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Projeto de investigação
Associate Laboratory of Energy, Transports and Aeronautics
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Publicações
On the Critical Velocity of Moving Force and Instability of Moving Mass in Layered Railway Track Models by Semianalytical Approaches
Publication . Dimitrovová, Zuzana; DEC - Departamento de Engenharia Civil; MDPI - Multidisciplinary Digital Publishing Institute
This article presents a comparison between layered models of a railway track. All analyses are based on semianalytical approaches to show how powerful they can be. Results are presented in dimensionless form, making them applicable to a wide range of possible real-world scenarios. The main results and conclusions are obtained using repeated exact calculations of the equivalent flexibility of supporting structure related to each model by contour integration. New terms and a fundamentally different approach with respect to other published works underline the scientific contribution to this field. Semianalytical methods demonstrate that the intended results can be obtained easily and accurately. However, this benefit cannot be extended to a large number of models due to the simplifications that must be introduced in order to apply such methods. It turns out that even though the one-layer model is the furthest away from reality, it is easy to handle analytically because it has a regular and predictable behavior. The three-layer model, on the other hand, has many unpredictable properties that will be detailed in this article.
Multi-material and strength-oriented microstructural topology optimization applied to discrete phase and functionally graded materials
Publication . Conde, Fábio M.; Coelho, Pedro G.; Guedes, José M.; UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial; Springer Science Business Media
Structural optimization plays an important role in lightweight construction, and stresses need to be controlled to avoid material failure. The multi-material design setting offers additional design freedom which can lead to structures with improved strength and stiffness properties compared to the single-material case. The present work addresses topology optimization of a periodic composite material unit cell, with properties predicted by homogenization, using strength and stiffness design criteria, under bulk and mixed loading cases. Plane stress and linear behavior are assumed. The compliance minimization with mass constraint problem is revisited here, but the paper focus is on multi-material stress-based topology optimization. Specifically, the maximal von Mises stress is minimized in the unit-cell where two solids are mixed amidst void. Depending on the material interpolation law settings, two design solutions are investigated. On one hand, the two solids coexist being bonded together across sharp interfaces. On the other hand, a functionally graded material is obtained as an extensive smooth variation of material properties on account of varying composition’s volume fractions of both solids throughout the design domain. A parallel MMA version is proposed to efficiently deal with several design constraints. The compliance-based optimization results show that multi-material microstructures can be stiffer compared to single-material ones for the same mass requirement. Regarding the stress-based problem, lower stress peaks are obtained in bi-material design solutions and, specially, in the case of graded material solutions. The latter approximates a fully stressed design which excels in stress mitigation. Therefore, the multi-material setting impacts favorably on structural performance, in both stiffness and strength-oriented designs.
On the predictability of postoperative complications for cancer patients
Publication . Gonçalves, Daniel; Henriques, Rui; Santos, Lúcio Lara; Costa, Rafael S.; LAQV@REQUIMTE; BioMed Central (BMC)
Postoperative complications are still hard to predict despite the efforts towards the creation of clinical risk scores. The published scores contribute for the creation of specialized tools, but with limited predictive performance and reusability for implementation in the oncological context. This work aims to predict postoperative complications risk for cancer patients, offering two major contributions. First, to develop and evaluate a machine learning-based risk score, specific for the Portuguese population using a retrospective cohort of 847 cancer patients undergoing surgery between 2016 and 2018, for 4 outcomes of interest: (1) existence of postoperative complications, (2) severity level of complications, (3) number of days in the Intermediate Care Unit (ICU), and (4) postoperative mortality within 1 year. An additional cohort of 137 cancer patients from the same center was used for validation. Second, to improve the interpretability of the predictive models. In order to achieve these objectives, we propose an approach for the learning of risk predictors, offering new perspectives and insights into the clinical decision process. For postoperative complications the Receiver Operating Characteristic Curve (AUC) was 0.69, for complications’ severity AUC was 0.65, for the days in the ICU the mean absolute error was 1.07 days, and for 1-year postoperative mortality the AUC was 0.74, calculated on the development cohort. In this study, predictive models which could help to guide physicians at organizational and clinical decision making were developed. Additionally, a web-based decision support tool is further provided to this end.
DI2
Publication . Alexandre, Leonardo; Costa, Rafael S.; Henriques, Rui; LAQV@REQUIMTE; DQ - Departamento de Química; BioMed Central (BMC)
Background: A considerable number of data mining approaches for biomedical data analysis, including state-of-the-art associative models, require a form of data discretization. Although diverse discretization approaches have been proposed, they generally work under a strict set of statistical assumptions which are arguably insufficient to handle the diversity and heterogeneity of clinical and molecular variables within a given dataset. In addition, although an increasing number of symbolic approaches in bioinformatics are able to assign multiple items to values occurring near discretization boundaries for superior robustness, there are no reference principles on how to perform multi-item discretizations. Results: In this study, an unsupervised discretization method, DI2, for variables with arbitrarily skewed distributions is proposed. Statistical tests applied to assess differences in performance confirm that DI2 generally outperforms well-established discretizations methods with statistical significance. Within classification tasks, DI2 displays either competitive or superior levels of predictive accuracy, particularly delineate for classifiers able to accommodate border values. Conclusions: This work proposes a new unsupervised method for data discretization, DI2, that takes into account the underlying data regularities, the presence of outlier values disrupting expected regularities, as well as the relevance of border values. DI2 is available at https://github.com/JupitersMight/DI2
Predictability of COVID-19 hospitalizations, intensive care unit admissions, and respiratory assistance in Portugal
Publication . Patrício, André; Costa, Rafael S.; Henriques, Rui; LAQV@REQUIMTE; JMIR Publications
Background: In the face of the current COVID-19 pandemic, the timely prediction of upcoming medical needs for infected individuals enables better and quicker care provision when necessary and management decisions within health care systems. Objective: This work aims to predict the medical needs (hospitalizations, intensive care unit admissions, and respiratory assistance) and survivability of individuals testing positive for SARS-CoV-2 infection in Portugal. Methods: A retrospective cohort of 38,545 infected individuals during 2020 was used. Predictions of medical needs were performed using state-of-the-art machine learning approaches at various stages of a patient's cycle, namely, at testing (prehospitalization), at posthospitalization, and during postintensive care. A thorough optimization of state-of-the-art predictors was undertaken to assess the ability to anticipate medical needs and infection outcomes using demographic and comorbidity variables, as well as dates associated with symptom onset, testing, and hospitalization. Results: For the target cohort, 75% of hospitalization needs could be identified at the time of testing for SARS-CoV-2 infection. Over 60% of respiratory needs could be identified at the time of hospitalization. Both predictions had >50% precision. Conclusions: The conducted study pinpoints the relevance of the proposed predictive models as good candidates to support medical decisions in the Portuguese population, including both monitoring and in-hospital care decisions. A clinical decision support system is further provided to this end.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
6817 - DCRRNI ID
Número da atribuição
UIDB/50022/2020