RESUMEN

Microbial fuel cell (MFC) power performance strongly depends on the biofilm growth, which in turn is affected by the feed flow rate. In this work, an artificial neural network (ANN) approach has been used to simulate the effect of the flow rate on the power output by ceramic MFCs fed with neat human urine. To this aim, three different second-order algorithms were used to train our network and then compared in terms of prediction accuracy and convergence time: Quasi-Newton, Levenberg-Marquardt, and Conjugate Gradient. The results showed that the three training algorithms were able to accurately simulate power production. Amongst all of them, the Levenberg-Marquardt was the one that presented the highest accuracy (R = 95%) and the fastest convergence (7.8 s). These results show that ANNs are useful and reliable tools for predicting energy harvesting from ceramic-MFCs under changeable flow rate conditions, which will facilitate the practical deployment of this technology.

RESUMEN

Microbial fuel cells (MFCs) are an environment-friendly technology, which addresses two of the most important environmental issues worldwide: fossil fuel depletion and water scarcity. Modelling is a useful tool that allows us to understand the behaviour of MFCs and predict their performance, yet the number of MFC models that could accurately inform a scale-up process, is low. In this work, a three-factor three-level Box-Behnken design is used to evaluate the influence of different operating parameters on the performance of air-breathing ceramic-based MFCs fed with human urine. The statistical analysis of the 45 tests run shows that both anode area and external resistance have more influence on the power output than membrane thickness, in the range studied. The theoretical optimal conditions were found at a membrane thickness of 1.55 mm, an external resistance of 895.59â€¯Ω and an anode area of 165.72 cm2, corresponding to a maximum absolute power generation of 467.63 µW. The accuracy of the second order model obtained is 88.6%. Thus, the three-factor three-level Box-Behnken-based model designed is an effective tool which provides key information for the optimisation of the energy harvesting from MFC technology and saves time in terms of experimental work.

RESUMEN

Power line interference is one of the main problems in surface electromyogram signals (EMG) analysis. In this work, a new method based on the stationary wavelet packet transform is proposed to estimate and remove this kind of noise from EMG data records. The performance has been quantitatively evaluated with synthetic noisy signals, obtaining good results independently from the signal to noise ratio (SNR). For the analyzed cases, the obtained results show that the correlation coefficient is around 0.99, the energy respecting to the pure EMG signal is 98-104%, the SNR is between 16.64 and 20.40dB and the mean absolute error (MAE) is in the range of -69.02 and -65.31dB. It has been also applied on 18 real EMG signals, evaluating the percentage of energy respecting to the noisy signals. The proposed method adjusts the reduction level to the amplitude of each harmonic present in the analyzed noisy signals (synthetic and real), reducing the harmonics with no alteration of the desired signal.

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After reviewing the antecedents of an PEG Percutaneous Endoscopic Gastrostomy Probe, the authors analyze the indications for its usage, the possible complications and the care procedures which the entire process for a PEG requires and the subsequent at home follow up patient care. Finally, the authors evaluate some recommendations to bear in mind so that this entire procedure is convenient for the patient and so that the patient's quality of life improves.

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RESUMEN

Tras repasar los antecedentes de la sonda PEG (gastrostomía endoscópica percutánea), se analizan sus indicaciones de colocación, las posibles complicaciones y los cuidados que requiere todo este proceso y su seguimiento domiciliario. Por último se valoran las recomendaciones a tener en cuenta para que todo el proceso sea idóneo para el paciente y aumente su calidad de vida (AU)

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