Abstract
In recent years, Positive Energy Districts (PEDs) have been interpreted in many—and often conflicting—ways. We recast PEDs as a vehicle for verifiable climate neutrality and present a declaration-ready assessment that integrates (i) a cumulative, science-based GHG budget per m2 gross floor area (GFA), (ii) full life-cycle accounting, and (iii) time-resolved conversion factors that include everyday motorized individual mobility and quantify flexibility. Two KPIs anchor the framework: the cumulative GHG LCA balance (2025–2075) against a maximum compliant budget of 320 kgCO2e·m-2GFA and the annual primary energy balance used to declare PED status with or without mobility. We follow EN 15978 and apply time-resolved emission factors that decline to zero by 2050. Its applicability is demonstrated on six Austrian districts spanning new builds and renovations, diverse energy systems, densities, and mobility contexts. The baseline scenarios show heterogeneous outcomes—only two out of six meet both the cumulative GHG budget and the positive primary energy balance—but design iterations indicate that all six districts can reach the targets with realistic, ambitious packages (e.g., high energy efficiency and flexibility, local renewables, ecological building materials, BESS/V2G, and mobility electrification). Hourly emission factors and flexibility signals can lower import-weighted emission intensity versus monthly or annual factors by up to 15% and reveal seasonal import–export asymmetries. Built on transparent, auditable rules and open tooling, this framework both diagnoses performance gaps and maps credible pathways to compliance—steering PED design away from project-specific targets toward verifiable climate neutrality. It now serves as the basis for the national labeling/declaration scheme klimaaktiv “Climate-Neutral Positive Energy Districts”. © 2025 by the authors.

Abstract
Deep learning in computational pathology (CPath) has rapidly advanced in recent years. Research has primarily focused on enhancing accuracy and interpretability across various histology image analysis tasks, from tile-level to slide-level foundation models and novel multiple instance learning (MIL) strategies. However, it is equally important for models to provide well-calibrated confidence estimates. Due to factors such as dataset bias, overfitting, and limited training data, existing models tend to be overly confident on test sets. Promising solutions to address this issue include temperature scaling, a post-hoc method that adjusts logits using a single scalar value. However, the role of calibration in CPath is yet to be clarified. In this study, we evaluate temperature scaling and linear temperature scaling for CPath tasks, analyzing their impact on recalibration in both in-domain and out-of-domain distributions. The results show the limitations of current probability calibration techniques and motivate future work. © 2025 IEEE.

Abstract
A investigação sobre o feedback e a autorregulação da aprendizagem tem granjeado interesse com a explosão da Inteligência Artificial e os desafios que coloca à educação e, em particular, à avaliação dos estudantes. Contudo, há mais de 30 anos que se estudam esses processos para compreender como os estudantes regulam a sua própria aprendizagem ao nível motivacional, cognitivo e metacognitivo. Ao assumirem um papel proativo na geração e utilização do feedback estão a avaliar o seu próprio trabalho, o que tem implicações na forma como os professores organizam a avaliação e o apoio na aprendizagem.  Este artigo elabora sobre os desafios múltiplos que se colocam à IA na avaliação digital da aprendizagem, no que respeita ao feedback e a autorregulação bem como na investigação sobre a avaliação digital. Após a discussão desses conceitos e de modelos enquadradores bem como a sua conexão com a avaliação digital conclui-se que é crucial considerar equipas multidisciplinares na investigação com IA e minimizar ou eliminar situações que podem introduzir enviesamentos em termos de género, etnias, culturas e estatutos económico ou social.

Abstract
Effective communication in engineering projects is pivotal for empowering the green transition, as it fosters multidisciplinary collaboration, ensures clarity across diverse stakeholders, and bridges technical and cultural gaps, ultimately driving sustainable innovation and project success. The main aim of this study is to give a contribution to overcome these communication limitations. This research explores the critical role of communication in engineering projects related to the green transition, as part of the ECO-GT project in Portugal. Through focus groups and interviews with different stakeholders, including engineers, product manufacturers and end-users, the research identifies communication challenges and essential skills required during project implementation. The findings show that the importance of multidisciplinary collaboration, adapted language depending on the target audience, and openness to feedback are essential to achieving project goals. Key findings include the need for tailored communication strategies at all project stages to overcome technical and cultural barriers. This research highlights the value of integrating communication training into engineering education to prepare future engineers for the complexities of green transition projects.

Abstract
In this paper we present results on the normalized temperature sensitivity of UV- and fs-induced fiber Bragg gratings in a singlemode fiber with similar to 4.7 mol% GeO2 and having an Ormocer coating. In the 1500-1600 nm wavelength range, the former shows an almost constant value of 6.165x10(-6) K-1, whilst the fs-induced present some variation not related with the strength of the grating but probably due to induced birefringence. The average value obtained was 6.191x10(-6) K-1 which is higher than the former. For the UV-induced gratings in the Corning SMF-28 fiber (3.67 mol% GeO2) the value obtained was 6.143x10(-6) K-1. The achieved values are compatible with the use of Corning 7980 silica-based cladding fiber. Preliminary results also show no measurable impact of the hydrogenation process or the strength of the grating on the normalized temperature sensitivity.

Abstract
This study investigates the use of SHAP (SHapley Additive exPlanations) values as an explainable artificial intelligence (xAI) technique applied on a facial attribute classification task. We analyse the consistency of SHAP value distributions across diverse classifier architectures that share the same feature extractor, revealing that key features driving attribute classification remain stable regardless of classifier architecture. Our findings highlight the challenges in interpreting SHAP values at the individual sample level, as their reliability depends on the model’s ability to learn distinct class-specific features; models exploiting inter-class correlations yield less representative SHAP explanations. Furthermore, pixel-level SHAP analysis reveals that superior classification accuracy does not necessarily equate to meaningful semantic understanding; notably, despite FaceNet exhibiting lower performance than CLIP, it demonstrated a more nuanced grasp of the underlying class attributes. Finally, we address the computational scalability of SHAP, demonstrating that KernelExplainer becomes infeasible for high-dimensional pixel data, whereas DeepExplainer and GradientExplainer offer more practical alternatives with trade-offs. Our results suggest that SHAP is most effective for small to medium feature sets or tabular data, providing interpretable and computationally manageable explanations.