Christ University, Bangalore
  • Bengaluru, Karnataka, India
Recent publications
In our groundbreaking exploration, we meticulously delve into the relationship between environmental policy stringency, international trade dynamics, and financial openness within the BRICS group (Brazil, Russia, India, China, and South Africa) spanning from 1996 to 2021. With a focus on critical variables such as economic growth and technological innovation, our empirical findings challenge conventional wisdom. Surprisingly, we found that those stringent environmental policies, when standing alone, do not invariably lead to reduce CO2 emissions. Equally interesting is our startling discovery that the anticipated moderating influence of environmental policy stringency, catalyzed by trade and foreign direct investment, on the well-being of our environment does not materialize; contrarily, both trade and foreign direct investment moderating channels exhibit unanticipated positive correlations with CO2 emissions. These revelations provoke us with the presence of a “pollution haven” phenomenon within the BRICS economies. Furthermore, our investigation reveals that, when examined individually, trade and foreign direct investment also appear to contribute to elevated emission levels. These findings provide a resolute solution to our research quandary, underlining the indispensable requirement for cutting-edge and robust environmental policies. These policies must possess the prowess to effectively counteract the adverse environmental consequences stemming from the amalgamation of global trade and financial integration. In doing so, they shall propel BRICS nations toward a future firmly grounded in principles of sustainability and ecological integrity.
The World Health Organization (WHO) suggests social isolation as a remedy to lessen the transmission of COVID-19 in public areas. Most countries and national health authorities have established the 2-m physical distance as a required safety measure in shopping malls, schools, and other covered locations. In this study, we use standard CCTV security cameras to create an automated system for people detecting crowds in indoor and outdoor settings. Popular computer vision algorithms and the CNN model are implemented to build up the system and a comparative study is performed with algorithms like Support Vector Machine and KNN algorithm. The created model is a general and precise people tracking and identifying the solution that may be used in a wide range of other study areas where the focus is on person detection, including autonomous cars, anomaly detection, crowd analysis, and many more.
Source Location Privacy (SLP) in Wireless Sensor Networks (WSNs) refers to a set of techniques and strategies used to safeguard the anonymity and confidentiality of the locations of sensor nodes (SNs) that are the source of transmitted data within the network. This protection is important in different WSN application areas like environmental monitoring, surveillance, and healthcare systems, where the revelation of the accurate location of SNs can pose security and privacy risks. Therefore, this study presents metaheuristics with sequential assignment routing based false packet forwarding scheme (MSAR-FPFS) for source location privacy protection (SLPP) on WSN. The contributions of the MSAR-FPFS method revolve around enhancing SLP protection in WSNs through the introduction of dual-routing, SAR technique with phantom nodes (PNs), and an optimization algorithm. In the presented MSAR-FPFS method, PNs are used for the rotation of dummy packets using the SAR technique, which helps to prevent the adversary from original data transmission. Next, the MSAR-FPFS technique uses an improved reptile search algorithm (IRSA) for the optimal selection of routes for real packet transmission. Moreover, the IRSA technique computes a fitness function (FF) comprising three parameters namely residual energy (RE), distance to BS (DBS), and node degree (ND). The experimental evaluation of the MSAR-FPFS system was investigated under different factors and the outputs show the promising achievement of the MSAR-FPFS system compared to other existing models.
After rice and wheat, potatoes are the third-largest crop grown for human use worldwide. The different illnesses that can harm a potato plant and lower the quality and quantity of the yield cause potato growers to suffer significant financial losses every year. While determining the presence of illnesses in potato plants, consider the state of the leaves. Early blight and late blight are two prevalent illnesses. A certain fungus causes early blight, while a specific bacterium causes late blight. Farmers can avoid waste and financial loss if they can identify these diseases early and treat them successfully. Three different types of data were used in this study's identification technique: healthy leaves, early blight, and late blight. In this study, I created a convolutional neural network (CNN) architecture-based system that employs deep learning to categorise the two illnesses in potato plants based on leaf conditions. The results of this experiment demonstrate that CNN outperforms every task currently being performed in the potato processing facility, which needed 32 batch sizes and 50 epochs to obtain an accuracy of about 98%.
Memes have now become a common medium of communication. There are multiple ways memes are considered in academia. Semiotics offers information on how the media and modes that memes consist of can be interpreted and how the characteristics of semiotic resources apply to memes. Drawing from a pool of memes collected during the Kerala assembly election in 2021, this research argues that certain memes need to be categorised as multimodal ensemble. Different modalities play different roles meaning construction, and they also collaborate with each other for a uniform purpose. By comparing existing memes defined in academia and multiple methodologies to analyse memes, the paper puts forth a framework to analyse memes.
