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Empowering Indigenous and Rural Women through Community-Based Sustainable Tourism Development: Building Resilient Communities
Gender inequality and climate change are pressing global issues, with income disparity further exacerbating the challenges faced by women, especially in rural and tribal communities. This chapter investigates the empowerment journey of rural and tribal women through rural entrepreneurship, specifically focusing on the efforts of the Uravu Organization in Wayanad, Kerala, India. Uravu, a non-profit non-governmental organization, aims to empower rural communities through sustainable practices, particularly emphasizing the bamboo industry and womens skill enhancement in handicrafts. Using Scheyvens theoretical framework, the qualitative research this chapter is based on in-depth interviews with female employees of Uravu to evaluate their empowerment levels. The findings reveal that while these women experience significant psychological and economic empowerment, they continue to face challenges in achieving political and social empowerment. This study underscores the potential of zero waste and environmentally friendly organizations in fostering job creation for rural and tribal women, thereby contributing to resilient community building and community-based sustainable development. CAB International 2026. -
Harvesting Innovation: Women Agripreneurs Revolutionizing with Technology
The operational world today increasingly considers agricultural sustainability and gender equality as twin requirements for a bright future. In such a scenario, the rise of women agripreneurs from rural India, showing passion towards sustainable agribusinesses, can be considered as a positive and powerful transformation. Sharing their success stories, these women elevate their own voices and contribute to an agricultural ecosystem that has become more inclusive and inspiring for future generations. This chapter presents the journey of women agripreneurs living in various rural areas of Tamil Nadu. The chapter covers how these women agripreneurs are using various technologies in driving innovation, fostering sustainable practices to enhance their productivity. It explores social media platforms, namely YouTube channels, to locate women agripreneurs from remote locations in Tamil Nadu and uses the opportunity sample method for data collection. On receiving consent to participate in the research, nine women agripreneurs were interviewed who are very active in social media in developing their businesses, in the process creating a unique identity for themselves. First, the chapter focuses on understanding their experiences as agripreneurs and the passion, drive, and motivation that has ignited them so far. Second, their approach to sustainable practices in their agri-venture is covered. Third, the chapter presents the challenges and mitigation strategies adopted for sustainable growth. The chapter offers valuable insights for policy makers, stakeholders, and aspiring agripreneurs seeking to leverage technology for agricultural advancement and community empowerment. CAB International 2026. -
Microbial Pigments in Textile and Dyeing Industries
[No abstract available] -
Mitigating Abiotic Stress Using Nanoparticles and Involved Cellular Processes Studied by Multiple Omics
Crop yield is greatly impacted by abiotic stress, necessitating creative methods to increase plant resilience. In order to reduce these stressors, this chapter focusses on the possibilities of integrating multi-omics technology with nanoparticle applications. Studies suggest that nanoparticles improve plant tolerance by improving antioxidant activity and minimizing oxidative damage. In addition, multi-omics techniques (genomics, transcriptomics, proteomics, and metabolomics) offer a comprehensive understanding of the molecular processes that stress initiates. The creation of nanoparticles designed to specifically target particular stress-response pathways is made possible by these findings. The chapter's case studies demonstrate how tailored nanoparticles made with multi-omics data might improve crop performance in difficult conditions. The chapter highlights a promising avenue for developing targeted, sustainable solutions to increase crop resilience under abiotic stress conditions by combining nanotechnology with omics techniques, providing long-term sustainability. CAB International 2025. All rights reserved. -
Future Crop Designing: Antistress Capacities Gained by CRISPRmediated Releasing the Potential of Functional Genomes
Abiotic stresses, including temperature fluctuations, salinity, and drought, as well as biotic stresses such as viral, bacterial, and fungal infections, exert detrimental effects on plant growth and development, thereby significantly impeding overall plant productivity and crop yield. Traditionally, the sustainable mitigation of abiotic stress has been achieved through the breeding of tolerant cultivars; however, this process is characterized by its timeconsuming and labor-intensive nature, as well as its inherent lack of precision. Thus, there is a pressing need to adopt advanced genome technology to address these limitations and enhance the efficacy of stress-tolerance breeding efforts. This can be addressed by facilitating site-specific modifications of selected functional genomic elements, thus providing a potential avenue for introducing desired traits to combat adverse stress conditions. Among various genome engineering methodologies, CRISPR-Cas9 has emerged as the most promising genomeediting tool, attributed to its notable efficiency, precision, and rapidity. This study offers insights into the prospective trajectory of crop improvement through the advancement of crop enhancement strategies, employing CRISPR technology to enhance crop resilience against stress conditions by selectively modifying or activating specific functional genomes. CAB International 2025. All rights reserved. -
