Influence of equal channel angular pressing in an acute angle die with a back pressure notch on grain refinement,torsion and mechanical properties of aluminium

Severe plastic deformation improves the strength of a metal by strain hardening. Of the various severe plastic deformation processes, equal channel angular pressing proves to be the right candidate for bulk metal processing. Extensive works were carried out on equal channel angular pressing with channel angle ranging from 90° to 120 ° with or without back pressure on the exit channel. Numerical analyses suggest that reducing the channel angle below 90° would enhance the magnitude of strain imparted and, with prediction of a lesser strain homogeneity in such cases. Hence an acute angled equal channel angular pressing die with a back‐pressure notch sunken into the roof of the exit channel was designed, fabricated and was used for processing pure aluminium. Various mechanical properties of the processed materials were tested and acute angle processing imparted superior tensile strength to the work pieces in a single pass that would require several passes in a conventional equal channel angular pressing die. Substantial improvement in grain refinement and torsional properties was identified.     

Alternative search techniques for face detection using location estimation and binary features

The sliding window approach is the most widely used technique to detect an object from an image. In the past few years, classifiers have been improved in many ways to increase the scanning speed. Apart from the classifier design (such as the cascade), the scanning speed also depends on a number of different factors (such as the grid spacing, and the scale at which the image is searched). When the scanning grid spacing is larger than the tolerance of the trained classifier it suffers from low detections. In this paper, we present a technique to reduce the number of missed detections when fewer subwindows are processed in the sliding window approach for face detection. This is achieved by using a small patch to predict the location of the face within a local search area. We use simple binary features and a decision tree for location estimation as it proved to be efficient for our application. We also show that by using a simple interest point detector based on quantized gradient orientation, as the front-end to the proposed location estimation technique, we can further improve the performance. Experimental evaluation on several face databases show better detection rate and speed with our proposed approach when fewer number of subwindows are processed compared to the standard scanning technique.     

Improved analysis of sinigrin in Ethiopian mustard

An ion‐pair reversed‐phase high‐performance liquid chromatography (HPLC) method was developed for the analysis of sinigrin in Ethiopian mustard (Brassica carinata A. Braun) seed and seed fractions. Separation was compared on several RP‐HPLC columns (Inertsil^® ODS‐4 C₁₈ and ZORBAX^® Eclipse XDB‐C₁₈) with an isocratic eluent containing 100% aqueous (aq.) tetramethylammonium bromide (10 mm , pH 5.0). Sinigrin retention was affected by HPLC variables including the type of ion‐pair reagent, buffer strength and pH, acetonitrile concentration, column temperature and eluent flow rate. Partial validation demonstrated this optimised chromatographic condition to be linear, accurate and precise. Multistage extraction using 70% (v/v) aq. methanol was more efficient than 50% (v/v) aq. acetonitrile. In addition, the matrix effect and recovery rate as well as processing efficiency of the analytical protocol were determined. This method is suitable for high throughput analysis of sinigrin in Ethiopian mustard seed and seed fractions.     

Investigation of Ambipolar Conduction and RF Stability Performance in Novel Germanium Source Dual Halo Dual Dielectric Triple Material Surrounding Gate TFET

Silicon - In this study, we present an ambipolar conduction and RF stability performance for a Germanium Source Dual Halo Dual Dielectric Triple Material Surrounding Gate Tunnel FET...     

Mobile data offloading based on minority game theoretic framework

Wireless Networks - Mobile data offloading is a current-day networking paradigm to channelize certain fraction of the cellular data traffic over unlicensed spectrum of WiFi. In this paper, we...     

Understanding e‐Government portal use in rural India: role of demographic and personality characteristics

Electronic government (e‐Government) is one of the most important ways to bridge the digital divide in developing countries. We develop a model of e‐Government portal use. We use various individual characteristics, namely demographics and personality, as predictors of e‐Government portal use. Specifically, our predictors were (1) gender, age, income and education; (2) the Big Five personality characteristics, i.e. extraversion, neuroticism, conscientiousness, agreeableness and openness to experience; and (3) personal innovativeness with information technology. We conducted a field study in a village in India. We collected data from over 300 heads of household. We found support for our model, with most variables being significant and explaining 40% of the variance in e‐Government portal use.     

Magnetization reversal mechanism of chemically synthesized linear chains of α-Fe nanospheres

Journal of Materials Science - We report upon synthesis of stable chains of magnetically aligned zero-valent α-Fe nanoparticles and investigation of their magnetization reversal mechanism. The...     

The Legendre wavelet method for solving initial value problems of Bratu-type

The aim of this work is to study the Legendre wavelets for the solution of initial value problems of Bratu-type, which is widely applicable in fuel ignition of the combustion theory and heat transfer. The properties of Legendre wavelets together with the Gaussian integration method are used to reduce the problem to the solution of nonlinear algebraic equations. Also a reliable approach for convergence of the Legendre wavelet method when applied to a class of nonlinear Volterra equations is discussed and an error estimation for the proposed method is also introduced. Illustrative examples have been discussed to demonstrate the validity and applicability of the technique and the results have been compared with the exact solution. We finally show the high accuracy and efficiency of the proposed method.     

Solid state synthesis of nano-mineral particles

Many researchers in academia and industries are interested in reducing particle sizes from few sub-micrometers to nano-meter levels. These nano-particles find application in several areas including ceramics, paints, cosmetics, microelectronics, sensors, textiles and biomedical, etc. This article reviews the present state of the art for solid state synthesis of mineral nano-particles by wet milling, including their operating variables such as ball size, solid mass fraction and suspension stability. This article concludes and recommends with a critical discussion of nano-particles synthesis and a few common strategies to overcome stability issues.     

Regularized nonnegative shared subspace learning

Joint modeling of related data sources has the potential to improve various data mining tasks such as transfer learning, multitask clustering, information retrieval etc. However, diversity among various data sources might outweigh the advantages of the joint modeling, and thus may result in performance degradations. To this end, we propose a regularized shared subspace learning framework, which can exploit the mutual strengths of related data sources while being immune to the effects of the variabilities of each source. This is achieved by further imposing a mutual orthogonality constraint on the constituent subspaces which segregates the common patterns from the source specific patterns, and thus, avoids performance degradations. Our approach is rooted in nonnegative matrix factorization and extends it further to enable joint analysis of related data sources. Experiments performed using three real world data sets for both retrieval and clustering applications demonstrate the benefits of regularization and validate the effectiveness of the model. Our proposed solution provides a formal framework appropriate for jointly analyzing related data sources and therefore, it is applicable to a wider context in data mining.