Tarun Kumar Vashishth

Digital transformation in microbiology laboratories is changing the generation, processing, and application of microbial data for research, diagnostics, and other industrial purposes. This chapter describes how IoT, Automation and digital workflow technologies have been integrated into this field (e.g. serverless computing platforms, electronic lab notebooks, wireless connectivity, connected sensors, automated instruments, cyber-physical infrastructure (CPI), etc.). It details how these technologies increase the accuracy, repeatability and efficiency of laboratory data and decrease human error and turnaround time. Additionally, the book details different system architectures, data interoperability, and cloud-based platforms that allow for seamless data acquisition and analysis throughout the microbiological workflow. The chapter examines challenges related to data security, standardisation, scalability, and regulatory compliance.

The advent of quantum computing introduces numerous new and severe challenges that arise from quantum computing's ability to break virtually any and all existing cryptography security infrastructures that secure modern digital communications systems. For example, Shor's algorithm and Grover's algorithm are two quantum algorithms that represent significant threats to the computational assumptions relied upon for securing most deployed cryptographic systems, therefore requiring new architectures that are resistant to the theoretical computational breaking of these architectures security through quantum computing techniques. This chapter explores the integration of post-quantum (i.e., non-quantum breaking) cryptographic mechanisms based on the theoretical foundation of quantum information theory, entropy, and computational complexity, and proposes a cyber-security framework incorporating that integration.

Digital transformation has created an opportunity for accountants and financial managers to create new efficiencies, add transparency to their work, and develop better strategies for decision-making. This chapter looks at how artificial intelligence, automation, big data analytics, and cloud-based financial management are changing how accountants use technology, as well as how those technologies are changing the practices and processes that accountants follow when doing their jobs. It also describes how the digital transformation of Finance will cause a dramatic shift in the way financial services are delivered to customers from being reactive to value-driven. By integrating intelligent systems into forecasting, performance management, risk assessment, and real-time reporting functions, the ability to make informed decisions is enhanced. The chapter identifies the organizational, regulatory, and ethical challenges that digital finance poses, including data privacy, cybersecurity, and governance risks.

Artificial Intelligence (AI) is the latest and best paradigm shift for library systems. It translates traditional information retrieval systems into user centered smart self-adjusting systems. This chapter discusses the implications of AI driven hardware, software and platforms processes or systems that can deliver higher efficiencies and broaden accessibility and interactivity of the modern library systems. The facets of AI influenced systems that offer better service delivery to researchers, digital cataloguing, smart retrieval, and optimum user utilization of resources are discussed while maintaining user cane protection and morally responsible applications of AI. The chapter examines personalized AI, systems of knowledge organization for library systems are also assessed along with associated technology including AR and VR as contributory systems in the scenario to build the next generation of smart libraries.

The rapid integration of Artificial Intelligence (AI) into human resource management (HRM) is fundamentally reshaping how careers are structured, managed, and experienced across the globe. Traditional linear career models are increasingly replaced by dynamic, boundaryless, and skills-oriented career trajectories, driven by algorithmic decision-making, digital platforms, and data-intensive HR practices. This chapter examines the transformative role of AI in HRM and its implications for contemporary career management in diverse organisational and national contexts. Particular attention is given to critical challenges, including algorithmic bias, ethical concerns, transparency, employee trust, and unequal access to AI-driven career opportunities across regions and demographic groups. The chapter further discusses how organisations can strategically align AI-enabled HRM systems with sustainable career principles to support adaptability, inclusion, and long-term workforce resilience.

The rapid evolution of cloud computing, edge infrastructures, and emerging quantum resources demands intelligent orchestration to ensure performance, scalability, security, and sustainability. This chapter explores AI-driven orchestration across the cloud continuum, integrating edge devices, multi-cloud environments, and quantum-ready services into cohesive, autonomous workflows. It highlights techniques such as reinforcement learning for predictive resource placement, AI-based autoscaling, and hybrid orchestration strategies that dynamically adapt to workload demands and system heterogeneity. The chapter also examines critical considerations for privacy-preserving federated learning, secure data aggregation, zero-trust security models, and blockchain-backed provenance to maintain data integrity and trust across distributed infrastructures. Practical deployment scenarios and case studies illustrate how AI-native orchestration improves latency-aware scheduling, energy efficiency, and compliance with carbon-aware and sustainability goals.

Cybersecurity is one of the most pressing concerns with regard to autonomous systems' ever-increasing adoption across multiple sectors, including transportation, health care, and smart city developments. The aim of this chapter is to focus on the various methods of securing autonomous systems through artificial intelligence (AI)-powered intrusion detection systems (IDS) and privacy-preserving mechanisms. For example, this chapter will explore the use of machine learning for anomaly detection as well as secure federated learning and blockchain technology to enhance the integrity of data in autonomous systems. Furthermore, it will provide an overview of the use of adversarially attacking AI models and provide recommendations for reducing cyber risk. Utilizing AI-based security frameworks, autonomous systems can identify threats and respond to them almost instantly, while ensuring user privacy. Finally, this chapter addresses the regulatory hurdles surrounding autonomous technology, as well as potential areas for future research related to security in autonomous systems.

Recent leaps in healthcare technology have given rise to Virtual Health Assistants (VHAs) and chatbots, reshaping the way patients interact with clinics and hospitals. These VHAs, built on natural language processing and conversational AI, now handle routine tasks such as filing paperwork, sorting symptoms, sending pill reminders, and offering light mental-health tips. The chapter reviews the design sets-classic rule-based tools, retrieval systems, and full-generative models-that let these assistants speak in near-human language. It also considers how tightly they connect to electronic health records and examines worries about tech integration, patient privacy, and rules like HIPAA. Real-world rollouts are described, together with measures of their impact on patient comfort, access to care, and the daily load carried by doctors and nurses.

Autonomous systems' growing use across various industries, including transportation, healthcare, and smart city development, has created a serious concern about cyber security. This chapter will provide a brief overview of some of the ways to secure autonomous systems through AI-based intrusion detection systems (IDS) and privacy-preserving mechanisms. In particular, it will identify the use of machine learning to detect anomalies and secure federated learning and blockchain technology to provide integrity of data in autonomous systems. It also provides an overview of how adversarially attacking AI models can be used to reduce cyber risk. Once security frameworks using AI are established, autonomous systems will be able to detect threats and quickly respond, while also preserving user privacy. Finally, this chapter will address regulatory issues regarding autonomous technology and potential future research issues related to the security of autonomous systems.