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- Multidrug resistance assessment of indoor air in Portuguese long-term and acute healthcare settingsPublication . Santos-Marques, C.; Teixeira, C.; Pinheiro, R.; Brück, W. M.; Pereira, Sónia Gonçalves; dos Santos Marques, Catarina; Silva Teixeira, Camila; Pinheiro, Rafael; Gonçalves Pereira, SóniaBackground: Knowledge about air as a pool of pathogens and multidrug resistance (MDR) in healthcare units apart from hospitals is scarce. Aim: To investigate these features in a Portuguese long-term healthcare unit (LTHU) and a central hospital (CH). Methods: Air samples were collected and their microbial load (bacteria and fungi) determined. Bacterial isolates were randomly selected for further characterization, particularly identification by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry, antimicrobial susceptibility testing, and polymerase chain reaction screening of extended-spectrum b-lactamases, carbapenemase genes and mecA gene, with RAPD profile assessment of positive results of the latter. Findings: A total of 192 samples were collected (LTHU: 86; CH: 106). LTHU showed a statistically significantly higher bacterial load. CH bacteria and fungi loads in inpatient sites were statistically significantly lower than in outpatients or non-patient sites. A total of 164 bacterial isolates were identified (MALDI-TOF: 78; presumptively: 86), the majority belonging to Staphylococcus genus (LTHU: 42; CH: 57). The highest antimicrobial resistance rate was to erythromycin and vancomycin the least, in both settings. Eighteen isolates (11%) were classified as MDR (LTHU: 9; CH: 9), with 7 MDR Staphylococcus isolates (LTHU: 4; CH: 3) presenting mecA. Nine non-MDR Staphylococcus (LTHU: 5; CH: 4) also presented mecA. Conclusion: The current study highlights that healthcare unit indoor air can be an important pool of MDR pathogens and antimicrobial resistance genes. Also, LTHUs appear to have poorer air quality than hospitals, as well as supportive areas compared to curative care areas. This may suggest possible yet unknown routes of infection that need to be explored.
- Review on hardware-in-the-loop simulation of wave energy converters and power take-offsPublication . Gaspar, José F.; Pinheiro, Rafael F.; Mendes, Mário J.G. C.; Kamarlouei, Mojtaba; Soares, C. GuedesThis paper reviews the state-of-the-art on Hardware-In-The-Loop simulation methodologies and technologies applied in the research field of wave energy converters. It reveals important issues, such as an unclear taxonomy and representations of these methodologies, which are critical for the success of the approach, mostly during the design of experiments and presentation of results. Moreover, a classification approach to these methodologies is not found in the literature. Thus, a generic taxonomical and classification framework is developed to support the review process. This framework is built based on three taxonomic subsystems that the review shows to be effective in organizing the reviewed methodologies: simulated, real and interface subsystems. In particular, the definition of the interface subsystem is key to overcoming the limitations found in the methodological representations. Furthermore, this review borrows the term actionability to this approach to better describe the nuances and gaps between the reviewed case studies. It is found that the different technical implementations are easily organized with the proposed framework, and the results cover a wide range of wave energy converter development phases. Likewise, this review shows opportunities for improvements in the methodology and application to a wider number of new case studies.
- On the μ-analysis and synthesis for uncertain time-delay systems with Padé approximationsPublication . Pinheiro, Rafael Fernades; Colón, DiegoReal problems in control engineering usually involve many uncertainties and delays. In the search for solutions which deal more adequately with these problems, this paper presents contributions to the theory of the -analysis and synthesis applied to uncertain linear fixed time time-delay systems by using Padé approximations. A new necessary and sufficient condition for robust stability for a class of uncertain time-delay systems is presented. From this condition, a novel robust controller synthesis technique is obtained. Furthermore, contributions are presented on the convergence theory of Padé approximations applied to the -theory via parametrized optimization technique. In order to better understand the theory presented and verify the effectiveness of the results, examples and comparisons are proposed. Finally, in the conclusions, new lines of research and application are pointed out.
- Level up! How Gamed-Based Activities Transform Learning and Alleviate Stress in Institutionalized ElderlyPublication . Lacomba-Arnau, Elena; Ribeiro, Anaísa; Sabino, Raquel; Pinheiro, Rafael; Lopes, Susana; Gaspar, Marisa; Navarro-Mateos, Carmen; Sousa, Micael; Rosa, MarleneMental health issues are a critical concern for the elderly, as the inability to manage stress during stimulation activities can significantly impair their ability to accept and effectively learn new tasks, thereby affecting their performance in daily life activities. Serious games are increasingly recognized as valuable in the context of rehabilitation; however, there is a paucity of studies examining how elderly individuals manage stress and learn in regular practice using such games. In this study, 10 institutionalized elderly participants underwent 6 game-based stimulation sessions playing the serious games Ta!Ti! and Mexerico. Learning variables, including time and error rates, were assessed at baseline (T0), mid-point (T1), and the final session (T2), along with stress management indicators, specifically cortisol levels, at T0 and T2. The findings revealed that learning profiles improved throughout the program, with more pronounced gains observed initially. Additionally, stress levels decreased following each game-based session. The study identified significant relationships between stress management and learning profiles, suggesting that game-based activities can effectively enhance both learning outcomes and stress reduction in the elderly.
