Correction to: Comparative analysis of limit equilibrium and numerical methods for prediction of a landslide

Bulletin of Engineering Geology and the Environment - Landslides that occur due to the rapid motion of a rock-mass are a primary risk in mountainous terrains and are a danger to human life and...     

Applications of Polymer Matrix Syntactic Foams

A collection of applications of polymer matrix syntactic foams is presented in this article. Syntactic foams are lightweight porous composites that found their early applications in marine structures due to their naturally buoyant behavior and low moisture absorption. Their light weight has been beneficial in weight sensitive aerospace structures. Syntactic foams have pushed the performance boundaries for composites and have enabled the development of vehicles for traveling to the deepest parts of the ocean and to other planets. The high volume fraction of porosity in syntactic foams also enabled their applications in thermal insulation of pipelines in oil and gas industry. The possibility of tailoring the mechanical and thermal properties of syntactic foams through a combination of material selection, hollow particle volume fraction, and hollow particle wall thickness has helped in rapidly growing these applications. The low coefficient of thermal expansion and dimensional stability at high temperatures are now leading their use in electronic packaging, composite tooling, and thermoforming plug assists. Methods have been developed to tailor the mechanical and thermal properties of syntactic foams independent of each other over a wide range, which is a significant advantage over other traditional particulate and fibrous composites.     

Genetic programming for simultaneous feature selection and classifier design.

This paper presents an online feature selection algorithm using genetic programming (GP). The proposed GP methodology simultaneously selects a good subset of features and constructs a classifier using the selected features. For a c-class problem, it provides a classifier having c trees. In this context, we introduce two new crossover operations to suit the feature selection process. As a byproduct, our algorithm produces a feature ranking scheme. We tested our method on several data sets having dimensions varying from 4 to 7129. We compared the performance of our method with results available in the literature and found that the proposed method produces consistently good results. To demonstrate the robustness of the scheme, we studied its effectiveness on data sets with known (synthetically added) redundant/bad features.     

Probing the dynamics of nanoparticle growth in a flame using synchrotron radiation

Flame synthesis is one of the most versatile and promising technologies for large-scale production of nanoscale materials. Pyrolysis has recently been shown to be a useful route for the production of single-walled nanotubes, quantum dots and a wide variety of nanostructured ceramic oxides for catalysis and electrochemical applications. An understanding of the mechanisms of nanostructural growth in flames has been hampered by a lack of direct observations of particle growth, owing to high temperatures (2,000 K), rapid kinetics (submillisecond scale), dilute growth conditions (10(-6) volume fraction) and optical emission of synthetic flames. Here we report the first successful in situ study of nanoparticle growth in a flame using synchrotron X-ray scattering. The results indicate that simple growth models, first derived for colloidal synthesis, can be used to facilitate our understanding of flame synthesis. Further, the results indicate the feasibility of studies of nanometre-scale aerosols of toxicological and environmental concern.     

Hierarchical learning architecture with automatic feature selection for multiclass protein fold classification

The structure classification of proteins plays a very important role in bioinformatics, since the relationships and characteristics among those known proteins can be exploited to predict the structure of new proteins. The success of a classification system depends heavily on two things: the tools being used and the features considered. For the bioinformatics applications, the role of appropriate features has not been paid adequate importance. In this investigation we use three novel ideas for multiclass protein fold classification. First, we use the gating neural network, where each input node is associated with a gate. This network can select important features in an online manner when the learning goes on. At the beginning of the training, all gates are almost closed, i.e., no feature is allowed to enter the network. Through the training, gates corresponding to good features are completely opened while gates corresponding to bad features are closed more tightly, and some gates may be partially open. The second novel idea is to use a hierarchical learning architecture (HLA). The classifier in the first level of HLA classifies the protein features into four major classes: all alpha, all beta, alpha + beta, and alpha/beta. And in the next level we have another set of classifiers, which further classifies the protein features into 27 folds. The third novel idea is to induce the indirect coding features from the amino-acid composition sequence of proteins based on the N-gram concept. This provides us with more representative and discriminative new local features of protein sequences for multiclass protein fold classification. The proposed HLA with new indirect coding features increases the protein fold classification accuracy by about 12%. Moreover, the gating neural network is found to reduce the number of features drastically. Using only half of the original features selected by the gating neural network can reach comparable test accuracy as that using all the original features. The gating mechanism also helps us to get a better insight into the folding process of proteins. For example, tracking the evolution of different gates we can find which characteristics (features) of the data are more important for the folding process. And, of course, it also reduces the computation time.     

Chemical modification of metallocene‐based polyethylene–octene elastomer through solution grafting of acrylic acid and its effect on the physico‐mechanical properties

In the present work, the metallocene‐based polyethylene–octene elastomer (POE) was chemically modified by solution grafting of acrylic acid in presence of benzoyl peroxide. The relative proportions of graft and gel formation were optimized through %weight gain, Fourier Transform infrared spectroscopy, elemental analysis and proton nuclear magnetic resonance spectroscopy. The gel formation in the POE matrix was found to be the prime competitor. The effect of grafting at its maximum level on various physicomechanical properties was thoroughly investigated, using X‐ray diffraction analysis, differential scanning calorimetry, mechanical, dynamic mechanical, and thermogravimetric analysis. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007     

Novel synthesis of ultra‐fine Sb2 O3 nanocubes using plant extract

In this study, the synthesis of ultra‐fine grade antimony trioxide (Sb₂ O₃) using plant extract for the first time is reported. Antimony chloride was used as a starting material and Dioscorea alata tuber extract was used as a reducing and capping agent. The synthesised nanoparticles were characterised by X‐ray diffraction (XRD), field emission scanning electron microscopy (FE‐SEM), dynamic light scattering (DLS), and Fourier transform infrared spectroscopy. XRD analysis indicates the formation of pure Sb₂ O₃ nanoparticles. The result from FE‐SEM and DLS showed that the particles have a cube‐like morphology and have an average size of 346.4 nm which falls within the range of ultra‐fine grade Sb₂ O₃.Inspec keywords: field emission electron microscopy, scanning electron microscopy, X‐ray diffraction, particle size, nanofabrication, light scattering, transmission electron microscopy, ultraviolet spectra, nanoparticles, antimony compounds, Fourier transform infrared spectraOther keywords: field emission scanning electron microscopy, FE‐SEM, dynamic light scattering, DLS, XRD analysis, antimony chloride, starting material, reducing agent, ultrafine grade antimony trioxide, plant extract, dioscorea alata tuber extract, capping agent, X‐ray diffraction, pure antimony trioxide nanoparticles, cube‐like morphology, Sb₂ O₃      

The differential induction machine: Theory and performance

This paper presents the theory and performance of a differential induction machine, which is a special type of induction machine having two shafts projected from the two ends of a single stator. Application of a differential load on the two shafts cause them to run at different speed as a motor, which permits true differential movement and thus can meet the requirements of a differential drive in an electric vehicle. The machine is also capable of regeneration in the differential mode. This paper presents the construction of the above machine and performance of the same based on experimental results from a laboratory prototype. The equivalent circuit of the motor has been presented and verified experimentally.