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Women in Mining: A global study of the past two centuries
No full textThe economic history of mining has largely overlooked the role of women, reflecting both the male dominance of the sector and the invisibility of women’s labour in historical sources. This chapter explores women’s roles in mining over the past two centuries, focusing on the Global South -particularly Africa- and includes a case study of copper mining in Rio Tinto, Spain, using company records. While mineral extraction was reserved for men, women played key supporting roles, especially in the 20th-century Global South, though this rarely translated into improved conditions or career opportunities. Within Africa, regional differences were stark: for instance, Angolan diamond mines increased female employment in the 1950s, while women were absent from company payrolls in the Central African Copperbelt. In Rio Tinto, most employed women were widows in vulnerable positions, suggesting that their work served as a form of social insurance rather than a step toward economic inclusion. These patterns highlight the need for further research using company records to better understand the influence of policy, culture, and industry structure on women’s roles in mining
Reglamento de los Colegios Mayores Residencias de Estudiantes de la Universidad Carlos III de Madrid, aprobado por el Consejo de Gobierno en sesión de 14 de julio de 2025
No full textEste Reglamento deroga el Reglamento de los Colegios Mayores-Residencias de Estudiantes de la Universidad Carlos III de Madrid, aprobado por el Consejo de Gobierno en sesión de 8 de mayo de 2006
From Lack of Concerns to Complicity: How Misinformation Concerns Influences Fake News Sharing on WhatsApp via the Partisan Legitimation of Fake News
Get PDFObjectives: Misinformation has emerged as an important threat to democracies, fostering severe concerns among citizens worldwide. However, even in this context, research emphasizes that citizens play a crucial role in the dissemination of falsehoods on social media. While previous studies argued that most users unintentionally share fake news when misjudging its accuracy, recent evidence points to partisan identity as a stronger predictor of this behavior. This study puts the literature on misinformation concerns and fake news sharing into dialogue to better understand the associations in time of this behavior. We focus on WhatsApp, a prominent platform for the circulation of falsehoods which has remained relatively understudied. Methods: This study draws on a two-wave panel survey fielded in Spain (N = 570) between May 2022 and July 2022. Results: We conceptualize the legitimation of partisan use of fake news—the belief that using falsehoods to criticize opposing ideologies is legitimate—and show that it mediates the relationship between misinformation concerns and fake news sharing on WhatsApp. We also show that social media news use and age predict, positively and negatively respectively, both legitimation of partisan use of fake news and fake news sharing. The direct effect of misinformation concerns on fake news sharing is non-statistically significant. Conclusion: Overall, the study illustrates how, in the current polarized environment, fake news may be weaponized to serve partisan interests.The first and third authors have benefited from the support of the Community of Madrid through the research plan “Estímulo a la Investigación de Jóvenes Doctores” of the multiannual agreement with the UC3M (DEPROFAKE-CM-UC3M), within the framework of the V PRICIT (V Regional Plan of Scientific Research and Technological Innovation). The title of the Project is “Defensa Proactiva Frente a la Ciberamenaza Desinformativa: Antecedentes, Consecuencias y Mecanismos de Detección y Predicción de Contenido Falso.” Funding for APC: Universidad Carlos III de Madrid (Agreement CRUE-Madroño 2025)
Are Robots More Engaging When They Respond to Joint Attention? Findings from a Turn-Taking Game with a Social Robot
Get PDFJoint attention, the capacity of two or more individuals to focus on a common event simultaneously, is fundamental to human–human interaction, enabling effective communication. When considering the field of social robotics, emulating this capability might be necessary for promoting natural interactions and thus improving user engagement. Responding to joint attention (RJA), defined as the ability to react to external attentional cues by aligning focus with another individual, plays a critical role in promoting mutual understanding. This study examines how RJA impacts user engagement during human–robot interaction. The participants play a turn-taking game against a social robot under two conditions: with our RJA system active and with the system inactive. Auditory and visual stimuli are introduced to simulate real-world dynamics, testing the robot’s ability to detect and follow the user’s focus of attention. We use a twofold approach to evaluate the system’s impact on the user’s experience during the interaction. On the one hand, we use head pose telemetry to quantify attentional aspects of engagement, including measures of distraction and focus during the interaction. On the other hand, we use a post-experimental questionnaire incorporating the User Engagement Scale Short Form to assess engagement. The results regarding telemetry data reveal reduced distraction and improved attentional consistency, highlighting the system’s ability to