107 20 16488 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Desai, Vinayak; M, Sahana; Mayegowda, Shilpa Borehalli; Nagalapur Gadilingappa, Manjula; Nagshetty, Kavita Title A name given to the resource Harnessing quorum sensing for disease management Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Harnessing Quorum Quenching for Disease Management;pp.137-151 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-34178-6.00015-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-34178-6.00015-4</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105023941542?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023941542?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Desai V., Department of Microbiology, Gulbarga University, Karnataka, Gulbarga, India; M S., Department of Microbiology, School of Basic and Applied Sciences, Dayananda Sagar University, Karnataka, Bengaluru, India; Mayegowda S.B., Department of Psychology, CHRIST University, Kengeri Campus, Karnataka, Bengaluru, India; Nagalapur Gadilingappa M., Department of Microbiology, School of Basic and Applied Sciences, Dayananda Sagar University, Karnataka, Bengaluru, India; Nagshetty K., Department of Microbiology, Gulbarga University, Karnataka, Gulbarga, India Description An account of the resource Quorum sensing (QS) is a bacterial communication system that allows bacteria to coordinate their behavior based on their population size. Both Gram-positive and Gram-negative bacteria use QS, but they employ different chemical signals to communicate. As bacteria grow, they release these signals, and when the attention of these signals reaches a certain position, it triggers specific genes that help the bacteria acclimate to their terrain. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource chemical signals; disease management; Gram-negative bacteria; Gram-positive bacteria; Quorum sensing Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044334178-6; 978-044334179-3; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Jadhav, Vikram; Pardeshi, Shaktising; More, Bhagyashri; Merugu, Ramchander; Mayegowda, Shilpa Borehalli Title A name given to the resource Quorum quenching for biocontrol of plant diseases Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Harnessing Quorum Quenching for Disease Management;pp.201-224 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-34178-6.00007-5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-34178-6.00007-5</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105023921004?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023921004?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jadhav V., Department of Chemistry, K. K. Wagh Arts. Science and Commerce College Pimpalgaon (B), Maharashtra, Nashik, India; Pardeshi S., Karmaveer Punjababa Goverdhane Arts, Commerce, and Science College Igatpuri, Maharashtra, Nashik, India; More B., Camp Education Society's, Dr. Arvind B. Telang Senior College Arts, Commerce and Science, Pradhikaran Nigdi, Maharashtra, Pune, India; Merugu R., Department of Biochemistry, Mahatma Gandhi University, Telengana, Nalgonda, India; Mayegowda S.B., Department of Psychology, CHRIST University, Kengeri Campus, Karnataka, Bengaluru, India Description An account of the resource Bacterial pathogens have become a significant threat to world agriculture, causing tremendous crop losses and economic instability. The traditional mode of disease control by chemical pesticides is quickly becoming obsolete with resistant strains and environmental issues. Quorum quenching (QQ) is thus a promising alternative that is sustainable enough to target the bacterial communication system known as quorum sensing (QS). QS enables bacteria to coordinate their virulence factors, biofilm formation, and host colonization according to population density. Attenuating bacterial virulence is achieved through QQ by enzymatic degradation, signal molecule mimicry, or inactivation of autoinducers but without directly killing the pathogens, thereby not allowing resistance. This chapter describes the mechanism of QQ and several applications of this principle in controlling plant diseases through biocontrol agents. Enzymatic hydrolysis catalyzed by lactonases, acylases, and oxidoreductases inactivate autoinducers that include acyl-homoserine lactones and autoinducer-2 (AI-2). Mimicry of QS signal molecules is through synthetic or natural analogs that competitively inhibit QS receptors; sequestration occurs when autoinducers are captured to avert their involvement in QS. QQ is quite efficient in the lab and the field, both as microbial biocontrol agents against the pathogens Pectobacterium carotovorum and Xanthomonas campestris, as well as in transgenic plants expressing QQ enzymes. QQ still faces challenges in developing resistance, delivering QQ enzymes, and integrating with integrated pest management strategies. Future research could be concentrated on optimizing QQ strategies, designing new enzymes with better stability and specificity, and assessing the environmental impact of QQ-based biocontrol. Breakthroughs in synthetic biology and protein engineering would give way to new opportunities in the form of more potent biocontrol agents. The method of disease control in an environment-friendly manner and ensuring food security across the globe will be targeted by overcoming the challenges and through novel research with QQ-based biocontrol. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource bacterial pathogens; biocontrol; plant diseases; Quorum quenching; quorum sensing Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044334178-6; 978-044334179-3; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource C., Balakrishnan; J., Mahalakshmi; R., Stephen; Warrier, Gayathry S. Title A name given to the resource Artificial intelligence-powered talent acquisition and onboarding Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Harnessing Artificial Intelligence to Ensure Diverse Global Teams;pp.89-104 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-34135-9.00003-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-34135-9.00003-9</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033410452?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033410452?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant C. B., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; J. M., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; R. S., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Warrier G.S., Department of Computer Science, Christ University, Karnataka, Bengaluru, India Description An account of the resource Artificial intelligence (AI)-powered instantaneous recruitment analysis and onboarding of the candidates has gained more popularity in recent years, mainly after the COVID-19 phase, largely due to the incredible growth of data-driven content. Basically, the art of summarizing the entire content of the resume of the candidates and leveraging the resumes that are relevant to the job description takes more time-consuming by using the traditional methods. AI is revolutionizing the landscape of talent acquisition and onboarding for applicants/selected candidates, making it much easier than conventional techniques. By harnessing the power of advanced AI-based algorithms and machine learning, organizations and multinational corporations (MNCs) can streamline their recruitment processes, from identifying top talent to integrating new hires seamlessly. AI-based tools automate time-consuming tasks such as screening of the resumes of the voluminous data, scheduling interviews based on each individual job role, and sending out offer letters for the selected applicants. This automation frees up human resource professionals to focus on strategic initiatives and build stronger relationships with candidates. AI algorithms can analyze vast amounts of data to identify candidates who possess the exact skills and qualifications required for a specific role. This precision reduces the risk of mismatches and ensures a higher quality of hires. This article specifically focuses on the tools and techniques based on AI for making the recruitment process easier and their impacts on the actual landscape, along with some recent case studies. The impact of AI on this field of recruitment and its significance on the job market is well analyzed in this article. By embracing AI, organizations can unlock new opportunities for growth and innovation. AI-driven talent acquisition and onboarding not only improve efficiency and accuracy but also enhance the overall candidate experience and foster a more diverse and inclusive workforce. