• Energy Research
• 2025-2025close
• other engineering and technologies close
• AU close
• JP close

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.

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.

Background: An image captioning system is a crucial component in the domains of computer vision and natural language processing. Deep neural networks have been an increasingly popular tool for the generation of descriptive captions for photos in recent years. Objective: On the other hand, these models frequently have the issue of providing captions that are unoriginal and repetitious. Beam search is a well-known search technique that is utilized for the purpose of producing descriptions for images in an effective and productive manner. Method: The algorithm keeps track of a set of partial captions and expands them iteratively by choosing the probable next word throughout each step until a complete caption is generated. The set of partial captions, also known as the beam, is updated at each step based on the predicted probabilities of the next words. This research paper presents an image caption generation system based on beam search. In order to encode the image data and generate captions, the system is trained on a deep neural network architecture. Results: This architecture brings together the benefits of CNN with RNN. After that, the beam search method is executed in order to provide the completed captions, resulting in a more diverse and descriptive set of captions compared to traditional greedy decoding approaches. The experimental outcomes indicate that the suggested system beats existing image caption generation techniques in terms of the precision and variety of the generated captions. Conclusion: This demonstrates the effectiveness of beam search in enhancing the efficiency of image caption generation systems.

Thermo-regulating ability of the MEPCM catalyst on ammonia decomposition.

The recently completed Horizon-2020 project “Management and Uncertainties of Severe Accidents (MUSA)” has reviewed uncertainty sources and Uncertainty Quantification methodology for assessing Severe Accidents (SA), and has made a substantial effort at stimulating uncertainty applications in predicting the radiological Source Term of reactor and Spent Fuel Pool accident scenarios. The key motivation of the project has been to bring the advantages of the Best Estimate Plus Uncertainty approach to the field of Severe Accident modelling. With respect to deterministic analyses, expected gains are avoiding adopting conservative assumptions, identifying uncertainty bands of estimates, and gaining insights into dominating uncertain parameters. Also, the benefits for understanding and improving Accident Management were to be explored. The reactor applications brought together a large group of participants that set out to apply uncertainty analysis (UA) within their field of SA modelling expertise – in particular reactor types, but also SA code used (ASTEC, MELCOR, MAAP, RELAP/SCDAPSIM), uncertainty quantification tools used (DAKOTA, SUSA, URANIE, self-developed tools based on Python code), detailed accident scenarios, and in some cases SAM actions. The setting up of the analyses, challenges faced during that phase, and solutions explored, are described in Brumm et al. ANE 191 (2023). This paper synthesizes the reactor-application work at the end of the project. Analyses of 23 partners are presented in different categories, depending on whether their main goal is/are (i) uncertainty bands of simulation results; (ii) the understanding of dominating uncertainties in specific sub-models of the SA code; (iii) improving the understanding of specific accident scenarios, with or without the application of SAM actions; or, (iv) a demonstration of the tools used and developed, and of the capability to carry out an uncertainty analysis in the presence of the challenges faced. A cross-section of the partners’ results is presented and briefly discussed, to provide an overview of the work done, and to encourage accessing and studying the project deliverables that are open to the public. Furthermore, the partners’ experiences made during the project have been evaluated and are presented as good practice recommendations. The paper ends with conclusions on the level of readiness of UA in SA modelling, on the determination of governing uncertainties, and on the analysis of SAM actions.

Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are critical factors. This paper benchmarks solutions from the IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling, focusing on forecasting renewable production and demand and optimizing energy cost. The competition attracted 49 participants in total. The top-ranked method employed stochastic optimization using LightGBM ensembles, and achieved at least a 2% reduction in energy costs compared to deterministic approaches, demonstrating that the most accurate point forecast does not necessarily guarantee the best performance in downstream optimization. The published data and problem setting establish a benchmark for further research into integrated forecasting-optimization methods for energy systems, highlighting the importance of considering forecast uncertainty in optimization models to achieve cost-effective and reliable energy management. The novelty of this work lies in its comprehensive evaluation of Predict+Optimize methodologies applied to a real-world renewable energy scheduling problem, providing insights into the scalability, generalizability, and effectiveness of the proposed solutions. Potential applications extend beyond energy systems to any domain requiring integrated forecasting and optimization, such as supply chain management, transportation planning, and financial portfolio optimization.

Lymphoma is a malignant tumor caused by abnormal proliferation of lymphocytes in the lymphatic system. Conventional treatments for lymphoma often have limitations, and new therapeutic strategies need to be explored. Realgar is an ancient Chinese medicine that has been used for centuries to treat a variety of ailments due to its therapeutic potential for various diseases, including cancer. However, it is a time-consuming waste and has a low absorption rate in the gastrointestinal tract, so it has the disadvantages of oral dose, potential toxicity, and low bioavailability. Recently, the development of nanotechnology has promoted the nanization of realgar particles, which have better physicochemical properties and higher bioavailability. The antitumor activity of Realgar nanoparticles against lymphoma has been demonstrated in preclinical studies. Realgar nanoparticles exhibit cytotoxic effects by inducing apoptosis and inhibiting the growth and proliferation of lymphoma cells. Moreover, these nanoparticles exert immunomodulatory effects by enhancing the activity of immune cells and promoting the cytotoxicity of T lymphocytes against lymphoma cells. Additionally, realgar nanoparticles have been shown to inhibit tumor angiogenesis, thereby restricting the blood supply and nutrient availability to lymphoma cells as exhibited in this patent comprehensive review. Despite promising preclinical data, further research on the role and mechanism of realgar nanoparticles in the treatment of lymphoma remains to be studied. Moreover, the translation of these findings into clinical practice requires rigorous evaluation through well-designed clinical trials. Realgar nanoparticles hold great potential as a novel therapeutic approach for lymphoma, and their development may contribute to the advancement of precision medicine in the field of oncology.

This study proposes the Higher Rotational Speed Maintaining (HRSM) control algorithm that is suitable for large wind speed fluctuations. One of the challenges faced by wind generators is operating with significant wind speed oscillations. A wind generator with a large moment of inertia increases the time constant in rotational speed control and delays the tracking of the optimal operating point. This delay could reduce generated power by up to 40% or more. There are numerous regions in Japan’s inland areas where average wind speeds exceed 6.5 m/s. However, frequent fluctuations in wind speeds make wind power generation challenging. Therefore, this paper proposes a control algorithm suitable for wind turbines operating under unstable wind conditions. Higher rotational speeds are required to convert sudden high wind speeds into higher power output, especially when wind speed oscillations are large. Hence, the proposed algorithm focuses on maintaining the rotational speed during periods of unstable wind conditions to improve generation efficiency. The effectiveness of the proposed control method for wind turbines is verified through numerical simulation results. The novel control algorithm aims to promote the spread of wind turbines in areas with unstable wind conditions by overcoming these disadvantages.