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Space Habitat Resilience: Integrating Neuroarchitecture for Indoor Living in Extreme Environment
(2026-09-30) Milão, Susana; Lima, Ana
Moon and Mars mission architectures are shifting from short stays to longer surface stays in isolated, confined and extreme (ICE) conditions, where small crews live almost entirely inside pressurized habitats. As transit durations increase and lunar outposts evolve into more permanent bases, crews are exposed for longer periods to environmental hazards and non-terrestrial gravity that disrupt usual sensorimotor patterns. In this context, the habitat becomes the primary interface between human bodies and extreme environments, shaping how inhabitants perceive, move, orient themselves and sustain everyday rou- tines away from Earth. This article develops a neuroarchitecture integrative model for indoor living in lunar and Martian habitats, treating space habitat resilience as a cognitive and experiential property of the human–habitat system. The model connects advances in space architecture and planetary science research with person–environment theories to show how interior form and indoor environmental quality (IEQ) influence attention, emotional regulation and social functioning under confinement. It distinguishes a macro scale, where planetary constraints compress human experience into Built Environments in Extreme Environments (BEXEs), from a micro scale, where habitability is organized into four functional clusters (somatic, operational, psychosocial and ludic-recreational). Conventional IEQ assessment addresses a small set of generic dimensions applicable to any building; here, these are reorganized into twelve cluster-specific dimensions, three per cluster, calibrated for confinement and for the absence of an accessible exterior. Focusing on room shape and proportions, degrees of enclosure and visual order as key interior variables, the model positions the habitat as an active co-regulator of cognition and argues for design agendas that move beyond minimum safety and volume standards toward evidence-informed cognitive habitability in emerging off-Earth settlements.
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Beyond the Pioneers: Visibility, Canon Formation and Exceptionalism in the feminist Historiography of Portuguese Architecture [abstract]
(2026-10-21) Sousa, Goreti; Alcindor, Mónica
For much of the twentieth century, architectural historiography was shaped by a paradigm centred on male authorship, individual genius and heroic exceptionalism. Within this framework, women were largely excluded from historical narratives, their professional trajectories rendered invisible or marginal. In recent decades, feminist scholarship has played a decisive role in challenging this model, contributing to the recovery of women architects’ biographies and to the revision of canonical architectural history. In Portugal, this shift has enabled the identification of the first formally qualified women architects and the recognition of their pioneering roles within the profession. [...]
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AI-Enhanced Histopathological Breast Cancer Diagnosis Using ACO-Tuned L2-Regularized ResNet50: Toward Early Detection and Clinical Integration
(2026-10-26) Swain, Debabrata; Shah, Mahi; Solanki, Dev; Shah, Harsh; Acharya, Biswaranjan; Singh, Davinder Paul; Moreira, Fernando
The most common form of cancer, which is found in females in society, is breast cancer. The diagnosis of the disease is very complex and hence often results in the mortality of the patient. Healthcare practitioners generally use different types of medical imaging-based reports for the precise and timely detection of the disease. In this work, a custom ResNet50 model is trained using the histopathological image samples of benign and malignant cases. A hybrid optimization approach has been used to improve the model performance by resizing the image to 224 × 224 normalization and handling the class imbalance by augmenting the benign samples. By limiting the augmentation to the training set, data leakage has been prevented. In this work, searching the hyperparameter space and finding the optimal values of batch size, learning rate, and epsilon is treated as one of the most crucial parts for which ant colony optimization (ACO) has been used. During model training, the amount of regularization is adjusted in such a way that it minimizes overfitting and enhances generalization based on the gradient's behavior, which has been introduced in an adaptive L2 ridge regularization scheme. Other than fine-tuning the model, ACO-based optimization and adaptive L2 regularization are performed to enhance the generalization of the model on the histopathological data. The model has shown a promising performance of 98.86% validation accuracy, which has proven the effectiveness of the customizations made to the model during implementation.