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource AI-driven talent acquisition; automation; data-driven insights; Large language models; OpenAI; predictive analysis; summarization Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044334135-9; 978-044334136-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Hukkeri, Sheetal V.; Kavatekar, Smita; Ashraf, Sharmila; Donni, Sacheth Kiran; Ghosh, Nabanita Title A name given to the resource Transforming potential into performance: harnessing AI-driven competency frameworks to nurture talent in higher education Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Harnessing Artificial Intelligence to Ensure Diverse Global Teams;pp.317-329 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-34135-9.00001-5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-34135-9.00001-5</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033440281?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033440281?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Hukkeri S.V., School of Commerce, JAIN (Deemed-to-be University), Karnataka, Bengaluru, India; Kavatekar S., School of Commerce, Finance and Accountancy, Christ University, Karnataka, Bengaluru, India; Ashraf S., School of Commerce, JAIN (Deemed-to-be University), Karnataka, Bengaluru, India; Donni S.K., School of Psychological Sciences, Christ University, Karnataka, Bengaluru, India; Ghosh N., School of Commerce, Finance and Accountancy, Christ University, Karnataka, Bengaluru, India Description An account of the resource The Indian Higher Education System is the backbone for economic expansion, creativity and innovation resulting in the progress of the nation. With a huge network of universities, colleges, and institutes, India's higher education system is one of the world's largest, serving millions of students. Despite its rapid growth the Higher Education System in India is witnessing issues like lower research output in comparison with global benchmarks, shortage of qualified academic staff and inadequate quality. Considering the available store house of literature and need gap analysis this paper aims to investigate how competence frameworks powered by artificial intelligence might help to turn promise into performance, to examine the changing talent needs in Higher Education by looking at how the demands of academia, industry, and research are always changing and also to investigate AI's function in Competency-Based Talent Management. Based on the discussions of multiple spectrums namely, Academic excellence, Students employability and Industry readiness, Research Innovation, Teaching effectiveness, Global and National Rankings and Faculty Satisfaction, the paper has suggested the implementation of AI to all pervasively enable the academic Institutions to grow, diversify and enrich the talent acquisition and retention. The paper also focussed on the significance of the inclusion of AI driven automation process to tide over the mundane difficulties of administrative work, to enrich the collaborative research initiatives, to benefit the teaching community to concentrate on modern andragogies, to upscale the Global and National Rankings, to gain academic excellence, to strengthen the employability skills among the students and ultimately to promote the overall satisfaction of the educators and educates. The paper concluded that well trained Higher Education Institutions can optimize their workload judiciously by incorporating AI tools and in turn can contribute to a better learning environment. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource artificial intelligence; automation; Higher education system; Learning; talent management Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044334135-9; 978-044334136-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Thampan, Manju; Joy, Rojal Treesa; Ramadani, Lauresha; Rajesh Kanna, R. Title A name given to the resource Collaborative security approaches for IoT ecosystems Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived IoT Security: Fundamentals and Key Enabling Technologies;pp.291-304 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-34125-0.00026-X" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-34125-0.00026-X</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105027006351?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105027006351?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Thampan M., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Joy R.T., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Ramadani L., Computer Science Faculty, AAB College Prishtina, Kosovo, Prishtina; Rajesh Kanna R., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India Description An account of the resource The surge in Internet of Things (IoT) devices has transformed numerous industries by enabling unparalleled connectivity and data sharing. Yet, this rapid growth has also exposed critical security vulnerabilities. This chapter delves into collaborative security strategies aimed at improving the safety and reliability of IoT ecosystems. A major vulnerability is weak authentication and authorization, often stemming from poor password practices or insufficient authentication mechanisms. Such flaws can result in unauthorized access, data breaches, and serious cyber threats, including Distributed Denial of Service (DDoS) attacks and Man-in-the-Middle (MITM) attacks. DDoS attacks can overwhelm essential IoT systems, like those in smart cities or healthcare, while MITM attacks can jeopardize data integrity during communication between devices and cloud services. Given that IoT devices frequently handle sensitive information, including personal and health data, ensuring their security is vital to avoid detrimental outcomes for users. Physical security risks also present a challenge, as the physical compromise of IoT devices can disrupt systems or pose risks to individuals. To address these threats, this chapter recommends several cybersecurity measures, such as secure design and development practices, robust authentication methods, and advanced encryption techniques. Secure device design should include mechanisms for safe firmware updates and employ Trusted Platform Modules for secure key storage. Effective authentication can be enhanced with multifactor methods, role-based access controls, and digital certificates. Data protection should involve encrypting data both in transit and at rest, as well as employing techniques like data anonymization and differential privacy. 