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Study of runaway electron dynamics in FTU using synchrotron spectra and imaging measurements
No full textThe behavior of runaway electrons of Frascati Tokamak Upgrade (FTU) discharges is investigated through the comparison of experimental synchrotron emission spectra and visible images with their synthetic counterparts. Synchrotron spectra are measured in an unprecedented wide wavelength range (450-4000 nm) while synchrotron images are collected by a visible CCD camera. The simulated spectra and images are calculated with the synthetic synchrotron radiation diagnostic SOFT (Synchrotron-detecting Orbit Following Toolkit) code. The aim of this work is to extend the study of runaway electrons dynamics in FTU also to post-disruption phases. The runaway number, radial profile, energy and pitch angle have been evaluated during their whole time evolution, from the start-up to the post-disruption phase, assuming a given runaway electrons (RE) distribution function. The runaway number is found to increase by two orders of magnitude after the disruption, while the energy and pitch angle maintain similar values before and after the disruption. The runaway electrons are mostly distributed in the core of the plasma. The inferred maximum RE energy and pitch angle are in agreement with the results of simulations based on a runaway electron test particle model.This work has been carried out within the framework of the EUROfusion Consortium, funded by the European Union via the Euratom Research and Training Programme (Grant Agreement No 101052200 EUROfusion). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them. Part of this work was done under financial support from Projects ENE2015-66444-R (MINECO/FEDER, UE) and PID2019-110734RB-I00 (AEI, Spain)
99mTc-DTPA-Collagen Radiotracer for the Noninvasive Detection of Infective Endocarditis
No full textInfective endocarditis (IE) represents a significant concern among hospital-acquired infections, frequently caused by the Gram-positive bacterium Staphylococcus aureus. Nuclear imaging is emerging as a noninvasive and precise diagnostic tool. However, the gold standard radiotracer [18F]-FDG cannot distinguish between infection and inflammation, resulting in false positives. Based on the presence of collagen-binding proteins in the cell wall of S. aureus, we propose the radiolabeling of collagen for its evaluation in IE animal models by single-photon emission computed tomography (SPECT) imaging. We radiolabeled rat tail collagen I using DTPA chelator and [99mTc]NaTcO4. Selectivity was evaluated in vitro using 3 Gram-positive bacteria, 1 Gram-negative bacteria and 1 yeast. In vivo SPECT/computed tomography (CT) imaging was conducted on 8 SD rat models of IE and 8 sterile sham model as controls. Ex vivo biodistribution and autoradiography were performed following imaging. Diagnosis of IE was confirmed through microbiological studies and H&E histopathology. [99mTc]-DTPA-Collagen was synthesized successfully with a yield of 42.86 ± 6.35%, a purity of 95.84 ± 1.85% and a stability higher than 90% after 50 h postincubation. In vitro uptake demonstrated the selectivity for Gram-positive bacteria (63.85 ± 15.15%). Ex vivo analysis confirmed hepato-splenic excretion. In vivo SPECT/CT imaging revealed highly localized uptake within the aortic valve with a sensitivity of 62.5% and specificity of 87.5%. We successfully synthesized and characterized a new SPECT radiotracer based on [99mTc]Tc-radiolabeled collagen. In vitro studies demonstrated the selectivity of the radiotracer for Gram-positive bacteria. In vivo SPECT/CT-based assessment in an IE model confirmed the potential of this approach to detect active IE.This study has been funded by Instituto de Salud Carlos III through projects "PI20/01632, PT20/0044", co-funded by European Regional Development Fund "A way to make Europe”, and "PI23/01405, PT23/00027", co-funded by the European Union. Work supported by Comunidad de Madrid, project S2022/BMD-7403 (RENIM-CM). Grant PTA2022-021556-I funded by MICIU/AEI /10.13039/501100011033 and by FSE+. The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the Ministerio de Ciencia, Innovación y Universidades (MICIU), and the Pro CNIC Foundation and is a Severo Ochoa Center of Excellence (grant CEX2020-001041-S funded by MICIU/AEI/10.13039/501100011033). Finally, the authors thank Universidad Carlos III de Madrid (Agreement CRUE-Madroño 2024) and Fundación Ramón Areces for their support
