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Effect of source-substrate distance on the transparent electrode properties of spray pyrolysed aluminium doped zinc oxide thin films
The wide band gap zinc oxide is a potential metal oxide that has been widely used in optoelectronic applications. The zinc oxide thin films demonstrate excellent conductivity and transparency enabling them for transparent electrode applications. The aluminium doping is an efficient route in further improving the conductivity without compromising the transparency and scalable spray pyrolysis is an effective approach in realizing high quality thin films. Our current study focuses on the effects of distance between the substrate and spray nozzle on the structural, morphological, optical, and electrical properties of aluminium doped zinc oxide. Our results suggests that this spray parameter has appreciable impact on the thin film properties and can be optimized for tuning properties. We explain this in detail backed by the characterization of thin films by X-ray diffraction, Atomic Force Microscopy, UVVis-NIR spectroscopy, Photoluminescence and Hall effect measurements. 2021 -
Intensified geopolitical conflicts and herding behavior: An evidence from selected Nifty sectoral indices during India-China tensions in 2020
The recent India-China geopolitical conflicts have presented enormous uncertainty to the investors in various sectoral indices of the Indian stock market. This empirical study aims to examine the impact of intensified India-China geopolitical conflicts 2020 on investors' herding behavior in the National Stock Exchange sectoral indices. The high-frequency data of three major NIFTY sectoral indices (Auto, Energy, and Pharma) are used in an intensified geopolitical event window to spot precisely the traces of the investors' herding behavior. Furthermore, multifractal detrended fluctuation analysis (MFDFA) is employed to obtain Hurst Exponent values (h(q)) for the NIFTY sectoral indices. The findings reveal that these NIFTY sectoral indices exhibited profound traces of herding behavior on the event day (t = 0) due to the heightened India-China geopolitical clashes. In addition, these indices depicted an overall higher level herding behavior with the (h(q)) values close to 0.72 throughout the intensified geopolitical event window. The study concludes that the sectors highly reliant on the Chinese supplies and with significant trade linkages with China depicted a higher level of herding behavior in their indices. Further, the presence of herding behavior in these sectoral indices is due to the operational and supply-chain risks posed by the geopolitical event. 2022 LLC CPC Business Perspectives. All rights reserved. -
Effects of variable thermal properties on thermoelastic waves induced by sinusoidal heat source in half space medium
Aim of the present study is to characterize the effects of changing thermal conductivity on the propagation of thermoelastic waves in the half space medium when it is exposed to a periodic heat source. Closed form solutions of all significant physical fields such as conductive temperature, stress and displacement are evaluated in their dimensionless form in the Laplace transform domain. Impact of changing thermal conductivity parameter is exhibited on all field variables with the help of quantitative outcomes in time-domain. Following this pattern, the effects of time parameter is also observed on the field quantities. 2022 -
Examination of thermal and velocity slip effects on the flow of blood suspended with aluminum alloys over a bi-directional stretching sheet: the ternary nanofluid model
The flow of a ternary nanofluid through a bi-directionally distending sheet has been analyzed by employing the Casson model. The ternary nanofluid is formed by suspending the aluminum alloys namely (Formula presented.) and (Formula presented.) along with the oxide (Formula presented.) into blood. The nanoparticles that are suspended in the base fluid are assumed to be in the shape of a blade so that the maximum surface of the nanoparticle will be intact with the base fluid and thus will absorb more heat from the surface. Also, the shape of the nanoparticle helps in faster movement within the nanofluid. The flow is further subjected to velocity and thermal slips. With these assumptions, the mathematical model is framed with the help of partial differential equations considering thermal radiation and heat source/sink to achieve realistic results. These equations are further transformed into non-linear differential equations that are solved using the (Formula presented.) technique. The results of this study are interpreted graphically for various parameters concerning fluid flow. It is observed that the fluid flow velocity is ebbed considerably with the increase in Casson parameter and the slips at the boundary have enhanced the corresponding fluid profiles. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Perusal of flexoelectric effect with deformed interface in distinct (PZT-7A, PZT-5A, PZT-6B, PZT-4, PZT-2) piezoelectric materials
