Green synthesis of silver nanoparticles using Glaucium corniculatum (L.) Curtis extract and evaluation of its antibacterial activity

The metal nanoparticles, due to interesting features such as electrical, optical, chemical and magnetic properties, have been investigated repeatedly. Also, the mentioned nanoparticles have specific uses in terms of their antibacterial activity. The biosynthesis method is more appropriate than the chemical method for producing the nanoparticles because it does not need any special facilities; it is also economically affordable. In the current study, the silver nanoparticles (AgNPs) were obtained by using a very simple and low‐cost method via Glaucium corniculatum (L.) Curtis plant extract. The characteristics of the AgNPs were investigated using techniques including: X‐ray diffraction, transmission electron microscopy (TEM), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy. The SEM and TEM images showed that the nanoparticles had a spherical shape, and the mean diameter of them was 53.7 and 45 nm, respectively. The results of the disc diffusion test used for measuring the anti‐bacterial activity of the synthesised nanoparticles indicated that the formed nanoparticles possessed a suitable anti‐bacterial activity.Inspec keywords: silver, nanoparticles, antibacterial activity, nanomedicine, nanofabrication, X‐ray diffraction, transmission electron microscopy, scanning electron microscopy, Fourier transform infrared spectraOther keywords: green synthesis, silver nanoparticles, Glaucium corniculatum Curtis extract, antibacterial activity, metal nanoparticles, biosynthesis method, X‐ray diffraction, transmission electron microscopy, scanning electron microscopy, Fourier transform infrared spectroscopy, SEM, TEM, spherical shape, disc diffusion test, Ag     

Double Auction-based Scheduling of Scientific Applications in Distributed Grid and Cloud Environments

Economy models have long been considered as a promising complement to the classical distributed resource management not only due of their dynamic and decentralized nature, but also because the concept of financial valuation of resources and services is an inherent part of any such model. In its broadest sense, scheduling of scientific applications in distributed Grid and Cloud environments can be regarded as a market-based negotiation between a scheduling service optimizing user-centric objectives (execution time, budget), and a resource manager optimizing provider-centric metrics (resource utilization, income, job throughput). In this paper, we propose a new instantiation of the negotiation protocol between the scheduler and resource manager using a market-based Continuous Double Auction (CDA) model. We analyze different scheduling strategies that can be applied and identify general strategic patterns that can lead to a fast and cheap work ow execution. In the experimental study, we demonstrate that under certain circumstances one can benefit by applying an aggressive scheduling strategy.     

Waveguide filter with improved stopband response using transversal slots

In this article, the design and realization of waveguide filter with improved stop‐band response is presented. By inserting transversal slots at the proper position of cavities, without disturbing the field distribution of the dominant mode, the second longitudinal resonant modes are radiated out. This caused the out‐of‐band frequency response of the filter to be improved. The validity of the proposed technique is confirmed by comparison between measurement values and simulation results from the finite element method through the commercial software (high frequency structure simulator). Also, to assure electromagnetic compatibility the slot surfaces are covered by absorbing material. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

Solving bus terminal location problems using evolutionary algorithms

Bus terminal assignment with the objective of maximizing public transportation service is known as bus terminal location problem (BTLP). We formulate the BTLP, a problem of concern in transportation industry, as a p-uncapacitated facility location problem (p-UFLP) with distance constraint. The p-UFLP being NP-hard (Krarup and Pruzan, 1990), we propose evolutionary algorithms for its solution. According to the No Free Lunch theorem and the good efficiency of the distinctive preserve recombination (DPX) operator, we design a new recombination operator for solving a BTLP by new evolutionary and memetic algorithms namely, genetic local search algorithms (GLS). We also define the potential objective function (POF) for the nodes and design a new mutation operator based on POF. To make the memetic algorithm faster, we estimate the variation of the objective function based on POF in the local search as part of an operator in memetic algorithms. Finally, we explore numerically the performance of nine proposed algorithms on over a thousand randomly generated problems and select the best two algorithms for further testing. The comparative studies show that our new hybrid algorithm composing the evolutionary algorithm with the GLS outperforms the multistart simulated annealing algorithm.     

