To guarantee the ubiquitous and fully autonomous Internet connections in our daily life, the new technical challenges of mobile communications lie on the efficient utilization of resource and social information. To facilitate the innovation of the fifth generation (5G) networks, the cloud radio access network (RAN) and fog network have been proposed to respond newly emerging traffic demands. The cloud RAN functions more toward centralized resource management to achieve optimal transmissions. The fog network takes advantage of social information and edge computing to efficiently alleviate the end-toend latency. In this paper, we conduct a comprehensive survey of these two network structures, and then investigate possible harmonization to integrate both for the diverse needs of 5G mobile communications. We analytically study the harmonization of cloud RAN and fog network from various points of view, including the cache of Internet contents, mobility management, and radio access control. The performance of transition between the cloud RAN and the fog network has been presented and the subsequent switching strategy has been proposed to ensure engineering flexibility and success.INDEX TERMS 5G, fog network, cloud radio access network, RAN, heterogeneous network, edge computing, cloud computing, cache, radio resource management, mobility, mobile communications, vehicular network.

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Natural stocks of Japanese eel (Anguilla japonica) have decreased drastically because of overfishing, habitat destruction, and changes in the ocean environment over the past few decades. However, to date, artificial mass production of glass eels is far from reality because of the lack of appropriate feed for the eel larvae. In this study, wild glass eel, leptocephali, preleptocephali, and embryos were collected to conduct RNA-seq. Approximately 279 million reads were generated and assembled into 224,043 transcripts. The transcript levels of genes coding for digestive enzymes and nutrient transporters were investigated to estimate the capacities for nutrient digestion and absorption during early development. The results showed that the transcript levels of protein digestion enzymes were higher than those of carbohydrate and lipid digestion enzymes in the preleptocephali and leptocephali, and the transcript levels of amino acid transporters were also higher than those of glucose and fructose transporters and the cholesterol transporter. In addition, the transcript levels of glucose and fructose transporters were significantly raising in the leptocephali. Moreover, the transcript levels of protein, carbohydrate, and lipid digestion enzymes were balanced in the glass eel, but the transcript levels of amino acid transporters were higher than those of glucose and cholesterol transporters. These findings implied that preleptocephali and leptocephali prefer high-protein food, and the nutritional requirements of monosaccharides and lipids for the eel larvae vary with growth. An online database (http://molas.iis.sinica.edu.tw/jpeel/) that will provide the sequences and the annotated results of assembled transcripts was established for the eel research community.

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Intersection management is one of the most representative applications of intelligent vehicles with connected and autonomous functions. The connectivity provides environmental information that a single vehicle cannot sense, and the autonomy supports precise vehicular control that a human driver cannot achieve. Intersection management solves the fundamental conflict resolution problem for vehicles—two vehicles should not appear at the same location at the same time, and, if they intend to do that, an order should be decided to optimize certain objectives such as the traffic throughput or smoothness. In this paper, we first propose a graph-based model for intersection management. The model is general and applicable to different granularities of intersections and other conflicting scenarios. We then derive formal verification approaches which can guarantee deadlock-freeness. Based on the graph-based model and the verification approaches, we develop a centralized cycle removal algorithm for the graph-based model to schedule vehicles to go through the intersection safely (without collisions) and efficiently without deadlocks. Experimental results demonstrate the expressiveness of the proposed model and the effectiveness and efficiency of the proposed algorithm.