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Terahertz-based optoelectronic properties of ZnS quantum dot-polymer composites: For device applications
Terahertz (THz) technology integration with nanomaterials is receiving excellent attention for next-generation applications, including enhanced imaging and communication. The excellent optical properties in THz domain can lead to preparation of low-cost CMOS camera which can convert THz radiation into optical signal in very efficient manner. In the present study, we have studied the properties of Zinc Sulfide quantum dots (ZnS QDs) embedded with Polyvinyl Alcohol (PVA) composites films using THz Signal at room temperature. The optical characterizations such as refractive index, absorption coefficients and dielectric constants of these samples were measured in the 0.12.0 THz range. Additionally, optical impedance, surface roughness, and reflection coefficient in TE and TM mode between 0.1 and 2.0 THz range were determined for these samples based on surface roughness-based reflection and scattering properties. The surface roughness factor was used to measure the optical impedance of the ZnS QDs based polymer films. The measured values of the absorption coefficient at 266 nm are compared with THz radiation, and the refractive indices of these samples range from 1.75 to 2.0. Finally, these samples were subjected to UV light excitation (?exe = 266 nm) of 0.15 ns duration and 400 nm for the fluorescence and corresponding life time measurements. We observed two numbers of fluorescence lines in nanosecond based excited domain whereas 400 nm excitation-based fluorescence life time lies between 13.811.39 ns range along with shift in fluorescence lines between 538.7 to 560.7 nm, respectively. 2024 -
Kingship and Vedic Literature: Inflections, Deflections and Reflections
This chapter seeks to examine the figurations and configurations of 'kingship' reflected in different Vedic literary narratives in general and particularly aims at foregrounding how 'kingship' that happens to be one of the oldest forms of political governance, originated in Vedic times and how it became multifaceted with the passage of time. This chapter particularly seeks to employ three epistemological lenses - govern(mentality), sacral (infra)structuralism and planetarity - to lay down how the Vedic notion of 'kingship' underwent 'intensive' changes and how it stood in conformity with varied dimensions of contemporary political ecology. Besides that, this chapter aims at bringing out how Vedic notion of 'kingship' embodies the limits of 'human' by means of performing a liaison between the Almighty and ordinary human beings. Finally, at the end, royal haecceities of Vedic 'kingship' are critically taken up to facilitate readers to grasp the ontological and onticological fluidity of Vedic understanding of 'kingship'. 2024 selection and editorial matter, Nizar Zouidi; individual chapters, the contributors. -
Porous 3D Printed System for Synergistic Tandem Water Cleaning-Energy Generation
Non-availability of fresh water is the dire consequence of rapid industrialization and the unregulated discharge of industrial effluents. In an attempt to recover water from highly contaminated industrial wastewater, researchers have relied on developing various materials that can treat polluted water efficiently and sustainably. 3D printed materials have proved to be an emerging technology in water treatment. 2Dmaterials have recently enhanced filter technology due to their morphological properties. This study focuses on removing salinity and organic dyes utilizing 2DGadolinium telluride (Gd2Te3) coated 3D printed (2D@3DP) complex architecture. The 2D@3DP structure can potentially increase the contact time of adsorbed saline water due to its complex architecture and can remove ?52% salinity from brackish water. Furthermore, methylene blue (MB) and Methyl Orange (MO) removal efficiencies are ?69% and 45%, respectively. Spectroscopic and microscopic results confirm the adsorption of negatively charged chlorine ions on a positively charged 2D surface. The removal of bleaching powder is also tested for real-life applications, and ?20% of the bleaching powder is adsorbed. Moreover, the 2D@3DP device exhibits an electrical signal due to impinging sodium chloride droplets from different heights, making it a sustainable solution to address water pollution. 2023 Wiley-VCH GmbH. -
NEWSPAPER IN EDUCATION: UNDERSTANDING THE MUTUAL BENEFIT AND APPROACH OF EDUCATIONAL INSTITUTIONS AND OF PRINT MEDIA
Newspaper in Education has been proved to help in a students academic achievements and not just a means of updating the current events, but a tool for a student versatile and resourceful. Newspapers are a teaching tool, not a subject. They are highly motivating materials that can be used to teach math, science, grammar, character education, reading comprehension, writing strategies, problem solving and so much more. - Vicki Whiting, former third grade teacher and founder of Kid Scoop. This research would explore two major factors: -
Biogenesis and Green Synthesis of Metal Nanoparticles and Their Pharmacological Applications
