OpenAIRE (Open Access Infrastructure for Research in Europe)
Documentos de investigaciones financiadas por la UE en FP7 (2007–2013), Horizon 2020 (2014–2020) y Horizon Europe (2021–2027) —incluido el Consejo Europeo de Investigación (ERC) y MSCA— en el marco de OpenAIRE , que promueve el acceso abierto en Europa. Desde 2021, todos los beneficiarios deben mencionar la financiación de la UE en sus resultados (obligación legal en los programas 2021–2027).
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Permanent URI for this collectionhttps://riunet.upv.es/handle/10251/9185
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Item type: Artículo , Access status: Abierto , A pressure-impact approach to assess contamination and risk in surface water bodies(MDPI AG, 2025-11-14) Aydi, Siwar; Paredes Arquiola, Javier; Bergillos-Meca, Rafael Jesús; Solera Solera, Abel; Andreu Álvarez, Joaquín; Departamento de Ingeniería Hidráulica y Medio Ambiente; Instituto Universitario de Ingeniería del Agua y del Medio Ambiente; Escuela Técnica Superior de Ingeniería de Caminos, Canales y Puertos; European Commission; AGENCIA ESTATAL DE INVESTIGACION[EN] This study assesses the chemical state of surface water bodies (SWBs) in the Júcar River Basin District (Spain), specifically focusing on contaminants such as nickel, lead, imazalil, and thiabendazole. To identify risky zones, the RREA model was combined with a Python-based subroutine to estimate the minimum non-compliance load (MNCL). The results show that many SWBs fail to meet water quality criteria due to point source pollution. The RREA (Rapid Response to Environmental status) model improves monitoring capacities by confirming SWB chemical statuses and detecting locations that have not been monitored or assessed thoroughly. The study also analyzes confidence levels by comparing MNCL to the current accumulated load (CAL), allowing for the identification and prioritization of important non-compliant SWBs and locations that require additional examination. This methodology not only enhances the accuracy of compliance evaluations but also serves as a useful tool for targeted water quality management initiatives. The results of this paper highlight the potential of the proposed pressure-impact approach to assess the chemical state of SWBs. This approach is useful to support sustainable management measures that mitigate water quality issues and preserve the environmental status of SWBs.Item type: Artículo , Access status: Abierto , The CHEWMA chart: A new statistical control approach for microclimate monitoring in preventive conservation of cultural heritage(MDPI AG, 2025-02) Díaz-Arellano, Ignacio; Zarzo Castelló, Manuel; Departamento de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Informática; Grupo de Ingeniería Estadística Multivariante GIEM; COMISION DE LAS COMUNIDADES EUROPEA[EN] A new statistical control chart denoted as CHEWMA (Cultural Heritage EWMA) is proposed for microclimate monitoring in preventive conservation. This tool is a real-time detection method inspired by the EN 15757:2010 standard, serving as an alternative to its common adaptations. The proposed control chart is intended to detect short-term fluctuations (STFs) in temperature (T) and relative humidity (RH), which would enable timely interventions to mitigate the risk of mechanical damage to collections. The CHEWMA chart integrates the Exponentially Weighted Moving Average (EWMA) control chart with a weighting mechanism that prioritizes fluctuations occurring near extreme values. The methodology was validated using RH time series recorded by seven dataloggers installed at the Alava Fine Arts Museum, and, from these, seventy simulated time series were generated to enhance the robustness of the analyses. Sensitivity analyses demonstrated that, for the studied dataset, the CHEWMA chart exhibits stronger similarity to the application of EN 15757:2010 than other commonly used real-time STF detection methods in the literature. Furthermore, it provides a flexible option for real-time applications, enabling adaptation to specific conservation needs while remaining aligned with the general framework established by the standard. To the best of our knowledge, this is the first statistical process control chart designed for the field of preventive conservation of cultural heritage. Beyond assessing CHEWMA¿s performance, this study reveals that, when adapting the procedures of the European norm by developing a new real-time approach based on a simple moving average (herein termed SMA-FT), a window of approximately 14 days is more appropriate for STF detection than the commonly assumed 30-day period in the literature.Item type: Artículo , Access status: Abierto , Task Offloading Optimization Using PSO in Fog Computing for the Internet of Drones(MDPI AG, 2025-01) Zaidi, Sofiane; Attalah, Mohamed Amine; Khamer, Lazhar; Tavares De Araujo Cesariny Calafate, Carlos Miguel; Departamento de Informática de Sistemas y Computadores; Escuela Técnica Superior de Ingeniería Informática; Grupo de Redes de Computadores; European Commission; GENERALITAT VALENCIANA; AGENCIA