Search Results (135)

The rapid digitalisation of the agri-food sector has generated unprecedented volumes of farm and value chain data, but also highly fragmented data ecosystems and asymmetric power relations between farmers, technology providers, and public authorities. In response, the European Union has developed a comprehensive data governance architecture—including the Data Governance Act, the Data Act, the GDPR and the EU Code of Conduct on Agricultural Data Sharing—and is building a Common European Agricultural Data Space (CEADS). This article examines that governance framework and explores its implications for the agri-food sector in Spain. Through a qualitative legal policy review, we map the regulatory landscape, analyse five major European and Spanish initiatives (CEADS/AgriDataSpace, AgData, Agdatahub, RegenAg-X, and DADS), and use Spain as a national case study. A multi-level actor model (meta-governance, data originators, transformation intermediaries, and data users) structures the comparative analysis. On this basis, six design principles for responsible agri-food data spaces are identified: clarity of use cases, inclusive multi-stakeholder governance, data life cycle mapping, privacy and sovereignty by design, a fair economic model, and regulatory compliance as a trust factor. The article identifies open research questions on anonymisation of georeferenced data, data sovereignty, and equitable value distribution, and outlines an agenda for future empirical and legal research. Full article

Automated short-answer grading (ASAG) in educational contexts faces a fundamental trade-off between predictive performance, interpretability, and methodological transparency, particularly under data-constrained educational settings. While recent approaches rely on deep learning architectures, these models require large annotated datasets and offer limited transparency, restricting their applicability in authentic classroom environments. This study proposes a fully specified and interpretable machine learning pipeline for ASAG across multiple educational concepts. The approach is based on a shared TF–IDF representation and evaluates three linear classifiers—Logistic Regression, Multinomial Naïve Bayes, and Linear Support Vector Machines—under a stratified cross-validation framework adapted to small datasets. Model performance is assessed using accuracy, precision, recall, and F1-score. Statistical comparisons using the Wilcoxon signed-rank test indicate exploratory evidence of statistically significant differences between classifiers, although the observed differences remain small in practical magnitude. Additionally, the methodology incorporates token-level analysis to identify discriminative lexical patterns and examine consensus across classifiers. To enhance interpretability, tokens are presented using a bilingual Spanish/English representation while preserving the original feature space. The results across ten concept-specific datasets show consistent performance across models (accuracy ≈ 0.82–0.88) and reveal stable lexical patterns consistently associated with model predictions of correctness. The findings highlight that lightweight, interpretable models can provide consistent and reliable performance under resource-constrained educational conditions. The proposed framework contributes a stability-oriented and interpretable evaluation paradigm for ASAG, offering a practical alternative to data-intensive approaches in educational assessment. It is intended as a methodological reference protocol rather than a performance benchmark. The findings should be interpreted as evidence of within-context consistency instead of broad external generalization. Full article

Adherence to the 24-h movement guidelines (24-HMG), which integrate physical activity (PA), sedentary behaviour, and sleep, may be relevant to university students’ mental health, yet evidence in this population remains limited. This cross-sectional study aimed to estimate adherence to the 24-HMG, identify associated correlates, and examine whether meeting a greater number of guidelines was associated with more favourable mental health profiles in Spanish university students. A total of 1469 students (mean age = 21.6 ± 3.14 years; 71% women) completed validated self-report measures of physical activity, sedentary behaviour, sleep duration, psychological distress, self-esteem, and resilience. Adherence was defined according to the Canadian adult 24-HMG, and a global adherence index (0–3) was calculated. Concurrent adherence to all three guidelines was low and varied markedly across academic fields, ranging from 22% in Sport Sciences to 1.6% in Engineering and Architecture. Women showed lower odds of meeting the physical activity recommendation and of meeting all three guidelines. Greater adherence was associated with a lower likelihood of unfavourable mental health profiles, particularly low self-esteem and low resilience. These findings suggest that adherence to the 24-HMG was low in this sample of university students and support the development of integrated, context-tailored interventions targeting movement behaviours and mental health in university settings. Full article

The Real Maestranza de Caballería in Seville features one of the most prominent Spanish bullrings, characterized by a notable architectural design. Its distinctive ovoid geometry resulted from a protracted construction history (1761–1881), during which the floor plan adapted to pre-existing urban structures. Beyond its architectural significance, the sounds perceived within such venues constitute traces of collective memory and form part of an intangible cultural heritage relevant for understanding the sociocultural context of such spaces. This work provides an acoustic characterisation of the bullring through field measurements. Reverberation time and other monaural and binaural descriptors were determined using 3D impulse responses obtained from strategically placed sources and receivers. This analysis is complemented by examining the sound energy distribution of early reflections in the time–frequency domain to define the acoustic signature of the venue, namely the characteristic pattern of early reflections that unequivocally determines its sound response, and identify the provenance of reflections. In the Maestranza, music and silence are hallmarks of its identity, contributing to a complex auditory environment. The results highlight how its geometry and tiered seating create a differentiated sound field, potentially contributing to the preservation of the site as a cultural landmark. Full article

