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
In this work, we present an evaluation of the performance and error robustness of RTP-based broadcast streaming of high-quality high-definition (HD) H.264/AVC video. Using a fully controlled IP test bed (Hillestad et ...In this work, we present an evaluation of the performance and error robustness of RTP-based broadcast streaming of high-quality high-definition (HD) H.264/AVC video. Using a fully controlled IP test bed (Hillestad et al., 2005), we broadcast high-definition video over RTP/UDP, and use an IP network emulator to introduce a varying amount of randomly distributed packet loss. A high-performance network interface monitoring card is used to capture the video packets into a trace file. Purpose-built software parses the trace file, analyzes the RTP stream and assembles the correctly received NAL units into an H.264/AVC Annex B byte stream file, which is subsequently decoded by JVT JM 10.1 reference software. The proposed measurement setup is a novel, practical and intuitive approach to perform error resilience testing of real-world H.264/AVC broadcast applications. Through a series of experiments, we evaluate some of the error resilience features of the H.264/AVC standard, and see how they perform at packet loss rates from 0.01% to 5%. The results confirmed that an appropriate slice partitioning scheme is essential to have a graceful degradation in received quality in the case of packet loss. While flexible macroblock ordering reduces the compression efficiency about 1 dB for our test material, reconstructed video quality is improved for loss rates above 0.25%.展开更多
While in the past the robustness of transportation networks was studied considering the cyber and physical space as isolated environments this is no longer the case.Integrating the Internet of Things devices in the se...While in the past the robustness of transportation networks was studied considering the cyber and physical space as isolated environments this is no longer the case.Integrating the Internet of Things devices in the sensing area of transportation infrastructure has resulted in ubiquitous cyber-physical systems and increasing interdependen-cies between the physical and cyber networks.As a result,the robustness of transportation networks relies on the uninterrupted serviceability of physical and cyber networks.Current studies on interdependent networks overlook the civil engineering aspect of cyber-physical systems.Firstly,they rely on the assumption of a uniform and strong level of interdependency.That is,once a node within a network fails its counterpart fails immedi-ately.Current studies overlook the impact of earthquake and other natural hazards on the operation of modern transportation infrastructure,that now serve as a cyber-physical system.The last is responsible not only for the physical operation(e.g.,flow of vehicles)but also for the continuous data transmission and subsequently the cy-ber operation of the entire transportation network.Therefore,the robustness of modern transportation networks should be modelled from a new cyber-physical perspective that includes civil engineering aspects.In this paper,we propose a new robustness assessment approach for modern transportation networks and their underlying in-terdependent physical and cyber network,subjected to earthquake events.The novelty relies on the modelling of interdependent networks,in the form of a graph,based on their interdependency levels.We associate the service-ability level of the coupled physical and cyber network with the damage states induced by earthquake events.Robustness is then measured as a degradation of the cyber-physical serviceability level.The application of the approach is demonstrated by studying an illustrative transportation network using seismic data from real-world transportation infrastructure.Furthermore,we propose the integration of a robustness improvement indicator based on physical and cyber attributes to enhance the cyber-physical serviceability level.Results indicate an improvement in robustness level(i.e.,41%)by adopting the proposed robustness improvement indicator.The usefulness of our approach is highlighted by comparing it with other methods that consider strong interdepen-dencies and key node protection strategies.The approach is of interest to stakeholders who are attempting to incorporate cyber-physical systems into civil engineering systems.展开更多
Numerous uncertainties in practical production and operation can seriously affect the drive performance of permanent magnet synchronous machines(PMSMs).Various robust control methods have been developed to mitigate or...Numerous uncertainties in practical production and operation can seriously affect the drive performance of permanent magnet synchronous machines(PMSMs).Various robust control methods have been developed to mitigate or eliminate the effects of these uncertainties.However,the robustness to uncertainties of electrical drive systems has not been clearly defined.No systemic procedures have been proposed to evaluate a control system's robustness(how robust it is).This paper proposes a systemic method for evaluating control systems'robustness to uncertainties.The concept and fundamental theory of robust control are illustrated by considering a simple uncertain feedback control system.The effects of uncertainties on the control performance and stability are analyzed and discussed.The concept of design for six-sigma(a robust design method)is employed to numerically evaluate the robustness levels of control systems.To show the effectiveness of the proposed robustness evaluation method,case studies are conducted for second-order systems,DC motor drive systems,and PMSM drive systems.Besides the conventional predictive control of PMSM drive,three different robust predictive control methods are evaluated in terms of two different parametric uncertainty ranges and three application requirements against parametric uncertainties.展开更多