ESTATAL DE INVESTIGACION; European Regional Development Fund[EN] Recently, task offloading in the Internet of Drones (IoD) is considered one of the most important challenges because of the high transmission delay due to the high mobility and limited capacity of drones. This particularity makes it difficult to apply the conventional task offloading technologies, such as cloud computing and edge computing, in IoD environments. To address these limits, and to ensure a low task offloading delay, in this paper we propose PSO BS-Fog, a task offloading optimization that combines a particle swarm optimization (PSO) heuristic with fog computing technology for the IoD. The proposed solution applies the PSO for task offloading from unmanned aerial vehicles (UAVs) to fog base stations (FBSs) in order to optimize the offloading delay (transmission delay and fog computing delay) and to guarantee higher storage and processing capacity. The performance of PSO BS-Fog was evaluated through simulations conducted in the MATLAB environment and compared against PSO UAV-Fog and PSO UAV-Edge IoD technologies. Experimental results demonstrate that PSO BS-Fog reduces task offloading delay by up to 88% compared to PSO UAV-Fog and by up to 97% compared to PSO UAV-Edge.Item type: Artículo , Access status: Abierto , Optimising flexibility in highly electrified energy communities: A Mediterranean perspective(Elsevier, 2025-10-01) Manso-Burgos, Álvaro; Ribó-Pérez, David Gabriel; Aparisi-Cerdá, Isabel; Gómez-Navarro, Tomás; Madani, Hatef; Departamento de Proyectos de Ingeniería; Escuela Técnica Superior de Ingeniería Aeroespacial y Diseño Industrial; Departamento de Ingeniería Eléctrica; Instituto Universitario de Investigación de Ingeniería Energética; Escuela Técnica Superior de Ingeniería Industrial; AGENCIA ESTATAL DE INVESTIGACION; COMISION DE LAS COMUNIDADES EUROPEA; Universitat Politècnica de València[EN] Energy communities are emerging as key enablers of the energy transition, facilitating greater electrification and providing essential flexibility services in urban energy systems. This study explores the economic performance of energy communities in Mediterranean cities, focusing on integrating solar generation with a battery energy storage system, electric vehicles, air-source heat pumps, and electric water heaters. Using a mathematical optimisation model applied to a case study in Catarroja, Spain, the research evaluates the techno-economic interactions of these technologies under different economic and regulatory conditions. The results highlight that combining solar generation with flexible technologies enhances economic viability. At the same time, the level of electrification plays a crucial role in determining the profitability of battery storage and photovoltaic systems. These findings underscore the importance of flexibility in designing and operating energy communities, offering valuable insights for policymakers and stakeholders aiming to optimise collective energy systems and support a sustainable transition.Item type: Artículo , Access status: Abierto , Personalised Risk Modelling for Older Adult Cancer Survivors: Combining Wearable Data and Self-Reported Measures to Address Time-Varying Risks(MDPI AG, 2025-03-27) Valero Ramon, Zoe; Gema Ibanez-Sanchez; Martinez-Millana, Antonio; Fernández Llatas, Carlos; Departamento de Ingeniería Electrónica; Escuela Técnica Superior de Ingeniería de Telecomunicación; Departamento de Sistemas Informáticos y Computación; Instituto Universitario de Tecnologías de la Información y Comunicaciones; Escuela Técnica Superior de Ingeniería Informática; COMISION DE LAS COMUNIDADES EUROPEA[EN] Recent advancements in wearable devices have significantly enhanced remote patient monitoring, enabling healthcare professionals to evaluate conditions within home settings. While electronic health records (EHRs) offer extensive clinical data, they often lack crucial contextual information about patients¿ daily lives and symptoms. By integrating continuous self-reported outcomes related to vulnerability, anxiety, and depression from older adult cancer survivors with objective data from wearables, we can develop personalised risk models that address time-varying risk factors in cancer care. Our study combines real-world data from wearable devices with self-reported information, employing process mining techniques to analyse dynamic risk models for vulnerability and anxiety. Unlike traditional static assessments, this approach recognises that risk factors evolve. Collaborating with healthcare professionals, we analysed data from the LifeChamps study to create two dynamic risk models. This collaborative effort revealed how activity and sleep patterns influence self-reported vulnerability and anxiety among participants. It underscored the potential of wearable sensors and artificial intelligence techniques for deeper analysis and understanding, making us all part of a larger effort in cancer care. Overall, patients with prolonged sedentary activity had a higher risk of vulnerability, while those with highly dynamic sleep patterns were more likely to report