Characterization of the structure and properties of processed alumina-graphene and alumina-zirconia composites

The onset of hybrid alumina-based composites, which combines two or more nano-particles within the alumina matrix has already shown promising improvements in the matrix material. However, variations in mechanical properties including the optimum compositions that give improved properties faced with the development of alumina-based composites require further studies to understand the underlying mechanisms and synergistic effects of the nano-particle additions on the alumina matrix. In the current study, the structure and properties of Al?O?-graphene (0.5 wt%) and Al?O?–ZrO? (4 wt% and 10 wt%) composites fabricated via hot-pressing was studied as a baseline for multiple combinations. Even though the addition of 10 wt%ZrO? resulted in a 23% reduction in the grain size of the alumina matrix, the 4 wt%ZrO? addition resulted in a 14% increase in grain size as compared to the parent alumina matrix. X-ray diffraction analysis revealed that there was approximately 85% monoclinic (m-ZrO₂) vs. 15% tetragonal (t-ZrO₂) crystal structures in the A4ZrO? sample whilst the A₁₀ZrO? had approximately 93% m-ZrO₂ vs. 7% t-ZrO₂. The high-volume fraction of the monoclinic crystal structures in the A₁₀ZrO? accounts for the induced microcracks in the sample since the transition from the ductile-tetragonal to brittle-monoclinic is associated with the exertion of compressive stresses on the alumina matrix by the associated elastic volume expansion of m-ZrO₂. Also, the addition of 0.5 wt%graphene resulted in about 37% reduction in the grain size of the alumina matrix, and approximately 10% increase in hardness as a result of the distribution of graphene along the grain boundaries of the parent alumina matrix, which restricts grain coalescence and growth during processing. Furthermore, an increase up to 115% and 164% were observed in the fracture toughness (K_IC) with the inclusion of 0.5 wt%graphene and 10 wt%ZrO? respectively, which was primarily ascribed to the fine-grained microstructures and toughening mechanisms of the intergranular graphene and ZrO? particles.     

A fog-based security framework for intelligent traffic light control system

Multimedia Tools and Applications - The world is facing many problems including that of traffic congestion. To highlight the issue of traffic congestion worldwide specially in urban areas and to...     

Reliability and scale effect in toughness of concrete structures

In the present work, the distribution of the random toughness characteristics (i.e. critical energy release rate, G_1c) has been evaluated on the basis of experimental observations. Fracture test results from three groups of geometrically similar concrete specimens of size (width×total depth×thickness), 420×420×50–1680×1680×200 mm³, made with different maximum aggregate size of 9.5, 19, 38, and 76 mm were analyzed using a recently proposed distribution of extremes. In applications of probability, it is important to use an appropriate distribution type and adequate techniques for estimating the parameters of distribution. In this study, a new type distribution of minima is employed for probability computations. It was noticed that the entropy of distribution increases with the crack length, i.e. the uncertainty of toughness, G_1c, value increases with crack length. A non-linear reduction of the maximum allowable splitting force with the defect size, a, was noticed. For large specimens, the maximum allowable splitting load is more sensitive to the required reliability level than that for small specimens. Reliability increases with aggregate size when all other conditions were constant.     

Evolving artificial neural network ensembles

Using a coordinated group of simple solvers to tackle a complex problem is not an entirely new idea. Its root could be traced back hundreds of years ago when ancient Chinese suggested a team approach to problem solving. For a long time, engineers have used the divide-and-conquer strategy to decompose a complex problem into simpler sub-problems and then solve them by a group of solvers. However, knowing the best way to divide a complex problem into simpler ones relies heavily on the available domain knowledge. It is often a manual process by an experienced engineer. There have been few automatic divide-and-conquer methods reported in the literature. Fortunately, evolutionary computation provides some of the interesting avenues to automatic divide-and-conquer methods. An in-depth study of such methods reveals that there is a deep underlying connection between evolutionary computation and ANN ensembles. Ideas in one area can be usefully transferred into another in producing effective algorithms. For example, using speciation to create and maintain diversity had inspired the development of negative correlation learning for ANN ensembles, and an in-depth study of diversity in ensembles. This paper will review some of the recent work in evolutionary approaches to designing ANN ensembles.     

Algorithms in the W-Hierarchy

We show new upper bounds for problems in the W-hierarchy of fixed-parameter complexity. A crucial ingredient of our proofs is an extension to the W-RAM model, which permits more-powerful operations but remains equivalent to the original. We use the extended model to give new upper bounds for Subsetsum, Maximum Irredundant Set, and various problems concerning intersection of finite-state machines.     

Estimation of the Substrate Concentration Profiles in Petroleum Pipelines Containing Biofilms

In industrial systems, the formation of biofilms can cause many problems, such as an increase in the flow resistance of pipelines, energy losses in fluid transport and heat exchangers, product contamination, materials deterioration, and biocorrosion. As a result, biofilms contribute substantially to economic losses in the industry. Corrosion is particularly an issue in the petroleum industry and its implications range from down-hole completion through petroleum processing units. Much of this corrosion is attributed to microbial activities. This paper proposes a mathematical model for predicting substrate concentration for such microbial growth. Substrate concentrations in the system and near the biofilm surface are one of the parameters that has a great effect in determining the extent of the problems associated with biofilms. In this study, a convective-diffusion model under various flow conditions (stagnant, laminar, and turbulent) has been solved using the finite difference technique, employing the alternating direction implicit (ADI) method. The model assumes that a liquid containing substrate and bacteria is flowing in a pipeline with known concentrations at the inlet and then predicts the variation of the transient (as a function of time) substrate concentration along the pipeline and as a function of the pipe radius. The model is then used to predict and estimate the substrate concentration profiles on the biofilm surface under different environmental conditions. A parametric study was also conducted to study the effect of the different parameters influencing the substrate concentration profiles in the system and on the biofilm surface.     

The growth and characterization of AlGaAs double heterostructures for the evaluation of reactor and source quality

AlGaAs double heterostructures are grown by low-pressure metalorganic chemical vapor deposition to evaluate the level of oxygen contamination in different trimethylaluminum sources. Effects of arsine purifiers, misoriented substrates, atmospheric exposure of the growth chamber, and possible phosphorus contamination are also studied. Extensive characterization is performed on these films by a variety of methods, including high-resolution x-ray diffraction, photoluminescence (PL), time-resolved photoluminescence, and secondary-ion mass spectrometry. The PL intensities for structures grown with the low-alkoxide grade are reproducibly much greater than those grown with the regular-grade TMA1. The use of AsH₃ purification improves the PL intensity.     

A mechanical model for the deformational behavior of the polymeric membranes operating in pressure-driven processes

A mechanical model consisting of three elements (elastic, viscoelastic, and plastic) is proposed to describe the deformational behavior of a polymeric membrane operating in a pressure-driven process. The changes in the membrane thickness are proposed to be calculated from the experimentally obtained flux data. The advantages of a mechanical model over the conventional method of hysteresis area measurement as the mechanical stability of the polymeric membranes are discussed. © 1992 John Wiley & Sons, Inc.