Abstract
As energy systems evolve, protecting and empowering consumers is vital, enabling participation in decentralized electricity markets and maximizing benefits from energy resources. The integration of Distributed Energy Resources (DER) and Renewable Energy Sources (RES) fosters new energy communities, shifting from centralized systems to distributed structures. Consumers can sell excess production to neighbors, increasing income, reducing bills, and advancing energy transition goals. This paper proposes a community-based peer-to-peer (P2P) energy market model that reduces costs while respecting network constraints. Using the Alternating Direction Method of Multipliers (ADMM), ensures privacy enhancement, decentralization, and scalability. The Relaxed Branch Flow Model (RBFM) manages constraints, and Electric Vehicles (EVs) reduce imports and costs through strategic discharging. Tested on a 33-bus distribution network, the ADMM-based approach aligns closely with a centralized benchmark, showing minor discrepancies while maintaining system reliability. This model underscores the potential of decentralized markets for consumer-centric, flexible, and efficient energy trading.

Abstract
Breast cancer locoregional treatment can cause significant and long-lasting alterations to a patient's body. As various surgical options may be available to a patient and considering the impact that the aesthetic outcome may have on the patient's self-esteem, it is critical for the patient to be adequately informed of the possible outcomes of each treatment when deciding on the treatment plan. With the purpose of simulating how a patient may look like after treatment, we propose a deep generative model to transfer asymmetries caused by treatment from post-operative breast patients into pre-operative images, taking advantage of the inherent symmetry of breast images. Furthermore, we disentangle asymmetries related with the breast shape from the nipple within the latent space of the network, enabling higher control over the alterations to the breasts. Finally, we show the proposed model's wide applicability in medical imaging, by applying it to generate counterfactual explanations for cardiomegaly and pleural effusion prediction in chest radiographs.

Abstract
Purpose: The 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/approach: Relying 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. Findings: The 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/value: This 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. © 2025, Fernando Almeida and Edet Okon.

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
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.