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This paper considers the sequential design of remedial control actions in response to system anomalies for the ultimate objective of preventing blackouts. A physics-guided reinforcement learning (RL) framework is designed to identify effective sequences of real-time remedial look-ahead decisions accounting for the long-term impact on the system's stability. The paper considers a space of control actions that involve both discrete-valued transmission line-switching decisions (line reconnections and removals) and continuous-valued generator adjustments. To identify an effective blackout mitigation policy, a physics-guided approach is designed that uses power-flow sensitivity factors associated with the power transmission network to guide the RL exploration during agent training. Comprehensive empirical evaluations using the open-source Grid2Op platform demonstrate the notable advantages of incorporating physical signals into RL decisions, establishing the gains of the proposed physics-guided approach compared to its black box counterparts. One important observation is that strategically~\emph{removing} transmission lines, in conjunction with multiple real-time generator adjustments, often renders effective long-term decisions that are likely to prevent or delay blackouts.

This study introduces a mixed-integer linear programming (MILP) model, effectively co-optimizing patrolling, damage assessment, fault isolation, repair, and load re-energization processes. The model is designed to solve a vital operational conundrum: deciding between further network exploration to obtain more comprehensive data or addressing the repair of already identified faults. As information on the fault location and repair timelines becomes available, the model allows for dynamic adaptation of crew dispatch decisions. In addition, this study proposes a conservative power flow constraint set that considers two network loading scenarios within the final network configuration. This approach results in the determination of an upper and a lower bound for node voltage levels and an upper bound for power line flows. To underscore the practicality and scalability of the proposed model, we have demonstrated its application using IEEE 123-node and 8500-node test systems, where it delivered promising results.

Abstract Future “net-zero” electricity systems in which all or most generation is renewable may require very high volumes of storage in order to manage the associated variability in the generation-demand balance. The physical and economic characteristics of storage technologies are such that a mixture of technologies is likely to be required. This poses nontrivial problems in storage dimensioning and in real-time management. We develop the mathematics of optimal scheduling for system adequacy, and show that, to a good approximation, the problem to be solved at each successive point in time reduces to a linear programme with a particularly simple solution. We argue that approximately optimal scheduling may be achieved without the need for a running forecast of the future generation-demand balance. We consider an extended application to GB storage needs, where savings of tens of billions of pounds may be achieved, relative to the use of a single technology, and explain why similar savings may be expected elsewhere.

Abstract This paper investigates the forced convection of alumina-water nanofluids within helical tubes, maintaining a constant wall temperature and assuming thermal equilibrium between the nanoparticles and the base fluid. The nanofluid model incorporates the effects of alumina (Al2O3) nanoparticle volume fraction, diameter, and temperature on thermophysical properties. The governing equations are solved using the Forward-Time Central-Space Finite Volume method in conjunction with the simple algorithm. Numerical results are validated against experimental data, demonstrating high accuracy. The study explores the effects of pitch size, curvature ratio, nanoparticle volume fraction, nanoparticle diameter, and Reynolds number on velocity contours, temperature profiles, secondary flow, thermophysical properties, friction coefficient, and heat transfer rate. Additionally, the figure of merit evaluates the impact of these parameters on the thermal performance of the system. The results indicate that an increase in Reynolds number and nanoparticle diameter negatively affects thermal performance, while higher nanoparticle volume fraction, curvature ratio, and pitch size enhance it. Furthermore, incorporating nanoparticles in straight tubes proves to be more advantageous compared to helical tubes. This study tested volumetric ratios of 1%, 2%, and 4%, which resulted in increases in heat transfer coefficients of 21%, 32%, and 43%, respectively, compared to pure water under similar conditions, such as Reynolds number and coil pitch.

