Using Clustering via Soccer League Competition Algorithm for Optimizing Power Consumption in WSNs (Wireless Sensor Networks)

Wireless Personal Communications - WSNs consist of small sensor nodes which have limited battery power, limited processing capability and limited memory unit. Self-organization and the lack of...     

Multi Objective Optimization with a New Evolutionary Algorithm

Various objectives are mainly met through decision making in real world. Achieving desirable condition for all objectives simultaneously is a necessity for conflicting objectives. This concept is called multi objective optimization widely used nowadays. In this study, a new algorithm, comprehensive evolutionary algorithm (CEA), is developed based on general concepts of evolutionary algorithms that can be applied for single or multi objective problems with a fixed structure. CEA is validated through solving several mathematical multi objective problems and the obtained results are compared with the results of the non-dominated sorting genetic algorithm II (NSGA-II). Also, CEA is applied for solving a reservoir operation management problem. Comparisons show that CEA has a desirable performance in multi objective problems. The decision space is accurately assessed by CEA in considered problems and the obtained solutions’ set has a great extent in the objective space of each problem. Also, CEA obtains more number of solutions on the Pareto than NSGA-II for each considered problem. Although the total run time of CEA is longer than NSGA-II, solution set obtained by CEA is about 32, 4.4 and 1.6% closer to the optimum results in comparison with NSGA-II in the first, second and third mathematical problem, respectively. It shows the high reliability of CEA’s results in solving multi objective problems.     

Time-triggered runtime verification

The goal of runtime verification is to monitor the behavior of a system to check its conformance to a set of desirable logical properties. The literature of runtime verification mostly focuses on event-triggered solutions, where a monitor is invoked when an event of interest occurs (e.g., change in the value of some variable). At invocation, the monitor evaluates the set of properties of the system that are affected by the occurrence of the event. This constant invocation introduces two major defects to the system under scrutiny at run time: (1) significant overhead, and (2) unpredictability of behavior. These defects are serious obstacles when applying runtime verification on safety-critical systems that are time-sensitive by nature. To circumvent the aforementioned defects in runtime verification, in this article, we introduce a novel time-triggered approach, where the monitor takes samples from the system with a constant frequency, in order to analyze the system’s health. We describe the formal semantics of time-triggered monitoring and discuss how to optimize the sampling period using minimum auxiliary memory. We show that such optimization is NP-complete and consequently introduce a mapping to Integer Linear Programming. Experiments on a real-time benchmark suite show that our approach introduces bounded overhead and effectively reduces the involvement of the monitor at run time by using negligible auxiliary memory. We also show that in some cases it is even possible to reduce the overall overhead of runtime verification by using our time-triggered approach when the structure of the system allows choosing a long enough sampling period.     

Flow induced orientation of multiwalled carbon nanotubes in polycarbonate nanocomposites: Rheology, conductivity and mechanical properties

We investigated the effect of flow field and deformation rate on the nanotube alignment and on the properties of PC/multiwalled carbon nanotube nanocomposites. Samples of various MWCNT loadings were prepared by diluting a commercial masterbatch containing 15 wt% nanotubes using optimized melt mixing conditions. Different processing conditions were then used to systematically change the degree of nanotube alignment, from random orientation to highly aligned. Morphological studies and Raman spectroscopy analysis revealed that the nanotubes are preferentially aligned in the flow direction, particularly at large injection or compression rates. Rheological measurements corresponding to high shear rate conditions showed drastic changes in the viscoelastic behavior. The complex viscosity significantly decreased and percolation threshold notably rose. High degrees of nanotube alignment also resulted in a significant increase in the electrical percolation threshold. The mechanical properties of the nanocomposites for different nanotube loadings were also shown to depend on the processing conditions, and somehow improved when the material was processed at higher rates. Finally, we used a power-law type equation to correlate the percolation behavior and the nanotube alignment. The estimated percolation threshold and the power index, q, significantly increase with the degree of nanotube alignment as determined by Raman analysis.     

Artificial Neural Network Modeling of the Deposition Rate of Lactose Powder in Spray Dryers

A spray dryer is the ideal equipment for the production of food powders because it can easily impart well-defined end product characteristics such as moisture content, particle size, porosity, and bulk density. Wall deposition of particles in spray dryers is a key processing problem and an understanding of wall deposition can guide the selection of operating conditions to minimize this problem. The stickiness of powders causes the deposition of particles on the wall. Operating parameters such as inlet air temperature and feed flow rate affect the air temperature and humidity inside the dryer, which together with the addition of drying aids can affect the stickiness and moisture content of the product and hence its deposition on the wall. In this article, an artificial neural network (ANN) method was used to model the effects of inlet air temperature, feed flow rate, and maltodextrin ratio on wall deposition flux and moisture content of lactose-rich products. An ANN trained by back-propagation algorithms was developed to predict two performance indices based on the three input variables. The results showed good agreement between predicted results using the ANN and the measured data taken under the same conditions. The optimum condition found by the ANN for minimum moisture content and minimum wall deposition rate for lactose-rich feed was inlet air temperature of 140°C, feed rate of 23 mL/min, and maltodextrin ratio of 45%. The ANN technology has been shown to be an excellent investigative and predictive tool for spray drying of lactose-rich products.     