Rural territories continue to face persistent structural challenges related to depopulation, limited economic diversification, and unequal access to specialized knowledge. Although scientific research and applied expertise are widely recognized as critical resources for addressing these challenges, their effective transmission to local actors remains fragmented. In recent years, digital platforms have emerged as potential mechanisms to bridge this gap; however, their role within rural development frameworks remains conceptually underdeveloped. This paper proposes a conceptual framework for knowledge transmission platforms oriented towards rural development, integrating scientific research, applied analysis, and structured dissemination within a unified operational architecture. Drawing on a structured review of the literature on rural development, knowledge transfer, and digital platforms, the framework identifies key functional dimensions and design principles that shape platform-based knowledge intermediation. The framework is illustrated through a qualitative case study of CreandoTuProvincia, a Spanish platform focused on territorial analysis and rural knowledge transmission. The findings highlight the relevance of hybrid platforms that combine scientific rigour, accessibility, and territorial embeddedness, offering a scalable model for strengthening evidence-informed rural development strategies. By conceptualizing platforms as structured knowledge intermediaries, this study contributes to the emerging literature on knowledge-based rural development and provides practical insights for policymakers, researchers, and platform designers. Full article

Heart failure management increasingly relies on heterogeneous clinical and real-world data generated through remote monitoring technologies. However, transforming these multimodal data streams into actionable insights requires robust interoperability infrastructures. This study presents the RETENTION interoperability framework, a federated HL7 Fast Healthcare Interoperability Resources (FHIR)-based architecture designed to support multi-site heart failure monitoring across five independent clinical environments. A semantic reference model comprising 444 clinical and contextual variables was developed and aligned with FHIR R4 resources and internationally recognised terminology systems. The platform adopts a selective profiling strategy, extending only the Patient resource while standardising the remaining variables through example-driven Implementation Guide documentation. Identifiable data are retained locally within Clinical Site Backends, whereas anonymised datasets are periodically aggregated into a Global Insights Cloud to enable centralised analytics and controlled third-party interactions. The framework was deployed across six hospitals (with two Spanish hospitals sharing the same deployment), supporting 390 patients and over 130,000 patient-days of monitoring, with more than 3.6 million remote device data points harmonised without schema conflicts. The results demonstrate that large-scale semantic harmonisation and privacy-preserving aggregation can be achieved using a lightweight profiling approach, providing a scalable and reproducible interoperability model for multi-centre digital health research infrastructures. Full article

Recent breakthroughs in large language models have made it feasible to effectively summarize cross-lingual dialogue information, proving essential for the global communication context. However, existing methodologies encounter difficulties in maintaining factual consistency across multiple dialogue exchanges and lack clear explanations of the summarization process. This paper presents a novel factually consistent prompting technology with large language models to address these challenges in cross-lingual dialogue summarization. First, we propose a factual replacement mechanism to enhance information analysis by incorporating noise information into summarization candidates. We adopt a self-guidance framework to enforce factual consistency, enhancing information flow tracking in cross-lingual hybrid dialogue scenarios with the assistance of GPT-based models. Furthermore, we introduce a view-aware chain-of-thought-driven architecture to improve the interpretability and transparency of the cross-lingual dialogue summarization process. Comprehensive experimental evaluations on cross-lingual summarization tasks, spanning English, French, Spanish, Russian, Chinese, and Arabic, and hybrid cross-lingual tasks substantiate that the proposed model achieves superior performance relative to state-of-the-art baselines. Full article

Rethinking architecture is an urgent task for creating caring, democratic, and sustainable environments for older adults. In Spain, architectural design has historically been disconnected from the complex dimensions of care, leaving a critical gap in the discipline’s engagement with the implementation of community-based, person-centered care typologies. Following the COVID-19 pandemic, the term caring architecture has rapidly proliferated in Spanish architectural discourse, in which care has become central to political debate and spatial strategies. In this context, this article develops a theoretical framework for transitioning from institutional architecture toward a caring architecture for older people. The study is based on a theory-oriented systematic literature review and critical analysis of key theoretical approaches that intersect architecture, urbanism, and the ethics of care. Through bibliometric, conceptual, and thematic analyses of eight selected publications, three dimensions of care ethics are identified: interdependence, economics of care, and eco-dependence. The research shows that these dimensions of care resonate closely with the democratic quintuple helix model and the sociocultural, economic, and environmental pillars of holistic sustainability. The alignment between care, democracy, and sustainability underpins the proposed conceptual framework of caring, democratic, and sustainable architecture for older people. This theoretical paradigm enables transitioning from institutional settings to built environments that promote well-being, community connectedness, and respect for both people and the planet. Full article

