Abstract
This study estimates alighting stops and transfers from entry-only Automatic Fare Collection (AFC) data. The methodology adopted includes two main steps: an implementation of the Trip Chaining Method (TCM) to estimate the alighting stops from AFC records and the subsequent application of criteria for the identification of transfers. For each pair of consecutive AFC records on the same smart card, a transfer is identified considering a threshold for the walking distance, a threshold for the time required to perform an activity, and the validation of different boarding routes. This methodology was applied to the case study of Porto, Portugal, considering all trips performed by a set of 19999 smart cards over one year. The results of this methodology allied with visualization techniques allowed to study Origin-Destination (OD) patterns by type of day, seasonally, and by user frequency, each analyzed at the stop level and at the geographic area level.

Abstract
In this paper, a solution to monitor the location of humans during their activity in the agriculture sector with the aim to boost productivity and efficiency is provided. Our solution is based on map-matching methods, that are used to track the path spanned by a worker along a specific activity in an agriculture culture. Two different cultures are taken into consideration in this study olives and vines. We leverage the symmetry of the geometry of these cultures into our solution and divide the problem three-fold initially, we estimate a path of a worker along the fields, then we apply the map-matching to such path and finally, a post-processing method is applied to ensure local continuity of the sequence obtained from map-matching. The proposed methods are experimentally evaluated using synthetic and real data in the region of Mirandela, Portugal. Evaluation metrics show that results for synthetic data are robust under several sampling periods, while for real-world data, results for the vine culture are on par with synthetic, and for the olive culture performance is reduced.

Abstract
Many Virtual Reality (VR) locomotion techniques have been proposed, but those explored for and with blind people are often custom-made or require specialized equipment. Consequently, it is unclear how popular techniques can support blind people's VR locomotion, blocking access to most VR experiences. We implemented three popular techniques - Arm Swinging, Linear Movement (joystick-based steering), and Point & Teleport - with minor adaptations for accessibility. We conducted a study with 14 blind participants consisting of navigation tasks with these techniques and a semi-structured interview. We found no differences in overall performance (e.g., completion time), but contrasting preferences. Findings highlight the challenges and advantages of each technique and participants' strategies. We discuss, among others, how augmenting the techniques enabled blind people to navigate in VR, the greater control of movement of Arm Swinging, the simplicity and familiarity of Linear Movement, and the potential for efficiency and for scanning the environment of Point & Teleport.

Abstract
Este artículo presenta un algoritmo para localización de fallas basado en un método de estimaciones de estado, válido para sistemas de distribución activos de media tensión. El algoritmo utiliza las mediciones registradas por unos pocos dispositivos de medición sincrofasoriales uPMUs, junto con pseudomediciones, para localizar con éxito la línea con falla. Primero se presenta la formulación de general de las estimaciones de estado bajo la suposición de que todas las barras son monitoreadas. Posteriormente, se define el método a seguir para conseguir detectar una falla con mínimo dos uPMUs. Finalmente, se desarrolla un algoritmo de localización óptima, cuyas restricciones se basan en mejorar los índices de confiabilidad del sistema. El método propuesto es validado en un sistema de distribución trifásico de 39 barras, donde el índice de confiabilidad de duración de interrupciones es reducido en un 22.01% con el despliegue de tan sólo dos uPMUs.

Abstract
Context. The dust- and gas-rich protoplanetary disks around young stellar systems play a key role in star and planet formation. While considerable progress has recently been made in probing these disks on large scales of a few tens of astronomical units (au), the central au requires further investigation. Aims.We aim to unveil the physical processes at play in the innermost regions of the strongly accreting T Tauri Star S CrA N by means of near-infrared interferometric observations. As recent spectropolarimetric observations suggest that S CrA N might undergo intense ejection processes, we focus on the accretion-ejection phenomena and on the star-disk interaction region. Methods. We obtained interferometric observations with VLTI/GRAVITY in the K-band during two consecutive nights in August 2022. The analysis of the continuum emission, coupled with the differential analysis across the Br gamma line, allows us to constrain the morphology of the dust and the gas distribution in the innermost regions of S CrA N and to investigate their temporal variability. These observations are compared to magnetospheric accretion-ejection models of T Tauri stars and to previous observations in order to elucidate the physical processes operating in these regions. Results. The K-band continuum emission is well reproduced with an azimuthally modulated dusty ring with a half-light radius of 0.24 au (similar to 20 R*), an inclination of similar to 30 degrees, and a position angle of similar to 150 degrees. As the star alone cannot explain such a large sublimation front, we propose that magnetospheric accretion is an important dust-heating mechanism leading to this continuum emission. The Br gamma-emitting region (0.05-0.06 au; 5-7 R*) is found to be more compact than the continuum, to be similar in size or larger than the magnetospheric truncation radius. The on-sky displacements across the Br gamma spectral channels are aligned along a position angle offset by 45 degrees from the disk, and extend up to 2 R*. This is in agreement with radiative transfer models combining magnetospheric accretion and disk winds. These on-sky displacements remain unchanged from one night to another, while the line flux decreases by 13%, suggesting a dominant contribution of wind to the origin of the Br gamma line. Conclusions. Our observations support the scenario where the Br gamma line originates from a combination of (variable) accretion-ejection processes in the inner disk region.

Abstract
Left ventricle (LV) segmentation of 2-D echocardiography images is an essential step in the analysis of cardiac morphology and function and-more generally-diagnosis of cardiovascular diseases (CVD). Several deep learning (DL) algorithms have recently been proposed for the automatic segmentation of the LV, showing significant performance improvement over the traditional segmentation algorithms. However, unlike the traditional methods, prior information about the segmentation problem, e.g., anatomical shape information, is not usually incorporated for training the DL algorithms. This can degrade the generalization performance of the DL models on unseen images if their characteristics are somewhat different from those of the training images, e.g., low-quality testing images. In this study, a new shape-constrained deep convolutional neural network (CNN)-called B-spline explicit active surface (BEAS)-Net-is introduced for automatic LV segmentation. The BEAS-Net learns how to associate the image features, encoded by its convolutional layers, with anatomical shape-prior information derived by the BEAS algorithm to generate physiologically meaningful segmentation contours when dealing with artifactual or low-quality images. The performance of the proposed network was evaluated using three different in vivo datasets and was compared with a deep segmentation algorithm based on the U-Net model. Both the networks yielded comparable results when tested on images of acceptable quality, but the BEAS-Net outperformed the benchmark DL model on artifactual and low-quality images.