The effects of different cement dosages, slumps and pumice aggregate ratios on the compressive strength and densities of concrete

Compressive strengths of concretes made up of mixtures of pumice aggregate (PA) and normal aggregate were measured. To determine the effect of PA ratio, different cement dosage and slumps on the compressive strength of concrete, (1) 25%, 50%, 75% and 100% pumice ratios were used instead of normal aggregate by volume, (2) 200, 250, 350, 400 and 500 kg/m³ cement dosages were used and (3) 3±1, 5±1 and 7±1 cm slumps were also used in this study.The analysis of the test results leads to the conclusion that PA decreased the density of concretes up to 41.5% and reductions occurred due to the increase of the PA ratio in the mixes. With the increase of cement dosage in the mixes, both density and compressive strength of concretes increased up to 3.2% and 265%, respectively, when compared to the control sample that contain 200 kg/m³ cement dosage. The effect of the slump on the density and compressive strength was varied. Elasticity moduli were decreased with an increase of PA ratio and increased with an increase of cement dosage. Water absorption improved with an increase of cement content.     

An optimization based method for simultaneous localization and mapping

In this study, we present an optimization based solution to the simultaneous localization and mapping (SLAM) problem. In the proposed algorithm, the SLAM problem is considered as two optimization problems. These problems are solved using forward dynamic programming. In the first problem, it is assumed that map is known perfectly and the robot path is estimated. In the second problem, the estimated robot path with their corresponding measurements is used to identify map. As optimization problem in each step of dynamic programming have high nonlinearity and also differential evolution (DE) tends to find the globally optimal solution without being trapped at local maxima, DE is developed to solve dynamic programming in each step of time. Some simulations and experiments are presented to illustrate the proposed algorithm and exhibit its performance.     

Hydrogenolysis and hydrocracking of the carbon-oxygen bond. 2. Thermal cleavage of the carbon-oxygen bond in guaiacol

Low-rank coals and their precursors contain, in addition to aromatic hydroxy groups, aromatic methoxy groups. In the present work a model compound, guaiacol, is used for the study of the behaviour of the carbon-oxygen bonds under thermolytic conditions. The thermolysis of guaiacol is studied in tetralin, naphthalene and without solvent under hydrogen or nitrogen pressure at 578–618 K. The compound is homolytically converted by first-order kinetics. The major product is pyrocatechol. Phenol, o-cresol, methyl catechols and methyl guaiacols are also formed. When tetralin is present it reacts in a molar ratio of 1:4 with guaiacol to form naphthalene. The source of hydrogen when tetralin is not present is guaiacol itself because molecular hydrogen does not participate in the reaction. The kinetics and reaction mechanism are discussed.     

A context layered locally recurrent neural network for dynamic system identification

This paper puts forward a novel recurrent neural network (RNN), referred to as the context layered locally recurrent neural network (CLLRNN) for dynamic system identification. The CLLRNN is a dynamic neural network which appears in effective in the input–output identification of both linear and nonlinear dynamic systems. The CLLRNN is composed of one input layer, one or more hidden layers, one output layer, and also one context layer improving the ability of the network to capture the linear characteristics of the system being identified. Dynamic memory is provided by means of feedback connections from nodes in the first hidden layer to nodes in the context layer and in case of being two or more hidden layers, from nodes in a hidden layer to nodes in the preceding hidden layer. In addition to feedback connections, there are self-recurrent connections in all nodes of the context and hidden layers. A dynamic backpropagation algorithm with adaptive learning rate is derived to train the CLLRNN. To demonstrate the superior properties of the proposed architecture, it is applied to identify not only linear but also nonlinear dynamic systems. The efficiency of the proposed architecture is demonstrated by comparing the results to some existing recurrent networks and design configurations. In addition, performance of the CLLRNN is analyzed through an experimental application to a dc motor connected to a load to show practicability and effectiveness of the proposed neural network. Results of the experimental application are presented to make a quantitative comparison with an existing recurrent network in the literature.     

Electrochemical deposition and characterization of NiFe coatings as electrocatalytic materials for alkaline water electrolysis

In this study, nickel (Cu/Ni), iron (Cu/Fe) and nickel-iron (Cu/NiFe) composite coatings with various chemical compositions were electrochemically deposited on a copper electrode and characterized using cyclic voltammetry (CV), atomic absorption spectroscopy (AAS), scanning electron microscopy (SEM) and atomic force microscopy (AFM) techniques in view of their possible applications as electrocatalytic materials for the hydrogen evolution reaction (HER) in an alkaline medium. The electrocatalytic activity of the coatings for the HER was studied in 1 M KOH solution using cathodic current-potential curves and electrochemical impedance spectroscopy (EIS) techniques. The presence of nickel along with iron increases the electrocatalytic activity of the electrode for the HER when compared to nickel and iron coatings individually. The HER activity of the composite coatings depends on the chemical composition of the alloys. The Cu/NiFe-3 electrode (with a molar concentration ratio of Ni²⁺:Fe²⁺ of 4:6 in the plating bath) was found to be the best suitable cathode material for the HER in an alkaline medium under the experimental conditions studied. Furthermore, the electrocatalytic activity of the Cu/NiFe-3 electrode for the HER was tested for extended periods of time in order to evaluate the change in the electrocatalytic activity of the electrode with operation time. The HER was activation controlled and has not been changed after electrolysis. A constant current density of 100 mA cm⁻² was applied to the electrolysis system, and the corrosion behavior of the Cu/NiFe-3 electrode was investigated after different operation times using EIS and linear polarization resistance (LPR) techniques. For comparison, the corrosion behavior of a Cu/NiFe-3 electrode to which current was not applied was also investigated. The corrosion tests showed that the corrosion resistance of the Cu/NiFe-3 cathode changed when a cathodic current was applied to the electrolysis system.     

