IoV and Blockchain-Enabled Driving Guidance Strategy in Complex Traffic Environment

Diversified traffic participants and complex traffic environment(e.g.,roadblocks or road damage exist)challenge the decision-making accuracy of a single connected and autonomous vehicle(CAV)due to its limited sensing and computing capabilities.Using Internet of Vehicles(IoV)to share driving rules between CAVs can break limitations of a single CAV,but at the same time may cause privacy and safety issues.To tackle this problem,this paper proposes to combine IoV and blockchain technologies to form an efficient and accurate autonomous guidance strategy.Specifically,we first use reinforcement learning for driving decision learning,and give the corresponding driving rule extraction method.Then,an architecture combining IoV and blockchain is designed to ensure secure driving rule sharing.Finally,the shared rules will form an effective autonomous driving guidance strategy through driving rules selection and action selection.Extensive simulation proves that the proposed strategy performs well in complex traffic environment,mainly in terms of accuracy,safety,and robustness.

Cooperative Content Caching and Delivery in Vehicular Networks: A Deep Neural Network Approach

The growing demand for low delay vehicular content has put tremendous strain on the backbone network.As a promising alternative,cooperative content caching among different cache nodes can reduce content access delay.However,heterogeneous cache nodes have different communication modes and limited caching capacities.In addition,the high mobility of vehicles renders the more complicated caching environment.Therefore,performing efficient cooperative caching becomes a key issue.In this paper,we propose a cross-tier cooperative caching architecture for all contents,which allows the distributed cache nodes to cooperate.Then,we devise the communication link and content caching model to facilitate timely content delivery.Aiming at minimizing transmission delay and cache cost,an optimization problem is formulated.Furthermore,we use a multi-agent deep reinforcement learning(MADRL)approach to model the decision-making process for caching among heterogeneous cache nodes,where each agent interacts with the environment collectively,receives observations yet a common reward,and learns its own optimal policy.Extensive simulations validate that the MADRL approach can enhance hit ratio while reducing transmission delay and cache cost.

Mechanism of chain propagation for the synthesis of polyoxymethylene dimethyl ethers

Polyoxymethylene dimethyl ethers(PODE)were synthesized from the reaction of paraformaldehyde with dimethoxymethane(DMM)over different acid catalysts at different conditions.Products were found to follow the Schulz-Flory distribution law.The chain propagation proceeds through the insertion of an individual segment of CH2O one by one,while the simultaneous insertion of a few CH2O segments or their assembly is unlikely.Due to the restriction of this law,it is difficult to increase the selectivity to the desired products(e.g.,PODE3 4).

Cluster-Based Content Download and Forwarding Scheme for Highway VANETs

The recent advances in wireless communication technology enable high-speed vehicles to download data from roadside units(RSUs). However, the data download volume of individual vehicle is rather restricted due to high mobility and limited transmission range of vehicles, bringing users poor performance. To address this issue, we exploit the combination of both clustering and carry-and-forward schemes in this paper. Our scheme coordinates the cooperation of multiple infrastructures, cluster formation in the same direction and data forwarding of reverse vehicles to facilitate the target vehicle to download large-size content in dark areas. The process of data dissemination and achievable data download volume are then derived and analyzed theoretically. Finally, we conduct extensive simulations to verify the performance of the proposed scheme. Results show significant benefits of the proposed scheme in terms of increasing data download volume and throughput.

Sulfonated carbon materials with hydrophilic and lipophilic properties

Two acidic carbon materials （H-PRC and HS-C） were used as catalysts for the condensation reaction of methanol with formaldehyde to produce dimethoxymethane （DMM） in aqueous solution （hydrophilic system） and for the etherification of isopentene with methanol to produce tert amyl methyl ether （TAME） in toluene solution （lipophilic system）. Microcalorimetric adsorptions of water and benzene showed that the HS-C was highly hydrophilic without the lipophilicity, while the H-PRC exhibited both the hydrophilicity and lipophilicity. Thus, the HS-C was well dispersed in aqueous solution and difficult to separate from it. On the other hand, the H-PRC was highly active, more active than the acidic resin （D008） and sulfuric acid, for the synthesis of DMM in aqueous solution. The H-PRC was also highly active, more active than the HS-C, for the etherification of isopentene with methanol to produce TAME in toluene solution, probably owing to its amphiphilic surface property as well as its strong surface acidity as measured by the microcalorirnetric adsorption of NH3.

Cooperative sensing,communication and computation resource allocation in mobile edge computing-enabled vehicular networks

The combination of integrated sensing and communication(ISAC)with mobile edge computing(MEC)enhances the overall safety and efficiency for vehicle to everything(V2X)system.However,existing works have not considered the potential impacts on base station(BS)sensing performance when users offload their computational tasks via uplink.This could leave insufficient resources allocated to the sensing tasks,resulting in low sensing performance.To address this issue,we propose a cooperative power,bandwidth and computation resource allocation(RA)scheme in this paper,maximizing the overall utility of Cramer-Rao bound(CRB)for sensing accuracy,computation latency for processing sensing information,and communication and computation latency for computational tasks.To solve the RA problem,a twin delayed deep deterministic policy gradient(TD3)algorithm is adopted to explore and obtain the effective solution of the RA problem.Furthermore,we investigate the performance tradeoff between sensing accuracy and summation of communication latency and computation latency for computational tasks,as well as the relationship between computation latency for processing sensing information and that of computational tasks by numerical simulations.Simulation demonstrates that compared to other benchmark methods,TD3 achieves an average utility improvement of 97.11%and 27.90%in terms of the maximum summation of communication latency and computation latency for computational tasks and improves 3.60 and 1.04 times regarding the maximum computation latency for processing sensing information.

腹直肌筋膜-肋软骨颗粒复合体在单侧唇裂二期鼻整形中的应用

目的:探讨腹直肌筋膜包裹肋软骨颗粒作为鼻背移植物应用于单侧唇裂继发鼻畸形整复中的治疗效果。方法:回顾性分析35例单侧唇裂继发鼻畸形患者,采用腹直肌筋膜包裹肋软骨颗粒作为鼻背移植物矫正鼻背畸形。通过拍摄术前、术后二维照片测量鼻侧貌美学角度。统计术后患者满意度。结果:患者术后鼻面角、鼻尖旋转度较术前明显增大,鼻颏角减小,鼻孔轴线角比值增大,差异有统计学意义(P<0.05)。患者鼻畸形矫正程度显著,患者对手术效果满意。结论:腹直肌筋膜包裹肋软骨颗粒在单侧唇裂继发鼻畸形整复中可有效抬高鼻背,改善鼻侧貌美学曲线,在临床上具有推广意义。