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Towards the underlying theories of artificial intelligence in customer engagement: A review and future research agenda
Artificial Intelligence (AI) and customer engagement have grabbed the attention of many business organizations in the recent past, especially with the advancement of machine and deep learning. Though there is significant development in this area, in reality customer engagement and customer experience has received less attention. Since there is less theoretical and empirical evidence related to AI application in customer engagement, the objective of this research is to lay down the theoretical underpinning for artificial intelligence and customer engagement research and to propose a future research agenda. In this context, the present study aims to full fill the gap based on 20 different theories and model identified from 53 A Star and A categories of Australian Business Deans Council (ABDC) journals (2011-2023), and result are analyzed using classifier variables. In associating the presence research aim, four prominently used theories like value co-creation theory, theory of planned behaviour, SOR (Stimulus- Organism-Response) theory, Relationship Marketing theories are selected to review. The selected theories are compared based on key attributes and outcomes to give a clear direction towards an optimum theory development. By validating the various theoretical perspective, the present study also gives first-hand insight to the practising manager to formulate organization strategy and researcher to explore the future research directions. 2025 Arabinda Bhandari and Mudita Sinha. All rights reserved. -
Towards various applications of Big Data and related issues and challenges
A new trend in feature abstraction is Big Data Analysis combined with computational techniques. This includes gathering knowledge from reputable data sources, analyzing information quickly, and forecasting the future. Big data entails vast amounts of data that are challenging to analyze using typical database and software approaches. When using big data applications, a technological hurdle arises when transporting data across several locations, which is quite expensive and necessitates a huge primary memory for storing data for processing. Big data refers to the transaction and interaction of datasets whose size and complexity transcend the usual technical capabilities of acquiring, organizing, and processing data in a cloud environment. This article provides an in depth study of various applications of big data. It also provides a detailed view on various problems and challenges in Big Data. 2021 IEEE. -
Towards Visibility: Subaltern Counterpublics in Paul Chirakkarodes Pulayathara
Christianity has always been celebrated as a catalyst towards modernity for the Dalits of Kerala. Though missionary accounts and ethnographic studies confirm the progress of the community, there was rampant casteism and separatism too. This is succinctly revealed in Dalit Christian texts. Pulayathara by Paul Chirakkarode stands as a testimony to the Dalit Christian dilemma and traces the history of the Kuttanadan Pulaya community in the pre- and post-conversion scenarios. Conversions could not change the existing public sphere of Kerala, where upper castes were the dominant party. They (Dalits) continued to be marginalized and subordinated and lacked a class consciousness. The article highlights the limitations in the public sphere that emerged in Kerala as part of the missionary endeavours in accommodating the converted Dalits. The article attempts to trace the emergence of subaltern counterpublics among the Dalit Christians to oppose the continued oppression and casteism by situating Pulayathara at the centre of the analysis. 2022 Indian Institute of Management, Ahmedabad. -
Toxic Effects of Nanoparticles on Fish Embryos
Nanoparticles are used widely in the in-drug delivery, diagnostics, cosmetics, paints, electronics, fabrics, solar cells, medicines etc. Their wide application is due to their special properties which include minute size, high reaction rate, increased surface area and quantum effect. Despite their multiple applications, nanoparticles have harmful effects too due to their improper disposal causing their entry into the aquatic environment greatly threatening the ecological security as well as our health. Zebra fishes (Danio rerio) are used more commonly for the evaluation of toxicity and are considered a promising animal model. Studies on Salmon and Labeo rohita have also been used for toxicity evaluation. Nanoparticles affect the embryo more easily than the adult since the embryo are more sensitive. Hence it becomes important for us to study the effect of the nanoparticles on the embryo of the fishes. These nanoparticles have the ability to cross the chorion layer and affect the developing embryo. Since the fishes are a part of the food chain, when these organisms get affected they will eventually harm the humans too. This review focuses on the effect of metal nanoparticles (NPs) like gold(Au), silver(Ag), copper(Cu), platinum and metal oxides nanoparticles like titanium dioxide, aluminium oxide, copper oxide, nickel oxide zinc oxide on the embryonic development of fish embryos. When compared to the other nanoparticles (NPs) like silver, copper and platinum, it has been observed that the gold nanoparticles (Au NPs) showed no toxicity to embryos of zebrafish though few studies contradict this. Aluminium nanoparticles showed no toxicity and role of reactive oxygen species (ROS) in enhancing the toxicity of nanoparticles have also been discussed. 2021 World Research Association. All rights reserved. -
