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Probing the disk-jet coupling in M 87
No full textContext. Recent GMVA observations of M 87 at event horizon scales revealed a ring-like structure that is ∼50% larger at 86 GHz than the ring observed by the Event Horizon Telescope at 230 GHz.
Aims. We studied a possible origin of the increased ring size at 86 GHz and the role the nonthermal electron population plays in the observed event horizon scales.
Methods. We carried out 3D general relativistic magnetohydrodynamic simulations followed by radiative transfer calculations. We incorporated synchrotron emission from both thermal and nonthermal electrons into the calculations. To better compare our results to observations, we generated synthetic interferometric data adjusted to the properties of the observing arrays. We fit geometrical models to these data in Fourier space through Bayesian analysis to monitor the variable ring size and width over the simulated time span.
Results. We find that the 86 GHz ring is always larger than the 230 GHz ring, which can be explained by the increased synchrotron self-absorption at 86 GHz and the mixed emission from both the accretion disk and the jet footpoints, as well as flux arcs ejected from a magnetized disk. We find agreement with the observations, particularly within the error range of the observational value of M/D for M 87.
Conclusions. We show that state-of-the art 3D general relativistic magnetohydrodynamic simulations combined with thermal and nonthermal emitting particles can explain the observed frequency-dependent ring size in M 87. Importantly, we find that MAD events triggered in the accretion disk can significantly increase the lower-frequency ring sizes
Introducing NewCluster: First half of the history of a high-resolution cluster simulation
No full textAims. We introduce NE
Evolution of galaxy attenuation curves driven by evolving dust mass and grain size distributions
No full textAims. We investigate the impacts of the evolution of dust mass and grain size distribution on the evolution of global attenuation curves, with a focus on the optical-ultraviolet (UV) slope and the 2175 Å bump, within a Milky Way-like (MW-like) galaxy simulation. In addition, we discuss the contributions of the star-dust geometry, scattering, and dust properties to the attenuation curves.
Methods. We performed the post-processing dust radiative transfer using the SKIRT code based on a MW-like galaxy simulation. The hydrodynamic simulation was carried out with the GADGET4-OSAKA code, which models the evolution of grain size distributions.
Results. For lower inclination angles (i.e., closer to face-on), the attenuation curve flattens over time up to t = 1 Gyr and becomes progressively steeper. The steeper slope of the attenuation curve is caused by the interplay between scattering and the dust disk becoming more extended over time (i.e., changes in the star-dust geometry). At higher inclination angles, the effect of scattering is suppressed and the attenuation curves steepen slightly over time due to the formation of small grains and the bias of observed UV emission toward old stars. The 2175 Å bump becomes stronger on a timescale of ∼250 Myr due to the formation of small carbonaceous grains. However, the bump strength is affected not only by the abundance of small grains, but also by star-dust geometry. At higher AV, or at higher inclination angles, the bump strengths become weaker. These results may help interpret flatter attenuation curves and less prominent bumps in high-redshift galaxies. Furthermore, we find that variations in the star-dust geometry alter the amount of scattered photons escaping the galaxy, thereby driving the anti-correlation between the slope and V-band attenuation, AV. The scatter in this relation arises from differences in dust optical depth along and perpendicular to the line of sight, reflecting differences in the inclination and star-dust geometry. Additional contributions to the scatter come from variations in the grain size distribution and the fraction of obscured young stars
A Comprehensive Review of Machine Learning Techniques for Optimized Urban Water Systems (UWSs)
No full textUrban Water Systems (UWSs) across the world are under mounting pressure as cities continue to expand, infrastructures age, and climate change introduces new levels of uncertainty into water availability and distribution. The traditional tools and hydraulic models that once guided urban water management are increasingly unable to cope with the highly dynamic, non-linear behavior of today's networks. These limitations have prompted a shift toward more adaptive and intelligent approaches capable of handling complex data environments. This review explores how Machine Learning (ML) is emerging as a powerful instrument for addressing these modern challenges. By examining research published largely in the past five years, the paper provides a structured overview of how ML has been applied to major UWS functions— such as forecasting future water demand, identifying leaks and pipe failures, monitoring water quality, and optimizing day-to-day system operations. A diverse range of techniques is discussed, from established learning models like Random Forests (RF) and Support Vector Machines (SVM), to more sophisticated deep learning methods including Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs). Across multiple studies, ML consistently surpasses traditional modelling approaches by learning intricate spatial and temporal relationships that conventional tools fail to capture. Certain algorithms have demonstrated notable advantages—for example, LSTM networks excel in predicting time-dependent water usage, while CNNs show strong performance in analyzing acoustic signals for leak detection. Beyond summarizing existing applications, the review highlights emerging themes and persistent gaps. Key concerns include the uneven availability and reliability of operational datasets, growing demands for data privacy and cybersecurity, and the ongoing challenge of interpreting decisions made by complex deep learning models. Additionally, hybrid frameworks, which combine the strengths of data-driven ML models with physically-based hydraulic models, are gaining interest as a promising direction for future research. Ultimately, the paper emphasizes the need for UWSs to evolve towards intelligent, autonomous, and sustainable systems. By integrating ML into standard practice—supported by robust data collection, careful preprocessing, and informed model selection—urban areas can significantly improve resource allocation, reduce water loss, and enhance overall resilience. This study demonstrates how data-driven strategies, when aligned with the realities of urban infrastructure, can play a pivotal role in shaping the next generation of efficient and sustainable urban water management
Impact of climate change on variations in groundwater storage in the Saïss aquifer (Northern Morocco)
No full textThis study examines the influence of rainfall intensity and drought regimes on groundwater levels in the Sais aquifer, located in a semi-arid region of Morocco. Using satellite-derived datasets and the Innovative Trend Analysis (ITA) method, the research analyses groundwater storage (GWS) trends, providing a robust approach to detect long-term changes compared to the Mann-Kendall test. The study employs Gravity Recovery and Climate Experiment (GRACE) gridded data to estimate groundwater fluctuations and assess time series trends in equivalent water thickness (EWT) and soil moisture. Results indicate a significant decline in groundwater levels, with 40% of monitoring sites showing a substantial downward trend and 60% experiencing pronounced declines. The estimated maximum groundwater storage loss is -0.244 cm/yr–1. These findings highlight the adverse effects of overexploitation and inefficient irrigation. While the study’s reliance on satellite data provides valuable information, it may overlook localized variations, and GRACE data may be less accurate in areas with complex geological features. Despite these limitations, the research informs water management strategies to mitigate groundwater depletion. The novelty of this study lies in the use of the ITA technique to enhance trend detection accuracy and support sustainable groundwater management in vulnerable regions such as the Sais aquifer
Human Emotion Identification Using Brain Waves and Facial Images
No full textEmotions carry considerable weight in any human interaction. This study presents a bimodal framework for detecting emotions through which brainwaves are combined with facial images to promote better identification of emotions. The EEG data sets acquired non-invasively resolve internal neuronal activities across frequency bands manifesting an emotional state. Simultaneously, the facial expressions including happiness, sadness, and neutrality provide an inhibitory complementary layer of observation. The merging of these two methodologies delivers more fine-tuned knowledge into the subject of emotion as such observations can greatly help in cases of stress management and supporting people who have an impaired ability to communicate
Le β-glucane favorise la tolérance à la grippe A via la reprogrammation des neutrophiles
No full textPaludisme : la nétose, arme de défense des neutrophiles impliquée dans les formes graves de la maladie, pourrait-elle être une cible thérapeutique ?
No full textBoundary-induced classical generalized Gibbs ensemble with angular momentum
No full textWe investigate how confinement geometry leads to the emergence of a Generalized Gibbs Ensemble (GGE) in classical systems. Unlike the standard Gibbs ensemble, the GGE includes additional conserved quantities, such as angular momentum, that arise from boundary-induced symmetries. Using analytical arguments based on the maximum entropy principle, we show that circular boundaries preserve angular momentum and drive the system toward a chiral, non-ergodic GGE that violates time-reversal symmetry. This ensemble differs fundamentally from the Gibbs case, producing near-boundary condensation and revealing how geometry alone can alter thermal equilibration. To quantify these effects, we introduce an order parameter measuring deviations from Gibbs behavior and demonstrate that conventional Monte Carlo methods must incorporate angular momentum conservation under such conditions. Our study highlights how geometric constraints shape non-equilibrium statistical ensembles and lead to subtle departures from the Bohr-van Leeuwen theorem. These predictions are validated through detailed simulations of confined classical hard-disk gases