anxiety and depression. Prostate-metastatic patients showed an increased risk of vulnerability compared to other cancer types.Item type: Artículo , Access status: Abierto , Intestinal microbiota shifts by dietary intervention during extreme heat summer episodes in farmed gilthead sea bream (Sparus aurata)(Elsevier, 2025-03-15) Domingo-Bretón, Ricardo; Cools, Steven; Moroni, Federico; Belenguer, Álvaro; Calduch-Giner, Josep Àlvar; Croes, Evi; Holhorea, Paul George; Naya-Català, Fernando; Boon, Hans; Pérez-Sánchez, J.; European Commission; Generalitat Valenciana[EN] Climate change and its associated extreme events alter a number of physiological processes that also affect the homeostatic relationship of the host with their microbial communities. The aim of this study was to gain more insights on this issue, examining the effect of the record breaking-heat summer of 2022 on the gut microbiota of farmed gilthead sea bream (Sparus aurata), reared from May to August at the IATS research infrastructure (Spain¿s Mediterranean coast). Fish were fed daily with four experimental diets, containing two different lipid levels (16 % and 14 %) with/without a commercial emulsifier (0.1 %; Volamel Aqua, Nukamel). On August 9th, concurrently with the historical record of water temperature (30.49 ºC), fish were sampled for analysis of bloodstress markers and water/intestinal microbiota. Gut microbiota analysis clearly evidenced the increased abun dance of bacteria of Spirochaetota phylum, mainly represented by the genus Brevinema. This microbiota shift was not driven by environmental colonization as this bacteria genus remained residual in water samples with the increase of temperature. Bayesian network and functional enrichment analyses suggested that the high abun dance of Brevinema exploits and negatively enhances a condition of imbalance in intestinal homeostasis, which was almost completely reversed by the use of dietary emulsifiers in combination with low energized diets. This phenotype restoration occurred in concomitance with changes in circulating levels of cortisol and glucose. Altogether this highlights the potential use of Brevinema as a heat-stress indicator, reinforcing the value of dietary intervention as a valuable solution to mitigate the negative impact of global warming on aquaculture production.Item type: Artículo , Access status: Abierto , A Resilient Distributed Pareto-Based PSO for Edge-UAVs Deployment Optimization in Internet of Flying Things(MDPI AG, 2025-10-24) Zerrougui, Sabrina; Zaidi, Sofiane; Tavares De Araujo Cesariny Calafate, Carlos Miguel; Departamento de Informática de Sistemas y Computadores; Escuela Técnica Superior de Ingeniería Informática; Grupo de Redes de Computadores; European Commission; Generalitat Valenciana; Agencia Estatal de Investigación; European Regional Development Fund[EN] Particle Swarm Optimization (PSO) has been widely employed to optimize the deployment of Unmanned Aerial Vehicles (UAVs) in various scenarios, particularly because of its efficiency in handling both single and multi-objective optimization problems. In this paper, a framework for optimizing the deployment of edge-enabled UAVs using Pareto-PSO is proposed for data collection scenarios in which UAVs operate autonomously and execute onboard distributed multi-objective PSO to maximize the total non-overlapping coverage area while minimizing latency and energy consumption. Performance evaluation is conducted using key indicators, including convergence time, throughput, and total non-overlapping coverage area across bandwidth and swarm-size sweeps. Simulation results demonstrate that the Pareto-PSO consistently attains the highest throughput and the largest coverage envelope, while exhibiting moderate and scalable convergence times. These results highlight the advantage of treating the objectives as a vector-valued objective in Pareto-PSO for real-time, scalable, and energy-aware edge-UAV deployment in dynamic Internet of Flying Things environments.Item type: Artículo , Access status: Abierto , Precision RNAi in Tomato Using Synthetic Trans-Acting Small Interfering RNAs Derived From Minimal Precursors(Blackwell Publishing, 2025-10) Tomassi, Ariel H.; Juárez Molina, María; Cisneros, Adriana E.; Alarcia-García, Ana; Orlando, Francesca; Toledano-Franco, Sara; Presa Castro, Silvia; GRANELL RICHART, ANTONIO; CARBONELL, ALBERTO; Instituto Universitario Mixto de Biología Molecular y Celular de Plantas; European Commission; Agencia Estatal de Investigación; Ministerio de Ciencia e Innovación[EN] RNA interference (RNAi) is a highly conserved gene silencing mechanism regulating gene expression at transcriptional and post-transcriptional levels in plants. Synthetic trans-acting small interfering RNAs (syn-tasiRNAs) have emerged as powerful tools for highly specific and efficient gene silencing. However, their application in crops has been constrained by the need for transgene integration and the relatively long length of TAS-derived precursors. Here, we developed a novel syn-tasiRNA platform for Solanum lycopersicum (tomato) based on minimal precursors targeted by endogenous