Toxic heavy metal ion detection by fluorescent nanocarbon sensor derived from a medicinal plant
In the twenty-first century, the importance of environmental pollution sensing cannot be overstated. Cadmium is a well-known poisonous heavy metal that seriously endangers human health. In terms of screening for poisons and diagnosing illnesses, the sensitive and focused detection of cadmium in cells is crucial. In this work, we developed Green fluorescent Carbon nanomaterial (Carbon nanomaterial) synthesized from a novel precursor, Justicia Wynaadensis, by the most eco-friendly, cost-effective hydrothermal method, which acts as a fluorescent probe for Cadmium fluorescence sensing technique with the concentration range of 1 nM1 M. The sensor displays remarkable linear detection with a 5.235 nM detection limit. 2022 The Author(s) -
Toxic text classification
The users of the Internet increase every moment with increasing population and accessibility of the Internet. With the increase in the number of users of the Internet, the number of controversies, arguments and abuses of all kinds increases. It becomes necessary for social media and other sites to identify toxic content amongst a large number of content being posted by the users of the sites every second. The traditional algorithms that depend on users reporting toxic content for it to be deleted and necessary actions to be taken against the users posting the content would take a long time, within which it would have gained media attention and would have lead to huge fights over the content. Thus, it becomes important for the content to be evaluated for toxicity at the time it is posted in order to stop it from being posted. Therefore, we have designed and trained a deep learning model that can be read through the textual content given through it and determine if it is toxic or not. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Toxic waste colonialism : A re-evaluation of global management of transboundary hazardous waste /
Journal On Environment Law Policy And Development, Vol.3, pp.85-119, ISSN: 2348-7046. -
Toxicity analysis of endocrine disrupting pesticides on non-target organisms: A critical analysis on toxicity mechanisms
Endocrine disrupting compounds are the chemicals which mimics the natural endocrine hormones and bind to the receptors made for the hormones. Upon binding they activate the cascade of reaction which leads to permanent activating of the signalling cycle and ultimately leads to uncontrolled growth. Pesticides are one of the endocrine disrupting chemicals which cause cancer, congenital birth defects, and reproductive defects in non-target organisms. Non-target organisms are keen on exposing to these pesticides. Although several studies have reported about the pesticide toxicity. But a critical analysis of pesticide toxicity and its role as endocrine disruptor is lacking. Therefore, the presented review literature is an endeavour to understand the role of the pesticides as endocrine disruptors. In addition, it discusses about the endocrine disruption, neurological disruption, genotoxicity, and ROS induced pesticide toxicity. Moreover, biochemical mechanisms of pesticide toxicity on non-target organisms have been presented. An insight on the chlorpyrifos toxicity on non-target organisms along with species names have been presented. 2023 Elsevier Inc. -
Toxicological evaluation of Calotropis gigantea (L.) W. T. Aiton (Apocynaceae) stem extract in zebrafish: A chronic exposure study
Calotropis gigantea is widely used in traditional medicine across rural and tribal regions for treating various ailments. The safety profile of this plant especially in concerning long term or high dose exposure, remains inadequately studied. This study aims to scientifically assess the toxicity of C. gigantea stem extract using zebrafish (Danio rerio) as a model organism. Chronic exposure over 10, 20 and 30 days revealed significant oxidative stress, mitochondrial impairment and histopathological alterations in vital organs. Key antioxidant enzymes glutathione reductase (GR), glutathione S-transferase (GST), succinate dehydrogenase (SDH), catalase (CAT) and superoxide dismutase (SOD) were analysed in the liver, gills, brain and muscles. Enzyme activity has initially increased but declined by the 30th day, indicating progressive oxidative damage. The liver and gills exhibited the most substantial biochemical and structural changes. The histological analysis confirmed cellular degeneration, inflammation and necrosis. These findings highlight the potential risks associated with unregulated therapeutic use of C. gigantea and emphasize the need for scientific validation and public awareness to ensure safe application. The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited (https://creativecommons.org/licenses/by/4.0/) -
