UPCommons (Universitat Politècnica de Catalunya)
Not a member yet
281550 research outputs found
Sort by
Evaluating risk-based hazard corridors in air traffic controller decisions during space launch failures
No full textThe increasing frequency and diversity of space launch activities challenge the safety and reliability of current air traffic management systems. In this study, we present a risk-based hazard corridor methodology for managing air traffic during space launch failures. Our method combines a debris propagation model with a hazard corridor construction approach that estimates the risk posed by debris to aircraft. We evaluated the constructed risk-based hazard corridors using high-fidelity human-in-the-loop simulations. In our experiments, air traffic controllers managed two strategies of hazard corridors. The dynamic hazard corridor updated the boundary in real-time while the static hazard corridor remained fixed by consolidating the dynamic boundaries over the entire activation period until the last piece of debris fell. The results show that controllers maintained safety separation across all scenarios, although their real-time workload increased significantly during hazard corridor activation. Overall, the controllers’ perceived workload and situation awareness remained stable, implying that the task demands were acceptable for all the experimental runs. Efficiency measure results indicate that the dynamic hazard corridor can reduce extra flight distance and delays, thus minimizing operational disruption caused by space launch failures. We also found that more experienced controllers tend to choose more cautious and conservative rerouting strategies. These findings offer practical guidance for improving resilience in air and space management integration. Furthermore, our study provides a basis for modeling air traffic controller behavior under emergency conditions in a way that is more in line with the real world patterns.This research is supported by the Office of Space, Technology and Industry, Singapore (OSTIn) / Singapore Economic Development Board (EDB) through the National Research Foundation (NRF) of Singapore
grant under the Space Technology Development Programme. Any opinions, findings, and conclusions or
recommendations expressed in this material are those of the author(s) and do not reflect the views of the
OSTIn, EDB, NRF Singapore, or the Civil Aviation Authority of SingaporePeer ReviewedPostprint (published version
Hydrogen peroxide pretreatment of aqueous phase product of hydrothermal sludge liquefaction for enhanced anaerobic and aerobic biodegradability
No full textWastewater treatment plants (WWTP) generate municipal sludge (MS) that contains high organic and inorganic matter, creating disposal challenges. Hydrothermal liquefaction (HTL) is a promising method for converting sludge into value-added products (biocrude oil, hydrochar) but generates a large volume of aqueous by-product (HTLaq) with soluble inhibitory organics to downstream biological treatment. This creates a bottleneck to incorporate HTL to WWTPs. This study investigated hydrogen peroxide (H2O2) pretreatment of HTLaq to improve its biodegradability for downstream treatment. Pretreatment with H2O2 dosages of 0.25, 0.50, and 0.75 g H2O2/g chemical oxygen demand (COD) of HTLaq, followed by quenching with sodium carbonate (Na2CO3), significantly reduced total COD (tCOD) and phenolic compounds. The highest tCOD removal (18%) occurred with 0.75 g H2O2/g COD, while the 0.25 g H2O2/g COD with Na2CO3 quencher showed the highest (63%) increase in cumulative methane yield under thermophilic conditions. Aerobic biodegradability index, quantified by biochemical oxygen demand (BOD)/tCOD ratio, also increased from 0.75 to 0.85. The results suggest that lowdosage H2O2 pretreatment enhances the biodegradability of HTLaq, making it more amenable for downstream biological treatment.The authors gratefully acknowledge the generous support of Metro Vancouver and the Natural Sciences and Engineering Research Council of Canada (NSERC) under the Industrial Research Chair Program in Advanced Resource Recovery from Wastewater (IRCPJ 548816–18). The authors thank Jacky Takeuchi (research technician) and Dr. Parmila Devi (research engineer) at the Bioreactor Technology Group, School of Engineering, University of British Columbia, for their help with the analysis of organic compounds using GC-MS and some of the laboratory procedures & set-up. The authors also acknowledge the Syilx Okanagan Nation for the use of their unceded ancestral traditional territory, the land on which the research was conducted.Peer Reviewed7 - Energia Assequible i No Contaminant6 - Aigua Neta i Sanejament9 - Indústria, Innovació i Infraestructura11 - Ciutats i Comunitats SosteniblesPostprint (published version
Topological reconstruction of sampled surfaces via Morse theory
