Voltammetric characterization of selective potassium ion transfer across micro-water/1,2-dichloroethane interface facilitated by a novel calix[4]arene derivative

In this study, transfer reactions of alkali and alkaline-earth metal ions across a micro-water/1,2-dichloroethane (1,2-DCE) interface facilitated by a novel calix[4]arene derivative, 5,11,17,23-tetra-tert-butyl-25,27-bis(2′amino-methylpyridine)-26,28-dihydroxy calix[4]arene (APHC4), were investigated by cyclic and differential pulse voltammetry techniques. Well-defined voltammetric behavior was obtained only for K⁺ ion among the used metal ions. The electrochemical data were used to determine the stoichiometry and the appropriate association constant of the occurring complex between K⁺ ion and APHC4. The obtained steady-state voltammograms indicated that the facilitated transfer process occurs with a TIC/TID mechanism according to 1:1 stoichiometry. The logarithm of the association constant () of K(APHC4)⁺ complex in the DCE phase was calculated to be 6.32. Also, the availability of the facilitated transfer for the design of an amperometric screening sensor for K⁺ ion was evaluated in the range of 50–500 μmol dm⁻³.     

Preparation of the Pb(Mg1/3Nb2/3)O3 films by aqueous tape casting

Lead magnesium niobate (PMN) is a relaxor ferroelectric material. Because of its high dielectric constant and superior electrostrictive properties it is commonly used in the manufacture of multilayer electronic devices which is typically produced by tape casting. However, preparation of PMN slurry formulations to use in aqueous tape casting process is not investigated in detail yet. Therefore, in this study aqueous PMN formulations were developed for tape casting and its relation with the final properties of PMN films were investigated. The slurries were prepared using poly(acrylic acid)-based comb polymer as the dispersant, nonionic acrylic latex as the binder and the hydroxypropyl methyl cellulose as the wetting agent. The results showed that it is possible to prepare flexible, crack-free PMN films using highly concentrated suspensions without using any plasticizer. The study gives guidelines for the aqueous tape casting of PMN and can be adapted to processing of other multi-component metal oxides.     

A comparative modeling study to estimate wear of concrete

This paper investigates the impacts of fuzzy genetic (FG), a new fuzzy logic model with genetic algorithm, artificial neural networks (ANN) and general linear model (GLM) approaches on abrasive wear of concrete. For this purpose, experimental studies were made to investigate the influence on wear of the following input parameters: hematite, cement, compressive strength and different loads on the experiments. In these models, 60 data sets were used. For training set, 48 data (80 %) were randomly selected and the residual data (12 data, 20 %) were test set. Model results were compared with experimental results. In this paper, main model performance criterion was root mean square errors. Also, sum of squared error and determination coefficient statistics were used as comparing criteria for the evaluation of models’ performances. Comparison results indicate that FG models are superior to ANN and GLM models in modeling of influence hematite, cement, compressive strength and loads on wear of concrete.     

Forecasting Bank Failure: Base Learners,Ensembles and Hybrid Ensembles

The prediction of bankruptcy for financial companies, especially banks, has been extensively researched area and creditors, auditors, stockholders and senior managers are all interested in bank bankruptcy prediction. In this paper, three common machine learning models namely Logistic, J48 and Voted Perceptron are used as the base learners. In addition, an attribute-base ensemble learning method namely Random Subspaces and two instance-base ensemble learning methods namely Bagging and Multi-Boosting are employed to enhance the prediction accuracy of conventional machine learning models for bank failure prediction. The models are grouped in the following families of approaches: (i) conventional machine learning models, (ii) ensemble learning models and (iii) hybrid ensemble learning models. Experimental results indicate a clear outperformance of hybrid ensemble machine learning models over conventional base and ensemble models. These results indicate that hybrid ensemble learning models can be used as a reliable predicting model for bank failures.     

Propagation of seismic waves in tall buildings

A discrete-time wave propagation formulation of the seismic response of tall buildings is introduced. The building is modeled as a layered medium, similar to a layered soil medium, and is subjected to vertically propagating seismic shear waves. Soil layers and the bedrock under the foundation are incorporated in the formulation as additional layers. Seismic response is expressed in terms of the wave travel times between the layers, and the wave reflection and transmission coefficients at the layer interfaces. The equations account for the frequency-dependent filtering effects of the foundation and floor masses. The calculation of seismic response is reduced to a pair of simple finite-difference equations for each layer, which can be solved recursively starting from the bedrock. Compared to the commonly used vibration formulation, the wave propagation formulation provides several advantages, including simplified calculations, better representation of damping, ability to account for the effects of the soil layers under the foundation, and better tools for identification and damage detection from seismic records. Examples presented show the versatility of the method. Copyright © 1998 John Wiley & Sons, Ltd.     