SlmiR482b microRNA. These minimal precursors, comprising only a 22-nt miRNA target site, an 11-nt spacer, and the syn-tasiRNA sequence(s), effectively produced functional syn-tasiRNAs in both transgenic and transient virus-induced gene silencing (syn-tasiR-VIGS) systems. To facilitate their broader application, we engineered a series of vectors for high-throughput cloning and efficient syn-tasiRNA expression from SlmiR482b-based minimal precursors in tomato. Our results show that minimal precursors induce robust gene silencing of endogenous tomato genes and confer antiviral resistance to the economically important tomato spotted wilt virus. Furthermore, we show that syn-tasiR-VIGS can be applied in a transgene-free manner through crude extract delivery, leading to efficient silencing of endogenous genes. This study establishes minimal syn-tasiRNA precursors as a versatile and efficient tool for precision RNAi in tomato, with applications in functional genomics and crop improvement.Item type: Artículo , Access status: Abierto , FEA-Assisted Test Bench to Enhance the Comprehension of Vibration Monitoring in Electrical Machines--A Practical Experiential Learning Case Study(MDPI AG, 2025-08-12) Ruiz-Sarrió, José Enrique; Madariaga-Cifuentes, Carlos; J. Antonino-Daviu; Instituto de Tecnología Eléctrica; Departamento de Ingeniería Eléctrica; Escuela Técnica Superior de Ingeniería Industrial; COMISION DE LAS COMUNIDADES EUROPEA[EN] Rotating electrical machine maintenance is a core component of engineering education curricula worldwide. Within this context, vibration monitoring represents a widespread methodology for electrical rotating machinery monitoring. However, the multi-physical nature of vibration monitoring presents a complex learning scenario, including concepts from both mechanical and electrical engineering domains. This article proposes a novel knowledge-based educational experience design leveraging an integrated FEA-assisted test bench aimed at comprehensively addressing the electromechanical link between stator current and frame vibration. To this aim, a Finite Element Analysis (FEA) model is utilized to link excitation electrical signals with airgap radial forces acting in the stator. The subsequent correlation of these FEA predictions with measured frame vibrations on a physical test bench provides students with the theoretical concepts and practical tools to adequately comprehend this complex multi-physical phenomenon of wide application in real industrial scenarios. The pedagogical potential of the method also includes the development of critical thinking and problem-solving soft skills, and foundational understanding for digital twin concepts. A Delphi-style expert survey conducted with 25 specialists yielded strong support for the pedagogical robustness and relevance of the method, with mean ratings between 4.32 and 4.64 out of 5 across key dimensions. These results confirm the potential to enhance deep understanding and practical skills in vibration-based electrical machine diagnosis.Item type: Artículo , Access status: Abierto , Subject grounding to reduce electromagnetic interference for MRI scanners operating in unshielded environments(John Wiley & Sons, 2025-10) Lena, Beatrice; de Vos, Bart; Guallart-Naval, Teresa; Parsa, Javad; García Cristóbal, Pablo; van den Broek, Ruben; Najac, Chloe; Alonso-Otamendi, Joseba; Webb, Andrew; European Commission; Agencia Estatal de Investigación; Netherlands Organization for Scientific Research[EN] Purpose Portable low-field (<0.1T) MRI is increasingly used for point-of-care imaging, but electromagnetic interference (EMI) presents a significant challenge, especially in unshielded environments. EMI can degrade image quality and compromise diagnostic utility. This study investigates whether subject grounding can effectively reduce EMI and improve image quality, comparing different grounding strategies. Methods Experiments were conducted using a 47 mT Halbach-based MRI scanner with a single receive channel. Reproducibility was evaluated at a second site using a 72 mT scanner with similar geometry. Turbo spin echo sequences were used to image the hand and brain. Subject grounding was implemented using conductive cloth sleeves or electrocardiography (ECG) electrodes, each connected between the subject's skin and scanner ground. Three EMI conditions were tested: ambient, added single-frequency EMI, and broadband EMI. SNRs were calculated under each configuration. Results Subject grounding significantly reduced EMI in both hand and brain scans. In hand imaging, conductive sleeves reduced noise from 85x to 1.25x the 50-ohm noise floor. In brain imaging, grounding alone reduced noise from 55x to 25x baseline; when combined with arc RF shields, noise was further reduced to 1.2x baseline, even under added EMI. These results were reproducible across different scanners and locations. Conclusion Subject grounding is a simple, effective, and reproducible strategy for mitigating EMI in portable low-field MRI. It is especially effective for hand imaging, while brain imaging benefits from additional RF shielding. The approach