Globally, cancer is the leading cause of death and morbidity, and skin cancer is the most common cancer diagnosis. Skin problems can be treated with nanoparticles (NPs), particularly with zinc oxide (ZnO) NPs, which have antioxidant, antibacterial, anti‐inflammatory, and anticancer properties. An antibacterial activity of zinc oxide nanoparticles prepared in the presence of 4‐nitrobenzaldehyde (4NB) was also tested in the present study. In addition, the influence of synthesized NPs on cell apoptosis, cell viability, mitochondrial membrane potential (MMP), endogenous reactive oxygen species (ROS) production, apoptosis, and cell adhesion was also examined. The synthesized 4‐nitro benzaldehyde with ZnO (4NBZnO) NPs were confirmed via characterization techniques. 4NBZnO NPs showed superior antibacterial properties against the pathogens tested in antibacterial investigations. As a result of dose‐based treatment with 4NBZnO NPs, cell viability, and MMP activity of melanoma cells (SK‐MEL‐3) cells were suppressed. A dose‐dependent accumulation of ROS was observed in cells exposed to 4NBZnO NPs. As a result of exposure to 4NBZnO NPs in a dose‐dependent manner, viable cells declined and apoptotic cells increased. This indicates that apoptotic cell death was higher. The cell adhesion test revealed that 4NBZnO NPs reduced cell adhesion and may promote apoptosis of cancer cells because of enhanced ROS levels.
The current investigation focuses on synthesizing copper oxide (CuO)‐titanium oxide (TiO 2 )‐chitosan‐farnesol nanocomposites with potential antibacterial, antifungal, and anticancer properties against Melanoma cells (melanoma cells [SK‐MEL‐3]). The nanocomposites were synthesized using the standard acetic acid method and subsequently characterized using an X‐ray diffractometer, scanning electron microscope, transmission electron microscopy, and Fourier transform infrared spectroscopy. The results from the antibacterial tests against Streptococcus pneumoniae and Stapylococcus aureus demonstrated significant antibacterial efficacy. Additionally, the antifungal studies using Candida albicans through the agar diffusion method displayed a considerable antifungal effect. For evaluating the anticancer activity, various assays such as MTT assay, acridine orange/ethidium bromide dual staining assay, reactive oxygen species (ROS) generation assay, and mitochondrial membrane potential (MMP) analysis were conducted on SK‐MEL‐3 cells. The nanocomposites exhibited the ability to induce ROS generation, decrease MMP levels, and trigger apoptosis in SK‐MEL‐3 cells. Collectively, the findings demonstrated a distinct pattern for the synthesized bimetallic nanocomposites. Furthermore, these nanocomposites also displayed significant ( p < 0.05) antibacterial, antifungal, and anticancer effects when tested on the SK‐MEL‐3 cell line.
There is a mainstream glorified image of motherhood. But is this the reality of every mother? An intersectional study of the play Labour Room by Sreeja K. V. is done to get an answer to this question. The play portrays three women from different backgrounds, their journey to motherhood, and their idea of being a mother. A Dalit feminist study of the play, focusing on the character woman three, can be employed to understand that being a mother is not always majestic and is a different experience for every mother. Does a Dalit woman lack motherly feelings, or do their circumstances mute those feelings? Through the character of a woman three, this article attempts to address this question using the theory of intersectionality. The article also tries to shift the focus from the conventional depiction of motherhood.
The entropy generation analysis for the nanofluid flowing over a stretching/shrinking curved region is performed in the existence of the cross-diffusion effect. The surface is also subjected to second-order velocity slip under the effect of mixed convection. The Joule heating that contributes significantly to the heat transfer properties of nanofluid is incorporated along with the heat source/sink. Furthermore, the flow is assumed to be governed by an exterior magnetic field that aids in gaining control over the flow speed. With these frameworks, the mathematical model that describes the flow with such characteristics and assumptions is framed using partial differential equations (PDEs). The bvp4c solver is used to numerically solve the system of non-linear ordinary differential equations (ODEs) that are created from these equations. The solutions of obtained through this technique are verified with the available articles and the comparison is tabulated. Meanwhile, the interpretation of the results of this study is delivered through graphs. The findings showed that the Bejan number was decreased by increasing Brinkman number values whereas it enhanced the entropy generation. Also, as the curvature parameter goes higher, the speed of the nanofluid flow diminishes. Furthermore, the increase in the Soret and Dufour effects have enhanced the thermal conduction and the mass transfer of the nanofluid.
