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Summary This study aims to detect voice disorders related to vocal fold nodule, Reinke’s edema and neurological pathologies through multiband cepstral features of the sustained vowel /a/. Detection is performed between pairs of study groups and multiband analysis is accomplished using the wavelet transform. For each pair of groups, a parameters selection is carried out. Time series of the selected parameters are used as input for four classifiers with leave-one-out cross validation. Classification accuracies of 100% are achieved for all pairs including the control group, surpassing the state-of-art methods based on cepstral features, while accuracies higher than 88.50% are obtained for the pathological pairs. The results indicated that the method may be adequate to assist in the diagnosis of the voice disorders addressed. The results must be updated in the future with a larger population to ensure generalization.

Despite the popularity of microservice architectures using container based virtualization, there are not many studies that describe how logging practices are being employed in this scenario. In this empirical study, we cloned 10,918 of the most stargazed GitHub Python repositories with a Docker/Kubernetes associated file. We were able to find 1,166 Python projects that used containers. A custom parser identified and saved log statements from Python source code. We discovered that certain licenses tend to have a higher log density; over 99% of projects use the built-in Python logging library; the repository age does not affect its log statements/LLOC ratio; logging verbosity levels debug and info are used almost twice as much as warning and error. We hope our study provides the community with useful data about this topic, possibly contributing to the improvement of techniques that stimulate its applications.

Achieving a balanced healthcare workforce requires health planners to adjust the supply of health human resources (HHR). Mathematical programming models have been widely used to assist such planning, but the way uncertainty is usually considered in these models entails methodological and practical issues and often disregards radical yet plausible changes to the future. This study proposes a new socio-technical methodology to factor in uncertainty over the future within mathematical programming modelling. The methodological approach makes use of foresight and scenario planning concepts to build tailor-made scenarios and scenario fit input parameters, which are then used within mathematical programming models. Health stakeholders and experts are engaged in the scenario building process. Causal map modelling and morphological analysis are adopted to digest stakeholders and experts’ information about the future and give origin to contrasting and meaningful scenarios describing plausible future. These scenarios are then adjusted and validated by stakeholders and experts, who then elicit their best quantitative estimates for coherent combinations of input parameters for the mathematical programming model under each scenario. These sets of parameters for each scenario are then fed to the mathematical programming model to obtain optimal solutions that can be interpreted in light of the meaning of the scenario. The proposed methodology has been applied to a case study involving HHR planning in Portugal, but its scope far extends HHR planning, being especially suited for addressing strategic and policy planning problems that are sensitive to input parameters. info:eu-repo/semantics/acceptedVersion

Output-only methods are widely used to characterize the dynamic behavior of very diverse structures. However, their application to floating structures may be limited due to their strong nonlinear behavior. Therefore, since there is very little experience on the application of these experimental tools to these very peculiar structures, it is very important to develop studies, either based on numerical simulations or on real experimental data, to better understand their potential and limitations. In an initial phase, the use of numerical simulations permits a better control of all the involved variables. In this work, the Covariance-driven Stochastic Subspace Identification (SSI-COV) algorithm is applied to numerically simulated data of two different solutions to Floating Offshore Wind Turbines (FOWT) and for its capability of tracking the rigid body motion modal properties and susceptibility to different modeling restrictions and environmental conditions tested. The feasibility of applying the methods in an automated fashion in the processing of a large number of datasets is also evaluated. While the structure natural frequencies were consistently obtained from all the simulations, some difficulties were observed in the estimation of the mode shape components in the most changeling scenarios. The estimated modal damping coefficients were in good agreement with the expected results. From all the results, it can be concluded that output-only methods are capable of characterizing the dynamic behavior of a floating structure, even in the context of continuous dynamic monitoring using automated tracking of the modal properties, and should now be tested under uncontrolled environmental loads.

This paper presents a summary of recent progress towards the development and upscaling of an emerging class of electrostatic power take-off (PTO) systems for wave energy converters (WECs), called dielectric elastomer generators (DEGs). DEGs are electromechanical devices able to convert mechanical energy into electrical energy by exploiting the deformation of rubber-like dielectric materials. The high power density (in the order of hundreds of Watts per kilogram), good efficiency and ease of assembling, combined with the low-cost of the employed materials (a few euros per kilogram) and their intrinsic resilient/reliable response to mechanical shocks make DEGs a very promising option for the deployment of a future generation of WECs. In the last decade, some specific concepts of WECs based on DEGs have been devised and a considerable interest in the topic has been aroused in the wave energy community. Among the candidate DEG topologies for wave energy harvesting, recent studies have suggested that a specific layout, namely the axial-symmetric inflating DEG diaphragm, could be a very promising candidate for future upscaling. This paper first describes the operating principle of DEG PTOs and the effect of electro-mechanical material parameters on their energetic performance. With reference to the above-mentioned inflating DEG diaphragm topology, an overview of concepts for integration on WECs is then provided, with a special focus on advanced concepts enabling the achievement of dynamical tuning with the incoming waves. A general lumped-parameter modelling approach for the design of DEG-based WECs is proposed. Experimental activity carried out to date, i.e. dry-run laboratory tests, wave-tank tests and preliminary sea trials is reviewed, with the aim of showing the progression in the device's scale and performance. Finally, economical and technological considerations are outlined, in order to point out challenges, future research opportunities and to draft a roadmap for future research and technological transfer.

Phylogenetic networks are a useful way of displaying relationships between nucleotide or protein sequences. They diverge from phylogenetic trees as networks present cycles, several possible evolutionary histories of the sequences analysed, while a tree presents a single evolutionary relationship. Networks are especially useful in studying markers with a high level of homoplasy (same mutation happening more than once during evolution) like the control region of mitochondrial DNA (mtDNA), where the researcher does not need to compromise with a single explanation for the evolution suggested by the data. However in many instances, trees are required. One case where this happens is in the founder analysis methodology that aims at estimating migration times of human populations along history and prehistory. Currently, the founder analysis methodology implicates the creation of networks, from where a probable tree will be extracted by hand by the researcher, a time-consuming process, prone to errors and to the ambiguous decisions of the researcher. In order to automate the founder analysis methodology an algorithm that extracts a single probable tree from a network in a fast, systematic way is presented here.