Robustness of the European power grids under intentional attack

The power grid defines one of the most important technological networks of our times and sustains our complex society. It has evolved for more than a century into an extremely huge and seemingly robust and well understood system. But it becomes extremely fragile as well, when unexpected, usually minimal, failures turn into unknown dynamical behaviours leading, for example, to sudden and massive blackouts. Here we explore the fragility of the European power grid under the effect of selective node removal. A mean field analysis of fragility against attacks is presented together with the observed patterns. Deviations from the theoretical conditions for network percolation (and fragmentation) under attacks are analysed and correlated with non topological reliability measures.Comment: 7 pages, 4 figure

Alternative Techniques of Neural Signal Processing in Neuroengineering

Neural signal processing is a discipline within neuroengineering. This interdisciplinary approach combines principles from machine learning, signal processing theory, and computational neuroscience applied to problems in basic and clinical neuroscience. The ultimate goal of neuroengineering is a technological revolution, where machines would interact in real time with the brain. Machines and brains could interface, enabling normal function in cases of injury or disease, brain monitoring, and/or medical rehabilitation of brain disorders. Much current research in neuroengineering is focused on understanding the coding and processing of information in the sensory and motor systems, quantifying how this processing is altered in the pathological state, and how it can be manipulated through interactions with artificial devices including brain–computer interfaces and neuroprosthetics

Avalanches from charged domain wall motion in BaTiO<inf>3</inf> during ferroelectric switching

© 2020 Author(s). We report two methods for direct observations of avalanches in ferroelectric materials during the motion of domain walls. In the first method, we use optical imaging techniques to derive changes in domain structures under an electric field. All changes occur through small jumps (jerks) that obey avalanche statistics. In the second method, we analyze jerks by their displacement current. Both methods reveal a power law distribution with an energy exponent of 1.6, in agreement with previous acoustic emission measurements, and integrated mean field theory. This new combination of methods allows us to probe both polarization and strain variations during the motion of domain walls and can be used for a much wider class of ferroelectrics, including ceramic samples, than acoustic emission

EEG Windowed statitical wavelet deviation for estimation of muscular artifacts

Electroencephalographic (EEG) recordings are, most of the times, corrupted by spurious artifacts, which should be rejected or cleaned by the practitioner. As human scalp EEG screening is error-prone, automatic artifact detection is an issue of capital importance, to ensure objective and reliable results. In this paper we propose a new approach for discrimination of muscular activity in the human scalp quantitative EEG (QEEG), based on the time-frequency shape analysis. The impact of the muscular activity on the EEG can be evaluated from this methodology. We present an application of this scoring as a preprocessing step for EEG signal analysis, in order to evaluate the amount of muscular activity for two set of EEG recordings for dementia patients with early stage of Alzheimer’s disease and control age-matched subjects

Coherency and sharpness measures by using ICA algorithms. An investigation for Alzheimer’s disease discrimination

In this paper, we present a comprehensive study of different Independent Component Analysis (ICA) algorithms for the calculation of coherency and sharpness of electroencephalogram (EEG) signals, in order to investigate the possibility of early detection of Alzheimer’s disease (AD). We found that ICA algorithms can help in the artifact rejection and noise reduction, improving the discriminative property of features in high frequency bands (specially in high alpha and beta ranges). In addition to different ICA algorithms, the optimum number of selected components is investigated, in order to help decision processes for future works

Investigation of ICA algorithms for feature extraction of EEG signals in discrimination of Alzheimer disease

In this paper we present a quantitative comparisons of different independent component analysis (ICA) algorithms in order to investigate their potential use in preprocessing (such as noise reduction and feature extraction) the electroencephalogram (EEG) data for early detection of Alzhemier disease (AD) or discrimination between AD (or mild cognitive impairment, MCI) and age-match control subjects

Missing energy in black hole production and decay at the Large Hadron Collider

Black holes could be produced at the Large Hadron Collider in TeV-scale gravity scenarios. We discuss missing energy mechanisms in black hole production and decay in large extra-dimensional models. In particular, we examine how graviton emission into the bulk could give the black hole enough recoil to leave the brane. Such a perturbation would cause an abrupt termination in Hawking emission and result in large missing-energy signatures.Comment: addressed reviewer comments and updated reference