Demos

Abstract The most general searches for gravitational wave transients (GWTs) rely on data analysis methods that do not assume prior knowledge of the signal’s waveform, direction, and arrival time on Earth. These searches provide data-driven signal reconstructions that are crucial both for testing available GW emission models and for discovering yet-to-be-unveiled sources. Here, we discuss the progresses in detection performances of the coherent WaveBurst second generation pipeline (cWB-2G), which is highly adaptable to minimally–modeled as well as model–informed searches for GWTs. Several search configurations for GWTs are examined using approximately 14.8 d of observation time from the third observing run by LIGO-Virgo-KAGRA (LVK). Recent enhancements include a ranking statistic fully based on multivariate classification with eXtreme gradient boosting, a thorough validation of the accuracy of the statistical significance of GWT candidates, and the measurement of the correlations of false alarms and simulated detections between different concurrent searches. For the first time, we provide a comprehensive comparison of cWB-2G performances on data from networks made of two and three detectors, and we demonstrate the advantage of combining concurrent searches for GWTs of generic morphology in a global observatory. This work offers essential insights for assessing our data analysis strategies in the ongoing and future LVK searches for generic GWTs.

Quantifying the evaporative flux from the land surface into the atmosphere is important for local irrigation management, regional water management and global weather prediction, but measuring evapotranspiration (ET) remains a challenge. Methods that directly measure ET, including eddy covariance and lysimeters, have high temporal resolution but limited spatial coverage, while indirect estimates, including remote sensing techniques, have broad spatial coverage but lower temporal frequency. Each principle for estimating ET comes with unique uncertainties, so a combined use of these methods under identical conditions, which is rarely done in field campaigns, should be promoted to assess its relative and overall performance. In order to quantify the relative uncertainty of different commonly-used ET estimation techniques, we performed an ET method intercomparison using data from the 2021 Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) experiment (Boone et al., 2025). Fifteen ET estimation methods were evaluated across six crop types, spanning footprint extents from individual fields to the regional scale and encompassing multiple measurement principles. We also include an upscaled “mixed-agriculture” estimate from the eddy-covariance data to match the footprints of methodologies that cover more than one field (e.g. satellite remote sensing data and long-path scintillometry) We find that the total sampling uncertainty in the eddy covariance measurements is between 10% and 20% of the latent heat flux regardless of time of day. Moreover, we find that standard deviation among different ET methods for a single crop type (between 2.0 and 3.7 mm day-1) is greater than the standard deviation among the different crop types (1.9 mm day-1) in the LIAISE domain.

Abstract Purpose Facial expression recognition has gained significance as a means of providing artificial agents with the capacity to discern the emotional states of users. Notable performance has been achieved by deep learning approaches; however, its direct use for recognizing facial expressions in individuals with intellectual disabilities has not yet been studied in the literature, to the best of our knowledge. The aim of this work is to thoroughly explore the applicability of these systems to people with intellectual disabilities. Methods To address this objective, we work with a set of 12 convolutional neural networks (CNNs) in different approaches, including an ensemble of datasets without individuals with intellectual disabilities and a dataset featuring such individuals. Further, we apply the explainable artificial intelligence technique LIME that allows understanding the important regions considered by the CNN. Results Our examination of the outcomes, both the performance and the important image regions for the models, reveals significant distinctions in facial expressions between individuals with and without intellectual disabilities, as well as among individuals with intellectual disabilities. Conclusions Our findings show the need of facial expression recognition within this population through tailored user-specific training methodologies, which enable the models to effectively address the unique expressions of each user.

Abstract The formation of black holes by the gravitational collapse of stars is known to spontaneously excite particle pairs out of the quantum vacuum. For the canonical vacuum state at past null infinity, the expected number of particles received at future null infinity can be obtained in full closed form at sufficiently late times. However, for intermediate times, or for more complicated astrophysical processes (e.g. binary black hole mergers), the problem is technically challenging and has not yet been resolved. We develop here a numerical approach to study scattering problems of massless quantum fields in asymptotically flat spacetimes, based on the hyperboloidal slice method used in numerical relativity and perturbation theory. This promising approach can reach both past and future null infinities, and therefore it has the potential to address the Hawking scattering problem more rigorously than evolution on the usual Cauchy slices. We test this approach with some dynamical toy models in Minkowski using effective potentials that mimic the effects of gravity, and compute the spectrum of particles received at future null infinity. We finally discuss future prospects for applying this framework in more relevant gravitational scenarios.

