The use of graph theory for analyzing network-like data has gained central importance with the rise of the Web 2.0. However, many graph-based techniques are not welldisseminated and neither explored at their full potential, what might depend on a complimentary approach achieved with the combination of multiple techniques. This paper describes the systematic use of graph-based techniques of different types (multimodal) combining the resultant analytical insights around a common domain, the Digital Bibliography & Library Project (DBLP). To do so, we introduce an analytical ensemble based on statistical (degree, and weakly-connected components distribution), topological (average clustering coefficient, and effective diameter evolution), algorithmic (link prediction/machine learning), and algebraic techniques to inspect non-evident features of DBLP at the same time that we interpret the heterogeneous discoveries found along the work. As a result, we have put together a set of techniques demonstrating over DBLP what we call multimodal analysis, an innovative process of information understanding that demands a wide technical knowledge and a deep understanding of the data domain. We expect that our methodology and our findings will foster other multimodal analyses and also that they will bring light over the Computer Science research.
Silicon carbide is a well-known wide-band gap semiconductor traditionally used in power electronics and solid-state lighting due to its extremely low intrinsic carrier concentration and high thermal conductivity. What is only recently being discovered is that it possesses excellent compatibility within the biological world. Since publication of the first edition of Silicon Carbide Biotechnology: A Biocompatible Semiconductor for Advanced Biomedical Devices and Applications five years ago [1], significant progress has been made on numerous research and development fronts. In this paper three very promising developments are briefly highlighted – progress towards the realization of a continuous glucose monitoring system, implantable neural interfaces made from free-standing 3C-SiC, and a custom-made low-power ‘wireless capable’ four channel neural recording chip for brain-machine interface applications.
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