Properties of na‐montmorillonite and cellulose nanocrystal reinforced poly(butyl acrylate‐co‐methyl methacrylate) nanocomposites

Poly(butyl acrylate‐co‐methyl methacrylate) (BA‐co‐MMA) nanocomposite latexes were synthesized in the presence of sodium montmorillonite (Na‐MMT) and cellulose nanocrystal (CNC) as fillers. Nanocomposite preparation with 3 wt% Na‐MMT based upon the total monomer amount was conducted by semi‐batch emulsion polymerization. Furthermore, direct blending of neat copolymer latex with Na‐MMT was performed for comparison. CNC/BA‐co‐MMA nanocomposites were obtained via blending process with varying CNC content (1, 2, and 3 wt %). Good dispersion of both Na‐MMT and CNC within the copolymer matrix was achieved as demonstrated by X‐ray diffraction and transmission electron microscope. Particle size of the nanocomposite latexes was around 120 nm. Thermal, mechanical, and barrier properties of the copolymer showed great improvement with the addition of both Na‐MMT and CNC. CNC nanocomposites displayed enhanced properties with increasing CNC level. Tensile strength of copolymer latex with 3 wt% CNC reached 262.5% of the pristine latex, while tensile strength of Na‐MMT nanocomposite at the same content was 187.5% of the pristine latex. POLYM. ENG. SCI., 55:2922–2928, 2015. © 2015 Society of Plastics Engineers     

Feature-Based Fusion of Medical Imaging Data

The acquisition of multiple brain imaging types for a given study is a very common practice. There have been a number of approaches proposed for combining or fusing multitask or multimodal information. These can be roughly divided into those that attempt to study convergence of multimodal imaging, for example, how function and structure are related in the same region of the brain, and those that attempt to study the complementary nature of modalities, for example, utilizing temporal EEG information and spatial functional magnetic resonance imaging information. Within each of these categories, one can attempt data integration (the use of one imaging modality to improve the results of another) or true data fusion (in which multiple modalities are utilized to inform one another). We review both approaches and present a recent computational approach that first preprocesses the data to compute features of interest. The features are then analyzed in a multivariate manner using independent component analysis. We describe the approach in detail and provide examples of how it has been used for different fusion tasks. We also propose a method for selecting which combination of modalities provides the greatest value in discriminating groups. Finally, we summarize and describe future research topics.     

A General Probabilistic Formulation for Supervised Neural Classifiers

We use partial likelihood (PL) theory to introduce a general probabilistic framework for the design and analysis of neural classifiers. The formulation allows for the training samples used in the design to have correlations in time, and for use of a wide range of neural network probability models including recurrent structures. We use PL theory to establish a fundamental information-theoretic connection, show the equivalence of likelihood maximization and relative entropy minimization, without making the common assumptions of independent training samples and true distribution information. We use this result to construct the information geometry of partial likelihood and derive the information geometric e- and m-projection (em) algorithm for class conditional density modeling by finite normal mixtures. We demonstrate the successful application of the algorithm by a channel equalization example and give simulation results to show the efficiency of the scheme.     

The transition from thin plates to moderately thick plates by using finite element analysis and the shear locking problem

Two simple plate bending elements, based on Mindlin theory for analysis of both moderately thick and thin plates, are presented in this paper. These elements have either four nodes or eight nodes with 12 and 24 DOF, respectively. To illustrate the accuracy of these finite elements named as TURE12 and TURE24, several numerical examples of displacements and stresses for both thin and moderately thick plate bending problems are presented and discussed with a range of finite element meshes and thickness-to-plate length ratios. In addition, the bending and shearing behaviours of a Mindlin plate are analyzed with respect to shear locking. In order to test the shear locking, the results obtained from the Mindlin plate analysis using 4- or 8-noded elements with full, reduced, and selective reduced integration are compared with the exact classical thin plate solution.     

A study regarding the environmental impact analysis of the building materials production process (in Turkey)

Because of the negative impact of buildings on the environment, building materials and, particularly, their production process are important factors. However, this impact differs from country to country and according to ecological applications in place at the production plants. Depending on the success rate of these applications, impact on environment during production of building materials is reduced. Turkey plays a major role in production of building materials, and consequently, the impacts of the global environmental problems are also experienced in this country. Therefore, reducing the environmental impact of any kind is a prominent issue, regionally and globally.     

Optimisation of laminated cylindrical pressure vessels under strength criterion

An approach for the optimisation of symmetrically laminated cylindrical pressure vessels is presented. The analysis is based on the membrane theory of shells and the optimisation is carried out with respect to the fibre orientations and thickness distributions subject to the Tsai-Wu failure criterion. The approach is equally applicable to balanced and unbalanced symmetrically laminated shells. Two examples are considered which involve the design of cylindrical shells for maximum burst pressure and minimum weight. Numerical results are given for pressure vessels subject to internal pressure only and to a combination of internal pressure and liquid pressure. The effect of axial loading and torque on the designs is discussed.     

Dimensionality reduction in conic section function neural network

This paper details how dimensionality can be reduced in conic section function neural networks (CSFNN). This is particularly important for hardware implementation of networks. One of the main problems to be solved when considering the hardware design is the high connectivity requirement. If the effect that each of the network inputs has on the network output after training a neural network is known, then some inputs can be removed from the network. Consequently, the dimensionality of the network, and hence, the connectivity and the training time can be reduced. Sensitivity analysis, which extracts the cause and effect relationship between the inputs and outputs of the network, has been proposed as a method to achieve this and is investigated for Iris plant, thyroid disease and ionosphere databases. Simulations demonstrate the validity of the method used.     

Application of refined theory in the nonlinear analysis of composite laminated structures

A higher order theory for the analysis of plates and shells is presented. The theory considers transverse shear and normal deformation and is based on hypotheses in which nonlinearly elastic or elastoplastic behavior of the materials is included. The number and sequence of transversely isotropic layers are arbitrary and the layers may differ significantly in their physical and mechanical properties. A discrete-continuous scheme of the finite element method are developed for the implementation of the nonlinear higher order theory. In this scheme, the degrees of freedom of the elements are independent of the number of layers. The results presented illustrate the importance of modelling both transverse shear and normal deformation in the nonlinear analysis of laminated structures.The research presented in this study was supported by a Core Programmes Grant from the Foundation of Research Development of South Africa     

Analog active filter component selection with nature inspired metaheuristics

The most crucial design step for analog active filter design is the optimal selection of passive component values due to manufactured constants. Since the search on possible combinations in preferred values for capacitors and resistors is an exhaustive process, it has to be automated with high accuracy within short computation time. In this work, two nature inspired metaheuristics, differential evolution (DE) and harmony search (HS) algorithms are utilized for optimal filter design considering different topologies and manufacturing series. Simulation results show that as global optimization methods both DE and HS minimize the total design error and reduce the elapsed time with respect to previously utilized methods.