Browsing by Type "book-chapter"

The media landscape is experiencing seismic shifts in the wake of economic crises, with certain media entities, especially those embracing digital transformation and benefiting the deep-pocketed investors, managing to survive and thrive. Conversely, independent journalism, particularly beyond the Western world, faces acute financial challenges. In today's globalized world, the media significantly impacts how we receive and process information. Corporate media has an agenda that determines what information is shared with the public because there is a strong affinity between the media and political organizations. The book chapter offers an analysis of how governments strategically employ funding mechanisms to exert control over the media. It scrutinizes the trends whereby governments allocate financial resources to media outlets that align with their own interests and objectives, be they political, ideological, or commercial, while sidelining independent journalism. It has been reported through different studies that the political and media nexus is damaging the free press in a systematic way. The book chapter primarily focused on an in-depth analysis of provisions pertaining to press freedom in diverse jurisdictions. The book chapter further takes into account on how globally the political parties are controlling media, both directly and indirectly, to fulfill their agenda. In the context of India, the media's role as a potent instrument in shaping public opinion and fostering democracy is under scrutiny. Freedom of the press is the fulcrum upon which the scales of truth and accountability delicately balance, and the guardian of societal enlightenment. The constraints placed upon freedom of the press aren't confined to India alone; they extend to countries like China and Russia, where the majority of media outlets operate under governmental control. In stark contrast, the United Kingdom, lacking a formal written provision for press freedom, excels in upholding this fundamental liberty. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

The development of wireless communication advances toward 6G, enabling high-speed data sharing and robust networking. Future 6G networks will use terahertz (THz) bands for continuous performance. AI, quantum communication, blockchain, and digital twins will optimize efficiency and security. 6G will modernize digital infrastructure, supporting smart cities, self-driving transport, XR programs, and IoE. However, challenges include high costs, radio wave safety, and energy efficiency. Research must focus on THz-based hardware, AI-network orchestration, and edge computing. Key directions include AI-driven networks, satellite-ground systems, and eco-friendly protocols. Power-saving solutions like IRS and RIM enhance signal propagation. Quantum communication and blockchain ensure security and reliability. Global research will drive 6G as a foundation for intelligent, hyper-connected systems, uniting demographics in industrial and social transformation. This study explores 6G advancements, future wireless techniques, and anticipated solutions.

This chapter reframes educational cybersecurity as a neurocognitively orchestrated socio-technical control regime rather than a peripheral IT safeguard. It integrates Cognitive Load Theory, dual-process reasoning, predictive processing, and neuroergonomics with affective computing, behavioral biometrics, and neuro-symbolic AI to explain how workload, vigilance, affect, and habit loops shape susceptibility to phishing, extortion, and configuration drift in digital learning ecologies. Brain-inspired anomaly detection, continuous authentication, and explainable risk analytics are embedded within human-in-the-loop governance, role-specific competency frameworks, and equity centric policy architectures. A staged implementation roadmap aligns identity fabrics, telemetry pipelines, federated learning, and impact audits with cost risk modelling and data protection mandates, yielding a globally relevant blueprint for neuro-intelligent, ethically constrained cyber defenses that preserve autonomy, inclusion, and instructional continuity.

A revolutionary approach to guaranteeing the security and longevity of critical infrastructures is the utilization of AI and quantum networks for real-time structural health monitoring (SHM). Machine learning and deep learning are artificial intelligence techniques that facilitate the real-time analysis of enormous quantities of data produced by sensors that are integrated into buildings. Consequently, structural anomalies and malfunctions can be identified and predicted with unparalleled accuracy. Quantum networks substantially enhance the efficiency and security of Structural Health Monitoring (SHM) systems by guaranteeing a secure connection and offering unparalleled computational capabilities. We can create structural health monitoring frameworks that are more adaptable to dynamic conditions, such as those caused by deteriorating infrastructure and increasing urbanization, by combining quantum technologies with artificial intelligence.