Superhydrophobic glass has inspiring development prospects in endoscopes,solar panels and other engineering and medical fields.However,the surface topography required to achieve superhydrophobicity will inevitably aff...Superhydrophobic glass has inspiring development prospects in endoscopes,solar panels and other engineering and medical fields.However,the surface topography required to achieve superhydrophobicity will inevitably affect the surface transparency and limit the application of glass materials.To resolve the contradiction between the surface transparency and the robust superhydrophobicity,an efficient and low-cost laser-chemical surface functionalization process was utilized to fabricate superhydrophobic glass surface.The results show that the air can be effectively trapped in surface micro/nanostructure induced by laser texturing,thus reducing the solid-liquid contact area and interfacial tension.The deposition of hydrophobic carbon-containing groups on the surface can be accelerated by chemical treatment,and the surface energy is significantly reduced.The glass surface exhibits marvelous robust superhydrophobicity with a contact angle of 155.8°and a roll-off angle of 7.2°under the combination of hierarchical micro/nanostructure and low surface energy.Moreover,the surface transparency of the prepared superhydrophobic glass was only 5.42%lower than that of the untreated surface.This superhydrophobic glass with high transparency still maintains excellent superhydrophobicity after durability and stability tests.The facile fabrication of superhydrophobic glass with high transparency and robustness provides a strong reference for further expanding the application value of glass materials.展开更多
Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy...Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.展开更多
In the face of disasters,a strong organizational network is the foundation for eff ectively accomplishing emergency relief tasks.In an emergency response network comprising tasks and organizations,the failure of certa...In the face of disasters,a strong organizational network is the foundation for eff ectively accomplishing emergency relief tasks.In an emergency response network comprising tasks and organizations,the failure of certain organizations may cause large systemic losses owing to internal component associations.To analyze the response system’s robustness,we developed emergency response networks based on the associations between organizations and tasks.A cascading failure model was established considering task reassignment after organizational failure,and indicators in terms of tasks and structures were identified to observe robustness.In the proposed model,we developed random,bond-based,and bridge-based organizational failure modes,and average,capacity-based,and surplus-based reassignment programs.To validate the model,simulation experiments were conducted in the context of extreme rainstorms.The results show that bridge-based failures were the most damaging to network systems,and the average reassignment program was the least eff ective.The analysis of model parameters illustrates the critical eff ectiveness of individual organizational capability in enhancing system robustness.The proposed framework and model enrich the study of emergency response networks with favorable applicability,and the results can provide theoretical references for emergency management practices.展开更多
Tag recommendation systems can significantly improve the accuracy of information retrieval by recommending relevant tag sets that align with user preferences and resource characteristics.However,metric learning method...Tag recommendation systems can significantly improve the accuracy of information retrieval by recommending relevant tag sets that align with user preferences and resource characteristics.However,metric learning methods often suffer from high sensitivity,leading to unstable recommendation results when facing adversarial samples generated through malicious user behavior.Adversarial training is considered to be an effective method for improving the robustness of tag recommendation systems and addressing adversarial samples.However,it still faces the challenge of overfitting.Although curriculum learning-based adversarial training somewhat mitigates this issue,challenges still exist,such as the lack of a quantitative standard for attack intensity and catastrophic forgetting.To address these challenges,we propose a Self-Paced Adversarial Metric Learning(SPAML)method.First,we employ a metric learning model to capture the deep distance relationships between normal samples.Then,we incorporate a self-paced adversarial training model,which dynamically adjusts the weights of adversarial samples,allowing the model to progressively learn from simpler to more complex adversarial samples.Finally,we jointly optimize the metric learning loss and self-paced adversarial training loss in an adversarial manner,enhancing the robustness and performance of tag recommendation tasks.Extensive experiments on the MovieLens and LastFm datasets demonstrate that SPAML achieves F1@3 and NDCG@3 scores of 22%and 32.7%on the MovieLens dataset,and 19.4%and 29%on the LastFm dataset,respectively,outperforming the most competitive baselines.Specifically,F1@3 improves by 4.7%and 6.8%,and NDCG@3 improves by 5.0%and 6.9%,respectively.展开更多
