A local asynchronous distributed privacy preserving feature selection algorithm for large peer-to-peer networks

In this paper we develop a local distributed privacy preserving algorithm for feature selection in a large peer-to-peer environment. Feature selection is often used in machine learning for data compaction and efficient learning by eliminating the curse of dimensionality. There exist many solutions for feature selection when the data are located at a central location. However, it becomes extremely challenging to perform the same when the data are distributed across a large number of peers or machines. Centralizing the entire dataset or portions of it can be very costly and impractical because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, dynamic nature of the data/network, and privacy concerns. The solution proposed in this paper allows us to perform feature selection in an asynchronous fashion with a low communication overhead where each peer can specify its own privacy constraints. The algorithm works based on local interactions among participating nodes. We present results on real-world dataset in order to test the performance of the proposed algorithm.     

Microstructure-Based Estimation of Strength and Ductility Distributions for $$\alpha +\beta $$ Titanium Alloys

Titanium alloys are processed to develop a wide range of microstructure configurations and therefore material properties. While these properties are typically measured experimentally, a framework for property prediction could greatly enhance alloy design and manufacturing. Here a microstructure-sensitive framework is presented for the prediction of strength and ductility as well as estimates of the bounds in variability for these properties. The framework explicitly considers distributions of microstructure via new approaches for instantiation of structure in synthetic samples. The parametric evaluation strategy, including the finite element simulation package FEpX, is used to create and test virtual polycrystalline samples to evaluate the variability bounds of mechanical properties in Ti-6Al-4V. Critical parameters for the property evaluation framework are provided by measurements of single crystal properties and advanced characterization of microstructure and slip system strengths in 2D and 3D. Property distributions for yield strength and ductility are presented, along with the validation and verification steps undertaken. Comparisons between strain localization and slip activity in virtual samples and in experimental grain-scale strain measurements are also discussed.

     

Identification of monofloral honey using voltammetric electronic tongue

Quality assessment of honey is often related to its floral origin which is a complex task to evaluate. Traditional technique of floral assessment is made by melissopalynological method. However, this method is quite time consuming and also often operator dependent. Thus, the fallout is a large range of error in interpretation of the result and hence there is considerable demand for instrumental methods to assess the identification of pollen in honey. In this pursuit, an electronic tongue based on cyclic voltammetry is developed to discriminate honey samples based on their floral types and is described in this paper. The technique has been investigated using platinum as the working electrode and the resultant current from the potentiostat has been considered for data analysis. The use of principal component analysis (PCA) and linear discriminate analysis (LDA) proves to be useful in clustering honey samples. Finally, classification performances are investigated using back-propagation multilayer perceptron (BP-MLP) and radial basis function (RBF) neural network models for identification of different floral origin of honey.     

Peanut proteins: Applications,ailments and possible remediation

Peanut is an annually grown plant and the chiefly cultivated species is Arachis hypogaea L. Mainly three types of proteins, arachin, conarachin I and conarachin II are present in peanut. It is an important and cheap source of protein. It can also cause allergic reactions in human beings. The peanut protein contains two components, Ara h 1 and Ara h 2 which are mainly responsible to cause allergy. Different remedial measures have been proposed to fight with the allergenic property of peanut proteins such as treatment with peroxidase, phytic acid, active charcoal, and various enzymes to minimize allergenic effect. Improper storage of peanuts causes growth of a fungal organism that releases aflatoxin, which is a carcinogen to mammals. Use of aluminosilicate clay, gamma irradiation, ozonation methods have been discussed for aflatoxin decontaminated from peanut. In this critical review, we have given an overview of the different extraction procedures of the peanut proteins and pointed out the methodologies to minimize allergenic actions and detoxification of the aflatoxin contamination in peanut.     

In situ gamma-radiation: one-step environmentally benign method to produce gold-palladium bimetallic nanoparticles

Synthesis of gold-palladium bimetallic nanoparticles using in situ radioactivity from (198)Au isotope is reported in this paper. Gold solution spiked with (198)Au(III) has been mixed with PdCl2 solution in measured proportions in 50% polyethylene glycol solution. Au(III) and Pd(II) are reduced, and nanoparticles are formed due to radiolysis of the polymer solution. The solution has been characterized by UV-visible spectra, and the actual size has been determined using transmission electron microscopy in conjugation with energy dispersive X-ray measurements.     

Species dependent aqueous biphasic extraction of some heavy metals

A clean liquid–liquid extraction system was designed for extraction of three different heavy metals (Hg²⁺, Pb²⁺ and Bi³⁺) using an Aqueous Biphasic System consisting of a nonionic surfactant Triton X-100 in combination with two different salts viz., magnesium sulphate and sodium citrate. The extraction was monitored in two ways, by direct partitioning as well as by forming a complex with diphenylthiocarbazone (dithizone) for each metal ion in the Triton phase and measuring the colour intensity using a spectrophotometer. The change in extraction pattern at different pH of the salt rich phase was monitored. The possible change in speciation of these metal ions with pH was checked with the software programme ‘CHEAQS’ (Chemical Equilibria in Aquatic Systems). These heavy metals were also found to have higher extraction in the Triton phase when a prior complexation with dithizone was performed. The method may find extensive applications for the removal and decontaminations of the above mentioned toxic heavy metals from a sample.     

A survey of cellular automata: types,dynamics, non-uniformity and applications

Cellular automata (CAs) are dynamical systems which exhibit complex global behavior from simple local interaction and computation. Since the inception of cellular automaton (CA) by von Neumann in 1950s, it has attracted the attention of several researchers over various backgrounds and fields for modeling different physical, natural as well as real-life phenomena. Classically, CAs are uniform. However, non-uniformity has also been introduced in update pattern, lattice structure, neighborhood dependency and local rule. In this survey, we tour to the various types of CAs introduced till date, the different characterization tools, the global behavior of CAs, like universality, reversibility, dynamics etc. Special attention is given to non-uniformity in CAs and especially to non-uniform elementary CAs, which have been very useful in solving several real-life problems.

     

TTCBT: two tier complete binary tree based wireless sensor network for FSR and LANMAR routing protocols

Microsystem Technologies - Wireless sensor network is extensively used for monitoring and surveillance systems. The primary challenges encountered in the area are energy minimization as well as...     

A review on graphene-based materials as versatile cancer biomarker sensors

Early detection of cancer has multitude of advantages like early diagnosis, reduced risk, ease in the treatment and follow up of recurrence. New and developed techniques are always under research to control the spreading malignancy. Graphene is an emerging star in biomedical field as it exhibits exceptional thermal, electrical and optical properties. Here, we review application of graphene-based materials in developing biosensing devices for the detection of different cancer biomarkers at concentrations down to sub-toxic levels. Different analytical methodologies chosen for sensing have been undertaken and their performance and background have been discussed. The trend of use of these methodologies can also be perceived from the graphical data presented.