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This paper aims to develop a mathematical model for four-lobe memristor (FLM) element. The four-lobe memristive behaviour can be used in realization of hyperchaotic oscillators and implementation of multi-bit memories. For verification of the developed mathematical framework, two FLM circuit emulators have been presented using VDCC and IC LM13700, respectively.

A mathematical model for FLM has been developed in which, the condition for the existence of symmetrical four lobes, instances and coordinates of the end points of lobes has been derived and presented. Using this mathematical framework, a FLM emulator based on VDCC has been developed. To validate the possibility of practical implementation of FLM concept, an IC LM13700-based circuit has also been developed. The workability of VDCC based circuit has been verified by running simulations in PSPICE environment using CMOS VDCC model. Similarly, the behaviour of LM13700 IC-based circuit has been confirmed by SPICE model of LM13700 IC.

It has been shown mathematically that under certain conditions, third-order flux dependent equation of memductance can be used to generate four lobes on the transient v-i plane. Also, two FLM emulators without using any voltage multiplier circuit/IC have been reported.

From the best knowledge of the authors, there are no such FLM emulators that have been reported in literature so far, which operates at practical operating frequencies.

The purpose of the paper is to report an emulation configuration of a three pinch-off memristor (TPM), whose transient characteristics consist three cross-over points on the voltage-current plane, which is dissimilar to a conventional memristor. These characteristics can be very useful in memristor-based multi-bit memory devices and hyperchaotic oscillators.

The work describes the Mathematical framework for TPM and a circuit emulator based on the derived conditions. The configuration is based on five operational transconductance amplifier (OTAs) and four grounded passive elements. After which, we have verified its operation using personal simulation program with integrated circuit emphasis simulation environment. Finally, the implementation of OTA-based TPM using commercial integrated circuit (IC) LM13700 has also been presented.

It has been shown that a flux-dependent memductance expression of cubic order can show three intersections on the VI contour under certain parameter related constraints. Moreover, the OTA-based emulator reported in the work is very compact in nature because of the no use of external multiplier IC/circuitry, which has been popular in previous emulators.

For the first time, a multiple cross-over memristor emulator has been reported which can operate under practical operating conditions such as at practical operating frequencies and sinusoidal excitation.

Recently, Unicode has been standardized with the penetration of social networking services, the use of emojis has become common. Emojis, as they are also known, are most effective in expressing emotions in sentences. Sentiment analysis in natural language processing manually labels emotions for sentences. The authors can predict sentiment using emoji of text posted on social media without labeling manually. The purpose of this paper is to propose a new model that learns from sentences using emojis as labels, collecting English and Japanese tweets from Twitter as the corpus. The authors verify and compare multiple models based on attention long short-term memory (LSTM) and convolutional neural networks (CNN) and Bidirectional Encoder Representations from Transformers (BERT).

The authors collected 2,661 kinds of emoji registered as Unicode characters from tweets using Twitter application programming interface. It is a total of 6,149,410 tweets in Japanese. First, the authors visualized a vector space produced by the emojis by Word2Vec. In addition, the authors found that emojis and similar meaning words of emojis are adjacent and verify that emoji can be used for sentiment analysis. Second, it involves entering a line of tweets containing emojis, learning and testing with that emoji as a label. The authors compared the BERT model with the conventional models [CNN, FastText and Attention bidirectional long short-term memory (BiLSTM)] that were high scores in the previous study.

Visualized the vector space of Word2Vec, the authors found that emojis and similar meaning words of emojis are adjacent and verify that emoji can be used for sentiment analysis. The authors obtained a higher score with BERT models compared to the conventional model. Therefore, the sophisticated experiments demonstrate that they improved the score over the conventional model in two languages. General emoji prediction is greatly influenced by context. In addition, the score may be lowered due to a misunderstanding of meaning. By using BERT based on a bi-directional transformer, the authors can consider the context.

The authors can find emoji in the output words by typing a word using an input method editor (IME). The current IME only considers the most latest inputted word, although it is possible to recommend emojis considering the context of the inputted sentence in this study. Therefore, the research can be used to improve IME performance in the future.

In the paper, the authors focus on multilingual emoji prediction. This is the first attempt of comparison at emoji prediction between Japanese and English. In addition, it is also the first attempt to use the BERT model based on the transformer for predicting limited emojis although the transformer is known to be effective for various NLP tasks. The authors found that a bidirectional transformer is suitable for emoji prediction.