Academic and industrial communities have been paying significant attention to the 6th Generation(6G)wireless communication systems after the commercial deployment of 5G cellular communications.Among the emerging techn...Academic and industrial communities have been paying significant attention to the 6th Generation(6G)wireless communication systems after the commercial deployment of 5G cellular communications.Among the emerging technologies,Vehicular Edge Computing(VEC)can provide essential assurance for the robustness of Artificial Intelligence(AI)algorithms to be used in the 6G systems.Therefore,in this paper,a strategy for enhancing the robustness of AI model deployment using 6G-VEC is proposed,taking the object detection task as an example.This strategy includes two stages:model stabilization and model adaptation.In the former,the state-of-the-art methods are appended to the model to improve its robustness.In the latter,two targeted compression methods are implemented,namely model parameter pruning and knowledge distillation,which result in a trade-off between model performance and runtime resources.Numerical results indicate that the proposed strategy can be smoothly deployed in the onboard edge terminals,where the introduced trade-off outperforms the other strategies available.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both g...Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both global and regional climates.This study assesses the performance of models participating in phase 6 of the Coupled Model Intercomparison Project in simulating interannual variability modes of Northern Hemisphere 500-hPa geopotential height during winter and summer,distinguishing predictable(potentially predictable on seasonal or longer timescales)and unpredictable(intraseasonal and essentially unpredictable at long range)components,using reanalysis data and a variance decomposition method.Although most models effectively capture unpredictable modes in reanalysis,their ability to reproduce dominant predictable modes-specifically the Pacific-North American pattern,Arctic Oscillation,and Western Pacific Oscillation in winter,and the East Atlantic and North Atlantic Oscillations in summer-varies notably.An optimal ensemble is identified to distinguish(a)predictable-external modes,dominated by external forcing,and(b)predictable-internal modes,associated with slow internal variability,during the historical period(1950-2014)and the SSP5-8.5 scenario(2036-2100).Under increased radiative forcing,the leading winter/summer predictable-external mode exhibits a more uniform spatial distribution,remarkably larger trend and annual variance,and enhanced height-sea surface temperature(SST)covariance under SSP5-8.5 compared to historical conditions.The dominant winter/summer predictable-internal modes also exhibit increased variance and height-SST covariance under SSP5-8.5,along with localized changes in spatial configuration.Minimal changes are observed in spatial distribution or variance for dominant winter/summer unpredictable modes under SSP5-8.5.This study,from a predictive perspective,deepens our understanding of model uncertainties and projected changes in circulations.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
目的分析西安汉族人群CYP2A6基因拷贝数多态性及其与吸烟行为的关联性。方法选取2021年9月—2023年6月在西安区域医学检验中心体检的西安汉族居民3500例为研究对象。根据受试者是否吸烟,将其分为吸烟组(2233例)和不吸烟组(1267例),根据...目的分析西安汉族人群CYP2A6基因拷贝数多态性及其与吸烟行为的关联性。方法选取2021年9月—2023年6月在西安区域医学检验中心体检的西安汉族居民3500例为研究对象。根据受试者是否吸烟,将其分为吸烟组(2233例)和不吸烟组(1267例),根据吸烟组受试者吸烟程度将其进一步分为轻度吸烟亚组(476例)和重度吸烟亚组(1757例)。采用TaqMan®Copy Number Assays试剂盒检测受试者CYP2A6基因拷贝数多态性。结果3500例受试者中,CYP2A6基因拷贝数为0者32例(0.91%),CYP2A6基因拷贝数为1拷贝者632例(18.06%),CYP2A6基因拷贝数为2拷贝者2737例(78.20%),CYP2A6基因拷贝数为≥3拷贝者99例(2.83%)。吸烟组CYP2A6基因拷贝数为0、1拷贝者占比低于不吸烟组,CYP2A6基因拷贝数为2拷贝、≥3拷贝者占比高于不吸烟组(P<0.05)。轻度吸烟亚组与重度吸烟亚组CYP2A6基因拷贝数多态性比较,差异无统计学意义(P>0.05)。结论西安汉族人群CYP2A6基因缺失率较高,达18.97%;西安汉族人群CYP2A6基因拷贝数多态性与吸烟行为可能存在关联,且CYP2A6基因缺失个体不易形成烟草依赖,而CYP2A6基因拷贝数多态性可能与吸烟程度不存在关联。展开更多
