1,461 research outputs found
Methods for multi-detector burst gravitational wave search
In this paper, a review of methods of data exchange and multi-detector analysis is given. Details of data exchange parameters will also be discussed. We focus on the coherent network search which turns out to be a fundamental step towards the overcoming of ambiguity in parameter estimation of a gravitational wave in the low signal-to-noise ratio regime
Laboratory investigation of the impact of air pollution on partial discharge inception voltage of insulators in a specific region
Studying the discharge characteristics of transmission line insulators in the presence of pollution, particularly when the contaminated layer is wet by rain, fog or condensation, is necessary for selecting the proper insulation. Therefore, identifying the major air pollutants as well as the most effective ones on the discharge performance of outdoor insulators is mandatory. A systematic approach has been proposed to evaluate the impact of dominant air pollutants of an area on partial discharge (P.D) inception voltage of specimen insulators. Based on the suggested method, determining the pollution constituents, defining the dominant pollutant of the area, finding the most commonly used insulators for medium and above distribution voltages within the geographical boundaries of the Central Province of Iran, as well as examining the effect of dominant air pollutant of the region on partial discharge inception voltage of designated insulators by laboratory measurements, are the necessary steps toward a comprehensive study of the subject
Enhancing the significance of gravitational wave bursts through signal classification
The quest to observe gravitational waves challenges our ability to
discriminate signals from detector noise. This issue is especially relevant for
transient gravitational waves searches with a robust eyes wide open approach,
the so called all- sky burst searches. Here we show how signal classification
methods inspired by broad astrophysical characteristics can be implemented in
all-sky burst searches preserving their generality. In our case study, we apply
a multivariate analyses based on artificial neural networks to classify waves
emitted in compact binary coalescences. We enhance by orders of magnitude the
significance of signals belonging to this broad astrophysical class against the
noise background. Alternatively, at a given level of mis-classification of
noise events, we can detect about 1/4 more of the total signal population. We
also show that a more general strategy of signal classification can actually be
performed, by testing the ability of artificial neural networks in
discriminating different signal classes. The possible impact on future
observations by the LIGO-Virgo network of detectors is discussed by analysing
recoloured noise from previous LIGO-Virgo data with coherent WaveBurst, one of
the flagship pipelines dedicated to all-sky searches for transient
gravitational waves
Prospects for intermediate mass black hole binary searches with advanced gravitational-wave detectors
We estimated the sensitivity of the upcoming advanced, ground-based
gravitational-wave observatories (the upgraded LIGO and Virgo and the KAGRA
interferometers) to coalescing intermediate mass black hole binaries (IMBHB).
We added waveforms modeling the gravitational radiation emitted by IMBHBs to
detectors' simulated data and searched for the injected signals with the
coherent WaveBurst algorithm. The tested binary's parameter space covers
non-spinning IMBHBs with source-frame total masses between 50 and 1050
and mass ratios between and 1. We found that
advanced detectors could be sensitive to these systems up to a range of a few
Gpc. A theoretical model was adopted to estimate the expected observation
rates, yielding up to a few tens of events per year. Thus, our results indicate
that advanced detectors will have a reasonable chance to collect the first
direct evidence for intermediate mass black holes and open a new, intriguing
channel for probing the Universe over cosmological scales.Comment: 9 pages, 4 figures, corrected the name of one author (previously
misspelled
Analysis of gut microbiota in rheumatoid arthritis patients. Disease-related dysbiosis and modifications induced by etanercept
A certain number of studies were carried out to address the question of how dysbiosis could affect the onset and development of rheumatoid arthritis (RA), but little is known about the reciprocal influence between microbiota composition and immunosuppressive drugs, and how this interaction may have an impact on the clinical outcome. The aim of this study was to characterize the intestinal microbiota in a groups of RA patients treatment-naïve, under methotrexate, and/or etanercept (ETN). Correlations between the gut microbiota composition and validated immunological and clinical parameters of disease activity were also evaluated. In the current study, a 16S analysis was employed to explore the gut microbiota of 42 patients affected by RA and 10 healthy controls. Disease activity score on 28 joints (DAS-28), erythrocyte sedimentation rate, C-reactive protein, rheumatoid factor, anti-cyclic citrullinated peptides, and dietary and smoking habits were assessed. The composition of the gut microbiota in RA patients free of therapy is characterized by several abnormalities compared to healthy controls. Gut dysbiosis in RA patients is associated with different serological and clinical parameters; in particular, the phylum of Euryarchaeota was directly correlated to DAS and emerged as an independent risk factor. Patients under treatment with ETN present a partial restoration of a beneficial microbiota. The results of our study confirm that gut dysbiosis is a hallmark of the disease, and shows, for the first time, that the anti-tumor necrosis factor alpha (TNF-α) ETN is able to modify microbial communities, at least partially restoring a beneficial microbiota
Nutrition, physical activity, and other lifestyle factors in the prevention of cognitive decline and dementia
Multiple factors combined are currently recognized as contributors to cognitive decline. The main independent risk factor for cognitive impairment and dementia is advanced age followed by other determinants such as genetic, socioeconomic, and environmental factors, including nutrition and physical activity. In the next decades, a rise in dementia cases is expected due largely to the aging of the world population. There are no hitherto effective pharmaceutical therapies to treat age-associated cognitive impairment and dementia, which underscores the crucial role of prevention. A relationship among diet, physical activity, and other lifestyle factors with cognitive function has been intensively studied with mounting evidence supporting the role of these determinants in the development of cognitive decline and dementia, which is a chief cause of disability globally. Several dietary patterns, foods, and nutrients have been investigated in this regard, with some encouraging and other disappointing results. This review presents the current evidence for the effects of dietary patterns, dietary components, some supplements, physical activity, sleep patterns, and social engagement on the prevention or delay of the onset of age-related cognitive decline and dementia. © 2021 by the authors. Licensee MDPI, Basel, Switzerland
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