is robust under various EMI conditions and may complement other denoising techniques.Item type: Artículo , Access status: Abierto , Toward Carbon Neutrality: A Methodological Approach for Assessing and Mitigating Urban Emissions at the Neighborhood Level, Applied to Benicalap, Valencia(MDPI AG, 2025-06-03) Vargas-Salgado, Carlos; Montagud- Montalvá, Carla; Alfonso-Solar, David; Izquierdo-De-Andrés, Lucía; Departamento de Termodinámica Aplicada; Departamento de Ingeniería Eléctrica; Instituto Universitario de Investigación de Ingeniería Energética; Escuela Técnica Superior de Ingeniería Industrial; COMISION DE LAS COMUNIDADES EUROPEA[EN] This study presents a methodology for estimating the carbon footprint of urban neighborhoods as a necessary step in proposing and evaluating potential GHG reduction measures to enhance the sustainability of cities. Additionally, this method has been applied to Benicalap, a district in Valencia, Spain. This research employs the Datadis, QGIS, and HOMER tools to assess emissions across Scopes 1, 2, and 3. Tailored mitigation strategies are proposed, primarily focusing on reducing emissions in Scopes 1 and 2. While previous studies have extensively examined CO2 emissions at broader geographic scales, like nations, regions, and cities, this study emphasizes the importance of neighborhood-level analysis to address localized environmental challenges effectively. The results reveal that Benicalap¿s emissions contribute 28.69 ktCO2 (15.56%) to Scope 1, 13.71 ktCO2 (7.43%) to Scope 2, and 142 ktCO2 (77%) to Scope 3. By 2030, targeted interventions could reduce emissions from Scopes 1 and 2 by 19,885 ktCO2, representing a 50.69% reduction. Among the proposed measures, sustainable transportation improvements and photovoltaic deployment stand out, contributing to 25.39% and 24.87% of the reduction, respectively. Enhancements in public lighting and nature-based solutions would offer a minor decrease of 0.43%. These insights underscore the need for strategic, localized interventions to achieve meaningful emission reductions and support sustainable urban development efforts.Item type: Artículo , Access status: Abierto , Using Transformers and Reinforcement Learning for the Team Orienteering Problem Under Dynamic Conditions(MDPI AG, 2025-07-20) Guerrero, Antoni; Escoto-Gomar, Marc; Ammouriova, Majsa; Yangchongyi, Men; Juan, Angel A.; Departamento de Estadística e Investigación Operativa Aplicadas y Calidad; Centro de Investigación en Gestión e Ingeniería de Producción; Escuela Politécnica Superior de Alcoy; European Commission; Generalitat Valenciana; AGENCIA ESTATAL DE INVESTIGACION; Agencia Estatal de Investigación; Ministerio de Ciencia e Innovación[EN] This paper presents a reinforcement learning (RL) approach for solving the team orienteering problem under both deterministic and dynamic travel time conditions. The proposed method builds on the transformer architecture and is trained to construct routes that adapt to real-time variations, such as traffic and environmental changes. A key contribution of this work is the model's ability to generalize across problem instances with varying numbers of nodes and vehicles, eliminating the need for retraining when problem size changes. To assess performance, a comprehensive set of experiments involving 27,000 synthetic instances is conducted, comparing the RL model with a variable neighborhood search metaheuristic. The results indicate that the RL model achieves competitive solution quality while requiring significantly less computational time. Moreover, the RL approach consistently produces feasible solutions across all dynamic instances, demonstrating strong robustness in meeting time constraints. These findings suggest that learning-based methods can offer efficient, scalable, and adaptable solutions for routing problems in dynamic and uncertain environments.Item type: Artículo , Access status: Abierto , Validation of an Artificial Intelligence Model for Breast Cancer Molecular Subtyping Using Hematoxylin and Eosin-Stained Whole-Slide Images in a Population-Based Cohort(MDPI AG, 2025-10-05) Kiraz, Umay; Fernández-Martín, Claudio; Rewcastle, Emma; Gudlaugsson, Einar G.; Skaland, Ivar; Naranjo Ornedo, Valeriana; Morales, Sandra; Janssen, Emiel A. M.; Escuela Técnica Superior de Ingeniería de Telecomunicación; Departamento de Matemática Aplicada; Departamento de Comunicaciones; Escuela Técnica Superior de Ingeniería Aeroespacial y Diseño Industrial; Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano; European Commission; Universitat Politècnica de València[EN] Simple Summary Breast cancer is a complex disease that can be classified into different biological subtypes. Correctly identifying these subtypes is essential in determining the most effective treatment for each patient. However, current methods such as gene expression testing and immunohistochemistry are either expensive, time-consuming, or not widely available in all healthcare settings. In this study, we explored whether a computer-based approach using artificial intelligence can accurately predict breast cancer subtypes by analyzing