Abstract
Due to the high variability in Hematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs), hidden stratification, and batch effects, generalizing beyond the training distribution is one of the main challenges in Deep Learning (DL) for Computational Pathology (CPath). But although DL depends on large volumes of diverse and annotated data, it is common to have a significant number of annotated samples from one or multiple source distributions, and another partially annotated or unlabeled dataset representing a target distribution for which we want to generalize, the so-called Domain Adaptation (DA). In this work, we focus on the task of generalizing from a single source distribution to a target domain. As it is still not clear which domain adaptation strategy is best suited for CPath, we evaluate three different DA strategies, namely FixMatch, CycleGAN, and a self-supervised feature extractor, and show that DA is still a challenge in CPath.

Abstract
This study presents the development and assessment of a mobile application - the WalkingPAD app - aimed at promoting adherence to physical exercise among patients with Peripheral Arterial Disease (PAD). The assessment of adherence, acceptability, and usability was performed using mixed methods. Thirty-eight patients participated in the study with a mean age of 63.4 years (SD = 6.8). Thirty patients used the application for three months, responded to a semi-structured interview, and completed a task test and the System Usability Scale (SUS, ranging from 0 to 100). The application's adherence rate was 73%. When patients were asked about their reasons for using the app, the main themes that emerged were motivation, self-monitoring, and support in fulfilling a commitment. The average SUS score was 82.82 (SD = 18.4), indicating high usability. An upcoming version of the WalkingPAD app is expected to redesign both tasks - opening the app and looking up the walking history - which were rated as the most difficult tasks to accomplish. The new version of the WalkingPAD app will incorporate participants' comments and suggestions to enhance usability for this population.

Abstract
Sound design plays an important role in serious games, influencing user experience and learning outcomes. However, deriving general principles and best practices remains challenging, as most literature relies on case-based studies in different application domains. Through a systematic review of the literature, 21 studies were analyzed to address two key questions: 1) what types of serious games and application domains incorporate sound design? and 2) what sound design strategies are implemented to enhance user experience and learning outcomes? The findings show that serious games have mainly focused on education, healthcare, and training, using sound to enhance motivation (50%), cognition (32%), and knowledge acquisition (18%). Furthermore, sound design strategies fulfill distinct roles: sound effects enhance feedback and engagement, background music influences motivation and cognitive processing, ambient sounds support navigation and emotional regulation, and dialogue facilitates knowledge acquisition. The findings highlight the need for further research to establish standardized sound design principles to optimize user experience and learning outcomes in serious games. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

Abstract
There are now multiple proposals for Conflict-free Replicated Data Types (CRDTs) in SQL databases aimed at distributed systems. Some, such as ElectricSQL, provide only relational tables as convergent replicated maps, but this omits semantics that would be useful for merging updates. Others, such as Pg\_crdt, provide access to a rich library of encapsulated column types. However, this puts merge and query processing outside the scope of the query optimizer and restricts the ability of an administrator to influence access paths with materialization and indexes. Our proposal, CRDV, overcomes this challenge by using two layers implemented as SQL views: The first provides a replicated relational table from an update history, while the second implements varied and rich types on top of the replicated table. This allows the definition of merge semantics, or even entire new data types, in SQL itself, and enables global optimization of user queries together with merge operations. Therefore, it naturally extends the scope of query optimization and local transactions to operations on replicated data, can be used to reproduce the functionality of common CRDTs with simple SQL idioms, and results in better performance than alternatives.

Abstract
One of the industry’s most common applications of lasers is engraving, which is generally performed on flat surfaces. However, there are many situations where the object to be engraved has an unevenly curved geometry. In those cases, the light power density will be different along the surface for a fixed head, leading to a poor engraving result. This work deals with this problem by designing a robotic application capable of detecting variations on the object surface and automatically creating a trajectory to engrave on it correctly. This was made possible through a robotic manipulator, a time-of-flight distance sensor, and a data processing algorithm over the measured data. Obtained results were acquired using a custom-made test rig and validated by delivering consistent engraving results on irregular surface shapes.

Abstract
Traumatic Brain Injury (TBI) is a form of brain injury caused by external forces, resulting in temporary or permanent impairment of brain function. Despite advancements in healthcare, TBI mortality rates can reach 30%–40% in severe cases. This study aims to assist clinical decision-making and enhance patient care for TBI-related complications by employing Artificial Intelligence (AI) methods and data-driven approaches to predict decompensation. This study uses learning models based on sequential data from Electronic Health Records (EHR). Decompensation prediction was performed based on 24-h in-mortality prediction at each hour of the patient's stay in the Intensive Care Unit (ICU). A cohort of 2261 TBI patients was selected from the MIMIC-III dataset based on age and ICD-9 disease codes. Logistic Regressor (LR), Long-short term memory (LSTM), and Transformers architectures were used. Two sets of features were also explored combined with missing data strategies by imputing the normal value, data imbalance techniques with class weights, and oversampling. The best performance results were obtained using LSTMs with the original features with no unbalancing techniques and with the added features and class weight technique, with AUROC scores of 0.918 and 0.929, respectively. For this study, using EHR time series data with LSTM proved viable in predicting patient decompensation, providing a helpful indicator of the need for clinical interventions. © 2025 Elsevier Ltd