No full textIn this work, we study the perception problem for sampled surfaces (possibly with boundary) using tools from computational topology, specifically, how to identify their underlying topology starting from point-cloud samples in space, such as those obtained with 3D scanners. We present a reconstruction algorithm based on a careful topological study of the point sample that allows us to obtain a cellular decomposition of it using a Morse function. No triangulation or local implicit equations are used as intermediate steps, avoiding in this way reconstruction-induced artifices. The algorithm can be run without any prior knowledge of the surface topology, density or regularity of the point-sample. The results consist of a piece-wise decomposition of the given surface as a union of Morse cells (i.e. topological disks), suitable for tasks such as mesh-independent reparametrization or noise-filtering, and a small-rank cellular complex determining the topology of the surface. The algorithm, which we test with several real and synthetic sampled surfaces, can be applied to clouds coming from smooth surfaces with or without boundary, embedded in an ambient space of any dimension.This research work was funded by the European Commission - NextGenerationEU, through Momentum CSIC Programme: Develop Your Digital Talent. Franco Coltraro is staff hired under the Generation D initiative, promoted by Red.es, an organization attached to the Ministry for Digital Transformation and the Civil Service, for the attraction and retention of talent through grants and training contracts, financed by the Recovery, Transformation and Resilience Plan through the European Union’s Next Generation funds. F. Coltraro was also partially supported by the ClothIRI (CSIC 202350E080) project and the RobIRI 2021-SGR-00514 AGAUR project. Jaume Amorós and Maria Alberich-Carramiñana have been partially supported by the projects PID2019-103849GB-I00 and PID2023-146936NB-I00 financed by the Spanish State Agency MICIU/AEI/10.13039/501100011033 and by ERDF/EU, and by the GEOMVAP 2021-SGR-00603 AGAUR project.Peer ReviewedPostprint (published version
Scalable energy-aware VM allocation on cloud data centers through mathematical programming models
No full textCloud data centers are becoming indispensable pillars of modern society, driving AI innovation, global connectivity, and data-driven advancements. As their size and complexity grow, so does the urgency for sustainable and efficient solutions to address operational and environmental challenges. The Virtual Machine (VM) allocation problem lies at the heart of these challenges, directly impacting energy consumption, scalability, and cost-effectiveness. While heuristics are traditionally favored for their fast execution times, they fail to adequately address the complexities of heterogeneous environments and the increasing need for energy-aware solutions. In this work, we redefine the potential of mathematical programming models — traditionally considered impractical due to scalability limitations — by defining a comprehensive VM allocation strategy that embeds the models into scalable algorithms that distribute computational workloads and exploit solver capabilities. This approach achieves linear scalability — an unprecedented milestone for mathematical programming — allowing us to integrate detailed and heterogeneous aspects of the VM allocation problem. The resulting algorithms dramatically outperform state-of-the-art heuristics and metaheuristics in both scalability and solution quality, delivering an average 16% increase in Net Profit, a 54% reduction in Total Energy Consumption, and a more-than-double improvement in Energy Efficiency. Designed to meet the evolving demands of modern Cloud data centers, our algorithms scale efficiently to manage growing workloads, adapt to heterogeneity, and comply with sustainability and regulatory requirements by prioritizing energy efficiency, facilitating the transition to next-generation Cloud environments.This research was partially supported by the EU-HORIZON programme under grant agreement EU-HORIZON GA.101092646, by the Spanish Ministry of Science and the Research State Agency (MICIU/AEI/ 10.13039/501100011033) and by European Regional Development Funds (ERDF/FEDER) under contract PID2021-126248OB-I00, and by the Generalitat de Catalunya (AGAUR) under contract 2021-SGR-00478.Peer ReviewedPostprint (published version
A Surrogate model for topology optimisation of elastic structures via parametric autoencoders