Computer vision is a multidisciplinary discipline that allows computers to interpret and comprehend the visual environment. It is generally called a subfield of artificial intelligence (AI). To provide numerical and figurative information, this field broadly processes, analyzes, and comprehends many sorts of pictures, as well as high-dimensional data from the actual world. Structure identification, which draws out the characteristics of pictures, is at the core of computer vision. The field of computer vision gathers and analyzes sensory data to enable an understanding of three-dimensional environments through 2D mapping. Recent technical and application advancements are addressed. Investigated and updated are the places where many sorts of approaches originated. The growth and development of knowledge may be advantageous to academic scholars. The applications of identification, visualization, semantic division, and picture restoration are explained in four essential scenarios. Finally, a description of the phases that have evolved over the last ten years is given, focusing on current trends in research for the subsequent work.

The incorporation of Artificial Intelligence (also known as AI) into workforce management is transforming traditional employee engagement tactics by providing creative ways to increase worker participation. This chapter investigates AI-enhanced worker involvement techniques, specifically how AI technology might be used to boost employee engagement, contentment, and productivity. Organisations may build increasingly flexible and adaptable work environments by implementing AI-driven analytics, customised feedback systems as a whole and intelligent automation. This chapter looks at how AI can provide real-time insights on employee behaviours and preferences, allowing for tailored interventions that promote a more engaged and inspired workforce. Furthermore, the ethical implications and obstacles of implementing AI in engagement among workers are examined, with a focus on openness and fairness. AI has already given some meaning at workforce, as: Automation is a key function of artificial intelligence. It handles tedious and time-consuming activities, freeing free human workers to concentrate on highly innovative and tactical responsibilities. This not only increases productivity but also lowers the likelihood of errors. AI analyses large databases and gives data-driven insights, allowing for better decision-making. This assistance includes anything from projecting trends to optimising resource allocation. Organisations may make more informed decisions by enhancing their human intelligence by AI-driven insights. This chapter highlights the usefulness of AI-enhanced tactics in promoting worker involvement and offers strategies for embedding AI into organisational processes using a variety of secondary resources, including book chapters, academic articles, and empirical data. The findings show that when used correctly, AI may considerably increase workforce engagement, resulting in increased organisational efficiency and employee well-being.

This chapter interrogates the strategic integration of artificial intelligence into environmental education as a transformative mechanism for invasive alien species prevention, detection, and control. It synthesises ecological urgency, technological acceleration, and pedagogical reform to propose governance frameworks that embed AI literacy, ethical reasoning, and biodiversity knowledge across educational systems. Drawing on global policy contexts and institutional innovations, the discussion illustrates how AI-enabled curricula can generate actionable data, strengthen conservation decision-making, and cultivate ecologically responsible citizenship. The chapter advances a vision of AI not as a detached tool but as a co-evolving partner in biotic stewardship, with education functioning as a conduit for aligning technological potential with measurable biodiversity outcomes. By linking algorithmic capacity to participatory ecological governance, it positions AI-enabled education as a critical vector for ecological resilience and long-term socio-environmental sustainability.

This chapter interrogates how artificial intelligence reshapes transnational education by reconfiguring pedagogy, faculty development, and governance across borders. It synthesizes learning sciences, intercultural theory, and critical algorithm studies to construct a multilevel framework that links macro policy, meso institutional design, and micro classroom practice. The analysis profiles adaptive tutoring, analytics, translation, and simulation while diagnosing risks of surveillance, bias, context collapse, and epistemic homogenization. It advances a program for AI literate faculty built on technical, critical, and pedagogical fluencies, and specifies equity strategies for disability, gender, language, and LGBTQ inclusion. The chapter proposes actionable mechanisms including participatory procurement, model transparency, low bandwidth pathways, human in the loop assessment, and cross-national accreditation for microcredentials. It concludes by reframing AI as public infrastructure governed by ethical reflexivity, contestability, and cultural responsiveness.

The advancement of artificial intelligence has made robust cybersecurity essential. As governments implement various policies to protect citizens, the utilization of AI by governmental agencies has significantly increased. Despite this, there is a significant gap between theoretical knowledge of AI and cybersecurity as separate fields and their practical integration. The challenge lies in the rapid evolution of both AI and cybersecurity, which often outpaces the legislative process, rendering many regulatory efforts outdated before they are fully realized. Due to the lack of dedicated laws for these dynamic areas, achieving optimal results is difficult. Therefore, it is crucial to explore how AI can be leveraged to improve cybersecurity. By using appropriate safeguards, AI can autonomously protect against threats like viruses, misuse, and hacking attacks. This paper examines the role of technology in AI and cybersecurity and investigates how AI can optimize cybersecurity, the opportunities & challenges of AI in cybersecurity, regulatory bodies, and strategies to merge these fields.

