DETERMINATION OF FAULT PLANE SOLUTIONS USING WAVEFORM AMPLITUDES AND RADIATION PATTERN

In the present work a modified version of the program FPFIT (Reasenberg and Oppenheimer, 1985) is developed, in order to improve the calculation of the fault plane solutions. The method is applied on selected earthquakes from short period waveform data in the Mygdonia basin (N. Greece) as recorded by the permanent network of the Seismological Station of Aristotle University of Thessaloniki during the period 1989-1999. The proposed modification of the FPFIT program was developed in order to minimize the derivation of multiple solutions, as well as the uncertainties in the location of Ρ and Τ axis of the determined fault plane solutions. Compared to the original version of FPFIT the modified approach takes also into account the radiation pattern of SV and SH waves. For each earthquake horizontal and vertical components of each station were used and the first arrivals of Ρ and S waves were picked. Using the maximum peak-to-peak amplitude of Ρ and S waves the ratio Pmax/(S/\/2max+SE2max)1/2 was estimated, where S/Vmax and SEmax are the maximum amplitudes of the two horizontal components (N-S, E-W) for the S waves and Pmax is the maximum amplitude of the vertical one for the P- waves. This ratio for the observed data, as well as the corresponding ratio Prad/iS/Aad+SlAad)1'2 of the synthetic data was used as a weight for the determination of the observed and theoretical P-wave polarities, respectively. The method was tested using synthetic data. A significant improvement of the results was found, compared to the original version of FPFIT. In particular, an improved approximation of the input focal mechanism is found, without multiple solutions and the best-estimated Ρ and Τ axes exhibit much smaller uncertainties. The addition of noise in the synthetic data didn't significantly change the results concerning the fault plane solutions. Finally, we have applied the modified program on a real data set of earthquakes that occurred in the Mygdonia basin

Letters

by S Gee, S Cotter, D O’Flanagan, on behalf of the national incident management tea

Early Component-Based System Reliability Analysis for Approximate Computing Systems

A key enabler of real applications on approximate computing systems is the availability of instruments to analyze system reliability, early in the design cycle. Accurately measuring the impact on system reliability of any change in the technology, circuits, microarchitecture and software is most of the time a multi-team multi-objective problem and reliability must be traded off against other crucial design attributes (or objectives) such as power, performance and cost. Unfortunately, tools and models for cross-layer reliability analysis are still at their early stages compared to other very mature design tools and this represents a major issue for mainstream applications. This paper presents preliminary information on a cross-layer framework built on top of a Bayesian model designed to perform component-based reliability analysis of complex systems

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype

Measure representation and multifractal analysis of complete genomes

This paper introduces the notion of measure representation of DNA sequences. Spectral analysis and multifractal analysis are then performed on the measure representations of a large number of complete genomes. The main aim of this paper is to discuss the multifractal property of the measure representation and the classification of bacteria. From the measure representations and the values of the $D_{q}$ spectra and related $C_{q}$ curves, it is concluded that these complete genomes are not random sequences. In fact, spectral analyses performed indicate that these measure representations considered as time series, exhibit strong long-range correlation. For substrings with length K=8, the $D_{q}$ spectra of all organisms studied are multifractal-like and sufficiently smooth for the $C_{q}$ curves to be meaningful. The $C_{q}$ curves of all bacteria resemble a classical phase transition at a critical point. But the 'analogous' phase transitions of chromosomes of non-bacteria organisms are different. Apart from Chromosome 1 of {\it C. elegans}, they exhibit the shape of double-peaked specific heat function.Comment: 12 pages with 9 figures and 1 tabl

Correlation property of length sequences based on global structure of complete genome