No full textA surrogate-based topology optimisation algorithm for linear elastic structures under parametric loads and boundary conditions is proposed. Instead of learning the parametric solution of the state (and adjoint) problems or the optimisation trajectory as a function of the iterations, the proposed approach devises a surrogate version of the entire optimisation pipeline. First, the method predicts a quasi-optimal topology for a given problem configuration as a surrogate model of high-fidelity topologies optimised with the homogenisation method. This is achieved by means of a feed-forward net learning the mapping between the input parameters characterising the system setup and a latent space determined by encoder/decoder blocks reducing the dimensionality of the parametric topology optimisation problem and reconstructing a high-dimensional representation of the topology. Then, the predicted topology is used as an educated initial guess for a computationally efficient algorithm penalising the intermediate values of the design variable, while enforcing the governing equations of the system. This step allows the method to correct potential errors introduced by the surrogate model, eliminate artifacts, and refine the design in order to produce topologies consistent with the underlying physics. Different architectures are proposed and the approximation and generalisation capabilities of the resulting models are numerically evaluated. The quasi-optimal topologies allow to outperform the high-fidelity optimiser by reducing the average number of optimisation iterations by 53% while achieving discrepancies below 4% in the optimal value of the objective functional, even in the challenging scenario of testing the model to extrapolate beyond the training and validation domain.The authors would like to express their sincere gratitude to Dr. Guillem Barroso, whose contributions were instrumental in initiat-ing and shaping this work. His efforts during the early stages of the project were essential to its development. This work was supported by MCIU/AEI/10.13039/501100011033, Spanish Ministry of Science, Innovation and Universities and Spanish State Research Agency (Grants No. TED2021-132021B-I00, PID2023-149979OB-I00) and by the Generalitat de Catalunya (Grant No. 2021-SGR-01049). MG is Fellow of the Serra Húnter Programme of the Generalitat de Catalunya.Peer ReviewedPostprint (published version
A GCN-GRU-KAN-Based Framework for UWB 3D localization in adverse geometric configurations
No full textThis study addressed the challenge of three-dimensional (3D) positioning using an ultra-wide band (UWB) in environments with poor geometric configurations of base stations. To improve the positioning accuracy in narrow spaces and with limited base station deployment, a deep learning model combining Graph Convolutional Networks (GCN), Gate Recurrent Units (GRU), and Kolmogorov-Arnold Networks (KAN) was proposed. The GCN extracted the spatial structural features, the GRU captured the temporal sequence information, and the KAN adaptively integrated the key features to further enhance accuracy. The experimental results indicated that introducing a GCN reduced the positioning error by 43.0 %–50.6 % and the standard deviation by 24.2 %–43.3 %, compared to Convolutional Neural Networks (CNN). Compared to the fully connected layers, KAN reduced the positioning error by 30.4 %–62.2 % and the standard deviation by 6.0 %–61.3 %. The model achieved an average positioning error of less than 10 cm, with accuracy improvements of 22.2 %–76.1 %, surpassing the traditional methods and other deep learning algorithms, and demonstrating exceptional robustness and adaptability.Peer ReviewedPreprin
Emerging therapies for improving stereoacuity in amblyopia. A systematic review and meta-analysis
No full textEmerging treatments, including virtual reality (VR)-based therapies, video games, and movies, have been proposed to enhance stereoacuity in individuals with binocular vision disorders such as amblyopia and strabismus. However, their comparative effectiveness remains uncertain. This systematic review and meta-analysis aimed to evaluate the effectiveness of these emerging treatments in improving stereoacuity through within-group analyses, and to compare their outcomes with occlusion, in studies with direct group comparisons. We conducted comprehensive literature searches in PubMed, MEDLINE, Cochrane Library, Scopus, and Web of Science. Eligible studies included randomized controlled trials, cohort studies, and case-control studies reporting stereoacuity outcomes. The primary outcome was the change in stereoacuity (log arcsec). A random-effects meta-analysis, subgroup comparisons, and meta-regressions were performed. Twenty-six studies were included. The pooled mean improvement in stereoacuity was 0.26 log arcsec, i.e. a factor of 1.82 (95 % CI: 0.19–0.33). Emerging treatments yielded significant within-group improvements, with no significant difference compared to occlusion therapy. VR-based interventions did not show statistically significant advantages over non-VR binocular treatments. Movies showed slightly greater gains than video games, but differences were not significant after correction. In regression analyses, no predictors remained significant after Bonferroni correction. Heterogeneity was moderate, reflecting variability across studies. In conclusion, emerging therapies demonstrate measurable benefits in enhancing stereoacuity. However, they have not consistently outperformed occlusion.Peer ReviewedPostprint (author's final draft
A Grey-box temperature model for digital twins of building-integrated rooftop greenhouses
