A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing values.This method achieves...A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing values.This method achieves precise adjustment of the network structure by constructing a preliminary random network model and introducing small-world network characteristics and combines L1 norm minimization regularization techniques to control model complexity and optimize the inference process of variable dependencies.In the experiment of game network reconstruction,when the success rate of the L1 norm minimization model’s existence connection reconstruction reaches 100%,the minimum data required is about 40%,while the minimum data required for a sparse Bayesian learning network is about 45%.In terms of operational efficiency,the running time for minimizing the L1 normis basically maintained at 1.0 s,while the success rate of connection reconstruction increases significantly with an increase in data volume,reaching a maximum of 13.2 s.Meanwhile,in the case of a signal-to-noise ratio of 10 dB,the L1 model achieves a 100% success rate in the reconstruction of existing connections,while the sparse Bayesian network had the highest success rate of 90% in the reconstruction of non-existent connections.In the analysis of actual cases,the maximum lift and drop track of the research method is 0.08 m.The mean square error is 5.74 cm^(2).The results indicate that this norm minimization-based method has good performance in data efficiency and model stability,effectively reducing the impact of outliers on the reconstruction results to more accurately reflect the actual situation.展开更多
Today, most people know that physical activity(PA) is beneficial for their health ^(1,2)and aspire to engage in regular PA.^(3,4)However, despite their awareness of the importance of PA, it is evident that the transit...Today, most people know that physical activity(PA) is beneficial for their health ^(1,2)and aspire to engage in regular PA.^(3,4)However, despite their awareness of the importance of PA, it is evident that the transition from intention to action is challenging-a situation that has important public health implications. According to the World Health Organization,^(5)1 person dies every 6 s worldwide from causes related to physical inactivity, which underscores the urgency of addressing this situation.展开更多
In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service delay.In this paper,we analyze the impact of vehicle movements o...In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service delay.In this paper,we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking scheduling.Then,a Bi-LSTM-based model is proposed to predict the trajectories of vehicles.The service area is divided into several equal-sized grids.If the actual position of the vehicle and the predicted position by the model belong to the same grid,the prediction is considered correct,thereby reducing the difficulty of vehicle trajectory prediction.Moreover,we propose a scheduling strategy for delay optimization based on the vehicle trajectory prediction.Considering the inevitable prediction error,we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers,thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task offloading.Simulation results show that,compared with other classical schemes,the proposed strategy has lower average task offloading delays.展开更多
Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is con...Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is considered,where multiple User Equipments(UEs)offload their computational tasks to the F-RAN through fog nodes.Each UE can select one of the fog nodes to offload its task,and each fog node may serve multiple UEs.The tasks are computed by the fog nodes or further offloaded to the cloud via a capacity-limited fronhaul link.In order to compute all UEs'tasks quickly,joint optimization of UE-Fog association,radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs.This min-max problem is formulated as a Mixed Integer Nonlinear Program(MINP).To tackle it,first,MINP is reformulated as a continuous optimization problem,and then the Majorization Minimization(MM)method is used to find a solution.The MM approach that we develop is unconventional in that each MM subproblem is solved inexactly with the same provable convergence guarantee as the exact MM,thereby reducing the complexity of MM iteration.In addition,a cooperative offloading model is considered,where the fog nodes compress-and-forward their received signals to the cloud.Under this model,a similar min-max latency optimization problem is formulated and tackled by the inexact MM.Simulation results show that the proposed algorithms outperform some offloading strategies,and that the cooperative offloading can exploit transmission diversity better than noncooperative offloading to achieve better latency performance.展开更多
