138 research outputs found

    Volcanogenic Dark Matter and Mass Extinctions

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    The passage of the Earth through dense clumps of dark matter, the presence of which are predicted by certain cosmologies, would produce large quantities of heat in the interior of this planet through the capture and subsequent annihilation of dark matter particles. This heat can cause large-scale volcanism which could in turn have caused the extinction of the dinosaurs and other mass extinctions. The periodicity of such volcanic outbursts agrees with the frequency of palaeontological mass extinctions as well as the observed periodicity in the occurrence of the largest flood basalt provinces on the globe.Comment: 6 pages in Latex fil

    Consequences of Streptococcal Pneumoniae Meningitis When it Remains Undiagnosed – Suggested Model of Investigational Process

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    Background: Whenever any patient presents with headache and slightly high total leukocytic counts, the pertinent question gets raised is either prompt lumbar puncture (LP) is indicated or not. Usually patients with bacterial meningitis characteristically exhibit fever, neck rigidity and deranged mental status or headache. In majority of cases the causes are non-infective. Whilst meningococcal meningitis has a distinctive non-blanching rash and is promptly diagnosable from the CSF. Our report describes disease presentation with merely one aspect of the triad for acute bacterial meningitis and it raises query regarding reliance on guidelines based on the triad

    BIOLOGICAL ASPECTS AND LIFE TABLE PARAMETERS OF THE EGGPLANT STEM BORER, Euzophera osseatella Treit. (Lep.: Pyralidae)

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    Biological aspects and life table parameters of the eggplant stem borer, Euzophera osseatella Treit. (Lep.: Pyralidae) were studied under laboratory conditions at 27℃ and 65% R.H. The egg, larval and pupal stages of E. osseatella lasted 5.8, 33.2 and 12.9 days, respectively. The mortality percentage in the corresponding stages reached 14, 37 and 13%, respectively. The sex ratio in E. osseatella was in favour of females (1:1.1). The preoviposition, oviposition and postoviposition periods of the pest averaged 1.8, 3.1 and 1.2 days, respectively. The daily and total numbers of eggs/female of E. osseatella were 46.6 and 147.2, respectively. The male and female of the insect lived 4.4 and 6.2 days, respectively. The mean generation time (T) of E. osseatella averaged 54.22 days. The net reproductive rate (Ro), the intrinsic rate of increase (rm) and the finite rate of increase (exp.rm/ λ) in the pest were 28.09, 0.062 and 1.063, respectively. The generation doubling time (Dt) of E. osseatella reached 14.01 days. The fecundity curve (Mx) of E. osseatella showed three peaks of 14.14, 20.14 and 12.32 female progeny/female/day at the female age (X) of 50.5, 61.5 and 63.5 days, respectively. The survival curve (Lx) of the insect revealed that most death of females occurred after an extended oviposition period to compensate failure of most immature stages to reach maturity (64%)

    Fabrication of TiO2 Doped ZnO UV Detector by Pulse Laser Deposition

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    In this work TiO2 doped with ZnO(5% , 7%)nbsp thin films werenbsp grown by pulse laser deposition technique on P-type (Si)substratenbsp at RT under vacuum 10-2 mbar. The properties of the photovoltaic detector with the increase ofnbsp ZnO concentrationnbsp is studied. All the films display photovoltaic in the near visible region.nbspIt was observed that the responsivity increased to 3.39 atnbsp RT and to 3.82 after annealingnbsp at 500 ordmC withnbsp the increase of ZnO concentrations from (5% to 7%).The Specific efficiency increases with the increase of ZnO concentration atnbsp RT 10.9and afternbsp annealing to 12.3

    Unveiling the Landscape of Big Data Analytics in Healthcare: A Comprehensive Bibliometric Analysis

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    In the rapidly evolving landscape of healthcare, the digital transformation marked by healthcare 4.0 has spurred a surge in data generation, giving rise to ‘big data’. Big data analytics has become an effective tool in the healthcare industry, revolutionising medical research, patient care, and healthcare management. This study undertakes a meticulous bibliometric analysis, drawing upon a dataset of 2212 articles from the Scopus database spanning 2014 to 2023, to unravel the trajectory of big data analytics in healthcare. The research explores diverse dimensions, from the distribution of studies across years to the productivity rankings of journals, countries, and institutions, elucidating the evolving trends and key contributors. Co-authorship networks and keyword co-occurrence analysis reveal thematic clusters and intellectual structures, contributing to a nuanced understanding of the field. The results underscore the escalating global interest in the fusion of big data and healthcare, illuminating collaborations, and identifying influential players. Additionally, the study identifies pressing challenges, including security concerns and skill shortages, emphasizing the imperative of overcoming these barriers for effective big data applications in healthcare. Serving as a valuable resource for researchers, practitioners, and policymakers, this research not only captures the current landscape but also provides insights for future exploration, contributing to strategic planning in this dynamic domain
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