An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e., cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graphbased pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e., support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e., there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis.

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Nowadays, millions of people are affected by heart diseases worldwide, whereas a considerable amount of them could be aided through an electrocardiogram (ECG) trace analysis, which involves the study of arrhythmia impacts on electrocardiogram patterns. In this work, we carried out the task of automatic arrhythmia detection in ECG patterns by means of supervised machine learning techniques, being the main contribution of this paper to introduce the optimum-path forest (OPF) classifier to this context. We compared six distance metrics, six feature extraction algorithms and three classifiers in two variations of the same dataset, being the performance of the techniques compared in terms of effectiveness and efficiency. Although OPF revealed a higher skill on generalizing data, the support vector machines (SVM)-based classifier presented the highest accuracy. However, OPF shown to be more efficient than SVM in terms of the computational time for both training and test phases.

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Bacteriophage particles have been reported as potentially useful in the development of diagnosis tools for pathogenic bacteria as they specifically recognize and lyse bacterial isolates thus confirming the presence of viable cells. One of the most representative microorganisms associated with health care services is the bacterium Pseudomonas aeruginosa, which alone is responsible for nearly 15% of all nosocomial infections. In this context, structural and functional stabilization of phage particles within biopolymeric hydrogels, aiming at producing cheap (chromogenic) bacterial biosensing devices, has been the goal of a previous research effort. For this, a detailed knowledge of the bacterial diffusion profile into the hydrogel core, where the phage particles lie, is of utmost importance. In the present research effort, the bacterial diffusion process into the biopolymeric hydrogel core was mathematically described and the theoretical simulations duly compared with experimental results, allowing determination of the effective diffusion coefficients of P. aeruginosa in the agar and calcium alginate hydrogels tested.

Digital presses have consistently improved their speed in the past ten years. Meanwhile, the need for document personalization and customization has increased. As a consequence of these two facts, the traditional RIP (Raster Image Processing) process has become a highly demanding computational step in the print workflow. Print Service Providers (PSP) are now using multiple RIP engines and parallelization strategies to speed up the whole ripping process which is currently based on a per-page basis. Nevertheless, these strategies are not optimized in terms of ensuring the best Return On Investment (ROI) for the RIP engines. Depending on the input document jobs characteristics, the ripping step may not achieve the print-engine speed creating a unwanted bottleneck. The aim of this paper is to present L.G. Fernandes ( ) GMAP-PPGCC-PUCRS, a way to improve the ROI of PSPs proposing a profiling strategy which enables the optimal usage of RIPs for specific jobs features ensuring that jobs are always consumed at least at engine speed. The profiling strategy is based on a per-page analysis of input Portable Document Format (PDF) jobs identifying their key components. This work introduces a PDF Profiler tool aimed at extracting information from jobs and some metrics to predict a job ripping cost based on its profile. This information is extremely useful to rasterize jobs in a clever way. The computational cost estimated using the information extracted by the PDF Profiler and the proposed metrics is useful for the print jobs queue management to improve the allocated RIPs load balance, resulting in a higher throughput for the ripping step. Experiments have been carried out in order to evaluate the PDF Profiler, the proposed metrics and their impact in the print jobs queue management.

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