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Feasibility of district heating in a mild climate: A comparison of warm and cold temperature networks in Bilbao
Get PDFPublisher Copyright: © 2024 The AuthorsDistrict heating and cooling systems can aid in decarbonisation and the provision of efficient heating and cooling in Europe. However, whereas these systems have achieved high penetration rates in colder climates of Northern, Central and Eastern Europe, they remain marginal in milder climates of Southern Europe. In terms of network design, district heating and cooling systems can be configured in different ways. In so-called warm networks, the required temperature for all the consumers is attained city-wide, and in so-called cold systems, the necessary temperature is achieved at the consumers' premises by ancillary equipment. The most cost-effective heating and cooling solution for urban areas requires investigation. This research models and compares cold and warm district energy systems with other heating and cooling solutions through a comprehensive case study executed in the city of Bilbao, Spain. The city is characterised by a mild climate and a high population density which is characteristic of many Southern European cities. The results show that district energy systems are economically advantageous compared to other low-carbon solutions, such as air-source heat pumps. However, these systems are not able to outcompete natural gas under current cost and taxation levels. Warm networks provide a cheaper source of heat compared to cold networks, but both network types lead to similar expenditures for combined heating and cooling supply. This paper, presents the study context and its results, and is complemented by an exhaustive detailed methodology document and a separate supplementary material repository.Peer reviewe
Tool Used to Assess Co-Benefits of Nature-Based Solutions in Urban Ecosystems for Human Wellbeing: Second Validation via Measurement Application
Get PDFPublisher Copyright: © 2025 by the authors.In recent years, nature-based solutions have been used in urban regeneration interventions to improve the adaptation and resilience of these places, contributing to improved environmental quality and cultural ecosystem functions, including people’s physiological, social, and mental health and wellbeing. However, when it comes to the assessment of psychological wellbeing and social benefits (psychosocial co-benefits), the existing evidence is still limited. To contribute to the advancement of knowledge on nature’s contribution to people in relation to this type of benefit, it is necessary for us to develop and test assessment tools to contribute to the development of a robust nature-based solutions monitoring framework. In this paper, the second phase of the validation of a psychosocial co-benefit assessment tool for nature-based urban interventions is presented. This tool is structured around two dimensions: the perceived health and wellbeing and social co-benefits. The first validation was carried out with experts using the Delphi method. The second validation presented in this paper was based on a sample of users, evaluating a set of eight urban spaces at different levels of naturalisation and openness. The results indicate that the tool is sensitive to the differences in naturalisation and openness in the public urban places analysed. The most relevant contextual variables to explain the psychosocial co-benefits are openness, the surfaces covered by tree branches, the water surface area, and naturalisation.Peer reviewe
Design and experimental characterization of a propane-based reversible dual source/sink heat pump
Get PDFPublisher Copyright: © 2024 The Author(s)The current paper presents the design and energy performance analysis of a propane-based reversible Dual Source/Sink Heat Pump (DSHP). DSHPs offer an alternative to conventional water to water and air to water heat pumps, leveraging the strengths of both technologies in an efficient manner. The developed prototype incorporates an innovative Dual Source/Sink Heat eXchanger (DSHX), enabling the unit operating in various modes, including space heating, space cooling, and domestic hot water production using brine, air or both simultaneously as a source/sink. The DSHX serves as as both a condenser or an evaporator, directly rejecting or absorbing heat from air and/or brine. By eliminating secondary loops and defrost cycles, the DSHX minimizes energy losses. The main novelty of this work lies in the DSHX that integrates external units typically duplicated in DSHPs into a single component, eliminating the need for split refrigerant flow rates, thus avoiding maldistribution, refrigerant charge increase and draining valves. A steady state experimental campaign was conducted in a climatic chamber to characterize the DSHP prototype and validate the DSHX performance models. Heating capacity up to 11.2 kW and COP values up to 4.7 were achieved at nominal compressor speed by supplying hot water at 35 °C with an ambient temperature of 7 °C. Similarly, when producing cold water at 7 °C, cooling capacity and EER reached 9.8 kW and 3.6, respectively, at nominal compressor speed using air as heat sink at 35 °C. The effects of various operating parameters on the overall coefficient of performance and heat duty in both heating and cooling modes, considering air or brine as heat source/sink are analyzed in detail. Results demonstrate enhancements of approximately 15 % in capacity and efficiency compared to earlier work. Moreover, four deterministic models were created in order to predict the behaviour of the DSHX and validated against experimental results, reaching deviation values below 15 %.Peer reviewe
