Smart Driving Vehicles (SDVs) are assisted by Mobile Edge Computing (MEC) to enhance the ability to deal with complex road conditions. Smart network composed of SDVs and MEC. The transmission of low delay is required in intelligent driving tasks. However, High Mobility Edge Computing Service Hand-Off (HMEC-HO) can cause retransmission delays and some security issues. To reduce delay in Smart network, a Hybrid Transmission and Reputation Management (HTRM) system is presented. Based on Fifth Generation Mobile Communications (5G) Vehicle-to-Everything (V2X) technology, an efficient hybrid Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) Scheduling (EH-V2VV2I) algorithm is designed, which uses relay vehicles to help initiating-vehicles for task transmission. To enhance the reliability of V2V connection, an algorithm of Relay Vehicle Selection and Reputation Management (RV-SRM) is proposed for predicting the link survival time, selecting highly reliable relay vehicles, and managing vehicle credibility simultaneously. The simulation results show that HTRM makes the throughput of the tasks of edge server to meet the requirements of vehicles. The experiments show that the delay of our method is reduced by 41% and the reliability of V2V connection is improved by 31% than the hard handover method.

The optimization of logistics distribution can be defined as the multiple traveling salesman problem (MTSP). The purpose of existing heuristic algorithms, such as Genetic Algorithm (GA), Ant Colony Algorithm (ACO), etc., is to find the optimal path in a short time. However, two important factors of logistics distribution optimization, including work time window and the carrying capacity of the vehicle in distribution system, have been ignored. In this paper, we consider the influences of time limitation of modern commercial logistics and carrying capacity of the vehicle on the logistics optimization, and then propose a MTSP with constraints of time window and capacity of each salesman. We design a novel hybrid algorithm by combining the minimum spanning 1-tree with ACO to find the optimal solution. In addition, we improve the pheromone update rules to increase the search efficiency of ACO algorithm. The experiments show that the novel hybrid algorithm achieves a shorter path than the other algorithms.

With the evolution of 6G technology, mobile edge computing is rapidly advancing as a crucial application scenario. This research presents an innovative method for vehicle-edge task offloading decision-making, leveraging real-time data inputs such as channel conditions, image entropy, and detector confidence levels. We propose a collaborative task processing framework for vehicle-edge computing that effectively combines lightweight and heavyweight models to cater to varying demands, ensuring efficient task execution. Additionally, the study introduces a custom-designed reinforcement learning algorithm aimed explicitly at optimizing offloading scheduling. This algorithm boosts decision-making accuracy and efficiency and features a comprehensive reward system to achieve a balanced trade-off between detection performance and latency. The frameworks efficacy is thoroughly evaluated in complex driving scenarios using the SODA10M dataset. Our results indicate the frameworks capability to achieve convergence, enhance precision, ensure stability, and maintain a lightweight operation, emphasizing its suitability for real-world implementation. This work provides practical and efficient strategies for intelligent driving task scheduling to meet the requirements of contemporary dynamic environments.

Pine wilt disease, which is caused by the pine wood nematode (PWN),Bursaphelenchus xylophilus, has caused huge damage to pine forests around the world. In this study, we analysed the PWN transcriptome to investigate the expression of genes related to the associated bacterial speciesPseudomonas fluorescensand found that the geneadh-1 encoding alcohol dehydrogenase (ADH) was upregulated. The open reading frame ofadh-1, which encoded a protein of 352 amino acid residues, was cloned fromB. xylophilus. Recombinant ADH with a relative molecular weight of 39 kDa, was present mainly in inclusion bodies and was overexpressed inEscherichia coliBL21 (DE3) and purified after refolding. The biochemical assay revealed that recombinant ADH could catalyse the dehydrogen reaction of eight tested alcohols including ethanol in the presence of NAD⁺. Quantitative real-time RT-PCR analysis indicated that ethanol upregulatedadh-1 expression in PWN. Results of RNA interference and inhibition of ADH treatment indicated that downregulating expression ofadh-1 or inhibition of ADH could reduce ethanol tolerance and the vitality and reproduction ability ofB. xylophilus, suggesting thatadh-1 is involved in pathogenicity of PWN.

To realize the encryption of document information, authority authentication, and traceability of historical records, we propose a trusted verification scheme (TVS) for office documents to ensure security. Specifically, the scheme is realized by timestamps, smart contracts (or chaincode), and other blockchain technologies. It is based on the features of blockchain, such as security, credibility, immutability, and traceability of network behavior. And the TVS stores users and documents information through blockchain; it can monitor the state changes of office documents in real time by setting the trigger conditions of smart contracts. The experiment indicates that we have realized the real-time monitoring of data and the traceability of historical records. Moreover, we have achieved the purpose of document encryption and authority authentication, ensuring the authenticity and objectivity of data, avoiding the illegal tampering of malicious users to realize the trusted verification for documents.

Cloud manufacturing (CMfg) platform consists of the cloud services, manufacturing technology, and the Internet of Things, which provides solutions for large-scale personalized customization through the service model. However, the service flexibility and resource allocation of CMfg are two factors that restrict the production time and cost of CMfg. A CMfg service model based on rewritable Petri nets (RPNs) is established, where the reconfiguration process of personalized customization is described by the rewritable rules of RPN. On this basis, the performance of the reconfiguration of the personalized customization service process is analyzed (this model analysis method can analyze the soundness of the reconfiguration process). In addition, we establish the resource allocation strategy of CMfg based on nondominated sorting genetic algorithm to obtain the best personalized customization scheme in terms of time and cost. The results of simulation and comparison experiments show that the method proposed in this article can obtain the optimal solution for both production time and cost.