Integrating Advanced Metabolomics with Plant Functional Genomics
Metabolomics encompasses the entire suite of small-molecule compounds or metabolites synthesized by an or ganism, whereas functional genomics refers to the gene-level functioning of an organism. The genome of a plant will dictate its metabolome, but the link between the two omics data may not always be clearly visible or properly explored. This chapter delves into the integration of advanced metabolomics with plant functional genomics, highlighting its pivotal role in advancing our understanding of plant biology and its applications in agriculture. Metabolomics provides a comprehensive analysis of small molecules, bridging the gap between genotype and phenotype by elucidating the dynamic interactions within plant systems. Key techniques such as mass spectrom etry and nuclear magnetic resonance are explored, emphasizing their importance in high-throughput and high-resolution metabolite profiling. The chapter further discusses the synergy between metabolomics and other omics technologies, including genomics, transcriptomics, and proteomics, underscoring its significance in iden tifying gene functions and metabolic pathways linked to complex traits such as stress tolerance. Applications in plant breeding are also highlighted, showcasing how metabolomics can drive the development of crops with en hanced stress resilience, yield, and nutritional quality. The chapter concludes by emphasizing the transformative potential of this integrated approach in shaping future agricultural practices and improving food security. CAB International 2025. All rights reserved. -
Tourist Accommodation Regional Demand Investment and Supply (TARDIS) Model
Amid the competition and dynamic environment, the travel and tourism industry strives to survive by integrating the efforts of the multiple stakeholders involved. Destinations that employ competent strategies and innovative management practices emerge successfully in the global context with the application of the Tourist Accommodation Regional Demand Investment and Supply (TARDIS) model. Global destinations aim to promote regional development through a sustainable orientation with the adoption of certain best practices in the industry for ensuring long-term growth and success. But Regional Demand Investment and Supply Model stratifies the growth with increased cost and cost-efficacy at touristic destinations. This research assesses how the interests of stakeholders can be managed effectively through TARDIS business model, which integrates profitability with the impact on touristic destinations and their hospitality industry. This study analyzes key stakeholders including local communities, government bodies, private sector entities, and tourists, focusing on their roles, responsibilities, and collaborative potential in fostering a sustainable destination. By the integration of the principles of TARDIS approach into the tourism and hospitality sector, this research proposes the practical solutions to regional growth and stakeholder prioritization, mapping with value co-creation. CAB International 2026. -
Structure and mechanism of zinc-finger nucleases-mediated genome editing in plants
The chapter provides a comprehensive exploration of zinc-finger nucleases (ZFNs), intricate molecular tools designed to integrate genes of interest into target sites, thereby instigating both phenotypic and genotypic transformations. The focal point of this genetic manipulation lies in the activity of the FokI endonuclease enzyme, which, through the formation of homodimers, orchestrates precise cleavage at integration sites. Leveraging the wealth of genomic information across diverse organisms, the chapter elucidates the mechanistic underpinnings of ZFNs. A particular emphasis is placed on the modular assembly of ZFNs, unravelling the formation of ??? motifs and explicating the nuanced mechanisms governing their actions. The application of this methodology in plant engineering holds paramount significance, particularly in the realm of augmenting stress tolerance and nutritional value. The chapter systematically examines the gene of interest for various plants, including tobacco, soybean, Arabidopsis thaliana, rice, and wheat, elucidating the corresponding ZFN mechanisms. The amalgamation of sophisticated genetic tools with a detailed understanding of plant genomes presents a promising avenue for tailoring crops to meet diverse agricultural challenges. This work not only contributes to the fundamental understanding of ZFNs but also underscores their practical implications in advancing crop improvement strategies for sustainable agriculture. CAB International 2025. All rights reserved. -
The integration of the Internet of Things (IoT) in healthcare analytics: a transformative force