Resilience to the Flowing Unknown: An Open Set Recognition Framework for Data Streams
No full textPublisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Modern digital applications extensively integrate Artificial Intelligence models for automated decision-making. However, these AI-based systems encounter reliability and safety challenges when handling continuous data streams in dynamic scenarios. This work explores the concept of resilient AI systems that must operate in the face of unexpected events and unseen patterns. This is a common issue that regular closed-set classifiers encounter in streaming scenarios, as they are designed to compulsory classify any new observation into one of the training patterns (i.e., the so-called over-occupied space problem). In batch learning, the Open Set Recognition field addresses this issue by requiring models to maintain classification performance when processing unknown patterns. This work investigates the application of an Open Set Recognition framework that combines classification and clustering to address the over-occupied space problem in streaming scenarios. We devise a benchmark comprising different classification datasets with varying ratios of known to unknown classes, and experiments compare the performance of the proposed framework with that of individual incremental classifiers. Discussions held over the obtained results highlight situations where the framework performs best and the limitations of incremental classifiers in open-world streaming environments.Peer reviewe
Decentralized Digital Product Passport Building Blocks for Enhancing Supply Chain Sovereignty and Circular Economy Practices
Get PDFPublisher Copyright: © IEEE. 2013 IEEE.This research article explores the decentralization of Digital Product Passport (DPP) building blocks for Asset Administration Shells (AAS), aiming to enhance the management and traceability of digital assets. The DPP system is conceptualized to incorporate three critical elements: identity management, traceability, and access permissions. We present a comprehensive architecture that integrates these elements, ensuring a resilient supply chain. Furthermore, we delve into the custodianship of the DPP data, examining the roles and responsibilities of various stakeholders in maintaining and securing the system. Finally, we experiment with existing technologies to conceptualize a decentralized DPP system for AASs, showcasing its practical application and effectiveness in real-world scenarios. This study sheds some light into the potential of decentralized systems in digital product management, highlighting the benefits and challenges associated with their deployment.Peer reviewe
Heterogeneous Catalysis in Environmental Applications
No full textPublisher Copyright: © 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.Global environmental issues are posing a major threat to human health and well-being. Heterogeneous catalysis is a promising technology to address these challenges not only by selectively removing pollutants, but also converting waste materials into valuable products. Heterogeneous catalysis is expected to have a beneficial impact on the environment.Peer reviewe
AiGAS-dEVL: An Adaptive Incremental Neural Gas Model for Drifting Data Streams under Extreme Verification Latency
No full textNowadays, the rapid generation of data and the high costs of labeling (in most cases, reliant on human supervision) often lead to partially labeled data streams, particularly in scenarios of extreme verification latency (EVL), where supervision is indefinitely unavailable. Additionally, streaming data can exhibit non-stationarities, known as concept drift CD), requiring models to adapt incrementally to evolving data patterns. This paper addresses the simultaneous occurrence of these two issues, where concept tracking and change adaptation mechanisms must operate without supervision. To this end we propose AiGAS-dEVL (Adaptive Incremental neural GAS model for drifting Streams under Extreme Verification Latency), a novel approach utilizing growing neural gas (GNG) to characterize concept distributions in a data stream over time. By analyzing the behavior of prototypical points, our method identifies changes in concept behavior and informs adaptation strategies to accommodate these shifts. Experimental results on various synthetic datasets demonstrate that AiGAS-dEVL outperforms baseline models, achieving an average prequential error improvement of 39.5% across datasets of varying drift characteristics, showcasing enhanced adaptability while maintaining a straightforward and interpretable instance-based adaptation strategy. Keyword: Stream Learning,Concept Drift , Extreme Verification Latency , Growing Neural Gas , Unsupervised Incremental LearningPeer reviewe
