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The ongoing COVID-19 pandemic has considerably promoted the usage of Digital Financial Services (DFS) in India. Therefore, exploring the various determinants influencing the DFS users is crucial for the DFS providers to understand their customers better. This study aims to identify, measure, and validate the determinants of Digital Financial Literacy (DFL) from the Indian adults who use Digital Financial Services. A sample of 384 adult DFS users from India was surveyed using a self-administered questionnaire in 2021. A multidimensional scale was developed to measure the Digital Financial Literacy in this study. The results exhibit that Digital Knowledge, Financial Knowledge, Knowledge of DFS, Awareness of Digital Finance Risk, Digital Finance Risk Control, Knowledge of Customer Right, Product Suitability, Product Quality, Gendered Social Norm, Practical Application of Knowledge and Skill, Self-determination to use the Knowledge and Skill and Decision Making are the determinants of DFL among the adults in India. Further, the users of DFS without DFL will face numerous challenges such as inability to complete the transaction, financial loss and privacy breach, etc. Hence, the study concludes that DFL is prerequisite to use DFS effectively.

In this work, the natural frequencies and mode shapes of a number of cantilever glass fiber reinforced polymer composites (GFRPCs) and carbon fiber reinforced polymer composites (CFRPCs) are numerically obtained using the commercial finite element analysis software (ANSYS). The laminates under study include 8 ply cantilevered plates having a plate aspect ratio of 2 and fiber volume fractions of 0.3, 0.4, 0.5, and 0.6. The finite element analysis procedure is described. The natural frequencies and mode shapes calculated using ANSYS are first validated with the results obtained from previous literature. The agreement between the two results is found to be excellent. The effect of change in the matrix material, hybridization, and laminate stacking sequence on the natural frequencies and mode shapes are also investigated. It is found that hybridization and orientation of the outermost layer has more significant influence on the natural frequencies of the laminated composite plates compared to fiber volume fraction and change in the matrix material.

This paper presented a flux controlled memristor using the most versatile analog block, a single Operational Amplifier (Op-Amp), an N-channel metal–oxide–semiconductor field-effect transistor (MOSFET), and four passive elements. The following benefits are offered by the suggested memristor design: (1) a lesser number of active and passive elements; (2) floating nature of the circuit; (3) wide-operating frequency range (200 Hz–20 kHz); and (4) simple and versatile design. The performance evaluation through simulation of the proposed memristor model including post-layout simulation of silicon components (Op-Amp and NMOS transistor ( M )) is verified with Cadence Virtuoso tool using standard CMOS 90 nm technology. In addition, the application of the proposed memristor in the field of analog and digital are also shown in the paper. Furthermore, the proposed circuit verification is also carried out experimentally using off-the-shelf components (IC LM741 and 2N6659) along with passive components.

This work explores the mechanical, thermal, and tribological characteristics of carbon fabric reinforced epoxy (CF-Ep) composites filled with halloysite nanotubes (HNT). The mechanical properties were evaluated, including hardness, interlaminar shear strength (ILSS), tensile strength, and flexural strength. The enhanced curing and even dispersion of HNTs in the epoxy matrix were validated by DSC, FTIR, and SEM measurements. Thermogravimetric analysis (TGA) and dynamic mechanical analysis (DMA) demonstrated improved thermal stability and damping, especially for the 0.75 wt.% HNT composite. Tribological performance was investigated utilizing a pin-on-disc configuration (60 N, 3 m/s) and silica sand (212 µm, 30 N, 2.38 m/s) under three-body abrasion and dry sliding wear, respectively. Hardness was highest at 1.75 wt.% HNT, and wear resistance and mechanical performance were best at 0.75 wt.% HNT composite. Surface damage, including matrix separation, micro-ploughing, and fragmentation, was lessened in 0.75 wt.% HNT composites, according to scanning electron microscopy worn surfaces. Using Minitab 17 and the Taguchi approach, wear experiments was created using an L16 orthogonal array. There were four levels of variation in three factors: load (10–40 N), abrading distance (250–1000 m), and HNT content (0–2.75 wt.%). The most important variables influencing wear volume loss were found to be load + distance and load + filler interactions using ANOVA and regression analysis. Scanning electron microscopy revealed that H0.75% HNT-filled composites had the best resistance to wear because they showed less surface damage mechanisms, such as fragmentation, micro-ploughing, and matrix detachment, when they were worn-out by dry sliding or abrasion. Overall, by strengthening interfacial bonding, improving load transfer, and creating a protective tribolayer that decreased material loss and surface damage during abrasion, HNTs improved mechanical and wear properties. Specifically, the 0.75 wt.% HNT composite showed outstanding heat stability and wear resistance, which made it a good option for high-performance uses such as power plant chute and automobile liners.

