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Demand Response Aggregator Participation Strategies in the French Electric Day-Ahead Market
No full textPublisher Copyright: © 2025 IEEE.This paper focuses on providing tools for the Demand Response Aggregator (DRA) and establishing an optimised strategy to maximise expected revenues. The authors concentrate on the French market, where the role of the Aggregator has been operational for several years, providing Demand Response and enabling demand flexibility. A thorough analysis is conducted of the mechanism through which DRAs participate in the Day-Ahead Market (DAM) to trade energy in the form of flexibility. This mechanism operates under defined rules, allowing DRAs to employ certain strategies and optimise their operations. Under this premise, the French context is analysed, and a DAM price predictor is defined to estimate prices before the DAM closure time. This, combined with existing mechanisms, enables the DRA to anticipate and implement its bidding strategy. As a result, the potential earnings that the DRA could achieve are quantified and analised, which can subsequently incentivise and mobilise end consumers to offer flexibility to the system. The DAM price predictor provides robust results and is a tool that a DRA can utilise.Peer reviewe
Bacterial cellulose/thiolated chitosan nanoparticles hybrid antimicrobial dressing for curcumin delivery
No full textPublisher Copyright: © 2024 The AuthorsThiolated chitosan (Cs-SH) nanoparticles were synthesized and incorporated into bacterial cellulose (BC) membranes through vacuum-assisted confinement. Thiolation significantly enhanced the intrinsic adhesion capacity of chitosan (Cs) as well as its solubility in neutral aqueous solutions. Subsequently, Cs-SH nanoparticles were successfully loaded with curcumin (Cur-Cs-SH), with nanoparticle sizes of 121 ± 2 nm for Cs-SH and 152 ± 6 nm for Cur-Cs-SH. Stability assessments revealed improved pH tolerance and colloidal stability due to the introduction of thiol groups and curcumin encapsulation. Notably, the retention yield of nanoparticles in BC was calculated to be 99 % (w/v) within 45 min. Nanoparticle and curcumin in vitro release studies demonstrated pH-dependent profiles, indicating controlled release kinetics influenced by initial loading and environmental acidity. Moreover, the enhanced adhesive properties of the developed BC membranes, verified by mucin disks and porcine skin adhesion tests, suggested their potential for targeted drug delivery to human tissue. Additionally, antimicrobial assays suggested a synergistic effect between Cs-SH and encapsulated curcumin, exhibiting antibacterial activity against S. aureus and E. coli. In this research, the bioavailability of curcumin was increased by encapsulating it in Cur-Cs-SH nanoparticles, which enhanced its antimicrobial properties and improved the adhesion of BC membranes, thereby expanding their applications in biomedicine.Peer reviewe
Health impact assessment of urban and transport developments in Barcelona: A case study
No full textPublisher Copyright: © 2024Background: Urban spaces need to be rethought to address growing health and environmental challenges. Urban density and transport systems contribute significantly to air pollution, negatively impacting public health. Barcelona has begun a transformation by introducing the Superblock model, an urban development with proven health benefits. However, there is a lack of understanding of the health impacts of various planned urban and transport interventions. This study aims to explore planned urban and transport developments in Barcelona (e.g. Superblocks, Low emission zone, tactical urban planning, port electrification) and estimates the health impacts of their related exposures. Methods: We utilized modelled NO2 reduction scenarios, which considered changes from implementing Barcelona's Urban Mobility Plan (UMP) of 2018–2024 and the Port electrification project. The UMP includes different interventions such as the low emission zones, tactical urban planning (reducing car traffic lanes), existing superblocks, and street greening. We established a baseline scenario for the year 2019, with no implementation of UMP or Port electrification. We devised three scenarios implementing the UMP: a) no change in private car use b) a 25% reduction in private car use, and c) a 25% reduction in private car use with port electrification. We estimated the effect on NO2 levels and conducted a health impact assessment following a comparative risk assessment methodology to demonstrate the impacts of these scenarios on natural cause of adult mortality. Results: The scenario with no change in private car use resulted in a 5.9 % reduction in NO2, preventing 67 (34–133 95% CI) premature deaths annually. The scenario with a 25% reduction in private car use led to a 17.6% reduction in NO2, preventing 199 (101–392 95% CI) premature deaths annually. Adding port electrification to the 25% reduction in private car use scenario resulted in a 19.4% reduction in NO2, preventing 228 (115–447 95% CI) premature deaths annually. Conclusion: Our findings suggest that implementing measures to reduce car use and electrifying the port in Barcelona can significantly reduce air pollution and prevent premature deaths in adults. This emphasizes the relevance of ambitious urban and transport policies in improving public health. Policymakers should consider assertive actions and broader implementation of such measures for greater health benefits. Further research is needed to explore additional measures and their potential impacts, facilitating the development of comprehensive urban and transport strategies.Peer reviewe
