Abstract
PurposeThe Internet of Things (IoT) is currently acting as a critical component of the digitalization process by connecting physical devices to the digital world. It is assumed that IoT serves as both a driver and enabler of digitalization. Accordingly, this study investigates the significance of digitalization in enhancing small and medium-sized enterprises (SMEs) firm performance using IoT as a mediator.Design/methodology/approachRelying on a sample of 393 SMEs in Portugal, the study used a survey method and questionnaire to gather data, while utilizing the structured equations model to explore the relationship between the constructs of the research model.FindingsThe findings show that digital infrastructure and value chains are central to digitalization. Technology, data analytics, digital skills and transformation strategies directly and jointly enhance firm performance. The study also highlights the mediating role of IoT in this relationship and stresses the need to consider industry dynamics, digital readiness and strategic goals when assessing IoT's impact on SME performance.Originality/valueThis study provides valuable insights into the core of digitalization, emphasizing the need for SMEs to effectively integrate digital infrastructure, digital value chains and IoT-driven technologies to drive performance and long-term success.

Abstract
Prognosis after intracranial hemorrhage (ICH) is influenced by a complex interplay between imaging and tabular data. Rapid and reliable prognosis are crucial for effective patient stratification and informed treatment decision-making. In this study, we aim to enhance image-based prognosis by learning a robust feature representation shared between prognosis and the clinical and demographic variables most highly correlated with it. Our approach mimics clinical decision-making by reinforcing the model to learn valuable prognostic data embedded in the image. We propose a 3D multi-task image model to predict prognosis, Glasgow Coma Scale and age, improving accuracy and interpretability. Our method outperforms current state-of-the-art baseline image models, and demonstrates superior performance in ICH prognosis compared to four board-certified neuroradiologists using only CT scans as input. We further validate our model with interpretability saliency maps. Code is available at https://github.com/MiriamCobo/MultitaskLearning_ICH_Prognosis.git.

Abstract

Abstract
End-users traditional ownership of devices is progressively being replaced by usage-based approaches. One of the most significant Device-as-a-Service (DaaS) challenges concerns protecting device usage outside of supplier control. In this work, we build on our previous blockchain framework for device management to propose a novel and enhanced pre-OS boot process for device validation in environments requiring both security and transparency. For that, we have customized a Unified Extensible Firmware Interface (UEFI) module to authorise device usage against the blockchain before system boot, preventing authorized manipulation at the earliest stage. Preliminary experiments show this approach is valid and effective.

Abstract
Caves and lava tubes offer ideal environments for testing and improving methodological approaches as natural space analogs and living laboratories. These underground environments hold natural records that help us understand the evolution of our planet. This research reflects on the relevance of lava tubes and caves as simulation sites for extraterrestrial exploration. This study will focus on the methodological approach used in Lanzarote (Canary Islands, Spain) and Selvagens Islands (Madeira, Portugal), as two space analog sites associated with astrobiology projects that demonstrated good practice and reliable science and can inspire other space-related programs. Finally, the lava tube system on Terceira Island (Azores) is presented for the first time in Portugal as a promising new experimental site for geoengineering research and space analog activities. The multisectoral and longitudinal investigations related to a geoengineering approach and the 5Gs project will leverage the unique geodiversity and biodiversity of Natal Cave. Lava tube habitats could ultimately enable the establishment of a sustainable human presence on the Moon or Mars. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Background: Aberrant or loss of cell adhesion drives invasion and metastasis, key hallmarks of cancer progression. In this work, we hypothesized that a gene signature related to cell adhesion could predict breast cancer prognosis. Methods: Highly variant genes were tested for association with overall survival using Cox regression. Adhesion-related genes were identified through gene ontology analysis and multivariate Cox regression, with AIC selection, defined the prognostic signature. The AdhesionScore was then calculated as a weighted sum of gene expression, with risk stratification assessed by Kaplan-Meier and log-rank tests. Results: We found that the AdhesionScore was a significant independent predictor of poor survival in three large independent datasets, as it provided a robust stratification of patient prognosis in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (HR: 2.65; 95% CI: 2.33-3.0, p = 2.34 x 10-51), The Cancer Genome Atlas (TCGA) (HR: 3.46; 95% CI: 2.35-5.09, p = 3.50 x 10-10), and the GSE96058 (HR: 2.83; 95% CI: 2.20-3.65, p = 6.29 x 10-16) datasets. The 5-year risk of death in the high-risk group was 32.41% for METABRIC, 27.8% for TCGA, and 17.54% for GSE96058 datasets. Consistently, HER2-enriched and triple-negative breast carcinomas (TNBC) cases showed higher AdhesionScores than luminal subtypes, indicating an association with aggressive tumor biology. Conclusions: We have developed, for the first time, a molecular signature based on cell adhesion, as well as an associated AdhesionScore that can predict patient prognosis in invasive breast cancer, with potential clinical application. We developed a novel adhesion-based molecular signature, the AdhesionScore, that robustly predicts prognosis in breast cancer across independent cohorts, highlighting its potential clinical utility for patient risk stratification.