Nanomaterial innovation is the primary catalyst of advancement in nanotechnology. Although there are many known chemical processes for creating nanoparticles that use harmful substances, it is now more important than ever to use processes that are safer, greener, and more environmentally friendly. The goal of research in this field is to use diverse life forms as "nanoparticle factories." Phytochemicals can convert salt into the appropriate nanoparticles thanks to their regular biosynthetic routes. In recent years, green chemistry methods for the synthesis of metallic nanoparticles have emerged as a fresh and exciting area of study. Metal nanoparticles, including gold (Au), silver (Ag), iron (Fe), and cadmium (Cd) along with certain oxides, can be synthesized using a variety of chemical and physical techniques as well as biological techniques carried out using plants. It has been discovered that methods involving plant-mediated synthesis are a more efficient and cost-effective way to create these metal nanoparticles. The plant-mediated nanoparticles are used as potential pharmaceutical agents for many diseases, including hepatitis, cancer, malaria, and HIV. Due to the higher efficacy and fewer side effects of nanodrugs compared to other commercial cancer drugs, the synthesis of nanoparticles targeting biological pathways has gained tremendous popularity. This review paper aims to cover the different green methods for the biogenesis of these nanoparticles, the different compounds and salts used, and the metals obtained. Ultimately, the significance and prospects of these metal nanoparticles especially in the fields of medicine, pharmacology, drug designing, and drug delivery engineering will also be commented on. The Author(s). -
Quantum vs. Classical: A Rigorous Comparative Study on Neural Networks for Advanced Satellite Image Classification
Navigating the intersection of quantum computing and classical machine learning in image classification, this study confronts prevailing challenges. Centered on the "Satellite Image dataset (RSI-CB256),"our investigation probes the early phases of quantum architectures, utilizing simulations to transform numerical data into a quantum format, the investigation highlights the existing limitations in traditional classical methodologies for image classification tasks. In light of the groundbreaking possibilities presented by quantum computing, this study underscores the need for creative solutions to push image classification beyond the usual methods. Additionally, the study extends beyond conventional CNNs, incorporating Quantum Machine Learning through the Qiskit framework. This dualparadigm approach not only underscores the limitations of current classical machine learning methods but also sets the stage for a more profound understanding of the challenges that quantum methodologies aim to address. The research offers valuable insights into the ongoing evolution of quantum architectures and their potential impact on the future landscape of image classification and machine learning. 2024 IEEE. -
FRA-CDS-VDAX based credit crash model: A German conundrum
Often credit crash opens up the glaring research warts in finding credit pits. Robust German credit and interest rate derivative market have been under scrutiny to develop credit crash predictor by effective utilisation of a cobbled methodology encompassing various research tools (such as econometric, mathematical and machine-learning etc.) and the logical trio, namely forward rate agreement (FRA), credit default swap (CDS) and volatility index constructed on CBOE methodology (VDAX). Though setting up VDAX predictor is the first objective, yet the cross-dependence of various derivatives, threshold finding for sudden change (steep) in VDAX and linking the results with real life events remain the secondary objectives. The results point out a clear threshold for detecting credit pit and a prominent behavioural trace as well. Serials Publications Pvt. Ltd. -
How well the log periodic power law works in an emerging stock market?
A growing body of research work on Log Periodic Power Law (LPPL) tries to predict market bubbles and crashes. Mostly, the fitment parameters remain con?ned within certain specific ranges. This paper examines these claims and the robustness of the reformulated LPPL model of Filimonov & Sornette (2013) for capturing large falls in the S&P BSE Sensex, an Indian heavyweight index over the period 20002019. Thirty-five mid to large-sized crashes are identified during this period, forming a clear LPPL signature. This confirms the possibility to predict the embedded risk of future uncertain events in the Indian stock market with the LPPL approach. 2020 Informa UK Limited, trading as Taylor & Francis Group. -
Identifying explosive behavioral trace in the CNX nifty index: A quantum finance approach
The fnancial markets are found to be fnite Hilbert space, inside which the stocks are displaying their wave-particle duality. Te Reynolds number, an age old fluid mechanics theory, has been redefned in investment fnance domain to identify possible explosive moments in the stock exchange. CNX Nify Index, a known index on the National Stock Exchange of India Ltd., has been put to the test under this situation. Te Reynolds number (its fnancial version) has been predicted, as well as connected with plausible behavioral rationale. While predicting, both econometric and machinelearning approaches have been put into use. Te primary objective of this paper is to set up an efcient econophysics' proxy for stock exchange explosion. Te secondary objective of the paper is to predict the Reynolds number for the future. Last but not least, this paper aims to trace back the behavioral links as well. 2018 Bikramaditya Ghosh, Emira Kozarevic. -