Calibration and uncertainty quantification for deep learning-based drought detection
No full textDroughts are hydrometeorological extreme events influenced by highly intricate land–atmosphere feedback mechanisms and climate variability. Deep learning models have recently succeeded in detecting extreme climate events and promise to uncover and understand droughts further. There are two main challenges of reliability and trustworthiness limiting their applications: miscalibration and inherent uncertainty. However, they remain rarely explored because deep learning models are overparameterized and seldom tractable. To address this shortcoming, we introduce methodologies for model calibration and entropy-based uncertainty quantification for deep learning-based drought detection. The calibration algorithm can deal with calibration errors by reducing distributional shifts and alleviating overconfident predictions. The uncertainty framework, in turn, decomposes and quantifies the total uncertainty according to several components: data uncertainty, procedural variability, parametric variability, and latent variability. Thus, our method identifies uncertain predictions and supports robust evaluations, benefiting the credibility of the decision-making process. Empirical evidence of performance in a wide range of European drought events is given, justifying the effectiveness of our approach. The calibration methodology yields the lowest expected calibration error (0.31%) and the precision of the uncertainty-based decision-making is improved from 72.27% to 74.06% and 76.59%, based on ensemble predictions and rejecting the predictions for the top 20% uncertain negative samples, respectively. In summary, our approach significantly enhances drought detection’s reliability and classification accuracy, constituting a key step toward more trustworthy and actionable climate decision-making.M Z appreciates the financial support from the China Scholarship Council (CSC) through the State Scholarship Fund for Overseas Study (No. 202106710031). All authors acknowledge the support from the European Research Council (ERC) under the ERC Synergy Grant USMILE (grant agreement 855187), the European Union‘s Horizon 2020 research and innovation program within the project ‘XAIDA: Extreme Events - Artificial Intelligence for Detection and Attribution,’ (GA 101003469), ‘AI4PEX: Artificial Intelligence and Machine Learning for Enhanced Representation of Processes and Extremes in Earth System Models’ (GA 101137682) and the computer resources provided by the Jülich Supercomputing Centre (JSC) (Project No. PRACE-DEV-2022D01-048), the computer resources provided by Artemisa (funded by the European Union ERDF and Comunitat Valenciana), as well as the technical support provided by the Instituto de Física Corpuscular, IFIC (CSIC-UV)
Mixed-type Fibonacci-Mittag-Leffler and Lucas-Mittag-Leffler polynomials: some properties
Full text linkWe introduce and investigate two new families of special polynomials: the mixed-type Fibonacci-Mittag-Leffler (FML) and Lucas-Mittag-Leffler (LML) polynomials. These are constructed by blending classical Fibonacci and Lucas polynomials with Mittag-Leffler polynomials, yielding novel recurrence relations and determinantal representations. Fundamental algebraic identities are established, and the zeros of these polynomials are analyzed through both analytic methods and computational visualization. The asymptotic behavior of zeros is further examined via a generalized version of Hurwitz theorem in two variables.Open Access funding provided by Colombia Consortium
Evaluation of the steady state cooling of flat plate systems with different channel shapes: Experimental measurements and numerical simulations.
