A hybrid GA–SQP optimization technique for determination of kinetic parameters of hydrogenation reactions

A hybrid optimization technique, GA–SQP, is developed in which the genetic algorithm (GA) which is a stochastic method is combined with the sequential quadratic programming (SQP) method which is a deterministic method. This method was used to determine the kinetic parameters of the set of highly nonlinear hydrogenation reactions. Catalyst deactivation was also taken into account. The ability of GA and SQP in solving this type of problem was investigated. It was shown that although the SQP is fast, it is not able to solve this problem properly and is very sensitive to the choice of initial point. The GA was able to solve the problem after a large number of generations. It was shown that the new GA–SQP hybrid method is able to determine the final solution considerably faster than the GA while it is not sensitive to the initial point.     

Versatile light-emitting-diode-based spectral response measurement system for photovoltaic device characterization

An absolute differential spectral response measurement system for solar cells is presented. The system couples an array of light emitting diodes with an optical waveguide to provide large area illumination. Two unique yet complementary measurement methods were developed and tested with the same measurement apparatus. Good agreement was observed between the two methods based on testing of a variety of solar cells. The first method is a lock-in technique that can be performed over a broad pulse frequency range. The second method is based on synchronous multifrequency optical excitation and electrical detection. An innovative scheme for providing light bias during each measurement method is discussed.     

EBSD and DTA Characterization of A356 Alloy Deformed by ECAP During Reheating and Partial Re-melting

Recrystallization and partial re-melting processes have been developed for producing semi-solid feedstock in a solid state in which a globular microstructure is obtained by plastic deformation followed by reheating. In this research, to induce strain, a cast- and solution-treated Aluminum A356 (7 wt pct Si) alloy was subjected to a repetitive equal channel angular pressing process using a 90 deg die, up to a total accumulated strain of approximately 8 in route A (increasing strain through a sequence of passes with no rotation of the sample after each pass) at ambient temperature. The microstructural evolutions of deformed and reheated materials were studied by optical microscopy, scanning electron microscopy, and electron back-scattered diffraction analysis. In addition, the influences of pre-deformation on the recrystallization mechanism and liquid formation of A356 alloy were presented and discussed. The results are also supported by differential thermal analysis experiments. Evaluation of the observations indicated that the average cell boundary misorientation increased with increasing strain, so this increased misorientation accelerated the mobility of boundaries and recrystallization kinetics. Therefore, the recrystallization mechanism and kinetics affected by deformation, reheating condition, and intrinsic material properties determined the particle size in the semi-solid state.     

LATOC: an enhanced load balancing algorithm based on hybrid AHP-TOPSIS and OPSO algorithms in cloud computing

Providing required level of service quality in cloud computing is one of the most significant cloud computing challenges because of software and hardware complexities, different features of tasks and computing resources and also, lack of appropriate distribution of tasks in cloud computing environments. The recent research in this field show that lack of smart prioritization and ordering of tasks in scheduling (as an NP-hard problem) has been very effective and resulted in lack of load balancing, response time increase, total execution time increase and also, average resource use decrease. In line with this, the proposed method of this research called LATOC considered first the key criteria of an input task like required processing unit, data length of task and execution time. Then, it addressed task prioritization in separate queues using the technique for order preference by similarity to ideal solution (TOPSIS) and analytic hierarchy process (AHP) in figure of a hybrid intelligent algorithm (AHP-TOPSIS). Each ordered task in separate priority queues was placed based on its priority level, and then, to assign each task from each priority queue to virtual machines, optimized particle swarm optimization was used. Many simulations based on various scenarios in Cloudsim simulator show that smart assignment of prioritized tasks by LATOC resulted in improvement of important cloud computing parameters such as total execution time and average resource use comparing similar methods.

     

GAZELLE: An Enhanced Random Network Coding Based Framework for Efficient P2P Live Video Streaming Over Hybrid WMNs

Wireless Personal Communications - Although Peer-to-Peer live video streaming over wireless mesh networks (WMNs) is considered a promising technology, some important challenges such as...     

