Dynamic behaviour of a magnetically actuated floating liquid marble

This paper reports the dynamic behaviour of a magnetically actuated floating liquid marble by analysing the oscillation of the marble. A liquid marble is a liquid droplet coated with hydrophobic powder. Magnetite particles inside the marble make it magnetic. The marble floats on a carrier liquid that contains aqueous glycerol of various concentrations. A permanent magnet located under the carrier liquid drives the floating marble with the initial velocity. Stopping the magnet abruptly causes the marble to oscillate around its final position for a few seconds. The oscillation was recorded and analysed using customised image processing and evaluation software. The damped harmonic motion model was then applied to the data and tested. Subsequently, critical parameters of the system such as the initial displacement, friction correction factor, the apparent frequency and the spring constant were determined and discussed. The simple experimental set-up and convenient theoretical approach allow us to characterise the marble motion under the influence of a magnet with good accuracy.     

Automatic classifier selection for non-experts

Choosing a suitable classifier for a given dataset is an important part of developing a pattern recognition system. Since a large variety of classification algorithms are proposed in literature, non-experts do not know which method should be used in order to obtain good classification results on their data. Meta-learning tries to address this problem by recommending promising classifiers based on meta-features computed from a given dataset. In this paper, we empirically evaluate five different categories of state-of-the-art meta-features for their suitability in predicting classification accuracies of several widely used classifiers (including Support Vector Machines, Neural Networks, Random Forests, Decision Trees, and Logistic Regression). Based on the evaluation results, we have developed the first open source meta-learning system that is capable of accurately predicting accuracies of target classifiers. The user provides a dataset as input and gets an automatically created high-performance ready-to-use pattern recognition system in a few simple steps. A user study of the system with non-experts showed that the users were able to develop more accurate pattern recognition systems in significantly less development time when using our system as compared to using a state-of-the-art data mining software.     

Liaison of phenolic acids and biological activity of escalating cultivars of Daucus carota

The objective of the present study was to analyze and compare the phenolic compounds and their antioxidant capacities of new lines of Dacus carota. The selected cultivars showed high variation in the contents of total phenolics (30.26–65.39 mg/100 g FW) and total ascorbic acid (41.12–58.36 mg/100 g FW). Analysis on RP-HPLC revealed that hydroxycinnamic acids and its derivatives were major phenolic compounds present in D. carota extracts, whereas 5-caffeolquinic acid was a major hydroxycinnamic acid (ranged from 30.26 to 65.39 mg/100 g FW). DCP cultivar showed high total antioxidant capacity (77.69 mg/100 g), 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging capacity (52.36 mg/100 g), superoxide radical scavenging capacity (53.69 mg/100 g), and hydroxyl radical scavenging capacity (51.91 mg/100 g). A linear relationship was found between total phenolic acid contents and antioxidant capacity. Both phenolic compounds and antioxidant capacities varied significantly (ρ < 0.05) among cultivars. DCP cultivar was found to be a rich source of phenolics and ascorbic acid with high antioxidant activity.     

Optimization of friction stir welding process using NSGA-II and DEMO

In welding processes, the selection of optimal process parameter settings is very important to achieve best weld qualities. In this work, neuro-multi-objective evolutionary algorithms (EAs) are proposed to optimize the process parameters in friction stir welding process. Artificial neural network (ANN) models are developed for the simulation of the correlation between process parameters and mechanical properties of the weld using back-propagation algorithm. The weld qualities of the weld joint, such as ultimate tensile strength, yield stress, elongation, bending angle and hardness of the nugget zone, are considered. In order to optimize those quality characteristics, two multi-objective EAs that are non-dominated sorting genetic algorithm II and differential evolution for multi-objective are coupled with the developed ANN models. In the end, multi-criteria decision-making method which is technique for order preference by similarity to the ideal solution is applied on the Pareto front to extract the best solutions. Comparisons are conducted between results obtained from the proposed techniques, and confirmation experiments are performed to verify the simulated results.

     

Dominant Mechanisms for Metal Removal from Acidic Aqueous Solutions by Cement Kiln Dust

Copper and zinc ions were removed from synthetic acidic aqueous solutions onto cement kiln dust (CKD) particles in a single component system. The objectives of this study were to: distinguish between adsorption and precipitation when both mechanisms are occurring simultaneously; define their individual contributions; and consequently, specify the dominant mechanism. This was achieved by conducting a new experimental procedure for the precipitation phase that depended on CKD leachate in combination with a derivation of a simultaneous adsorption-precipitation equation. High removal efficiencies, approaching 100?%, of the Cu and Zn ions, were attained. Precipitation was the dominant mechanism for removing low concentrations of these metals, while adsorption appears to be more significant in removal of high metal concentrations.     

A novel Arabic OCR post-processing using rule-based and word context techniques

International Journal on Document Analysis and Recognition (IJDAR) - Optical character recognition (OCR) is the process of recognizing characters automatically from scanned documents for editing,...     

Ontology based E-learning framework: A personalized,adaptive and context aware model

Multimedia Tools and Applications - Enhancing the degree of learner productivity, one of the major challenges in E-Learning systems, may be catered through effective personalization, adaptivity and...     

Meta-learning for evolutionary parameter optimization of classifiers

The performance of most of the classification algorithms on a particular dataset is highly dependent on the learning parameters used for training them. Different approaches like grid search or genetic algorithms are frequently employed to find suitable parameter values for a given dataset. Grid search has the advantage of finding more accurate solutions in general at the cost of higher computation time. Genetic algorithms, on the other hand, are able to find good solutions in less time, but the accuracy of these solutions is usually lower than those of grid search.     

Dynamic mechanical analysis and broadband electromagnetic interference shielding characteristics of poly (vinyl alcohol)-poly (4-styrenesulfonic acid)-titanium dioxide nanoparticles based tertiary nanocomposites

ABSTRACT

Novel tertiary nanocomposite films comprising of poly (vinyl alcohol) (PVA), poly (4-styrenesulfonic acid) (PSSA) and titanium dioxide (TiO₂) nanoparticles (NP_S) were prepared using simple solvent casting method. The structural, thermal, morphological, thermo-mechanical and electromagnetic interference (EMI) shielding properties of PVA/PSSA/TiO₂ nanocomposite films were investigated. The EMI shielding effectiveness (SE) of PVA/PSSA/TiO₂ nanocomposite films in the X and Ku band was found to be 12 dB and 13 dB respectively at 25 wt% TiO₂ NPs loading. These results demonstrate the possible applications of PVA/PSSA/TiO₂ nanocomposite films as low cost, lightweight and flexible material for EMI shielding.