Travelers of tropical or subtropical regions in particular are at higher risk of developing heat-related illnesses such as heat stroke. Heat stroke occurs when the body temperature reaches high levels and can cause damage to the body and nervous system. There are many factors that contribute to the risk. Internal factors include age, previous medical treatment, dehydration, physical fatigue, and the use of certain medications. On the other hand, high ambient temperature, high humidity, prolonged sun exposure, wearing inappropriate clothing, engaging in strenuous activities, inadequate rest time, lack of cooling mechanisms, and high-pressure relationships are other important factors that make it difficult to survive in high temperatures. When heat stroke occurs, immediate cooling measures should be taken, and then medical attention should be sought to ensure appropriate treatment. The findings highlight the serious effects of dehydration, inappropriate clothing choices, and intense exercise in hot weather.

Disaster relief management can be seen as a supply chain problem where the system should ensure effective design, deployment, and process management for dealing with present and future events. This also should be able to manage, coordinate, and interact with other processes of the same or different supply chain models. Disasters happen in a dynamic environment. There are numerous bottlenecks in disbursing adequate reliefs such as poor infrastructure or uncertainty about the volume of relief measures required. The disaster response model should be able to collaborate, coordinate, and show equality in distributing the relief. There is a need for a reliable and efficient system for facilitating the overall process of relief distribution from interactions to delivery. Blockchain technologies are being adopted in recent times as they can enable transparency. Security and real-time data transfer through intelligent contracts. The proposed chapter analyzes the use of blockchain technology on resilience in disaster management. A model combining IoT technology and an intelligent supply network for deploying a network-aided simulation was proposed. The results reveal that the proposed model performs well in disaster relief supply networks through intelligent contracts and collaborations. The experiments prove that the proposed methods are efficient in terms of the time taken to process a transaction in disaster supply when compared with other methods.

This study aims to explore the effectiveness of climate resilience measures in mitigating climate-related adversities, with a particular focus on the role of blue-green infrastructure. It investigates how blue-green infrastructure contributes to climate resilience and assesses its success in urban development both in India and globally. The study also examines the challenges and opportunities associated with implementing blue-green infrastructure in the Indian context, identifying key barriers and enablers for its adoption. The study highlights the alignment of blue-green infrastructure with Sustainable Development Goals, particularly those related to climate action and sustainable urbanization, offering insights into how such infrastructure can support broader sustainability objectives. Through these analyses, the research provides a comprehensive understanding of the potential of blue-green infrastructure as a climate-resilient solution for urban development.

This chapter conceptualizes the “braided archive” as a hybridized epistemic framework that entwines informal archival modalities including personal, community-driven, digital, and ephemeral, with institutionalised formal repositories to reconfigure pedagogical praxis. Anchored in experiential learning theory, constructivist paradigms, postcolonial epistemologies, and critical archival studies, it interrogates the ontological and epistemological underpinnings of archival legitimacy within educational ecologies. The chapter delineates typological taxonomies, methodological scaffolds, and didactic applications that operationalise informal archives as legitimate cognitive artefacts. By foregrounding counter-hegemonic narratives and subaltern epistemes, the braided archive functions as an agent of epistemic justice and curricular decolonisation. Empirical illustrations and transnational exemplars elucidate its efficacy in augmenting learner agency, enhancing cognitive engagement, and nurturing dialogic, multimodal knowledge construction.

This chapter explores the integration of neuroscience into educational practice through the lens of neuropedagogy, to enhance student engagement and learning outcomes. It presents foundational principles such as neuroplasticity, synaptic plasticity, and cognitive load theory, and connects them with practical strategies targeting attention, memory, executive function, and emotional regulation. Applications across early childhood, secondary, higher, and inclusive education are critically examined, with attention to cultural and developmental contexts. The chapter addresses infrastructural, ethical, and epistemic challenges, cautioning against neuromyths, technological overreach, and inequitable access. It concludes with forward-looking policy recommendations grounded in responsible innovation, critical teacher education, equity-oriented funding, and pluralistic research. Positioning neuropedagogy as a transformative and context-sensitive framework, the chapter emphasizes its potential to relook education as both brain-aligned and socially just.