No full textThis study presents an experimental and numerical evaluation of the steady-state cooling of flat plate systems, with different shapes for the inner channel through which a coolant circulates. 3D-printed aluminum prototypes with five different channel configurations, namely square, fork, crateriform, salverform, and cruciform, were tested in an innovative experimental facility to collect high-accuracy experimental measurements of the impact of their shape on the cooling performance of the plates. The pressure drop and temperature distribution across each configuration were thoroughly analyzed. The original experimental results were compared against numerical simulations, which were validated and demonstrated to be capable of precisely capturing the fluid-dynamics and heat transfer characteristics of the different channel shapes. Deviations below 15 % were obtained among the different configurations for the pressure drop measurements and an average temperature deviation of less than 1.5 °C was predicted for both the mean and the maximum temperatures of the plates. Both the experimental and the numerical results demonstrated that the cruciform configuration presented superior performance than the rest of the configurations for equal pumping power consumption, being the best or second best in terms of maximum and average temperatures, maximum temperature variation, and temperature homogeneity. In contrast, the other configurations resulted in either higher maximum temperatures or lower temperature uniformity. These findings provide valuable information for the design of efficient cooling systems, particularly in applications like batteries for electric vehicles or photovoltaic cells, where temperature homogeneity and low maximum temperatures are critical.This work has been supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M (EVIACOOL-CM-UC3M), and the grant “Ayudas para la Formación de Profesorado Universitario” (FPU21/02952) awarded by the Spanish Ministry of Universities
Aerodynamic characterisation of a flapping wing in turbulent free stream conditions
No full textBiologically inspired aero/hydrodynamics attracts considerable interest because of promising efficiency and manoeuvring capabilities. Yet, the influence that external perturbations, typical of realistic environments, can have over the flow physics and aerodynamic performance remains a scarcely investigated issue. In this work, we focus on the impact of free stream turbulence (FST) on the aerodynamics of a flapping wing with a prescribed (heaving and pitching) motion at a chord-based Reynolds number of 1000. The problem is tackled by means of direct numerical simulations using an immersed boundary method and a synthetic turbulence generator. The effect of two key parameters, i.e. the turbulence intensity and integral length scale of FST, is described by characterising the phase- and spanwise-averaged flows and aerodynamic coefficients. In particular, we show how FST effectively enhances the dissipation of the vortices generated by the flapping wing once they are sufficiently downstream of the leading edge. The net (i.e. time-averaged) thrust is found to be marginally sensitive to the presence of FST, whereas the characteristic aerodynamic fluctuations appear to scale linearly with the turbulence intensity and sublinearly with the integral length scale. Moreover, we reveal a simple mechanism where FST triggers the leading-edge vortex breakup, which in turns provides the main source of aerodynamic disturbances experienced by the wing. Finally, we show how the frequency spectra of the aerodynamic fluctuations are governed by the characteristic time scales involved in the problem.This work is supported by grant PID2022-142135NA-I00 by MCIN/AEI/10.13039/501100011033 and grants FJC2021-047652-I and TED2021-131282B-I00 by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR
Efficient 5G Mobile Network Management: network slicing and global roaming optimization
No full textMención Internacional en el título de doctorThe rapid transformation of mobile network infrastructures from hardware-based systems to software-defined networks (SDNs) has introduced several novel paradigms that aim to enhance scalability, flexibility, and efficiency. One of the most significant advancements in this shift is the concept of network slicing. In essence, network slicing allows a single physical network to be divided into multiple virtual networks, each tailored to the specific requirements of different use cases. These slices can vary in terms of bandwidth, latency, security, and resource allocation, making them highly adaptable to different industries, such as autonomous vehicles, smart cities, and Internet of Things (IoT) applications. This capability offers unprecedented flexibility for service providers, as they can deploy and manage various services on the same physical infrastructure without the need for expensive and complex hardware upgrades. However, realizing the full potential of network slicing presents several challenges. One of the key issues is the complex management of multiple network slices that may have conflicting requirements. For example, ensuring ultra-reliable