Deep neural network for damage detection in Infante Dom Henrique bridge using multi-sensor data
Full text linkPublisher Copyright: © The Author(s) 2024.This paper proposes a data-driven approach to detect damage using monitoring data from the Infante Dom Henrique bridge in Porto. The main contribution of this work lies in exploiting the combination of raw measurements from local (inclinations and stresses) and global (eigenfrequencies) variables in a full-scale structural health monitoring application. We exhaustively analyze and compare the advantages and drawbacks of employing each variable type and explore the potential of combining them. An autoencoder-based deep neural network is employed to properly reconstruct measurements under healthy conditions of the structure, which are influenced by environmental and operational variability. The damage-sensitive feature for outlier detection is the reconstruction error that measures the discrepancy between current and estimated measurements. Three autoencoder architectures are designed according to the input: local variables, global variables, and their combination. To test the performance of the methodology in detecting the presence of damage, we employ a finite element model to calculate the relative change in the structural response induced by damage at four locations. These relative variations between the healthy and damaged responses are employed to affect the experimental testing data, thus producing realistic time-domain damaged measurements. We analyze the receiver operating characteristic curves and investigate the latent feature representation of the data provided by the autoencoder in the presence of damage. Results reveal the existence of synergies between the different variable types, producing almost perfect classifiers throughout the performed tests when combining the two available data sources. When damage occurs far from the instrumented sections, the area under the curve in the combined approach increases (Formula presented.) compared to using local variables only. The classificatoin metrics also demonstrate the enhancement of combining both sources of data in the damage detection task, reaching close to (Formula presented.) precision values for the four considered test damage scenarios. Finally, we also investigate the capability of local variables to localize the damage, demonstrating the potential of including these variables in the damage detection task.Peer reviewe
Improvement opportunities on fatigue and corrosion behaviors in offshore fastener threads combining a maraging steel skin with a class 10.9 32CRB4 core
No full textPublisher Copyright: © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.The new generation of large offshore wind turbines (+ 15 MW) will require an improvement in the bolted joints used in these turbines. From the material perspective, this improvement will involve the development of fasteners that combine both corrosion-resistant properties and enhanced high-cycle fatigue performance, reducing the frequency of maintenance in the installations. This work explores the possibility of using L-DED (laser-directed energy deposition) technology to create hybrid fasteners consisting of a high-toughness 10.9 class carbon steel core and a 1.5 mm maraging steel coating that provides good resistance to marine corrosion. C300, 15–5 PH, and 17–4 PH maraging steel grades were tested. The results show that, in addition to corrosion protection, the 15–5 PH and 17–4 PH coatings improve high cycle fatigue performance compared to the fastener fully made in 32CrB4 used as a reference. Fifty percent of the coated fasteners tested in this work were fatigue runouts after 5 × 106 cycles, while none of the reference specimens reached such condition. The average fatigue life in the coated specimens was around 3.5 × 106 cycles, 3 times higher than the reference (average fatigue life slightly higher than 1 × 106 cycles). Another advantage to consider is that the application of the coating via L-DED does not impose limitations on the subsequent turning and thread-rolling operations, nor does it require additional finishing steps.Peer reviewe
Development of advanced Cu and Ni temperature sensors on ceramic-coated tubes via electroless plating
Full text linkPublisher Copyright: © 2025 Elsevier B.V.Considering the high cost of precious elements, it is necessary to replace metals like platinum or gold in the sensor sector. In this work, copper and nickel sensor circuits based on autocatalytic techniques were proposed with the aim of monitoring the temperature of cylindrically shaped (and electrically conductive) components. After electrically insulating the tube, a lithographic masking strategy adapted to the cylindrical surface was employed. The surface was functionalized with silanes to deposit a catalytic surface composed of palladium and tin. Non-critical materials such as copper and nickel were selected as sensor materials, depositing them over the catalytic surface by electroless plating. These circuits were electrically characterized on a home-designed testing equipment. Electrical resistance was measured over a range of temperatures: from room temperature up to 250 °C. The electrical results for the copper sensing layer evidenced the need for a protective layer. Silicon oxide was chosen as a protective material. However, it was observed that the sensing and protective layers interact due to their chemical affinity. In contrast, the electrical response of the nickel circuit was stable and repeatable after thermal cycling (heating and cooling), indicating that protection was not necessary for the working temperature range. The sensors developed performed well with regards to functionalization of tubes in terms of time response, repeatability, and stability at the maximum operating temperature.Peer reviewe