Modeling with ANN and effect of pumice aggregate and air entrainment on the freeze–thaw durabilities of HSC

The objective of this work is to calculate the compressive strength, ultrasound pulse velocity (UPV), relative dynamic modulus of elasticity (RDME) and porosity induced into concrete during freezing and thawing. Freeze–thaw durability of concrete is of great importance to hydraulic structures in cold areas. In this paper, freezing of pore solution in concrete exposed to a freeze–thaw cycle is studied by following the change of concrete some mechanical and physical properties with freezing temperatures. The effects of pumice aggregate (PA) ratios on the high strength concrete (HSC) properties were studied at 28 days. PA replacements of fine aggregate (0–2 mm) were used: 10%, 20%, and 30%. The properties examined included compressive strength, UPV and RDME properties of HSC. Results showed that compressive strength, UPV and RDME of samples were decreased with increase in PA ratios. Test results revealed that HSC was still durable after 100, 200 and 300 cycles of freezing and thawing in accordance with ASTM C666. After 300 cycles, HSC showed a reduction in compressive strength between 6% and 21%, and reduction in RDME up to 16%. For 300 cycles, the porosity was increased up to 12% for HSC with PA. In this paper, feed-forward artificial neural networks (ANNs) techniques are used to model the relative change in compressive strength and relative change in UPV in cyclic thermal loading. Then genetic algorithms are applied in order to determine optimum mix proportions subjected to 300 thermal cycling.     

Knowledge extraction for water gas shift reaction over noble metal catalysts from publications in the literature between 2002 and 2012

In this work, a database (containing 4360 experimental data points) on water gas shift reaction (WGS) over Pt and Au based catalysts was constructed using the data obtained from the published papers between the years 2002 and 2012. Then, the database was analyzed using three data mining tools to extract knowledge in three areas: Decision trees to determine the empirical rules and conditions that lead to high catalytic performance (high CO conversion); artificial neural networks (ANNs) to determine the relative importance of various catalyst preparation and operational variables and their effects on CO conversion; support vector machines (SVMs) to predict the outcome of unstudied experimental conditions. It was concluded that, all three models were quite successful and they complement each other to extract knowledge from the past published works and to deduce useful trends, rules and correlations, which are not easily comprehensible by the naked eyes.     

Preparation,characterization and application of alkaline leached CuNiZn ternary coatings for long-term electrolysis in alkaline solution

The NiCuZn ternary coating was electrochemically deposited on a copper electrode. Then, it was etched in a concentrated alkaline solution (30% NaOH) to produce a porous and electrocatalytic surface suitable for use in the hydrogen evolution reaction (HER). The surface composition of coating before and after alkaline leaching was determined by energy dispersive X-ray (EDX) analysis. The surface morphologies were investigated by scanning electron microscopy (SEM). The long-term stability of electrode prepared for alkaline water electrolysis was investigated in 1 M KOH solution with the help of cathodic current-potential curves and electrochemical impedance spectroscopy (EIS) techniques. It was found that, the NiCuZn coating has a compact and porous structure with good physical stability. Alkaline leaching process further improved the activity of NiCuZn coating in comparison with binary NiCu deposit for the HER. The long-term operation at −100 mA cm⁻² showed good electrochemical stability over 120 h.     

Electrospinning of single and multilayered scaffolds for tissue engineering applications

Optimized electrospinning conditions were applied to produce single and multilayered (ML) scaffolds composed of polycaprolactone, collagen and elastin. The ML scaffold was cross-linked with glutaraldehyde to increase the stability. Morphological and structural characteristics of the scaffolds were measured by SEM and FTIR analyses. Results revealed that polymers combined to each other well and uniform fibers were obtained with the diameters ranging from 156 ± 53 to 1536 ± 293 nm. Contact angle measurements were performed to investigate the hydrophilic character of each structure. It was observed that incorporation of the natural polymers into the blends increased the hydrophilicity. Mechanical tests proved that collagen contributed to fabricate stiffer structures while elastin provided more elasticity. Biocompatibility of the scaffolds was examined by SEM analysis and WST-1 test with mouse fibroblast cells (L929) in vitro. Results exhibited that the addition of natural polymers increased the cell growth, and none of the single and ML scaffolds presented cytotoxic effect.