We propose the Dantzig selector based on the l_(1-q)(1<q≤2)minimization model for the sparse signal recovery.First,we discuss some properties of l_(1-q)minimization model and give some useful inequalities.Then,we ...We propose the Dantzig selector based on the l_(1-q)(1<q≤2)minimization model for the sparse signal recovery.First,we discuss some properties of l_(1-q)minimization model and give some useful inequalities.Then,we give a sufficient condition based on the restricted isometry property for the stable recovery of signals.The l_(1-2)minimization model of Yin-Lou-He is extended to the l_(1-q)minimization model.展开更多
Carpooling is a sustainable,economical,and environmentally friendly solution to reduce air pollution and ease traffic congestion in urban areas.However,existing regret theories lack consideration of the heterogeneity ...Carpooling is a sustainable,economical,and environmentally friendly solution to reduce air pollution and ease traffic congestion in urban areas.However,existing regret theories lack consideration of the heterogeneity of attribute perception in different ways and the psychological factors that affect regret,so they cannot accurately portray urban residents’carpool travel decisions and cannot provide a correct explanation of the actual carpool choice behavior.In this paper,based on the analysis of classical random regret minimization models and random regret minimization models considering heterogeneity,the concept of psychological distance is introduced to address shortcomings of the existing models and construct an improved random regret minimization model considering heterogeneity and psychological distance.The results show that the fit and explanatory effect of the improved model proposed in this paper is better than that of the other two models.The psychological distance of travel residents during the Corona Virus Disease 2019(COVID-19)affects the anticipated regret value and the willingness to carpool.The model can better describe the carpool travel choice mechanism of travelers and effectively explain the carpool travel choice behavior of travelers.展开更多
Impulse noise removal is an important task in image restoration.In this paper,we introduce a general nonsmooth nonconvex model for recovering images degraded by blur and impulsive noise,which can easily include some p...Impulse noise removal is an important task in image restoration.In this paper,we introduce a general nonsmooth nonconvex model for recovering images degraded by blur and impulsive noise,which can easily include some prior information,such as box constraint or low rank,etc.To deal with the nonconvex problem,we employ the proximal linearized minimization algorithm.For the subproblem,we use the alternating direction method of multipliers to solve it.Furthermore,based on the assumption that the objective function satisfies the KurdykaLojasiewicz property,we prove the global convergence of the proposed algorithm.Numerical experiments demonstrate that our method outperforms both the l1TV and Nonconvex TV models in terms of subjective and objective quality measurements.展开更多
Puncturing has been recognized as a promising technology to cope with the coexistence problem of enhanced mobile broadband(eMBB) and ultra-reliable low latency communications(URLLC)traffic. However, the steady perform...Puncturing has been recognized as a promising technology to cope with the coexistence problem of enhanced mobile broadband(eMBB) and ultra-reliable low latency communications(URLLC)traffic. However, the steady performance of eMBB traffic while meeting the requirements of URLLC traffic with puncturing is a major challenge in some realistic scenarios. In this paper, we pay attention to the timely and energy-efficient processing for eMBB traffic in the industrial Internet of Things(IIoT), where mobile edge computing(MEC) is employed for data processing. Specifically, the performance of eMBB traffic and URLLC traffic in a MEC-based IIoT system is ensured by setting the threshold of tolerable delay and outage probability, respectively. Furthermore,considering the limited energy supply, an energy minimization problem of eMBB device is formulated under the above constraints, by jointly optimizing the resource blocks(RBs) punctured by URLLC traffic, data offloading and transmit power of eMBB device. With Markov's inequality, the problem is reformulated by transforming the probabilistic outage constraint into a deterministic constraint. Meanwhile, an iterative energy minimization algorithm(IEMA) is proposed.Simulation results demonstrate that our algorithm has a significant reduction in the energy consumption for eMBB device and achieves a better overall effect compared to several benchmarks.展开更多
