Abstract
In clinical practice and in particular in the diagnostic process, the assessment of cardiac and respiratory functions is supported by electrocardiogram and auscultation. These exams are non-invasive, quick and inexpensive to perform and easy to interpret. For these reasons, this type of assessment is a constant in the daily life of a clinician and the information obtained is central to the decision-making process. Therefore, it is essential that during their training, students of health-related subjects acquire skills in the acquisition and evaluation of the referred physiological signals. Simulation, considering the technological possibilities of today, is an excellent preparation tool since it exposes trainees to near real contexts but without the associated risks. Hence, the simulation of physiological signals plays an important role in the education of healthcare professionals, bioengineering professionals and also in the development and calibration of medical devices. This paper describes a project to develop synchronized electrocardiogram (ECG), phonocardiogram (PCG) and breathing sounds simulators that aims to improve an existing phantom simulator. The developed system allows, in an integrated way, to generate normal and pathological signals, being contemplated several distinct pathologies. For engineering education, it is also possible to simulate the introduction of signal disturbances or hardware malfunctions.

Abstract
One of the teacher's first goals should be to inspire students to learn. Using project-based learning (PBL) to involve students in the learning process could be a useful and powerful tool to prepare the students for their professional future. As part of a degree course in Biomedical Engineering, students were asked to look at society and identify a possible biomedical-related failure or daily-life problem. From this, the students were challenged to work towards a solution, by preparing a project and creating a prototype or a minimum viable product. In this article we present the case study of a students' team, whose project was candidate and winner of a national prize. This prize was related to health innovation. Despite the particularization of this case study case, the students considered the experience innovative, motivating, and challenging. They also underlined the added value of a project whose impact goes beyond the classroom. Therefore, this method of teaching and learning, based on projects, may have a special effect on the students and, therefore on the civil society. The PBL can help higher education institutions to have a more prominent social presence, as innovation drivers and as forces of intervention.

Abstract
Background:Amyotrophic lateral sclerosis (ALS) is a fatal progressive motor neuron disease. People with ALS demonstrate various speech problems.Summary:We aim to provide an overview of studies concerning the diagnosis of ALS based on the analysis of voice samples. The main focus is on the feasibility of the use of voice and speech assessment as an effective method to diagnose the disease, either in clinical or pre-clinical conditions, and to monitor the disease progression. Specifically, we aim to examine current knowledge on: (a) voice parameters and the data models that can, most effectively, provide robust results; (b) the feasibility of a semi-automatic or automatic diagnosis and outcomes; and (c) the factors that can improve or restrict the use of such systems in a real-world context.Key Messages:The studies already carried out on the possibility of diagnosis of ALS using the voice signal are still sparse but all point to the importance, feasibility and simplicity of this approach. Most cohorts are small which limits the statistical relevance and makes it difficult to infer broader conclusions. The set of features used, although diverse, is quite circumscribed. ALS is difficult to diagnose early because it may mimic several other neurological diseases. Promising results were found for the automatic detection of ALS from speech samples and this can be a feasible process even in pre-symptomatic stages. Improved guidelines must be set in order to establish a robust decision model.

Abstract
In a multimodal world the contact time between the teacher and the students is not always sufficient to ensure the effectiveness of the learning process. For the assimilation of concepts, students often endeavor on a search for the materials that best suit their learning needs. With the application of new technologies in teaching, study materials and support platforms are increasingly abundant and diverse. Additionally, recommendation algorithms overwhelm students with several options, sometimes hard to resist and select, especially after the COVID-19 restrictions, where the amount of connected time as increased. In this context, it is important for the teacher, to know which methods and materials the students use when they are autonomously developing their knowledge and skills. A survey was conducted within a group of engineering students at a Portuguese higher education institution with the main goal of characterizing the study habits and the materials that students. The obtained results are here reported and analyzed and compared with previous results from pre-pandemic study.

Abstract
To provide a safe and fair-value health service that ensures quality, hospitals must provide efficient processes, trained and committed personnel, appropriate technology and a strategic platform which integrates these aspects effectively. At present, a broad set of tools and methodologies are available, associated to the reconfiguration of processes for enhancing efficiency and enabling excellence and sustainability. Of these, the most noteworthy are Lean and Six-Sigma methodologies. A literature review was performed covering the implementation of these methodologies in health services over the last 5 years. The aim was to determine the current approach in this sector and propose guidelines aligned with the future challenges and the needs of healthcare managers. The influence of team management strategies in the final project outcomes has also been addressed representing a novelty.

Abstract
Background: Alzheimer's disease (AD) has paramount importance due to its rising prevalence, the impact on the patient and society, and the related healthcare costs. However, current diagnostic techniques are not designed for frequent mass screening, delaying therapeutic intervention and worsening prognoses. To be able to detect AD at an early stage, ideally at a pre-clinical stage, speech analysis emerges as a simple low-cost non-invasive procedure. Objectives: In this work it is our objective to do a systematic review about speech-based detection and classification of Alzheimer's Disease with the purpose of identifying the most effective algorithms and best practices. Methods: A systematic literature search was performed from Jan 2015 up to May 2020 using ScienceDirect, PubMed and DBLP. Articles were screened by title, abstract and full text as needed. A manual complementary search among the references of the included papers was also performed. Inclusion criteria and search strategies were defined a priori. Results: We were able: to identify the main resources that can support the development of decision support systems for AD, to list speech features that are correlated with the linguistic and acoustic footprint of the disease, to recognize the data models that can provide robust results and to observe the performance indicators that were reported. Discussion: A computational system with the adequate elements combination, based on the identified best-practices, can point to a whole new diagnostic approach, leading to better insights about AD symptoms and its disease patterns, creating conditions to promote a longer life span as well as an improvement in patient quality of life. The clinically relevant results that were identified can be used to establish a reference system and help to define research guidelines for future developments.