Using first-principles approaches, we investigate the thermoelectric efficiency, characterized by the figure of merit ZT, in metallic atomic junctions and insulating molecular junctions. To gain insight into the properties of ZT, an analytical theory is also developed to study the dependence of ZT on lengths (l) and temperatures (T). The theory considers the combined heat current carried by electrons and phonons. We observe a characteristic temperature: T(0) = (beta/gamma(/))(1/2). When T << T(0), the electronic heat current dominates the combined heat current and ZT is proportional to T(2). When T >> T(0), the phononic heat current dominates the combined heat current and ZT tends to a saturation value. Moreover, the metallic atomic junctions and the insulating molecular junctions have opposite trend for the dependence of ZT on lengths, that is, ZT increases as the length increases for aluminum atomic junctions, while ZT decreases as the length increases for alkanethiol molecular junctions.
Software Defined Network separates the control plane from network equipment and has great advantage in network management as compared with traditional approaches. With this paradigm, the security issues persist to exist and could become even worse because of the flexibility on handling the packets. In this paper we propose an effective framework by integrating SDN and machine learning to detect and categorize P2P network traffics. This work provides experimental evidence showing that our approach can automatically analyze network traffic and flexibly change flow entries in OpenFlow switches through the SDN controller. This can effectively help the network administrators manage related security problems.
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