maintain attention on the current task effectively. Furthermore, the questionnaire responses show that RJA significantly enhances self-reported engagement when the system is active. We believe these findings confirm the value of attentional mechanisms in promoting engaging human–robot interactions.The research leading to these results has received funding from the following projects: Robots sociales para mitigar la soledad y el aislamiento en mayores (SOROLI), PID2021-123941OA-I00, funded by Agencia Estatal de Investigación (AEI), Spanish Ministerio de Ciencia e Innovación. Robots sociales para reducir la brecha digital de las personas mayores (SoRoGap), TED2021-132079B-I00, funded by Agencia Estatal de Investigación (AEI), Spanish Ministerio de Ciencia e Innovación. Mejora del nivel de madurez tecnológica del robot Mini (MeNiR) PDC2022-133518-I00, funded by MCIN/AEI/10.13039/501100-011033 and by the European Union NextGeneration EU/PRTR. Portable Social Robot with High Level of Engagement (PoSoRo) PID2022-140345OB-I00, funded by MCIN/AEI/10.13039/501100-011033 and ERDF A way of making Europe
Corrosion and mechanical behavior of novel alumina forming steels in molten lead
No full textThree new multi-phase alumina-forming steels with compositions Fe-(10–14.5)Cr-(10–12)Ni-3.5Al (wt.%) were exposed to stagnant lead at 550 and 650 °C for up to 1000 h The experimental alloys formed stable and protective alumina (Al2O3) layers at both temperatures, crucial for preventing lead penetration and material degradation. In contrast, 316 L and T91 steels, candidate materials for nuclear applications, showed significant oxidation and lead penetration, particularly at the higher temperature. The designed alloys retained their mechanical properties after exposure, with one of them even increasing yield strength due to phase transformations. The findings highlight the potential of these new alloys with no reactive elements and no thermomechanical treatments, to operate in environments with high-temperature liquid lead, such as Gen IV nuclear reactors or high-temperature concentrated solar power plants.This work has been developed through project AFORMAR through the PID2019–109334RB-C32 grant funded by the National Research Agency of Spain (Agencia Estatal de Investigación -AEI)
Essays on Debiased Machine Learning
No full textPrograma de Doctorado en Economía por la Universidad Carlos III de MadridPresidente: Martin Weidner.- Secretario: Jesús María Carro Prieto.- Vocal: Clément De Chaisemarti
Protocolo de respuesta rápida ante situaciones de emergencia, aprobado por el Consejo de Gobierno en sesión de 14 de julio de 2025
No full textObservability Analysis for Structural System Identification Based on Static-State Estimation
No full textThe concept of observability analysis has garnered substantial attention in the field of structural system identifcation. Its primary aim is to identify a specifc set of structural characteristics, such as Young's modulus, area, inertia, and possibly their combinations (e.g., fexural or axial stifness). These characteristics can be uniquely determined when provided with a suitable subset of deflections, forces, and/or moments at the nodes of the structure. This problem is particularly intricate within the realm of structural system identification, mainly due to the presence of nonlinear unknown variables, such as the product of vertical defection and fexural stifness, in accordance with modern methodologies. Consequently, the mechanical and geometrical properties of the structure are intricately linked with node defections and/or rotations. The paper at hand serves a dual purpose: firstly, it introduces the concept of static-state estimation, especially tailored for the identifcation of structural systems; and secondly, it presents a novel observability analysis method grounded in static-state estimation principles, designed to overcome the aforementioned challenges. Computational experiments shed light on the algorithm's potential for practical structural systemidentifcation applications, demonstrating signifcant advantages over the existing state-of-the-art methods found in the literature. It is noteworthy that these advantages could potentially be further amplifed by addressing the static-state estimation principles problem, which constitutes a subject for future research. Solving this problem would help address the additional challenge of developing efficient techniques that can accommodate redundancy and uncertainty when estimating the current state of the structure.This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. These projects have been generously funded by MICIN (Ministry of Science and Innovation), AEI (State Research Agency) and the “A way to make Europe” FEDER Funds. Dr. Mínguez’s research was supported by project “SENSEI: Smart watEr NetworkS using artificial intElligence” with code CNS2022-135472, funded by MCIN/AEI/10.13039/ 501100011033 and by the European Union Next Generation EU/PRTR, and R&D project “Algorithms for Stochastic Optimization Using Data-driven and Learning Analysis (ASTRAL)” with code PID2023-151013NB-I00, funded by MCIN/AEI/10.13039/501100011033 and the European Union Next Generation EU/PRTR. Finally, scholarship funding was obtained from L’Agencia de Gesti´o d’Ajuts Universitaris i de Recerca (AGAUR) through the “Ajuts de suport a departaments i unitats de recerca universitaris per a la contractacio´ de personal investigador predoctoral en formacio´ (FI SDUR 2022).