routine pathology slides stained with hematoxylin and eosin. This real-world validation study shows that this method can identify certain subtypes with promising accuracy, offering a faster and more accessible alternative to existing techniques. This research may help improve diagnostic processes, especially in hospitals with limited resources, and support more personalized treatment decisions for patients with breast cancer.Abstract Background/Objectives: Breast cancer (BC) is the most commonly diagnosed cancer in women and the leading cause of cancer-related deaths globally. Molecular subtyping is crucial for prognosis and treatment planning, with immunohistochemistry (IHC) being the most commonly used method. However, IHC has limitations, including observer variability, a lack of standardization, and a lack of reproducibility. Gene expression profiling is considered the ground truth for molecular subtyping; unfortunately, this is expensive and inaccessible to many institutions. This study investigates the potential of an artificial intelligence (AI) model to predict BC molecular subtypes directly from hematoxylin and eosin (H&E)-stained whole-slide images (WSIs). Methods: A pretrained deep learning framework based on multiple-instance learning (MIL) was validated on the Stavanger Breast Cancer (SBC) dataset, consisting of 538 BC cases. Three classification tasks were assessed, including two-class [triple negative BC (TNBC) vs. non-TNBC], three-class (luminal vs. HER2-positive vs. TNBC), and four-class (luminal A vs. luminal B vs. HER2-positive vs. TNBC) groups. Performance metrics were used for the evaluation of the AI model. Results: The AI model demonstrated strong performance in distinguishing TNBC from non-TNBC (AUC = 0.823, accuracy = 0.833, F1-score = 0.824). However, performance declined with an increasing number of classes. Conclusions: The study highlights the potential of AI in BC molecular subtyping from H&E WSIs, offering an easily applicable and standardized method to IHC. Future improvements should focus on optimizing multi-class classification and validating AI-based methods against gene expression analyses for enhanced clinical applicability.Item type: Artículo , Access status: Abierto , On the Axiomatic of GV-Fuzzy Metric Spaces and Its Completion(MDPI AG, 2025-02) Gregori Gregori, Valentín; Miñana, Juan-José; Roig, Bernardino; Sapena Piera, Almanzor; Departamento de Matemática Aplicada; Escuela Politécnica Superior de Gandia; Instituto de Investigación para la Gestión Integrada de Zonas Costeras; European Commission; Generalitat Valenciana; Agencia Estatal de Investigación; Universitat Politècnica de València[EN] The concept of fuzzy metric space introduced by Kramosil and Michalek was later slightly modified by George and Veeramani who imposed three additional restrictions on it. A significant difference between these two concepts of fuzzy metrics is that fuzzy metric spaces in the sense of George and Veeramani do not admit completion, in general. This paper is devoted to go into detail on completable fuzzy metric spaces by means of the study of the impact on the completion of each one of the restrictions imposed by George and Veeramani in their definition of fuzzy metric. In this direction, we characterize those completable fuzzy metric spaces, in which just one of the three restrictions imposed by George and Veeramani is required. Various examples illustrate and justify the main results.Item type: Artículo , Access status: Abierto , Optimizing Bifacial Solar Modules with Trackers: Advanced Temperature Prediction Through Symbolic Regression(MDPI AG, 2025-04-15) Lara-Vargas, Fabian Alonso; Vargas-Salgado, Carlos; Águila-León, Jesús; Díaz-Bello, Dácil; Departamento de Ingeniería Eléctrica; Instituto Universitario de Investigación de Ingeniería Energética; Escuela Técnica Superior de Ingeniería Industrial; Ministerio de Universidades; European Commission[EN] Accurate temperature prediction in bifacial photovoltaic (PV) modules is critical for optimizing solar energy systems. Conventional models face challenges to balance accuracy, interpretability, and computational efficiency. This study addresses these limitations by introducing a symbolic regression (SR) framework based on genetic algorithms to model nonlinear relationships between environmental variables and module temperature without predefined structures. High-resolution data, including solar radiation, ambient temperature, wind speed, and PV module temperature, were collected at 5 min intervals over a year from a 19.9 MW bifacial PV plant with trackers in San Marcos, Colombia. The SR model performance was compared with multiple linear regression, normal operating cell temperature (NOCT), and empirical regression models. The SR model outperformed others by achieving a root mean squared error (RMSE) of 4.05 °C, coefficient of determination (R2) of 0.91, Spearman¿s rank correlation coefficient of 0.95, and mean absolute error (MAE) of 2.25 °C. Its hybrid structure combines linear ambient temperature dependencies with nonlinear trigonometric terms capturing solar radiation dynamics. The