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                <text>Financial Vulnerability in Households: Dissecting the Roots of Financial Instability</text>
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                <text>Biju D.S., Department of Commerce, Christ (Deemed to be University), Bangalore, India; Tantia V., Department of Commerce, Christ (Deemed to be University), Bangalore, India</text>
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                <text>The phenomenon of household financial vulnerability, defined by unexpected shocksin income and expenditures, carries major implications for both individual households and the overall economy of a nation. For a considerable time, household debt has been widely acknowledged as the primary determinant of household financial vulnerability. This study aims to extend the analysis beyond the scope of household debt. Middle-income households may experience financial difficulties when faced with unexpected changes in income and expenses. These challenges can arise from several circumstances, including the inability to engage in discretionary activities such as dining out or vacations. For a very long time, it has been posited that low-income households exclusively experience financial vulnerability. Hence, it is imperative to thoroughly examine the concept of household financial vulnerability and its underlying factors to enhance households' ability to withstand adversities and better clarify the matter. In light of the prevailing economic recession triggered by the global pandemic and the ongoing confrontation between Russia and Ukraine, the significance of the matter is further underscored. This study aims to comprehensively define household financial vulnerability and examine its relationship with financial capability, digitalized payments, financial stress, and financial socialization. The current study anticipates establishing a foundational framework for future research endeavors in this specific field. Moreover, this paper also explores potential avenues for future research.  The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.</text>
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                <text>Fernandes R.L., Christ University, Hosur Road, Bangalore, 560029, India; Tantia V., Christ University, Hosur Road, Bangalore, 560029, India</text>
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                <text>Predicting Financial Distress (FD) and shielding companies from reaching that stage is vital, even indispensable for every business. FD, if not attended to on time, ultimately leads to bankruptcy. Prediction variables are essential to forecast the wreckage in the business; however, the prediction is successful when suitable models are used. This study aims to predict FD at three levels: from mild to severe, by applying a machine learning algorithm. The study identifies modern models using the machine learning approach for predicting multi-level FD and summarises the significance of modern models through machine learning technology, to sustain the future development of the economy. The modern models are free from rigid assumptions and have proved to be the best in the prediction of FD. The results show that FD prediction is important at multiple stages. The models performance will be high when the best features are selected using the Pearson Correlation and SFS Feature selection approach. Among the ten models used in the study, LightGBM Classifier shows the highest performance of 80.43% accuracy without feature selection. However, with Pearson Correlation Approach and SFS Feature Selection methods, the accuracy is 82.68% and 86.95% respectively. This study has major implications for the stakeholders of the company to take timely decisions on their investment and for the management as a yardstick to check the performance of the business.  The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.</text>
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                <text>Shaji A.K., Department of Professional Accounting and Finance, Kristu Jayanti College (Autonomous), Karnataka, Bengaluru, India; Sankar S., Department of Professional Accounting and Finance, Kristu Jayanti College (Autonomous), Karnataka, Bengaluru, India; Uma V.R., Department of Commerce, CHRIST (Deemed to be University), Karnataka, Bengaluru, India</text>
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                <text>This article explores the potential transformative impact of integrating augmented reality (AR) technology with personalized product recommendations in the retail industry. By leveraging ARs ability to overlay digital information onto the physical world, retailers can offer tailored suggestions based on individual preferences, past purchases, and real-time contextual cues, thereby enhancing customer satisfaction and driving sales. Through a comprehensive literature review and empirical analysis, the study investigates user experience, adoption factors, and the long-term effectiveness of AR-deep learning integration in retail settings. Findings reveal significant improvements in customer satisfaction, sales performance, inventory management, and employee productivity with the implementation of AR-Deep Learning technology. Additionally, the article presents an innovative framework that seamlessly integrates AR and deep learning models, demonstrating high accuracy in object recognition, real-time interaction, and enhanced user experience across various industries. While highlighting the studys limitations and areas for further research, this article underscores the importance of customer-centric strategies and technological innovation in optimizing the retail experience and driving business growth.  The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.</text>