This paper considers three kinds of length sequences of the complete genome. Detrended fluctuation analysis, spectral analysis, and the mean distance spanned within time $L$ are used to discuss the correlation property of these sequences. The values of the exponents from these methods of these three kinds of length sequences of bacteria indicate that the long-range correlations exist in most of these sequences. The correlation have a rich variety of behaviours including the presence of anti-correlations. Further more, using the exponent $\gamma$, it is found that these correlations are all linear ($\gamma=1.0\pm 0.03$). It is also found that these sequences exhibit $1/f$ noise in some interval of frequency ($f>1$). The length of this interval of frequency depends on the length of the sequence. The shape of the periodogram in $f>1$ exhibits some periodicity. The period seems to depend on the length and the complexity of the length sequence.Comment: RevTex, 9 pages with 5 figures and 3 tables. Phys. Rev. E Jan. 1,2001 (to appear

Decoherence, einselection, and the quantum origins of the classical

Decoherence is caused by the interaction with the environment. Environment monitors certain observables of the system, destroying interference between the pointer states corresponding to their eigenvalues. This leads to environment-induced superselection or einselection, a quantum process associated with selective loss of information. Einselected pointer states are stable. They can retain correlations with the rest of the Universe in spite of the environment. Einselection enforces classicality by imposing an effective ban on the vast majority of the Hilbert space, eliminating especially the flagrantly non-local "Schr\"odinger cat" states. Classical structure of phase space emerges from the quantum Hilbert space in the appropriate macroscopic limit: Combination of einselection with dynamics leads to the idealizations of a point and of a classical trajectory. In measurements, einselection replaces quantum entanglement between the apparatus and the measured system with the classical correlation.Comment: Final version of the review, with brutally compressed figures. Apart from the changes introduced in the editorial process the text is identical with that in the Rev. Mod. Phys. July issue. Also available from http://www.vjquantuminfo.or

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype.- Pfizer Pharmaceuticals(undefined

Composition differences between organic and conventional meat: a systematic literature review and meta-analysis

Demand for organic meat is partially driven by consumer perceptions that organic foods are more nutritious than non-organic foods. However, there have been no systematic reviews comparing specifically the nutrient content of organic and conventionally produced meat. In this study, we report results of a meta-analysis based on sixty-seven published studies comparing the composition of organic and non-organic meat products. For many nutritionally relevant compounds (e.g. minerals, antioxidants and most individual fatty acids (FA)), the evidence base was too weak for meaningful meta-analyses. However, significant differences in FA profiles were detected when data from all livestock species were pooled. Concentrations of SFA and MUFA were similar or slightly lower, respectively, in organic compared with conventional meat. Larger differences were detected for total PUFA and n-3 PUFA, which were an estimated 23 (95 % CI 11, 35) % and 47 (95 % CI 10, 84) % higher in organic meat, respectively. However, for these and many other composition parameters, for which meta-analyses found significant differences, heterogeneity was high, and this could be explained by differences between animal species/meat types. Evidence from controlled experimental studies indicates that the high grazing/forage-based diets prescribed under organic farming standards may be the main reason for differences in FA profiles. Further studies are required to enable meta-analyses for a wider range of parameters (e.g. antioxidant, vitamin and mineral concentrations) and to improve both precision and consistency of results for FA profiles for all species. Potential impacts of composition differences on human health are discussed

Revisiting detrended fluctuation analysis

Half a century ago Hurst introduced Rescaled Range (R/S) Analysis to study fluctuations in time series. Thousands of works have investigated or applied the original methodology and similar techniques, with Detrended Fluctuation Analysis becoming preferred due to its purported ability to mitigate nonstationaries. We show Detrended Fluctuation Analysis introduces artifacts for nonlinear trends, in contrast to common expectation, and demonstrate that the empirically observed curvature induced is a serious finite-size effect which will always be present. Explicit detrending followed by measurement of the diffusional spread of a signals' associated random walk is preferable, a surprising conclusion given that Detrended Fluctuation Analysis was crafted specifically to replace this approach. The implications are simple yet sweeping: there is no compelling reason to apply Detrended Fluctuation Analysis as it 1) introduces uncontrolled bias; 2) is computationally more expensive than the unbiased estimator; and 3) cannot provide generic or useful protection against nonstationaries