Large deviations and fluctuations of real eigenvalues of elliptic random matrices
No full textSB is partially supported by the POSCO TJ Park Foundation (POSCO Science Fellowship), by the New Faculty Startup Fund at Seoul National University and by the National Research Foundation of Korea funded by the Korea government (NRF-2016K2A9A2A13003815, RS-2023-00301976, RS-2025-00516909). LDM is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 1283/2 2021– 317210226 “Taming uncertainty and profiting from randomness and low regularity in analysis, stochastics and their applications”, and the Royal Society grant RF\ERE\210237. NS acknowledges financial support from the Royal Society grant URF\R1\180707
Market Making Strategies with Reinforcement Learning
No full textThis dissertation is organized into six chapters, each serving a distinct purpose within the overall research narrative. The journey begins with this introductory segment, which sets the stage for the comprehensive exploration to follow. The following Chapter 2 takes a deep dive into the current state of the art and the main background in several interconnected fields: market making, RL, multi-objective reinforcement learning (MORL), and the nuanced application of RL within the context of market making. Additionally, it introduces the main concepts of financial markets and describes the simulated environment used throughout this work. This entire analysis is pivotal in understanding the research.
The heart of the dissertation resides in Chapter 3, 4, and 5, where the bulk of the research unfolds. In Chapter 3, the narrative begins with the development of an RL market making agent capable of operating within a simulated stock market environment. This agent is designed to enhance profitability while providing liquidity, even in the presence of competitors. Through rigorous policy analysis and competitive testing against other RL-based MM agents, the chapter describes the agent’s strategic efficacy.
Chapter 4 shifts the focus to the critical aspect of inventory management, approached from two distinct angles. The initial perspective, presented in Section 4.1, treats inventory management as a problem of reward engineering. Here, a novel reward function is proposed, leveraging dynamic factors that adjust rewards based on the cash-to-inventory ratio in real-time. This model is shown to outperform existing strategies, marking a significant advancement in the field. However, a more robust way to solve this multi-objective problem was sought. Reward engineering, although the main approach used in RL to deal with these kinds of challenges, suffers from many problems. This is why, in Section 4.2, a more sophisticated multi-objective approach is explored, allowing the agent to focus on both objectives independently through the use of dual NNs and Pareto front (PF) optimization. This approach is demonstrated to not only enhance stability but also significantly improve performance compared to the reward-engineered model.
With these achievements, this research has not yet addressed the problem of the non-stationarity of financial markets. To this point, a policy able to perform well under some predefined circumstances has been learned. However, the performance is not guaranteed if the market evolves due to any of the aforementioned factors. For this reason, in Chapter 5, a non-stationary algorithm based on Thompson sampling (TS), Policy Weighting through Discounted Thompson sampling (POW-dTS), tailored for RL market making agents is introduced. This algorithm cleverly balances a suite of pre-trained policies, derived from the multi-objective RL agent, adjusting dynamically to the ever-changing market conditions. With a set of pre-trained policies, the algorithm can adapt autonomously to the best combination of known policies in terms of rewards.
In the concluding Chapter 6, summarizes the key findings of this study, discussing their impact on market making and RL. It highlights the contributions to academic knowledge and suggests directions for future research. This final chapter aims to encapsulate the essence of the dissertation, offering both a summation of the research conducted and a visionary outlook toward the future of market making strategies empowered by RL.
The evolution of this research is visually represented in Figure 1.3, which not only chronicles the progression of the study through its main milestones but also lists the articles published or undergoing review at each pivotal moment. This graphical timeline reveals the dissertation’s logical and methodical advancement.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidenta: Inés María Galván León.- Secretario: Daniel Borrajo Millán.- Vocal: Juan Ramón Piñeiro Sous