Abstract
The capabilities of the most recent language models have increased the interest in integrating them into real-world applications. However, the fact that these models generate plausible, yet incorrect text poses a constraint when considering their use in several domains. Healthcare is a prime example of a domain where text-generative trustworthiness is a hard requirement to safeguard patient well-being. In this paper, we present Physio, a chat-based application for physical rehabilitation. Physio is capable of making an initial diagnosis while citing reliable health sources to support the information provided. Furthermore, drawing upon external knowledge databases, Physio can recommend rehabilitation exercises and over-the-counter medication for symptom relief. By combining these features, Physio can leverage the power of generative models for language processing while also conditioning its response on dependable and verifiable sources. A live demo of Physio is available at https://physio.inesctec.pt.

Abstract
This paper presents a simulation model of a Time of Flight distance sensor applying SimTwo robotics simulator in order to contribute to a mobile robotics application, in an educational context. The objective is to observe the sensor behavior, inside the simulation environment, face a set of experiments, such as an abrupt difference of distance, several angle inclinations and measurements to the maximum sensor range. The tests were performed using SimTwo being a high performance, open source, versatile, real time simulation environment, in which is possible to configure an specific sensor adding its features, which allows to achieve a realistic simulation. The results represented the expected sensor behavior for the proposed scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
The adoption of sustainable dietary patterns that consider simultaneously nutritional well-being and reduced environmental impact is of paramount importance. This paper introduces the Dietary Pattern Sustainability Index (DIPASI), as a method to assess the sustainability of dietary patterns by covering the environmental, nutritional, and economic dimensions in a single score. Environmental indicators include carbon footprint, water footprint, and land use, the nutritional quality is evaluated through the Nutritional Rich Diet 9.3 score, and the economic aspects are considered using diet cost. DIPASI measures the deviation (in %) of an individual's diet in relation to a reference diet. The case study utilized dietary data from the Portuguese National Food, Nutrition, and Physical Activity Survey (IAN-AF 2015-2016), which included 2999 adults aged 18 to 64. The Portuguese dietary patterns (covering 1492 food products consumed), were compared against the reference Mediterranean diet. Results indicated that the Portuguese dietary pattern had a higher environmental impact (CF: 4.32 kg CO2eq/day, WF: 3162.88 L/day, LU: 7.03 m(2)/day), a lower nutritional quality (NRD9.3: 334), and a higher cost (6.65 euros/day) when compared to the Mediterranean diet (CF: 3.30 kg CO2eq/day, WF: 2758.84 L/day, LU: 3.67 m(2)/day, NRD9.3: 668, cost: 5.71 euros/day). DIPASI reveals that only 4% of the sample's population does not deviate or presents a positive deviation (> - 0.5%) from the Mediterranean diet, indicating that the majority of Portuguese individuals have lower sustainability performance. For the environmental sub-score, this percentage was 21.3%, for the nutritional sub-score was 10.9%, and for the economic sub-score was 34.2%. This study provides a robust framework for assessing dietary sustainability on a global scale. The comprehensive methodology offers an essential foundation for understanding and addressing challenges in promoting sustainable and healthy dietary choices worldwide.

Abstract
The emergence of Digital Twins (DT) in Industry 4.0 has enabled the decision support systems taking advantage of more effective recommendation systems (RS). Despite the RS's growing popularity and ability to support decision-makers, these face two significant challenges, cold-start and data sparsity, which limits the system's capability to provide effective and accurate decision support. This paper aims to address these issues by conducting a literature review, analysing the current research landscape, and identifying the main enabling methods, algorithms, and similarity measures to mitigate these challenges. The performed analysis enables the point out of future research directions for developing effective and accurate RS that empower decision-makers.

Abstract
One of the indicators of possible occurrences of cardiovascular diseases is the amount of coronary artery calcium. Recently, approaches using new technologies such as deep learning have been used to help identify these indicators. This work proposes a segmentation method for calcification of the coronary arteries that has three steps: (1) extraction of the ROI using U-Net with batch normalization after convolution layers, (2) segmentation of the calcifications and (3) removal of false positives using Modified U-Net with EfficientNet. The method uses histogram matching as preprocessing in order to increase the contrast between tissue and calcification and normalize the different types of exams. Multiple architectures were tested and the best achieved 96.9% F1-Score, 97.1% recall and 98.3% in the OrcaScore Dataset.

Abstract
Electrochemical impedance spectroscopy (EIS) is a reliable technique for gathering information about electrochemical process occurring at the electrode surface and investigating properties of materials. Furthermore, EIS technique can be a very versatile and valuable tool in analytical assays for detection and quantification of several chemically and biologically relevant (bio)molecules. The first part of this Review (Introduction) provides brief insights into (i) theoretical aspects of EIS, (ii) the instrumentation required to perform the EIS studies and (iii) the most relevant representations of impedance experimental data (such as Nyquist and Bode plots). In the end of this section, (iv) theoretical aspects regarding the fitting of the Randles circuit to experimental data are addressed, not only to obtain information about electrochemical processes but also to illustrate its utility for analytical purposes. The second part of the Review (Impedimetric Detection of Disease Biomarkers) focuses on the applications of EIS in the biomedical field, particularly as analytical technique in electrochemical sensors and biosensors for screening disease biomarkers. In the last section (Conclusions and Perspectives), we discuss main achievements of EIS technique in analytical assays and provide some perspectives, challenges and future applications in the biomedical field.