2026 Elsevier Inc. All rights reserved.. Subject The topic of the resource Collaborative architectures; IoT security; Proactive threat hunting; Resilient IoT ecosystems; Scalable IoT design Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044334125-0; 978-044334126-7; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Jimmy, Mathew Jyyothis; John, Naveen; Sreenath, V.; Etemi, Blerta Prevalla; Rajesh Kanna, R. Title A name given to the resource Identity and access management for IOT devices Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived IoT Security: Fundamentals and Key Enabling Technologies;pp.137-151 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-34125-0.00020-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-34125-0.00020-9</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105027023704?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105027023704?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jimmy M.J., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; John N., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Sreenath V., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Etemi B.P., Department of Computing and Information Technologies, RIT Kosovo (A.U.K), Rochester Institute of Technology - RIT Global Campus, Kosovo, Prishtina; Rajesh Kanna R., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India Description An account of the resource Identity and Access Management (IAM) for Internet of Things (IoT) devices is crucial in safeguarding the security and integrity of interconnected systems. This critical analysis explores the unique challenges and solutions associated with IAM in the context of IoT environments. IoT devices, characterized by their diverse types and widespread deployment, present significant complexities in managing identities and controlling access. Traditional IAM frameworks often fall short when applied to the dynamic and distributed nature of IoT networks, where devices frequently interact autonomously and may lack standardization. The analysis identifies key issues such as scalability, interoperability, and real-time authentication. It highlights the need for advanced IAM solutions that can handle the vast number of devices, support diverse protocols, and ensure robust security measures. Emerging technologies, including blockchain and AI-driven authentication, offer promising avenues for enhancing IAM in IoT contexts. Blockchain can provide decentralized, tamper-proof identity management, while AI can enhance real-time threat detection and adaptive access control. The review underscores the importance of developing IAM frameworks that are both flexible and scalable to address the evolving security requirements of IoT environments. By addressing these challenges, organizations can better secure their IoT infrastructure and mitigate risks associated with unauthorized access and identity breaches. 2026 Elsevier Inc. All rights reserved.. Subject The topic of the resource Federated identity management; IoT security; Machine learning authentication; Privacy-preserving techniques; Risk-based access control; Scalable IAM solutions Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044334125-0; 978-044334126-7; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Muloor, Kiran Hemanthaj; Shankar Iyer, Lakshmi; Saseekala, M.; Manu, K.S. Title A name given to the resource The role of IoT in optimizing quick commerce operations: a comprehensive analysis of micro-fulfillment centers and consumer satisfaction Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived IoT Security: Fundamentals and Key Enabling Technologies;pp.473-487 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-34125-0.00014-3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-34125-0.00014-3</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105026989292?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105026989292?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Muloor K.H., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Shankar Iyer L., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Saseekala M., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Manu K.S., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India Description An account of the resource This research focuses on integrating Internet of Things (IoT) and AI-driven machine learning algorithms to optimize operations within micro-fulfillment centers for quick commerce in India. The study addresses the growing demand for faster delivery times, driven by the rise of e-commerce, and the challenges associated with managing inventory, logistics, and last-mile delivery. By implementing IoT-enabled devices to monitor real-time data and deploying machine learning algorithms for demand forecasting and route optimization, the study aimed to enhance operational efficiency and reduce delivery times. The research utilized a dataset comprising various operational metrics from micro-fulfillment centers across major Indian cities. A comparative analysis revealed that the AI and IoT integration led to a 22% reduction in delivery time and a 15% improvement in order accuracy. Additionally, the predictive maintenance system, powered by IoT sensors, resulted in a 30% decrease in equipment downtime. The results demonstrate that combining IoT with machine learning optimizes supply chain operation and significantly contributes to meeting consumer expectations in the quick commerce sector. This research provides a comprehensive framework for leveraging technology to address the unique challenges of the Indian market, offering valuable insights for industry stakeholders. 2026 Elsevier Inc. All rights reserved.. Subject The topic of the resource Artificial intelligence; Computer in society; Computer science; Computing methodology; Consumer decisions; Database management system; Fast delivery; Geomatics; Information systems; Internet of Things; Machine learning; Micro-fulfillment centers; Operations management; Product assortment; Product demand; Product quality; Quick commerce Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044334125-0; 978-044334126-7; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Santhosh Kumar, P.; Etemi, Blerta Prevalla; Rajesh Kanna, R. Title A name given to the resource Emerging trends and security in IoT devices Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived IoT Security: Fundamentals and Key Enabling Technologies;pp.275-290 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-34125-0.00012-X" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-34125-0.00012-X</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105027071557?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105027071557?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Santhosh Kumar P., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Etemi B.P., Department of Computing and Information Technologies, RIT Kosovo (A.U.K), Rochester Institute of Technology-RIT Global Campus, Kosovo, Prishtina; Rajesh Kanna R., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India Description An account of the resource The Internet of Things has rapidly evolved, connecting billions of devices and transforming various industries. This chapter explores the latest emerging trends in IoT devices, focusing on advancements in connectivity, edge computing, and artificial intelligence (AI) integration. As IoT adoption grows, so does the importance of security, given the increasing vulnerabilities and potential threats. This chapter delves into key security challenges, including data privacy, secure communication protocols, and device authentication. It also discusses cutting-edge security solutions, such as blockchain technology and AI-driven anomaly detection, to safeguard IoT ecosystems. By examining both technological advancements and security considerations, this chapter provides a comprehensive overview of the current landscape and future directions for IoT devices. 