An Improvement in Congestion Control Using Multipath Routing in MANET

The ad hoc connections, which opens many opportunities for MANET applications. In ad hoc network nodes are movable and there is no centralised management. Routing is an important factor in mobile ad hoc network which not only works well with a small network, but also it can also work well if network get expanded dynamically. Routing in Manets is a main factor considered among all the issues. Mobile nodes in Manet have limited transmission capacity, they intercommunicate by multi hop relay. Multi hop routing have many challenges such as limited wireless bandwidth, low device power, dynamically changing network topology, and high vulnerability to Failure. To answer those challenges, many routing algorithms in Manets were proposed. But one of the problems in routing algorithm is congestion which decreases the overall performance of the network so in this paper we are trying to identify the best routing algorithm which will improve the congestion control mechanism among all the Multipath routing protocols. Keywords: Disjoint Multipath, Multi hop, reliability, Congestion Control, Optimizatio

An Improvement in Congestion Control Using Multipath Routing in Manet

The ad hoc connections, which opens many opportunities for MANET applications. In ad hoc network nodes are movable and there is no centralised management. Routing is an important factor in mobile ad hoc network which not only works well with a small network, but also it can also work well if network get expanded dynamically. Routing in Manets is a main factor considered among all the issues. Mobile nodes in Manet have limited transmission capacity, they intercommunicate by multi hop relay. Multi hop routing have many challenges such as limited wireless bandwidth, low device power, dynamically changing network topology, and high vulnerability to Failure. To answer those challenges, many routing algorithms in Manets were proposed. But one of the problems in routing algorithm is congestion which decreases the overall performance of the network so in this paper we are trying to identify the best routing algorithm which will improve the congestion control mechanism among all the Multipath routing protocols

Node Disjoint Multipath Routing Approach for Controlling Congestion in MANETs

Mobile Ad hoc Networks are highly dynamic networks. Quality of Service (QoS) routing in such networks is usually limited by the network breakage due to either node mobility or energy depletion of the mobile nodes. Nodedisjoint routing becomes inessential technique in communication of packets among various nodes in networks. Meanwhile AODV (Ad Hoc On-demand Multipath Distance Vector) creates single-path route between a pair of source and destination nodes. Some researches has done so far to make multipath node-disjoint routing based on AODV protocol. But however their overhead and end-to-end delay are relatively high, while the detail of their code is not available too. In an ad hoc network, identification of all node-disjoint paths between a given pair of nodes is a challenging task. The phenomena that a protocol is not able to identify all node-disjoint paths that exist between a given pair of nodes is called path diminution. In this paper, we discuss that path diminution is unavoidable when a protocol discovers multiple node-disjoint paths in a single route discovery and working of node disjoint multipath protocol

A patient with mexiletine-related psychosis

Mexiletine is a commonly used Class IB (Vaughan William classification) antiarrhythmic drug. We report a case of mexiletine-induced psychosis that was successfully managed by decreasing the dose and using alternative medications for management of ventricular tachycardia

Identification of Co-Existing Mutations and Gene Expression Trends Associated With K13-Mediated Artemisinin Resistance in Plasmodium falciparum

Plasmodium falciparum infects millions and kills thousands of people annually the world over. With the emergence of artemisinin and/or multidrug resistant strains of the pathogen, it has become even more challenging to control and eliminate the disease. Multiomics studies of the parasite have started to provide a glimpse into the confounding genetics and mechanisms of artemisinin resistance and identified mutations in Kelch13 (K13) as a molecular marker of resistance. Over the years, thousands of genomes and transcriptomes of artemisinin-resistant/sensitive isolates have been documented, supplementing the search for new genes/pathways to target artemisinin-resistant isolates. This meta-analysis seeks to recap the genetic landscape and the transcriptional deregulation that demarcate artemisinin resistance in the field. To explore the genetic territory of artemisinin resistance, we use genomic single-nucleotide polymorphism (SNP) datasets from 2,517 isolates from 15 countries from the MalariaGEN Network (The Pf3K project, pilot data release 4, 2015) to dissect the prevalence, geographical distribution, and co-existing patterns of genetic markers associated with/enabling artemisinin resistance. We have identified several mutations which co-exist with the established markers of artemisinin resistance. Interestingly, K13-resistant parasites harbor α-ß hydrolase and putative HECT domain–containing protein genes with the maximum number of SNPs. We have also explored the multiple, publicly available transcriptomic datasets to identify genes from key biological pathways whose consistent deregulation may be contributing to the biology of resistant parasites. Surprisingly, glycolytic and pentose phosphate pathways were consistently downregulated in artemisinin-resistant parasites. Thus, this meta-analysis highlights the genetic and transcriptomic features of resistant parasites to propel further exploratory studies in the community to tackle artemisinin resistance.</p

Gradient Information and Regularization for Gene Expression Programming to Develop Data-Driven Physics Closure Models

Learning accurate numerical constants when developing algebraic models is a known challenge for evolutionary algorithms, such as Gene Expression Programming (GEP). This paper introduces the concept of adaptive symbols to the GEP framework by Weatheritt and Sandberg (2016) to develop advanced physics closure models. Adaptive symbols utilize gradient information to learn locally optimal numerical constants during model training, for which we investigate two types of nonlinear optimization algorithms. The second contribution of this work is implementing two regularization techniques to incentivize the development of implementable and interpretable closure models. We apply $L_2$ regularization to ensure small magnitude numerical constants and devise a novel complexity metric that supports the development of low complexity models via custom symbol complexities and multi-objective optimization. This extended framework is employed to four use cases, namely rediscovering Sutherland's viscosity law, developing laminar flame speed combustion models and training two types of fluid dynamics turbulence models. The model prediction accuracy and the convergence speed of training are improved significantly across all of the more and less complex use cases, respectively. The two regularization methods are essential for developing implementable closure models and we demonstrate that the developed turbulence models substantially improve simulations over state-of-the-art models