(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

Inspired by many biological systems such as lotus leaves, insect wings and rose petals, great attention has been devoted to the study and fabrication of artificial superhydrophobic surfaces with multiple functionalities. In the present study, a simple and ecological synthesis route has been employed for large scale fabrication of self-assembled, sustainable nanostructures on unprocessed and micro imprinted aluminum surfaces named ‘Nano’ and ‘Hierarchy’. The processed samples show extreme wettability ranging from superhydrophilicity to superhydrophobicity depending on post-processing conditions. The densely packed ellipsoidal nanostructures exhibited superhydrophobicity with excellent water, bacterial and dust repellency when modified by low surface energy material 1H,1H,2H,2H-perfluorooctyltriethoxysilane (FOTES), characterized by a static contact angle of 163 ± 1° and contact angle hysteresis (CAH) ~3°. These coated surfaces show significant corrosion resistance with current density of 6 nA/cm2 which is 40 times lower than unprocessed counterpart and retain chemical stability after prolonged immersion in corrosive media. These surfaces show excellent self-cleaning ability with significantly low water consumption (&lt; 0.1 µl/mm2-mg) and prevent biofouling which ensures its applicability in biological environment and marine components. The nanostructured superhydrophilic aluminum shows maximum antibacterial activity due to disruption of cell membrane. This work can offer a simple strategy to large scale fabrication of multifunctional biomimetic metallic surfaces. © 2021

World University root node

Physics

(SoNS) School of Natural Sciences

Mechanical Engineering

Colloids and Surfaces B: Biointerfaces

Bioinspired micro/nano structured aluminum with multifaceted applications

Micro/nano scale surface modifications of titanium based orthopedic and cardiovascular implants has shown to augment biocompatibility. However, bacterial infection remains a serious concern for implant failure, aggravated by increasing antibiotic resistance and over usage of antibiotics. Bacteria cell adhesion on implant surface leads to colonization and biofilm formation resulting in morbidity and mortality. Hence, there is a need to develop new implant surfaces with high antibacterial properties. Recent developments have shown that superhydrophobic surfaces prevent protein and bacteria cell adhesion. In this study, a thermochemical treatment was used modify the surface properties for high efficacy antibacterial activity on titanium surface. The modification led to a micro-nano surface topography and upon modification with polyethylene glycol (PEG) and silane the surfaces were superhydrophilic and superhydrophobic, respectively. The modified surfaces were characterized for morphology, wettability, chemistry, corrosion resistance and surface charge. The antibacterial capability was characterized with Staphylococcus aureus and Escherichia coli by evaluating the bacteria cell inhibition, adhesion kinetics, and biofilm formation. The results indicated that the superhydrophobic micro-nano structured titanium surface reduced bacteria cell adhesion significantly (>90%) and prevented biofilm formation compared to the unmodified titanium surface after 24 h of incubation. © 2022 Wiley Periodicals LLC.

Journal of Biomedical Materials Research - Part A

Enhanced antibacterial properties on superhydrophobic micro-nano structured titanium surface

Antimicrobial coatings on metallic surfaces are rapidly emerging to combat bacterial pathogens. The excess use of conventional antibiotics increases the number of resistant strains at an alarming rate, which in turn leads to detrimental implications in the healthcare sector. Hence, it is of great importance in developing a new class of material with inherent bactericidal activity and good biocompatibility. In the present study, we have modified the aluminium surfaces by a coating of graphene oxide (GO) due to its excellent physicochemical properties, water dispersity and low cytotoxicity. Coatings were developed through facile and enviornment-friendly transfer method. The antimicrobial properties of GO coated aluminium are investigated against Gram-negative strain E. coli through agar plate counting and ‘Live/Dead’ fluorescence staining. Further, to shed light into the mechanism of antibacterial activity of GO at the molecular level, we have performed X-ray reflectivity (XRR) study considering a phospholipid multilayer as a model system to mimic bacteria cell membrane. Results show a significant bactericidal activity of the GO coatings compared to uncoated aluminium with lower concentration showing slightly better antibacterial property due to higher roughness. The obtained results may pave the way for engineering graphene-based antimicrobial coatings on a material surface using an easy, environment-friendly, cost-effective and straight forward processing route. © 2022 The Author(s)