The healthcare landscape is undergoing a dynamic transformation driven by a confluence of factors. Patient expectations for personalized and accessible care are rising, fueled by rapid technological advancements and demographic shifts. To meet these demands, the healthcare sector is actively integrating emerging technologies such as big data analytics, electronic health records (EHRs), telemedicine, and remote patient monitoring (RPM) - all contributing to a value-based care model. This paradigm shift prioritizes preventative care and patient-centered approaches, leveraging technological innovations to fundamentally alter how healthcare is delivered and experienced. Advances in artificial intelligence (AI) and machine learning (ML) algorithms empower doctors with early disease diagnosis and prompt decision-making, potentially preventing illnesses before their onset. However, a relatively new development with significant transformative potential is the integration of the Internet of Things (IoT) into healthcare analytics systems. The core concept of IoT in healthcare revolves around facilitating seamless data sharing, networking, and communication between various entities. This encompasses patients, medical devices, sensors, and healthcare professionals, creating a fully interconnected ecosystem. However, the true power of IoT lies in its data generation. This data fuels sophisticated analytics systems that utilize ML algorithms, predictive modeling, and data visualization techniques to uncover hidden patterns and relationships within the vast information pool. These analytical methods empower healthcare professionals with early detection of abnormalities, accurate diagnoses, and robust disease monitoring capabilities. The resulting connectivity fostered by IoT translates into numerous benefits for the healthcare industry- increased operational efficiency, improved patient care, and advancements in medical research. This convergence of technologies is redefining how healthcare data is collected, exchanged, and analyzed, ultimately providing crucial insights to support clinical decision-making and evidence-based guidance for healthcare practitioners. This chapter delves into the multifaceted integration of IoT into healthcare analytics systems, highlighting its transformative potential for patient outcomes, data-driven decision-making, and healthcare delivery itself. We explore the diverse applications of IoT technology in healthcare analytics, encompassing population health management, remote diagnostics, real-time patient monitoring, and clinical research. Furthermore, we investigate the role of IoT gadgets such as wearables, sensors, and smart medical instruments in data collection. These devices capture a comprehensive picture of a patient's health through information on behavior, environmental factors, and physiological parameters, providing healthcare professionals with a holistic and continuous view. Additionally, the chapter addresses critical challenges associated with IoT integration, including data interoperability, security, and scalability. We examine how technologies like edge computing, blockchain, and cloud computing play a vital role in safeguarding patient privacy and ensuring data integrity. The Institution of Engineering and Technology and its licensors 2026. -
Graphene-metal oxide composite materials for supercapacitor applications
Recently, supercapacitors have emerged as one of the potential candidates for electrochemical energy storage applications owing to their excellent capacity properties, high power density, appreciable cyclic stability, and environmentally benign nature. Graphene has paved the way as a supercapacitor electrode because of its exceptional attributes, including its conductivity, and mechanical and electrical properties. The efficiency of supercapacitors has been significantly impeded by the aggregation of graphene layers brought on by the considerable van der Waals attractions. Numerous methods have been developed to get over the limitations and make graphene a prime choice for supercapacitors. It is anticipated that combining graphene with metal oxides will improve its capacitive properties due to the mutual contribution of the individual components. In this chapter, various synthetic methods for graphene-metal oxide-based binary and ternary composites, as well as their application as supercapacitor electrodes, are explained in detail. The current research directions and future scope of graphene-metal oxide-based composites for supercapacitor application are also included. The Royal Society of Chemistry 2025. -
Graphene-metal oxide composites for electrochemical energy storage and conversion
The development of clean and renewable alternative energy sources is essential due to the rising energy consumption. Advancements in energy conversion and storage technologies, such as fuel cells, batteries, and solar cells, are currently the subject of active research. The unique structure and properties of two-dimensional graphene materials are explored in developing energy devices. The efficient utilization of graphene's enormous specific surface area and exceptional electrical, chemical, and mechanical properties still remains a challenge to researchers due to the agglomeration of its layers. The introduction of metal oxides into these 2D layers helps to enhance their structural and electrochemical stability, which helps in the production of energy storage devices. This chapter discusses the synthesis protocols, tunable properties, as a function of size and shape, and characterization tools. It also provides a deeper understanding of graphene-metal oxide composites in various energy storage devices, highlighting the importance of the synergistic effects between graphene and metal oxides. The chapter concludes with the prospects and potential of graphene-metal oxide composites for energy storage applications. The Royal Society of Chemistry 2025. -
