Thapar University

About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Computer science & Cloud computing. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.

Investigation of machining parameters and surface integrity in wire electric discharge machining of pure titanium

Abstract: This research work explores the relationship of machining parameters such as pulse on time (Ton), pulse off time (Toff), peak current (Ip), spark gap voltage, wire feed and wire tension on the material removal rate, wire wear ratio and surface roughness for pure titanium in wire electric discharge machining process. The Box–Behnken design had been utilized to plan the experiments, and response surface methodology was employed for developing the empirical models. The peak current, spark gap voltage, pulse on time, pulse off time and interaction between pulse on time and peak current affected the surface roughness significantly. Multi-response optimization of process parameters was obtained using desirability approach. Furthermore, the selected machined samples were analyzed using energy-dispersive X-ray analysis, scanning electron microscope and X-ray diffraction techniques. The predictions from this model were validated by conducting confirmatory experiments.

Photoluminescent properties of ZnS:Mn nanoparticles with in-built surfactant

Abstract: Mn-doped ZnS nanoparticles, having average diameter 3–5 nm, have been synthesized using chemical precipitation technique without using any external capping agent Zinc blende crystal structure has been confirmed using the X-ray diffraction studies The effect of various concentrations of Mn doping on the photoluminescent properties of ZnS nanoparticles has been studied The time-resolved photoluminescence spectra of the ZnS:Mn quantum dots have been recorded and various parameters like lifetimes, trap depths, and decay constant have been calculated from the decay curves at room temperature The band gap was calculated using UV–Visible absorption spectra

Intelligent failure prediction models for scientific workflows

Abstract: Intelligent task failure models using machine learning approaches are proposed.The accuracy of proposed models is validated in Pegasus and Amazon EC2.The prediction accuracy of (94%) is achieved using Naive Bayes approach. The ever-growing demand and heterogeneity of Cloud Computing is garnering popularity with scientific communities to utilize the services of Cloud for executing large scale scientific applications in the form of set of tasks known as Workflows. As scientific workflows stipulate a process or computation to be executed in the form of data flow and task dependencies that allow users to simply articulate multi-step computational and complex tasks. Hence, proactive fault tolerance is required for the execution of scientific workflows. To reduce the failure effect of workflow tasks on the Cloud resources during execution, task failures can be intelligently predicted by proactively analyzing the data of multiple scientific workflows using the state of the art of machine learning approaches for failure prediction. Therefore, this paper makes an effort to focus on the research problem of designing an intelligent task failure prediction models for facilitating proactive fault tolerance by predicting task failures for Scientific Workflow applications. Firstly, failure prediction models have been implemented through machine learning approaches using evaluated performance metrics and also demonstrates the maximum prediction accuracy for Naive Bayes. Then, the proposed failure models have also been validated using Pegasus and Amazon EC2 by comparing actual task failures with predicted task failures.