Toxicological Profiling of Onion-Peel-Derived Mesoporous Carbon Nanospheres Using In Vivo Drosophila melanogaster Model
Toxicological profiling of the novel carbon materials has become imperative, owing to their wide applicability and potential health risks on exposure. In the current study, the toxicity of mesoporous carbon nanospheres synthesized from waste onion peel was investigated using the genetic animal model Drosophila melanogaster. The survival assays at different doses of carbon nanoparticles suggested their non-toxic effect for exposure for 25 days. Developmental and behavioral defects were not observed. The biochemical and metabolic parameters, such as total antioxidant capacity (TAC), protein level, triglyceride level, and glucose, were not significantly altered. The neurological toxicity as analyzed using acetylcholinesterase activity was also not altered significantly. Survival, behavior, and biochemical assays suggested that oral feeding of mesoporous carbon nanoparticles for 25 days did not elicit any significant toxicity effect in Drosophila melanogaster. Thus, mesoporous carbon nanoparticles synthesized from waste onion peel can be used as beneficial drug carriers in different disease models. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Tracing Early Enrichment Pathways: Chemical and Chemodynamical Analysis of Two CEMP-no Stars**Based on the data collected from HDS/SUBARU
We present a detailed high-resolution spectroscopic and chemodynamic analysis of two carbon stars, HE 1148?0037 and HE 1246?1344, using SUBARU High Dispersion Spectrograph spectra (R ? 50,000). Our analysis confirms that both stars are extremely metal-poor and belong to the Carbon-Enhanced Metal-Poor (CEMP)-no class ([C/Fe] > 0.70 and [Ba/Fe] < ?1.0). Both of the stars are found to be group II CEMP-no stars in the A(C) versus [Fe/H] diagram. While both stars exhibit slightly enhanced ?-elements (Ca, Sc), their Mg, Fe-peak, and neutron-capture element abundances show distinct trends, indicating different progenitor pathways. From [O/Fe] and [Sr/Ba] ratios, we identify both metal-poor asymptotic giant branch stars and fast-rotating massive stars as potential progenitors. Further elemental abundance analysis suggests a multienrichment origin for these stars. Kinematic analysis reveals that HE 1148?0037 belongs to the halo, with a spatial velocity of 464.99 14 km s?1. Orbital calculations show that HE 1148?0037 follows a retrograde orbit and is associated with the high-energy retrograde halo, likely a member of the Iitoi substructure. In contrast, HE 1246?1344 follows a prograde orbit and is linked to Gaia-Sausage/Enceladus. 2026. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the https://creativecommons.org/licenses/by/4.0/. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. -
Tracing Fe K X-Ray Reverberation Lag in the Energy-resolved Spectra of Narrow-line Seyfert 1 Galaxy Ton S180
We report the Fe K relativistic reverberation feature for the first time in the narrow-line Seyfert 1 galaxy Ton S180. Using a long observation from XMM-Newton we find that the Fe K emission lag peaks at 117 49 s in the lag energy spectrum computed for frequencies (0.3-8.5) 10?4 Hz. The lag amplitude drops to 22.85 14.20 s as the frequency increases to (8.5-30) 10?4 Hz. The time-averaged spectrum of the source shows a relatively narrow Fe K line at ?6.4 keV, indicating a low black hole spin ( a = 0 . 4 3 ? 0.14 + 0.10 ) based on the reflection modeling. We perform general relativistic transfer function modeling of the lag energy spectra individually. This provides an independent timing-based measure of the spin at a = 0.3 0 ? 0.17 + 0.34 , a black hole mass M BH = 0.2 9 ? 0.16 + 0.01 1 0 8 M ? , comparable to the previous measurement, and a coronal height h = 2.5 9 ? 0.33 + 5.17 r g . Further, we observe that the Fe K lag and the black hole mass fit well in the linear lag-mass relation shown by other Seyfert 1 galaxies. 2026. The Author(s). Published by the American Astronomical Society. -
Tracing the Evolution of Digital Strategy with AI, Blockchain, Cloud, and Cryptocurrencies