Municipal solid waste generation is strongly linked to rising human population and expanding urban areas, with significant implications on urban metabolism as well as space and place values redefinition. Effective man...Municipal solid waste generation is strongly linked to rising human population and expanding urban areas, with significant implications on urban metabolism as well as space and place values redefinition. Effective management performance of municipal solid waste management underscores the interdisciplinarity strategies. Such knowledge and skills are paramount to uncover the sources of waste generation as well as means of waste storage, collection, recycling, transportation, handling/treatment, disposal, and monitoring. This study was conducted in Dar es Salaam city. Driven by the curiosity model of the solid waste minimization performance at source, study data was collected using focus group discussion techniques to ward-level local government officers, which was triangulated with literature and documentary review. The main themes of the FGD were situational factors (SFA) and local government by-laws (LGBY). In the FGD session, sub-themes of SFA tricked to understand how MSW minimization is related to the presence and effect of services such as land use planning, availability of landfills, solid waste transfer stations, material recovery facilities, incinerators, solid waste collection bins, solid waste trucks, solid waste management budget and solid waste collection agents. Similarly, FGD on LGBY was extended by sub-themes such as contents of the by-law, community awareness of the by-law, and by-law enforcement mechanisms. While data preparation applied an analytical hierarchy process, data analysis applied an ordinary least square (OLS) regression model for sub-criteria that explain SFA and LGBY;and OLS standard residues as variables into geographically weighted regression with a resolution of 241 × 241 meter in ArcMap v10.5. Results showed that situational factors and local government by-laws have a strong relationship with the rate of minimizing solid waste dumping in water bodies (local R square = 0.94).展开更多
目的探讨天玑骨科手术机器人(TiRobot)辅助微创治疗老年脆性骨盆骨折(fragility fractures of the pelvis,FFP)的疗效。方法回顾分析2018年7月—2024年7月收治且符合选择标准的176例FFP患者临床资料。95例采用TiRobot辅助下闭合复位微...目的探讨天玑骨科手术机器人(TiRobot)辅助微创治疗老年脆性骨盆骨折(fragility fractures of the pelvis,FFP)的疗效。方法回顾分析2018年7月—2024年7月收治且符合选择标准的176例FFP患者临床资料。95例采用TiRobot辅助下闭合复位微创空心螺钉内固定术(机器人组),81例采用传统切开复位钢板螺钉内固定术(对照组)。两组患者性别、年龄、骨折分型、病程及术前疼痛视觉模拟评分(VAS)等基线资料比较,差异均无统计学意义(P>0.05)。记录并比较两组手术时间、术中失血量、术中输血率、术中输血量、最大切口长度、住院时间、最大残余位移、复位质量、骨折愈合时间、并发症发生情况及VAS评分、Majeed骨盆功能评分及分级。结果两组手术均顺利完成。机器人组手术时间、术中失血量、术中输血率、术中输血量、最大切口长度、住院时间均少于对照组(P<0.05)。机器人组共植入14个INFIX内固定支架和280枚空心螺钉,其中螺钉位置优250枚、良17枚、差13枚,螺钉位置优良率为95.36%。影像学复查示,机器人组复位优良率为91.58%(87/95),对照组为81.48%(66/81),两组术后最大骨折残余位移、复位质量差异无统计学意义(P>0.05)。两组患者均获随访,随访时间12~66个月,平均28.9个月,两组随访时间差异无统计学意义(P>0.05)。机器人组骨折愈合时间短于对照组(P<0.05)。末次随访时,两组VAS评分均较术前显著改善(P<0.05);机器人组VAS评分变化值、Majeed评分和Majeed骨盆功能优良率均高于对照组(P<0.05)。术后并发症方面,两组在步态改变、二次手术、异位骨化、切口感染、行走困难、内固定失效、死亡发生率方面差异均无统计学意义(P>0.05);机器人组切口延期愈合发生率低于对照组(P<0.05)。结论TiRobot辅助微创治疗老年FFP在手术创伤控制、术后康复速度及功能恢复效果方面均优于传统切开复位内固定术。展开更多
基金supported by the Scientific and Technological Developing Scheme of Jilin Province,China(No.20240101371JC)the National Natural Science Foundation of China(No.62107008).
文摘A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing values.This method achieves precise adjustment of the network structure by constructing a preliminary random network model and introducing small-world network characteristics and combines L1 norm minimization regularization techniques to control model complexity and optimize the inference process of variable dependencies.In the experiment of game network reconstruction,when the success rate of the L1 norm minimization model’s existence connection reconstruction reaches 100%,the minimum data required is about 40%,while the minimum data required for a sparse Bayesian learning network is about 45%.In terms of operational efficiency,the running time for minimizing the L1 normis basically maintained at 1.0 s,while the success rate of connection reconstruction increases significantly with an increase in data volume,reaching a maximum of 13.2 s.Meanwhile,in the case of a signal-to-noise ratio of 10 dB,the L1 model achieves a 100% success rate in the reconstruction of existing connections,while the sparse Bayesian network had the highest success rate of 90% in the reconstruction of non-existent connections.In the analysis of actual cases,the maximum lift and drop track of the research method is 0.08 m.The mean square error is 5.74 cm^(2).The results indicate that this norm minimization-based method has good performance in data efficiency and model stability,effectively reducing the impact of outliers on the reconstruction results to more accurately reflect the actual situation.
基金supported by The Shenzhen Educational Research Funding(zdzb2014)The Shenzhen Science and Technology Innovation Commission(202307313000096)+4 种基金The Social Science Foundation from the China's Ministry of Education(23YJA880093)The Post-Doctoral Fellowship(2022M711174)The National Center for Mental Health(Z014)BC is supported by the Chaires de recherche Rennes Métropole(23C 0909)SM is supported by the National Insti-tutes of Health(R01AG72445).
文摘Today, most people know that physical activity(PA) is beneficial for their health ^(1,2)and aspire to engage in regular PA.^(3,4)However, despite their awareness of the importance of PA, it is evident that the transition from intention to action is challenging-a situation that has important public health implications. According to the World Health Organization,^(5)1 person dies every 6 s worldwide from causes related to physical inactivity, which underscores the urgency of addressing this situation.
