Abstract
The business models (BM) for renewable energy communities (REC) are often based on their promoters being the sole or primary investors in energy assets, such as photovoltaic panels (PV) and battery energy storage systems (BESS), operating these assets centrally, and selling the locally produced energy to the REC members. This research addresses the computation of fixed local energy prices that the REC developer may apply under the optimal operation of the energy assets to maximize its revenues, while guaranteeing that all REC members benefit from belonging to the REC. We do this from two perspectives, depending on who operates the storage systems: i) maximizing the investor's benefits and ii) minimizing the REC cost by maximizing its self-consumption, ensuring maximization of the energy sold by the REC promoter/investor. The optimization framework includes energy production and demand balance constraints, peak load limitations, and constraints coming from the Portuguese regulatory framework. It also considers the opportunity costs of the members for buying the energy deficit from the grid or selling the energy surplus to the grid.

Abstract
Capricious data streams make no assumptions on feature space dynamics and are mainly handled based on feature correlation, linear classifier or ensemble of trees. There exist deficiencies such as limited learning capacity, high time cost and low interpretability. To enhance effectiveness and efficiency, capricious data streams are handled through a single tree in this paper, and the proposed algorithm is named OCFHT (Online learning from Capricious data streams with Flexible Hoeffding Tree). OCFHT does not rely on the correlation pattern among features and can achieve non-linear modeling. Its performance is verified by various experiments on 18 public datasets, showing that it is not only more accurate than state-of-the-art algorithms, but also runs faster.

Abstract

Abstract
Soil degradation is a critical challenge to global agricultural sustainability, driven by intensive land use, unsustainable farming practices, and climate change. Conventional soil monitoring techniques often rely on invasive sampling methods, which can be labor-intensive, disruptive, and limited in spatial coverage. In contrast, non-invasive geophysical techniques, particularly ground-penetrating radar, have gained attention as tools for assessing soil properties. However, an assessment of ground-penetrating radar's applications in agricultural soil research-particularly for detecting soil structural changes related to degradation-remains undetermined. To address this issue, a systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. A search was conducted across Scopus and Web of Science databases, as well as relevant review articles and study reference lists, up to 31 December 2024. This process resulted in 86 potentially relevant studies, of which 24 met the eligibility criteria and were included in the final review. The analysis revealed that the ground-penetrating radar allows the detection of structural changes associated with tillage practices and heavy machinery traffic in agricultural lands, namely topsoil disintegration and soil compaction, both of which are important indicators of soil degradation. These variations are reflected in changes in electrical permittivity and reflectivity, particularly above the tillage horizon. These shifts are associated with lower soil water content, increased soil homogeneity, and heightened wave reflectivity at the upper boundary of compacted soil. The latter is linked to density contrasts and waterlogging above this layer. Additionally, ground-penetrating radar has demonstrated its potential in mapping alterations in electrical permittivity related to preferential water flow pathways, detecting shifts in soil organic carbon distribution, identifying disruptions in root systems due to tillage, and assessing soil conditions potentially affected by excessive fertilization in iron oxide-rich soils. Future research should focus on refining methodologies to improve the ground-penetrating radar's ability to quantify soil degradation processes with greater accuracy. In particular, there is a need for standardized experimental protocols to evaluate the effects of monocultures on soil fertility, assess the impact of excessive fertilization effects on soil acidity, and integrate ground-penetrating radar with complementary geophysical and remote sensing techniques for a holistic approach to soil health monitoring.

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.