(SoE) School of Engineering

<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

According to a report by the World Health Organization(WHO) of diseases responsible for deaths, heart disease tops the chart. It is revealed that diagnosing heart disease in an earlier stage is an important issue and researchers across the world have investigated to develop intelligent support systems that could help a physician in better medical discernment beforehand. Machine Learning can be used to develop a medical diagnosis system for predicting heart disease which not only promises a more accurate diagnosis but also reduces the cost of diagnosis. In this paper, we are automating the task of predicting heart disease by using statistical methods/machine learning algorithms. We are exploiting some of the most important algorithms used these days like Logistic Regression, Support Vector Machine (SVM), Decision trees and also Ensemble Learning algorithms (Bagging, Boosting and Random Forests). We conduct these experiments on Cleveland Heart Dataset available on UCI KDD Archive. The dataset distribution judged by our research fits well with decision trees and gives better results with ensemble learning methods, we justify this claim by comparing ensemble results with other Machine Learning approaches. Random forest classifier works best for this data set distribution, the random-nature of this ensemble approach fits the dataset well providing good test accuracies of upto 96.26\% © 2020 IEEE.

Electrical Engineering

Proceedings of the 2020 International Conference on Computing, Communication and Security, ICCCS 2020

Ensemble learning classification for medical diagnosis

Friction stir processing of an Al0.1CoCrFeNi high entropy alloy (HEA) was performed at controlled cooling conditions (ambient and liquid submerged). Microstructural and mechanical characterization of the processed and as-cast HEAs was evaluated using electron backscat-ter diffraction, micro-hardness testing and nanoindentation. HEA under the submerged cooling condition showed elongated grains (10 µm) with fine equiaxed grains (2 µm) along the boundary compared to the coarser grain (∼2 mm) of as-cast HEA. The hardness showed remarkable improvements with four (submerged cooling condition) and three (ambient cooling condition) times that of as-cast HEA (HV ∼150). The enhanced hardness is attributed to the significant grain refinement in the processed HEAs. Cavitation erosion behavior was observed for samples using an ultrasonication method. All of the HEAs showed better cavitation erosion resistance than the stainless steel 316L. The sample processed under a submerged liquid condition showed approximately 20 and 2 times greater erosion resistance than stainless steel 316L and as-cast HEA, respectively. The enhanced erosion resistances of the processed HEAs correlate to their increased hardness, resistance to plasticity, and better yield strength than the as-cast HEA. The surface of the tested samples showed nucleation and pit growth, and plastic deformation of the material followed by fatigue-controlled disintegration as the primary material removal mechanism. © 2020, University of Science and Technology Beijing and Springer-Verlag GmbH Germany, part of Springer Nature.

Mechanical Engineering

International Journal of Minerals, Metallurgy and Materials

Enhanced cavitation erosion resistance of a friction stir processed high entropy alloy

Thermal spray coatings (TSCs) are widely utilized for limiting degradation of structural components. However, the performance of TSCs is significantly impaired by its inherent non-homogeneous microstructure, comprising of splat boundaries, porosities, secondary phase-formation, and elemental segregation. Herein, we report a simplistic approach for significantly enhancing the corrosion resistance of TSCs. Ni-Cr-5Al2O3 coatings were deposited on stainless steel using high-velocity oxy-fuel technique. The microstructure of as-sprayed coating showed significant inhomogeneities in the form of isolated splats and elemental segregation. The microstructure of developed coatings was modified using a novel processing technique, known as stationary friction processing (SFP). The SFP treatment resulted in complete refinement of coating microstructure with elimination of splat boundaries and pores along with elemental homogenization. The corrosion behavior of as-sprayed and SFP treated coating was evaluated in 3.5\% NaCl solution using potentiodynamic polarization and electrochemical impedance spectroscopy. The SFP treatment reduced the corrosion rate of as-sprayed coating by an order of magnitude. Long-time immersion studies showed continuously decreasing impedance of the as-sprayed coating due to the penetration of the electrolyte along the splat boundaries. In contrast, impedance for the SFP treated coating increased with the immersion time due to the removal of all microstructural defects. Copyright © 2020 American Chemical Society.

