Tecnalia

Fully Homomorphic Encryption (FHE) enables arbitrary computations on encrypted data but lacks mechanisms to ensure the integrity of those computations. In particular, verifying that algorithm inputs are correct or that the intended algorithm was indeed executed remains an open challenge. This article addresses the issue by making two key contributions. First, we perform a comprehensive analysis of integrity faults in FHE, culminating in the definition of verifiable FHE as a novel concept to tackle these concerns. Second, we present a systematic review of existing approaches aimed at providing verifiable FHE, assessing their strengths and weaknesses, as well as their applicability in practical settings. Our findings reveal that, despite promising advances, significant gaps persist in both the theoretical foundations and the practical deployment of verifiable FHE. We conclude by outlining future research directions necessary to achieve verifiable FHE computations.

Ensuring the safety and effectiveness of first responder teams during critical missions requires real-time health monitoring and responsive intervention systems. This study presents a novel system comprising a multimodal wearable device integrated with a remote command centre, designed to support the physiological monitoring and guidance of first responders in the field. The wearable device includes three main components: a physiological and biochemical signal acquisition unit, an electrotactile stimulation unit and a powerful communication interface. The acquisition unit continuously samples heart rate, body temperature, and biochemical markers from sweat, transmitting this data wirelessly to the remote command centre. The transmitted physiological data could be analyzed at the command centre and, based on the inferred first responder condition, appropriate feedback commands could be issued back to the corresponding wearer. The commands are then executed by the electrotactile stimulation unit on the wearable device. Initial testing in laboratory settings confirmed the system's ability to generate accurate electrochemical readings and dehydration assessment through changes in bulk ionic conductivity. Electrochemical impedance spectroscopy showed good agreement with a commercial potentiostat. Heart rate and temperature readings demonstrated satisfying accuracy with minor removable artifacts. Field trials with first responders validated continuous signal transmission and electrotactile feedback with over 80% success. These results confirm the system's robustness and modularity, supporting its application in operational environments.

With the ever-increasing realisation that measures against climate change have to be local, many cities opted to become NetZero (i.e., CO2-Neutral, emissions compensated) in the near future. Especially within the EU, a large-scale project takes place driven in close collaboration between the EU and cities. The “EU Cities Mission” facilitates urban transition via supporting actions and strategies towards neutrality by offering official labelling to cities that create successful climate city contracts and commit to achieve CO2-Neutrality by 2030. The Horizon project CLMS-Cities targets to support cities with quantifying and trenching their CO2 emissions based on existing and freely available Copernicus Services, in particular within CLMS (Copernicus Land Monitoring Service Cities) and in-situ data such as GNSS based mobility data. Within CLMS-Cities a CO2 exchange model for local-scale scope 1 CO2 emissions is developed at 10m resolution at hourly scale for the five sectors: mobility, buildings, industrial sources, AFOLU (Agriculture, Forestry and other land uses), and human respiration following closely typical city inventories. We here present first results and the background of the model for the case study city Vitoria-Gasteiz, Spain (Tier 1 city). The model is mainly based on the Urban Atlas and produces estimates of CO2 exchange by integrating relevant Copernicus services, satellite products and third-party mobility data. To ensure robustness, the model is paired with local-scale eddy covariance observations in the city centre of Vitoria-Gasteiz. Following this validation process, the model will be extended and rolled out to ten additional EU Mission cities to ensure that it accommodates a wide range of spatial, urban and environmental contexts, that is seen across the real cities in the EU. A co-design approach underpins this work, with continuous engagement with cities to ensure that their requirements are fully integrated in the design, development and operationalisation of the model.

To translate, culturally adapt and validate the first Spanish version of the Person-centred Practice Inventory-Care (PCPI-C) instrument. Cross-cultural adaptation and psychometric validation. Two-phase research design: (1) the PCPI-C's translation and cultural adaptation from English to Spanish following the 'Translation and Cultural Adaptation of Patient-Reported Outcomes Measures-Principles Guide of Good Practice' tool; and (2) a cross-sectional quantitative survey to assess the Spanish version's psychometric properties. A sample of 200 patients participated to obtain the PCPI-C's Spanish version. No significant issues arose during the translation process or the consulting sessions. No item exhibited an inadequate value following adjustment via the weighted kappa index (-scale-level content validity average of 0.95 for clarity and 0.97 for relevance). Psychometric evaluation revealed acceptable internal consistency (Cronbach's alpha from 0.67 to 0.84) and strong construct validity. Exploratory and confirmatory factor analyses supported a five-dimensional structure consistent with the domain Person-Centred Processes. Fit indices improved after model refinements, achieving CFI = 0.92, SRMR = 0.05 and RMSEA = 0.07. This study's observed psychometric properties confirm that the PCPI-C's Spanish version retains the original instrument's theoretical integrity, while showing strong reliability and validity in the new context. The PCPI-C's Spanish translation was psychometrically valid when tested with Spanish patients, thus providing a culturally appropriate, psychometrically sound tool to evaluate Spanish-speaking patients' perception of person-centred care. This study provides a validated instrument that allows for the assessment of person-centred practice in Spanish-speaking clinical environments. It enables healthcare professionals to measure patients' perceptions, track the implementation of person-centred principles and supports international comparative studies, contributing to the development of more ethical and responsive models of care. Patients participated in cognitive consultations and completed the survey for psychometric testing, ensuring that the translated items were understandable, culturally appropriate and reflective of their experiences of person-centred care.