Transfer of knowledge through reverse annealing: a preliminary analysis of the benefits and what to share: a preliminary analysis of the benefits and what to share
Full text linkPublisher Copyright: Copyright © 2025 Osaba and Villar-Rodriguez.Being immersed in the noisy intermediate-scale quantum (NISQ) era, current quantum annealers present limitations for solving optimization problems efficiently. To mitigate these limitations, D-Wave Systems developed a mechanism called reverse annealing, a specific type of quantum annealing designed to perform local refinement of good states found elsewhere. Despite the research activity around reverse annealing, no study has theorized about the possible benefits related to the transfer of knowledge under this paradigm. This work moves in that direction and is driven by experimentation focused on answering two key research questions: i) is reverse annealing a paradigm that can benefit from knowledge transfer between similar problems? and ii) can we infer the characteristics that an input solution should meet to help increase the probability of success? To properly guide the tests in this paper, the well-known knapsack problem has been chosen for benchmarking purposes, using a total of 34 instances composed of 14 and 16 items.Peer reviewe
Application of Symmetric Neural Networks for Bead Geometry Determination in Wire and Arc Additive Manufacturing (WAAM)
Full text linkPublisher Copyright: © 2025 by the authors.The accurate prediction of weld bead geometry is crucial for ensuring the quality and consistency of wire and arc additive manufacturing (WAAM), a specific form of directed energy deposition (DED) that utilizes arc welding. Despite advancements in process control, predicting the shape and dimensions of weld beads remains challenging due to the complex interactions between process parameters and material behavior. This paper addresses this challenge by exploring the application of symmetrical neural networks to enhance the accuracy and reliability of geometric predictions in WAAM. By leveraging advanced machine learning techniques and incorporating the inherent symmetry of the welding process, the proposed models aim to precisely forecast weld bead geometry. The use of neuronal networks and experimental validation demonstrate the potential of symmetrical neural networks to improve prediction precision, contributing to more consistent and optimized WAAM outcomes.Peer reviewe
Edge-Computing Framework for Human-Robot Collaboration in Industry 5.0: Enhancing Operator Well-Being and Efficiency in Manufacturing
No full textPublisher Copyright: © 2025 IEEE.Industry 5.0 emphasizes human-centric automation, integrating advanced robotics and artificial intelligence to enhance operator well-being and manufacturing efficiency. This paper presents an edge-computing framework for human-robot collaboration, incorporating real-time physiological monitoring, machine learning-based fatigue prediction, and adaptive decision-making. The proposed system consists of two core modules: a fatigue monitor that continuously evaluates operator conditions and a decision-making system that dynamically adjusts task distribution. The framework optimises workload balancing by leveraging 10T-enabled sensors, artificial vision, and fuzzy logic-based decision-making while ensuring safety and productivity. Experimental validation in an industrial setting demonstrates significant improvements in reducing operator fatigue and enhancing production efficiency. Future research will focus on refining fatigue prediction models and expanding the system's applicability across various industrial domains.Peer reviewe
Improving Building Heat Load Forecasting Models with Automated Identification and Attribution of Day Types
Full text linkPublisher Copyright: © 2025 by the authors.This paper introduces a comprehensive methodology for predicting hourly heat loads in buildings. The approach employs unsupervised learning to identify distinct day types based on daily load profiles. A classification process then assigns each day to one of these day types, followed by the application of various supervised learning techniques to forecast heat loads. The methodology is both simple and robust, facilitating its use in load prediction across a wide range of buildings. The process is validated using data from three distinct building types (Residential, Educational, and Commercial) located in Tartu, Estonia. The results indicate that the day type identification and attribution process significantly reduce model complexity and computational time while achieving high prediction accuracy (MAPE ~<2%) with minimal computational requirements.Peer reviewe