Basalt Fiber Composites in High-Performance Sports Equipment
This chapter explores the revolutionary role of basalt fiber composites in the development and production of high-performance sports equipment. Basalt fibers, produced from naturally occurring volcanic rocks, are a strong alternative to conventional reinforcement materials such as carbon and glass fibers. Their outstanding resistance to heat, moisture, and chemicals is complemented by their remarkable mechanical properties, including high tensile strength, impact resistance, and good vibration damping. These attributes make basalt fiber composites particularly suited for sports equipment subjected to dynamic loads and harsh environments, such as bicycles, tennis rackets, skis, snowboards, and surfboards. The chapter explores the most modern scientific and commercial advancements, demonstrating how basalt fiber composites can improve user comfort, reduce weight, and increase performance, durability, and safety. Illustrative examples compare basalt fiber composites with traditional materials to demonstrate their cost-effectiveness, reduced environmental impact, and successful applications. The manufacturing processes and potential challenges in adopting basalt fibers are discussed in detail. Finally, the chapter addresses emerging trends and prospects, positioning basalt fiber composites as a key material in the evolution of sustainable, high-performance sports equipment. This comprehensive overview provides valuable insights for researchers, manufacturers, and sports technology innovators. 2026 American Chemical Society -
A comparative evaluation of machine learning and deep learning models across diverse datasets for early detection of lung cancer
Lung cancer is among the most fatal types of cancer, accounting for millions of fatalities globally. The capacity for its early detection has the potential to greatly enhance the outcome of treatments, and in recent times, machine learning (ML) and deep learning (DL) algorithms have emerged as mighty resources in aiding radiologists and doctors. This article describes a comparison study of research articles in which they employed different ML and DL models in lung cancer detection on a wide range of datasets. The analysis establishes that the variety, quality, and source of the dataset are central to determining how reliable reported model performance is. Reproducibility has been made possible with public datasets such as LIDC-IDRI, NSCLC, and Kaggle datasets, whereas private clinical datasets typically lead to improved accuracy since they consist of high-quality curated annotations. Subsequent research has shown that DL models, especially state-of-the-art architectures such as convolutional neural networks (CNNs) and EfficientNet-B3, are well-suited to image classification tasks and consistently outperform classical ML models when large, well-balanced datasets are available. Hybrid approaches that blend CNN-based feature learning with traditional classifiers like support vector machines have also proven highly promising, particularly when applied to overcome challenges such as small sample sizes and noisy images. Directions for future work point toward the integration of standardized, multicenter datasets, explainable AI models, and multimodal learning techniques to reach reliable in-clinic deployment. 2026 Elsevier Inc. All rights reserved. -
Chatbots in health care: AI-based personalization and EHR integration in patientdoctor communication
The artificial intelligence (AI)-driven chatbots in healthcare integration revolutionizes patientprovider interactions for real-time support, communication streamline, and patient engagement. These chatbots connected to natural language processing (NLP) and machine learning provide medical queries resolution, chronic condition management, and scheduling appointments. Despite the advancements, there are gaps remain in the chatbot personalization interactions and Electronic Health Records (EHR) seamless integration. Personalization is crucial for satisfied patient and medical advice. EHR integration enables context-aware responses, error reduction, and better healthcare outcomes. This study effectiveness fosters the evaluation of AI-driven chatbots in healthcare communication personalization and potential benefits examination and EHR integration challenges. Using a mixed-methods approach includes sentiment analysis for patient satisfaction sentiments understanding, thematic analysis for key themes and findings from Patient Message, regression analysis for personalization, EHR integration, and patient outcomes understanding, and structural equation modeling (SEM) highlights the personalization and EHR integration impact on patient satisfaction, engagement, and trust in chatbot technology. The findings reinforcing the healthcare providers need to adopt AI-driven solutions and personalized communication priorities and seamless data integration for patient experience improvement and overall healthcare efficiency. 2026 Elsevier Inc. All rights reserved. -