This chapter explores the transformative role of key technologies - artificial intelligence (AI), blockchain, cloud computing, and cryptocurrencies - in shaping contemporary digital strategies. It traces the historical evolution of these technologies and highlights their individual and synergistic contributions to business, governance, and society. AI has progressed from theoretical concepts to practical applications across diverse industries, enhancing decision-making, automation, and operational efficiency. Initially conceived for cryptocurrencies, blockchain technology now plays a pivotal role in securing and streamlining finance, healthcare, and supply chain management transactions. Cloud computing has democratized access to advanced technologies, accelerating the integration and scalability of AI and blockchain. Cryptocurrencies, built on blockchain frameworks, are reshaping global financial systems through decentralization and security. The chapter also addresses the challenges and opportunities of technological convergence, including ethical considerations, regulatory challenges, and the strategic need for multidisciplinary collaboration. By analyzing these intersections, this article provides a comprehensive understanding of how AI, blockchain, cloud computing, and cryptocurrencies drive digital strategies' future. 2026 Manjari Sharma, Sharad Gupta. All rights reserved. -
Tracing the impact of social media on social cognition: Bibliometric analysis
The words "misinformation, " "fake news, " and "post-truth" have filled social media posts. It is a serious social threat, especially post COVID-19. In this chapter, the authors provide bibliometric analysis of research on social media and its impact on social cognition. This can be useful for identifying gaps for future research in the field. Publication data was obtained from the Web of Science database using a search algorithm. A total of 22,935 articles were extracted, and 22,909 eligible articles were included for analysis. Document co-citation analysis revealed that themes on social engagement, fake news, problematic social media use, and healthcare emerged as trends on shaping the social cognition through social media. Further, India achieved 9th position on the list based on citations and 8th on centrality and did not appear on any of the top-10 lists based on Burst value and Sigma. This indicates that neither sudden trend-setting articles nor scientific novelty-based articles have been published in this domain thus far. There is a considerable research gap in India to counter misinformation. 2024, IGI Global. -
Tracing the Legal Invisibility and Challenges of Same-Sex Couples in India
Although the Supreme Court's decision in Supriyo v. Union of India (2023) concerning marriage equality rights of same-sex couples has gained public attention, couples continue to face legal invisibility as the Court left the question of legal recognition to parliament. This study examines the legal challenges faced by same-sex couples in India in accessing marriage, family formation, healthcare and housing rights and seeks to understand their lived experiences in the context of limited legal recognition. It highlights how the lack of recognition deepens social vulnerability while also exposing couples to discrimination and, in some cases, abuse. Semi-structured, in-depth interviews were conducted with 25 couples recruited via snowball sampling, and data were thematically analyzed. The findings reveal that the participants continue to face legal blockades in exercising their rights, underscoring the need for urgent legislative reform. 2026 Policy Studies Organization. -
Tracing the outer disk of NGC300: An ultraviolet view
We present an ultra-violet (UV) study of the galaxy NGC300 using GALEX far-UV (FUV) and near-UV (NUV) observations. We studied the nature of UV emission in the galaxy and correlated it with optical, HI and mid-infrared (3.6 ?m) wavelengths. Our study identified extended features in the outer disk, with the UV disk extending up to a radius of 12 kpc (> 2 R25). We estimated the FUV and NUV disk scale-length as 3.05 0.27 kpc and 2.66 0.20 kpc respectively. The scale-length in FUV is 2.3 times larger than that at 3.6 ?m, and we also find the disk to gradually become flatter from longer to shorter wavelengths. We performed a statistical source subtraction to eliminate the background contaminants and identified 261 unresolved UV sources between the radii 5.3 kpc and 10 kpc (1 ? 2 R25). The identified UV sources show an age range between 1300 Myr with a peak at 25 Myr and a mass range between 10 3M? to 10 6M?, estimated using Starburst99 models. The north-eastern spiral arm is found to be populated by young low mass sources suggesting that the star formation in this spiral arm is a recent phenomenon. The UV emission beyond the R25 radius has contribution from these low mass sources and is extended up to ? 2 R25 radius. We conclude that NGC300 has an extended UV disk, mainly populated by young low mass sources. The star formation rate is measured to be ?0.46M?/yr which is comparable to its near optical twin M33. 2019, Indian Academy of Sciences. -
Tracking and Localization of Devices - An IoT Review