This chapter elucidates the construction of applied models from observational research in education as a transdisciplinary, praxis-oriented methodology for capturing and transforming complex pedagogical ecologies. Integrating theories from constructivism, experiential learning, systems thinking, and situated cognition, it outlines the methodological arc from data collection to model abstraction and validation. Through diverse case illustrations, the chapter demonstrates how applied models function as diagnostic, design, and governance tools across classroom, institutional, and policy levels. Emphasizing ethical reflexivity, epistemic justice, and methodological transparency, it positions applied models as dynamic epistemic instruments for educational transformation in volatile, diverse, and inequitable learning systems.

The chapter reviews consumer behavior fundamentals such purchasing decision criteria, quantification, and analysis. Next, it discusses decision trees, ensemble approaches, neural networks, regression analysis, and support vector machines for customer behavior prediction. Each algorithm's technique, merits, and weaknesses are examined. We then show how these algorithms can predict purchase intent, customer turnover, life cycle value, and other consumer behaviors. This setting presents challenges when using machine learning models, including data quality, model interpretability, and the ethics of using consumer data. The industry's current and future innovations, such as AI-driven personalization and deep learning to understand client behavior, are also highlighted. This chapter explores the intersection of machine learning and consumer behavior analysis to help researchers, data scientists, and business professionals acquire more accurate and valuable consumer insights.

This chapter explores the pivotal yet underexamined role of instructional materials design in reducing foreign language anxiety (FLA) in second language acquisition (SLA). Drawing on interdisciplinary frameworks, such as, affective filter hypothesis, cognitive load theory, growth mindset, self-determination theory, sociocultural theory, and universal design for learning, the chapter outlines how content, layout, and emotional cues in learning materials can mitigate anxiety and enhance learner confidence. It examines strategies such as scaffolded task design, culturally responsive content, visual supports, emotional regulation techniques, and gamified activities. The chapter also reviews global empirical studies and case-based evidence to demonstrate the impact of anxiety-sensitive materials. By shifting the locus of intervention from teacher behaviour to instructional content, the chapter offers practical, research-informed recommendations for educators, curriculum designers, and EdTech developers aiming to create inclusive, emotionally supportive language learning environments.

AI biases can induce existing imbalances and affect the most affected populations more severely. The study underlines the need to introduce the imperative of transparency and explainability of AI systems. The fact that many algorithmic systems are correspondence opaque raises questions about how such decisions are made and who is accountable when using artificial intelligence, which leads to wrongful arrest or unfair sentencing. The research calls for effective legislative frameworks that would protect constitutional entitlement due to the widespread use of AI systems. The criminal justice system can effectively embrace AI and avoid the risk of infringing individual rights to make technology serve justice rather than inimical or detrimental to justice and basic human rights.

This chapter reframes digital literacy as a justiciable facet of the right to education. Blending learning science and ethics, it defines competencies that unite critical judgment, data literacy, multimodal authorship, computational reasoning, safety, and civic responsibility. Using the A4A lens of availability, accessibility, acceptability, and adaptability, it links pedagogy to enablers such as teacher capacity, universal design, trustworthy analytics, and multilingual content. Comparative examples from Finland, Singapore, India, and sub-Saharan Africa show divergent governance and equity gaps. The analysis examines datafication, algorithmic bias, and surveillance risk while assessing the affordances of AI and immersive media. A pragmatic framework specifies design levers, learning evidence, and policy tools that align finance and accountability. Digital literacy is advanced as public infrastructure for democratic participation and dignified work across the life course.

As the world advances towards the integration of technology, more emphasis is placed on the relationship between cybersecurity and mental health. In this chapter, the authors discuss the impact of cybersecurity on the preservation of mental health, as well as the processes of safeguarding the related rights upon pointing out the directions of protection against the use of the internet as a weapon, cyber bullying, and harassment. In this chapter, they explore the effects of cybersecurity threats on mental health and the topic of safe online space. In order to illustrate the best current practices and new technologies protecting users from mental health information leakage, they present the most effective cybersecurity measures. Proof of the successful application of these measures is provided through relevant case studies and examples from practice. In that context, this chapter offers practical advice and directions for organisational policymakers, mental health experts, and tech gurus in the creation and deployment of efficiency cybersecurity structures that embrace and support mental health.