Abstract
O conceito de cidades criativas envolve a existência de uma polis, geograficamente localizada, no espaço físico, com toda sua paisagem urbana, artefatos, pessoas, produções manuais e físicas. No entan

Abstract

The digitalization of health care has accelerated the adoption of mobile health applications (mHealth apps) in Family and General Medicine in Portugal. These tools may support chronic disease management and clinical decision-making. However, limited high-quality scientific evidence and the absence of a national framework for certification and quality standards create uncertainty about their safe integration into clinical practice.

Abstract
Net Zero Energy Building (NZEB) is a concept that promotes the reduction of energy consumption in buildings by applying energy efficiency measures. The energy supply for the remaining demand should only come from sources with low CO2 emissions. Despite abundant research on NZEB for new buildings, only a small number of studies address its application to those already existing. This study aims to bridge this research gap by organizing the proposed methods to transform existing buildings into NZEB. The research method is a systematic literature review covering the methodological development and the application of the concept. We conducted a bibliometric and Scientometric analysis of 117 articles and a content analysis of 48 of them. The results highlighted that the methods identified follow similar stages: (i) planning, (ii) data collection, (iii) pre-design, (iv) design, and (v) delivery. The sub-stage with the highest frequency (88%) was the presentation of the efficiency measure package, making it an essential step in the transformation process. The review did not find specific topics, such as equipment listing and performance, occupant engagement, and charrette design. Finally, the study established guidelines for future research.

Abstract
Background The Endoscopic Grading of Gastric Intestinal Metaplasia (EGGIM) classification correlates with histological assessment of gastric intestinal metaplasia and enables stratification of gastric cancer risk. We developed and evaluated an artificial intelligence (AI) approach for EGGIM estimation. Methods Two datasets (A and B) with 1280 narrow-band imaging images were used for per-image analysis. Still images with manually selected patches of 224 x 224 pixels, annotated by experts, were used. Dataset A was retrospectively collected from clinical routine; Dataset B (used for per-patient analysis) was prospectively collected and included 65 fully documented patients. To mimic clinical practice, a deep neural network classified image patches into three EGGIM classes (0, 1, 2) and calculated the total per-patient EGGIM score (0-10). Results On per-image analysis, an accuracy of 87% (95%CI 71%-100%) was obtained. Per-patient EGGIM estimation had an average error of 1.15 (out of 10) and showed 88% (95%CI 80%-96%) accurate clinical decisions for surveillance (EGGIM >= 5), with 85% (95%CI 75%-94%) specificity, no false negatives, and positive and negative predictive values of 62% (95%CI 32%-92%) and 100% (95%CI 100%-100%), respectively. Conclusions EGGIM was estimated with high accuracy using AI tools in endoscopic image analyses. Automated assessment of EGGIM may provide a greener strategy for gastric cancer risk stratification, prospective studies, and interventional trials.

Abstract
Due to the high variability in Hematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs), hidden stratification, and batch effects, generalizing beyond the training distribution is one of the main challenges in Deep Learning (DL) for Computational Pathology (CPath). But although DL depends on large volumes of diverse and annotated data, it is common to have a significant number of annotated samples from one or multiple source distributions, and another partially annotated or unlabeled dataset representing a target distribution for which we want to generalize, the so-called Domain Adaptation (DA). In this work, we focus on the task of generalizing from a single source distribution to a target domain. As it is still not clear which domain adaptation strategy is best suited for CPath, we evaluate three different DA strategies, namely FixMatch, CycleGAN, and a self-supervised feature extractor, and show that DA is still a challenge in CPath.

Abstract
The potential of Augmented Reality (AR) has been harnessed to create immersive game settings, present layers of relevant information in museums, streamline procedures in healthcare and industry, and captivate consumers through innovative marketing strategies. Certain artifacts lend themselves well to representation in AR, especially those requiring a seamless fusion of the information layer with physical space. This integration underscores the suitability of information design artifacts for AR implementation. This study aims to delineate the distinctive attributes of AR in remediating information design, effectively catering to the user's informational needs. To this end, we analyzed the Google Translate app, examining it through the analytical lens of body schema and haptic engagement. The findings reveal that AR manifests as a performative, personalized, crafted image that fosters involvement through agency. The performative nature of the image directs attention, while individual images collectively form a collection. It is recommended that AR design be centered around achieving harmony among body, media, and space.