Background: Depression is a common comorbidity in Diabetes Mellitus (DM) and is associated with poor glycaemic control. Although pharmacotherapy can reduce depressive symptoms, nearly half of DM patients remain severely depressed, highlighting the need for effective non-pharmacological interventions. Cognitive Behavioural Therapy (CBT) combined with dhikr therapy offers a potential approach that integrates psychological and spiritual healing. Objective: This study aimed to determine the effect of CBT combined with dhikr therapy on depression levels and glycaemic control among Type II DM patients. Methods: A quasi-experimental pretest–posttest control group design was conducted over four weeks at Tanete and Bontobangun Health Centres, Bulukumba Regency, Indonesia. Eighty-four Type II DM patients with depression were recruited through purposive sampling, comprising 44 participants in the intervention group and 40 in the control group. The intervention group received structured CBT sessions integrated with dhikr once weekly for four weeks, while the control group received standard care. Depression levels were measured using the DASS-21 instrument, and blood glucose levels were assessed with a glucometer. Data were analyzed using the Wilcoxon test in SPSS. Results: CBT combined with dhikr therapy significantly reduced depression scores and blood glucose levels in the intervention group (p < 0.05), whereas the control group showed no significant changes. Conclusion: The integration of CBT and dhikr therapy effectively improves psychological well-being and glycaemic control in Type II DM patients. This combined therapy provides an evidence-based, non-pharmacological approach that supports holistic diabetes management. Further studies should explore optimal duration and frequency for sustained outcomes.

Cuando un pensador iconoclasta como Nietzsche o Cioran se vuelve referente dentro de la academia, paradigma interpretativo, o incluso asignatura curricular, no deja de surgir cierta incomodidad. Una punzada en lo profundo de la sangre advierte que algo no estamos haciendo bien. Estamos abandonando la ironía, petrificando la crítica al volverla institución. Ambos pensadores comparten ciertas similitudes, ya que, al igual que Nietzsche, Cioran es hijo de un representante del absoluto. El padre de Cioran es un sacerdote ortodoxo rumano, mientras que el del intempestivo fue un pastor luterano. Lo que marca irremediablemente su pensamiento contra la religión y sus representantes, que no es sino una forma de afirmación de la subjetividad en contra de la ley paterna, que según Freud es de donde tomamos nuestra idea de Dios. Es fácil creer que Dios es bueno cuando has tenido un padre bueno. Pero cuando has tenido un padre que se presenta como el representante de la divinidad, es complicado sobrevivir, y mucho más complejo encontrar un lugar, una manera particular para desarrollar la personalidad, pues la figura de un Dios deja muy poco espacio para nadie más. Así, tanto Nietzsche como Cioran construirán su pensamiento como una añoranza de esa debilidad paterna que les permita ser.

Abstract Standardizing the definition of eccentricity is necessary for unambiguous inference of the orbital eccentricity of compact binaries from gravitational wave observations. In previous works, we proposed a definition of eccentricity for systems without spin-precession that relies solely on the gravitational waveform, is applicable to any waveform model, and has the correct Newtonian limit. In this work, we extend this definition to spin-precessing systems. This simple yet effective extension relies on first transforming the waveform from the inertial frame to the coprecessing frame, and then adopting an amplitude and a phase with reduced spin-induced effects. Our method includes a robust procedure for filtering out spin-induced modulations, which become non-negligible in the small eccentricity and large spin-precession regime. Finally, we apply our method to a set of Numerical Relativity and Effective One Body waveforms to showcase its robustness for generic eccentric spin-precessing binaries. We make our method public via Python implementation in gw_eccentricity.