Optical spectroscopy offers the most direct view of the stellar properties and the accretion indicators. Standard accretion tracers, such as H\(\beta \), H\(\alpha \) and Ca II triplet lines, and most photospheric features fall in the optical wavelengths. However, these tracers are not readily observable from deeply embedded protostars because of the large line of sight extinction (\(A_v \sim 50\)–100 mag) toward them. In some cases, however, it is possible to observe protostars at optical wavelengths if the outflow cavity is aligned along the line-of-sight that allows observations of the photosphere, or the envelope is very tenuous and thin, such that the extinction is low. In such cases, we not only detect these protostars at optical wavelengths, but also follow up spectroscopically. We have used the HOPS catalog (Furlan et al. in 2016) of protostars in Orion to search for optical counterparts for protostars in the Gaia DR3 survey. Out of the 330 protostars in the HOPS sample, an optical counterpart within 2\(''\) is detected for 62 of the protostars. For 17 out of 62 optically detected protostars, we obtained optical spectra (between 5500 and 8900 Å) using nt Object Spectrograph and Camera (ADFOSC) on the 3.6-m Devasthal Optical Telescope (DOT) and Hanle Faint Object Spectrograph Camera (HFOSC) on 2-m Himalayan Chandra Telescope (HCT). We detect strong photospheric features, such as the TiO bands in the spectra (of 4 protostars), hinting that photospheres can form early in the star-formation process. We further determined the spectral types of protostars, which show photospheres similar to a late M-type. Mass accretion rates derived for the protostars are similar to those found for T-Tauri stars, in the range of 10\(^{-7}\)–10\(^{-8}\) \(M_\odot \) yr\(^{-1}\).
In an era driven by massive information, the data economy’s utilization fuels profound transformation across various sectors. This chapter presents the application, challenges, and benefits of instituting the Data Economy Framework in multiple sectors. The healthcare domain has witnessed a remarkable shift as data-driven insights, diagnosis, and treatment assist in precision medicine characterized by customized approaches. The finance sector, too, finds its pulse in data, optimizing investment decisions and risk management while reshaping the way customers experience financial services. Education experiences a tremendous shift, with personalized learning paths and predictive analytics to shape the educational journey, empowering learners with customized approaches. Data is a powerful streamlining force in productivity and efficiency, unlocking innovative efficiencies and promoting optimal resource allocation. Data helps decision-making and threat assessment in the defense sector, strengthening national security through enhanced data-based awareness. Lastly, the tourism industry embraces the power of data to curate mesmerizing experiences, customize adventures to individual preferences, and promote sustainable destination management practices. A common thread runs through all these sectors—how the power of data can revolutionize industries, unlocking unexplored potential and carving a future defined by excellence driven by actionable insights.
This chapter provides a comprehensive exploration of the profound impact of the exponential growth of data in the twenty-first century. The first part of the chapter discusses the unprecedented increase in data volume and its implications for data management and processing. The massive influx of data demands efficient storage, processing, and analysis strategies to derive valuable insights. The chapter also emphasizes the evolving significance of data in the modern era, driving innovation and influencing decision-making processes across various industries and societal domains. Challenges with data are examined, including issues related to data security, privacy, quality, and the complexities of handling diverse data sources. Finding practical solutions to these challenges is critical to harnessing the full potential of the data deluge. Moreover, the chapter explores the emergence of a data economy at the global level, highlighting the strategic importance of data as a valuable resource for economic growth and development. Building a better data economy is proposed as a strategic goal, involving initiatives for data sharing, promoting data literacy, and establishing responsible data governance frameworks. Finally, the chapter highlights the pivotal role of policy strategies in shaping the data value chain. Policy measures for promoting data access, safeguarding data privacy, and encouraging data-driven innovation are discussed in detail.
The significance of personal data in markets has attracted economists’ attention due to the growing digitization and technological advancements in data processing. The economics of privacy aims to examine how people, businesses, and policymakers interact in marketplaces where personal data is a crucial concern. Academics face challenges when investigating privacy in these markets due to their complex nature, which requires them to draw attention to diverse disciplines and employ research methodologies. This chapter presents the literature and emphasizes the significance of considering the complex interactions between economic players. The chapter also highlights specific strategies concerning privacy matters. First, individuals face a difficult trade-off between sharing their data to access personalized services and safeguarding their personal information from potential misuse. Second, industrial organizations and marketing scholars are researching how companies can integrate personal data into their approaches to propose innovative business models. Policymakers need help to establish a transparent framework that benefits individuals and businesses. Finally, it discusses the necessity for further research to dig into the role of privacy as a discriminating factor for businesses, specifically in building a strong connection between consumer demand and corporate offerings.
