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Software‐defined networking (SDN) is one of the most used network architecture that divides the forwarding plane and control plane. The SDN centrally observes and regulates the network through a software control in the control plane, called as controller. Multiple controllers are needed to manage the software‐defined WAN (SD‐WAN) for handling the scalability and reliability issues of the network as one controller is not enough. Deploying numerous controllers efficiently to improve the performance of the network is known as controller placement problem (CPP). This paper proposes a Varna‐based optimization (VBO) for a reliable CPP that minimizes the total average latency of SDN. To the best of our knowledge, the proposed work is a novel approach, which is compared with particle swarm optimization, teacher learning‐based optimization, and Jaya algorithms to solve reliable CPP. The experimental results show that VBO gives better performance than teacher learning‐based optimization (TLBO), PSO, and Jaya algorithms for the popular topologies that are publicly available.

Malaria caused by protozoan parasites belonging to the genus Plasmodium is a dreaded disease, second only to tuberculosis. The emergence of parasites resistant to commonly used drugs and the lack of availability of vaccines aggravates the problem. One of the preventive approaches targets adhesion of parasites to host cells and tissues. Adhesion of parasites is mediated by proteins called adhesins. Abrogation of adhesion by either immunizing the host with adhesins or inhibiting the interaction using structural analogs of host cell receptors holds the potential to develop novel preventive strategies. The availability of complete genome sequence offers new opportunities for identifying adhesin and adhesin-like proteins. Development of computational algorithms can simplify this task and accelerate experimental characterization of the predicted adhesins from complete genomes. A curated positive dataset of experimentally known adhesins from Plasmodium species was prepared by careful examination of literature reports. "Controversial" or "hypothetical" adhesins were excluded. The negative dataset consisted of proteins representing various intracellular functions including information processing, metabolism, and interface (transporters). We did not include proteins likely to be on the surface with unknown adhesin properties or which are linked even indirectly to the adhesion process in either of the training sets. A nonhomology-based approach using 420 compositional properties of amino acid dipeptide and multiplet frequencies was used to develop MAAP Web server with Support Vector Machine (SVM) model classifier as its engine for the prediction of malarial adhesins and adhesin-like proteins. The MAAP engine has six SVM classifier models identified through an exhaustive search from 728 kernel parameters set. These models displayed an efficiency (Mathews correlation coefficient) of 0.860-0.967. The final prediction P(maap) score is the maximum score attained by a given sequence in any of the six models. The results of MAAP runs on complete proteomes of Plasmodium species revealed that in Plasmodium falciparum at P(maap) scores above 0.0, we observed a sensitivity of 100% with two false positives. In P. vivax and P. yoelii an optimal threshold P(maap) score of 0.7 was optimal with very few false positives (upto 5). Several new predictions were obtained. This list includes hypothetical protein PF14_0040, interspersed repeat antigen, STEVOR, liver stage antigen, SURFIN, RIFIN, stevor (3D7-stevorT3-2), mature parasite-infected erythrocyte surface antigen or P. falciparum erythrocyte membrane protein 2, merozoite surface protein 6 in P. falciparum, circumsporozoite proteins, microneme protein-1, Vir18, Vir12-like, Vir12, Vir18-like, Vir18-related and Vir4 in P. vivax, circumsporozoite protein/thrombospondin related anonymous proteins, 28 kDa ookinete surface protein, yir1, and yir4 of P. yoelii. Among these, a few proteins identified by MAAP were matched with those identified by other groups using different experimental and theor...

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Insects and small animals capable of adhering reversibly to a variety of surfaces employ the unique design of the distal part of their legs. In the case of mosquitoes, their feet are composed of thousands of micro- and nanoscale protruding structures, which impart superhydrophobic properties. Previous research has shown that the superhydrophobic nature of the feet allows mosquitoes to land on water, which is necessary for their reproduction cycle. Here, we show that van der Waals interactions are the main adhesion mechanism employed by mosquitoes to adhere to various surfaces. We further demonstrate that the judicious creation of surface roughness on an opposing surface can increase the adhesion strength because of the increased number of surface elements interacting with the setae through multiple contact points. Although van der Waals forces are shown to be the predominant mechanism by which mosquitoes adhere to surfaces, capillary forces can also contribute to the total adhesion force when the opposing surface is hydrophilic and under humid conditions. These fundamental properties can potentially be applied in the development of superior Long Lasting Insecticidal Nets (LLINs), which represent one of the most effective methods to mitigate mosquito-transmitted infectious diseases such as Malaria, Filaria, Zika, and Dengue.