Environmental Technology and Innovation

Graphene oxide coated aluminium as an efficient antibacterial surface

It is a difficult task for an assembly line manager to select an appropriate worker from the available workers' list to assign to an assembly line workstation. Since each available worker has a unique set of skills and abilities, this research considers the worker differences in their work performance. The worker's work performance is considered based on their working speed or productivity. Task execution time (TET) is the measure used to distinguish the worker's work performance. The TET prediction is made by the application of a knowledge-based system framework. Workers' historical work-time data is used to model the knowledge objects. Workers are classified as skilled and semi-skilled respective methodologies are given for both categories of workers. Statistical-based learning algorithms are proposed for skilled workers based on the worker's age, gender, and work skill. Similarly, worker's learning patterns are used for semi-skilled workers. The predicted TET is used in solving the assembly line worker assignment problem. The second part of this work is to prioritise the aged worker during the worker selection without increment of worker count. The illustrative example helps understand the scope of the proposed methodology in an assembly line worker assignment problem. © 2022 Elsevier B.V.

International Journal of Production Economics

Modelling and prediction of worker task performance using a knowledge-based system application

In drug review sentimental analysis (SA), users can share their experiences after consuming the drugs, which provides an accurate decision about the safety of the drug and public health. Patient-written medical and health-care reviews are among the most valuable and informative textual content on social media, but researchers in the areas of natural language processing (NLP) and data mining have not researched them thoroughly. These reviews provide insight into patients' interactions with doctors, treatment, and satisfaction or dissatisfaction with health services. The existing approaches have some problems like exploding/vanishing gradients and do not have sequential modeling. When learning long reviews, the exploding and vanishing gradient problems occurs. This problem makes it hard to tune parameters and learn in the network. The existing methods do not have sequential modeling because they fail to extract long dependencies for long reviews in both backward and forward directions. To overcome these issues, we proposed a Modular Lexicon Generation and a Fusion of Bidirectional threshold weighted mapping CNN-RNN (MLBTWCR) for classifying drug reviews based on users opinions. The Aspect based Modular Lexicon generation using the Advanced Dragon Fly Algorithm (AMLDA) is used to generate the score values for the lexicon and labels based on aspect. The Bidirectional Dropout Long and Short-Term Memory (Bi-DLSTM) and Bidirectional Gated Recurrent Unit (Bi-GRU) used for extracting long dependencies and for performing the sequence of arbitrary length in both backward and forward directions. The experimental results are evaluated using Drugslib.com and Drugs.com datasets. Based on evaluation result, the proposed MLBTWCR gives accuracy of 93.02%, recall of 88.72%, error rate of 11.2, false positive rate (FPR) of 11.3, false negative rate (FNR) of 13.6, running time of 15 s, and convergence speed of 0.2 and F-measure of 92.64%. Hence, our method performs well for the drug reviews classification based on aspects. © 2022 John Wiley & Sons, Ltd.

Computer Science and Engineering

Concurrency and Computation: Practice and Experience

Drug review sentimental analysis based on modular lexicon generation and a fusion of bidirectional threshold weighted mapping CNN-RNN

This paper describes a novel method to estimate the five parameters (R {s}, I {ph}, I {o1}, I {o2}, and Rsh) of double diode model (DDM) of a photovoltaic (PV) module. For estimations of the parameters I {o1}, I {o2}, and Rsh, three new explicit expressions are derived. The value of parameters R {sh}, I {o1} and Io2 are obtained from the proposed explicit expressions by putting the value of independent parameters Rs and Iph. Further, modified bisection method is utilized to find the error between the estimated maximum power point (MPP) voltage and MPP voltage given by the manufacturer. The root mean square (RMS) error of the estimated parameters using the proposed method is compared with different existing methods. The main contribution of the paper is three accurate explicit expressions of three dependent parameters in terms of two suitable independent parameters for DDM of a PV module. Since this novel method is simple and accurate for parameter estimation of a DDM PV module. Hence, it is useful for the PV professional. © 2022 IEEE.

