Velagapudi Ramakrishna Siddhartha Engineering College

About: Velagapudi Ramakrishna Siddhartha Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Antenna (radio). The organization has 1307 authors who have published 1155 publications receiving 6163 citations.

Consolidation of Nano-Oxide Dispersion Strengthened Austenitic Stainless Steels for High Temperature Applications–A Review

Abstract: Austenitic stainless steels play vital role in high temperature applications. The components used in fission reactors should have better tensile properties at high temperature. To overcome the issues raised by conventional steels, a new group of steels known as oxide dispersion strengthened (ODS) steels are created. Nowadays, two groups of ODS steels such as the ferritic and austenitic ODS steels are being used. The present work mainly focusses on the processing of ODS austenitic steels. The consolidation processes adopted for ODS austenitic steels are discussed in detail. The microstructural features and precipitation of nano-oxides during compaction are also reported. In addition to this, the mechanical properties such as tensile strength and elongation values obtained by various manufacturing processes are also discussed.

Verification of numerical homogenization approach in predicting thermal conductivities of fiber reinforced composites with voids and randomly distributed fibers

Abstract: The aim of this paper is to establish the homogenization approach that eliminates the difficulties encountered by the conventional numerical methods in analyzing thermal behavior of the multi-mater...

A Novel Approach for Efficient Packet Transmission in Volunteered Computing MANET

Abstract: A mobile ad hoc network (MANET) is summarized as a combination device that can move, synchronize and converse without any preceding management. Enhancing the lifetime energy is based on the status of the concerned channel. The node is accomplished of control the control messages. Due to unplanned methods of energy conservation, the node lifespan and quality of packet flow is defaced in the existing solution. It results in a network-to-node-energy trade-off, ensuing in a failure of the post-network. This failure results in reduced time-to-live and higher overhead. This paper discusses an effective buffer management mechanism, in addition to proposing a novel performance modeling in Volunteered Computing MANET and tactile internet Next, the best execution the nodes can accomplish under fractional data is completely portrayed for utilities for a general purpose. To associate the space between network efficiency and energy conservation based on the minimal overhead, this article proposes a switch state promoting mutual Optimized MAC protocol for conservation of a node's energy and the optimal use of available nodes before their energy drain. Simulation results are provided as proof of the proposed solution. The simulation results are compared with the existing system with performance measures of delay, throughput, energy consumption, and availability of the node.

Computational Investigations of Fixed-Free and Fixed-Fixed Types Single-Wall Carbon Nanotube Mass Sensing Biosensor

Abstract: Using carbon nanotubes for sensing the mass in a biosensor is recently proven as an emerging technology in healthcare industry. This study investigates relative frequency shifts and sensitivity studies of various biological objects such as insulin hormone, immunoglobulin G (IgG), the most abundant type of antibody, and low-density lipoproteins (LDL) masses using the single-wall carbon nanotubes as a biomass sensor via continuum mechanics. Uniform distributed mass is applied to the single-wall carbon nanotube mass sensor. In this study, fixed-free and fixed-fixed type single-wall carbon nanotubes with various lengths of relative frequency shifts are studied. Additionally, the sensitivity analysis of fixed-free and fixed-fixed type CNT biological mass sensors is carried out. Moreover, mode shapes studies are performed. The sensitivity results show better, if the length of the single-wall carbon nanotube is reduced.

Big Data Technologies for Predicting Epidemics and Enhancing the Quality of Human Life

Abstract: With a lot of medical data coming from various sources, steer decisions can be made from the insights expand through big data by using various Machine Learning Algorithms. Traditionally, physicians use their knowledge while making treatment assessments, but in the last few years there has been a shift towards evidence-based medicine. This involves systematical appraisal of clinical data and making treatment decisions based on the finest accessible information. In this paper, we apply the predictive analysis technique in Hadoop/Map Reduce background to predict and classify the type of diabetes. And also focuses on how MapReduce is used, how map and reduce evaluations are adapted, implemented in various situations such as in medical field to produce medical reports by processing large medical data sets. This system provides capable way to care and cure the patients at low cost with improved outcomes like affordability and accessibility.