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                <text>Manuel A., School of Commerce, Finance and Accountancy, Christ University, Karnataka, Banglore, 560 029, India; Arumugam S.K., School of Commerce, Finance and Accountancy, Christ University, Karnataka, Banglore, 560 029, India</text>
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                <text>Nanotechnology has great potential for developing nano-enabled equipment and products in a variety of industries, including personal care, medical, food, and agriculture. Despite the increasing use of metal nanoparticles in various domains, concerns concerning biological and environmental safety during manufacture remain. Traditional commercial methods for generating nanoparticles often entail chemical procedures and high-energy physical approaches that are both environmentally damaging and expensive. As an alternative, green synthesis employing plants has arisen, which reduces the requirement for toxic chemicals and severe reaction conditions in nanoparticle synthesis. The utilization of mangrove plants for nanoparticle synthesis has recently gained popularity due to their abundance of unique phytochemicals that aid in nanoparticle synthesis. Microorganisms in mangroves and enzymatic activities in plants can be utilized for a range of biotechnological and environmental uses. Bioactive compounds from mangrove resources show potential for creating bionanomaterials that can be utilized in environmental and biomedical fields. Bionanomaterials created from mangroves are incredibly effective in medical uses and cleaning up the environment. Bionanomaterials are produced by utilizing mangrove and various biomolecules obtained from mangrove plants as substances for the creation of nanoparticles. Bionanomaterials made from biomolecules offer benefits for the sustainable use of mangroves because of their large surface area, biocompatibility, and minimal toxicity. Here focuses on the potential of mangroves as a natural resource for producing bionanomaterials in various applications, promoting an eco-friendly approach. This chapter investigates various types of mangrove species and their elements utilized in creating nanoparticles, as well as the applications of the nanoparticles in therapy, agriculture, and industry. It also investigates the obstacles hindering the extensive utilization of plant-based nanoparticle synthesis.  Springer Nature Switzerland AG 2025.</text>
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                <text>Abraham J.S., Department of Life Sciences, School of Sciences, Christ University, Bengaluru, India; Ponmudiraj C., Department of Life Sciences, School of Sciences, Christ University, Bengaluru, India; Al-Khayri J.M., Department of Agricultural Biotechnology, College of Agriculture and Food Sciences, King Faisal University, Al-Ahsa, Saudi Arabia; Nagella P., Department of Life Sciences, School of Sciences, Christ University, Bengaluru, India</text>
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                <text>Mangroves are used by folklore in indigenous medicine for the treatment of diseases. They contain an array of pharmacologically significant bioactive compounds. The endophytes of the mangroves have the capability of producing biologically active compounds which may be similar to their host plant. They are also able to produce novel and unique bioactive compounds which can be used in therapeutics. Bacillus and Streptomyces are the major genera of bacterial endophytes found in the mangroves. The major groups of bioactive compounds produced by the bacterial endophytes of mangroves include terpenoids, alkaloids, polyketides, etc. The bioactive compounds produced by the endophytes possesses biological activities such as antibacterial, cytotoxic, antioxidant activity, etc. These compounds have profound applications in the discovery of drugs. The present chapter focuses on the bacterial endophytes found in the mangroves, the bioactive molecules produced by them, and the pharmacological activities associated with these endophytes.  Springer Nature Switzerland AG 2025.</text>
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                <text>Murthy H.N., Department of Botany, Karnatak University, Dharwad, India, Department of Horticultural Science, Chungbuk National University, Cheongju, South Korea, Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India; Yadav G.G., Department of Botany, Karnatak University, Dharwad, India; Paek K.Y., Department of Horticultural Science, Chungbuk National University, Cheongju, South Korea; Park S.Y., Department of Horticultural Science, Chungbuk National University, Cheongju, South Korea</text>
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                <text>Lamani S., Department of Botany, DVS College of Arts and Science, Shimoga, India, Department of Botany, Karnatak University, Dharwad, India; Murthy H.N., Department of Botany, Karnatak University, Dharwad, India, Department of Biotechnology, KLE Technological University, Hubballi, India, Department of Horticultural Science, Chungbuk National University, Cheongju, South Korea, Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India</text>
