Prediction of the mechanical characteristics of multi-walled carbon nanotube/epoxy composites using a new form of the rule of mixtures

Multi-walled carbon nanotubes (MWCNTs) were used in the low-viscosity, thermosetting polyester epoxy/amine resin LY-5052 with high temperature resistance to fabricate MWCNT/epoxy composites. Tensile tests of the specimens were carried out to obtain mechanical properties of MWCNT/epoxy composites for various weight-percents (wt.%) of MWCNTs. Experimental results show that the Young’s modulus and the tensile strength of the composites can be significantly improved by adding a small percentage of MWCNT. A new form of the rule of mixtures, including an exponential shape function, length efficiency parameter, orientation efficiency factor and a waviness parameter, is proposed for a more accurate prediction of the mechanical properties of MWCNT-reinforced epoxy composites, for both low and high wt.% ranges. In order to verify the suitability of the model, the ensuing predictions are compared to the available experimental data in the literature. Results demonstrate a good predictability of the modified form over a wide range of tests.     

An evolutionary‐based adaptive neuro‐fuzzy inference system for intelligent short‐term load forecasting

The continuing growth in size and complexity of electric power systems requires the development of applicable load forecasting models to estimate the future electrical energy demands accurately. This paper presents a novel load forecasting approach called genetic‐based adaptive neuro‐fuzzy inference system (GBANFIS) to construct short‐term load forecasting expert systems and controllers. At the first stage, all records of data are searched by a novel genetic algorithm (GA) to find the most suitable feature of inputs to construct the model. Then, determined inputs are fed into the adaptive neuro‐fuzzy inference system to evolve the initial knowledge‐base of the expert system. Finally, the initial knowledge‐base is searched by another robust GA to induce a better cooperation among the rules by rule weight derivation and rule selection mechanisms. We show the superiority and applicability of our approach by applying it to the Iranian monthly electrical energy demand problem and comparing it with the most frequently adopted approaches in this field. Results indicate that GBANFIS outperforms its rival approaches and is a promising tool for dealing with short‐term load forecasting problems.     

Viscosity of a CaO-MgO-Al2O3-SiO2 melt containing spinel particles at 1646K

This article reports an experimental investigation into the effect of solid suspension on the viscosity of molten slags. Up to about 20 vol pct of spinel (MgAl₂O₄) particles of three size ranges (fine: 0.10 to 0.21 mm; medium: 0.21 to 0.44 mm; and coarse: 0.44 to 0.99 mm) were added to a CaO-MgO-Al₂O₃-SiO₂ melt at 1646 (±10)K. A Brookfield DVII+ viscometer was used. The viscosity determined for the solid-free melts was in good agreement with the results of published work. The viscosity for the solid-containing melt was found to increase with the addition of the particles. With more than 10 vol pct solid particles, particularly the fine and the coarse ones, the melt showed an apparent “Bingham” behavior, i.e., the shear stress increased linearly with the shear rate but had a residual shear stress (up to 3 Pa depending on the amount and size of solid added) at zero shear rate. The viscosity of the solid-containing slag, η, was found to fit an Einstein-Roscoe type equation, η=η ₀ (1−af)⁻ⁿ, where η ₀ is the viscosity of the solid-free melt, f is the volume fraction of solid particles in the melt, and a and n are parameters taking the value of 4.24, 3.29, and 3.56 and 1.28, 2.36, and 2.24 for the fine, medium, and coarse particles, respectively, for best fit.     

Density functional theory investigation of the mechanical properties of single-walled carbon nanotubes

We detail the results of our first-principles study based on density functional theory on the elastic properties of (6, 6) single-walled carbon nanotubes (SWCNTs) in both periodic and non-periodic systems. The Young’s modulus and the shear modulus of nanotubes were evaluated through applying axial and torsion strains on periodic, H-, and C-capped nanotubes. Based on our first-principles calculations, the Young’s modulus of the periodic nanotube tens to increase as the nanotube’s length increases, and finally approaches a constant value at long tube lengths. It was found that the Young’s modulus characteristic of H- and C-capped nanotubes exhibit contradictory behaviors during compression with the periodic nanotube. Our calculations also predict that the Young’s and Shear moduli of C-capped nanotubes are larger than those of other types of nanotubes.     

Fractal-fractional neuro-adaptive method for system identification

Engineering with Computers - Neuronal networks are used in different fields of science and technology due to their capacity to approximate nonlinear functions through the synaptic weights...     

GAPSO-SVM: An IDSS-based Energy-Aware Clustering Routing Algorithm for IoT Perception Layer

Wireless Personal Communications - With the emergence of Internet of Things (IoT) having large scale and generating huge amount of data, Intelligent Decision Support Systems (IDSSs) have attracted...     

Deep brain stimulation of the subthalamic nucleus improves set shifting but does not affect dual task performance in Parkinson's disease.

Problems with shifting attentional set and concurrent performance of tasks are key cognitive deficits in Parkinson's disease (PD). Our aim was to examine the effects of deep brain stimulation of the subthalamic nucleus on tests of set shifting and dual task performance in patients with PD. Twelve patients with PD were assessed on tests of set shifting and on dual task performance with subthalamic nucleus (STN) stimulation switched on and off in a counterbalanced order. All patients obtained a clinical benefit from deep brain stimulation (DBS) of the STN. STN stimulation significantly improved set shifting. The effect of DBS on dual task performance was not significant. Change in measures of set shifting was significantly associated with the change in the motor symptoms of PD with DBS. The improved set shifting with DBS of the STN in PD supports the critical role of the striato-frontal circuits in this cognitive function.