The evolution of digital and data economies has catalyzed unprecedented economic transformations across the globe, shaping the trajectories of developed, developing, and underdeveloped nations in distinctive ways. This chapter delves into the multifaceted impact of the data economy across these categories, dissecting their varying priorities, challenges, and accomplishments. In developed nations like the United States, Japan, and Germany, the data economy thrives on innovation and technological prowess, though they grapple with balancing innovation and data privacy. Developing nations in Latin America, Southeast Asia, and Africa prioritize inclusion through initiatives like Kenya’s M-Pesa, yet disparities in access persist. Underdeveloped nations focus on digital inclusion and infrastructure despite limited resources. Across these categories, diverse performances in parameters like technological infrastructure, digital literacy, and data governance are evident. Innovation ecosystems thrive in the United States and Nigeria, while Haiti’s constraints highlight challenges. This chapter explores the data economy’s impact on global economies, showcasing data-driven innovation, the imperative of bridging divides, and challenges that underscore equitable progress. The chapter unveils a tapestry of influence, highlighting shared growth potential.
The data economy, an intricate amalgamation of data and economic principles, has emerged as a transformative force in the modern world. Defined by the intricate interplay between data and economic activities, it encompasses a diverse range of sectors and stakeholders. This paradigm shift stems from the recognition that once a mere record, data has evolved into a prized resource, profoundly influencing business strategies, policy decisions, and societal progress. The data economy’s essence lies in its multifaceted components and stakeholders. It involves individuals, businesses, governments, data intermediaries, nonprofit organizations, academia, and society, forming a complex web of contributions and collaborations. However, this burgeoning landscape is challenging. Data privacy, security, access, interoperability, governance, and the digital divide emerge as significant obstacles, necessitating ethical guidelines, robust infrastructure, and a skilled workforce to navigate these complexities. The data economy’s impact is far-reaching, evident in its diverse types. The big data economy thrives on vast datasets and algorithmic analysis, revolutionizing industries, and decision-making. Personal data economy capitalizes on digital footprints to tailor experiences, while the industrial data economy optimizes processes through real-time insights. The algorithm economy ushers in automation and AI-led decision-making, albeit raising concerns of bias and job displacement. Amid this evolution, principles guide responsible data use: ownership, privacy, security, fairness, transparency, interoperability, innovation, and responsibility. These principles ensure equitable value distribution, mitigate risks, and drive innovation. The data economy’s significance is underscored by its role in fostering innovation, improving efficiency, enhancing customer experiences, job creation, and economic growth. It thrives on the relentless growth of data, which, by 2025, is projected to reach 175 zettabytes. As industries harness this wealth of information, the data economy acts as a cornerstone, propelling societies toward digital transformation, economic prosperity, and unparalleled innovation.
Data monetization refers to leveraging data generated by a company to create measurable economic advantages. It can lead to various business benefits, such as increased revenue or reduced expenses. This book chapter delves into the dynamic realm of data monetization, investigating its significance and implications across various dimensions. The chapter is structured into seven sections. Beginning with the “Need for Data Monetization,” the first section underscores the rationale behind harnessing data as an economic asset. The second section, “The Data Revolution: A Global Perspective,” explores the widespread impact of data in transforming industries and societies worldwide. Focusing on digital behemoths, the third section reveals the “Data Dominance Unveiled,” dissecting the duopoly of Google and Facebook in the data economy. Thereafter, the fourth section delves into the sectors of data monetization, identifying diverse domains capitalizing on data-driven strategies. Shifting the focus to industry dynamics, the fifth delves into the “Data Economy Leading to Mergers and Acquisitions,” uncovering how data-driven strategies shape corporate consolidations. The sixth section examines the complexities faced by both individuals and entities in navigating data monetization. Finally, the last section introduces data monetization tools, shedding light on technological enablers facilitating efficient data monetization strategies. By dissecting the multifaceted facets of data monetization, this chapter offers a comprehensive understanding of its global impact, ranging from industrial paradigms to individual experiences, thereby contributing to the broader discourse on the data-driven economy.
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22,942 members
Santhosh Kareepadath Rajan
  • Department of Psychology
Anish Nag
  • Department of Life Sciences
Dr Neeraj Panwar
  • Department of Psychology
Hosur Road, 560029, Bengaluru, Karnataka, India
Head of institution
Dr. Fr. Thomas C. Mathew