S everal IoT applications have immediate impacts on daily lives. The notion of "connected life, which includes IoT has been discussed. Apps that rely on localization are also featured. IoT is originally used to determine the precise position of things, animals, and people. The second tracks everyone and everything that's on the move, including pets, kids, and the elderly people. Localization and tracking are integral parts of security and surveillance systems in interconnected homes. This study reviews the state-of-the-art IoT-based localization and tracking approaches and outlines the key technical aspects, and contrast localization initiatives based on Internet of Things (IoT) with those that do not show how they might be used in a variety of contexts. It is now well established that localization and tracking methods based on the Internet of Things (IoT) are more pervasive and accurate than their predecessors. 2023 IEEE. -
Tracking Greenfield FDI During the COVID-19 Pandemic: Analysis by Sectors
We study the trends and fluctuations in greenfield foreign direct investment (GFDI) during the first wave of the COVID-19 pandemic crisis on a global scale. We analyse the data of a data set of GFDI provided by fDi Markets (Financial Times) to understand the contraction of GFDI during the first three quarters of the year 2020, taking into account the sector of the investment and the host and home country. We analyse both the long-run trends and the quarter-over-quarter changes in GFDI to capture its fluctuations before and during the first wave of the COVID-19 crisis and the 2008 global financial crisis. Our findings cast light on which countries and industries GFDIs were most affected by the pandemic crisis and draw a comparison to the global financial crisis. To our surprise, many services industries have shown unexpected resilience of GFDI due to the flexibility for remote work. On the contrary, GFDI in the manufacturing industries, as well as the extractives and the utility industries, has shown a dramatic decline during the pandemic. These contractions raise questions of stability and resilience of the global supply chains these industries are a part of. JEL Codes: F21. 2021 Indian Institute of Foreign Trade. -
Tracking Sigmoid Regression with Multicollinearity in Phase I: An Approach Incorporating Control Charts
Regression and quality control are two crucial techniques that data analysis employs in improving the decision-making process. We use the sigmoid function to model the connection between independent factors and the dependent variable in sigmoid regression. When there is a significant correlation among the independent variables in a regression model, multicollinearity a statistical phenomenon exists. Multicollinearity presents problems with higher uncertainty when estimating individual coefficients possibly making it harder to identify each variable's distinct contribution to the model. By suggesting a control chart specifically designed for the sigmoid regression model, this research presents a strategy to address the impact of influential observations using regression control charts, by making use of principal component regression class estimators. Principal component regression merges from the principal component analysis and linear regression methodologies, aiming to alleviate multicollinearity issues and enhances the stability of regression models. The performance of the model is evaluated using Pearson's residuals, Deviance residuals, and residuals. This strategy is proven to be useful in real world situations demonstrated through an application in the field of sleep wellness disorder. In conclusion, this study introduces a unique control chart to manage multicollinearity in sigmoid regression, providing a new perspective on the topic to spot differences in the underlying process by highlighting trends in the residuals. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Tracking the transmission channels of fiscal deficit and food inflation linkages: A structural var approach
This empirical analysis aspired to unearth the transmission channels of fiscal deficit and food inflation linkages in the Indian perspective by reasonably exerting the data for 1991 to 2017. The precise results of structural vector autoregressive (SVAR) analysis proffered that there were three different mechanisms of transmission such as consumption, general inflation, and import channels that led to food inflation in response to the high fiscal deficit. The first channel revealed that government deficit spending had a positive impact on income which further led to food inflation through surging the household consumption expenditure. It was concluded that fiscal deficit passed through general inflation finally leading to a food price surge in the economy and seemed to work as cost-push inflation for the food and agricultural industry. The outcome also revealed that the impact of fiscal deficit passed to food inflation through external linkages such as import and export. 2020 The Society of Economics and Development, except certain content provided by third parties.