Adaptive video protection in large scale peer‐to‐peer video streaming over mobile wireless mesh networks

Because video streaming over mobile handheld devices has been of great interest, the necessity of introducing new methods with low implementation cost and scalable infrastructures is a strong demand of the service. In particular, these requirements are present in popular wireless networks such as wireless mesh networks (WMN). Peer‐to‐peer (P2P) networks promise an efficient scalable network infrastructure for video streaming over wired and wireless networks. Limited resources of the peers in P2P networks and high error rate in wireless channels make it more challenging to run P2P streaming applications over WMNs. Therefore, it is necessary to design efficient and improved error protection methods in P2P video streaming applications over WMNs. In this paper, we propose a new adaptive unequal video protection method specially intended for large scale P2P video streaming over mobile WMNs. Using this method, different frames have different priorities in receivers along the recovery process. Moreover, we precisely and completely evaluate different aspects related to frame protection in these networks using five important performance metrics including video distortion, late arrival distortion, end‐to‐end delay, overhead and initial start‐up delay. The results obtained from a precise simulation in OMNeT++ show that the proposed adaptive method significantly outperforms other solutions by providing better video quality on mobile wireless nodes. Copyright © 2015 John Wiley & Sons, Ltd.     

Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran

This paper presents application of PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) techniques to estimate oil demand in Iran, based on socio-economic indicators. The models are developed in two forms (exponential and linear) and applied to forecast oil demand in Iran. PSO–DEM and GA–DEM (PSO and GA demand estimation models) are developed to estimate the future oil demand values based on population, GDP (gross domestic product), import and export data. Oil consumption in Iran from 1981 to 2005 is considered as the case of this study. The available data is partly used for finding the optimal, or near optimal values of the weighting parameters (1981–1999) and partly for testing the models (2000–2005). For the best results of GA, the average relative errors on testing data were 2.83% and 1.72% for GA–DEM_exponential and GA–DEM_linear, respectively. The corresponding values for PSO were 1.40% and 1.36% for PSO–DEM_exponential and PSO–DEM_linear, respectively. Oil demand in Iran is forecasted up to year 2030.     

A model of the rat phrenic motor neuron

We have developed a model for the rat phrenic motor neuron (PMN) that robustly replicates many experimentally observed behaviors of PMNs in response to pharmacological, ionic, and electrical perturbations using a single set of parameters. Our model suggests that the after-depolarization (ADP) response seen in action potentials is a result of the slow deactivation of the fast sodium channel in the range of the ADP coupled with the activation of the L-type calcium channel (I(CaL)). This current and its interactions with the small and large conductance calcium-activated potassium currents (I(KCaSK) and I(KCaBK), respectively) is also important in the generation of spike frequency adaptation in the repetitive firing mode of activity. Other aspects of the model conform very well to experimental observations in both the action potential and repetitive firing mode of activity, including the role of I(KCaSK) in the medium after-hyperpolarization (AHP) and the role of I(KCaBK) in the fast AHP. We have made a number of predictions using the model, including the characterization of two putative sodium currents (fast and persistent), as well as functional roles for the N- and T-type calcium currents.     

A novel cost-optimizing demand response control for a heat pump heated residential building

The present article describes the integration of a data-driven predictive demand response control for residential buildings with heat pump and on-site energy generation. The data driven control approach schedules the heating system of the building. In each day, the next 24 hours heating demand of buildings, including space heating and domestic hot water consumption, are predicted by means of a hybrid wavelet transformation and a dynamic neural network. Linear programming is implemented to define a cost-optimal schedule for the heat pump operation. Moreover, the study discusses the impact of heat demand prediction error on performance of demand response control. In addition, the option of energy trading with the electrical grid is considered in order to evaluate the possibility of increasing the profit for private householders through on-site energy generation. The results highlight that the application of the proposed predictive control could reduce the heating energy cost up to 12% in the cold Finnish climate. Furthermore, on-site energy generation declines the total energy cost and consumption about 43% and 24% respectively. The application of a data-driven control for the demand prediction brings efficiency to demand response control.