Optimal neuro‐fuzzy control of hepatitis C virus integrated by genetic algorithm

Hepatitis C blood born virus is a major cause of liver disease that more than three per cent of people in the world is dealing with, and the spread of hepatitis C virus (HCV) infection in different populations is one of the most important issues in epidemiology. In the present study, a new intelligent controller is developed and tested to control the hepatitis C infection in the population which the authors refer to as an optimal adaptive neuro‐fuzzy controller. To design the controller, some data is required for training the employed adaptive neuro‐fuzzy inference system (ANFIS) which is selected by the genetic algorithm. Using this algorithm, the best control signal for each state condition is chosen in order to minimise an objective function. Then, the prepared data is utilised to build and train the Takagi–Sugeno fuzzy structure of the ANFIS and this structure is used as the controller. Simulation results show that there is a significant decrease in the number of acute‐infected individuals by employing the proposed control method in comparison with the case of no intervention. Moreover, the authors proposed method improves the value of the objective function by 19% compared with the ordinary optimal control methods used previously for HCV epidemic.Inspec keywords: epidemics, diseases, blood, medical computing, microorganisms, genetic algorithms, fuzzy control, neurocontrollers, adaptive control, medical control systemsOther keywords: genetic algorithm, hepatitis C blood born virus, liver disease, hepatitis C virus infection, epidemiology, intelligent controller, optimal adaptive neuro‐fuzzy controller, adaptive neuro‐fuzzy inference system, ANFIS, genetic algorithm, control signal, state condition, objective function minimisation, Takagi‐Sugeno fuzzy structure, acute‐infected individuals, ordinary optimal control methods, HCV epidemic     

New efficient and practical verifiable multi-secret sharing schemes

In 2006, Zhao et al. proposed a practical verifiable multi-secret sharing based on Yang et al.’s and Feldman’s schemes. In this paper we propose two efficient, computationally secure (t,n), and verifiable multi-secret sharing schemes based on homogeneous linear recursion. The first scheme has the advantage of better performance, a new simple construction and various techniques for the reconstruction phase. The second scheme requires fewer public values with respect to Zhao et al.’s and Shao and Cao schemes. These schemes are easy to implement and provide great capabilities for many applications.     

The power of ants in solving Distributed Constraint Satisfaction Problems

Distributed Constraint Satisfaction (DCSP) has long been considered an important area of research for artificial intelligence and multi-agent systems. Also, Ant Colony Optimization (ACO) is an important evolutionary method for solving various optimization problems. This paper demonstrates the power of ants in solving DCSPs and describes a new approach for such a solution, showing how it differs from previous ACO-based DCSP solvers. The presented algorithm is designed to provide the special requirements that are important in the distributed form of Constraint Satisfaction Problem (CSP). The paper describes the important criteria for distributed CSP and then demonstrates how the presented algorithm stands out over similar DCSP solvers considering these criteria. Finally, the proposed approach is evaluated on random binary problems. The practical results show that this method, in most of the cases, outperforms the Asynchronous Backtracking Algorithm (ABT) and Distributed Breakout Algorithm (DBA) two important algorithms in this field of research.     

Influence of solvent,Lewis acid–base complex,and nanoparticles on the morphology and gas separation properties of polysulfone membranes

A series of polysulfone (PSF) membranes were prepared using different solvents: dimethylformamide (DMF), tetrahydrofuran, dimethylacetamide, and n-methyl-2-pyrrolidone (NMP). The PSF membrane prepared by NMP showed the highest gas permeability. The influence of propionic acid as a Lewis acid on gas separation properties of the PSF was explored. The PSF membrane prepared by the casting solution containing 25 wt% PSF, 35 wt% propionic acid, and 40 wt% NMP showed a superior gas separation performance. The gas permeation measurements indicated that incorporating 30 wt% γ-alumina nanoparticles into the PSF matrix resulted in about the respective 43% and 41% increase in CO₂ and O₂ permeability together with a rise in CO₂/CH₄ and O₂/N₂ selectivities (13% and 7%, respectively). Furthermore, by rearranged modified Maxwell model, the role and nature of the interfacial layer in the PSF-based mixed matrix membranes were mathematically analyzed considering a reduced permeability factor.     

Studies on syntheses and morphology characteristic of chiral novel poly(ester-imide)/TiO2 bionanocomposites derived from l-phenylalanine based diacid

In this study, a new optically active poly(ester-imide) (PEI) was synthesized from the polymerization reaction of N,N′-(pyromellitoyl)-bis-l-phenylalanine diacid with 4,4′-thiobis(2-tert-butyl-5-methylphenol) using tosyl chloride, pyridine and N,N-dimethyl formamide as a condensing agent. The obtained polymer and inorganic metal oxide bionanocomposites composed of poly(ester-imide)/titanium dioxide were synthesized through ultrasonic irradiation. The formation of PEI was confirmed by ¹H NMR, fourier transform IR spectroscopy (FT-IR), specific rotation and elemental analysis. The resulting bionanocomposites were characterized by FT-IR, powder X-ray diffraction (XRD), atomic force microscopy)AFM(, scanning electron microscopy (SEM), field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM) and thermogravimetric analysis (TGA). The TEM, SEM and FE-SEM results indicated that the nanoparticles were dispersed homogeneously in PEI matrix on nanoscale. TGA confirmed that the heat stability of the nanocomposite was improved in the presence of TiO₂ nanoparticles.