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Les proveedores de salud en Puerto Rico se han convertido en expertos en brindar atención médica de manera ética en el contexto de desastres múltiples. A lo largo de las consecuencias devastadoras del Huracán María en el 2017, un enjambre sísmico que comenzó en el 2019 y la pandemia de SARS-COV-2 a partir del 2020, les trabajadores de la salud en Puerto Rico han servido a sus pacientes, colegas, familiares, vecinos y amistades de una forma basada en el cuidado mutuo. Basado en el análisis de ciento cincuenta y una (151) entrevistas etnográficas y nueve (9) grupos focales, este informe documenta el desgaste emocional y físico que han vivido les trabajadores de salud durante esta cascada de desastres. Señalar los problemas institucionales que causan el desgaste y la herida moral entre les trabajadores de la salud en Puerto Rico pudiese contribuir a mejorar la seguridad, sustentabilidad y resiliencia del sistema de salud puertorriqueño. En este informe enumeramos los desafíos sistémicos que causan y contribuyen al agotamiento y la herida moral en les trabajadores de la salud en Puerto Rico, como lo son: los salarios bajos, las reducciones de personal, los abusos de las compañías de seguro y la falta de preparación y planificación institucional y estatal para los desastres. Además, constatamos que muchas personas que entrevistamos identificaron desafíos en sus vidas laborales y personales vinculados al desgaste y a la presencia de una herida moral. Algunos de estos desafíos incluyen: la dificultad para regular sus emociones, el reto de balancear el trabajo con la vida personal, el manejo de estrés, la tensión en sus relaciones personales, incluyendo la demanda incremental de personas queridas con necesidades de salud y una gran dificultad para poder relajarse. En este informe enfatizamos las experiencias de agotamiento de les trabajadores de salud, así como los cambios políticos y protocolarios recomendados por elles mismes. Los cambios sugeridos y descritos aquí pudieran mejorar las condiciones laborales de este sector tan importante y mejorar la salud de todes les puertorriqueñes. Frente a la crisis prolongada existente, nuestra investigación evidencia la existencia de propuestas concretas y del deseo de aprovechar la sabiduría acumulada del pueblo. 

The Collaboration reports a new determination of the jet transport parameter $\hat{q}$in the quark-gluon plasma (QGP) using Bayesian inference, incorporating all available inclusive hadron and jet yield suppression data measured in heavy-ion collisions at the BNL Relativistic Heavy Ion Collider (RHIC) and the CERN Large Hadron Collider (LHC). This multi-observable analysis extends the previously published Bayesian inference determination of $\hat{q}$, which was based solely on a selection of inclusive hadron suppression data. is a modular framework incorporating detailed dynamical models of QGP formation and evolution, and jet propagation and interaction in the QGP. Virtuality-dependent partonic energy loss in the QGP is modeled as a thermalized weakly coupled plasma, with parameters determined from Bayesian calibration using soft-sector observables. This Bayesian calibration of $\hat{q}$utilizes active learning, a machine-learning approach, for efficient exploitation of computing resources. The experimental data included in this analysis span a broad range in collision energy and centrality, and in transverse momentum. In order to explore the systematic dependence of the extracted parameter posterior distributions, several different calibrations are reported, based on combined jet and hadron data; on jet or hadron data separately; and on restricted kinematic or centrality ranges of the jet and hadron data. Tension is observed in comparison of these variations, providing new insights into the physics of jet transport in the QGP and its theoretical formulation. Published by the American Physical Society2025 

We present predictions and postdictions for a wide variety of hard jet-substructure observables using a multistage model within the framework. The details of the multistage model and the various parameter choices are described in []. A novel feature of this model is the presence of two stages of jet modification: a high-virtuality phase [modeled using the modular all twist transverse-scattering elastic-drag and radiation model ()], where modified coherence effects diminish medium-induced radiation, and a lower virtuality phase [modeled using the linear Boltzmann transport model ()], where parton splits are fully resolved by the medium as they endure multiple scattering induced energy loss. Energy-loss calculations are carried out on event-by-event viscous fluid dynamic backgrounds constrained by experimental data. The uniform and consistent descriptions of multiple experimental observables demonstrate the essential role of modified coherence effects and the multistage modeling of jet evolution. Using the best choice of parameters from [], and with no further tuning, we present calculations for the medium modified jet fragmentation function, the groomed jet momentum fraction $z_g$and angular separation $r_g$distributions, as well as the nuclear modification factor of groomed jets. These calculations provide accurate descriptions of published data from experiments at the Large Hadron Collider. Furthermore, we provide predictions from the multistage model for future measurements at the BNL Relativistic Heavy Ion Collider. Published by the American Physical Society2024 

We study parton energy-momentum exchange with the quark gluon plasma (QGP) within a multistage approach composed of in-medium Dokshitzer-Gribov-Lipatov-Altarelli-Parisi evolution at high virtuality, and (linearized) Boltzmann transport formalism at lower virtuality. This multistage simulation is then calibrated in comparison with high- $p_T$charged hadrons, $D$mesons, and the inclusive jet nuclear modification factors, using Bayesian model-to-data comparison, to extract the virtuality-dependent transverse momentum broadening transport coefficient $\hat{q}$. To facilitate this undertaking, we develop a quantitative metric for validating the Bayesian workflow, which is used to analyze the sensitivity of various model parameters to individual observables. The usefulness of this new metric in improving Bayesian model emulation is shown to be highly beneficial for future such analyses. Published by the American Physical Society2024