used to treat skin diseases like atopic dermatitis(AD)in China,and the aim of this study is to investigate the therapeutic effects and molecular mechanisms of WQY o...Background:Wenqing Yin(WQY)is a classic prescription used to treat skin diseases like atopic dermatitis(AD)in China,and the aim of this study is to investigate the therapeutic effects and molecular mechanisms of WQY on AD.Methods:The DNFB-induced mouse models of AD were established to investigate the therapeutic effects of WQY on AD.The symptoms of AD in the ears and backs of the mice were assessed,while inflammatory factors in the ear were quantified using quantitative real-time-polymerase chain reaction(qRT-PCR),and the percentages of CD4^(+)and CD8^(+)cells in the spleen were analyzed through flow cytometry.The compounds in WQY were identified using ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)analysis and the key targets and pathways of WQY to treat AD were predicted by network pharmacology.Subsequently,the key genes were tested and verified by qRT-PCR,and the potential active components and target proteins were verified by molecular docking.Results:WQY relieved the AD symptoms and histopathological injuries in the ear and back skin of mice with AD.Meanwhile,WQY significantly reduced the levels of inflammatory factors IL-6 and IL-1βin ear tissue,as well as the ratio of CD4^(+)/CD8^(+)cells in spleen.Additionally,a total of 142 compounds were identified from the water extract of WQY by UPLC-Orbitrap-MS/MS.39 key targets related to AD were screened out by network pharmacology methods.The KEGG analysis indicated that the effects of WQY were primarily mediated through pathways associated with Toll-like receptor signaling and T cell receptor signaling.Moreover,the results of qRT-PCR demonstrated that WQY significantly reduced the mRNA expressions of IL-4,IL-10,GATA3 and FOXP3,and molecular docking simulation verified that the active components of WQY had excellent binding abilities with IL-4,IL-10,GATA3 and FOXP3 proteins.Conclusion:The present study demonstrated that WQY effectively relieved AD symptoms in mice,decreased the inflammatory factors levels,regulated the balance of CD4^(+)and CD8^(+)cells,and the mechanism may be associated with the suppression of Th2 and Treg cell immune responses.展开更多
This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for ...This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels.展开更多
Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the u...Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.展开更多
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in us...Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.展开更多
Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This st...Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.展开更多
Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay di...Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.展开更多
Network pharmacology has gained widespread application in drug discovery,particularly in traditional Chinese medicine(TCM)research,which is characterized by its“multi-component,multi-target,and multi-pathway”nature....Network pharmacology has gained widespread application in drug discovery,particularly in traditional Chinese medicine(TCM)research,which is characterized by its“multi-component,multi-target,and multi-pathway”nature.Through the integration of network biology,TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms,establishing a novel research paradigm for TCM modernization.The rapid advancement of machine learning,particularly revolutionary deep learning methods,has substantially enhanced artificial intelligence(AI)technology,offering significant potential to advance TCM network pharmacology research.This paper describes the methodology of TCM network pharmacology,encompassing ingredient identification,network construction,network analysis,and experimental validation.Furthermore,it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods.Finally,it addresses challenges and future directions regarding cell-cell communication(CCC)-based network construction,analysis,and validation,providing valuable insights for TCM network pharmacology.展开更多
Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single ...Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 62225103,U22B2003,U2441227,and U24A20211the Beijing Natural Science Foundation under Grant L241008+3 种基金the Defense Industrial Technology Development Program JCKY2022110C010the National Key Laboratory of Wireless Communications Foundation under Grant IFN20230201the Fundamental Research Funds for the Central Universities under Grant FRFTP-22-002C2the Xiaomi Fund of Young Scholar。
文摘In the sixth generation mobile communication(6G) system,Non-Terrestrial Networks(NTN),as a supplement to terrestrial network,can meet the requirements of wide area intelligent connection and global ubiquitous seamless access,establish intelligent connection for wide area objects,and provide intelligent services.Due to issues such as massive access,doppler shift,and limited spectrum resources in NTN,research on resource management is crucial for optimizing NTN performance.In this paper,a comprehensive survey of multi-pattern heterogeneous NTN resource management is provided.Firstly,the key technologies involved in NTN resource management is summarized.Secondly,NTN resource management is discussed from network pattern and resource pattern.The network pattern focuses on the application of different optimization methods to different network dimension communication resource management,and the resource type pattern focuses on the research and application of multi-domain resource management such as computation,cache,communication and sensing.Finally,future research directions and challenges of 6G NTN resource management are discussed.