基金supported in part by the National Science Foundation of China(Grant No.62172450)the Key R&D Plan of Hunan Province(Grant No.2022GK2008)the Nature Science Foundation of Hunan Province(Grant No.2020JJ4756)。
文摘In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service delay.In this paper,we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking scheduling.Then,a Bi-LSTM-based model is proposed to predict the trajectories of vehicles.The service area is divided into several equal-sized grids.If the actual position of the vehicle and the predicted position by the model belong to the same grid,the prediction is considered correct,thereby reducing the difficulty of vehicle trajectory prediction.Moreover,we propose a scheduling strategy for delay optimization based on the vehicle trajectory prediction.Considering the inevitable prediction error,we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers,thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task offloading.Simulation results show that,compared with other classical schemes,the proposed strategy has lower average task offloading delays.
基金supported in part by the Natural Science Foundation of China (62171110,U19B2028 and U20B2070)。
文摘Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is considered,where multiple User Equipments(UEs)offload their computational tasks to the F-RAN through fog nodes.Each UE can select one of the fog nodes to offload its task,and each fog node may serve multiple UEs.The tasks are computed by the fog nodes or further offloaded to the cloud via a capacity-limited fronhaul link.In order to compute all UEs'tasks quickly,joint optimization of UE-Fog association,radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs.This min-max problem is formulated as a Mixed Integer Nonlinear Program(MINP).To tackle it,first,MINP is reformulated as a continuous optimization problem,and then the Majorization Minimization(MM)method is used to find a solution.The MM approach that we develop is unconventional in that each MM subproblem is solved inexactly with the same provable convergence guarantee as the exact MM,thereby reducing the complexity of MM iteration.In addition,a cooperative offloading model is considered,where the fog nodes compress-and-forward their received signals to the cloud.Under this model,a similar min-max latency optimization problem is formulated and tackled by the inexact MM.Simulation results show that the proposed algorithms outperform some offloading strategies,and that the cooperative offloading can exploit transmission diversity better than noncooperative offloading to achieve better latency performance.
基金supported by the National Natural Science Foundation of China“Variable exponential function spaces on variable anisotropic Euclidean spaces and their applications”(12261083),“Harmonic analysis on affine symmetric spaces”(12161083).
文摘We propose the Dantzig selector based on the l_(1-q)(1<q≤2)minimization model for the sparse signal recovery.First,we discuss some properties of l_(1-q)minimization model and give some useful inequalities.Then,we give a sufficient condition based on the restricted isometry property for the stable recovery of signals.The l_(1-2)minimization model of Yin-Lou-He is extended to the l_(1-q)minimization model.
基金the National Natural Science Foundation of China(No.52062026)the Educational Commission of Gansu Province of China(No.2019A-041)the Double-First Class Major Research Programs of Educational Department of Gansu Province(No.GSSYLXM-04)。
文摘Carpooling is a sustainable,economical,and environmentally friendly solution to reduce air pollution and ease traffic congestion in urban areas.However,existing regret theories lack consideration of the heterogeneity of attribute perception in different ways and the psychological factors that affect regret,so they cannot accurately portray urban residents’carpool travel decisions and cannot provide a correct explanation of the actual carpool choice behavior.In this paper,based on the analysis of classical random regret minimization models and random regret minimization models considering heterogeneity,the concept of psychological distance is introduced to address shortcomings of the existing models and construct an improved random regret minimization model considering heterogeneity and psychological distance.The results show that the fit and explanatory effect of the improved model proposed in this paper is better than that of the other two models.The psychological distance of travel residents during the Corona Virus Disease 2019(COVID-19)affects the anticipated regret value and the willingness to carpool.The model can better describe the carpool travel choice mechanism of travelers and effectively explain the carpool travel choice behavior of travelers.
基金Supported by the National Natural Science Foundations of China(Grant No.12061045,12031003)the Guangzhou Education Scientific Research Project 2024(Grant No.202315829)the Natural Science Foundation of Jiangxi Province(Grant No.20224ACB211004)。
文摘Impulse noise removal is an important task in image restoration.In this paper,we introduce a general nonsmooth nonconvex model for recovering images degraded by blur and impulsive noise,which can easily include some prior information,such as box constraint or low rank,etc.To deal with the nonconvex problem,we employ the proximal linearized minimization algorithm.For the subproblem,we use the alternating direction method of multipliers to solve it.Furthermore,based on the assumption that the objective function satisfies the KurdykaLojasiewicz property,we prove the global convergence of the proposed algorithm.Numerical experiments demonstrate that our method outperforms both the l1TV and Nonconvex TV models in terms of subjective and objective quality measurements.
