The development of wireless sensor networks (WSNs) in the underwater environment leads to underwater WSN (UWSN). It has severe impact over the research field due to its extensive and real-time applications. However effective execution of underwater WSNs undergoes several problems. The main concern in the UWSN is sensor nodes’ energy depletion issue. Energy saving and maintaining quality of service (QoS) becomes highly essential for UWASN because of necessity of QoS application and confined sensor nodes (SNs). To overcome this problem, numerous prevailing methods like adaptive data forwarding techniques, QoS-based congestion control approaches, and various methods have been devised with maximum throughput and minimum network lifesp

In this paper, we characterize the percolation condition for a continuum secondary cognitive radio network under the SINR model. We show that the well-established condition for continuum percolation does not hold true in the SINR regime. Thus, we find the condition under which a cognitive radio network percolates. We argue that due to the SINR requirements of the secondaries along with the interference tolerance of the primaries, not all the deployed secondary nodes necessarily contribute towards the percolation process- even though they might participate in the communication process. We model the invisibility of such nodes using the concept of Poisson thinning, both in the presence and absence of primaries. Invisibility occurs due to nodes

The development of wireless sensor networks (WSNs) in the underwater environment leads to underwater WSN (UWSN). It has severe impact over the research field due to its extensive and real-time applications. However effective execution of underwater WSNs undergoes several problems. The main concern in the UWSN is sensor nodes’ energy depletion issue. Energy saving and maintaining quality of service (QoS) becomes highly essential for UWASN because of necessity of QoS application and confined sensor nodes (SNs). To overcome this problem, numerous prevailing methods like adaptive data forwarding techniques, QoS-based congestion control approaches, and various methods have been devised with maximum throughput and minimum network lifesp

 The aim of this paper, is to discuss several high performance training algorithms fall into two main categories. The first category uses heuristic techniques, which were developed from an analysis of the performance of the standard gradient descent algorithm. The second category of fast algorithms uses standard numerical optimization techniques such as: quasi-Newton . Other aim is to solve the drawbacks related with these training algorithms and propose an efficient training algorithm for FFNN

 In this paper, an intelligent tracking control system of both single- and double-axis Piezoelectric Micropositioner stage is designed using Genetic Algorithms (GAs) method for the optimal Proportional-Integral-Derivative (PID) controller tuning parameters. The (GA)-based PID control design approach is a methodology to tune a (PID) controller in an optimal control sense with respect to specified objective function. By using the (GA)-based PID control approach, the high-performance trajectory tracking responses of the Piezoelectric Micropositioner stage can be obtained. The (GA) code was built and the simulation results were obtained using MATLAB environment. The Piezoelectric Micropositioner simulation model with th

The objective of an Optimal Power Flow (OPF) algorithm is to find steady state operation point which minimizes generation cost, loss etc. while maintaining an acceptable system performance in terms of limits on generators real and reactive powers, line flow limits etc. The OPF solution includes an objective function. A common objective function concerns the active power generation cost. A Linear programming method is proposed to solve the OPF problem. The Linear Programming (LP) approach transforms the nonlinear optimization problem into an iterative algorithm that in each iteration solves a linear optimization problem resulting from linearization both the objective function and constrains. A computer program, written in MATLAB environme

Sphingolipids are key components of eukaryotic membranes, particularly the plasma membrane. The biosynthetic pathway for the formation of these lipid species is largely conserved. However, in contrast to mammals, which produce sphingomyelin, organisms such as the pathogenic fungi and protozoa synthesize inositol phosphorylceramide (IPC) as the primary phosphosphingolipid. The key step involves the reaction of ceramide and phosphatidylinositol catalysed by IPC synthase, an essential enzyme with no mammalian equivalent encoded by the AUR1 gene in yeast and recently identified functional orthologues in the pathogenic kinetoplastid protozoa. As such this enzyme represents a promising target for novel anti-fungal and anti-protozoal drugs. Given