基金supported by the National Research Foundation,Singapore and Infocomm Media Development Authority under its Future Communications Research&Development Programme.
文摘From fifth-generation(5G)communication technology onward,non-terrestrial networks(NTNs)have emerged as a key component of future network architectures.Especially through the rise of low-Earth-orbit satellite constellations,NTNs enable a space Internet and present a paradigm shift in delivering reliable services to even the most remote regions on Earth.However,the extensive coverage and rapid movement of satellites pose unique challenges in user equipment access and inter-satellite transmission,impacting the quality of service and service continuity.This paper offers an in-depth review of NTN networking technologies in the context of six-generation(6G)mobile networks evolution,focusing on access management,satellite mobility,and hetero-network slicing.Building on this foundation and considering the latest trends in NTN development,we then present innovative perspectives on emerging challenges,including satellite beamforming,handover mechanisms,and service delivery.Lastly,we identify key open research areas and propose future directions to improve NTN performance and accelerate satellite Internet deployment.
基金supported in part by the National Natural Science Foundation of China under Grant U2441250 and 62231027in part by Natural Science Basic Research Programof Shaanxi under Grant 2024JC-JCQN-63+2 种基金in part by InnovationCapability Support Program of Shaanxi under Grant2024RS-CXTD-01in part by New Technology Research University Cooperation Project under Grant SKX242010031in part by the FundamentalResearch Funds for the Central Universities and theInnovation Fund of Xidian University under GrantYJSJ25007.
文摘With the widespread application of com-munication technology in the non-terrestrial network(NTN),the issue of the insecure communication due to the inherent openness of the NTN is increasingly being recognized.Consequently,safeguarding com-munication information in the NTN has emerged as a critical challenge.To address this issue,we pro-pose a beamforming and horizontal trajectory joint op-timization method for unmanned aerial vehicle(UAV)covert communications in the NTN.First,we formu-late an optimization problem that considers constraints such as the transmitting power and the distance.More-over,we employ the integrated communication and jamming(ICAJ)signal as Alice’s transmitting signal,further protecting the content of communication in-formation.Next,we construct two subproblems,and we propose an alternate optimization(AO)algorithm based on quadratic transform and penalty term method to solve the proposed two subproblems.Simulation re-sults demonstrate that the proposed method is effective and has better performance than benchmarks.
文摘With the coming of digital era,profound changes are happening in communication field.Within these,non-terrestrial network(NTN)is considered as a leading-edge technology.NTN not only represents an innovation,but also signifies the main development trend of future global communication.As a layered heterogeneous network,NTN will integrate multiple communication platforms,including satellites,high altitude platform systems(HAPS),and unmanned aerial systems(UAS),these provide flexible and composable solutions for achieving ubiquitous global communication coverage.
文摘With the advent of the digital era,the field of communications is undergoing profound transformation.Among the most groundbreaking developments is the emergence of non-terrestrial networks(NTN),which represent not only a technological leap forward but also a major trend shaping the future of global connectivity.As a layered heterogeneous network,NTN integrates multiple aerial platforms—including satellites,high-altitude platform systems(HAPS),and unmanned aerial systems(UAS)—to provide flexible and composable solutions aimed at achieving seamless worldwide communication coverage.
基金the National Key R&D Program of China(2018YFA0701601 and 2020YFA0711301)the National Natural Science Foundation of China(61771286,61941104,and 61922049)the Tsinghua University-China Mobile Communications Group Co.,Ltd.Joint Institute.