No full textUrban agriculture is increasingly recognized as a strategy for enhancing food security, sustainability, and climate resilience in cities. Building-Integrated Rooftop Greenhouses (BIRTGs) offer a compelling solution by combining food production with building energy synergies. However, accurately predicting indoor temperature in such systems remains challenging due to their low thermal inertia, highly variable heat transfer dynamics, and the complex, time-dependent thermal interactions introduced by crops. This study presents a grey-box temperature model tailored for BIRTGs and designed for integration into Digital Twin (DT) frameworks. The model captures heat exchanges with the outdoor environment, host building, solar radiation, ventilation and plant evapotranspiration. Unlike traditional models, the evapotranspiration component is calibrated using sensor data, bypassing the need for complex crop-specific inputs. The model was calibrated and validated using experimental data from a BIRTG at Universitat Politècnica de Catalunya, both with and without crops. Validation results showed high predictive accuracy, with RMSE values of 2.2 °C without plants and 2.0 °C with plants. Furthermore, a systematic evaluation of 70 dataset combinations revealed that a single day of calibration data is sufficient to accurately forecast temperature over a 24-hour period. These results highlight the suitability of the model for real-time climate control and automation in urban agriculture using DTs.This research is part of the projects MOVE4EDU (ref. no. PID2021- 126845OB-C22), funded by MCIN/AEI/10.13039/501100011033/ FEDER, UE, and BINAFET (ref. no. TED2021-130047B-C22), funded by MCIN/AEI/10.13039/501100011033 and the European Union through “NextGenerationEU”/PRTR. Additionally, this study is part of the WEF4Build research and development project (ref. no. 2023 CLIMA 00041), funded by the Catalan agency AGAUR. It also received support from AGAUR through its research group support programme (ref. no. 2021 SGR 00341). Francesc Pardo-Bosch acknowledges the support of the Serra Húnter Program.Peer ReviewedPostprint (published version
Nonlinear projection-based model order reduction with machine learning regression for closure error modeling in the latent space
No full textA significant advancement in nonlinear projection-based model order reduction (PMOR) is presented through a highly effective methodology. This methodology employs Gaussian process regression (GPR) and radial basis function (RBF) interpolation for closure error modeling in the latent space, offering notable gains in efficiency and expanding the scope of PMOR. Moving beyond the limitations of deep artificial neural networks (ANNs), previously used for this task, this approach provides crucial advantages in terms of interpretability and a reduced demand for extensive training data. The capabilities of GPR and RBFs are showcased in two demanding applications: a two-dimensional parametric inviscid Burgers problem, featuring propagating shocks across the entire computational domain, and a complex three-dimensional turbulent flow simulation around an Ahmed body. The results demonstrate that this innovative approach preserves accuracy and achieves substantial improvements in efficiency and interpretability when contrasted with traditional PMOR and ANN-based closure modeling.Charbel Farhat and Radek Tezaur acknowledge partial support by the Office of Naval Research under Grant N00014-23-1-2877 and Grant N00014-23-1-2413; and partial support by the Air Force Office of Scientific Research under Grant FA9550-22-1-0004 and Grant FA9550-20-1-0358. Sebastian Ares de Parga acknowledges partial support by the Departament de Recerca i Universitats de la Generalitat de Catalunya under Grant FI-SDUR 2021; partial support by the Fulbright Commission Spain through a Fulbright Predoctoral Research Fellowship (2024–2025); and partial support by the Department of Aeronautics and Astronautics at Stanford University.Peer ReviewedPostprint (published version
Self-catalytic flash sintering: a versatile approach for energy-efficient sintering and enhanced densification of ceramics
No full textThis work introduces the concept of self-catalytic flash sintering as a versatile and energy-efficient method for densifying ceramic materials. By exploiting differences in local conductivity within a single composition, self-catalytic flash sintering enables the activation of the flash event at significantly lower furnace temperatures and electric fields than conventional flash sintering. Using barium titanate (BaTiO3) as a model system, the study demonstrates that self-catalytic flash sintering achieves both a lower onset temperature for the flash event and improved densification compared to conventional flash sintering and catalytic flash sintering. This dual improvement offers substantial energy savings and enhanced mechanical stability while maintaining the functional performance of the material. Overall, the self-catalytic flash sintering emerges as a promising and generalizable approach for energy-efficient ceramic processing.Peer ReviewedPostprint (published version