The purpose of this study is to investigate and evaluate the impact of varying proportions of reinforcement on the mechanical properties of a modified Al₂O₃-LM6 cast composite under self-pouring temperature conditions. This study aims to determine the optimal mixture proportion of fine powders of Al, Si and xAl₂O₃ (with x values of 2%, 3% and 4%) through the application of design of experiment (DoE) and statistical analysis using the Minitab software. This study also involved evaluating the microstructural estimation and other physical properties of the cast composite to understand the combined effect of the reinforcement proportion on the material’s properties.

The researchers initially mixed the powders through ball milling and then compacted the moisture-free powder mix in a closed steel die. The resulting preforms were heated at the self-pouring temperature in an inert environment to fabricate the final cast composite. By applying DoE and performing an analysis of variance (ANOVA), the researchers sought to optimize the mixture proportion that would yield the best mechanical properties.

The experimental results indicated that a mixture combination of 83.5% Al blended with 12.5% Si and 4% Al₂O₃ led to the greatest improvement in mechanical properties, specifically in terms of increased density, hardness and impact strength. The ANOVA further supported the interaction effect of each processing parameter on the observed results. The results of this study offer valuable insights for the fabrication of modified Al₂O₃-LM6 cast composites under self-pouring temperature conditions. The identified optimal mixture proportion provides guidance for manufacturing processes and material selection to achieve improved mechanical properties in similar applications.

This study focuses on a specific composite material consisting of modified Al₂O₃ and LM6. Although Al₂O₃ and LM6 have been studied individually in various contexts, the combination of these materials and their impact on mechanical properties under self-pouring temperature conditions is a novel aspect of this research. The researchers use DoE methodology, along with statistical analysis using Minitab software, to optimize the mixture proportion and analyze the data. This systematic approach allows for a comprehensive exploration of the parameter space and the identification of significant factors that influence the mechanical properties of the composite.

This paper aims to propose to study the control of tube parameters in terms of diameter, separation between adjacent tubes and length, on delay and power dissipation in single-walled carbon nanotube (SWCNT) bundle interconnect for VLSI circuits.

The paper considers a distributed-RLC model of interconnect. A CMOS-inverter driving a distributed-RLC model of interconnect with load of 1 pF. A 0.1 GHz pulse of 2 ns rise time provides input to the CMOS-inverter. For SPICE simulation, predictive technology model (PTM) is used for the CMOS-driver. The performance of this setup is studied by SPICE simulation in 22 nm technology node. The results are compared with those of currently used copper interconnect.

SPICE simulation results reveal that delay increases with increase in separation between tubes and diameter whereas the reverse is true for power dissipation. The authors also find that SWCNT bundle interconnects are of lower delay than copper interconnect at various lengths and higher power dissipation due to dominance of larger capacitance of tube bundle.

The investigations show that tube parameters can control delay and this can also be utilized to decrease power dissipation in SWCNT bundle interconnects for VLSI applications.

The research examines how the principle of Dharma can be included in the Indian National Education Policy (NEP) 2020. The study seeks to know how Dharma as an educational framework can be infused with contemporary education to promote moral and ethical development as well as intellectual growth among students in India.

This research uses qualitative analysis of NEP 2020 documents and literature related to Indian Knowledge Systems (IKS) and the concept of Dharma. It analyses the philosophical bases behind NEP including traditional texts like Vedas, Upanishads, among others. Moreover, this study also evaluates how current Dharma teachings are implemented into curriculum by assessing its present status of incorporation as a part of Indian Knowledge System.

This research has found that national educational reforms have considered many traditional Indian beliefs and values, but they have not done enough when it comes to incorporating Indian Knowledge System and the concept of Dharma into practice through the curriculum. The paper advocates for a system that is grounded on morals, thus blending the current learning standards with cultural heritage. These findings call for continuous endeavouring in order to embed ethical and moral dimensions of Dharma across all levels within India’s education systems.

The integration of Dharma and IKS in education can enhance the development of well-rounded individuals who are not only intellectually competent but also ethically and morally grounded. This strong foundation of Dharma will also act as a personal and professional guide. Educators and policymakers can use these insights to design curricula that promote holistic growth, aligning with both global educational standards and cultural values.

This paper examines the philosophical and ethical foundation of NEP 2020 instead of only considering its pedagogical and skill-enhancement characteristics, thereby giving a fresh viewpoint. It contributes to the discussion on educational reforms in India by highlighting how present educational needs should be linked with traditional values. The research shows that the concept of dharma can help learners develop holistically so that they can meet present-day problems without losing touch with timeless morals.