2026 Elsevier Inc. All rights reserved.. Subject The topic of the resource AI integration; Anomaly detection; Blockchain; Connectivity; Data privacy; Device authentication; Edge computing; Emerging trends; IoT; Secure communication; Security Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044334125-0; 978-044334126-7; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Dash, Adarsh Kumar; Sehgal, Chiranjeev; Dey, Hemangshu; Ramadani, Lauresha; Rajesh Kanna, R. Title A name given to the resource Modern privacy preserving strategies for IoT security Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived IoT Security: Fundamentals and Key Enabling Technologies;pp.125-136 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-34125-0.00003-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-34125-0.00003-9</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105026966910?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105026966910?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Dash A.K., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Sehgal C., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Dey H., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Ramadani L., Computer Science Faculty, AAB College Prishtina, Kosovo, Prishtina; Rajesh Kanna R., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India Description An account of the resource The proliferation of Internet of Things (IoT) devices has brought about a revolution in various industries and everyday life, enhancing connectivity and efficiency. Nevertheless, this rapid adoption has also given rise to notable security and privacy challenges, leading to the need for robust solutions to safeguard sensitive data. This study delves into contemporary strategies for preserving privacy specifically designed for IoT security, with a focus on the most recent trends and technologies. By the year 2024, it is projected that the global count of IoT devices will exceed 30 billion, exhibiting a compound annual growth rate (CAGR) of 26.7% from 2021 to 2024. This exponential growth has led to a significant increase in the amount of data produced and transmitted by IoT devices, thereby creating fresh opportunities as well as vulnerabilities. Privacy apprehensions are crucial, given that these devices frequently amass sensitive personal and organizational data. The research scrutinizes cutting-edge privacy-preserving techniques, such as federated learning, homomorphic encryption, and differential privacy, which present promising resolutions for safeguarding data while upholding functionality. Federated learning has garnered attention as a decentralized approach that permits data processing to occur locally on devices rather than being sent to central servers, thereby reducing data exposure. Homomorphic encryption facilitates data processing while encrypted, ensuring a high level of security without disclosing the underlying information. Conversely, differential privacy introduces statistical noise to data, guaranteeing that individual data points are not easily discernible, thus preserving user privacy. This section also accentuates the significance of adhering to regulations and the impact of frameworks like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) on shaping the advancement and acceptance of privacy-preserving technologies. Moreover, an exploration is made into the incorporation of blockchain for immutable and transparent data management in IoT environments. This manuscript furnishes an exhaustive overview of the prevalent trends and technologies within this realm, providing insights into the future trajectory of IoT security and privacy. 2026 Elsevier Inc. All rights reserved.. Subject The topic of the resource Computer security; Computer security and privacy; Cryptography; Information systems; Network (computer science); Operations management Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044334125-0; 978-044334126-7; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Baby, Annmary; Thomas, Rithin Paul; Ramadani, Lauresha; Rajesh Kanna, R. Title A name given to the resource Human factors and social engineering in IoT attacks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived IoT Security: Fundamentals and Key Enabling Technologies;pp.93-104 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-34125-0.00002-7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-34125-0.00002-7</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105027093334?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105027093334?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Baby A., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Thomas R.P., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Ramadani L., Computer Science Faculty, AAB College Prishtina, Kosovo, Prishtina; Rajesh Kanna R., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India Description An account of the resource This chapter explores how social engineering and human factors contribute to IoT assaults, emphasizing how human mistake and psychological manipulation weaken linked systems. Cybercriminals are increasingly using social engineering techniques to trick people into disclosing private information or jeopardizing security measures as IoT devices become more and more integrated into everyday life and vital infrastructure. This study examines important strategies like baiting, pretexting, and phishing, highlighting their unique uses in Internet of Things settings. It also looks into how human behavior affects security procedures, showing how dangers are increased by insufficient awareness and training. The chapter offers important insights into how human factors and IoT security interact by examining current case studies and actual attack scenarios. It ends with tactical suggestions for enhancing security awareness, fortifying authentication procedures, and putting in place efficient defenses against social engineering risks in IoT ecosystems. Future research should focus on developing AI-driven security measures, flexible defense strategies, and strong policy frameworks to strengthen IoT security. Tackling these human-related vulnerabilities is essential to building a safer and more reliable IoT ecosystem. 2026 Elsevier Inc. All rights reserved.. Subject The topic of the resource AI integration; Human factors; IoT security; Social engineering; Threat detection; User education Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044334125-0; 978-044334126-7; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Rai, Rashmi; Rajesh, Roshan; Agarwal, Kushagra; Srivastava, Anubha Title A name given to the resource Cementing the future from gray to green Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Future Smart Cities: A Blueprint for Inclusive and Sustainable Living;pp.143-165 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-33667-6.00011-2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-33667-6.00011-2</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032981778?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032981778?