The neuroinflammatory response mediated by microglial activation plays an important role in the secondary nerve injury of traumatic brain injury.The post-transcriptional modification of N^(6)-methyladenosine is ubiqui...The neuroinflammatory response mediated by microglial activation plays an important role in the secondary nerve injury of traumatic brain injury.The post-transcriptional modification of N^(6)-methyladenosine is ubiquitous in the immune response of the central nervous system.The fat mass and obesity-related protein catalyzes the demethylation of N^(6)-methyladenosine modifications on mRNA and is widely expressed in various tissues,participating in the regulation of multiple diseases’biological processes.However,the role of fat mass and obesity in microglial activation and the subsequent neuroinflammatory response after traumatic brain injury is unclear.In this study,we found that the expression of fat mass and obesity was significantly down-regulated in both lipopolysaccharide-treated BV2 cells and a traumatic brain injury mouse model.After fat mass and obesity interference,BV2 cells exhibited a pro-inflammatory phenotype as shown by the increased proportion of CD11b^(+)/CD86^(+)cells and the secretion of pro-inflammatory cytokines.Fat mass and obesity-mediated N^(6)-methyladenosine demethylation accelerated the degradation of ADAM17 mRNA,while silencing of fat mass and obesity enhanced the stability of ADAM17 mRNA.Therefore,down-regulation of fat mass and obesity expression leads to the abnormally high expression of ADAM17 in microglia.These results indicate that the activation of microglia and neuroinflammatory response regulated by fat mass and obesity-related N^(6)-methyladenosine modification plays an important role in the pro-inflammatory process of secondary injury following traumatic brain injury.展开更多
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.
基金Project supported by the Research Council of Norway, Norwegian University of Science and Technology (NTNU), and the Norwegian Resarch Network (UNINETT)
文摘In this work, we present an evaluation of the performance and error robustness of RTP-based broadcast streaming of high-quality high-definition (HD) H.264/AVC video. Using a fully controlled IP test bed (Hillestad et al., 2005), we broadcast high-definition video over RTP/UDP, and use an IP network emulator to introduce a varying amount of randomly distributed packet loss. A high-performance network interface monitoring card is used to capture the video packets into a trace file. Purpose-built software parses the trace file, analyzes the RTP stream and assembles the correctly received NAL units into an H.264/AVC Annex B byte stream file, which is subsequently decoded by JVT JM 10.1 reference software. The proposed measurement setup is a novel, practical and intuitive approach to perform error resilience testing of real-world H.264/AVC broadcast applications. Through a series of experiments, we evaluate some of the error resilience features of the H.264/AVC standard, and see how they perform at packet loss rates from 0.01% to 5%. The results confirmed that an appropriate slice partitioning scheme is essential to have a graceful degradation in received quality in the case of packet loss. While flexible macroblock ordering reduces the compression efficiency about 1 dB for our test material, reconstructed video quality is improved for loss rates above 0.25%.
文摘While in the past the robustness of transportation networks was studied considering the cyber and physical space as isolated environments this is no longer the case.Integrating the Internet of Things devices in the sensing area of transportation infrastructure has resulted in ubiquitous cyber-physical systems and increasing interdependen-cies between the physical and cyber networks.As a result,the robustness of transportation networks relies on the uninterrupted serviceability of physical and cyber networks.Current studies on interdependent networks overlook the civil engineering aspect of cyber-physical systems.Firstly,they rely on the assumption of a uniform and strong level of interdependency.That is,once a node within a network fails its counterpart fails immedi-ately.Current studies overlook the impact of earthquake and other natural hazards on the operation of modern transportation infrastructure,that now serve as a cyber-physical system.The last is responsible not only for the physical operation(e.g.,flow of vehicles)but also for the continuous data transmission and subsequently the cy-ber operation of the entire transportation network.Therefore,the robustness of modern transportation networks should be modelled from a new cyber-physical perspective that includes civil engineering aspects.In this paper,we propose a new robustness assessment approach for modern transportation networks and their underlying in-terdependent physical and cyber network,subjected to earthquake events.The novelty relies on the modelling of interdependent networks,in the form of a graph,based on their interdependency levels.We associate the service-ability level of the coupled physical and cyber network with the damage states induced by earthquake events.Robustness is then measured as a degradation of the cyber-physical serviceability level.The application of the approach is demonstrated by studying an illustrative transportation network using seismic data from real-world transportation infrastructure.Furthermore,we propose the integration of a robustness improvement indicator based on physical and cyber attributes to enhance the cyber-physical serviceability level.Results indicate an improvement in robustness level(i.e.,41%)by adopting the proposed robustness improvement indicator.The usefulness of our approach is highlighted by comparing it with other methods that consider strong interdepen-dencies and key node protection strategies.The approach is of interest to stakeholders who are attempting to incorporate cyber-physical systems into civil engineering systems.