Fulltext

ACS Omega

Facile and Green Engineering Approach for Enhanced Corrosion Resistance of Ni-Cr-Al2O3Thermal Spray Coatings

Three dimensional dynamics of rising bubbles in gradually mixing fluids due to the effect of Rayleigh-Taylor instability have been numerically investigated. The effect of various parameters such as Eotvos (Eo) number and Morton (M) number for three different cases represented as single bubble rising, inline bubbles rising and offset bubbles rising are comprehensively investigated. It is observed that, two raising bubbles either inline or offset exhibits higher terminal Reynolds number (ReT) in quiescent liquids when compared with single rising bubble. They enhances ReT by 120.22\% for Eo = 38.8 and M = 9.71 × 10−4. Moreover, in gradually mixing fluids due to the presence of Rayleigh-Taylor instability, single rising bubble also exhibits higher ReT and at earlier time. In fluids with low Eo and high M values, mixing effect is very slow because of the presence of higher value of fluids internal resistance as well as high cohesive forces. This causes a delay in the formation of bubble behind the rising pre-defined bubbles. Whereas, in the opposite case, this bubble forms much quicker. But the rising pre-defined bubble breaks up and gradually vanishes off with time. Due to significant decrement in the fluids internal resistance, it is also observed that; at same Eo = 10, by decreasing the value of M from 9.71 × 10−4 to 9.71 × 10−8, the ReT value is increased by 48.84\% for inline and offset bubbles. Furthermore, at same M = 9.71 × 10−4, by increasing the value of Eo from 10 to 38.8, 18.3\% lower time is required for single bubble to attain higher velocity. This is due to the presence of low cohesive forces and high fluids internal resistance. © 2020

World University root node

International Journal of Multiphase Flow

Dynamics of rising bubbles in gradually mixing fluids due to the effect of Rayleigh–Taylor instability

This chapter focuses on investigating the phenomenon of intermittency in the dynamical behavior of aeroelastic systems. To that end, a classical two degree-of freedom pitch plunge aeroelastic system is considered as the representative aeroelastic model. Investigations are first carried out on the route to aeroelastic flutter in fluctuating flow conditions through wind tunnel experiments. The recurring nature of intermittent periodic bursts observed in the pre-flutter response are subsequently utilized to develop quantitative measures using time series based tools, that can serve as precursors to flutter instability. To gain further insights into the experimental observations, numerical investigations are carried out using a well known two degree-of-freedom pitch plunge mathematical model for the aeroelastic system. A stochastic bifurcation analysis is also carried out that provide insights into the noise induced dynamical stability characteristics. Finally, a detailed study is undertaken to investigate the physical mechanisms that lead to the appearance of intermittency. Particularly, the effect of time scales of the flow fluctuations are investigated. © 2020, Springer Nature Singapore Pte Ltd.

Energy, Environment, and Sustainability

Intermittency in a Pitch-Plunge Aeroelastic System

This work models high current snapback behavior in n-FET transistors with bottom body contact under high current stress at the drain for ZRAM (Zero capacitor RAM). We analyze 2D current flow in n-FET near the pinch-off region and relate the results to the S-shaped snapback characteristics under high injection avalanche generated carriers conditions. The role of surface bipolar effects on the first snapback phenomenon in the GG-NMOS (Gate-Grounded NMOS) is investigated. A novel physical insight of the bipolar activity is modeled through current flow and barrier lowering at the source-substrate junctions. Moreover, the coupling of electron and hole injection is described. Finally, a model for surface potential inside the substrate before and after snapback is derived and compared with TCAD simulation results. A very good agreement is observed between the model and the TCAD results. © 2020 The Author(s).

Journal of Physics Communications

Bipolar effects in snapback mechanism in advanced n-fet transistors under high current stress conditions

In this study, remote sensing-based algorithm, METRIC is used to estimate evapotranspiration (ET) over a mountainous watershed in the lesser Himalayas in Uttarakhand, India. ET estimates of METRIC are compared with the ET values determined by using FAO Penman-Monteith equation and crop coefficients. In its original form, METRIC underestimated the ET values considerably with mean absolute error (MAE) of 0.95 mm/day. A modification is incorporated in METRIC for better representation of the effect of rainfall on the ET process. The proposed modification estimates ET at the hot pixel using daily rainfall and meteorological data. Using the modification, the MAE is reduced to 0.44 mm/day and underestimation of ET is reduced from 20.9% to 8.4%. This study contributes to the better understanding of ET in the lesser Himalayas of Uttarakhand. It also underlines the importance of accurate assignment of ET to the hot pixel for accurate estimation of ET using METRIC.