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                <text>Kathirvel P., Department of Microbial Biotechnology, Bharathiar University, Tamil Nadu, Coimbatore, India; Saravanan K., Department of Microbial Biotechnology, Bharathiar University, Tamil Nadu, Coimbatore, India; Umesh M., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, India</text>
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                <text>Environmental Science and Engineering;Volume;Part F1;pp.337-358</text>
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                <text>Sudheesh A.K., Department of Life Sciences, CHRIST (Deemed to Be University), Karnataka, Bangalore, India; Antony A., Department of Life Sciences, CHRIST (Deemed to Be University), Karnataka, Bangalore, India; George A., Department of Life Sciences, CHRIST (Deemed to Be University), Karnataka, Bangalore, India; Kavana Somaiah C., Department of Life Sciences, CHRIST (Deemed to Be University), Karnataka, Bangalore, India; Umesh M., Department of Life Sciences, CHRIST (Deemed to Be University), Karnataka, Bangalore, India; Thazeem B., Waste Management Division, Integrated Rural Technology Centre (IRTC), Kerala, Palakkad, India</text>
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                <text>Microalgae boasts unique advantages regarding biofuel production, where researchers have made significant strides in increasing biofuel yields while reducing costs by improving lipid profiles in various microalgal strains, incorporating nano-additives, and improving extraction techniques. They have achieved this through genetic engineering, nanotechnology, and innovative cultivation techniques, optimizing the entire process. Moreover, microalgae are rich in bioactive compounds, and this chapter emphasizes advanced methods for extracting and purifying compounds like carotenoids, phycobiliproteins, polysaccharides, and omega-3 fatty acids, which have broad applications in food, medicine, and various industries. In energy production, microalgae generate renewable and eco-friendly energy through microbial fuel cells, employing techniques such as thermochemical liquefaction and producing energy from fermentation processes. Advancements in microalgae-based photobioreactors and strategies to improve photosynthetic efficiency further contribute to the efficiency and scalability of energy production. The book chapter highlights the importance and sustainability of microalgae in addressing energy and environmental challenges. By harnessing the latest advancements in biofuel production, valuable product synthesis, and renewable energy generation, microalgae have the potential to steer in a greener and brighter future. Their rapid growth, carbon dioxide absorption, and valuable compound synthesis make them a powerful tool in our pursuit of sustainable solutions. Embracing the potential of microalgae can lead us to an environmentally friendly and economically prosperous future.  The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.</text>
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                <text>: Synthesis, Photocatalytic Properties, and Applications;pp.87-114</text>
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                <text>Khan N., Department of Chemistry, Amity Institute of Applied Sciences, Amity University, Uttar Pradesh, Noida, India; Garg S., Department of Chemistry, Amity Institute of Applied Sciences, Amity University, Uttar Pradesh, Noida, India; Arora I., Department of Chemistry, Amity Institute of Applied Sciences, Amity University, Uttar Pradesh, Noida, India; Hegde G., Department of Chemistry, Centre for Advanced Research and Development (CARD), CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Sagadeven S., Nanotechnology &amp;amp; Catalysis Research Centre, University of Malaya, Kuala Lumpur, Malaysia; Ledwani L., Manipal University Jaipur, Dehmi Kalan, Jaipur-Ajmer Expressway, Rajasthan, Jaipur, India; Chandra A., Lexgin IP LLP, T21/1602, Paras Tierea, Sector 137, Uttar Pradesh, Noida, India</text>
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                <text>Recovery of photocatalytic materials after the degradation of organic pollutants remains a challenge. To address this issue, immobilizing the material on a suitable substrate presents a viable solution. Immobilization of the commonly used titanium dioxide (TiO2) photocatalyst onto various substrates is typically achieved through adsorption, hydrogen bonding, or chemical bonding. Coating TiO2 onto different substrates is a common approach to enhance its durability, reusability, and catalytic efficiency across multiple applications, such as photocatalysis, sensors, and heterogeneous catalysis. The choice of substrate depends on the specific application, desired properties, and its ability to improve the photocatalytic performance. Substrates such as glass/quartz, polymeric materials, metal oxides, carbon-based materials, textiles, and cellulose each offer unique characteristics that enhance the potential of the photocatalytic material.  2026 WILEY-VCH GmbH.</text>
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                <text>atomic layer deposition; chemical vapor deposition; dip coating; heat treatment; immobilization; microplasma; photodegradation; PVA matrix; sintering; substrate coating</text>
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                <text>Rajeev T., School of Psychological Sciences, CHRIST, Deemed to be University, Karnataka, Bengaluru, India; Chakraborty R., School of Psychological Sciences, CHRIST, Deemed to be University, Karnataka, Bengaluru, India; Kumar A., School of Psychological Sciences, CHRIST, Deemed to be University, Karnataka, Bengaluru, India</text>