文摘Numerous uncertainties in practical production and operation can seriously affect the drive performance of permanent magnet synchronous machines(PMSMs).Various robust control methods have been developed to mitigate or eliminate the effects of these uncertainties.However,the robustness to uncertainties of electrical drive systems has not been clearly defined.No systemic procedures have been proposed to evaluate a control system's robustness(how robust it is).This paper proposes a systemic method for evaluating control systems'robustness to uncertainties.The concept and fundamental theory of robust control are illustrated by considering a simple uncertain feedback control system.The effects of uncertainties on the control performance and stability are analyzed and discussed.The concept of design for six-sigma(a robust design method)is employed to numerically evaluate the robustness levels of control systems.To show the effectiveness of the proposed robustness evaluation method,case studies are conducted for second-order systems,DC motor drive systems,and PMSM drive systems.Besides the conventional predictive control of PMSM drive,three different robust predictive control methods are evaluated in terms of two different parametric uncertainty ranges and three application requirements against parametric uncertainties.
基金Projects(52105175,52305149)supported by the National Natural Science Foundation of ChinaProject(2242024RCB0035)supported by the Zhishan Young Scholar Program of Southeast University,China+5 种基金Project(BK20210235)supported by the Natural Science Foundation of Jiangsu Province,ChinaProject(2023MK042)supported by the State Administration for Market Regulation,ChinaProject(KJ2023003)supported by the Jiangsu Administration for Market Regulation,ChinaProjects(KJ(Y)202429,KJ(YJ)2023001)supported by the Jiangsu Province Special Equipment Safety Supervision Inspection Institute,ChinaProject(JSSCBS20210121)supported by the Jiangsu Provincial Innovative and Entrepreneurial Doctor Program,ChinaProject(1102002310)supported by the Technology Innovation Project for Returnees in Nanjing,China。
文摘Superhydrophobic glass has inspiring development prospects in endoscopes,solar panels and other engineering and medical fields.However,the surface topography required to achieve superhydrophobicity will inevitably affect the surface transparency and limit the application of glass materials.To resolve the contradiction between the surface transparency and the robust superhydrophobicity,an efficient and low-cost laser-chemical surface functionalization process was utilized to fabricate superhydrophobic glass surface.The results show that the air can be effectively trapped in surface micro/nanostructure induced by laser texturing,thus reducing the solid-liquid contact area and interfacial tension.The deposition of hydrophobic carbon-containing groups on the surface can be accelerated by chemical treatment,and the surface energy is significantly reduced.The glass surface exhibits marvelous robust superhydrophobicity with a contact angle of 155.8°and a roll-off angle of 7.2°under the combination of hierarchical micro/nanostructure and low surface energy.Moreover,the surface transparency of the prepared superhydrophobic glass was only 5.42%lower than that of the untreated surface.This superhydrophobic glass with high transparency still maintains excellent superhydrophobicity after durability and stability tests.The facile fabrication of superhydrophobic glass with high transparency and robustness provides a strong reference for further expanding the application value of glass materials.
基金supported by the National Natural Science Foundation of China(Grant No.82151302)the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-B-113)+1 种基金the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-A-019)the CAMS Innovation Fund for Medical Sciences(Grant No.2021-12M-1-014).
文摘Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.