SR model effectively balances accuracy and interpretability, providing information for modeling bifacial PV systems.Item type: Artículo , Access status: Abierto , NGATHA carpel development genes evolved in the common ancestor of seed plants(Blackwell Publishing, 2025-10-06) Cota, Ignacio; Moschin, Silvia; Offer, Elisabetta; Martinez-Fernandez, Irene; Magnanimi, Francesco; Ambrose, Barbara; Nigris, Sebastiano; Baldan, Barbara; FERRANDIZ MAESTRE, CRISTINA; Pelaz, Soraya; Instituto Universitario Mixto de Biología Molecular y Celular de Plantas; European Commission; Generalitat de Catalunya; Agencia Estatal de Investigación; European Regional Development Fund; Ministerio de Ciencia e Innovación[EN] The evolution of the carpel, the defining feature of angiosperms, remains a fundamental question in plant biology. Understanding how this organ originated is crucial because it underpins the reproductive success and diversity of flowering plants. Here, we investigated the functional conservation between gymnosperms and angiosperms of key transcription factors involved in carpel development. We found that Ginkgo biloba homologs can functionally substitute for their angiosperm counterparts in stigma development. We discovered that GbRAV5 is related to angiosperm NGA genes, challenging previous notions that these are exclusive to angiosperms, and we found a parallel loss of the AP2 domain in gymnosperms providing a rare snapshot of how protein families evolve. Conserved protein interactions and overlapping expression patterns of GbRAV5 and GbHEC in Ginkgo ovules suggest that the molecular toolkit for carpel development was largely present in the last common ancestor of seed plants, offering new insights into the evolution of reproductive structures.Item type: Artículo , Access status: Cerrado , Advancing solar cell efficiency: insights from cesium lead halide perovskite analysis(Springer-Verlag, 2025-09) El-Mrabet, M.; Bouich, Amal; Tarbi, A.; Chtouki, T.; Erguig, H.; Zawadzka, A.; Marjanowska, A.; Migalska-zalas, A.; Kityk, A.; Andrushchak, A.; Myronchuk, G.; Sahraoui, B.; Departamento de Física Aplicada; Escuela Técnica Superior de Ingeniería Aeroespacial y Diseño Industrial; Instituto de Diseño para la Fabricación y Producción Automatizada; European Commission[EN] In this study, we investigated the performance of cesium lead halide perovskite solar cells through numerical simulations using a Solar Cell Capacitance Simulator (SCAPS). Three cell configurations are proposed: Yb/ZnO/CsPbI3/CuI/Au, Sm/TiO2/CsPbBr3/CuO3/Au, and Yb/TiO2/CsPbCl3/CuO2/Au. The solar cell was optimized by adjusting parameters such as the active layer thickness, hole transport layer (HTL), electron transport layer (ETL), metal work function (WF), defects, doping levels, series resistance (RS), and shunt resistance (RSh). These studies indicate that photovoltaic performance is strongly influenced by critical factors, including the quality of the ETL and HTL layers, metal work function, series and shunt resistances, defect density, as well as the thickness and doping concentration of the absorber layers. The efficiencies of CsPbX3 (X = I, Br, and Cl)-based perovskite solar cells were 26.68%, 16.76%, and 14.97%, respectively. The cells based on CsPbCl3 and CsPbBr3 exhibited superior stability, while the external quantum efficiency (EQE) measurements revealed that the CsPbI3 cell responded across the entire visible and near-infrared spectrum, indicating its higher potential for photovoltaic applications compared to CsPbBr3 and CsPbCl3. In conclusion, we found that for diffusion lengths (L) greater than similar to 600 nm, the reduction in the internal electric field in CsPbBr3 and CsPbCl3 cells promotes electron-hole recombination within the core layers.Item type: Artículo , Access status: Abierto , A Safe In-Flight Reconfiguration Solution for UAV Swarms Based on Attraction/Repulsion Principles(MDPI AG, 2025-09-25) Sarabia Sauquillo, Nicolás; Gashaw, Henok; Wubben, Jamie; Hernández-Orallo, Enrique; Tavares De Araujo Cesariny Calafate, Carlos Miguel; Departamento de Informática de Sistemas y Computadores; Escuela Técnica Superior de Ingeniería Informática; Grupo de Redes de Computadores; European Commission; GENERALITAT VALENCIANA; AGENCIA ESTATAL DE INVESTIGACION; European Regional Development Fund[EN] The increasing use of UAV swarms for collaborative autonomous missions presents significant challenges in coordination, safety, and scalability, especially during dynamic formation reconfigurations. This study introduces the Magnetic Swarm Reconfiguration (MSR) protocol, a fully distributed navigation method that enables UAV swarms to transition smoothly and safely between geometric formations. MSR achieves this by combining two main components: first, it employs the Hungarian algorithm to compute an optimal assignment of UAVs to target positions within the new formation, thereby minimizing trajectory overlap and interference; second, it utilizes virtual magnetic attraction and repulsion forces for real-time navigation, drawing each UAV toward its assigned destination while dynamically repelling