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                <text>Pedagogical considerations play a vital role in shaping the development of individuals, especially in the context of rapid technological advancements. There is a growing understanding that the multidirectional teaching-learning process extends beyond cognitive processes to include social and cultural dimensions. In psychology education, it becomes an imminent necessity to adapt to pedagogical advances to fulfil diverse student needs and to balance between the academic and practitioner approaches. This chapter explores the importance of contextualising psychology pedagogy within specific cultural and Indigenous frameworks, presenting contextualisation as a dynamic process that requires ongoing adaptation by educators to promote inclusive and effective learning for all students. The discussion begins by outlining the need to contextualise pedagogy, exploring how learning environments are influenced by cultural, political and social factors, supported by theoretical frameworks like classical approaches of Vygotsky, and more recent ones like Melanie Walker's critical capability approach. The chapter will explore the diverse contexts that could be considered for localised, context-sensitive equity and justice-oriented pedagogies, such as the digital age, multicultural classrooms, Indigenous settings and other diverse learning environments. It will also explore challenges to contextualising pedagogy, including reconciling Western models with specific cultural values and managing the discord between standardisation and localisation. The chapter concludes with strategies for contextualising, underscoring the importance of balancing international educational standards with the realities and needs of local contexts.  2026 by John Wiley &amp;amp; Sons, Ltd. All rights reserved.</text>
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                <text>Geetha P., Department of CINTEL, School of Computing, SRM Institute of Science and Technology (KTR Campus), Chennai, India; Abirami G., Department of Computer Science and Engineering, B.S. Abdur Rahman Crescent Institute of Science &amp;amp; Technology, Chennai, India; Padmavathy T., Department of Database Systems, SCOPE, VIT University, Vellore, India; Sivagami S., Department of CSE, Saveetha School of Engineering, SIMATS, Thandalam, Chennai, India; Vinodha D., Department of Computer Science and Engineering, CHRIST University, Bangalore, India</text>
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                <text>The modern cyber security environment changed through increased sophistication and complexity of cyber threats that requires organizations to use artificial intelligence (AI) technologies for strengthened security frameworks. It also limits the adverse impact within the populace by a decrease in carbon dioxide emissions, energy conservation, decrease wastage of electronic gadgets and assistance to sustainability with renewable resources. Among the practices of creating the green environment are the measures of virtualization, improving the quality of the hardware to increase the energy efficiency and using the cooling technologies efficiently. The Sustainable Cyber security practices are examining the measures and innovation for minimizing energy usage by integrated cyber security tools/infrastructure, green data center/network, and practicing green software engineering. In this domain, Sustainability Cyber security employs energy conservative cryptographic algorithms and software architecture, low energy cryptographic physical devices, power-conscious security protocols; efficient virtualization by integrating these approaches, they can advance sustainability into higher security statures. Besides, the enforcement of those security practices will help to address green data center objectives like server virtualization, and other efficiency data storage products. Cyber Security algorithms will act to lessen the time construct of cryptographic operations to less computational power, and hence less energy consumption. It also looks at the direction that sustainability is likely to take in the future, which may include such policies and structures as are likely to promote green computing in cyber security. Also through this case study, we have been able to incorporate green computing into its cyber security programs for a better and environmentally friendly future. The advanced strategic planning through automation enables organizations to develop stronger defense capabilities as they adjust to security threats which keep evolving in the present-day landscape.  2026 Scrivener Publishing LLC.</text>
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                <text>Designing Inclusive Classrooms: Integrating Emerging Technologies for Equity and Social Justice;pp.295-327</text>
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                <text>&lt;a href="https://doi.org/10.1002/9781394389223.ch14" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1002/9781394389223.ch14&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105034021951?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105034021951?origin=resultslist&lt;/a&gt;</text>