low-latency communication for one slice while maximizing bandwidth for another slice can be difficult, especially when they share the same underlying physical resources. The dynamic nature of network slicing, where slices are created, modified, and terminated on demand, also requires highly efficient orchestration and automation systems. Furthermore, maintaining security and isolation between slices is essential to prevent interference or data breaches. As mobile networks continue to evolve, addressing these challenges will be crucial to fully unlocking the benefits of network slicing and realizing the vision of highly flexible, software-driven networks. This thesis explores innovative approaches to improve the profitability of Mobile Network Operators (MNO) through advanced network slicing strategies in 5G networks. The research focuses on three key areas: overbooking network slices for maximize financial gains, cost-efficient network slice management, and operational performance of Mobile Network Aggregators (MNA) within Network Slicing as a Service (NSaaS) context under roaming scenarios. These innovations leverage state-of-the-art techniques, including deep learning, classical optimization, and data-driven algorithms, to tackle the complex challenges of resource provisioning, network function lifecycle management, and global service optimization. This document delves into these advancements, structured across several chapters that explore these key areas in depth. In the first contribution, we explore the NSaaS concept and introduce slice overbooking as a promising strategy to maximize resource utilization and boost net profit in cloud-native mobile networks. Network slicing enables the creation of virtualized, custom-tailored network slices, but managing these slices efficiently remains a challenge. Overbooking allows network operators to admit more slices than available physical resources by capitalizing on the fact that tenants seldom use their total reserved capacity simultaneously. The chapter presents a complete NSaaS management solution, overbooKing-Aware Network Slicing as a Service (kaNSaaS), which integrates deep learning with classical optimization to address the dual problems of admission control and resource allocation. Through extensive experimentation with large-scale real-world data on tenant demands, the results show that kaNSaaS boosts operator profits potentially multiplying it by four compared to non-overbooking strategies under real-world conditions. In the second contribution, the focus shifts to zero-touch management systems, which promise autonomous network operation with minimal human intervention. As modern network infrastructures have evolved into systems with numerous virtualized network functions, traditional manual approaches to lifecycle management are increasingly inadequate. In response, this chapter introduces AZTEC+, a data-driven solution for anticipatory resource provisioning in network slicing environments. Using a hybrid and modular deep learning architecture, AZTEC+ forecasts future service demands and determines optimal trade-offs between resource provisioning, instantiation, and reconfiguration costs and performance requirements. Tested on a large-scale network, AZTEC+ outperformed existing state-of-the-art management strategies by up to 5.85 times, proving its effectiveness in reducing network costs and addressing the complexity of virtualized mobile networks. It effectively balances costs associated with resource instantiation and reconfiguration, making it a highly efficient solution for managing dynamic network slices autonomously. This chapter emphasizes how zero-touch management, paired with anticipatory resource provisioning, offers a scalable approach to future network management. In the third contribution, we explore the added complexity introduced by MNAs, which represent a new frontier in global mobile telecommunications in NSaaS. MNAs, such as Google Fi, Twilio, and Truphone, operate by leveraging multiple MNOs to provide mobile communication services across different regions. Unlike traditional MNOs, which are limited by geographic boundaries, MNAs dynamically connect to the MNO that offers the best performance based on location and time, ensuring optimal service quality for users, especially those frequently crossing borders. However, the dynamic nature of MNAs introduces a new layer of complexity in meeting network slicing’s Quality of Service (QoS) guarantees as the isolation and management of slices become more intricate with the involvement of multiple, regionally dispersed MNOs. To address this, we quantify and compare the performance of MNA-driven models against traditional MNOs, offering insights into the challenges and trade-offs in achieving reliable QoS in these advanced operator frameworks. This section presents a detailed performance analysis of the three aforementioned MNAs, comparing their performance for key applications like web browsing and video streaming within NSaaS across diverse geographical regions, namely the USA and Spain. While MNAs may introduce slight delays compared to local MNOs in certain regions, they significantly outperform the traditional