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Rai R., Christ University, School of Business and Management, Karnataka, Bangalore, India; Rajesh R., Christ University, School of Business and Management, Karnataka, Bangalore, India; Agarwal K., Christ University, School of Business and Management, Karnataka, Bangalore, India; Srivastava A., Christ University, School of Business and Management, Karnataka, Bangalore, India Description An account of the resource This chapter explores the potential of green structures and nature-based solutions in the context of Bangalore, a city that is rapidly urbanizing and struggling with the environmental impacts of traditional building materials such as concrete. Concrete, while being a mainstay of modern construction, contributes significantly to global CO2 emissions (Fischetti etal., 2023). The problem is how we can maintain Bangalore's rapid growth without concrete's ill effects. What are the alternatives to concrete, how do the different materials compare with each other, and how will it affect Bangalore? In Bangalore, using materials like clay and mud blocks instead of bricks, simple Kota tiles and clay tiles for flooring, or Hempcrete would have a significant environmental benefit. This chapter also discusses nature solutions, which are nature-based strategies for creating sustainable and climate-resilient solutions to address infrastructure needs for Bangalore. These solutions often offer higher quality, lower cost, and more excellent resiliency than traditional infrastructure. Composition and Properties of Hempcrete is a biocomposite building material that combines the internal woody core of the industrial hemp plant with a lime-based binder. The result is a lightweight, insulating, and flame-retardant material with excellent thermal properties. The porous cellulosic structure of the hemp core provides exceptional bonding strength with lime, making foam concrete a versatile material that can be used in both new construction and renovation of existing buildings. One of the main thermal properties and advantages is its high thermal mass. This means they can store heat and keep buildings warmer in the winter and cooler in the summer (Hemp). This property reduces the need for energy-intensive heating and cooling systems, making buildings made from Hempcrete more energy-efficient and cost-effective in the long run. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource Bangalore; Ferrock; Hempcrete; Smart city; Sustainable development; Urban planning Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044333667-6; 978-044333668-3; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Chandra, J.; Joseph, Alwin; Joseph, Joel; Upadhyay, Priyansha; Kumar, Satyam Title A name given to the resource Impact of fine-tuning large language model in society: a comprehensive study Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Challenges and Applications of Generative Large Language Models;pp.23-45 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-33592-1.00008-5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-33592-1.00008-5</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032982650?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032982650?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandra J., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Joseph A., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Joseph J., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Upadhyay P., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Kumar S., Department of Computer Science, Christ University, Karnataka, Bengaluru, India Description An account of the resource The fine-tuned large language models (LLMs) have revolutionized artificial intelligence (AI) and natural language processing (NLP) with key innovations in neural architectures, particularly with the transformer. The recent advancements of LLM have witnessed that models like bidirectional encoder representations from transformer (BERT), generative pretrained transformer (GPT)-2, GPT-3, and text-to-text transfer transformer (T5) show outstanding performance in understanding and generating human-like text at scale. Researchers use fine-tuned models to excel in their responses to specific tasks or domains. The purpose of fine-tuning the LLM models is to improve the performance of LLM in special fields such as education, research, literature summarization, contract analysis, and creative content generation. Fine-tuning LLM models also has issues like amplifying biases, ethical issues, and regulatory implications, remarkably when LLMs are fine-tuned for emerging domains that may hold harmful stereotypes or misinformation. Fine-tuned LLMs also provide substantial societal benefits, including expert-level knowledge to underserved regions and personalizing educational resources for self-directed learning. The study also discusses the technical aspects of fine-tuning LLMs by examining how general-purpose models are transformed into efficient models. The impact on society and the need for a framework that can shape the deployment of models, with ethical guardrails, transparency, and public engagement to ensure responsible development and use of fine-tuned LLMs. The current work explores the various steps that can be taken for bias mitigation and transparent documentation for different stakeholder engagements. The purpose of the chapter is to analyse the perspectives from technical foundations with ethical, cultural, and policy considerations and provides an integral view of the societal impact of fine-tuned LLMs. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource artificial intelligence; computing; information management; information retrieval; knowledge management; Machine learning; natural language processing; transformers Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044333592-1; 978-044333593-8; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Chandra, J.; Malviya, Meenakshi; Sabu, Samson; Rajendran, Rajesh Kanna; Joseph, Alwin Title A name given to the resource Applications and future directions in multimodal large language model: opportunities and challenges Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Challenges and Applications of Generative Large Language Models;pp.219-241 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-33592-1.00006-1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-33592-1.00006-1</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032983757?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032983757?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandra J., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Malviya M., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Sabu S., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Rajendran R.K., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Joseph A., Department of Computer Science, Christ University, Karnataka, Bengaluru, India Description An account of the resource Multimodal large language models (MLLMs) are an application of artificial intelligence that is rapidly growing by integrating numerous use cases. MLLMs have the capability to process data from several sources, including structured and unstructured. It enables large language models (LLMs) to give insights to the user by analyzing data from various formats. The traditional way of analyzing the data was done with a single data format. While using MLLMs, multiple data modalities are handled to manage complicated multimodal tasks, like generating content, multimodal perception, and augmenting human-computer interaction. This chapter discusses in detail the insights from data from multiple modalities and domains of the use cases of MLLMs. We also discuss the advantages of MLLMs and explain the transformative benefits from unimodal systems to multimodal systems in different sectors. We also focus on the ethical usage of MLLMs by addressing the challenges related to privacy, operational limits, bias, computational difficulties, and data scarcity. The scarce assessment metrics and trials in accomplishing robust explainability are also discussed. To train these MLLMs, acquiring and training the data utilizes more computation and power consumption, along with addressing the data security and privacy concerns. The chapter also discusses the sensible usage of AI through different problems and practices. Detailed analysis and strategies for addressing global challenges and promoting novelty in model development, outlining how these MLLMs shape the upcoming technological innovation focusing on ethical application of technology with an advantage on society. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource automation; customer care; healthcare assistant; information management; machine learning; Multimodal large language models Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044333592-1; 978-044333593-8; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Noel, Joe Karunya; Mahadevan, Madhumitha; Umesh, Mridul; Suresh, Sreehari Title A name given to the resource Human gut microbiota and regulation of human behavior Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Advancing Science and Innovation in Healthcare Research: Health Horizons;pp.295-316 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-33351-4.00020-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-33351-4.00020-9</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105019729127?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105019729127?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Noel J.K., Department of Life Sciences, Christ University, Karnataka, Bangalore, India; Mahadevan M., Department of Life Sciences, Christ University, Karnataka, Bangalore, India; Umesh M., Department of Life Sciences, Christ University, Karnataka, Bangalore, India; Suresh S., Center for Global Health Research, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Tamil Nadu, Chennai, India Description An account of the resource The human body is often referred to as a microbiome, embodying a multitude of microbes. Specifically, the gut microbiome constitutes a cluster of bacteria within the gastrointestinal (GI) system. In both healthy individuals and those facing health issues, these bacteria significantly impact human physiology. They form an integral part of the unconscious system that exerts influence over human behavior. Globally, researchers are actively investigating the components of a good gut microbiome to enhance our comprehension of the role gut microorganisms play in health and disease. The gut harbors a diverse array of bacteria, encompassing both hazardous and beneficial types. Furthermore, studies on specific health-improving parasitic species are yielding biological insights that may propel the development of novel drugs. The gut microbiome directly impacts an individual's health by secreting physiologically active compounds like vitamins, essential amino acids, and lipids. Moreover, it indirectly influences the immune system and metabolic functions, thereby affecting moods and cognitive functions. Recognition of the role played by gut microbiota extends to various diseases and disorders. Evidence demonstrates significant interactions between the microbiome and certain drugs, profoundly shaping their intended effects. The interplay between the immune system, gut microbiota, and psychiatric disorders such as eating disorders, as well as conditions like cancer, autoimmune diseases, and autism spectrum disorder, is increasingly evident through ongoing research. This is unsurprising given the pivotal roles played by daily caloric intake, eating habits, and food composition in regulating various biological systems. Recent research sheds light on the intricate gut-brain axis (GBA), a two-way communication system where the gut receives signals from the brain, and vice versa. Microbes in the GI tract produce neurotransmitters that influence crucial functions like learning, memory, attention, and emotions. Even a slight change in the gut microbiome can lead to inflammatory reactions, connecting to the hypothalamic-pituitary-adrenal (HPA) axis and influencing human behavior. Under stressed conditions, changes in the gut microbiome can harm helpful bacteria, reduce the variety of bacteria in our gut, and allow harmful bacteria to grow. This makes us more likely to get sick and causes inflammation in our gut. Some studies even suggest that chemicals produced by the gut during an infection can affect our brain, making us more prone to feeling anxious or depressed. Knowing that our gut microbiome influences our behavior, it's essential to pay attention to what we eat, avoid drugs that harm our gut bacteria, and consider taking probiotic supplements in our diet. 2025 Elsevier Inc. All rights reserved. Subject The topic of the resource Bacteria; Behavior; Gut microbiota; Hormones; Regulation Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044333351-4; 978-044333352-1; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kannan, M.; Sindhu, V.; Umamageshwari, D.; Keerthika, K. Title A name given to the resource Explainable AI and computational intelligence in healthcare: Application to clinical decision support and personalized medicine Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Mathematical Modeling and AI-Driven Computational Techniques for Epidemiology and Disease Dynamics;pp.245-271 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-44-333234-0.00020-0" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-44-333234-0.00020-0</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033423078?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033423078?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kannan M., Department of Computer Science, CHRIST University, Bangalore, India; Sindhu V., Department of Computer Science, CHRIST University, Bangalore, India; Umamageshwari D., Department of Computer Science, CHRIST University, Bangalore, India; Keerthika K., Department of Computer Science, School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, India Description An account of the resource Human intelligence system simulation has made significant strides in several areas, including clinical decision-making using medical imaging and electronic health records, health referral systems, discovering recommended medications and vaccines, recognizing prescribed errors, and real-time data analysis. Therefore it is essential to discover patterns and transfer knowledge in the medical domain. The obstacles at the level of data collection, data analysis, model development, decision-making, and ethical concerns need to be addressed. It is vital that clinical interpretation tools associated with both hardware and software employed by medical professionals be precisely examined when rendering decisions regarding diagnoses and therapies related to the diagnosis. Computer scientists generally lack training in medical concepts specific to their field. Another crucial aspect is that black box algorithms based on artificial and computational intelligence are opaque and devoid of logical justification. Owing to these limitations, the technique of eXplainable Artificial Intelligence (XAI) models is explored in this chapter, primarily focusing on improving the interpretability of computational models. Specific objectives of this chapter are to: a) discuss the role that CI techniques and methods in the construction of an intelligent health prediction system; b) demonstrate the multiple CI paradigms utilized in medical prediction; and c) present recent case studies to showcase the performance of the computational intelligent models. