Effect of Cu2+ Ions and Buffering Capacity on Smectite Microstructure and Performance

Smectite is usually used as buffer clay for contaminant of high level nuclear waste canisters. Corrosion of the canisters will expose the buffer clay to Cu2+ ions as one of the corrosion products. This study investigates the effect of Cu2+ cations in a smectite, with and without addition of sodium carbonate in the clay, on its consolidation behavior and transmission characteristics. Characterization experiments conducted in the study included determination of changes in the buffering capacity and permeability properties of the clay in relation to the presence of varying concentrations of Cu2+, and consolidation experiments. Double-layer swelling forces and buffering capacity are seen to be dominant factors in the performance of the clays—apparently through their influence on formation and restructuring of microstructures in the overall smectite clay mixtures.     

Three-dimensional free vibration of variable thickness thick circular and annular isotropic and functionally graded plates on Pasternak foundation

This paper describes a study of three-dimensional free vibration analysis of thick circular and annular isotropic and functionally graded (FG) plates with variable thickness along the radial direction, resting on Pasternak foundation. The formulation is based on the linear, small strain and exact elasticity theory. Plates with different boundary conditions are considered and the material properties of the FG plate are assumed to vary continuously through the thickness according to power law. The kinematic and the potential energy of the plate-foundation system are formulated and the polynomial-Ritz method is used to solve the eigenvalue problem. Convergence and comparison studies are done to demonstrate the correctness and accuracy of the present method. With respect to geometric parameters, elastic coefficients of foundation and different boundary conditions some new results are reported which may be used as benchmark solutions for future researches.     

Energy advantages of microprocessor platforms with on-chip configurable logic

System chips that incorporate configurable logic can reduce the energy consumed in executing software. The key is to use the configurable logic to execute performance-critical loops, producing average energy savings of 25% to 71% for embedded-system benchmarks.     

Effect of added salt and increase in ionic strength on skim milk electroacidification performances

Bipolar-memibrane electroacidification (BMEA) technology which uses the property of bipolar membranes to split water and the demineralization action of cation-exchange membranes (CEM), was tested for the production of acid casein. BMEA has numerous advantages in comparison with conventional isoelectric precipitation processes of proteins used in the dairy industry. BMEA uses electricity to generate the desired ionic species to acidify the treated solutions. The process can be precisely controlled, as electro-acidification rate is regulated by the effective current density in the cell. Water dissociation at the bipolar membrane interface is continuous and avoids local excess of acid. In-situ generation of dangerous chemicals (acids and bases) reduces the risks associated with the handling, transportation, use and elimination of these products. The aim of this study was to evaluate the performance of BMEA in different conditions of added ionic strength (p(added) = 0, 0.25, 0.5 and 1.0 M) and added salt (CaCl2, NaCl and KCl). The combination of KCl and p(added) = 0.5 M gave the best results with a 45% decrease in energy consumption. The increased energy efficiency was the result of a decrease in the anode/cathode voltage difference. This was due to an increase of conductivity, produced by addition of salt, necessary to compensate for the lack of sufficiently mobile ions in the skim milk. However, the addition of salts, irrespective of type or ionic strength, increased the required operation time. The protein profile of isolates were similar under all experimental conditions, except at 1.0 M-CaCl2.     

Parameter identification of deep-bar induction motors using genetic algorithm

In recent years, the use of three-phase deep-bar induction motors in power systems has increased. Proper modeling and precise parameter identification of the model are essential for motors’ operating analysis. In this paper, among the proposed models of deep-bar induction motors, a model based on two-axis theory is discussed and developed to improve precision. A real coded genetic algorithm estimates the parameters of the model. The accuracy and validity of the model and its identified parameters are verified with the help of a 5.5 kW, 380 V, 50 Hz, 1,450 rpm deep-bar induction motor.     

A Critical Study of the Compressible Lattice Boltzmann Methods for Riemann Problem

In this paper, the discrete velocity model proposed by Kataoka and Tsutahara (Phys. Rev. E 69(5):056702, 2004) for simulating inviscid flows is employed. Three approaches for improving the stability and the accuracy of this model, especially for high Mach numbers, are suggested and implemented in this research. First, the TVD scheme (Harten in J. Comput. Phys. 49:357?C393, 1983) is used for space discretization of the convective term in the Lattice Boltzmann equation. Next, the modified Lax-Wendroff with artificial viscosity is employed to increase the robustness of the method in supersonic flows. Finally, a combination of TVD and the 2nd order derivative of the distribution function is employed using a differentiable switch. It is found that the recent technique is a more suitable approach for a wide range of Mach numbers. Moreover, the WENO scheme for space discretization has been applied and compared with these newly applied methods.     

Decision fusion of horizontal and vertical trajectories for recognition of online Farsi subwords

Online handwriting is formed by a combination of horizontal and vertical trajectories. If these trajectories are treated separately, new recognition methods are emerged. In contrast, one classifier is often used to recognize handwriting. In this work, some features for x(t) and y(t) signals were proposed and used to make two separate classifiers. After initial recognition by these classifiers, their results were fused for final recognition. Using HMM classifiers and simple product rule for decision fusion, the recognition results of 42 classes of Farsi subwords showed promising achievements.     

A multi-objective facility location model with batch arrivals: two parameter-tuned meta-heuristic algorithms

Many research works in mathematical modeling of the facility location problem have been carried out in discrete and continuous optimization area to obtain the optimum number of required facilities along with the relevant allocation processes. This paper proposes a new multi-objective facility-location problem within the batch arrival queuing framework. Three objective functions are considered: (I) minimizing the weighted sum of the waiting and the traveling times, (II) minimizing the maximum idle time pertinent to each facility, and (III) minimizing the total cost associated with the opened facilities. In this way, the best combination of the facilities is determined in the sense of economical, equilibrium, and enhancing service quality viewpoints. As the model is shown strongly NP-hard, two meta-heuristic algorithms, namely genetic algorithm (GA) and simulated annealing (SA) are proposed to solve the model. Not only new coding is developed in these solution algorithms, but also a random search algorithm is proposed to justify the efficiency of both algorithms. Since the solution-quality of all meta-heuristic algorithms severely depends on their parameters, design of experiments and response surface methodologies have been utilized to calibrate the parameters of both algorithms. Finally, computational results obtained by implementing both algorithms on several problems of different sizes demonstrate the performances of the proposed methodology.     

A survey of shaped-based registration and segmentation techniques for cardiac images

Heart disease is the leading cause of death in the modern world. Cardiac imaging is routinely applied for assessment and diagnosis of cardiac diseases. Computerized image analysis methods are now widely applied to cardiac segmentation and registration in order to extract the anatomy and contractile function of the heart. The vast number of recent papers on this topic point to the need for an up to date survey in order to summarize and classify the published literature. This paper presents a survey of shape modeling applications to cardiac image analysis from MRI, CT, echocardiography, PET, and SPECT and aims to (1) introduce new methodologies in this field, (2) classify major contributions in image-based cardiac modeling, (3) provide a tutorial to beginners to initiate their own studies, and (4) introduce the major challenges of registration and segmentation and provide practical examples. The techniques surveyed include statistical models, deformable models/level sets, biophysical models, and non-rigid registration using basis functions. About 130 journal articles are categorized based on methodology, output, imaging system, modality, and validations. The advantages and disadvantages of the registration and validation techniques are discussed as appropriate in each section.