基金supported by the National Natural Science Foundation of China(Grant Nos.72504070,72404024)。
文摘In the face of disasters,a strong organizational network is the foundation for eff ectively accomplishing emergency relief tasks.In an emergency response network comprising tasks and organizations,the failure of certain organizations may cause large systemic losses owing to internal component associations.To analyze the response system’s robustness,we developed emergency response networks based on the associations between organizations and tasks.A cascading failure model was established considering task reassignment after organizational failure,and indicators in terms of tasks and structures were identified to observe robustness.In the proposed model,we developed random,bond-based,and bridge-based organizational failure modes,and average,capacity-based,and surplus-based reassignment programs.To validate the model,simulation experiments were conducted in the context of extreme rainstorms.The results show that bridge-based failures were the most damaging to network systems,and the average reassignment program was the least eff ective.The analysis of model parameters illustrates the critical eff ectiveness of individual organizational capability in enhancing system robustness.The proposed framework and model enrich the study of emergency response networks with favorable applicability,and the results can provide theoretical references for emergency management practices.
基金supported by the Key Research and Development Program of Zhejiang Province(No.2024C01071)the Natural Science Foundation of Zhejiang Province(No.LQ15F030006).
文摘Tag recommendation systems can significantly improve the accuracy of information retrieval by recommending relevant tag sets that align with user preferences and resource characteristics.However,metric learning methods often suffer from high sensitivity,leading to unstable recommendation results when facing adversarial samples generated through malicious user behavior.Adversarial training is considered to be an effective method for improving the robustness of tag recommendation systems and addressing adversarial samples.However,it still faces the challenge of overfitting.Although curriculum learning-based adversarial training somewhat mitigates this issue,challenges still exist,such as the lack of a quantitative standard for attack intensity and catastrophic forgetting.To address these challenges,we propose a Self-Paced Adversarial Metric Learning(SPAML)method.First,we employ a metric learning model to capture the deep distance relationships between normal samples.Then,we incorporate a self-paced adversarial training model,which dynamically adjusts the weights of adversarial samples,allowing the model to progressively learn from simpler to more complex adversarial samples.Finally,we jointly optimize the metric learning loss and self-paced adversarial training loss in an adversarial manner,enhancing the robustness and performance of tag recommendation tasks.Extensive experiments on the MovieLens and LastFm datasets demonstrate that SPAML achieves F1@3 and NDCG@3 scores of 22%and 32.7%on the MovieLens dataset,and 19.4%and 29%on the LastFm dataset,respectively,outperforming the most competitive baselines.Specifically,F1@3 improves by 4.7%and 6.8%,and NDCG@3 improves by 5.0%and 6.9%,respectively.
基金supported by the National Key Research and Development Program of China(2020YFB1807500)the National Natural Science Foundation of China(62072360,62001357,62172438,61901367)+4 种基金the key research and development plan of Shaanxi province(2021ZDLGY02-09,2020JQ-844)the Natural Science Foundation of Guangdong Province of China(2022A1515010988)Key Project on Artificial Intelligence of Xi'an Science and Technology Plan(2022JH-RGZN-0003)Xi'an Science and Technology Plan(20RGZN0005)the Xi'an Key Laboratory of Mobile Edge Computing and Security(201805052-ZD3CG36).
文摘Academic and industrial communities have been paying significant attention to the 6th Generation(6G)wireless communication systems after the commercial deployment of 5G cellular communications.Among the emerging technologies,Vehicular Edge Computing(VEC)can provide essential assurance for the robustness of Artificial Intelligence(AI)algorithms to be used in the 6G systems.Therefore,in this paper,a strategy for enhancing the robustness of AI model deployment using 6G-VEC is proposed,taking the object detection task as an example.This strategy includes two stages:model stabilization and model adaptation.In the former,the state-of-the-art methods are appended to the model to improve its robustness.In the latter,two targeted compression methods are implemented,namely model parameter pruning and knowledge distillation,which result in a trade-off between model performance and runtime resources.Numerical results indicate that the proposed strategy can be smoothly deployed in the onboard edge terminals,where the introduced trade-off outperforms the other strategies available.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2342210 and 42275043)the National Institute of Natural Hazards,Ministry of Emergency Management of China(Grant Nos.J2223806,ZDJ2024-25 and ZDJ2025-34)。
文摘Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both global and regional climates.This study assesses the performance of models participating in phase 6 of the Coupled Model Intercomparison Project in simulating interannual variability modes of Northern Hemisphere 500-hPa geopotential height during winter and summer,distinguishing predictable(potentially predictable on seasonal or longer timescales)and unpredictable(intraseasonal and essentially unpredictable at long range)components,using reanalysis data and a variance decomposition method.Although most models effectively capture unpredictable modes in reanalysis,their ability to reproduce dominant predictable modes-specifically the Pacific-North American pattern,Arctic Oscillation,and Western Pacific Oscillation in winter,and the East Atlantic and North Atlantic Oscillations in summer-varies notably.An optimal ensemble is identified to distinguish(a)predictable-external modes,dominated by external forcing,and(b)predictable-internal modes,associated with slow internal variability,during the historical period(1950-2014)and the SSP5-8.5 scenario(2036-2100).Under increased radiative forcing,the leading winter/summer predictable-external mode exhibits a more uniform spatial distribution,remarkably larger trend and annual variance,and enhanced height-sea surface temperature(SST)covariance under SSP5-8.5 compared to historical conditions.The dominant winter/summer predictable-internal modes also exhibit increased variance and height-SST covariance under SSP5-8.5,along with localized changes in spatial configuration.Minimal changes are observed in spatial distribution or variance for dominant winter/summer unpredictable modes under SSP5-8.5.This study,from a predictive perspective,deepens our understanding of model uncertainties and projected changes in circulations.
