The technological developments in the Internet of Things (IoT), data science, artificial intelligence, wearable sensors, remote monitoring, decision support systems, fog, and edge systems have transformed digital healthcare. Especially after the pandemic, there has been a rapid transformation of healthcare infrastructure from a conventional to a digital approach. Now, specifically, technologies such as the Internet of Things play a vital role in the transformation of the healthcare system. In this paper, an effort has been made to encompass the transformation of healthcare with a focus on the Internet of Medical Things (IoMT). In particular, it provides a detailed overview of the Internet of Medical Things whilst discussing the design goals and challenges, the resource constraints and limitations of the complex healthcare systems. The paper also provides a detailed account of the research initiatives as well as off-the-shelf wireless motes, internet-enabled sensors and open-source platforms. A thorough account of the next-generation digital healthcare technologies and future research opportunities is provided. This work not only covers the state-of-the-art but also offers critical insight into the digital healthcare challenges. The work attempts to summarise the extensive literature in the domain and present a new perspective on the internet of medical things, affiliate technologies and their role in healthcare.

A person-centered approach was employed to investigate how students' evaluation of perceived teacher utility value messages, i.e., fear appeals, as a threat and as a challenge, combined within individuals and how these combinations related to student engagement. Two studies were conducted with students in their final two years of secondary education. Empirically distinct clusters emerged at two time points in the academic year. Evaluating the message in the fear appeal at a higher level of challenge than threat was beneficial. Unexpectedly, high threat was associated with high engagement, as long as high challenge was also present, however, this combination was also related to high emotional disaffection. Moderate threat combined with moderate challenge had the most detrimental relationship with student engagement. Educational interventions should aim to increase the likelihood of a challenge evaluation.

This chapter investigates the profound impact of fear of failure on academic motivation and engagement among higher education students. Despite its significance, fear of failure remains underexplored in educational literature. Using self-report instruments, data were collected and analysed through structural equation modelling. Findings reveal that fear of failure significantly influences academic motivation and engagement, exhibiting both direct and indirect effects. While fear negatively affects performance and participation, it can indirectly enhance these aspects for extrinsically motivated students, underscoring the importance of extrinsic motivation. Cluster analysis identified distinct student profiles based on their levels of fear and motivation, establishing a positive correlation between fear of failure and extrinsic motivation. The chapter concludes with practical implications, emphasising the necessity for educators to recognise and address the complexities surrounding fear of failure in the learning environment, ultimately fostering a more supportive academic experience.

The rapid spread of multimodal fake news (FN) on Online Social Networks (OSNs) threatens digital information ecosystems, particularly in low-resource languages. Existing multimodal fake news detection (FND) methods are largely limited to high-resource settings, restricting their global applicability. We propose an M&M-RAG, a Multilingual & Multimodal Retrieval-Augmented Generation framework, that leverages Large Vision-Language Models (LVLMs) and Large Language Models (LLMs) to verify news claims across English, Chinese and Urdu. M&M-RAG integrates real-time multilingual evidence retrieval, language-aware prompting, and cross-modal reasoning for fact verification. We further propose Multi-Ax-to-Grind Urdu, the first large-scale, multi-domain multimodal benchmark for FND in Urdu. Experiments on typologically diverse monolingual multimodal datasets demonstrate that M&M-RAG achieves state-of-the-art (SOTA) performance, with 94.6% accuracy and 94.2% F1 score, surpassing models such as SpotFake, MPFN, MMCFND, and Semi-FND. The proposed framework remains robust in zero-shot and cross-lingual scenarios under frozen-model inference without task-specific fine-tuning. The results underscore the scalability and interpretability of LVLM-based approaches for combating multimodal misinformation, particularly in under-represented and typologically diverse languages.

This chapter explores the shift from traditional statistical analysis to advanced deep learning in quantitative decision-making. It argues for integrating classical paradigms—such as descriptive and inferential statistics conducted in tools like SPSS—with modern computational techniques to address complex, large-scale challenges. The discussion highlights the pivotal role of big data platforms, including Hadoop and Spark, in enabling real-time analytics and distinguishes core machine learning algorithms from deep neural architectures designed for unstructured data. Interdisciplinary case studies in healthcare, finance, and engineering demonstrate the practical synergy of these approaches. By embracing multi-paradigm strategies, the chapter offers insights into building transparent, scalable, and effective decision-support systems across diverse domains. It concludes by addressing challenges of ethics and governance and by pointing to future directions such as explainable AI and federated learning, emphasising that robust decision-making depends on a hybridised analytical framework.

Teachers often employ various techniques to motivate and engage students. They may choose to use positive fear appeals as a motivational tactic to stimulate fear that will result in students making greater efforts to avoid failure or they may employ extrinsic incentives to engage students. This study examined the modulatory role of extrinsic motivation, as a differentiated construct, in the relationship between fear of failure and student engagement. Data were collected using self-reported instruments and analysed using moderation and mediation analyses. Extending the motivation literature, this study, sheds new light on the positive modulatory role of extrinsic motivation regulations in the relationship between fear of failure and student engagement. Contributions to practice are implied; there is a need for educators to understand the role of self-imposed and self-endorsed behaviours in influencing engagement among students with high and low fear of failure. Comprehending the complexity of the learning environment in light of the complex nature of human behaviours is considered essential to improving teaching and learning.