nearby agents to avoid collisions. To evaluate the performance of the MSR protocol, six representative formation transitions were simulated across swarm sizes of up to 100 UAVs. Results show that MSR reduces reconfiguration time significantly compared to existing methods, maintains strict safety standards by achieving minimal to zero collisions, and supports fully decentralized and simultaneous maneuvering. The scalability and robustness of the MSR protocol make it suitable for complex, large-scale swarm operations requiring rapid and reliable formation changes.Item type: Artículo , Access status: Abierto , Hybrid Energy Solutions for Enhancing Rural Power Reliability in the Spanish Municipality of Aras de los Olmos(MDPI AG, 2025-03-30) Motevakel, Pooriya; Roldán-Blay, Carlos; Roldán-Porta, Carlos; Escrivá-Escrivá, Guillermo; Dasí-Crespo, Daniel; Departamento de Ingeniería Eléctrica; Instituto Universitario de Investigación de Ingeniería Energética; Escuela Técnica Superior de Ingeniería Industrial; European Commission[EN] Featured Application The methodologies and results presented in this study can serve as a blueprint for rural municipalities, cooperative energy planners, or agricultural communities seeking to enhance microgrid reliability and reduce costs. By leveraging locally available biomass (e.g., livestock waste) and optimal solar photovoltaic capacity, stakeholders can tailor hybrid energy systems to local resource conditions, lower greenhouse gas emissions, and foster community-level economic development. Additionally, regulatory bodies and policymakers can use these insights to formulate supportive policies-such as feed-in tariffs or biogas incentives-that accelerate the adoption of clean, resilient hybrid solutions in other semi-remote or grid-constrained settings.Abstract As global energy demand increases, ensuring a reliable electricity supply in rural or semi-remote areas remains a significant challenge. Hybrid energy systems, which integrate renewables, generators, storage, and grid connections, offer a promising solution for addressing energy reliability issues. In this context, the rural community of Aras de los Olmos, Spain, serves as the focal point because of its frequent power outages despite being connected to the main grid. This study investigates innovative solutions tailored to the community's unique needs. It highlights critical challenges in achieving reliable energy access and bridges the gap between existing limitations and sustainable, future-oriented energy systems. This is achieved by analyzing the current energy setup and evaluating potential alternatives. Two scenarios were evaluated: one optimizing the existing configuration for economic efficiency while retaining the grid as the primary energy source, and another introducing a biomass generator to enhance reliability by partially replacing the grid. Detailed technical, financial, and environmental assessments were performed using HOMER. These assessments identified an optimal configuration. This optimal configuration improves reliability, enhances stability, reduces disruptions, and meets growing energy demands cost-effectively. As will be indicated, the first scenario can reduce total costs to approximately USD 90,000 compared to the existing setup, whereas the second scenario can lower grid dependence by approximately 70%. In addition, introducing renewable energy sources, such as solar and biomass, significantly reduces greenhouse gas emissions and reliance on fossil fuels. Additionally, these solutions create local job opportunities, promote community engagement, support energy independence, and align with broader sustainability goals.Item type: Artículo , Access status: Abierto , Artificial Intelligence Decision Systems to Support Industrial Equipment Manufacturing(Springer, 2024-04) Andres, B.; Mateo-Casalí, Miguel Ángel; Fiesco-Muñoz, Juan Pablo; Poler, R.; Departamento de Organización de Empresas; Centro de Investigación en Gestión e Ingeniería de Producción; Escuela Politécnica Superior de Alcoy; GENERALITAT VALENCIANA; European Commission[EN] This article discusses how integrating artificial intelligence (AI) into Industry 4.0 can promote sustainability and resilience in production systems. It addresses the lifecycle manufacturing concept, which aims to minimise waste and reduce the environmental impact of manufacturing operations. This paper focuses on the specific machine tool production sector and how AI technology can optimise production processes by reducing downtimes and improving overall manufactur- ing efficiency. Accordingly, the article aims to identify the needs that industrial equipment manufacturers have during the replenishment, production and delivery processes, and how AI could fulfil these needs. By leveraging AI technologies, manufacturers can significantly improve efficiency, profitability and customer sat- isfaction, which results in improved performance and business growth. The paper also introduces European HORIZON project AIDEAS, which aim to develop AI technologies to support the manufacturing phase of the industrial equipment life cycle.