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Monitoring a deep geological repository for radioactive waste during the operational phases relies on a combination of fit-for-purpose numerical simulations and online sensor measurements, both producing complementary massive data, which can then be compared to predict reliable and integrated information (e.g., in a digital twin) reflecting the actual physical evolution of the installation over the long term (i.e., a century), the ultimate objective being to assess that the repository components/processes are effectively following the expected trajectory towards the closure phase. Data prediction involves using historical data and statistical methods to forecast future outcomes, but it faces challenges such as data quality issues, the complexity of real-world data, and the difficulty in balancing model complexity. Feature selection, overfitting, and the interpretability of complex models further contribute to the complexity. Data reconciliation involves aligning model with in situ data, but a major challenge is to create models capturing all the complexity of the real world, encompassing dynamic variables, as well as the residual and complex near-field effects on measurements (e.g., sensors coupling). This difficulty can result in residual discrepancies between simulated and real data, highlighting the challenge of accurately estimating real-world intricacies within predictive models during the reconciliation process. The paper delves into these challenges for complex and instrumented systems (multi-scale, multi-physics, and multi-media), discussing practical applications of machine and deep learning methods in the case study of thermal loading monitoring of a high-level waste (HLW) cell demonstrator (called ALC1605) implemented at Andra's underground research laboratory. [ABSTRACT FROM AUTHOR]

High-intensity activities are related to success in football. We looked at whether high-intensity activity differed between home and away matches and its impact on the final score. Thirty football players (20.3 ± 0.8 years) were recruited from a Spanish semi-professional team. Footballers wore a GPS device to monitor high-intensity parameters from competition matches. The final score of every match was also recorded. Playing at home showed greater total distance (TD) > 24 km/h, >27 km/h, >85% Vmax, and the number of sprints > 24 km/h (all p < 0.05) than playing away. Positive correlations were also found between the match score and high-speed running (HSR) distance covered by the team per minute (r = 0.401), TD > 21 km/h (r = 0.417), TD > 24 km/h (r = 0.343), number of sprints > 24 km/h (r = 0.337), and HSR per minute (r = 0.459) (all p < 0.05). The results suggest that playing at home is associated with greater high-intensity effort than playing away. Moreover, the volume of high-intensity effort influences the final score. [ABSTRACT FROM AUTHOR]

Compound flood (CF) modeling enables the simulation of nonlinear water level dynamics in which concurrent or successive flood drivers synergize, producing larger impacts than those from individual drivers. However, CF modeling is subject to four main sources of uncertainty: (i) the initial condition, (ii) the forcing (or boundary) conditions, (iii) the model parameters, and (iv) the model structure. These sources of uncertainty, if not quantified and effectively reduced, cascade in series throughout the modeling chain and compromise the accuracy of CF hazard assessments. Here, we characterize cascading uncertainty using linked process-based and machine learning (PB–ML) models for a well-known CF event, namely, Hurricane Harvey in Galveston Bay, TX. For this, we run a set of hydrodynamic model scenarios to quantify isolated and cascading uncertainty in terms of maximum water level residuals; additionally, we track the evolution of residuals during the onset, peak, and dissipation of Hurricane Harvey. We then develop multiple linear regression (MLR) and PB–ML models to estimate the relative and cumulative contribution of the four sources of uncertainty to total uncertainty over time. Results from this study show that the proposed PB–ML model captures "hidden" nonlinear associations and interactions among the sources of uncertainty, thereby outperforming conventional MLR models. The model structure and forcing conditions are the main sources of uncertainty in CF modeling, and their corresponding model scenarios, or input features, contribute to 56 % of variance reduction in the estimation of maximum water level residuals. Following these results, we conclude that PB–ML models are a feasible alternative for quantifying cascading uncertainty in CF modeling. [ABSTRACT FROM AUTHOR]

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In this paper, we provide a framework for the study of Hecke operators acting on the Bredon (co)homology of an arithmetic discrete group. Our main interest lies in the study of Hecke operators for Bianchi groups. Using the Baum–Connes conjecture, we can transfer computations in Bredon homology to obtain a Hecke action on the K -theory of the reduced C ∗ -algebra of the group. We show the power of this method giving explicit computations for the group SL 2 (ℤ [ i ]). In order to carry out these computations we use an Atiyah–Segal type spectral sequence together with the Bredon homology of the classifying space for proper actions. [ABSTRACT FROM AUTHOR]