The Zero Trust Architecture (ZTA) model has emerged as a foundational cybersecurity paradigm that eliminates implicit trust and enforces continuous verification across users, devices, and networks. This study presents a systematic literature review of 74 peer-reviewed articles published between 2016 and 2025, spanning domains such as cloud computing (24 studies), Internet of Things (11), healthcare (7), enterprise and remote work systems (6), industrial and supply chain networks (5), mobile networks (5), artificial intelligence and machine learning (5), blockchain (4), big data and edge computing (3), and other emerging contexts (4). The analysis shows that authentication, authorization, and access control are the most consistently implemented ZTA components, whereas auditing, orchestration, and environmental perception remain underexplored. Across domains, the main challenges include scalability limitations, insufficient lightweight cryptographic solutions for resource-constrained systems, weak orchestration mechanisms, and limited alignment with regulatory frameworks such as GDPR and HIPAA. Cross-domain comparisons reveal that cloud and enterprise systems demonstrate relatively mature implementations, while IoT, blockchain, and big data deployments face persistent performance and compliance barriers. Overall, the findings highlight both the progress and the gaps in ZTA adoption, underscoring the need for lightweight cryptography, context-aware trust engines, automated orchestration, and regulatory integration. This review provides a roadmap for advancing ZTA research and practice, offering implications for researchers, industry practitioners, and policymakers seeking to enhance cybersecurity resilience.

This study investigates the impact of green innovation (GI) on corporate environmental performance (CEP), while examining the moderating roles of corporate governance practices (CGPs) and environmental policy pressure (EPP). This study uses advanced statistical methods to ensure the reliability of the results. These include techniques such as propensity score matching (PSM), difference-in-differences (DID) analysis with China’s National Green Development Fund (2020) as the policy intervention, and the generalized method of moments (GMM), and this study examines how internal governance mechanisms and external regulatory pressures influence the GI-CEP relationship. The results show that GI significantly increases CEP, and that this effect is amplified by robust CGPs and EPP. By analyzing data from 4026 firm-year observations of A-share listed Chinese companies (2017–2022), the findings highlight the importance of innovation, governance, and policy in achieving sustainable environmental outcomes. This study provides valuable insights for firms and policymakers to foster green innovation and align corporate strategies with global sustainability goals.

The ever-growing volume of global electronic waste (e-waste) poses significant environmental and health challenges. Printed circuit boards (PCBs), which form the core of most electronic devices, contain valuable metals as well as hazardous materials. The efficient disassembly and recycling of e-waste is critical for both economic and environmental sustainability. The traditional manual disassembly methods are time-consuming, labor-intensive, and often hazardous. The integration of edge computing and the Internet of Things (IoT) provides a novel approach to automating the disassembly process, potentially transforming the way e-waste is managed. Automated disassembly of WPCBs involves the use of advanced technologies, specifically edge computing and the IoT, to streamline the recycling process. This strategy aims to improve the efficiency and sustainability of e-waste management by leveraging real-time data analytics and intelligent decision-making at the edge of the network. This paper explores the application of edge computing and the IoT in the automated disassembly of WPCBs, discussing the technological framework, benefits, challenges, and future prospects. The experimental results show that the YOLOv10 model achieves 99.9% average precision (AP), enabling accurate real-time detection of electronic components, which greatly facilitates the automated disassembly process.

The rapid advancement of technology has led to a substantial increase in Waste Electrical and Electronic Equipment (WEEE), which poses significant environmental threats and increases pressure on the planet’s limited natural resources. In response, Artificial Intelligence (AI) has emerged as a key enabler of the Circular Economy (CE), particularly in improving the speed and precision of waste sorting through machine learning and computer vision techniques. Despite this progress, to our knowledge, no comprehensive, systematic review has focused specifically on the role of AI in disassembling and recycling Waste-Printed Circuit Boards (WPCBs). This paper addresses this gap by systematically reviewing recent advancements in AI-driven disassembly and sorting approaches with a focus on machine learning and vision-based methodologies. The review is structured around three areas: (1) the availability and use of datasets for AI-based WPCB recycling; (2) state-of-the-art techniques for selective disassembly and component recognition to enable fast WPCB recycling; and (3) key challenges and possible solutions aimed at enhancing the recovery of critical raw materials (CRMs) from WPCBs.

The exponential growth of electronic waste is a direct result of nowadays fast technological progress. European Union directives prioritize resource optimization, particularly the circular utilization of Critical Raw Materials (CRMs) present in electronic devices. In our study, we introduce an advanced computer vision system based on the deep learning model YOLOv9, designed to support the robotic selective disassembly of Waste Printed Circuit Boards (WPCBs). This is an effective approach for enhancing the density of specific CRMs and making their extraction more efficient. Our approach leverages chemical-physical processes to efficiently extract CRMs from electronic components. By utilizing distinctive features, we classify these components based on their recyclability, thereby enhancing recycling efforts.