Universitat de les Illes Balears

Purpose : The purpose of this review was to examine and describe the characteristics of sport and physical education-focused interventions that have been implemented in educational contexts to raise awareness toward disability. Method : A systematic review of journal-based literature published on disability awareness sport and physical education interventions impacting on students and preservice teachers was conducted. The article selection process included three phases based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Results : A total of 43 articles, spanning from 1992 to 2023, were analyzed in this study. Most reviewed research was from authors in Spain or the United States, and quantitative designs were most often used. Interventions were carried out across primary schools, secondary schools, and university settings, with most research coming from the university setting. Discussion/Conclusion : The results of this review demonstrate an abundance of empirically tested resources that can be utilized within elementary, secondary, and university physical education contexts to help enhance attitudes and self-efficacy toward students with disabilities.

A partir del análisis de las principales áreas turísticas de sol y playa del mundo, Mediterráneo, Caribe y Sudeste Asiático, este artículo estudia el papel de las migraciones circulares durante los procesos de formación de destinos turísticos. Los resultados indican que estas migraciones han jugado un rol clave en los procesos de transformación radical de los espacios turísticos con implicaciones diferenciales para el desarrollo económico y social de estas regiones. Las características de estos flujos migratorios sugieren un abordamiento específico desde el punto de vista de las políticas públicas con el objetivo de mejorar la inserción y movilidad social de estos migrantes y lograr una mayor cohesión social. Asimismo, el trabajo enfatiza la importancia de incluir el impacto diferencial de estas migraciones, a menudo negligidas en la literatura, en los análisis socio-económicos del desarrollo turístico.

¿Qué modelos de masculinidad produce el deporte profesional, también en la intimidad entre hombres? La serie ‘Más que rivales’ muestra situaciones que la sociología del deporte ha estudiado y descrito.

Abstract. Rip currents are hazardous, fast-moving seaward flows and remain one of the leading causes of rescues and drownings on surf beaches, yet their automated detection remains a significant challenge due to their amorphous, dynamic morphology and the environmental complexity of the surf zone. This study introduces a novel platform-agnostic deep learning–based framework for automated rip current detection from beach imaging platforms, integrating three core contributions: a diverse new dataset, a rigorous architectural benchmark, and a deployable operational tool. We first present RipAID, a comprehensive dataset enriched with multi-platform imagery and multiple viewing angles to ensure scale-invariant learning. Building on this resource, a systematic evaluation of state-of-the-art architectures demonstrates that geometric fidelity is critical; specifically Oriented Bounding Boxes (OBB) significantly outperform standard axis-aligned methods. Our optimized YOLOv11n-OBB model achieves robust performance (mAP50: 0.927), with inference speeds from 2.4 to 60 FPS on hardware ranging from edge devices to GPU workstations. To bridge the gap between research and practice, and ensure that the results are reusable and reproducible, the framework and model weights have been released as an open-source, containerized module (socib-rip-currents-detection), providing the coastal safety community with a scalable, ready-to-use and standardized tool for continuous, automated rip current monitoring.

The growing adoption of deep learning in medical image processing requires careful consideration of energy usage during model training. This study investigates the impact of batch size and training epochs on energy consumption during the fine-tuning of ResNet-50, a widely used convolutional neural network. The experiments were conducted on the ODIR-5K ocular disease dataset, using a controlled hardware configuration with GPU-level monitoring to ensure reproducibility. Fine-tuning was performed with batch sizes of 32, 64, 128, and 256 across four epoch schedules: 1, 10, 80, and 160. For each configuration, training time, average power draw, total energy consumption, GPU utilization, and memory usage were recorded. The results show that the number of epochs is the dominant factor influencing energy consumption, as training time and total energy scale almost linearly with epoch count. Batch size, in contrast, introduced a more complex trade-off. Larger batches reduced the average power drawn by the GPU but significantly increased training time, leading to higher overall energy consumption. Additionally, GPU utilization decreased as batch size increased, suggesting reduced hardware efficiency, while memory usage rose with larger batches. These findings highlight the importance of jointly considering batch size and epoch count when optimizing training configurations. Simply reducing instantaneous power draw does not guarantee lower total energy consumption. The results provide empirical evidence that can inform more energy-aware strategies in deep learning, particularly in medical imaging tasks where both computational efficiency and sustainability are critical.

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.

Long paleoclimate time series combine strong persistence, multiple orbital cycles, and regime shifts, which complicates the analysis of dynamical coupling and predictability. We analyze Cenozoic variability using the Cenozoic global reference benthic foraminiferal carbon and oxygen isotope dataset, in a regime based time series framework that integrates deterministic decomposition, cyclical fractional cointegration, and regime aware forecasting. We divide the record into segments in line with the major Cenozoic climate states. Within each segment, deterministic components are estimated and removed, including linear trends, orbital forcing variables, and harmonic cycles identified via a GARMA based filtering procedure. We then apply cyclical fractional cointegration tests at shared orbital frequencies to assess whether common spectral peaks reflect a stable frequency specific linkage (cointegration) between the proxies and orbital variables. The results reveal pronounced regime dependence. The long eccentricity cycle (405 kyr) shows recurrent evidence of cointegration with both proxies across different climate states. For obliquity, an indication of frequency specific linkage is primarily found after the middle Miocene Climate Transition. Finally, we fit regime specific VAR(2) models to the residuals and report in-sample forecasts, and we generate a 100 kyr out-of-sample projection based on the Icehouse specific dynamics. Forecast behaviour varies across climate states, highlighting that non-stationarity and regime specific dynamics place strong constraints on predictability in long paleoclimate records.

ABSTRACT The present study contributes to the growing body of research showing absence of first language (L1) attrition when the L1 and second language (L2) are structurally aligned and regularly used. It investigates possible language attrition in the morphosyntactic features of 3rd-person Italian accusative clitics across three clitic structures in sequential L1-Italian–L2-Spanish speakers immersed in a Spanish-predominant environment. A total of 19 Italian–Spanish bilinguals and 18 Italian monolinguals completed a structural priming task to assess knowledge of clitic form and placement in spoken production. In the data analysis, key variables such as L2 exposure or L2 performance were also considered. We hypothesise that bilinguals will not exhibit significant variation in clitic form and placement compared to monolinguals due to the combination of continued L1 use and complete structural overlap between Italian and Spanish. However, syntactic complexity, particularly in modal constructions, was expected to influence placement and form in both groups due to processing demands. The results supported our predictions, revealing no significant differences between bilinguals and monolinguals in clitic accuracy, and lower structural accuracy in modal structures compared to lexical or causative structures in both groups. Although the present design does not allow us to isolate the mechanisms responsible for L1 maintenance, our findings offer empirical evidence that structural overlap and ongoing L1 activation can preserve grammatical stability even under intense L2 exposure.