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                <text>Haseena S.V., Christ University, Bengaluru, India, Department of Computer Applications, Presidency College, Bengaluru, India; Jasawani N., Department of Computer Applications, Presidency College, Bengaluru, India; Ayasha, Department of Computer Science, Christ College of Science and Management, Bengaluru, India; Suresh G.B., Department of Computer Applications, Presidency College, Bengaluru, India; Suresh G.B., Department of Computer Applications, CMR University, Presidency College, Bengaluru, India; Shanavas S., Department of Computer Applications, Presidency College, Bengaluru, India</text>
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                <text>Fraud has always posed problems to financial institutions and with the rapid growth of digital transactions, and its complexity has increased beyond detection. Normal methods of fraud detection that depend on rules only are severely outdated and ineffective against newer types of schemes. It is now imperative to employ more sophisticated mechanisms for fraud detection considering the evolvement of financial crimes. The massive amounts of transnational data that need to be analyzed to detect fraudulent patterns can now be processed with medium to high levels of accuracy using AI with the help of machine learning, deep learning, and natural language processing (NLP), and fraud detection and prevention have been transformed for the better. Algorithms of machine learning like supervised, unsupervised, and reinforcement learning are central to the features of fraud detection. Suspicious transactions are detected during supervised learning by using already existing fraudulent data, whereas unsupervised learning detects all anomalies without any prior defined labels. Through real-time data input, reinforcement learning adjusts its detection methodologies. Deep learning models such as convolutional neural networks and recurrent neural networks identify and process fraud indicators hidden within messages or intricate datasets. Moreover, through intricate analysis of customer interactions, NLP techniques detect fraudulent activities by identifying phishing attempts and deceptive communications. The chapter touches upon the issues of implementing AI oriented fraud detection in realms like e-commerce and entertainment. Identifying fraud from e-commerce is complicated by factors like high volume of transactions, false positives, privacy issues, and the endless frameworks of fraud. Finally, the chapter provides a summary of the main insights and makes recommendations for further investigation like incorporating blockchain, federated learning, and higher explainability to bolster AI powered fraud detection systems.  2026 Scrivener Publishing LLC.</text>
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                <text>Adaptive Artificial Intelligence: Fundamentals, Challenges and Applications;pp.73-100</text>
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                <text>Meena Kumari P., ICFAITech, ICFAI Foundation for Higher Education, Hyderabad, India; Ramakrishna Reddy K., ICFAITech, ICFAI Foundation for Higher Education, Hyderabad, India; Manikandakumar M., Artificial Intelligence and Data Science, School of Engineering &amp;amp; Technology, Christ University, Bengaluru, India</text>
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                <text>Adaptive Artificial Intelligence (AI) is an innovative development in the domain of intelligent systems because machines have attained the ability to independently learn, modify, and optimize their operations based on experience and other real-time information. While traditional artificial intelligence (AI) is programmed to follow a certain set of algorithms and rules step-by-step, adaptive AI systems have a level of independence that affords them the flexibility to change their actions. As a result, healthcare, finance, education, manufacturing, and many other industries can now employ AI, which was formerly not possible due to its inflexible nature, for enhanced and efficient decision-making, improved consumer practice, and effective working processes. There is significant scope for adaptive AI to transform Natural Language Processing (NLP) as well as business practices and solve complex problems. On the other hand, there is also the advancement of dependency on data, algorithmic perception, and ethical inquiries concerning the application of these technologies which pose a challenge. This proposed chapter emphasizes the techniques and applications of Adaptive AI in NLP and the ethical challenges that make it authoritative to adopt responsible development of artificial intelligence.  2025 Scrivener Publishing LLC.</text>
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                <text>Vinodha D., School of Engineering and Technology, CHRIST (Deemed to be University), Bengaluru, India; Jenefa J., School of Engineering and Technology, CHRIST (Deemed to be University), Bengaluru, India; Anita E.A.M., School of Engineering and Technology, CHRIST (Deemed to be University), Bengaluru, India; Al-Dahidi S., School of Applied Technical Sciences, German Jordanian University, Amman, Jordan</text>
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                <text>Rani S., Amity Institute of Information Technology, Amity University, Noida, India; Anjum M., Amity Institute of Information Technology, Amity University, Noida, India; Chawla S., Department of Computer Science, Jamia Millia Islamia, Delhi, India; Sharma V., Computer Science Department, Christ University, Bengaluru, India</text>
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