Emulating opportunistic networks with KauNet Triggers

In opportunistic networks the availability of an end-to-end path is no longer required. Instead opportunistic networks may take advantage of temporary connectivity opportunities. Opportunistic networks present a demanding environment for network emulation as the traditional emulation setup, where application/transport endpoints only send and receive packets from the network following a black box approach, is no longer applicable. Opportunistic networking protocols and applications additionally need to react to the dynamics of the underlying network beyond what is conveyed through the exchange of packets. In order to support IP-level emulation evaluations of applications and protocols that react to lower layer events, we have proposed the use of emulation triggers. Emulation triggers can emulate arbitrary cross-layer feedback and can be synchronized with other emulation effects. After introducing the design and implementation of triggers in the KauNet emulator, we describe the integration of triggers with the DTN2 reference implementation and illustrate how the functionality can be used to emulate a classical DTN data-mule scenario

Vrcholovo tranzitivní Haarovi grafy kterí nejsou Cayleyho

V nedávním článku Electron. J. Combin. 23 (2016) Estélyi a Pisanski se ptali, jestli existují vrcholovo tranzitivní Haarovi grafy kterí nejsou Cayleyho. V tomhle článku se konštruuje nekoneční třída kubických Haarových grafů, které jsou vrcholovo tranzitivní, avšak nejsou Cayleyho. Nejmenší takový graf má 40 vrcholů a je známy jako Kroneckerove nakrytí dodekaedra G(10,2), a ve Fosterověm cenzusu je uveden jako graf "40".The function of enzymatic proteins is given by their ability to bind specific small molecules into their active sites. These sites can often be found in pockets on a hypothetical boundary between the protein and its environment. Detection, analysis, and visualization of pockets find its use in protein engineering and drug discovery. Many definitions of pockets and algorithms for their computation have been proposed. Kawabata and Go defined them as the regions of empty space into which a small spherical probe can enter but a large probe cannot and developed programs that can compute their approximate shape. In this article, this definition was slightly modified in order to capture the existence of large internal holes, and a Voronoi‐based method for the computation of the exact shape of these modified regions is introduced. The method first puts a finite number of large probes on the protein exterior surface and then, considering both large probes and atomic balls as obstacles for the small probe, the method computes the exact shape of the regions for the small probe. This is all achieved with Voronoi diagrams, which help with the safe navigation of spherical probes among spherical obstacles. Detected regions are internally represented as graphs of vertices and edges describing possible movements of the center of the small probe on Voronoi edges. The surface bounding each region is obtained from this representation and used for visualization, volume estimation, and comparison with other approaches