Extraction and Characterization of Novel Natural Cellulosic Fibers from Pigeon Pea Plant

ABSTRACT

This study is mainly focused on the extraction and characterization of the lingo-cellulosic fibers from the Pigeon Pea plant. The chemical components of the fibers such as cellulose content (55.03%), lignin (18.32%), wax content (2.38%), moisture content (8.13%), and density (1.7389 g cc^?1) were identified. Functional groups present in the fibers were obtained by infrared spectroscopy. By using X-ray diffraction analysis, Crystallinity Index is found to be 68% and thermogravimetric analysis showed that thermal degradation of the fiber begins at 225°C.The results suggest that the Pigeon pea fibers can be used as reinforcement in polymer-matrix composites.     

Effect of magneto rheological minimum quantity lubrication on machinability,wettability and tribological behavior in turning of Monel K500 alloy

Abstract

The proposed work deals with the investigation of magnetorheological based minimum quantity lubrication of graphene oxide (GO) based jojoba oil as bio-lubricant on machinability and tool wear mechanism of turning Monel K500 alloy. Experiments were carried out for dry, flooded, minimum quantity lubrication (MQL) and magnetorheological (MR–MQL) conditions using medium duty lathe. The process parameters include the cutting speed 95, 110, 125?m/min, feed rate 0.050, 0.075, 0.1?mm/rev and depth of cut 0.25, 0.50, 0.75?mm for the output responses such as surface roughness, cutting temperature and tool flank wear. The results indicated that GO-based bio-lubricant MR–MQL reduced coefficient of friction (COF) of 0.051 and wetting angle of 6°, as well as improved machining performance such as cutting temperature of 145?°C, the surface roughness of 0.614?µm, flank wear of 0.18?mm with enhanced lubrication regime under extreme wear conditions.     

Compressive Sensing Node Localization Method Using Autonomous Underwater Vehicle Network

This framework attempts to introduce a new Distributed denial-of-service (DDoS) attack detection and mitigation model. It is comprised of two stages, namely DDoS attack detection and mitigation. The first stage consists of three important phases like feature extraction, optimal feature selection, and classification. In order to optimally select the features of obtained feature sets, a new improved algorithm is implanted named Improved Update oriented Rider Optimization Algorithm (IU-ROA), which is the modification of the Rider Optimization Algorithm (ROA) algorithm. The optimal features are subjected to classification using the Deep Convolutional Neural Network (CNN) model, in which the presence of network attacks can be detected. The second stage is the mitigation of the attacker node. For this, a bait detection mechanism is launched, which provides the effective mitigation of malicious nodes having Distributed Denial-of-Service (DDoS) attacks. The experimentation is done on the KDD cup 99 dataset and the experimental analysis proves that the proposed model generates a better result which is 90.06% in mitigation analysis and the overall performance analysis of the proposed model on DDoS Attack Detection is 96% better than conventional methods.

     

Network Support for IP Trace Back Model in Wireless Sensor Networks Using Quantum Annealing Method

Wireless Personal Communications - Distributed Denial-Of-Service (DDoS) attacks are one of the all the more difficult security issues on the Internet today. They can without much of a stretch,...