home-routed roaming model in terms of service quality. Moreover, emulation studies using open-source 5G implementations deployed across AmazonWeb Services (AWS) locations illustrate the performance gains MNAs can achieve through advanced network function virtualization. This chapter highlights the potential of the MNA model to reshape global mobile services, offering more flexible, efficient, and seamless experiences for end-users. Overall, this research provides valuable insights into NSaaS overbooking management, zero-touch resource provisioning, and the global reach of MNAs. Together, these innovations represent significant strides toward realizing the next generation of mobile networks, where resource efficiency, automation, and global service quality are paramount. The research highlights the economic benefits of slice overbooking, demonstrating how operators can significantly increase profitability by intelligently managing their resources. Furthermore, by integrating hybrid models of AI and optimization, it lays a strong foundation for future developments in network slicing and cost optimization, offering practical implications for both MNO and application developers. As 5G networks continue to evolve, this work sheds light on the shifting landscape of global network operators and provides a roadmap for addressing the growing complexities of resource allocation and service management in a cloud-native, AI-driven environment. Through a blend of deep learning, anticipatory algorithms, and network virtualization, the telecommunications industry is better positioned to meet the ever-evolving demands of users while optimizing network performance across a wide range of dynamic environments.This work has been supported by IMDEA Networks InstitutePresidente: Vincenzo Mancuso. - Vocal: Evgenia Christoforou. - Secretario: Chrysoula Papagiann
Signalized Traffic Management Optimizing Energy Efficiency Under Driver Preferences for Vehicles With Heterogeneous Powertrains
No full textThis paper presents a novel approach to signalized traffic management that optimizes energy efficiency while considering driver preferences. In order to address the heterogeneous nature of vehicles, our methodology is applicable to both electric and internal combustion engine (ICE) vehicles. Traffic flow in signalized intersections is improved, taking into account that drivers want to reach the desired destination as soon as possible, under consideration of the acceleration preference of each individual driver. Furthermore, road safety is ensured by maintaining a safe distance from the preceding vehicle. Simulation results demonstrate that our approach significantly improves energy efficiency and reduces fuel consumption, while also accommodating driver preferences to enhance overall satisfaction. A comparison with train-like velocity profiles revealed energy reductions of 2.14%, 8.37%, and 9.67% for electric, gasoline, and diesel vehicles, respectively, when the proposed methodology was employed.This work was supported in part by the National Key R&D Program of China under Grant 2023YFE0197900; in part by MCIN/AEI/10.13039/501100011033 under Grant PID2022-136468OBI00, "ERDF A way of making Europe"; and in part by UC3M's Grants for Young Doctors and by Universidad Carlos III de Madrid (Agreement CRUE-Madrono 2025
Análisis de las transformaciones religiosas de Creta y Cirene entre los siglos I a.C. y IV d.C.
No full textMención Internacional en el título de doctorPrograma de Doctorado en Humanidades por la Universidad Carlos III de MadridPresidenta: Corinne Bonnet.- Secretario: Valentino Gasparini.- Vocal: Elena Muñiz Grijalv
Damping Identification Sensitivity in Flutter Speed Estimation
No full textPredicting flutter remains a key challenge in aeroelastic research, with certain models relying on modal parameters, such as natural frequencies and damping ratios. These models are particularly useful in early design stages or for the development of small Unmanned Aerial Vehicles (maximum take-off mass below 7 kg). This study evaluates two frequency-domain system identification methods, Fast Relaxed Vector Fitting (FRVF) and the Loewner Framework (LF), for predicting the flutter onset speed of a flexible wing model. Both methods are applied to extract modal parameters from Ground Vibration Testing data, which are subsequently used to develop a reduced-order model with two degrees of freedom. The results indicate that FRVF- and LF-informed models provide reliable flutter speed, with predictions deviating by no more than 3% (FRVF) and 5% (LF) from the N4SID-informed benchmark. The findings highlight the sensitivity of flutter speed predictions to damping ratio identification accuracy and demonstrate the potential of these methods as computationally efficient alternatives for preliminary aeroelastic assessments.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) [grant number 2277626]. The third author is supported by the Centro Nazionale per la Mobilità Sostenibile (MOST–Sustainable Mobility Center), Spoke 7 (Cooperative Connected and Automated Mobility and Smart Infrastructures), Work Package 4 (Resilience of Networks, Structural Health Monitoring and Asset Management)