文摘In the upcoming sixth-generation(6G)era,the demand for constructing a wide-area time-sensitive Internet of Things(IoT)continues to increase.As conventional cellular technologies are difficult to directly use for wide-area time-sensitive IoT,it is beneficial to use non-terrestrial infrastructures,including satellites and unmanned aerial vehicles(UAVs).Thus,we can build a non-terrestrial network(NTN)using a cell-free architecture.Driven by the time-sensitive requirements and uneven distribution of IoT devices,the NTN must be empowered using mobile edge computing(MEC)while providing oasisoriented on-demand coverage for devices.Nevertheless,communication and MEC systems are coupled with each other under the influence of a complex propagation environment in the MEC-empowered NTN,which makes it difficult to coordinate the resources.In this study,we propose a process-oriented framework to design communication and MEC systems in a time-division manner.In this framework,large-scale channel state information(CSI)is used to characterize the complex propagation environment at an affordable cost,where a nonconvex latency minimization problem is formulated.Subsequently,the approximated problem is provided,and it can be decomposed into sub-problems.These sub-problems are then solved iteratively.The simulation results demonstrated the superiority of the proposed process-oriented scheme over other algorithms,implied that the payload deployments of UAVs should be appropriately predesigned to improve the efficiency of using resources,and confirmed that it is advantageous to integrate NTN with MEC for wide-area time-sensitive IoT.
文摘Complicated radio resource management,e.g.,handover condition,will trouble the user in non-terrestrial networks due to the impact of high mobility and hierarchical layouts which co-exist with terrestrial networks or various platforms at different altitudes.It is necessary to optimize the handover strategy to reduce the signaling overhead and im⁃prove the service continuity.In this paper,a new handover strategy is proposed based on the convolutional neural network.Firstly,the handover process is modeled as a directed graph.Suppose a user knows its future signal strength,then he/she can search for the best handover strategy based on the graph.Secondly,a convolutional neural network is used to extract the underlying regularity of the best handover strategies of different users,based on which any user can make near-optimal handover decisions according to its historical signal strength.Numerical simulation shows that the proposed handover strategy can effi⁃ciently reduce the handover number while ensuring the signal strength.
基金supported by the National Natural Science Foundation of China under Grant 62271168in part by the Key Research and Development Program of Heilongjiang Province under Grant JD22A001.
文摘In the advent of the 6G era, non-terrestrial networks (NTN) with expansive coverage are being increasingly recognized as a vital supplement to cellular networks for facilitating seamless communication. The intricate interplay between network performance and service quality necessitates a thorough investigation into the modeling and analysis of services for efficient construction of NTN.Previous studies on service analysis, predominantly focused on terrestrial networks,fall short in addressing the unique challenges posed by NTN,particularly those related to platform distribution and antenna gain modeling. This deficiency in research,coupled with the varying preferences of users for different network types, forms the basis of this study. This paper explores the spatio-temporal characteristics of services within a multi-layered NTN framework.In this context,the spatial distribution of the platforms is modeled using a binomial point process, and the antennas are characterized by a sectorized beam pattern. We derive the closed-form expressions for the association probability,the number of accessed users, and the arrival rate of services with certain delay requirements towards different types of NTN. Simulation results are provided to evaluate the influence of various parameters on the association probability, the number of accessed users, and the total arrival rate of services. The number of satellites can be determined to achieve the optimal system utility,balancing the accessed services, offloading effects, and launching costs. This initial investigation lays the groundwork for further theoretical progress in the service analysis and platform deployment of NTN.
基金Supported by Key Research and Development Program of Shaanxi Province,China,No.2024SF-YBXM-078.
文摘BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes that may relate to NSSI through distinct psychological mechanisms.However,how these subtypes interact with specific NSSI behaviors remains unclear.AIM To examine associations between rumination subtypes and specific NSSI behaviors in adolescents.METHODS We conducted a cross-sectional study with 305 hospitalized adolescents diagnosed with depressive disorders.The subjects ranged from 12-18 years in age.Rumi-nation subtypes were assessed using the Ruminative Response Scale,and 12 NSSI behaviors were evaluated using a validated questionnaire.Network analysis was applied to explore symptom-level associations and identify central symptoms.RESULTS The network analysis revealed close connections between rumination subtypes and NSSI behaviors.Brooding was linked to behaviors such as hitting objects and burning.Scratching emerged as the most influential NSSI symptom.Symptomfocused rumination served as a key bridge connecting rumination and NSSI.CONCLUSION Symptom-focused rumination and scratching were identified as potential intervention targets.These findings highlight the psychological significance of specific cognitive-behavioral links in adolescent depression and suggest directions for tailored prevention and treatment.However,the cross-sectional,single-site design limits causal inference and generalizability.Future longitudinal and multi-center studies are needed to confirm causal pathways and verify the generalizability of the findings to broader adolescent populations.