- Lurie Control Systems Applied to the Sudden Cardiac Death Problem Based on Chua Circuit DynamicsPublication . Pinheiro, Rafael F.; Colón, Diego; Antunes, Alexandre; Fonseca-Pinto, RuiSudden cardiac death (SCD) represents a critical public health challenge, emphasizing the need for predictive techniques that model complex physiological dynamics. Studies indicate that the “V-trough” pattern in sympathetic nerve activity (SNA) could act as an early indicator of potentially fatal cardiac events, which can be effectively modeled using a modified version of Chua’s chaotic system, incorporating the variables of heart rate (HR), SNA, and blood pressure (BP). This paper introduces a Chua circuit with delay, and proposes a novel control design technique based on Lurie-type control systems theory combined with mixed-sensitivity H∞ (S/KS/T) methodology. The proposed controller enables precise regulation of HR in Chua’s circuit, both with and without delay, paving the way for the development of advanced devices capable of preventing SCD. Furthermore, the developed theory allows for the project of robust controllers for delayed Lurie systems within the single-input–single-output (SISO) framework. The presented theoretical framework, supported by numerical simulations, demonstrates the effectiveness of the conceptualization, marking a considerable advance in the understanding and early intervention of SCD through robust and nonlinear control systems.
- Prediabetes risk classification algorithm via carotid bodies and K-means clustering techniquePublication . Pinheiro, Rafael F.; Guarino, Maria P.; Lages, Marlene; Fonseca-Pinto, RuiDiabetes is a disease that affects millions of people in the world and its early screening prevents serious health problems, also providing relief in the demand for healthcare services. In the search for methods to support early diagnosis, this article introduces a novel prediabetes risk classification algorithm (PRCA) for type-2 diabetes mellitus (T2DM), utilizing the chemosensitivity of carotid bodies (CB) and K-means clustering technique from the field of machine learning. Heart rate (HR) and respiratory rate (RR) data from eight volunteers with prediabetes and 25 without prediabetes were analyzed. Data were collected in basal conditions and after stimulation of the CBs by inhalation of 100% of oxygen and after ingestion of a standardized meal. During the analysis, a greater variability of groups was observed in people with prediabetes compared to the control group, particularly after inhalation of oxygen. The algorithm developed from these results showed an accuracy of 86% in classifying for prediabetes. This approach, centered on CB chemosensitivity deregulation in early disease stages, offers a nuanced detection method beyond conventional techniques. Moreover, the adaptable algorithm and clustering methodology hold promise as risk classifications for other diseases. Future endeavors aim to validate the algorithm through longitudinal studies tracking disease development among volunteers and expand the study’s scope to include a larger participant pool.
- A memory failure computational model in Alzheimer-like disease via continuous delayed Hopfield network with Lurie control system based healingPublication . Pinheiro, Rafael Fernandes; Colón, Diego; Fonseca-Pinto, RuiAlzheimer’s disease (AD) is a degenerative neurological condition that impacts millions of individuals across the globe and remains without a healing. In the search for new possibilities of treatments for this terrible disease, this work presents the improved Alzheimer-like disease (IALD) model for memory failure and connects it to a new control technique that establishes a cure for the memory lost, either in biological or in artificial neural networks. For the IALD model, continuous Hopfield neural networks (HNN) with time delay are used. From the healing side, a robust control technique is used, which is based on new discoveries in Lurie control systems. In addition, this paper reviews the development of Alzheimer-like disease (ALD) model, as well as, the relationship of HNN with Lurie system. Simulations are executed to validate the model and to show the efficacy of applying a new theorem from Lurie problem. With the results presented, this work proposes a new conceptual paradigm that could potentially be applied in memory failure treatments in AD, as well as in hardware implemented HNN under adversarial attacks or adverse environmental conditions.
- On the development of diagnostic support algorithms based on CPET biosignals data via machine learning and waveletsPublication . Pinheiro, Rafael F.; Fonseca-Pinto, Rui; Brunello, AndreaFor preventing health complications and reducing the strain on healthcare systems, early identification of diseases is imperative. In this context, artificial intelligence has become increasingly prominent in the field of medicine, offering essential support for disease diagnosis. This article introduces an algorithm that builds upon an earlier methodology to assess biosignals acquired through cardiopulmonary exercise testing (CPET) for identifying metabolic syndrome (MS), heart failure (HF), and healthy individuals (H). Leveraging support vector machine (SVM) technology, a wellknown machine learning classification method, in combination with wavelet transforms for feature extraction, the algorithm takes an innovative approach. The model wastrained on CPETdatafrom 45participants, including 15 with MS, 15 with HF, and 15 healthy controls. For binary classification tasks, the SVM with a polynomial kernel and 5-level wavelet transform (SVM-POL-BW5) outperformed similar methods described in the literature. Moreover, one of the main contributions of this study is the development of a multi-class classification algorithm using the SVM employing a linear kernel and 3-level wavelet transforms (SVM-LIN-MW3), reaching an average accuracy of 95%. In conclusion, the application of SVM-based algorithms combined with wavelet transforms to analyze CPET data shows promise in diagnosing various diseases, highlighting their adaptability and broader potential applications in healthcare.