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource Clinical decision-making; Computational intelligence; Electronic health records; Explainable artificial intelligence (XAI); Health prediction systems; interpretability; Medical data analysis Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044333234-0; 978-044333235-7; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Goswami, Mausumi Title A name given to the resource Air quality index improvement through machine learning and quantum computing: a framework for advancing air quality prediction using quantum-inspired metaheuristics on climate change to achieve positive health Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Multilevel Quantum Metaheuristics: Applications in Data Exploration;pp.341-384 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-33136-7.00021-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-33136-7.00021-9</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032975207?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032975207?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Goswami M., AI, ML and Data Science Department, CHRIST (Deemed to be University), Karnataka, Bangalore, India Description An account of the resource Climate change significantly exacerbates air quality deterioration, intensifying health risks and environmental instability. Air pollution poses significant challenges to public health and environmental sustainability. Accurate prediction of the Air Quality Index (AQI) is crucial for timely interventions and policy-making. As urbanization and industrial activities intensify, there is an urgent need for accurate and real-time air quality monitoring systems. Advanced machine learning (ML) techniques have shown promise in air quality forecasting and classification. Recently, quantum-inspired computational paradigms have emerged as innovative tools to overcome the limitations of traditional models, particularly in areas like feature selection, optimization, and spatial-temporal pattern recognition. This study presents a comprehensive analysis of various machine learning and deep learning models for AQI prediction, utilizing pollutant concentration data. It also explores quantum computing-inspired approaches. We explore the efficacy of different algorithms, datasets, and preprocessing techniques. This paper critically reviews high-impact research that explores the intersection of climate-induced changes and air quality prediction using ML. It identifies trends, gaps, and emerging methodologies. We conduct a comparative analysis of datasets, prediction models, and performance metrics. The paper focuses on three case studies. The first case study focuses on the Indian aspect using an Indian dataset and the global aspects with different global datasets, and the second case study uses quantum-inspired approaches. We further evaluate the performance of 10 state-of-the-art ML models, offering a roadmap for future research and deployment. Effective air quality forecasting is vital in urban planning decisions. This also plays an essential role need in environmental management and the protection of public health. This issue directly deals with Sustainable Development Goal (SDG) 3 and SDG 13. SDG 3 is related to positive health and SDG 13 is related to climate action. Conventional predictive models in ML face challenges due to multiple reasons. Effective feature selection is one such challenge as well as effective hyperparameter tuning. These challenges limit the effectiveness of artificial intelligence models. In the proposed framework, searching is enhanced using quantum jump- and quantum mechanics-related principles. This approach leads to the development of a quantum-inspired particle swarm optimization called QPSO. QPSO is able to provide more promising results by bridging the gaps of traditional optimization techniques. Model convergence is accelerated by using quantum-inspired feature selection techniques. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource Climate change, machine learning, Sustainable Development Goals, quantum-inspired metaheuristics, climate action, artificial intelligence Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044333136-7; 978-044333137-4; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Gunjan, Abhishek; Bhattacharyya, Siddhartha Title A name given to the resource Multiobjective portfolio optimization using multilevel quantum inspired optimization algorithms: a comparative study Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Multilevel Quantum Metaheuristics: Applications in Data Exploration;pp.155-204 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-33136-7.00007-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-33136-7.00007-4</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032958834?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032958834?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Gunjan A., Christ (Deemed to be) University, Karnataka, Bangalore, India; Bhattacharyya S., VSB Technical University of Ostrava, Ostrava, Czech Republic, Algebra Bernays University, Zagreb, Croatia Description An account of the resource The study of portfolio optimization has been a significant focus for computer science and finance researchers, with frequent publication of innovative methods. Numerous works have illustrated that conventional approaches like quadratic programming struggle with nonlinear constraints. This chapter compares ant colony optimization and particle swarm intelligence optimization within classical and quantum inspired frameworks, utilizing qubits and qutrits. This study analyzes benchmark datasets from the NASDAQ, Dow Jones, and BSE spanning over a decade. A pioneering effort has been made to develop a multiobjective portfolio optimization technique through a multilevel quantum inspired optimization algorithm. The experimental results demonstrate that the quantum inspired metaheuristic technique that utilizes qutrits slightly outperforms classical and qubit based quantum inspired methods. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource evolutionary algorithm; multilevel quantum-inspired techniques; Portfolio optimization; quantum-inspired ant colony optimization (QiACO); quantum-inspired particle swarm optimization (QiPSO) Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044333136-7; 978-044333137-4; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Shakya, Richa; Srivastava, Shivam; Paul Richards, M.; Agrawal, Ankur; Saharan, Renu; Bhise, Manish R.; Suresh, Venkata; Anjali, P.K. Title A name given to the resource Ethical considerations in nanomedicine research involving women Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Nanomedicine Advancements and Intersectional Perspectives for Women's Health;pp.237-263 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-33056-8.00005-1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-33056-8.00005-1</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105026848032?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105026848032?