This chapter examines the transformative impact of Virtual Reality (VR) and Augmented Reality (AR) technologies on vocational education and training (VET). By providing immersive and interactive learning environments, VR and AR enhance skill development, improve knowledge retention, and facilitate the practical application of concepts in vocational training. These technologies enable the simulation of real-world scenarios, allowing trainees to safely practice complex tasks such as operating machinery or performing medical procedures, thereby boosting safety and confidence. AR adds value by overlaying digital information onto physical tasks, offering real-time guidance in industries requiring precision and hands-on expertise. Furthermore, the chapter underscores the potential of VR and AR in promoting remote learning and accessibility, empowering trainees to engage with high-quality simulations and training programs from virtually any location.

The future of smart contracts in decentralized finance (DeFi) is a dynamic and evolving field that holds immense potential for transforming traditional financial systems The integration of artificial intelligence (AI) with smart contracts is enhancing their capabilities, enabling efficient processing of data and intelligent decision-making. Smart contracts provide an open and effective replacement for traditional financial structures as they continue to gain popularity. The potential of smart contracts to improve transparency, optimize operating procedures, and transform industries like healthcare, finance, and education is what will determine their future. To support blockchain-based apps and grow the DeFi ecosystem, smart contract platforms like Polkadot, Cardano, and Ethereum must continue to innovate and evolve. The future of smart contracts is largely being shaped by the possibility of multi-chain smart contracts, the incorporation of AI and machine learning technology, and the support for decentralized autonomous organizations (DAOs).

As such, backscatter communication with RIS's support is the suitable technology for using in 6G IoT networks, according to the efficiency, reliability, security, and low energy consumption. The analysis indicates that the integration of the RIS with backscatter communication enables achieving additional enhancements of up to 2.5 dB in the channel gain and up to 17% in the data rate as well as up to 3 dBm/Hz in energy efficiency. In particular, this book chapter will be devoted to an introduction to RIS-assisted backscatter communication, as well as to the elaboration of certain technical principles of this system, the definition of its possibilities in the context of improving large-scale IoT networks for the sixth generation. This chapter will also contain information concerning these problems, directions of further research, and potential applications of this technology.

Sleep stage classification is crucial for diagnosing sleep disorders, yet traditional manual scoring methods are labor-intensive and error-prone. This chapter presents an overview of automated sleep stage classification systems, focusing on integrating nature-inspired optimization algorithms to improve classification accuracy. We will discuss various techniques, including clustering, dimensionality reduction, feature extraction, and classification algorithms, emphasizing their roles in enhancing the performance of sleep stage classification systems. The chapter will detail recent advancements in deep learning approaches, such as Long short-term memory (LSTM) networks, and their application in analyzing electroencephalogram (EEG) signals. By employing optimization algorithms like Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), we aim to demonstrate how these methods can refine feature selection processes and improve overall classification outcomes.