Characterization of electrotactile stimulation intensity to exploit the funneling illusion
No full textExploiting the funneling illusion is a promising approach to increase the spatial resolution of tactile sensory feedback, by eliciting a phantom sensation located midway two stimuli on the human skin. To date, this illusion has been tested with vibrotactile stimulation only, although it could be used also with the well-established alternative of electrotactile stimulation. To ensure a seamless feedback delivery combining electrotactile stimulation and funneling illusion, a characterization of the key stimulation parameters is required. Our work aims to characterize the variation of the perceived intensity generated by two electrodes simultaneously activated, depending on the variation of their individual intensities. We asked 6 healthy volunteers to complete a two-alternative forced choice task in three conditions (75%, 50% and 25%) corresponding to different phantom locations. We found that when a two-pad stimulation is delivered, the cumulative intensity of the two pads should be increased to elicit a phantom sensation with the same intensity as a single pad. Specifically, this increment depends on the phantom location, with a peak at the midpoint (i.e., a cumulative activation of 110% and 113,27% for phantom sensations close to the real pads, and 124.6% for the middle point). These findings represent a first step toward the characterization of the funneling illusion with electrotactile stimulation and could be exploited to enhance haptic feedback in fields such as human movement augmentation, prostheses and teleoperation.Peer reviewe
Parameters optimization of short carbon fiber-reinforced polyamide 6 printed using Big Area Additive Manufacturing (BAAM)
No full textPublisher Copyright: © The Author(s) 2025.Big Area Additive Manufacturing (BAAM) of thermoplastic polymers is increasingly adopted for components, tooling and, especially, moulds across various industries and sectors as aerospace, automotive, energy, marine or construction due to its high productivity and low investment costs. However, geometric accuracy, layer adhesion and anisotropic properties of 3D printed parts are the main detrimental aspects for the successful implementation of this technology in the industry. As 3D printed parts need to withstand similar loads and stresses as those manufactured by traditional methods to be viable in industrial and engineering applications, enhancing mechanical properties ensures that parts do not break or deform under normal use. In this study, the effects of the main process parameters in the geometry distortion, inner defects and interfacial adhesion of 20% carbon fiber-reinforced polyamide 6 printed by pellet extrusion BAAM were determined, and the optimal process window was identified in terms of substrate deposition temperature, layer height and extruder motor rotation. With optimal parameters, the voids formed inbetween deposited beads were removed and the mechanical performance at room temperature in the z-direction compared to the bead direction was highly improved to 42% and 35% for tensile strength and modulus, respectively. It means a significant enhancement compared to the results reported by the state of the art, which show average values below 25%, and a highly notable improvement of the mechanical properties in the z-direction.Peer reviewe
A Statistical Review of Hydrogen Effects on the Fatigue and Fracture Behavior of Steel
Get PDFPublisher Copyright: © 2025 The Author(s). Fatigue & Fracture of Engineering Materials & Structures published by John Wiley & Sons Ltd.This study conducts a statistical re-analysis of experimental data from the literature to assess the influence of hydrogen on key mechanical properties, including the medium-/high-cycle fatigue strength and the threshold value of the stress intensity factor range. The analysis employs linear regression, S-N curve plotting, and Paris' law regression. The results indicate that hydrogen has a minimal effect on the endurance limit of steel (estimated at (Formula presented.) cycles to failure), in contrast to the reductions in lifespan observed in the medium-cycle fatigue regime. Regarding crack propagation, the threshold value of the stress intensity factor range is reduced in the presence of hydrogen, particularly in conventional steel, which is more susceptible to hydrogen embrittlement than stainless steel. Conversely, systematic evaluation of constants linked to Paris' equation across various material types revealed considerable variability, suggesting a non-discernible trend in the response to hydrogen.Peer reviewe