Synthesis, characterization and magnetic properties of polyaniline/γ-Fe2O3 composites

Conducting polyaniline/γ-Fe₂O₃ (PANI/FE) composites have been synthesized using an in situ deposition technique by placing fine-graded γ-Fe₂O₃ in a polymerization mixture of aniline. The composites are characterized by using scanning electron microscopy (SEM), X-ray diffraction (XRD) and infrared (IR) spectroscopy. The electrical properties such as d.c. and a.c. conductivities are studied by sandwitching the pellets of these composites between the silver electrodes. It is observed that the conductivity increases up to a composition of 20 wt.% of γ-Fe₂O₃ in polyaniline and decreases thereafter. The initial increase in conductivity is attributed to the extended chain length of polyaniline, where polarons possess sufficient energy to hop between favourable sites. Beyond 20 wt.% of γ-Fe₂O₃ in polyaniline, the blocking of charge carrier hop occurs, reducing conductivity values. The magnetic properties such as hysteresis characteristics and normalized a.c. susceptibility are also measured, which show a strong dependence on content of γ-Fe₂O₃ in polyaniline. Because of superparamagnetic behaviour of these composites, they may find extensive technological applications, especially for absorbing and shielding applications in microwave frequencies.     

TecDEM: A MATLAB based toolbox for tectonic geomorphology, Part 2: Surface dynamics and basin analysis

We present the analytical capability of TecDEM, a MATLAB toolbox used in conjunction with Global DEMs for the extraction of tectonic geomorphologic information. TecDEM includes a suite of algorithms to analyze topography, extracted drainage networks and sub-basins. The aim of part 2 of this paper series is the generation of morphometric maps for surface dynamics and basin analysis. TecDEM therefore allows the extraction of parameters such as isobase, incision, drainage density and surface roughness maps. We also provide tools for basin asymmetry and hypsometric analysis. These are efficient graphical user interfaces (GUIs) for mapping drainage deviation from basin mid-line and basin hypsometry. A morphotectonic interpretation of the Kaghan Valley (Northern Pakistan) is performed with TecDEM and the findings indicate a high correlation between surface dynamics and basin analysis parameters with neotectonic features in the study area.     

TecDEM: A MATLAB based toolbox for tectonic geomorphology, Part 1: Drainage network preprocessing and stream profile analysis

We present TecDEM, a software shell implemented in MATLAB that applies tectonic geomorphologic tasks to digital elevation models (DEMs). The first part of this paper series describes drainage partitioning schemes and stream profile analysis. The graphical user interface of TecDEM provides several options: determining flow directions, stream vectorization, watershed delineation, Strahler order labeling, stream profile generation, knickpoints selection, Concavity, Steepness and Hack indices calculations. The knickpoints along selected streams as well as stream profile analysis, and Hack index per stream profile are computed using a semi-automatic method. TecDEM was used to extract and investigate the stream profiles in the Kaghan Valley (Northern Pakistan). Our interpretations of the TecDEM results correlate well with previous tectonic evolution models for this region. TecDEM is designed to assist geoscientists in applying complex tectonic geomorphology tasks to global DEM data.     

View-invariant motion trajectory-based activity classification and recognition

Motion trajectories provide rich spatio-temporal information about an object's activity. The trajectory information can be obtained using a tracking algorithm on data streams available from a range of devices including motion sensors, video cameras, haptic devices, etc. Developing view-invariant activity recognition algorithms based on this high dimensional cue is an extremely challenging task. This paper presents efficient activity recognition algorithms using novel view-invariant representation of trajectories. Towards this end, we derive two Affine-invariant representations for motion trajectories based on curvature scale space (CSS) and centroid distance function (CDF). The properties of these schemes facilitate the design of efficient recognition algorithms based on hidden Markov models (HMMs). In the CSS-based representation, maxima of curvature zero crossings at increasing levels of smoothness are extracted to mark the location and extent of concavities in the curvature. The sequences of these CSS maxima are then modeled by continuous density (HMMs). For the case of CDF, we first segment the trajectory into subtrajectories using CDF-based representation. These subtrajectories are then represented by their Principal Component Analysis (PCA) coefficients. The sequences of these PCA coefficients from subtrajectories are then modeled by continuous density hidden Markov models (HMMs). Different classes of object motions are modeled by one Continuous HMM per class where state PDFs are represented by GMMs. Experiments using a database of around 1750 complex trajectories (obtained from UCI-KDD data archives) subdivided into five different classes are reported.     