Humancomputer interaction for cognitive, emotional and learning well-being
HumanComputer Interaction (HCI) has revolutionized the way humans engage with technology, shaping cognitive, emotional, and learning experiences. This chapter explores HCI's impact on well-being, focusing on cognitive load reduction, emotional stability, and adaptive learning. HCI technologies such as AI-driven decision support, emotion-aware systems, and personalized education platforms enhance user engagement by fostering efficiency and well-being. Cognitive well-being benefits from AI-powered cognitive tools that improve memory, decision-making, and mental agility. Emotional well-being is facilitated by affect-sensitive systems, digital therapeutics, and social HCI that reduce stress and increase emotional engagement. Adaptive learning systems, gamification, and assistive technologies also ensure inclusive education by making learning more personalized and accessible to special needs students. Future HCI trends involve neuroadaptive interfaces, wearable-integrated health technology, and AI-based mental health solutions that all improve personalization and user experience. Yet ethical issues, such as data privacy, digital addiction, and algorithmic biases, need to be addressed in order to maintain responsible technology utilization. Achieving balance between digital and physical interactions is important in the preservation of general well-being. The future of HCI, where it becomes empathetic, adaptive, and ethical interfaces, is a future that sees technology enhance not just efficiency but cognitive, emotional, and learning well-being. By making ethical AI design and user-centric experiences a priority, HCI will continue to unleash human potential and develop durable, well-being-oriented technological solutions. 2026 Elsevier Inc. All rights reserved. -
Transforming sectors: VR and AR applications in healthcare, education, and social roboticsA case study
Virtual reality (VR) and augmented reality (AR) are transforming multiple fields, including healthcare, education, social robotics, and mental health. In medicine, they enhance surgical precision, medical training, rehabilitation, and remote consultations by providing immersive and real-time data-driven environments. Education benefits from interactive and skill-based learning, making complex subjects more engaging and accessible. In social robotics, AR/VR improves human-robot interactions, emotional intelligence in AI assistants, and robotic telepresence. Mental health applications include VR-based exposure therapy, stress management, and PTSD treatment through controlled, immersive simulations. Industrial training leverages VR for hazardous work simulations and AR for real-time assistance, increasing workplace safety and efficiency. In addition, AR supports precision medicine by overlaying real-time visuals during surgeries for improved accuracy. The integration of AI, haptic feedback, and 5G is expected to further advance these technologies. However, ethical concerns regarding data privacy and prolonged VR exposure require careful regulation. Overall, VR and AR continue to revolutionize various sectors, enhancing engagement, efficiency, and personalization in professional and therapeutic settings. 2026 Elsevier Inc. All rights reserved. -
Data-driven education: Leveraging big data, AI, and machine learning for smarter learning environments
Big Data, artificial intelligence (AI), and machine learning are transforming education with self-paced learning, precise insights, and automated decisions. The effects these technologies have on education are transformative, especially when it comes to improving student achievement, advancing administrative operations, and personalizing the learning experience, as stated in this chapter. Big Data collects and analyzes immense volumes of data, while AI powers automation, makes predictions, automates evaluations, and enables the possibility of adaptive learning. Chatbots, recommendation engines, and tutoring systems enhance student-focused digital education. Still, integrating these technologies comes with challenges such as privacy and ethical issues, algorithm discrimination, and data security concerns. Some of the new trends are explainable AI (XAI) for ethical decision-making, blockchain and federated learning for privacy-preserving analytic systems, and verifiable credentials. In addition, XR, AI-based virtual laboratories, and neurosymbolic AI will have great consequences on the future learning environment. AI in education offers scalability and inclusivity but demands ethical regulation, governance, and resource management. This chapter recommends sustained effort in research, policy changes, and ethical integration of AI for the best possible use thereof. The education sector, by incorporating human-centered AI approaches, can create a just, sustainable future of learning that is accessible to all students around the world. 2026 Elsevier Inc. All rights reserved. -
Harnessing quorum sensing for disease management
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. -
Quorum quenching for biocontrol of plant diseases
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. -
Artificial intelligence-powered talent acquisition and onboarding
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.