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.
文摘目的分析西安汉族人群CYP2A6基因拷贝数多态性及其与吸烟行为的关联性。方法选取2021年9月—2023年6月在西安区域医学检验中心体检的西安汉族居民3500例为研究对象。根据受试者是否吸烟,将其分为吸烟组(2233例)和不吸烟组(1267例),根据吸烟组受试者吸烟程度将其进一步分为轻度吸烟亚组(476例)和重度吸烟亚组(1757例)。采用TaqMan®Copy Number Assays试剂盒检测受试者CYP2A6基因拷贝数多态性。结果3500例受试者中,CYP2A6基因拷贝数为0者32例(0.91%),CYP2A6基因拷贝数为1拷贝者632例(18.06%),CYP2A6基因拷贝数为2拷贝者2737例(78.20%),CYP2A6基因拷贝数为≥3拷贝者99例(2.83%)。吸烟组CYP2A6基因拷贝数为0、1拷贝者占比低于不吸烟组,CYP2A6基因拷贝数为2拷贝、≥3拷贝者占比高于不吸烟组(P<0.05)。轻度吸烟亚组与重度吸烟亚组CYP2A6基因拷贝数多态性比较,差异无统计学意义(P>0.05)。结论西安汉族人群CYP2A6基因缺失率较高,达18.97%;西安汉族人群CYP2A6基因拷贝数多态性与吸烟行为可能存在关联,且CYP2A6基因缺失个体不易形成烟草依赖,而CYP2A6基因拷贝数多态性可能与吸烟程度不存在关联。
基金supported by grants from the Major Projects of Health Science Research Foundation for Middle-Aged and Young Scientist of Fujian Province,China,No.2022ZQNZD01010010the National Natural Science Foundation of China,No.82371390Fujian Province Scientific Foundation,No.2023J01725(all to XC).
文摘The neuroinflammatory response mediated by microglial activation plays an important role in the secondary nerve injury of traumatic brain injury.The post-transcriptional modification of N^(6)-methyladenosine is ubiquitous in the immune response of the central nervous system.The fat mass and obesity-related protein catalyzes the demethylation of N^(6)-methyladenosine modifications on mRNA and is widely expressed in various tissues,participating in the regulation of multiple diseases’biological processes.However,the role of fat mass and obesity in microglial activation and the subsequent neuroinflammatory response after traumatic brain injury is unclear.In this study,we found that the expression of fat mass and obesity was significantly down-regulated in both lipopolysaccharide-treated BV2 cells and a traumatic brain injury mouse model.After fat mass and obesity interference,BV2 cells exhibited a pro-inflammatory phenotype as shown by the increased proportion of CD11b^(+)/CD86^(+)cells and the secretion of pro-inflammatory cytokines.Fat mass and obesity-mediated N^(6)-methyladenosine demethylation accelerated the degradation of ADAM17 mRNA,while silencing of fat mass and obesity enhanced the stability of ADAM17 mRNA.Therefore,down-regulation of fat mass and obesity expression leads to the abnormally high expression of ADAM17 in microglia.These results indicate that the activation of microglia and neuroinflammatory response regulated by fat mass and obesity-related N^(6)-methyladenosine modification plays an important role in the pro-inflammatory process of secondary injury following traumatic brain injury.
