Self-Sustaining Caching Stations: Towards Cost-Effective 5G-Enabled Vehicular Networks

In this article, we investigate the cost-effective 5G-enabled vehicular networks to support emerging vehicular applications, such as autonomous driving, in-car infotainment and location-based road services. To this end, self-sustaining caching stations (SCSs) are introduced to liberate on-road base stations from the constraints of power lines and wired backhauls. Specifically, the cache-enabled SCSs are powered by renewable energy and connected to core networks through wireless backhauls, which can realize "drop-and-play" deployment, green operation, and low-latency services. With SCSs integrated, a 5G-enabled heterogeneous vehicular networking architecture is further proposed, where SCSs are deployed along roadside for traffic offloading while conventional macro base stations (MBSs) provide ubiquitous coverage to vehicles. In addition, a hierarchical network management framework is designed to deal with high dynamics in vehicular traffic and renewable energy, where content caching, energy management and traffic steering are jointly investigated to optimize the service capability of SCSs with balanced power demand and supply in different time scales. Case studies are provided to illustrate SCS deployment and operation designs, and some open research issues are also discussed.Comment: IEEE Communications Magazine, to appea

Network Prominence in Social Network Services and Seller Performance in Social Marketplaces: An Exploratory Study

Social network services (SNS) have been used to support sellers in e-commerce marketplaces for years. It offers multiple affordances to sellers to promote their products, maintain relationships with customers and learn from other sellers. However, the open nature of online SNS also fosters competition as sellers can closely follow each other. How SNS impacts the sales performance of online sellers has not been studied in the literature. This research presents a preliminary study to explore the role of SNS on the performance of online sellers, with a focus on seller’s network prominence in SNS. Four types of network prominences are proposed in the research and data from 83,462 stores were collected over two years to empirically verify our hypotheses

Understanding the ego network structure of followers in social marketplaces: Structural capital or liability?

In social marketplaces, follower network is one of the most important digital assets that an online seller owns. The existing literature repeatedly reports the strong positive effect of the number of followers on seller performance. However, to date, limited research has been done to understand the ego network structure of a seller’s follower network. The structure of social network may characterize different resources that a seller can leverage to enhance its sales performance. This research studies three network structural properties, including network density, network component and fragmentation, and network centralization, and their impacts on seller performance. A panel data of 1,150 sellers were collected and analyzed. The results show that network density, and centralization are negatively related to seller performance. This suggests that sellers in social marketplaces should avoid highly dense and centralized network when they build and maintain their follower network

Using multitask classification methods to investigate the kinase-specific phosphorylation sites

<p>Abstract</p> <p>Background</p> <p>Identification of phosphorylation sites by computational methods is becoming increasingly important because it reduces labor-intensive and costly experiments and can improve our understanding of the common properties and underlying mechanisms of protein phosphorylation.</p> <p>Methods</p> <p>A multitask learning framework for learning four kinase families simultaneously, instead of studying each kinase family of phosphorylation sites separately, is presented in the study. The framework includes two multitask classification methods: the Multi-Task Least Squares Support Vector Machines (MTLS-SVMs) and the Multi-Task Feature Selection (MT-Feat3).</p> <p>Results</p> <p>Using the multitask learning framework, we successfully identify 18 common features shared by four kinase families of phosphorylation sites. The reliability of selected features is demonstrated by the consistent performance in two multi-task learning methods.</p> <p>Conclusions</p> <p>The selected features can be used to build efficient multitask classifiers with good performance, suggesting they are important to protein phosphorylation across 4 kinase families.</p

(1R,1′S)-1,1′-Dihydr­oxy-1,1′-biisobenzofuran-3,3′(1H,1′H)-dione

In the title compound, C16H10O6, the complete mol­ecule is generated by a crystallographic centre of symmetry. In the crystal, O—H⋯O hydrogen bonds link the mol­ecules into (100) sheets and C—H⋯O links also occur