The reliable and continuous acquisition of seismic data from multiple open sources is essential for real-time monitoring, hazard assessment, and early-warning systems. However, the heterogeneity among existing data providers such as the United States Geological Survey, the European-Mediterranean Seismological Centre, and the Spanish National Geographic Institute creates significant challenges due to differences in formats, update frequencies, and access methods. To overcome these limitations, this paper presents a modular and automated framework for the scheduled near-real-time ingestion of global seismic data using open APIs and semi-structured web data. The system, implemented using a Docker-based architecture, automatically retrieves, harmonizes, and stores seismic information from heterogeneous sources at regular intervals using a cron-based scheduler. Data are standardized into a unified schema, validated to remove duplicates, and persisted in a relational database for downstream analytics and visualization. The proposed framework adheres to the FAIR data principles by ensuring that all seismic events are uniquely identifiable, source-traceable, and stored in interoperable formats. Its lightweight and containerized design enables deployment as a microservice within emerging data spaces and open environmental data infrastructures. Experimental validation was conducted using a two-phase evaluation. This evaluation consisted of a high-frequency 24 h stress test and a subsequent seven-day continuous deployment under steady-state conditions. The system maintained stable operation with 100% availability across all sources, successfully integrating 4533 newly published seismic events during the seven-day period and identifying 595 duplicated detections across providers. These results demonstrate that the framework provides a robust foundation for the automated integration of multi-source seismic catalogs. This integration supports the construction of more comprehensive and globally accessible earthquake datasets for research and near-real-time applications. By enabling automated and interoperable integration of seismic information from diverse providers, this approach supports the construction of more comprehensive and globally accessible earthquake catalogs, strengthening data-driven research and situational awareness across regions and institutions worldwide. Full article

After the Spanish Civil War, the shortage of building materials in the country and the restrictions imposed by the Dirección General de Arquitectura limited the use of steel in construction, encouraging solutions that reduced the consumption of this material. In this context, the thin-tile vault gained new relevance due to its low cost, speed of execution and good structural and fire performance. Among the architects who revisited this system, Ignasi Bosch Reitg (1910–1985) developed an innovative procedure for the construction of continuous ceilings, based on double-curved vaults with a single layer of brick. His cousin, Josep Maria Bosch Aymerich (1917–2015), an industrial engineer and architect trained in the United States, brought a business vision to the table when he discovered the potential of this system. This paper proposes an in-depth study of the patents requested on this system by the two architects, questioning the reasons for their success or failure in different countries, both in terms of dissemination and exploitation, in regard to the historical context in which it was developed. The analysis, based on original documents from the Bosch Aymerich Archive, uncovers the tensions that the reinterpretation and global projection of a traditional technique can generate. Full article

Background/Objectives: Despite decades of technological progress, the diagnosis of dental caries still depends largely on subjective, operator-dependent assessment, leading to inconsistent detection of early lesions and delayed intervention. Artificial intelligence (AI) has emerged as a transformative approach capable of standardizing diagnostic performance and, in some cases, surpassing human accuracy. This scoping review critically synthesizes the current evidence on AI for caries detection and examines its true translational readiness for clinical practice. Methods: A comprehensive literature search was conducted in PubMed, Scopus, and Web of Science (WoS), covering studies published from January 2019 to June 2024, in accordance with PRISMA-ScR guidelines. Eligible studies included original research evaluating the use of AI for dental caries detection, published in English or Spanish. Review articles, editorials, opinion papers, and studies unrelated to caries detection were excluded. Two reviewers independently screened, extracted, and charted data on imaging modality, sample characteristics, AI architecture, validation approach, and diagnostic performance metrics. Extracted data were summarized narratively and comparatively across studies using tabulated and graphical formats. Results: Thirty studies were included from an initial pool of 617 records. Most studies employed convolutional neural network (CNN)-based architectures and reported strong diagnostic performance, although these results come mainly from experimental settings and should be interpreted with caution. Bitewing radiography dominated the evidence base, reflecting technological maturity and greater reproducibility compared with other imaging modalities. Conclusions: Although the reported metrics are technically robust, the current evidence remains insufficient for real-world clinical adoption. Most models were trained on small, single-source datasets that do not reflect clinical diversity, and only a few underwent external or multicenter validation. Until these translational and methodological gaps are addressed, AI for caries detection should be regarded as promising yet not fully clinically reliable. By outlining these gaps and emerging opportunities, this review offers readers a concise overview of the current landscape and the key steps needed to advance AI toward meaningful clinical implementation. Full article

