Preparation and characterization of low-halogen and nonhalgoen fire-resistant low-smoke (FRLS) cable sheathing compound from blends of functionalized polyolefins and PVC

An attempt is made to develop a novel series of cable sheathing compounds with variation in chlorine content and sufficient fire retardance and unique low-smoke characteristics. These are prepared by blending PVC and functionalized polyolefins in different compositions. PE and EPDM have been functionalized by grafting dibutyl maleate (DBM) using DCP as initiator. FRLS compounds made from PVC-functionalized polyolefin blends possess the special characteristics of low-smoke, low-acid-gas generation, increased fire retardance, and improved volume resistivity, which are much better in comparison with a typical PVC sheathing compound. Thermoplastic elastomer (TPE) based nonhalogen FRLS compounds are also reported. The mechanisms for grafting, polymer-polymer and polymer-filler interactions have been presented.     

Anisotropy of X-ray structural parameters of the three phase model in cellulose I

Anisotropy of X-ray structural parameters of the three phase model proposed by Mitra and Mukherjee (ref.2), has been investigated in the case of cellulose fibres of ramie, hemp and jute. The structural parameters have been determined separately from each of the observable equatorial reflections. It has been found that the parameter degree of crystallinity as defined in the model is in fact anisotropic at least in the samples studied. The parameter degree of paracrystallinity introduced by Mitra and Mukherjee was also found to be anisotropic. Several important correlations between the structural parameters have been drawn. It has been observed, in agreement with the recent findings of Hosemann and Balta Calleja (ref.10) that the greater the paracrystalline distortion, the smaller the ‘paracrystallite size’.     

Pixel rearrangement based statistical restoration scheme reducing embedding noise

In this paper, a block based steganographic algorithm has been proposed where a sequence of secret bits are embedded into a set of pixels by rearranging the pixel locations. This algorithm has been devised as an improvement over existing statistical restoration based algorithms in order to reduce the additive noise which occurs due to embedding. It is shown that the proposed scheme substantially reduces the additive noise compared to existing statistical restoration based schemes.     

Femtocell based green power consumption methods for mobile network

This paper presents the analytical models of power consumption in macrocell, microcell, picocell and femtocell based networks. Five case studies are presented in this paper where macrocells, microcells, picocells and femtocells are deployed based on the number of mobile subscribers present in a region, mobile user traffic in that region and the area of the region where cellular coverage has to be provided. A comparative study is performed between the power consumption by the base stations in each of these five cases and that of the only macrocell based network. The simulation results demonstrate that using each of these five strategies the power consumption by the base stations can be minimized than that of only macrocell based network. Based on the power consumption by the base stations in these five schemes, we have categorized the networks into five classes, A, B, C, D and E, each of which contains cells of different types to reduce power consumption to achieve green cellular network.     

Distributed probabilistic inferencing in sensor networks using variational approximation

This paper considers the problem of distributed inferencing in a sensor network. It particularly explores the probabilistic inferencing problem in the context of a distributed Boltzmann machine-based framework for monitoring the network. The paper offers a variational mean-field approach to develop communication-efficient local algorithm for variational inferencing in distributed environments (VIDE). It compares the performance of the proposed approximate variational technique with respect to the exact and centralized techniques. It shows that the VIDE offers a much more communication-efficient solution at very little cost in terms of the accuracy. It also offers experimental results in order to substantiate the scalability of the proposed algorithm.     

Approximation Bounds for Minimum Information Loss Microaggregation

The NP-hard microaggregation problem seeks a partition of data points into groups of minimum specified size k, so as to minimize the sum of the squared euclidean distances of every point to its group's centroid. One recent heuristic provides an O(k³) guarantee for this objective function and an O(k²) guarantee for a version of the problem that seeks to minimize the sum of the distances of the points to its group's centroid. This paper establishes approximation bounds for another microaggregation heuristic, providing better approximation guarantees of O(k²) for the squared distance measure and O(k) for the distance measure.     

Bacteria-Responsive Self-Assembly of Antimicrobial Peptide Nanonets for Trap-and-Kill of Antibiotic-Resistant Strains

Bacterial trapping using nanonets is a ubiquitous immune defense mechanism against infectious microbes. These nanonets can entrap microbial cells, effectively arresting their dissemination and rendering them more vulnerable to locally secreted microbicides. Inspired by this evolutionarily conserved anti-infective strategy, a series of 15 to 16 residue-long synthetic β-hairpin peptides is herein constructed with the ability to self-assemble into nanonets in response to the presence of bacteria, enabling spatiotemporal control over microbial killing. Using amyloid-specific K114 assay and confocal microscopy, the membrane components lipoteichoic acid and lipopolysaccharide are shown to play a major role in determining the amyloid-nucleating capacity as triggered by Gram-positive and Gram-negative bacteria respectively. These nanonets displayed both trapping and killing functionalities, hence offering a direct improvement from the trap-only biomimetics in literature. By substituting a single turn residue of the non-amyloidogenic BTT1 peptide, the nanonet-forming BTT1-3A analog is produced with comparable antimicrobial potency. With the same sequence manipulation approach, BTT2-4A analog modified from BTT2 peptide showed improved antimicrobial potency against colistin-resistant clinical isolates. The peptide nanonets also demonstrated robust stability against proteolytic degradation, and promising in vivo efficacy and biosafety profile. Overall, these bacteria-responsive peptide nanonets are promising clinical anti-infective alternatives for circumventing antibiotic resistance.     

Yet another cybersecurity risk assessment framework

IT systems pervade our society more and more, and we become heavily dependent on them. At the same time, these systems are increasingly targeted in cyberattacks, making us vulnerable. Enterprise and cybersecurity responsibles face the problem of defining techniques that raise the level of security. They need to decide which mechanism provides the most efficient defense with limited resources. Basically, the risks need to be assessed to determine the best cost-to-benefit ratio. One way to achieve this is through threat modeling; however, threat modeling is not commonly used in the enterprise IT risk domain. Furthermore, the existing threat modeling methods have shortcomings. This paper introduces a metamodel-based approach named Yet Another Cybersecurity Risk Assessment Framework (Yacraf). Yacraf aims to enable comprehensive risk assessment for organizations with more decision support. The paper includes a risk calculation formalization and also an example showing how an organization can use and benefit from Yacraf.