Abstract
We present an analysis of performance configurations in Portuguese traditional music, using computational methods to process field recordings from the A Musica Portuguesa A Gostar Dela Propria (MPAGDP) archive. Our approach employs YOLOv11s (You Only Look Once), a computer vision system that can detect and count performers in archival footage, allowing us to automatically classify performances into meaningful categories: solo, duo, small, and large ensembles. This computational classification method processed 8122 field recordings with 96% classification accuracy, enabling systematic examination of performance contexts that would be time-consuming through manual analysis. Our analysis shows relationships between performance configuration and musical practice across Portuguese traditions. Solo performers, comprising 48% of vocal recordings, predominantly appear in narrative and poetic traditions requiring individual expression. Large ensembles (21%) maintain collective practices like polyphonic singing traditions. The geographic distribution shows regional traits-Alentejo features large-ensemble singing traditions, while northern regions favor solo performances. The temporal analysis traces how traditional forms maintain continuity through specific performance configurations, while contemporary adaptations emerge primarily in small group formats, illuminating the social dimensions of musical transmission and adaptation in Portuguese traditional music.

Abstract
This article describes the steps involved in creating a prototype with a gamified approach aimed at highlighting the challenges encountered by immigrants in foreign countries. This serious game sought to provide an interactive experience that mirrored the real-life obstacles faced by immigrants, fostering empathy among non-immigrant players in these scenarios, with the goal of improving attitudes toward immigrants. During the development phase of the game, a user-centered design approach was employed. The project was divided into several phases: understanding the context, comprehending user needs, iterative prototyping, and usability testing. Both immigrants and non-immigrants participated in the study, directly contributing to defining requirements and evaluating the game. The serious game Typical Day, designed to simulate everyday situations faced by immigrants through interactive scenarios and critical decisions, demonstrated positive acceptance in terms of usability and engagement. The results indicated that Typical Day provided an engaging and educational gaming experience, successfully balancing entertainment and information. Positive feedback from 45 non-immigrant participants highlighted its potential as an educational tool to raise awareness about the experiences of immigrants. However, further studies are needed to evaluate its long-term impact on attitudes and behaviors. In conclusion, this study contributes to the literature by addressing a gap in gamified approaches to immigrant challenges, laying the foundation for future developments in serious games aimed at promoting attitude change.

Abstract

Abstract
Monitoring vineyard diseases such as downy mildew (Plasmopara viticola) is important for viticulture, enabling an early intervention and optimized disease management. This is crucial for disease monitoring, and the use of high-spatial-resolution multispectral data from unmanned aerial vehicles (UAVs) can allow to for a better understanding of disease progression. This study explores the application of UAV-based multispectral data for monitoring downy mildew infection in vineyards through multi-temporal analysis. This study was conducted in a vineyard plot in the Vinho Verde region (Portugal), where 84 grapevines were monitored, half of which received phytosanitary treatments while the other half were left untreated in this way during the growing season. Seven UAV flights were performed across different phenological stages to assess the effects of infection using spectral bands, vegetation indices, and morphometric parameters. The results indicate that downy mildew affects canopy area, height, and volume, restricting the vegetative growth. Spectral analysis reveals that infected grapevines show increased reflectance in the visible and red-edge bands and a progressive decline in near-infrared (NIR) reflectance. Several vegetation indices demonstrated a suitable response to the infection, with some of them being capable of detecting early-stage symptoms, while vegetation indices using red edge and NIR allowed us to track disease progression. These results highlight the potential of UAV-based multi-temporal remote sensing as a tool for vineyard disease monitoring, supporting precision viticulture and the assessment of phytosanitary treatment effectiveness.

Abstract
This article investigates sampling strategies in latent space navigation to enhance co-creative music systems, focusing on timbre latent spaces. Adopting Villa-Rojo's 'Lamento' for tenor saxophone and tape as a case study, we conducted two experiments. The first assessed traditional corpus-based concatenative synthesis sampling within the RAVE model's latent space, finding that sampling strategies gradually deviate from a given target sonority while still relating to the original morphology. The second experiment aims at defining sampling strategies for creating variations of an input signal, namely parallel, contrary, and oblique motions. The findings expose the need to explore individual model layers and the geometric transformation nature of the contrary and oblique motions that tend to dilate the original shape. The findings highlight the potential of motion-aware sampling for more contextually aware and expressive control of music structures via CBCS.

Abstract
To get products delivered, clients and retailers agree on a delivery time window. We collaborated with an online retailer to develop a real-world application aimed at dynamically determining the delivery fee for each time window while ensuring the explainability of the pricing policy. This sequential decision-making problem arises as new customers continuously arrive. The objective is to maximize the final profit, given by the sum of baskets and delivery fees, discounted by the transportation and fleet costs. As multiple customers share the same delivery route, the costs are distributed among them, complicating the calculation of the marginal cost of each customer. Our study employs Genetic Programming (GP) to create explainable and easy-to-compute pricing policies to determine the delivery fees. These policies, expressed as mathematical formulas, rank price panels combinations of time slots and corresponding fees to identify optimal prices for each customer. The inputs to the GP algorithm capture the current state of the system, including factors such as capacity, customer location, and basket value. The resulting expressions offer operational managers a transparent pricing policy that allows them to maximize total profit.