Quantifying explainable discrimination and removing illegal discrimination in automated decision making

Recently, the following discrimination-aware classification problem was introduced. Historical data used for supervised learning may contain discrimination, for instance, with respect to gender. The question addressed by discrimination-aware techniques is, given sensitive attribute, how to train discrimination-free classifiers on such historical data that are discriminative, with respect to the given sensitive attribute. Existing techniques that deal with this problem aim at removing all discrimination and do not take into account that part of the discrimination may be explainable by other attributes. For example, in a job application, the education level of a job candidate could be such an explainable attribute. If the data contain many highly educated male candidates and only few highly educated women, a difference in acceptance rates between woman and man does not necessarily reflect gender discrimination, as it could be explained by the different levels of education. Even though selecting on education level would result in more males being accepted, a difference with respect to such a criterion would not be considered to be undesirable, nor illegal. Current state-of-the-art techniques, however, do not take such gender-neutral explanations into account and tend to overreact and actually start reverse discriminating, as we will show in this paper. Therefore, we introduce and analyze the refined notion of conditional non-discrimination in classifier design. We show that some of the differences in decisions across the sensitive groups can be explainable and are hence tolerable. Therefore, we develop methodology for quantifying the explainable discrimination and algorithmic techniques for removing the illegal discrimination when one or more attributes are considered as explanatory. Experimental evaluation on synthetic and real-world classification datasets demonstrates that the new techniques are superior to the old ones in this new context, as they succeed in removing almost exclusively the undesirable discrimination, while leaving the explainable differences unchanged, allowing for differences in decisions as long as they are explainable.     

Crown Structures for Vertex Cover Kernelization

Crown structures in a graph are defined and shown to be useful in kernelization algorithms for the classic vertex cover problem. Two vertex cover kernelization methods are discussed. One, based on linear programming, has been in prior use and is known to produce predictable results, although it was not previously associated with crowns. The second, based on crown structures, is newer and much faster, but produces somewhat variable results. These two methods are studied and compared both theoretically and experimentally with each other and with older, more primitive kernelization algorithms. Properties of crowns and methods for identifying them are discussed. Logical connections between linear programming and crown reductions are established. It is shown that the problem of finding an induced crown-free subgraph, and the problem of finding a crown of maximum size in an arbitrary graph, are solvable in polynomial time.     

Optimization of Gear Blank Casting Process by Using Taguchi's Robust Design Technique

This study demonstrates optimization of the gear blank casting process by using Taguchi's Robust Design technique. The metal casting process involves a large number of parameters affecting the various casting quality features of the product. Some of the parameters are controllable and some are uncontrollable, e.g., noise factors. In order to optimize the process, six control factors—namely, clay content, moisture content, ramming, sand particle size, metal fluidity, and gating design—were selected. Each process factor was considered at three levels. The quality characteristic selected was casting defects. The reduction in the weight of casting as compared to the target weight was taken to be proportional to the casting defects. An orthogonal array was constructed for the six factors undertaken, and performing 18 sets of experiments generated the data. The weights of the finished castings were obtained and signal-to-noise ratios were calculated by using the nominal best approach of parameter design. The average values of signal-to-noise ratios for each factor at three levels were calculated and were plotted on the graph. Considering the maximum signal-to-noise ratios from the graph, the optimum levels of process factors in the type of task considered were obtained. A validation experiment was performed using the levels obtained. The weight of the castings produced was found to have a value close to the target value.     

Semiconductor-mediated photocatalysed degradation of two selected azo dye derivatives, amaranth and bismarck brown in aqueous suspension

Semiconductor-mediated photocatalysed degradation of two selected azo dye derivatives such as amaranth (1) and bismarck brown (2) has been investigated in aqueous suspension by monitoring the change in substrate concentration employing UV spectroscopic analysis technique as a function of irradiation time. The degradation was studied under different conditions such as types of TiO(2), pH, substrate concentration, catalyst concentration, and in the presence of electron acceptors such as hydrogen peroxide (H(2)O(2)), potassium bromate (KBrO(3)) and ammonium persulphate (NH(4))(2)S(2)O(8) besides air. The degradation rates were found to be strongly influenced by all the above parameters. The photocatalyst Degussa P25 showed comparatively highest photocatalytic activity. The dye derivative, bismarck brown (2) was found to degrade faster than amaranth dye (1).