Biosynthesised AgCl NPs using Bacillus sp. 1/11 and evaluation of their cytotoxic activity and antibacterial and antibiofilm effects on multi‐drug resistant bacteria

Silver nanoparticles (AgNPs) have attracted the attention of researchers due to their properties. Biological synthesis of AgNPs is eco‐friendly and cost‐effective preferred to physical and chemical methods, which utilize environmentally harmful agents and large amounts of energy. Microorganisms have been explored as potential biofactories to synthesize AgNPs. Bacterial NP synthesis is affected by Ag salt concentration, pH, temperature and bacterial species. In this study, Bacillus spp., isolated from soil, were screened for AgNP synthesis at pH 12 with 5 mM Ag nitrate (AgNO₃) final concentration at room temperature. The isolate with fastest color change and the best ultraviolet‐visible spectrum in width and height were chosen as premier one. AgNO₃ and citrate salts were compared in terms of their influence on NP synthesis. Spherical Ag chloride (AgCl) NPs with a size range of 35–40 nm were synthesized in 1.5 mM Ag citrate solution. Fourier transform infrared analysis demonstrated that protein and carbohydrates were capping agents for NPs. In this study, antimicrobial and antitumor properties of the AgNP were investigated. The resulting AgCl NPs had bacteriostatic activity against four standard spp. And multi‐drug resistant strain of Pseudomonas aeruginosa. These NPs are also cytotoxic to cancer cell lines MCF‐7, U87MG and T293.Inspec keywords: silver compounds, nanoparticles, nanomedicine, nanofabrication, particle size, biomedical materials, microorganisms, ultraviolet spectra, visible spectra, Fourier transform infrared spectra, proteins, macromolecules, antibacterial activity, tumours, cancer, cellular biophysics, toxicologyOther keywords: citrate salts, spherical Ag chloride, particle size, Ag citrate solution, Fourier transform infrared analysis, protein, carbohydrates, capping agents, antitumour properties, bacteriostatic activity, Pseudomonas aeruginosa, multidrug resistant strain, cancer cell lines MCF‐7,U87MG, size 35 nm to 40 nm, temperature 293 K to 298 K, AgCl, ultraviolet‐visible spectrum, colour change, room temperature, Ag nitrate final concentration, soil, bacterial species, temperature effect, pH, Ag salt concentration, biofactories, microorganisms, environmentally harmful agents, chemical methods, physical methods, antibacterial properties, electrical properties, mechanical properties, silver nanoparticles, multidrug resistant bacteria, antibiofilm effects, antibacterial effects, cytotoxic activity, Bacillus sp. 1/11, biosynthesised AgCl NPs     

Magnetic soy protein isolate–bovine serum albumin nanoparticles preparation as a carrier for inulinase immobilisation

Magnetic nanoparticles (NPs) were functionalised with soy protein isolate (SPI) and bovine serum albumin (BSA) for inulinase immobilisation. The results revealed the nanomagnetite size of about 50 nm with a polydispersity index (PDI) of 0.242. The average size of the SPI NPs prepared by using acetone was 80–90 nm (PDI, 0.277), and SPI–BSA NPs was 80–90 nm (PDI, 0.233), and their zeta potential was around −34 mV. The mean diameter of fabricated Fe₃ O₄ @SPI–BSA NPs was <120 nm (PDI, 0.187). Inulinase was covalently immobilised successfully through glutaraldehyde on Fe₃ O₄ @SPI–BSA NPs with 80% enzyme loading. Fourier transform infrared spectra, field emission scanning electron microscopy, and transmission electron microscopy images provided sufficient proof for enzyme immobilisation on the NPs. The immobilised inulinase showed maximal activity at 45°C, which was 5°C higher than the optimum temperature of the free enzyme. Also, the optimum pH of the immobilised enzyme was shifted from 6 to 5.5. Thermal stability of the enzyme was considerably increased to about 43% at 75°C, and K _m value was reduced to 25.4% after immobilisation. The half‐life of the enzyme increased about 5.13‐fold at 75°C as compared with the free form. Immobilised inulinase retained over 80% of its activity after ten cycles.Inspec keywords: magnetic particles, nanoparticles, proteins, molecular biophysics, nanofabrication, enzymes, Fourier transform spectra, infrared spectra, scanning electron microscopy, field emission ion microscopy, transmission electron microscopy, pH, biochemistry, nanobiotechnology, biomagnetism, electrokinetic effects, iron compoundsOther keywords: magnetic nanoparticles, soy protein isolate, bovine serum albumin, inulinase immobilisation, nanomagnetite, polydispersity index, SPI‐BSA NP, zeta potential, inulinase, glutaraldehyde, enzyme loading, Fourier transform infrared spectra, field emission scanning electron microscopy, transmission electron microscopy images, enzyme immobilisation, pH, size 80 nm to 90 nm, temperature 45 degC, temperature 75 degC, Fe₃ O₄      

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.     

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.     

The bipartite edge frustration of graphs under subdivided edges and their related sums

The bipartite edge frustration of a graph G, denoted by φ(G), is the smallest number of edges that have to be deleted from G to obtain a bipartite spanning subgraph of G. This topological index is related to the well-known Max-cut problem, and has important applications in computing stability of fullerenes. In this paper, the bipartite edge frustration of an infinite family of fullerenes is computed. Moreover, this quantity for four classes of graphs arising from a given graph under different types of edge subdivisions is investigated.