Weighted network analysis of high frequency cross-correlation measures

In this paper we implement a Fourier method to estimate high-frequency correlation matrices from small data sets. The Fourier estimates are shown to be considerably less noisy than the standard Pearson correlation measures and thus capable of detecting subtle changes in correlation matrices with just a month of data. The evolution of correlation at different time scales is analyzed from the full correlation matrix and its minimum spanning tree representation. The analysis is performed by implementing measures from the theory of random weighted networks. © 2007 The American Physical Society

Cross-correlation measures in the high-frequency domain

On a high-frequency scale the time series are not homogeneous, therefore standard correlation measures can not be directly applied to the raw data. To deal with this problem the time series have to be either homogenised through interpolation or methods that can handle raw non-synchronous time series need to be employed. This paper compares two traditional methods that use inter-polation with an alternative method applied directly to the actual time series. The three methods are tested on simulated data and actual trades time series

Cross-correlation measures in the high-frequency domain

On a high-frequency scale the time series are not homogeneous, therefore standard correlation measures cannot be directly applied to the raw data. To deal with this problem the time series have to be either homogenized through interpolation, or methods that can handle raw non-synchronous time series need to be employed. This paper compares two traditional methods that use interpolation with an alternative method applied directly to the actual time series. The three methods are tested on simulated data and actual trades time series

Co-morbidities in the multiple victims of the silent killer in carbon monoxide poisoning

Carbon monoxide (CO) remains an insidious and silent killer due to its physical and chemical properties; its lethal effects are encountered in cases of household accidents, occupational hazards or suicide. Deaths due to CO poisoning were studied retrospectively in the period 2000–2018 at the Institute of Forensic Medicine, Timişoara, Romania. These cases represent 1.75% of all the autopsies and 0.63% of all violent deaths. There have been cases of single deaths and cases with multiple victims – concomitant deaths. The analysis of lethal CO intoxication cases that occurred in different circumstances (incomplete burning with CO accumulation, fires – associated with burns, death in the fountain – due to fossil fuel pump failure, suicide due to exhaust gases) was based on the examination of 298 autopsy files. In this type of poisoning, the forensic examination of the body is marked by the non-specific character of most of the macroscopic and microscopic changes. Although inconstant, these types of changes (e.g., red discoloration of livor mortis) raise the suspicion of death by CO poisoning; the essential contribution to establishing cause of death resides in the determination of carboxyhemoglobin (COHb) concentration by spectroscopy. In all cases, the cerebral and cardio-pulmonary modification and their contribution to the cause of death were studied. Comorbidities interfere with the cause of death in cases with average COHb concentrations, in the 20–50% range, where CO blood levels alone are not reason enough to explain the onset of death. © 2019, Editura Academiei Romane. All rights reserved

Electro-Oxidation Method Applied for Activated Sludge Treatment: Experiment and Simulation Based on Supervised Machine Learning Methods

In the present research, an electro-oxidation method was applied to decrease the organic compounds and remove the available micro-organisms in activated sludge of the sewage. Within this method, low cost electrodes were used, including stainless steel, graphite, and Pb/PbO2, and the operating parameters (pH, current density, and operating time) were experimentally optimized. In order to determine sludge stabilization (removal of organic matters and microorganisms), the decrease of parameters like chemical oxygen demand, the increase of electroconductivity and the total dissolved solids, total coli form, and fecal coli form were investigated. Two machine learning techniques (artificial neural networks and support vector machines) were applied comparatively for prediction of the process efficiency. Accurate results were obtained by simulation, in agreement with experimental data