The transport sector is a crucial yet challenging area to decarbonize, given its heavy reliance on fossil fuel usage, carbon-intensive infrastructure and car-centric lifestyles. It remains the largest contributor to local air pollution in cities yet has the potential to improve people's physical and mental health. This research investigated the potential contribution of transport energy demand reduction to climate change mitigation and improving public health. Using a comprehensive bottom-up modelling framework, the Transport Energy and Air pollution Model (TEAM), this study provides an integrated assessment of the impacts of deep mobility-related energy demand reductions, including lifecycle carbon emissions, local air pollution and health impacts. Using a sociotechnical scenario approach and the UK as a case study, this research reveals that energy demand reductions of up to 61 % by 2050 compared to baseline levels are achievable and can enhance citizens' quality of life. Business as usual approaches which rely on a technical transition miss the legislated carbon budgets and result in higher energy demand in 2050. More comprehensive scenarios deliver a reduction of up to 72 % in total lifecycle carbon emissions by 2050, with approximately half of the reduction achieved through mode shifting and avoiding travel, while the other half comes from vehicle energy efficiency, electrification, and downsizing of the vehicle fleets. The research shows that it can lead to significant co-benefits such as improved local air pollution and public health. The feasibility and practicality of policy measures and integrated strategies identified for achieving deep transport-energy demand reductions are discussed.

Adding an infrared transparent spacer to far-field thermophotovoltaic (TPV) devices boosts power density. This scalable zero-gap design surpasses vacuum blackbody limit and achieves performance comparable to near-field TPV with nanoscale gaps.

Introduction: Object detection has been an essential task in computer vision for decades, and modern developments in computer vision and deep learning have greatly increased the accuracy of detecting systems. However, the high computational requirements of deep learningbased object detection algorithms limit their applicability to resource-constrained systems, such as embedded devices. Methods: With the advent of Tiny Machine Learning (TinyML) devices, such as Raspberry Pi, it has become possible to deploy object detection systems on small, low-power devices. Due to their accessibility and cost, Tiny-ML devices, such as Raspberry Pi, a single-board tiny-ML device that is extremely well-liked, have recently attracted a lot of attention. Results: In this study, we present an enhanced SSD-based object detection approach and deploy the model using a tinyML device, i.e., Raspberry Pi. Conclusion: The proposed object detection model is lightweight and built utilizing Mobilenet- V2 as the underlying foundation.

Graphene oxide, a derivative of graphene, has recently emerged as a promising nanomaterial in the biomedical field due to its unique properties. Its potential as a nanocarrier in the treatment of Alzheimer's disease represents a significant advancement. This abstract outlines a study focused on utilizing graphene oxide to reduce the toxicity of Alzheimer's proteins, marking a revolutionary approach in treatment strategies. The pathological features of Alzheimer’s disease, primarily focusing on the accumulation and toxicity of amyloid-beta proteins, have been described in this review. These proteins are known to form plaques in the brain, leading to neuronal damage and the progression of Alzheimer's disease. The current therapeutic strategies and their limitations are briefly reviewed, highlighting the need for innovative approaches. Graphene oxide, with its high surface area, biocompatibility, and ability to cross the blood-brain barrier, is introduced as a novel nanocarrier. The methodology involves functionalizing graphene oxide sheets with specific ligands that target amyloid-beta proteins. This functionalization facilitates the binding and removal of these toxic proteins from the brain, potentially alleviating the symptoms of Alzheimer's disease. Preliminary findings indicate a significant reduction in amyloid-beta toxicity in neuronal cell cultures treated with graphene oxide nanocarriers. The study also explores the biocompatibility and safety profile of graphene oxide in biological systems, ensuring its suitability for clinical applications. It calls for further research and filing patents for its translational potential and benefits of this nanotechnology paying the way for a new era in neurodegenerative therapy.

The importance of early diagnosis of hepatitis B virus infection to treat and follow up this disease has led to many advances in diagnostic techniques and materials. Conventional diagnostic tests are not very useful, especially in the early stages of infection; it is therefore suggested that nanomaterials can enhance them by changing and strengthening their performance for a more accurate and rapid diagnosis. Electrochemical immunosensors with unique features such as miniaturization, low cost, specificity and simplicity have become a suitable and vital tool in the rapid diagnosis of hepatitis B since the patent. Different strategies have been presented, such as graphene oxide and gold nanorods (GO-GNRs), graphene oxide (GO), copper metal–organic framework/ electrochemically reduced graphene oxide (Cu-MOF/ErGO) composite, Label-free graphene oxide/ Fe₃O₄/Prussian Blue (GO/Fe₃O₄/PB) immunosensor, and graphene oxide–ferrocene-CS/Au (GOFc- CS/Au) nanoparticle layered electrochemical immunosensor. In this review, we discuss a group of the most widely used nanostructures, such as graphene and carbon nanotubes, which are used to develop electrochemical immunosensors for the early diagnosis of the hepatitis B virus.