Civil Engineering

Geocarto International

Estimation of evapotranspiration in lesser Himalayas using remote sensing based surface energy balance algorithm

The attributed multiplex network is a set of attributed networks in which each network represents a different type of interaction between the same set of nodes. Individual networks are termed as layers or dimensions and network nodes are characterized by attribute vectors. Neighborhood, in general, refers to any dense connected subgraph. We refer neighborhood1 as subgraph induced on graph node and its neighbors. It is usually observed that majority of the nodes in multilayer networks are active only on small number of layers except some outliers [18]. However, node activity is not strictly correlated to the edges incident in a node. A node might be active at few layers with relatively large number of incident edges and at the same time, multi-active node might not have many links even on single layer. Moreover, each layer has distinct importance in the multiplex networks2 and the structure and size of neighborhood formed by these multiplex nodes are different on each layer. Nodes with different attributes come together on different layers in the attributed multiplex networks. This node and layer heterogeneity should be considered while identifying anomalous neighborhoods in the attributed multiplex networks. Thus, a measure is required to quantify the quality of neighborhoods formed by active nodes on different layers. Existing approaches do not consider heterogeneity among network layers and do quantify the structure of networks either separately for each layer or its aggregated network and ignore the attributes of nodes. In this work, we define a novel quality measure Multi-Normality which utilizes the structure and attributes together of each layer and detect attribute coherence in neighborhoods between layers. We also propose an algorithm exhausting multi-normality to identify anomalous neighborhoods in multiplex networks and is named as Anomaly Detection of Entity Neighborhoods in Multiplex Networks (ADENMN). We evaluate the effectiveness of the proposed algorithm in anomaly detection by comparing its performance with three existing baseline approaches including ADOMS, AMM and AGG+AD on five real-world attributed multiplex networks including Amazon, YouTube, Noordin top terrorist network, DBLP_C, and Aarhus. The results of experiments demonstrate that multi-normality outperforms baseline algorithms. © 2020 Association for Computing Machinery.

Computer Science and Engineering

ACM International Conference Proceeding Series

Ranking and discovering anomalous neighborhoods in attributed multiplex networks

Structural materials are subjected to severe forms of material degradation for a prolonged life period. The most common material degradation is wear, erosion, and corrosion which severely impairs the service life of the components. State-of-art-of materials are of paramount importance to mitigate these forms of degradation phenomenon. NiTi alloy exhibits outstanding properties in terms of superelasticity, high fatigue strength, and exceptional corrosion resistance. In this work, the tribological behavior of NiTi alloy synthesized using arc melter (TIG) under different cooling conditions (ambient and water quenching) was studied via cavitation erosion, sliding wear, slurry erosion-corrosion and electrochemical corrosion. The microstructural study disclosed the formation of dendritic structures for both the samples. Hardness observed for the ambient cooled sample was around 980 HV which is 1.3 times higher than the quenched NiTi alloy. The difference in the hardness was attributed to the presence of pores for the latter. Both the NiTi alloy samples showed significant improvement in the degradation behavior compared to SS316L under pure erosion and erosion-corrosion conditions. Despite the lower hardness obtained for the quenched NiTi alloy, almost similar degradation resistance in terms of cavitation erosion and dry sliding wear was observed for both the alloyed samples. This is explained based on the work hardening ability behavior of the NiTi alloy. Slurry and sliding wear performed under 3.5\% NaCl solution portrayed greater results for quenched alloy which is attributed to its higher corrosion and passivation resistance compared to the ambient NiTi alloy and SS316L. In conclusion, the microstructural, mechanical and tribological behavioral study of NiTi alloy indicated its dominance over SS316L in dry as well as wet conditions and hereby, emphasizing its importance as an alternative material for marine applications in hydrodynamic environment. © 2019 Elsevier Ltd. All rights reserved.

Materials Today: Proceedings

Tribological behavior of NiTi alloy processed under different cooling conditions

Nowadays robots have been inching towards living beings in terms of performance, agility, accuracy and performance. But there is one domain which is yet to come to limelight i.e. autonomous rescue robots. This involves a parallel use of robot design and control systems to get it executed. Basically, in simpler terms, there is a risk of life is involved in such scenarios and it's always hoped that a robot deployed for such operations be working with high precision and recall. So, in this paper we have described the design and control of an autonomous amphibian all terrain six-legged robot with adaptive gait for rescue operations. To fulfill this, we have used LiDAR and camera in parallel to assist bot in locomotion and detect the victim. Achieving all of this needs a significant amount of on-board processing power, but at the same time has to be compact enough. Keeping this in mind, NVIDIA Jetson Nano seems to be promising choice to get all the processing done onboard. © 2020 IEEE.