Accessing operational information across industrial systems (ERP, MES, SCADA, PLC) in concrete plants requires 15–30 min and specialized knowledge. This work addresses this accessibility gap by developing a conversational AI system that democratizes industrial information access through natural language. A five-layer cognitive architecture was implemented integrating the Mistral-7B model quantized in GGUF Q4_0 format (3.82 GB) on a Raspberry Pi 5, Spanish speech recognition/synthesis, and heterogeneous industrial protocols (OPC UA, MQTT, REST API) across all automation pyramid levels. Experimental validation at Frumecar S.L. (Murcia, Spain) characterized performance, thermal stability, and reliability. Results show response times of 14.19 s (simple queries, SD = 7.56 s), 16.45 s (moderate, SD = 6.40 s), and 23.24 s (complex multilevel, SD = 6.59 s), representing 26–77× improvement over manual methods. The system maintained average temperature of 69.3 °C (peak 79.6 °C), preserving 5.4 °C margin below throttling threshold. Communication latencies averaged 8.93 ms across 10,163 readings (<1% of total latency). During 30 min of autonomous operation, 100% reliability was achieved with 39 successful queries. These findings demonstrate the viability of deploying quantized LLMs on low-cost edge hardware, enabling cognitive democratization of industrial information while ensuring data privacy and cloud independence. Full article

The bell tower of Murcia Cathedral (1521–1793) exhibits a documented inclination whose origin and structural significance have never been examined through an integrated geotechnical–structural approach. This study aims to identify the causes, quantify the magnitude, and assess the safety implications of the tower’s long-term differential settlement. A multidisciplinary methodology is adopted, combining historical construction records, geological and geotechnical data from the Segura alluvial plain, non-destructive testing of masonry, and classical analytical modelling based on Heyman’s masonry theory, consolidation mechanics, and elastic column behaviour. This approach is selected in place of finite element modelling because the tower’s geometry, construction sequence, and material parameters are sufficiently constrained to allow a non-invasive and verifiable assessment suited to heritage structures. Results indicate a total horizontal displacement of approximately 0.56 m toward the northwest, produced by the slow consolidation of compressible silty–clayey deposits influenced by groundwater fluctuations and by historical eccentric load redistributions during the eighteenth-century construction phase. The calculated working compressive stresses (0.83–1.02 N/mm²) remain far below the estimated strength of the limestone masonry, and the bearing capacity analysis suggests a safety factor of about 1.5 against foundation failure. These findings confirm that the tower’s deformation reflects the long-term geotechnical response of the subsoil rather than structural instability. The study provides a non-destructive analytical framework for interpreting settlement mechanisms in historic masonry towers and contributes a quantitatively grounded explanation of the Murcia Cathedral tower’s inclination, offering guidance for future assessment of similar heritage structures. Full article

This study examines how domain-general (processing speed and receptive vocabulary) and domain-specific (symbolic and non-symbolic comparison) cognitive skills contribute to early informal mathematical thinking in preschoolers. The aim was to assess the invariance of these predictive relationships across two sociocultural contexts: Chilean and Spanish samples. A total of 130 children participated, and structural equation modeling was used to estimate latent structures and test multigroup invariance. The results revealed a consistent latent structure across samples and a significant contribution of symbolic and non-symbolic comparison to early math performance, while processing speed and vocabulary showed context-specific variations. These findings indicate that although foundational mathematical competencies rely on common cognitive mechanisms, cultural and educational contexts modulate the strength of these associations. This study contributes to understanding the cognitive architecture underlying early numeracy and highlights the importance of culturally sensitive assessment and intervention strategies. Full article

This paper presents a qualitative exploratory study based on the analysis of a representative sample of 35 projects carried out during the period 2020–2022 by six Spanish newspapers: elDiario.es, ABC, IDEAL, El Correo, ElConfidencial.com and El País. This study aims to detect and analyze the main elements of multimedia, crossmedia and transmedia content in the selected projects using an original analysis sheet designed for this research. In relation to the categories proposed in the categorization model, in this work we will focus on analyzing two in particular: authorship and information architecture. The projects were selected based on criteria of appropriateness, quality and innovation, as well as the results of semi-structured interviews with the heads and innovation managers (laboratories) of the media included in the framework of the projects ‘NEWSNET: News, Networks, and Users in the Hybrid Media System: Transformation of the Media Industry and the News in the Post-Industrial Era’ and ‘IAMEDIA: Impact of Artificial Intelligence and Algorithms on Online Media, Journalist and Audiences’. The aim of the qualitative analysis is to propose a list of aspects, characteristics, and fundamentals in the ideation, elaboration, and distribution of these types of products. We conclude that the results of applying the designed analysis sheet help us to understand these processes and also to propose alternatives and improvements in its design and implementation Full article