基金supported by grants from the National Natural Science Foundation of China(82004252)the Project of Administration of Traditional Chinese Medicine of Guangdong Province(202405112017596500)the Basic and Applied Basic Research Foundation of Guangzhou Municipal Science and Technology Bureau(202102020533).
文摘Background:Wenqing Yin(WQY)is a classic prescription used to treat skin diseases like atopic dermatitis(AD)in China,and the aim of this study is to investigate the therapeutic effects and molecular mechanisms of WQY on AD.Methods:The DNFB-induced mouse models of AD were established to investigate the therapeutic effects of WQY on AD.The symptoms of AD in the ears and backs of the mice were assessed,while inflammatory factors in the ear were quantified using quantitative real-time-polymerase chain reaction(qRT-PCR),and the percentages of CD4^(+)and CD8^(+)cells in the spleen were analyzed through flow cytometry.The compounds in WQY were identified using ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)analysis and the key targets and pathways of WQY to treat AD were predicted by network pharmacology.Subsequently,the key genes were tested and verified by qRT-PCR,and the potential active components and target proteins were verified by molecular docking.Results:WQY relieved the AD symptoms and histopathological injuries in the ear and back skin of mice with AD.Meanwhile,WQY significantly reduced the levels of inflammatory factors IL-6 and IL-1βin ear tissue,as well as the ratio of CD4^(+)/CD8^(+)cells in spleen.Additionally,a total of 142 compounds were identified from the water extract of WQY by UPLC-Orbitrap-MS/MS.39 key targets related to AD were screened out by network pharmacology methods.The KEGG analysis indicated that the effects of WQY were primarily mediated through pathways associated with Toll-like receptor signaling and T cell receptor signaling.Moreover,the results of qRT-PCR demonstrated that WQY significantly reduced the mRNA expressions of IL-4,IL-10,GATA3 and FOXP3,and molecular docking simulation verified that the active components of WQY had excellent binding abilities with IL-4,IL-10,GATA3 and FOXP3 proteins.Conclusion:The present study demonstrated that WQY effectively relieved AD symptoms in mice,decreased the inflammatory factors levels,regulated the balance of CD4^(+)and CD8^(+)cells,and the mechanism may be associated with the suppression of Th2 and Treg cell immune responses.
基金supported by Istanbul Technical University(Project No.45698)supported through the“Young Researchers’Career Development Project-training of doctoral students”of the Croatian Science Foundation.
文摘This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels.
文摘Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
文摘Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.
基金Supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004)Supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-00155885,Artificial Intelligence Convergence Innovation Human Resources Development(Hanyang University ERICA)).
文摘Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.
文摘Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.
基金supported by the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(No.2024C03106,X.F.)the National Natural Science Foundation of China(No.82474160,X.S.)+2 种基金the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(No.LBZ24H270001,X.P.)the Major Joint Projects Supported by the National Administration of TCM and Zhejiang Province(No.GZY-ZI-KJ-23037,X.P.)the Ningbo Top Medical and Health Research Program(No.2022030309,X.P.)。
文摘Network pharmacology has gained widespread application in drug discovery,particularly in traditional Chinese medicine(TCM)research,which is characterized by its“multi-component,multi-target,and multi-pathway”nature.Through the integration of network biology,TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms,establishing a novel research paradigm for TCM modernization.The rapid advancement of machine learning,particularly revolutionary deep learning methods,has substantially enhanced artificial intelligence(AI)technology,offering significant potential to advance TCM network pharmacology research.This paper describes the methodology of TCM network pharmacology,encompassing ingredient identification,network construction,network analysis,and experimental validation.Furthermore,it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods.Finally,it addresses challenges and future directions regarding cell-cell communication(CCC)-based network construction,analysis,and validation,providing valuable insights for TCM network pharmacology.
文摘Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
