As quantum computing advances, current cryptographic protocols are increasingly vulnerable to quantum attacks, particularly those based on Public Key Infrastructure (PKI) like RSA or Elliptic Curve Cryptography (ECC). This paper presents a comprehensive review of Post-Quantum Cryptography (PQC) as a solution to protect digital systems in the quantum era. We provide an in-depth analysis of various quantum-resistant cryptographic algorithms, including lattice-based, code-based, hash-based, isogeny-based, and multivariate approaches. The review highlights the National Institute of Standards and Technology (NIST) PQC standardization process, highlighting key algorithms, such as CRYSTALS–Kyber, CRYSTALS–Dilithium, Falcon, and SPHINCS+, and discusses the strengths, vulnerabilities, and implementation challenges of the leading algorithms. In addition, we explore transition strategies for organizations, emphasizing hybrid cryptography to ensure backward compatibility during migration. This study offers key insights into the future of cryptographic standards and the critical steps necessary to prepare for the transition from classical to quantum-resistant systems.

The Internet of Things (IoT) revolutionizes connectivity, enabling innovative applications across healthcare, industry, and smart cities but also introducing significant cybersecurity challenges due to its expanded attack surface. Intrusion Detection Systems (IDSs) play a pivotal role in addressing these challenges, offering tailored solutions to detect and mitigate threats in dynamic and resource-constrained IoT environments. Through a rigorous analysis, this study classifies IDS research based on methodologies, performance metrics, and application domains, providing a comprehensive synthesis of the field. Key findings reveal a paradigm shift towards integrating artificial intelligence (AI) and hybrid approaches, surpassing the limitations of traditional, static methods. These advancements highlight the potential for IDSs to enhance scalability, adaptability, and detection accuracy. However, unresolved challenges, such as resource efficiency and real-world applicability, underline the need for further research. By contextualizing these findings within the broader landscape of IoT security, this work emphasizes the critical importance of developing IDS solutions that ensure the reliability, privacy, and security of interconnected systems, contributing to the sustainable evolution of IoT ecosystems.

This study explores the process of creating avatars for Virtual Reality and metaverse environments using a variety of specialized applications, including VRChat, Ready Player Me, VRoidStudio, Mixamo, Convai, and MetaHuman. By evaluating these platforms, the research identifies the strengths and limitations of each tool in terms of customization, integration, and overall user experience. The practical implementation focuses on avatar creation within Unity and Unreal Engine, highlighting the technical aspects of rigging, animation, and real-time rendering. The study also delves into the broader implications of avatar use, particularly concerning privacy and security. Our findings reveal that while each platform offers unique advantages, the choice of tool significantly impacts the visual fidelity and performance of avatars in virtual environments. For the ease of use, Ready Player Me stands out with its intuitive interface. VRoidStudio is notable for its high degree of customizability, allowing for detailed avatar personalization. In terms of high-quality graphics, MetaHuman leads the way with its advanced graphical fidelity. At the same time, while some platforms excel in personalizing avatars or integrating with development environments (VRoidStudio, Ready Player Me), others are constrained by their limited flexibility or their platform-specific availability (MetaHuman, Mixamo). Understanding the technical intricacies of these tools is crucial for developing more immersive and secure metaverse experiences.

Agritech uses advanced technologies to boost the efficiency, sustainability, and productivity of farming. The Internet of Things (IoT) in agriculture has brought sensors and networked technology to gather and analyse environmental and crop data, enabling precision farming that optimises resource usage and increases yields. Yet, current agricultural methods suffer from unsecured and decentralised data management, causing inefficiencies and complicating traceability across the supply chain. The integration of IoT with blockchain technology is seen as a promising solution to enhance data‐driven agriculture. Blockchain provides a secure, decentralised, and transparent ledger that enhances data integrity, reduces fraud, and improves traceability, which complements IoT applications. The authors detail the development of an innovative system that orchestrates IoT and blockchain technologies to facilitate the adoption of new technologies in agriculture and overcomes the lacked of comprehensive data connectivity. It outlines a conceptual framework and its preliminary empirical implementation. The system consists of three integrated layers: the IoT layer, which creates digital twins of field crops; the blockchain layer, which secures and manages data from the field and external stakeholders for dynamic applications such as track and tracing; and the orchestration layer, which fuses physical and digital data to optimise business models, enhance supply chain productivity, and support governmental policy‐making, thereby improving field productivity and food sector innovation.

The COVID-19 pandemic has recently brought attention to several acute human needs. However, it has also demonstrated how crisis can spark innovation in the context of information asymmetry and uncertainty. It has raised the importance of early warning systems (EWS) to prevent similar future events and prepare governments, organizations, and individuals in advance. Such efforts in innovating under uncertainty resemble the process entrepreneurs face when creating new ventures and launching new products and services. Moreover, the increasing adoption of mobile technologies associated with the surging Internet of Things (IoT) devices and applications provides cues to a more extensive discussion about the adoption of sensor networks and applied artificial intelligence algorithms to benefit society and improve its relationship with data. Implementing IoT sensor location-based networks to help reduce community-transmitted infections can be a cost-effective solution that adds to broader pandemic control warning systems. In this paper, a novel predictive location-based early warning system is proposed. The system is able to measure people’s density, people flow, and behavior in specific areas of indoor and outdoor environments. Its implementation has been tested in a real public scenario, showing the capacity to operate flawlessly in real-time, thus addressing the needs of a trusted EWS for governments and organizations to manage event-led situations.