Abstract
Folk music and particularly children's folk songs serve as vital repositories of cultural identity, emotional expression, and social values. This study presents a computational thematic analysis of Portuguese and Spanish children's folk songs using the I-Folk corpus, comprising 800 annotated entries in the Music Encoding Initiative (MEI) format. Despite shared historical influences on the Iberian Peninsula, the lyrical content of each tradition reveals distinct thematic orientations. Through a methodological framework that combines traditional text pre-processing, frequency analysis, and semantic embedding using large language models (LLMs), we uncover cross-cultural similarities and divergences in content, form, and emotional register. Spanish lyrics focus primarily on caregiving, emotional development, and moral-religious motifs, while Portuguese songs emphasize performative rhythm, localized identity, and folkloric references. Our results highlight the need for tailored analytical strategies when working with children's repertoire and demonstrate the utility of LLMs in capturing culturally embedded patterns that are often obscured in conventional analyses. This work contributes to digital folklore scholarship, corpus-based ethnomusicology, and the preservation of underrepresented cultural expressions in computational humanities.

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
This paper systematically maps how digital transformation technologies associated with Industry 4.0 (I4.0) are conceptualised in relation to concerns within the built environment regarding sustainability. Employing a hybrid methodology that combines scoping review, bibliometric mapping, and thematic analysis, an initial corpus of 1513 documents from Web of Science and Scopus was refined to 83 influential peer-reviewed publications. The analysis identified thirteen key I4.0 technologies for sustainability in the built environment that are frequently discussed, including Building Information Modelling (BIM), Digital Twins (DT), Internet of Things (IoT), Blockchain, Artificial Intelligence (AI), and others, each demonstrating varying degrees of explicit sustainability relevance. Notably, the study uncovered recurrent combinations, or "technology stacks", such as BIM integrated with DT and IoT, or Blockchain combined with BIM and IoT, which significantly enhance sustainability outcomes through interoperability and functional synergies. Despite these findings, substantial conceptual fragmentation and inconsistent terminology were noted across the literature, limiting theoretical coherence and empirical validation. The study highlights the importance of standardised taxonomies and comprehensive empirical studies to rigorously validate technology stacks and their sustainability impacts across diverse built environment contexts. © 2025 IEEE.

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.