Sodium sulfate as an efficient additive of negative paste for lead-acid batteries

This paper is devoted to the effect of sodium sulfate as negative paste additive on the performance of the lead-acid battery. Six different percentages of sodium sulfate were added to negative paste. The effect of sodium sulfate on discharge capacity, cycle life and cold cranking ability of the sealed lead-acid batteries were investigated. Batteries containing sodium sulfate in negative plates at low amount (0.1 wt%) showed a remarkable electrical behavior during the test. Results indicate that negative electrodes containing 0.1 wt% sodium sulfate exhibit discharge capacity of the more than 3% and 12% with respect to negative electrodes without sodium sulfate before and after cycling test, respectively. Addition of sodium sulfate also increases the time of reaching to cut off voltage of 6 V at cold cranking test more than 17%. The main effect of sodium sulfate is to increase the cycle life of the lead-acid batteries to more than 18%. Scanning electron microscopy (SEM) was used for the investigation of paste morphology.     

ANN modeling of water consumption in the lead-acid batteries

Due to importance of the quantity of water loss in the life cycle of lead-acid batteries, water consumption tests were performed on 72 lead-acid batteries with low antimony grid alloy at different charge voltages and temperatures. Weight loss of batteries was measured during a period of 10 days. The behavior of batteries in different charge voltages and temperatures were modeled by artificial neural networks (ANNs) using MATLAB 7 media. Four temperatures were used in the training set, out of which three were used in prediction set and one in validation set. The network was trained by training and prediction data sets, and then was used for predicting water consumption in all three temperatures of prediction set. Finally, the network obtained was verified while being used in predicting water loss in defined temperatures of validation set. To achieve a better evaluation of the model ability, three models with different validation temperatures were used (model 1 = 50 °C, model 2 = 60 °C and model 3 = 70 °C). There was a good agreement between predicted and experimental results at prediction and validation sets for all the models.     

Importance sampling for Jackson networks with customer impatience until the end of service

Importance sampling is a technique that is commonly used to speed up Monte Carlo simulation of rare events. The standard approach, which simulates the system using an a priori fixed change of measure, has been shown to fail in even the simplest network settings. Estimating probabilities associated with rare events has been a topic of great importance in queuing theory, and in applied probability at large. In this paper, we estimate the probability of two rare events known as total population overflow and individual buffer overflow in an open Jackson network in which the customers should receive the needed service in a definite deadline. we use parallel computing in implementing the estimator. Moreover, we consider the effect of various network parameters on aforementioned overflow probabilities, and we have also shown that how these parameters affect the probability of missing the deadline.     

Correction to: Novel design and simulation of reversible ALU in quantum dot cellular automata

The Journal of Supercomputing -     

An efficient energy-aware method for virtual machine placement in cloud data centers using the cultural algorithm

Excessive consumption of energy in cloud data centers whose number is increasing day by day has led to substantial problems. Hence, offering efficient schemes for virtual machine (VM) placement to decrease energy consumption in cloud computing environments has become a significant research field in recent years. In this paper, with the goal of reducing energy consumption in cloud data centers, we present a VM placement method using the cultural algorithm. In the proposed algorithm called balance-based cultural algorithm for virtual machine placement (BCAVMP), a new fitness function is introduced to evaluate VM allocation solutions. In this function, by using the sum of balance vector lengths for each VM placement, balanced utilization of resources is considered. Also, by applying the amount of energy usage in the fitness function, solutions with lower energy consumption are intended. The performance of the proposed method is evaluated using CloudSim simulator. The simulation results indicate that by appropriate VM assignment and resource wastage reduction, energy consumption in cloud data centers can be decreased.

     

Reduce energy consumption in sensors using a smartphone,smartwatch, and the use of SFLA algorithms (REC-SSS)

The Journal of Supercomputing - Wireless body area networks are a technology for remote medical care. Because of the limited energy of the sensors, one of the problems of long-distance medical care...