Abstract
PurposeThe present study aims to analyse the presumed relationship between VLC use and students' grades.Design/methodology/approachThe research strategy unfolds as a case study (Yin, 1994), framed by how undergraduate students of pharmaceutical sciences used video lecture capture (VLC) and the impact of VLC on pedagogic differentiation. Looking at the course of Mechanistic Toxicology (MecTox), the objective is to describe this case of pharmaceutical sciences in depth.FindingsThe findings reveal that over 90% of students engaged with VLC videos, with the average viewing time exceeding the total available video minutes, indicating strong student engagement. The study particularly highlights VLC's positive impact on students with lower academic performance (grades D and E), suggesting that VLC can help reduce the performance gap and support a more inclusive educational environment.Research limitations/implicationsThe findings may have limited generalisability beyond the specific context and sample used. However, this study allows the research findings to be compared with previous research (Remi & atilde;o et al., 2022), contributing to the debate on how pedagogic research can promote evidence-based decisions regarding innovative strategies. The meaning of educational inclusion processes and diversity is, thus, contingent on the institutionalisation of research as a practice of teaching and learning.Practical implicationsThe results of this study thus provide interesting insights for the design of strategic action, considering the diversity of students as seen in parents' academic qualifications and students' conditions (e.g. student-workers, living away from home, holding a grant of economic and social support).Social implicationsThe implications of research findings for society bring the issue of equity in education to the fore. By addressing the diverse needs of students, HEIs can contribute to greater educational equity.Originality/valueUsing VLC as a differentiated pedagogic device might give diversity real content insofar as institutional and national policies can mitigate the possible negative effects of parents' low academic qualifications and the students' conditions of living away from their residence area and holding a grant of economic and social support.

Abstract
Understanding the orbits of giant planets is critical for testing planet formation models, particularly at wide separations (>10 au) where traditional core accretion becomes inefficient. However, constraining orbits at these separations has historically been challenging due to sparse orbital coverage and related degeneracies in the orbital parameters. In this work, we use existing high-resolution (R similar to 100,000) spectroscopic measurements from CRIRES+, astrometric data from SPHERE, NACO, and Atacama Large Millimeter/submillimeter Array, and combine it with new high-precision GRAVITY astrometry data to refine the orbit of GQ Lup B, a similar to 30 M-J companion at similar to 100 au, in a system that also hosts a circumstellar disk and a wide companion, GQ Lup C. Including radial velocity (RV) data significantly improves orbital constraints by breaking the degeneracy between inclination and eccentricity that plagues astrometry-only fits for long-period companions. Our work is one of the first to combine high-precision astrometry with the companion's relative radial velocity measurements to achieve significantly improved orbital constraints. The eccentricity is refined from e=0.47(-0.16)(+0.14 )(GRAVITY only) to e=0.35(-0.09)(+0.10) when RVs and GRAVITY data are combined. We also compute the mutual inclinations between the orbit of GQ Lup B, the circumstellar disk, the stellar spin axis, and the disk of GQ Lup C. The orbit is misaligned by 63(-14)(+6) degrees relative to the circumstellar disk, 52(-24)(+19 )degrees with the host star's spin axis, but appears more consistent ( 34-13+6 degrees) with the inclination of the wide tertiary companion GQ Lup C's disk. These results support a formation scenario for GQ Lup B consistent with cloud fragmentation. They highlight the power of combining companion RV constraints with interferometric astrometry to probe the dynamics and formation of wide-orbit substellar companions.

Abstract
When the available data for a target domain is limited, transfer learning (TL) methods leverage related data-rich source domains to train and evaluate models, before deploying them on the target domain. However, most TL methods assume fixed levels of labeled and unlabeled target data, which contrasts with real-world scenarios where both data and labels arrive progressively over time. As a result, evaluations based on these static assumptions may not reflect how methods perform in practice. To support a more realistic assessment of TL methods in dynamic settings, we propose an evaluation framework that (1) simulates varying data availability over time, (2) creates multiple domains via resampling of a given dataset and (3) introduces inter-domain variability through controlled transformations, e.g., including time-dependent covariate and concept shifts. These capabilities enable the systematic simulation of a large number of variants of the experiments, providing deeper insights into how algorithms may behave when deployed. We demonstrate the usefulness of the proposed framework by performing a case study on a proprietary real-world suite of card payment datasets. To support reproducibility, we also apply the framework on the publicly available Bank Account Fraud (BAF) dataset. By providing a methodology for evaluating TL methods over time and in different data availability conditions, our framework supports a better understanding of model behavior in real-world environments, which enables more informed decisions when deploying models in new domains.

Abstract

Abstract
Language models have become foundational to many widely used systems. However, these seemingly advantageous models are double-edged swords. While they excel in tasks related to resource-rich languages like English, they often lose the fine nuances of language forms, dialects, and varieties that are inherent to languages spoken in multiple regions of the world. Languages like European Portuguese are neglected in favor of their more popular counterpart, Brazilian Portuguese, leading to suboptimal performance in various linguistic tasks. To address this gap, we introduce the first open-source translation model specifically tailored for European Portuguese, along with a novel dataset specifically designed for this task. Results from automatic evaluations on two benchmark datasets demonstrate that our best model surpasses existing open-source translation systems for Portuguese and approaches the performance of industry-leading closed-source systems for European Portuguese. By making our dataset, models, and code publicly available, we aim to support and encourage further research, fostering advancements in the representation of underrepresented language varieties.

Abstract
Precision agriculture seeks to optimize crop yields while minimizing resource use. A key challenge is achieving uniform pesticide spraying to prevent crop damage and environmental contamination. Water-sensitive paper (WSP) is a common tool used for assessing spray quality, as it visually registers droplet impacts through color change. This work introduces a smartphone-based solution for capturing WSP images within vegetation, offering a tool for farmers to assess spray quality in real-world conditions. To achieve this, two approaches were explored: classical computer vision techniques and machine learning (ML) models (YOLOv8, Mask-RCNN, and Cellpose). Addressing the challenges of limited real-world data and the complexity of manual annotation, a programmatically generated synthetic dataset was employed to enable sim-to-real transfer learning. For the task of WSP segmentation within vegetation, YOLOv8 achieved an average Intersection over Union of 97.76%. In the droplet detection task, which involves identifying individual droplets on WSP, Cellpose achieved the highest precision of 96.18%, in the presence of overlapping droplets. While classical computer vision techniques provided a reliable baseline, they struggled with complex cases. Additionally, ML models, particularly Cellpose, demonstrated accurate droplet detection even without fine-tuning.