The hard drive is one of the important components of a computing system, and its failure can lead to both system failure and data loss. Therefore, the reliability of a hard drive is very important. Realising this importance, a number of studies have been conducted and many are still ongoing to improve hard drive failure prediction. Most of those studies rely solely on machine learning, and a few others on semantic technology. The studies based on machine learning, despite promising results, lack context-awareness such as how failures are related or what other factors, such as humidity, influence the failure of hard drives. Semantic technology, on the other hand, by means of ontologies and knowledge graphs (KGs), is able to provide the context-awareness that machine learning-based studies lack. However, the studies based on semantic technology lack the advantages of machine learning, such as the ability to learn a pattern and make predictions based on learned patterns. Therefore, in this paper, leveraging the benefits of both machine learning (ML) and semantic technology, we present our study, knowledge graph-based hard drive failure prediction. The experimental results demonstrate that our proposed method achieves higher accuracy in comparison to the current state of the art.

Collaborative artificial intelligence systems (CAISs) aim to work with humans in a shared space to achieve a common goal, but this can pose hazards that could harm human beings. We identify emerging problems in this context and report our vision of and progress toward a risk-driven assurance process for CAISs.

Attack graphs are a tool for analyzing security vulnerabilities that capture different and prospective attacks on a system. As a threat modeling tool, it shows possible paths that an attacker can exploit to achieve a particular goal. However, due to the large number of vulnerabilities that are published on a daily basis, they have the potential to rapidly expand in size. Consequently, this necessitates a significant amount of resources to generate attack graphs. In addition, generating composited attack models for complex systems such as self-adaptive or AI is very difficult due to their nature to continuously change. In this paper, we present a novel fragment-based attack graph generation approach that utilizes information from publicly available information security databases. Furthermore, we also propose a domain-specific language for attack modeling, which we employ in the proposed attack graph generation approach. Finally, we present a demonstrator example showcasing the attack generator's capability to replicate a verified attack chain, as previously confirmed by security experts.

Context: Cyber-physical systems (CPS) are increasingly self-adaptive, i.e. they have the ability to introspect and change their behavior. This self-adaptation process must be considered when modeling the safety and security aspects of the system.Objective: This study collects and compares security attacks and safety hazards on self-adaptive systems (SAS) described in the literature. In addition, mitigation and treatment strategies as well as the modeling and analysis approaches are investigated.Method: We conducted a systematic literature review on 21 selected papers. The selection process involved a database search and snowballing methodologies. The coding scheme to analyze the content of the papers was obtained through our research questions, existing domain-specific taxonomies, and open coding.Results: Safety and Security are not jointly modeled in the context of self-adaptive systems. The adaptation process is often not considered in the attack and hazard analysis due to naïve assumptions and modeling. The proposed approaches are mostly verified and validated through simulations.Conclusion: A thorough and joint modeling approach for safety and security in self-adaptive systems is still an open challenge that needs to be addressed. Further work is needed to address the gap between safety and security modeling in self-adaptive systems.

Plastic pollution constitutes a critical planetary health challenge, undermining the integrity of Earth systems while generating cascading harms to human health, livelihoods, and social equity particularly in low- and middle-income countries. Conventional top-down regulatory and technological responses have proven insufficient to address the complexity of plastic pollution, often excluding those most affected from decision-making and solution design. This paper examines how democratizing plastic governance through grassroots leadership can advance planetary health by simultaneously protecting ecosystems, improving human well-being, and strengthening socio-ecological resilience. Drawing on empirical evidence from the #RestorationX10000 initiative led by Community Action Against Plastic Waste (CAPws), this paper documents implementation processes and outcomes achieved between 2021 and 2025 across 71 impacted communities in 21 countries spanning Africa, Asia-Pacific, and Latin America. The initiative was designed to empower 10,000 youths and women as community leaders, practitioners, and advocates by equipping them with leadership, technical, and policy engagement skills to drive systemic change in plastic governance and circular economy practice. Using a transdisciplinary, community-based action research approach aligned with planetary health principles, the initiative integrates capacity building, citizen science, circular economy interventions (collection, sorting, repair, reuse, repurposing, and recycling), and policy advocacy. Quantitative and qualitative evidence demonstrates that grassroots-led interventions can simultaneously reduce plastic leakage, create decent green livelihoods, and strengthen environmental governance. We argue that inclusive, community-centered plastic governance is not only an environmental intervention but a planetary health strategy, offering policy-relevant insights for national plastic action plans, extended producer responsibility frameworks, and global negotiations toward a legally binding instrument on plastic pollution.