Nuclear energy is widely being regarded as a lowcarbon power source. However, today’s geopolitical environment which includes the high-intensity interstate conflict, accelerating drone warfare, cyber operations, as well as systemic stress on power grids has significantly exposed stark gaps in the legal frameworks that govern nuclear safety, security, and liability across countries in the world. This paper attempts to properly survey the international nuclear governance architecture (that is, the Non-Proliferation Treaty (NPT) safeguards and the International Atomic Energy Agency (IAEA) with all their Additional Protocols including the Convention on Nuclear Safety and the Joint Convention; the Convention on the Physical Protection of Nuclear Material and its 2005 Amendment; the United Nations Security Council (UNSC) Resolution 1540, as well as the International Convention for the Suppression of Acts of Nuclear Terrorism). Its pertinent to note that all these diverse legislations help to place these instruments in conversation with international humanitarian law that protects works and installations containing all such dangerous forces like nuclear power plants and during armed conflict at both national and international warfront. For instance, using the ongoing peril at Ukraine’s Zaporizhzhia Nuclear Power Plant and the horrific war disasters in the Middle East as principal case studies, the paper analyses how real-world hostilities interact with the known seven pillars of nuclear safety and security articulated by the IAEA Director General, and how power-grid attacks, drone strikes as well as their hybrid methods of conducting war fares create pathways to radiological catastrophe without a deliberate strike on a reactor as such. It also examines persistent domestic-international mismatches in nuclear liability in the terrains of the regulatory implications of Small Modular Reactors (also known as SMRs) and micro reactors, the unresolved safeguards lacuna surrounding naval nuclear propulsion (also known as AUKUS), and the cyber risk governance difficulties. Ultimately, the paper concludes with cordial suggestions for necessary reforms and better approaches to achieve the highest utility benefits without jeopardizing the basic interests of nuclear exploration: harvesting nuclear relevance for non-military purposes.
Digital twin frameworks have been standardized for manufacturing implementations in ISO 23247. Despite the standardized framework for the manufacturing domain, other frameworks exist both within the manufacturing domain and across all other industries. Typical frameworks include hierarchical and collaborative frameworks similar to the ISO 23247 framework as well as frameworks based on digital twin services and implementation strategies. Hierarchical and collaborate frameworks generally follow a layered approach similar to the OSI and internet protocol frameworks. Service and implementation based frameworks may not include hierarchical layers, but can still be abstracted in terms of layers/services within the overall framework. Despite all the available frameworks, no unified framework exists for the complete digital twin life cycle (design, implementation, and operation). However, service based frameworks can be combined with hierarchical/collaborative frameworks to provide a comprehensive understanding of the system to better aid digital twin implementation beginning with enduser requirements definition and finishing with end-user access.
This study explores the feasibility of using additive manufacturing for constructing hydraulic prototypes by comparing experimental data with computational simulations of pressure loss in perforated plates. Based on the experimental results presented by Souza (2024), numerical simulations were performed using Ansys CFX, which applies the Reynolds-Averaged Navier-Stokes (RANS) model and the geometric ones—likely due to the plate’s short height and relatively large diameter. short height and relatively large diameter. The findings suggest that additive manufacturing is a promising alternative for building experimental components, and that well-calibrated computational models may accurately predict their hydraulic performance. Future studies should investigate geometries that enhance the influence of surface roughness, enabling a more detailed assessment of its impact on flow behavior
Malaria continues to pose a significant global health challenge, especially in areas with limited resources, requiring diagnostic tools that are both precise and comprehensible for healthcare professionals. This study offers a comprehensive framework for malaria detection through Machine Learning (ML) models, enhanced by explainable artificial intelligence (XAI) tools, to examine both predictive efficacy and the interpretability of model predictions. The framework is utilized on a new dataset comprising P. vivaxinfected human blood smear images. To deal with class imbalance and feature variability, a lot of data preprocessing was done, such as synthetic minority oversampling and feature scaling. An ensemble model got a test accuracy of 96.8% and a ROC-AUC of 0.992. XAI techniques found that cell texture, color intensity, and parasite density were the most important morphological predictors. The findings suggest that combining machine learning with explainable AI offers a viable approach to developing precise, transparent, and clinically applicable diagnostic tools, aligning with the principles of sustainable engineering by facilitating early, accurate, and accessible healthcare interventions.
Industry 4.0 represents a paradigm shift towards smarter, more interconnected systems in the evolving manufacturing landscape including maintenance practices for these systems. This paper specifically explores how Industry 4.0 (I.4.0) technologies are transforming preventative maintenance practices. It also showcases the potential of these I.4.0 technologies for minimizing operational downtime. By leveraging advancements such as the Internet of Things (IoT), artificial intelligence (AI), and digital twins, organizations can move beyond traditional maintenance strategies to implement more data-driven proactive, and realtime approaches. This transformation drives maintenance accuracy and efficiency. The paper reviews key Industry 4.0 technologies and their applications in preventative maintenance, examining their impact on reducing unplanned downtime and improving overall equipment effectiveness. Case studies and industry examples are presented to illustrate successful implementations, and the tangible benefits realized. The discussion also addresses challenges and considerations associated with integrating these advanced technologies into existing maintenance frameworks. This comprehensive analysis provides insights into how Industry 4.0 can revolutionize preventative maintenance strategies, offering practical recommendations for organizations aiming to optimize their maintenance processes and significantly reduce downtime.