Prediction of Lateral Effective Stresses in Sand using Artificial Neural Network

Predicting the lateral effective stress and the coefficient of lateral earth pressure at rest values is an important task in geotechnical engineering since it is used in the design and analysis of earth retaining structures, slope stability, piles and pier foundations. It needs sophisticated test procedures. The laboratory and in situ tests are also expensive and time consuming. In this study, an artificial neural network model is developed to predict the σ′_h, lateral effective stress in cohesionless soils. Back propagation neural networks are used for function approximation and model has been trained by Levenberg-Marqurdt (LM) learning algorithm. The data used in the running of network models have been obtained from extensive series of oedometer tests on Kilyos, Ayvalik and Yalikoy sands. Tests were carried out on loose, medium dense and dense state of compactness in normal loading, unloading and reloading conditions. The test results demonstrate that there is a linear relationship between vertical and lateral stresses for normally loaded cohesionless soils under K₀ conditions. K₀ values obtained for the loose state of compactness are higher than for the dense state of compactness. The results of the artificial neural network model indicate that the model serves as simple and reliable tool to predict σ′_h and also K₀ in cohesionless soils. The variation of K₀ values with internal friction angles is obtained and a simple expression is derived from this relationship.     

Transformation of Bromine Species During Decomposition of Bromate under UV Light from Low Pressure Mercury Vapor Lamps

Bromate decomposition with low pressure mercury vapor lamps (LPMVL) was studied in buffer-free and buffered Milli-Q water by following the fate of bromine species BrO₃ ^?, Br^?, and free bromine. BrO₃ ^? was converted over time to Br^? with total free bromine (TFBr) as secondary reaction product. BrO₃ ^? decay followed pseudo-first-order kinetics and was independent of [BrO₃ ^?]_o (0.06–0.6 mM), pH_o (6.9–9.5) and HCO₃ ^? (0.05–1.0 mM), slightly dependent on acetate (0.06–0.27 mM), highly dependent on and adversely affected by humic acids (HA) and an increasing function of photon flux I as measured by potassium ferrioxalate actinometry. Reaction pH dropped by as much as 2.2 units at pH_o ≤ 8.5 in the buffer-free experiments, while it remained within 0.5 unit at pH_o > 9. TFBr decay due to exposure to LPMVL resulted in formation of Br^? (major) and BrO₃ ^? (minor). Decay of HA in the presence of BrO₃ ^? was highly augmented by photon emission of LPMVL at 185 nm and was found to substantially contribute to BrO₃ ^? decay in the presence of HA. Correlations on the dependency of bromine species decay or formation rates as a function of photon flux are presented.     

A new benchmark for pose estimation with ground truth from virtual reality

The development of programming paradigms for industrial assembly currently gets fresh impetus from approaches in human demonstration and programming-by-demonstration. Major low- and mid-level prerequisites for machine vision and learning in these intelligent robotic applications are pose estimation, stereo reconstruction and action recognition. As a basis for the machine vision and learning involved, pose estimation is used for deriving object positions and orientations and thus target frames for robot execution. Our contribution introduces and applies a novel benchmark for typical multi-sensor setups and algorithms in the field of demonstration-based automated assembly. The benchmark platform is equipped with a multi-sensor setup consisting of stereo cameras and depth scanning devices (see Fig. 1). The dimensions and abilities of the platform have been chosen in order to reflect typical manual assembly tasks. Following the eRobotics methodology, a simulatable 3D representation of this platform was modelled in virtual reality. Based on a detailed camera and sensor simulation, we generated a set of benchmark images and point clouds with controlled levels of noise as well as ground truth data such as object positions and time stamps. We demonstrate the application of the benchmark to evaluate our latest developments in pose estimation, stereo reconstruction and action recognition and publish the benchmark data for objective comparison of sensor setups and algorithms in industry.     

Electricity consumption and economic growth: Evidence from Turkey

This study investigates the causal relationship between electricity consumption and real GDP in Turkey during the period of 1950–2000. Both of the series were found to be a stationary process around a structural break by the Zivot and Andrews test. Thus, two different methodologies have been employed to test the Granger non-causality: the Dolado–Lütkepohl test using the VARs in levels, and the standard Granger causality test using the detrended data. Both tests have yielded a strong evidence for unidirectional causality running from the electricity consumption to the income. This implies that the supply of electricity is vitally important to meet the growing electricity consumption, hence to sustain the economic growth in Turkey.