Performance analysis of opportunistic broadcast for delay-tolerant wireless sensor networks

This paper investigates a class of mobile wireless sensor networks that are unconnected most of the times; we refer to them as delay-tolerant wireless sensor networks (DTWSN). These networks inherit their characteristics from both delay tolerant networks (DTN) and traditional wireless sensor networks. After introducing DTWSNs, three main problems in the design space of these networks are discussed: routing, data gathering, and neighbor discovery. A general protocol is proposed for DTWSNs based on opportunistic broadcasting in delay-tolerant networks with radio device on-off periods. Three performance measures are defined in the study: the energy for sending queries to ask for data from possible neighbors (querying energy), data transfer energy, and absorption time (delay). A simple yet accurate approximation for the data-transfer energy is proposed. An analytic model is provided to evaluate the querying energy per contact (epc). Simulation results for the data propagation delay show that the querying energy per contact measure obtained from the analytic model is proportional to the product of the querying energy and the delay. A practical rule of thumb for an optimal query interval in terms of delay and energy is derived from different parts of the study.     

Adaptive sliding mode control of chaotic dynamical systems with application to synchronization

We address the problem of control and synchronization of a class of uncertain chaotic systems. Our approach follows techniques of sliding mode control and adaptive estimation law. The adaptive algorithm is constructed based on the sliding mode control to ensure perfect tracking and synchronization in presence of system uncertainty and external disturbance. Stability of the closed-loop system is proved using Lyapunov stability theory. Our theoretical findings are supported by simulation results.     

Deriving operating policies for multi-objective reservoir systems: Application of Self-Learning Genetic Algorithm

Optimal multi-reservoir operation is a multi-objective problem in nature and some of its objectives are nonlinear, non-convex and multi-modal functions. There are a few areas of application of mathematical optimization models with a richer or more diverse history than in reservoir systems optimization. However, actual implementations remain limited or have not been sustained.Genetic Algorithms (GAs) are probabilistic search algorithms that are capable of solving a variety of complex multi-objective optimization problems, which may include non-linear, non-convex and multi-modal functions. GA is a population based global search method that can escape from local optima traps and find the global optima. However GAs have some drawbacks such as inaccuracy of the intensification process near the optimal set.In this paper, a new model called Self-Learning Genetic Algorithm (SLGA) is presented, which is an improved version of the SOM-Based Multi-Objective GA (SBMOGA) presented by Hakimi-Asiabar et al. (2009) [45]. The proposed model is used to derive optimal operating policies for a three-objective multi-reservoir system. SLGA is a new hybrid algorithm which uses Self-Organizing Map (SOM) and Variable Neighborhood Search (VNS) algorithms to add a memory to the GA and improve its local search accuracy. SOM is a neural network which is capable of learning and can improve the efficiency of data processing algorithms. The VNS algorithm can enhance the local search efficiency in the Evolutionary Algorithms (EAs).To evaluate the applicability and efficiency of the proposed methodology, it is used for developing optimal operating policies for the Karoon-Dez multi-reservoir system, which includes one-fifth of Iran's surface water resources. The objective functions of the problem are supplying water demands, generating hydropower energy and controlling water quality in downstream river.     

Unsupervised adaptive neural-fuzzy inference system for solving differential equations

There has been a growing interest in combining both neural network and fuzzy system, and as a result, neuro-fuzzy computing techniques have been evolved. ANFIS (adaptive network-based fuzzy inference system) model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. In this paper, a novel structure of unsupervised ANFIS is presented to solve differential equations. The presented solution of differential equation consists of two parts; the first part satisfies the initial/boundary condition and has no adjustable parameter whereas the second part is an ANFIS which has no effect on initial/boundary conditions and its adjustable parameters are the weights of ANFIS. The algorithm is applied to solve differential equations and the results demonstrate its accuracy and convince us to use ANFIS in solving various differential equations.     

Best position algorithms for efficient top-k query processing

The general problem of answering top-k queries can be modeled using lists of data items sorted by their local scores. The main algorithm proposed so far for answering top-k queries over sorted lists is the Threshold Algorithm (TA). However, TA may still incur a lot of useless accesses to the lists. In this paper, we propose two algorithms that are much more efficient than TA. First, we propose the best position algorithm (BPA). For any database instance (i.e. set of sorted lists), we prove that BPA stops as early as TA, and that its execution cost is never higher than TA. We show that there are databases over which BPA executes top-k queries O(m) times faster than that of TA, where m is the number of lists. We also show that the execution cost of our algorithm can be (m−1) times lower than that of TA. Second, we propose the BPA2 algorithm, which is much more efficient than BPA. We show that the number of accesses to the lists done by BPA2 can be about (m−1) times lower than that of BPA. We evaluated the performance of our algorithms through extensive experimental tests. The results show that over our test databases, BPA and BPA2 achieve significant performance gains in comparison with TA.