The present research article aims to describe that the flexoelectric affects the propagation of Love-type in various piezoelectric (PE) materials bars (PZT-7A, PZT-5A, PZT-6B, PZT-4, PZT-2) that rest over a silicon oxide plate under the presence of a deformed interface. With the help of material properties of these various piezoelectric, this article elucidates the impact of flexoelectric (FE) and piezoelectric (PE) on propagation characteristics of Love-type waves. Before this, the desire dispersion relation in the form of a complex, for both electrically unlocked/locked conditions, have been obtained by using mechanical as well as electrical quantities for the respective medium under suitable boundary and interface conditions. The complex dispersion relation is separated into real and imaginary terms which give Real(c) and Imag(c). Further, the effect of flexoelectric (FE) and piezoelectric (PE) on Real(c) and Imag(c) have been observed. In addition, a comparative study among various types of piezoelectric materials is also performed which serve as a major highlight of the present research work. The outcomes of this study may be very helpful in the specific problems of monitoring structural health system design with the help of simulation and alesser number of elaborate trials. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Implementation of a Heart Disease Risk Prediction Model Using Machine Learning
Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease. The chi-square statistical test is performed to select specific attributes from the Cleveland heart disease (HD) dataset. Support vector machine (SVM), Gaussian Naive Bayes, logistic regression, LightGBM, XGBoost, and random forest algorithm have been employed for developing heart disease risk prediction model and obtained the accuracy as 80.32%, 78.68%, 80.32%, 77.04%, 73.77%, and 88.5%, respectively. The data visualization has been generated to illustrate the relationship between the features. According to the findings of the experiments, the random forest algorithm achieves 88.5% accuracy during validation for 303 data instances with 13 selected features of the Cleveland HD dataset. 2022 K. Karthick et al. -
Cattaneo-Christov Theory to model heat flux effect on nanoliquid slip flow over a spinning disk with nanoparticle aggregation and Hall current
The heat transport of a nanoliquid on a spinning disk with velocity slip and thermal jump boundary conditions is modeled. The effects of external magnetism and the aggregation of nanoparticles are analyzed. The Cattaneo-Christov heat flux model and the Joule heating phenomenon are incorporated in the thermal analysis. The central composite design (CCD) of the response surface methodology is implemented to optimize heat transfer in the nanoliquid. The sensitivity of the heat transport is analyzed. The partial differential governing model is converted into a system of ordinary differential equations using a novel von Karmans transformation, the consequent system is solved numerically. The significance of physical operating parameters is analyzed through a detailed parametric study. Optimal levels of Hall parameter, Hartmann number, and Eckert number, that optimize the heat transport are determined. The Lorentz force expands the structure of the thermal layer and subsequently reduces the heat transport of the system. The Hall current improves the thickness of the velocity layer in the radial direction, while the thickness of the thermal layer is reduced. Viscous dissipation improves the thickness of the thermal boundary layer. The isothermal boundary condition causes less heat transport in the system than the temperature jump condition. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Yttrium(III) oxide catalyzed facile synthesis of novel hydrazinyl thiazoles by multicomponent approach
A facile and a one-pot procedure for the synthesis of novel hydrazinyl thiazoles by the cyclo condensation reaction between substituted phenacyl bromides, aromatic aldehydes, and semicarbazides using Yttrium(III) oxide as reusable catalyst under acetic acid as a solvent medium is described. The chromatography-free methodology has several benefits such as being facile, atom economic, higher functional group tolerance and provides excellent yield in shorter reaction time. All the synthesized compounds were characterized by 1H NMR, 13C NMR, and HRMS (ESI) analysis. 2021 -
Hycons Renewable Private Limited: decision to accept or reject an equity investment
Learning outcomes: This study will help students determine the economic value of a firm particularly in case of a small business. The crux of the case is to help students estimate an enterprise value for a company and figure the actual worth of the company to aid in decision-making. Case overview/Synopsis: This case is about a decision dilemma faced by Shashi Hegde, Director, Hycons Renewable Private Ltd, a company ventured into the production of Bio-CNG. It is about a recent proposal received by the firm from APL Ltd for equity investment with 40% stake in the firm. The case reflects the dilemma faced by small businesses to choose between investment or loss of control. Accepting the proposal will bring in additional funds, whereas the Board loss control on the firm. The case revolves around this dilemma. To help Hegde in this task, he seeks advice from his CFO and his confidant Kumar. Complexity academic level: This case is most appropriate for a core finance class for both under-graduate and graduate programs. Supplementary materials: Teaching notes are available for educators only. Subject code: CSS 1: Accounting and Finance. 2022, Emerald Publishing Limited. -