This research article presents a meminductor emulator without multiplier using double output second generation current conveyor (DO-CCII) and operational trans-conductance amplifiers (OTA) and minimum numbers of passive elements. The mathematical expression of meminductor is obtained and verified through various simulation i.e., hysteresis analysis, non-volatile analysis and process corner analysis. Also, presented post-layout simulation of silicon components (DO-CCII and OTA). Application of meminductor emulator as Amoeba behaviour is also incorporated in the Neuromorphic circuit. Furthermore, an experimental setup was also build using the off the shelf ICs AD844AN (for DO-CCII) and CA3080EZ (for OTA) to examine the experimental results. The proposed meminductor emulator is simulated in Cadence Virtuoso tool using standard CMOS 90 nm technology.

The firm size and value anomalies are the global-level counterpart for explaining the cross-sectional variations of equity returns. The purpose of this paper is to examine the size, value effects and the explanatory power of three well-known pricing models - CAPM, three-and five-factor across and within 15 Indian industries. The study considers all firms listed on the Indian largest stock exchange, BSE (Bombay stock exchange), between 1999-2021 by developing portfolios using firm size/value, size/investment and size/profitability risk characteristics. The study employs both univariate and multivariate methods, including time-series, GRS statistic, and cross-sectional models within and across industries' portfolios. Results indicated that size and value effects exist in almost all industries, presenting that size and value anomalies are the most prominent determinants for industry-level equity returns. In addition, the profitability and investment effects were also investigated; however, the results are mixed by industry to industry. In the case of the explanatory power of pricing models, the five-factor performs much better within and across industry portfolios than other pricing models; however, the models' effectiveness varies by industry. We also reported that investors who seek to allocate funds within and across the industries tend to be expected reasonably stable returns and conceivably predictable; findings of this study contribute to the existing literature on assets pricing and portfolio management to the emerging markets.

This research focuses on the tensile properties, dynamic-mechanical and thermogravimetric analysis of carbon fibre-reinforced epoxy (CF/Ep) composites with graphene nanoplatelets (GnP) nanofiller (G-CF/Ep). These composites were portrayed regarding tensile properties, surface morphology, dynamic-mechanical and thermogravimetric analysis. The dynamic-mechanical properties studied were storage modulus, loss modulus, dynamic capacity and glass transition temperature (Tg) through the dynamic-mechanical analyser. The thermogravimetric property studied was weight loss% through the thermogravimetric analyser. The carbon fabric reinforcement along with the GnP-Epoxy matrix improved the tensile properties. Further, it was exhibited that 1.75 wt% GnP into CF/Ep prompts predominant damping capacity and thermal stability. Generally speaking, it was presumed that GnP-filled CF/Ep composites even with very small wt% GnP gracefully and successfully improved tensile, dynamic-mechanical and thermogravimetric properties as well as the morphology of epoxy for various engineered applications.

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. AcknowledgmentsThe authors express their sincere thanks of gratitude to Dr. Bikramaditya Ghosh (Associate Professor, Symbiosis Institute of Business and Management, Bangalore, India) and Dr. Iqbal Thonse Hawaldar (Professor, College of Business Administration, Kingdom University, Riffa, Bahrain) for their instrumental role in encouraging and motivating them to accomplish this publication. The authors also extend their sincere thanks to Dr. Manu K.S and Dr. Surekha Nayak (Assistant Professor, School of Business and Management, CHRIST (Deemed to be university), Bangalore, India) for their continued support throughout this empirical investigation.

Geopolitical tensions between nations play a crucial role in triggering volatility and affecting the investors’ behavior in stock markets. This empirical work attempts to detect the traces of herding and bubble embedded in the Indian stock indices of CNX Nifty 50 and CNX Nifty 100 (both in High-Frequency Trading domains) during the latest events of geopolitical tensions escalated between India-China and India-Pakistan. An event window approach is employed to capture the impact of these events on herding behavior and information uncertainty in the considered stock indices. Multifractal Detrended Fluctuation Analysis (MFDFA) is applied to compute the Hurst value in all the trading days of the event window. The results of both indices exhibit conclusive evidence of herding and bubble formation during the overall period of geopolitical tensions between India-China and India-Pakistan. However, the degree of herding in the stock indices intensifies to a profound pattern when the tensions between India and China escalated into deadly violent clashes, and also during the heightened tensions between India and Pakistan that eventually ended up in airstrikes across the boundaries. The overall level of information uncertainty depicted by entropy is within control. The volatility in these stock indices has been confirmed to follow a unidirectional pattern. AcknowledgementsThe authors express their sincere thanks of gratitude to Dr. Bikramaditya Ghosh (Professor, Department of Finance and Analytics, RV Institute of Management, Bangalore, India) for his instrumental role in encouraging and motivating them to accomplish this research task. The authors also extend their sincere thanks to Dr. Manu K.S. (Assistant Professor, School of business and management, CHRIST (Deemed to be university), Bangalore, India) for his continued support throughout this empirical investigation.