This study aims to evaluate Netemeyer and colleagues' much cited financial well-being scale in Brazil and India and compare responses from different demographics. It also compares the results using two analysis techniques, item response theory (IRT) and confirmatory factor analysis (CFA).

A total of 994 survey responses from Brazil and 1,081 from India were collected. IRT and CFA models were used to analyse the data.

The results demonstrate the two-dimensional structure of the financial well-being scale and show that different items are differentially useful in measuring the construct across different groups. These findings may support the scale's future refinement and use in applied studies that will target specific groups (e.g. males, females, younger respondents and older respondents).

This study serves as an example to others who can explore the advantages of IRT over classical test theory methods to assess the psychometric properties of scales aimed at measuring latent constructs of interest in the field of marketing.

The correct diagnosis of financial well-being is important to guide interventions by governments and non-governmental entities, as well as by financial institutions interested in better understanding individuals.

The authors show how the identification of the characteristics of scale items provided by the IRT technique allows for a better understanding of its properties and how it can be improved.

This study examines current dynamics, consolidates current knowledge, elicits trends, identifies and analyzes primary research clusters, offers future directions, and develops an integrated framework for Product Advantage (PA) research.

Using the SCOPUS database, this study applied bibliometric analysis (208 articles) and conducted content analysis on the 35 curated articles selected using a combination of bibliographic coupling and the most cited articles.

This study presents the field’s publication trends, most relevant authors, articles, journals, and knowledge structures. It identifies six primary research themes and four major clusters using the thematic map and bibliographic coupling. Marketing and PA, New Product Development (NPD) and PA, Product Innovation and PA, and New product speed and PA are the main clusters. Finally, this study offers directions for future research and provides an integrated framework for PA research.

By developing an ADO framework of PA, the study offers key insights into how PA shapes product outcomes and identifies key antecedents of PA. Firms must focus on firm factors such as market and technological orientation; product factors, such as development time and pre-announcement proficiency; external factors, such as competition; and environmental factors, such as competitive intensity and technological turbulence. It enables firms to create products with high PA, shaping product outcomes and contributing to their competitive advantage.

This is the first study to conduct a two-stage sequential hybrid review of quality articles on PA. It offers an Antecedents-Decisions-Consequences (ADO) framework based on significant studies and offers cluster-wise directors for future research.

This study aims to assess the current state and impact of the RegTech industry on financial regulation and compliance by providing a comprehensive overview of its evolution and identifying key challenges and opportunities.

A hybrid review approach was employed, involving a detailed bibliometric analysis of 89 scholarly articles and a content analysis of 47 key studies, covering the period from 2010 to 2023.

The research identifies critical trends and challenges within the RegTech industry, focusing on the roles of regulatory bodies and technological innovations. It explores four major themes: (1) RegTech applications in FinTech, financial services and banking regulations; (2) RegTech’s role in compliance management and fraud prevention; (3) the impact of digital transformation, governance and regulations; and (4) the integration of Big Data, AI, ML and blockchain in regulatory systems.

This study provides a comprehensive framework for understanding the complicated applications of RegTech, highlighting its potential to enhance compliance efficiency, mitigate risks and foster innovation within the financial sector. The insights provided are valuable for policymakers and financial institutions aiming to develop more robust regulatory frameworks and practices.

This study uniquely integrates bibliometric and content analysis to provide an up-to-date and nuanced overview of RegTech, focusing on recent advancements in AI, ML and blockchain technologies. It not only maps current trends but also identifies research gaps and offers new directions for future research.

The purpose of this paper is to examine the impact of perceived social media marketing activities (SMMAs) on customer loyalty via customer equity drivers (CEDs) in an e-commerce context.

The study surveyed 371 students from a large university in India. The data were analyzed via confirmatory factor analysis and the research hypotheses were examined using SEM.

The study revealed three key findings. First, perceived SMMAs of e-commerce comprise five dimensions, namely, interactivity, informativeness, word-of-mouth, personalization and trendiness. Second, perceived SMMAs of e-commerce have significantly and positively influenced all the drivers of customer equity (CEDs). Third, the CEDs of e-commerce exhibit a significant and positive influence on customer loyalty toward the e-commerce sites.

This study will help e-commerce managers to boost customer loyalty toward the e-commerce sites through perceived SMMA.

The study is the first to identify five dimensions of e-commerce perceived SMMA. The current study also introduces the stimulus–organism–response model as a theoretical support to connect perceived SMMAs of e-commerce to customers’ loyalty via CEDs. This is supposed to be the first study to examine the impact of perceived SMMA on customer loyalty toward the e-commerce sites via CEDs in the e-commerce industry.