Sixth-Generation (6G) Mobile Cloud Security and Privacy Risks for AI System Using High-Performance Computing Implementation
The exchange of information from one person to another is called communication. Telecommunication makes it possible with electronic devices and their tools. The scientist Alexander Graham Bell has invented the basic telephone in 1876 in the USA. Telephones now have the new format in the form of mobile phones, which are the primary media for communicating and transmitting data. We are using 5th-generation mobile network standards. Still, there are some requirements for the users that are believed to be solved in the 6th-generation mobile network standards. By 2030, all of the people would be using 6G. The computing model in the cloud is not dependent on either the location or any specific device that would provide the service. It is an on-demand computational service-oriented mechanism. Combining these two technologies as mobile cloud computing provides customized options with more flexible implementations. Artificial intelligence is being used in devices in many fields. AI can be used in mobile network services (MNS) to provide more reliable and customized services to the users, such as network operation monitoring, network operation management, fraud detection, and reduction in mobile transactions and security to the cyber devices. Combining cloud with AI in mobile network services in the 6th generation would improve human beings' lives, such as zero road accidents, advanced level special health care, and zero crime rates in society. However, the most vital needs for sixth-generation standards are the capability to manage large volumes of records and excessive-statistics-fee connectivity in step with gadgets. The sixth-generation mobile network is under development. This generation has many exciting features. Security is the central issue that needs to be sorted out using appropriate forensic mechanisms. There is a need to approach high-performance computing for improved services to the end-user. Considering three-dimensional research methodologies (technical dimension, organizational dimension, and applications hosted on the cloud) in a high-performance computing environment leads to two different cases such as real-time stream processing and remote desktop connection and performance test. By 'narrowing the targeted worldwide audience with a wide range of experiential opportunities,' this paper is aimed at delivering dynamic and varied resource allocation for reliable and justified on-demand services. 2022 Srinivasa Rao Gundu et al. -
Evaluation on effect of alkaline activator on compaction properties of red mud stabilised by ground granulated blast slag
Any industrial waste has a potential to be used as a civil engineering material with an effective and appropriate waste management system. Like many industrial wastes, red mud (RM) and Ground granulated blast slag (GGBS) are some of the industrial wastes produced from aluminium and steel industries respectively. Utilization of only waste materials will not be effective without a suitable stabilizer, which forced to use an alkaline activator to satisfy the needs of a building materials. This paper evaluates measurements to assess the effect of alkaline activator on the compaction properties of GGBS stabilized RM. Different ratios of NaOH to Na2SiO3 was used as an alkaline activator with 10, 20 and 30 percentage replacement of GGBS to RM and measured the compaction properties by using a mini compaction apparatus. Upon conducting standard and modified proctor compaction tests for various combinations of RM and GGBS, the compaction curves depicted that huge variation in maximum dry density and optimum moisture content with the change of GGBS percentage and different ratios of NaOH to Na2SiO3 are measured and analysed. Further the influence of compaction energy on the density characteristics of these trails were assessed for better understanding. Copyright: This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. -
Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches
