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In recent years, IoV (Internet of Vehicles) has become one of the most active research fields in network and intelligent transportation system. As an open converged network, IoV plays an important role in solving various driving and traffic problems by advanced information and communications technology. We review the existing notions of IoV from different perspectives. Then, we provide our notion from a network point of view and propose a novel IoV architecture with four layers. Particularly, a novel layer named coordinative computing control layer is separated from the application layer. The novel layer is used for solving the coordinative computing and control problems for human-vehicle-environment. After summarizing the key technologies in IoV architecture, we construct a VV (Virtual Vehicle), which is an integrated image of driver and vehicle in networks. VVs can interact with each other in cyber space by providing traffic service and sharing sensing data coordinately, which can solve the communication bottleneck in physical space. Finally, an extended IoV architecture based on VVs is proposed.

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Huangjiu (Chinese rice wine) has been consumed for centuries in Asian countries and is known for its unique flavor and subtle taste. The flavor compounds of Huangjiu are derived from a wide range of sources, such as raw materials, microbial metabolic activities during fermentation, and chemical reactions that occur during aging. Of these sources, microorganisms have the greatest effect on the flavor quality of Huangjiu. To enrich the microbial diversity, Huangjiu is generally fermented under an open environment, as this increases the complexity of its microbial community and flavor compounds. Thus, understanding the formation of flavor compounds in Huangjiu will be beneficial for producing a superior flavored product. In this paper, a critical review of aspects that may affect the formation of Huangjiu flavor compounds is presented. The selection of appropriate raw materials and the improvement of fermentation technologies to promote the flavor quality of Huangjiu are discussed. In addition, the effects of microbial community composition, metabolic function of predominant microorganisms, and dynamics of microbial community on the flavor quality of Huangjiu are examined. This review thus provides a theoretical basis for manipulating the fermentation process by using selected microorganisms to improve the overall flavor quality of Huangjiu.

An evolution and resequencing strategy was used to research the genetic basis of Saccharomyces cerevisiae BR20 (with 18 vol% ethanol tolerance) and the evolved strain F23 (with 25 vol% ethanol tolerance). Whole-genome sequencing and RNA sequencing (RNA-seq) indicated that the enhanced ethanol tolerance under 10 vol% ethanol could be attributed to amino acid metabolism, whereas 18 vol% ethanol tolerance was due to fatty acid metabolism. Ultrastructural analysis indicated that F23 exhibited better membrane integrity than did BR20 under ethanol stress. At low concentrations (<5 vol%), the partition of ethanol into the membrane increased the membrane fluidity, which had little effect on cell growth. However, the toxic effects of medium and high ethanol concentrations (5 to 20 vol%) tended to decrease the membrane fluidity. Under high ethanol stress (>10 vol%), the highly tolerant strain was able to maintain a relatively constant fluidity by increasing the content of unsaturated fatty acid (UFA), whereas less-tolerant strains show a continuous decrease in fluidity and UFA content. OLE1, which was identified as the only gene with a differential single-nucleotide polymorphism (SNP) mutation site related to fatty acid metabolism, was significantly changed in response to ethanol. The role of OLE1 in membrane fluidity was positively validated in its overexpressed transformants. Therefore, OLE1 lowered the rate of decline in membrane fluidity and thus enabled the yeast to better fight the deleterious effects of ethanol. IMPORTANCE Yeasts with superior ethanol tolerance are desirable for winemakers and wine industries. In our previous work, strain F23 was evolved with superior ethanol tolerance and fermentation activity to improve the flavor profiles of Chinese rice wine. Therefore, exploring the genomic variations and ethanol tolerance mechanism of strain F23 could contribute to an understanding of its effect on the flavor characteristics in the resulting Chinese rice wine. The cellular membrane plays a vital role in the ethanol tolerance of yeasts; however, how the membrane is regulated to fight the toxic effect of ethanol remains to be elucidated. This study suggests that the membrane fluidity is variably regulated by OLE1 to offset the disruptive effect of ethanol. Current work will help develop more ethanol-tolerant yeast strains for wine industries and contribute to a deep understanding of its high flavor-producing ability.

So far, the handoff management involved in the wireless local area network (WLAN) has mainly fallen into the handoff mechanism and the decision algorithm. The traditional handoff mechanism generates noticeable delays during the handoff process, resulting in discontinuity of service, which is more evident in dense WLANs. Inspired by software-defined networking (SDN), prior works put forward many seamless handoff mechanisms to ensure service continuity. With respect to the handoff decision algorithm, when to trigger handoff and which access point to reconnect to, however, are still tricky problems. In this paper, we first design a self-learning architecture applicable to the SDN-based WLAN frameworks. Along with it, we propose DCRQN, a novel handoff management scheme based on deep reinforcement learning, specifically deep Q-network. The proposed scheme enables the network to learn from actual users' behaviors and network status from scratch, adapting its learning in time-varying dense WLANs. Due to the temporal correlation property, the handoff decision is modeled as the Markov decision process (MDP). In the modeled MDP, the proposed scheme depends on the real-time network statistics at the time of decisions. Moreover, the convolutional neural network and the recurrent neural network are leveraged to extract fine-grained discriminative features. The numerical results through simulation demonstrate that DCRQN can effectively improve the data rate during the handoff process, outperforming the traditional handoff scheme.