The efficient sequencing techniques have significantly increased the number of genomes that are now available, including the Crenarchaeon Sulfolobus solfataricus P2 genome. The genome-scale metabolic pathways in Sulfolobus solfataricus P2 were predicted by implementing the “Pathway Tools” software using MetaCyc database as reference knowledge base. A Pathway/Genome Data Base (PGDB) specific for Sulfolobus solfataricus P2 was created. A curation approach was carried out regarding all the amino acids biosynthetic pathways. Experimental literatures as well as homology-, orthology- and context-based protein function prediction methods were followed for the curation process. The “PathoLogic”

The present study explores numerically the energy storage and energy regeneration during Melting and Solidification processes in Phase Change Materials (PCM) used in Latent Heat Thermal Energy Storage (LHTES) systems. Transient two-dimensional (2-D) conduction heat transfer equations with phase change have been solved utilizing the Explicit Finite Difference Method (FDM) and Grid Generation technique. A Fortran computer program was built to solve the problem. The study included four different Paraffin's. The effects of container geometrical shape, which included cylindrical and square sections of the same volume and heat transfer area, the container volume or mass of PCM, variation of mass flow rate of heat transfer fluid (HTF), and temp

Transmission lines are generally subjected to faults, so it is advantageous to determine these faults as quickly as possible. This study uses an Artificial Neural Network technique to locate a fault as soon as it happens on the Doukan-Erbil of 132kv double Transmission lines network. CYME 7.1-Programming/Simulink utilized simulation to model the suggested network. A multilayer perceptron feed-forward artificial neural network with a back propagation learning algorithm is used for the intelligence locator's training, testing, assessment, and validation. Voltages and currents were applied as inputs during the neural network's training. The pre-fault and post-fault values determined the scaled values. The neural network's p

Through recent years many researchers have developed methods to estimate the self-similarity and long memory parameter that is best known as the Hurst parameter. In this paper, we set a comparison between nine different methods. Most of them use the deviations slope to find an estimate for the Hurst parameter like Rescaled range (R/S), Aggregate Variance (AV), and Absolute moments (AM), and some depend on filtration technique like Discrete Variations (DV), Variance versus level using wavelets (VVL) and Second-order discrete derivative using wavelets (SODDW) were the comparison set by a simulation study to find the most efficient method through MASE. The results of simulation experiments were shown that the performance of the meth

This paper investigates the performance evaluation of two state feedback controllers, Pole Placement (PP) and Linear Quadratic Regulator (LQR). The two controllers are designed for a Mass-Spring-Damper (MSD) system found in numerous applications to stabilize the MSD system performance and minimize the position tracking error of the system output. The state space model of the MSD system is first developed. Then, two meta-heuristic optimizations, Simulated Annealing (SA) optimization and Ant Colony (AC) optimization are utilized to optimize feedback gains matrix K of the PP and the weighting matrices Q and R of the LQR to make the MSD system reach stabilization and reduce the oscillation of the response. The Matlab softwar

Computer modeling has been used to investing the Coulomb coupling parameter ?. The effects of the structure parameter K, grain charge Z, plasma density N, temperature dust grain Td, on the Coulomb coupling parameter had been studied. It was seen that the ? was increasing with increasing Z and N, and decrease with increasing K and T. Also the critical value of ? that the phase transfer of the plasma state from liquid to solid was studied.

Abstract

Robust controller design requires a proper definition of uncertainty bounds. These uncertainty bounds are commonly selected randomly and conservatively for certain stability, without regard for controller performance.  This issue becomes critically important for multivariable systems with high nonlinearities, as in Active Magnetic Bearings (AMB) System. Flexibility and advanced learning abilities of intelligent techniques make them appealing for uncertainty estimation. The aim of this paper is to describe the development of robust H₂/H_∞ controller for AMB based on intelligent estimation of uncertainty bounds using Adaptive Neuro Fuzzy Inference System (ANFIS).  Simulatio