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Shakya R., Department of Pharmacy, Faculty of Pharmaceutical Sciences, Rama University, Uttar Pradesh, Kanpur, India; Srivastava S., Truba College of Pharmacy, Madhya Pradesh, Bhopal, India; Paul Richards M., Aditya Bangalore Institute of Pharmacy Education and Research, Affiliated to Rguhs, Karnataka, Bangalore, India; Agrawal A., Department of Pharmacy, Jai Institute of Pharmaceutical Sciences and Research, Madhya Pradesh, Gwalior, India; Saharan R., Department of Pharmacy, M. M. College of Pharmacy, MMDU, Haryana, Ambala, India; Bhise M.R., Department of Pharmaceutics, SGSPS, Institute of Pharmacy, Affiliated to Sant Gadge Baba Amravati University, Maharashtra, Amravati, India; Suresh V., Department of Pharmacy, Vathsalya College of Pharmacy, Telangana, Bhuvanagiri, India; Anjali P.K., Department of Economics, CHRIST (Deemed to be University), Karnataka, Bangalore, India Description An account of the resource This chapter elaborates social impact and perspectives of advanced technologies as well as critically discuss the ethical issues of womens participation in nano-medicine research. It starts by addressing the issue that women have been under sampled in clinical research but it is moral to take their samples farther into research most notably with certain special health conditions and unique reactions to drug treatments that are likely to be unique to women. One of the most emphasized concepts is informed consent, for the purpose of precisions and clarity in explaining its purpose and usefulness, as well as the probable consequences in the form of some narrowly speculative treatments with particular relevance to certain probabilities remaining beyond the range of perception on the female body after some or other nano-scale procedures. The chapter also talks about the ethical implications in the present strategies to increase the efficacy and safety for women only therapies like nanomedicine; the author urges for stringent preclinical and clinical guidelines which should have examined the following gender variations. It also examines the regulation with guidance and suggests contingency structures that are employed in the management of the ethical use of nanomedicine in women health care. Therefore this chapter tries to develop ethics together with science, incorporate these met ethical considerations into methods and guidelines in research, to contribute to enhancement of beneficial impact of the nanomedicine for womens rights and well-being. 2026 Elsevier Inc. All rights reserved.. Subject The topic of the resource clinical trials; ethical considerations; gender; Nanomedicine; women Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044333056-8; 978-044333057-5; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Biju, Anu K.; Viswambharan, Ashna; Thomas, Jince; Thomas, Minu Elizabeth; Dixon, Ditty; Thomas, Sabu Title A name given to the resource Water purification membranes: state of the art, fundamentals, challenges, and opportunities Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Membrane Assisted Water Purificatio;pp.3-25 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-32968-5.00008-5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-32968-5.00008-5</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105027165947?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105027165947?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Biju A.K., School of Polymer Science and Technology, Mahatma Gandhi University, Kerala, Kottayam, India; Viswambharan A., School of Polymer Science and Technology, Mahatma Gandhi University, Kerala, Kottayam, India; Thomas J., International and Inter University Centre for Nanoscience and Nanotechnology, Mahatma Gandhi University, Kerala, Kottayam, India; Thomas M.E., School of Polymer Science and Technology, Mahatma Gandhi University, Kerala, Kottayam, India; Dixon D., School of Chemical Sciences, Mahatma Gandhi University, Kerala, Kottayam, India; Thomas S., Mahatma Gandhi University, Kerala, Kottayam, India, Christ University, Karnataka, Bangalore, India, Center of Excellence in Polymeric Materials for Medical Practice Devices, Department of Chemical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand Description An account of the resource The rising global demand for clean and safe water has intensified the necessity for effective and sustainable purification technologies. This chapter thoroughly overviews membrane-based water purification systems, highlighting their basic principles, material types, operational mechanisms, and evolving roles in addressing water scarcity. It begins with the historical progression of membrane technology, discussing the various membrane types alongside essential separation processes including ultrafiltration, nanofiltration, reverse osmosis, and membrane distillation. The text also covers recent innovations in nanocomposite membranes, cutting-edge material design, and membrane module configurations, focusing on enhancing performance and energy efficiency. Special focus is placed on membrane fouling, its causes, effects, and strategies for mitigation, backed by computational modeling and machine learning insights. The chapter begins by exploring emerging trends, such as the development of fit-for-purpose membranes, their integration into zero liquid discharge systems, and scalable fabrication methods. Together, this information highlights the transformative capacity of membrane technology in tackling global water issues. 2026 Elsevier Inc. All rights reserved.. Subject The topic of the resource desalination; membrane fouling; Membrane technology; polymeric membranes; sustainable water treatment; zero liquid discharge Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044332968-5; 978-044332969-2; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Manuel, Manju; Kanchi, Suvardhan Title A name given to the resource Detection and management of sucralose pollution: innovations in sustainable monitoring, mitigation, and energy utilization Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Waste Derived Catalysts: Sustainable Materials for Energy and Environment;pp.345-358 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-32958-6.00024-8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-32958-6.00024-8</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032956736?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032956736?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Manuel M., Department of Chemistry, Christ University, Karnataka, Bengaluru, India; Kanchi S., Department of Chemistry, Christ University, Karnataka, Bengaluru, India Description An account of the resource Sucralose, a widely utilized artificial sweetener, has emerged as a potential environmental contaminant, persisting in aquatic environments and causing ecological risks. The use of suitable methods and sustainable materials can be applied for the detection of sucralose in wastewater, sludge, and other waste matrices Using sustainable materials and novel techniques for discerning sucralose. Leveraging waste-derived components, particularly in electrochemical sensing systems with competent nanomaterials can be a promising avenue for sucralose detection. Moreover, integrating waste-derived carbon materials like activated carbon or carbon black as electrodes enhances sensor sensitivity by augmenting organic molecule adsorption capacities, thus amplifying detection capabilities. Furthermore, waste-derived substrates such as cellulose fibers or agricultural residues can be engineered to serve as substrates or immobilization matrices in enzyme-based sensors for sucralose detection. While the electrochemical systems current output may not directly serve energy applications, its potential utilization for energy generation or storage, through technologies like electrochemical capacitors or batteries, underscores the broader sustainability objectives by repurposing energy from the detection process for additional beneficial applications. The amalgamation of sustainable methodologies in sucralose pollution detection and management not only contributes to environmental preservation but also aligns with the broader trajectory toward sustainable resource utilization and conservation. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource catalyst; environmental nanotechnology; Environmental pollution; mitigation; sucralose Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044332958-6; 978-044332959-3; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online