Multifractal analysis of volatility for detection of herding and bubble: Evidence from CNX Nifty HFT
This study delves into the herding and bubble detection in the volatility domain of a capital market underlying. Furthermore, it focuses on creating heuristics, so that common investors find it relatively easy to understand the state of the market volatility. Hence, it can be termed that this study is focused on the specific financial innovation regarding bubble and herding detection coupled with investor awareness. The traces of possible volatility bubble emerge when it is positioned against its own lags (both lag1 and lag2). The volatility trigger indicated clear traces of herding and an embedded parabola function. Continuous and repetitive parabola function hinted at a subtle presence of "fractals". Firstly, the detrended fluctuation analysis has been used with its multifractal variant. Secondly, the regularized form of Hurst calculation and analysis have been used. Both tests reveal the traces of nascent bubble formation owing to prominent herding in CNX Nifty HFT environment. They also indicate a clear link with Hausdorff topological patterns. These patterns would help to create heuristics, enabling investors to be aware of possible bubble and herd situations. Bikramaditya Ghosh, Emira Kozarevic, 2019. -
Power law in tails of bourse volatility evidence from India
Inverse cubic law has been an established Econophysics law. However, it has been only carried out on the distribution tails of the log returns of different asset classes (stocks, commodities, etc.). Financial Reynolds number, an Econophysics proxy for bourse volatility has been tested here with Hill estimator to find similar outcome. The Tail exponent or ? ? 3, is found to be well outside the Levy regime (0 < ? < 2). This confirms that asymptotic decay pattern for the cumulative distribution in fat tails following inverse cubic law. Hence, volatility like stock returns also follow inverse cubic law, thus stay way outside the Levy regime. This piece of work finds the volatility proxy (econophysical) to be following asymptotic decay with tail exponent or ? ? 3, or, in simple terms, inverse cubic law. Risk (volatility proxy) and return (log returns) being two inseparable components of quantitative finance have been found to follow the similar law as well. Hence, inverse cubic law truly becomes universal in quantitative finance. Bikramaditya Ghosh, M. C. Krishna, 2019. -
Predictability and herding of bourse volatility: An econophysics analogue
Financial Reynolds number works as a proxy for volatility in stock markets. This piece of work helps to identify the predictability and herd behavior embedded in the financial Reynolds number (time series) series for both CNX Nifty Regular and CNX Nifty High Frequency Trading domains. Hurst exponent and fractal dimension have been used to carry out this work. Results confirm conclusive evidence of predictability and herd behavior for both the indices. However, it has been observed that CNX Nifty High Frequency Trading domain (represented by its corresponding financial Reynolds number) is more predictable and has traces of significant herd behavior. The pattern of the predictability has been found to follow a quadratic equation. Bikramaditya Ghosh, Krishna M.C., Shrikanth Rao, Emira Kozarevi?, Rahul Kumar Pandey, 2018. -
Fear estimation-evidence from BRICS and UK
The paper aims to build a composite Fear Index for the BRICS countries and UK by adding new dimensions to the initial structure, such as overbought/oversold conditions and commodity impacts. The main purpose is to identify the degree in which fear really percolates down to all the market participants, respectively if this generates a certain asset transfer to Gold. The results point out the GMM model as the best fit for explaining the link between the Fear Index and the behaviour of market participants. It also confirms the transfer of assets to a safer asset class during the phases of high volatility on the market. Serials Publications Pvt. Ltd. -
Investigation of the fractal footprint in selected EURIBOR panel banks
EURIBOR emerged as a conventional proxy for a risk-free rate for a reasonably long period of time after the creation of the Eurozone. However, the joy was short-lived, as the global credit crisis shook the markets in mid-2008. Significant counterparty risk embedded in a derivative transaction cannot be left out. EURIBOR reflects the credit spread on borrowing. Hence, risk and uncertainty are inextricably linked here. This study investigates five banks out of 19 panel banks that manage EURIBOR in various Eurozone countries. These banks, HSBC, ING, Deutsche Bank, the National Bank of Greece and Barclays, are tested from January 2009 to December 2017 on a daily basis. Bank specific EURIBOR can be predicted in all five cases with different degrees. The trace of a profound herd is observed in the case of the National Bank of Greece, others were relatively mild in nature. The customer base and their risk grade were recognized as the main factor. Their information asymmetry and derived information entropy suggest embedded chaos and uncertainty. Bikramaditya Ghosh, Corlise Le Roux, Anjali Verma, 2020. -
Econophysical bourse volatility-Global Evidence