The rate of infant mortality (IMR) in a population under one year of age is a marker for infant mortality. It is a major sensitive marker of a community's overall physical health. Protecting the lives of newborns has become a challenging issue in public health, development programs, and humanitarian initiatives. Almost 10.1% infants died in the United States of America (USA) in 2021. Therefore, this paper aims to extract and understand the various influential factors causing infant deaths in the USA. A crowding distance-based multi-objective ant lion optimization (MOALO-CD) is proposed here with statistical evidence for feature selection. The proposed technique is compared with competitive metaheuristic models such as multi-objective genetic algorithm based on crowding distance (MOGA-CD), multi-objective filter approaches, and recursive feature elimination. Various machine learning classifiers are applied to the selected feature subset obtained from MOALO-CD on the USA's infant dataset. Extensive experimental results indicate that the proposed model outperforms the existing metaheuristic approaches in terms of Generational Distance, Inverted Generational Distance, Spread, and Hyper volume. Also, the comparative analysis of various machine learning models reveals that random forest achieves significantly better performance on the feature subset obtained from MOALO-CD. 2013 IEEE. -
Detection and Classification of Colorectal Polyp Using Deep Learning
Colorectal Cancer (CRC) is the third most dangerous cancer in the world and also increasing day by day. So, timely and accurate diagnosis is required to save the life of patients. Cancer grows from polyps which can be either cancerous or noncancerous. So, if the cancerous polyps are detected accurately and removed on time, then the dangerous consequences of cancer can be reduced to a large extent. The colonoscopy is used to detect the presence of colorectal polyps. However, manual examinations performed by experts are prone to various errors. Therefore, some researchers have utilized machine and deep learning-based models to automate the diagnosis process. However, existing models suffer from overfitting and gradient vanishing problems. To overcome these problems, a convolutional neural network- (CNN-) based deep learning model is proposed. Initially, guided image filter and dynamic histogram equalization approaches are used to filter and enhance the colonoscopy images. Thereafter, Single Shot MultiBox Detector (SSD) is used to efficiently detect and classify colorectal polyps from colonoscopy images. Finally, fully connected layers with dropouts are used to classify the polyp classes. Extensive experimental results on benchmark dataset show that the proposed model achieves significantly better results than the competitive models. The proposed model can detect and classify colorectal polyps from the colonoscopy images with 92% accuracy. 2022 Sushama Tanwar et al. -
The impact of the movement of the gyrotactic microorganisms on the heat and mass transfer characteristics of Casson nanofluid
This article focuses on analyzing the impact of the movement of gyrotactic microorganisms on the heat and mass transfer characteristics of Casson nanofluid flowing between divergent. Further, the analysis is performed through simulation to have a better understanding of the impact. Since the microorganisms are self-propelled, they move on their own in the nanofluid due to the concentration gradient and stabilize the nanoparticle suspension. This movement of microorganisms constitutes the formation of bioconvection. Further, the random motion of nanoparticles gives rise to two major slip mechanisms termed thermophoresis and Brownian motion. The mathematical model comprising these effects is designed using partial differential equations that are converted to ordinary differential equations with the help of suitable similarity transformation. The resulting system of equations is then solved using the differential transformation method and the outcomes are interpreted through graphs. It is indicated that the nanoparticle concentration and the motile density profiles increase with the increase in Schmidt number and also, the concentration profile is found to be increasing for higher Brownian motion parameter and lower thermophoretic parameter. The simulations performed through the finite element method portrayed that the heat flow in the diverging channel is occurring along the isothermal planes. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Model independent analysis in (?, n) reactions using deuterium targets
Photonuclear reactions play an important role in nuclear physics, astrophysics and in various applications such as non-destructive measurement of nuclear materials (NDT). The study of (?, n) reactions using deuterium targets i.e., photodisintegration of deuterons in addition to all the other (?, n) reactions, is of considerable interest to these fields. In this contribution, we have studied the photodisintegration of deuterons with unpolarized photons. The angular dependence of the differential cross section is studied by expressing it in terms of Legendre polynomials. The analysis of differential cross-section is presented using the model-independent irreducible tensor formalism. 2021 -
Design of Grovers Algorithm over 2, 3 and 4-Qubit Systems in Quantum Programming Studio