Electrical Engineering

2022 IEEE 2nd International Conference on Sustainable Energy and Future Electric Transportation, SeFeT 2022

Parameter Estimation of a DDM PV Module Using a Simplistic Novel Method

The stepwise hydration of clay minerals has been observed repeatedly in studies, but the underlying mechanism remains unclear. Previous numerical studies confirmed the presence of one-water layer (1W) and two-water layer (2W) hydration states. However, the undisturbed transition between these hydration states has never been captured. Using molecular dynamics simulation, this study (i) simulated for the first time the free 1W–2W transition during clay hydration and (ii) identified the underlying mechanism to be the detachment of cations from the clay surface and the formation of a shell of water molecules around the cation. The swelling dynamics of clay was found to be affected by the clay charge, clay mineralogy, and counterions through complex cation–clay interactions, cation hydration capacity, and cation migration rate. © 2022 The Authors. Published by American Chemical Society

ACS Omega

Clay Swelling: Role of Cations in Stabilizing/Destabilizing Mechanisms

This paper presents experimental and simulation results for two different types of static harmonic compensators for removal of current harmonics from 3-phase supply lines. The comparative advantages and disadvantages of both the schemes are discussed. The first scheme uses sinusoidal signal integrators (SSIs) to extract, amplify and correct particular harmonic current errors in supply lines. Multiple SSIs are required for elimination of harmonic current errors of different orders. The SSI based schemes were reported by many researchers in last couple of decades. The second scheme uses a simplified repetitive controller (RC) to simultaneously remove some dominant current harmonic errors from supply current. Both the schemes have been verified experimentally and by simulation. Emphasis of the present work is to compare the two schemes in respect of performance and controller complexity. © 2022 IEEE.

Comparison of Two Current Harmonic Elimination Methods for Improving Supply Side Current Waveforms

Device reliability issues like BTI and HCI degrade the circuit performance over the device lifetime. The current work presents the degradation analysis of Axon Hillock (AH) and Simplified Leaky Integrate & Fire (SLIF) neuron circuits. The AH and SLIF circuits are implemented to generate spike output in two frequency ranges (KHz and MHz). We have analysed the impact of BTI, HCI and their combined effect on the circuit performance. Neuron circuits are characterized for their time-zero and aged performance by using spike frequency and spike output ON and OFF durations. 1000 Monte-Carlo (MC) simulations are performed to analyse the impact of time-zero process variability on the spike frequency. © 2022 IEEE.

2022 IEEE Region 10 Symposium, TENSYMP 2022

Impact of Reliability Issues and Process Variability in Neuromorphic Circuits

The main goal of the present study is to generate the GIS-based landslide susceptibility map (LSM) of Meghalaya, India. For this purpose, two bivariate statistical (FR and SE) and multi-criteria decision analysis (AHP and Fuzzy-AHP) methods are utilised. The study area is situated over the Shillong Plateau in the North Eastern Region of India and is subjected to numerous landslides due to heavy rainfall and seismic activities. To obtain the LSMs, a landslide inventory of 1330 events and 15 conditioning factors are prepared. The inventory dataset is randomly split into a 70/30 ratio to make training and testing datasets for the study. The results show that the southern escarpment, the southeast region and the hillslopes along the roadsides are highly susceptible to a landslide. The LSMs are validated using the area under the curve (AUC) values and F1-scores, in which AHP (AUCAHP = 0.913; F1-score = 0.884) shows the highest prediction accuracy for Meghalaya. The generated LSMs can help in landslide risk mitigation strategies and sustainable infrastructure development. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Civil Engineering

Fulltext

All Earth

Assessment of landslide susceptibility for Meghalaya (India) using bivariate (frequency ratio and Shannon entropy) and multi-criteria decision analysis (AHP and fuzzy-AHP) models