The integration of artificial intelligence (AI) into soil research presents significant opportunities to advance the understanding, management, and conservation of soil ecosystems. This paper reviews the diverse applications of AI in soil health assessment, predictive modeling of soil properties, and the development of pedotransfer functions within the context of agriculture, emphasizing AI’s advantages over traditional analytical methods. We identify soil organic matter decline, compaction, and biodiversity loss as the most frequently addressed forms of soil degradation. Strong trends include the creation of digital soil maps, particularly for soil organic carbon and chemical properties using remote sensing or easily measurable proxies, as well as the development of decision support systems for crop rotation planning and IoT-based monitoring of soil health and crop performance. While random forest models dominate, support vector machines and neural networks are also widely applied for soil parameter modeling. Our analysis of datasets reveals clear regional biases, with tropical, arid, mild continental, and polar tundra climates remaining underrepresented despite their agricultural relevance. We also highlight gaps in predictor–response combinations for soil property modeling, pointing to promising research avenues such as estimating heavy metal content from soil mineral nitrogen content, microbial biomass, or earthworm abundance. Finally, we provide practical guidelines on data preparation, feature extraction, and model selection. Overall, this study synthesizes recent advances, identifies methodological limitations, and outlines a roadmap for future research, underscoring AI’s transformative potential in soil science. • AI advances soil health assessment and supports sustainable management strategies. • New predictor–response combinations are key for future soil parameter modeling. • Need for more data being collected regularly using consistent, standardized methods. • Soil data lacks representation from Central/South Asia, Australia, and tundras. • GNNs, PINNs, RL, Multi-task learning & SHAP/LIME remain underused in soil research.

In medical imaging, deep learning (DL) models often struggle to delineate ambiguous structures such as tumors or organ boundaries, leading to uncertainty in defining precise contours. This challenge is amplified by inter-rater variability, where experts may disagree on boundary delineations, resulting in inconsistent segmentation outcomes. Addressing these issues requires robust algorithms capable of quantifying uncertainty, standardizing annotation practices, and improving calibration to ensure reliable predictions, particularly in multi-class and multi-rater scenarios. When models are miscalibrated and overconfident, their outputs can mislead clinical decision-making, potentially influencing radiologists to over- or under-estimate malignancy risks. The CURVAS challenge (Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation) was established to address these challenges by jointly assessing uncertainty, calibration, and segmentation quality, as well as promoting clinical relevance by evaluating organ volumes while accounting for annotation variability. To support this, a dataset of 90 contrast-enhanced CT scans from University Hospital Erlangen was curated, containing pancreas, liver, and kidney segmentations annotated by three experts. This resource provides a foundation for developing and benchmarking algorithms that balance segmentation accuracy, calibration, and reliability. A quantitative analysis of the annotations shows that kidney and liver segmentations exhibit strong consistency, whereas the pancreas remains challenging, emphasizing the need for refined labeling protocols and improved training strategies.

ABSTRACT One of the primary challenges in ensuring road safety is effectively alerting drivers to adverse weather conditions, such as fog, which can severely impair visibility and increase the risk of accidents. Timely and accurate fog detection is crucial for providing drivers with the necessary warnings to adapt their driving behaviour and enhance safety. This paper presents a system designed to detect foggy scenarios and classify visibility levels, thereby enabling timely alerts for drivers to minimise the risks associated with reduced visibility. To achieve this, we have developed two new image datasets of road fog scenarios – Foggy‐Ceit 2023 and an extension to the Foggy CityScapes – DBF dataset – featuring both real and synthetic fog. Additionally, we compare various algorithms developed using classical vision techniques and deep learning methods (vision transformers [ViT] and EfficientNet). Finally, eXplainable artificial intelligence techniques are utilised to provide visual explanations and evaluate the performance of these models.