In this paper, we design and analyse the Circuit for Grovers Quantum Search Algorithm on 2, 3 and 4-qubit systems, in terms of number of gates, representation of state vectors and measurement probability for the state vectors. We designed, examined and simulated the quantum circuit on IBM Q platform using Quantum Programming Studio. We present the theoretical implementation of the search algorithm on different qubit systems. We observe that our circuit design for 2 and 4-qubit systems are precise and do not introduce any error while experiencing a small error to our design of 3-qubit quantum system. 2022 Polish Academy of Sciences. All rights reserved. -
A Discrete Kumaraswamy Marshall-Olkin Exponential Distribution
Finding new families of distributions has become a popular tool in statistical research. In this article, we introduce a new flexible four-parameter discrete model based on the Marshall-Olkin approach, namely, the discrete Kumaraswamy MarshallOlkin exponential distribution. The proposed distribution can be viewed as another generalization of the geometric distribution and enfolds some important distributions as special cases. Some properties of the new distribution are derived. The model parameters are estimated by the maximum likelihood method, with validation through a complete simulation study. The usefulness of the new model is illustrated via counttype real data sets. 2022. Journal of the Iranian Statistical Society. All Rights Reserved. -
Transformer performance enhancement by optimized charging strategy for electric vehicles
Transformer efficiency and regulation, are to be maintained at maximum and minimum respectively by optimal loading, control, and compensation. Charging of electric vehicles at random charging stations will result in uncertain loading on the distribution transformer. The efficiency reduces and regulation increases as a consequence of this loading. In this work, a novel optimization strategy is proposed to map electric vehicles to a charging station, that is optimal with respect to the physical distance, traveling time, charging cost, the effect on transformer efficiency and regulation. Consumer and utility factors are considered for mapping electric vehicles to charging stations. An Internet of Things platform is used to fetch the dynamic location of electric vehicles. The dynamic locations are fed to a binary optimization problem to find an optimal routing table that maps electric vehicles to a charging station. A comparative study is carried out, with and without optimization, to validate the proposed methodology. 2022. The Author(s) -
Computational techniques to study the dynamics of generalized unstable nonlinear Schringer equation
In this paper, a more general form of unstable nonlinear Schringer equation which describe the time evolution of disturbances in marginally stable or unstable media is studied. A new modification of the Sardar sub-equation method is discussed and employed to retrieve solitons and other solutions of the suggested nonlinear model. A variety of solutions, including bright solitons, dark solitons, singular solitons, combo bright-singular solitons, periodic, exponential, and rational solutions are provided with considerable physical perspective. Using the q-homotopy analysis algorithm in combination with the Laplace transform, we present the approximate solutions of the bright and dark solitons, including the physical nature of the attained solutions. The computation complexity and results indicate that the given techniques are simple, effective, uncomplicated, and that they may be used to a wide range of unstable and stable nonlinear evolution equations encountered in mathematics, mathematical physics, and other applied disciplines. 2022 -
Wireless Communication for Robotic Process Automation Using Machine Learning Technique
Machine intelligence is what has been generated by programming computers with certain aspects of human intellect, like training, solving problems, and priority setting. A machine can solve a number of complicated issues using these capabilities. In major industries, such as customer support and manufacturing, machine intelligence is now being employed. The growth and quick development of digital technology and artificial intelligence (AI) technologies are becoming more and more difficult. At now, sophisticated manufacturing, the world of invention, and broad acceptance are undergoing a fast transition. Robotics is much more vital as it may now be related to the human brain by the connection between machine and brain, as AI develops. The world's economy faces substantial difficulties by increasing productivity in the manufacturing industry. This study examines the present progress of robotic communication styles of artificial intelligence (AI). In many specific applications, communication between members of a robotic group or even people becomes vital. The paper solves the problem of implementation of an independent industry mobile robot in all fields in the major business, live interactive, planning, mobile robot technologies, and intending. In order to identify the best solution to this issue, a mixed integer robotic model has been developed. 2022 C. Murugamani et al.