Financial Reynolds number (Re) has been proven to have the capacity to predict volatility, herd behaviour and nascent bubble in any stock market (bourse) across the geographical boundaries. This study examines forty two bourses (representing same number of countries) for the evidence of the same. This study finds specific clusters of stock markets based on embedded volatility, herd behaviour and nascent bubble. Overall the volatility distribution has been found to be Gaussian in nature. Information asymmetry hinted towards a well-discussed parameter of 'financial literacy' as well. More than eighty percent of indices under consideration showed traces of mild herd as well as bubble. The same indices were all found to be predictable, despite being stochastic time series. In the end, financial Reynolds number (Re) has been proved to be universal in nature, as far as volatility, herd behaviour and nascent bubble are concerned. 2020 Bikramaditya Ghosh et al., published by Sciendo 2020. -
Diagnosis and prediction of iigps countries bubble crashes during brexit
We herein employ an alternative approach to model the financial bubbles prior to crashes and fit a log-periodic power law (LPPL) to IIGPS countries (Italy, Ireland, Greece, Portugal, and Spain) during Brexit. These countries represent the five financially troubled economies of the Eurozone that have suffered the most during the Brexit referendum. It was found that all 77 crashes across the five IIGPS nations from 19 January 2015 until 17 February 2020 strictly followed a log-periodic power law or other LPPL signature. They all had a speculative bubble phase (following the power law growth) that was then followed by a sudden crash immediately after reaching a critical point. Furthermore, their pattern coefficients were similar as well. This study would surely assist policymakers around the Eurozone to predict future crashes with the help of these parameters. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
Long memory investigation during demonetization in India
Long-range dependence (LRD) in financial markets remains a key factor in determining whether there is market memory, herding traces, or a bubble in the economy. Usually referred to as 'Long Memory', LRD has remained a key parameter even today since the mid-1970s. In November 2016, a sudden and drastic demonetization measure took place in the Indian market, aimed at curbing money laundering and terrorist funding. This study is an attempt to identify market behavior using long-range dependence during those few days in demonetization. Besides, it tries to identify nascent traces of bubble and embedded herding during that time. Auto Regressive Fractionally Integrated Moving Average (ARFIMA) is used for three consecutive days around the event. Tick-by-tick data from CNX Nifty High Frequency Trading (CNX Nifty HFT) is used for three consecutive days around demonetization (approximately, 5000 data points from morning trading sessions on each of the three days). The results show a clear and profound presence of herd behavior in all three data sets. The herd intensity remained similar, indicating a unique mixture of both 'Noah Effect' and 'Joseph Effect', proving a clear regime switch. However, the results on the event day show stable and prominent herding. Mandelbrot's specified effects were tested on an uncertain and sudden financial event in India and proved to function perfectly. Bikramaditya Ghosh, Saleema J. S., Aniruddha Oak, Manu K. S., Sangeetha R., 2020. -
Self in schizophrenia: Current issues and future directions
Background: The objective of this review is to discuss the current advancements, and critical issues, in the area of studying disturbances of self in schizophrenia. The critical and systematic review of the self in schizophrenia is significant because it has been regarded as a prodrome and a predictor of the development of future psychosis. In addition, it has been found to be over and above clinical symptoms and is common in people with schizophrenia. Methodology: A systematic electronic literature search was done using PubMed, MEDLINE, and PMC (PubMed Central) databases were searched systematically, and relevant articles published in English peer-reviewed journals were selected. Results: The findings were discussed, and critical analysis of the studies revealed methodological and conceptual issues in the literature studying self in schizophrenia. Conclusion: The review has concluded with the discussion on future directions in terms of research and clinical applications. 2018 Archives of Mental Health | Published by Wolters Kluwer - Medknow. -
Empirical analysis of ensemble methods for the classification of robocalls in telecommunications
With the advent of technology, there has been an excessive use of cellular phones. Cellular phones have made life convenient in our society. However, individuals and groups have subverted the telecommunication devices to deceive unwary victims. Robocalls are quite prevalent these days and they can either be legal or used by scammers to trick one out of their money. The proposed methodology in the paper is to experiment two ensemble models on the dataset acquired from the Federal Trade Commission (DNC Dataset). It is imperative to analyze the call records and based on the patterns the calls can classify as a robocall or not a robocall. Two algorithms Random Forest and XgBoost are combined in two ways and compared in the paper in terms of accuracy, sensitivity and the time taken. 2019 Institute of Advanced Engineering and Science. All rights reserved.