基金supported by the Natural Science Foundation of China (No.62171051)。
文摘Puncturing has been recognized as a promising technology to cope with the coexistence problem of enhanced mobile broadband(eMBB) and ultra-reliable low latency communications(URLLC)traffic. However, the steady performance of eMBB traffic while meeting the requirements of URLLC traffic with puncturing is a major challenge in some realistic scenarios. In this paper, we pay attention to the timely and energy-efficient processing for eMBB traffic in the industrial Internet of Things(IIoT), where mobile edge computing(MEC) is employed for data processing. Specifically, the performance of eMBB traffic and URLLC traffic in a MEC-based IIoT system is ensured by setting the threshold of tolerable delay and outage probability, respectively. Furthermore,considering the limited energy supply, an energy minimization problem of eMBB device is formulated under the above constraints, by jointly optimizing the resource blocks(RBs) punctured by URLLC traffic, data offloading and transmit power of eMBB device. With Markov's inequality, the problem is reformulated by transforming the probabilistic outage constraint into a deterministic constraint. Meanwhile, an iterative energy minimization algorithm(IEMA) is proposed.Simulation results demonstrate that our algorithm has a significant reduction in the energy consumption for eMBB device and achieves a better overall effect compared to several benchmarks.
文摘Municipal solid waste generation is strongly linked to rising human population and expanding urban areas, with significant implications on urban metabolism as well as space and place values redefinition. Effective management performance of municipal solid waste management underscores the interdisciplinarity strategies. Such knowledge and skills are paramount to uncover the sources of waste generation as well as means of waste storage, collection, recycling, transportation, handling/treatment, disposal, and monitoring. This study was conducted in Dar es Salaam city. Driven by the curiosity model of the solid waste minimization performance at source, study data was collected using focus group discussion techniques to ward-level local government officers, which was triangulated with literature and documentary review. The main themes of the FGD were situational factors (SFA) and local government by-laws (LGBY). In the FGD session, sub-themes of SFA tricked to understand how MSW minimization is related to the presence and effect of services such as land use planning, availability of landfills, solid waste transfer stations, material recovery facilities, incinerators, solid waste collection bins, solid waste trucks, solid waste management budget and solid waste collection agents. Similarly, FGD on LGBY was extended by sub-themes such as contents of the by-law, community awareness of the by-law, and by-law enforcement mechanisms. While data preparation applied an analytical hierarchy process, data analysis applied an ordinary least square (OLS) regression model for sub-criteria that explain SFA and LGBY;and OLS standard residues as variables into geographically weighted regression with a resolution of 241 × 241 meter in ArcMap v10.5. Results showed that situational factors and local government by-laws have a strong relationship with the rate of minimizing solid waste dumping in water bodies (local R square = 0.94).
文摘目的探讨天玑骨科手术机器人(TiRobot)辅助微创治疗老年脆性骨盆骨折(fragility fractures of the pelvis,FFP)的疗效。方法回顾分析2018年7月—2024年7月收治且符合选择标准的176例FFP患者临床资料。95例采用TiRobot辅助下闭合复位微创空心螺钉内固定术(机器人组),81例采用传统切开复位钢板螺钉内固定术(对照组)。两组患者性别、年龄、骨折分型、病程及术前疼痛视觉模拟评分(VAS)等基线资料比较,差异均无统计学意义(P>0.05)。记录并比较两组手术时间、术中失血量、术中输血率、术中输血量、最大切口长度、住院时间、最大残余位移、复位质量、骨折愈合时间、并发症发生情况及VAS评分、Majeed骨盆功能评分及分级。结果两组手术均顺利完成。机器人组手术时间、术中失血量、术中输血率、术中输血量、最大切口长度、住院时间均少于对照组(P<0.05)。机器人组共植入14个INFIX内固定支架和280枚空心螺钉,其中螺钉位置优250枚、良17枚、差13枚,螺钉位置优良率为95.36%。影像学复查示,机器人组复位优良率为91.58%(87/95),对照组为81.48%(66/81),两组术后最大骨折残余位移、复位质量差异无统计学意义(P>0.05)。两组患者均获随访,随访时间12~66个月,平均28.9个月,两组随访时间差异无统计学意义(P>0.05)。机器人组骨折愈合时间短于对照组(P<0.05)。末次随访时,两组VAS评分均较术前显著改善(P<0.05);机器人组VAS评分变化值、Majeed评分和Majeed骨盆功能优良率均高于对照组(P<0.05)。术后并发症方面,两组在步态改变、二次手术、异位骨化、切口感染、行走困难、内固定失效、死亡发生率方面差异均无统计学意义(P>0.05);机器人组切口延期愈合发生率低于对照组(P<0.05)。结论TiRobot辅助微创治疗老年FFP在手术创伤控制、术后康复速度及功能恢复效果方面均优于传统切开复位内固定术。
