1,721,437 research outputs found
Development of the Reporting Infographics and Visual Abstracts of Comparative studies (RIVA-C) checklist and guide
Full text linkPeople often use infographics (also called visual or graphical abstracts) as a substitute for reading the full text of an article. This is a concern because most infographics do not present sufficient information to interpret the research appropriately and guide wise health decisions. The Reporting Infographics and Visual Abstracts of Comparative studies (RIVA-C) checklist and guide aims to improve the completeness with which research findings of comparative studies are communicated and avoid research findings being misinterpreted if readers do not refer to the full text. The primary audience for the RIVA-C checklist and guide is developers of infographics that summarise comparative studies of health and medical interventions. The need for the RIVA-C checklist and guide was identified by a survey of how people use infographics. Possible checklist items were informed by a systematic review of how infographics report research. We then conducted a two-round, modified Delphi survey of 92 infographic developers/designers, researchers, health professionals and other key stakeholders. The final checklist includes 10 items. Accompanying explanation and both text and graphical examples linked to the items were developed and pilot tested over a 6-month period. The RIVA-C checklist and guide was designed to facilitate the creation of clear, transparent and sufficiently detailed infographics which summarise comparative studies of health and medical interventions. Accurate infographics can ensure research findings are communicated appropriately and not misinterpreted. By capturing the perspectives of a wide range of end users (eg, authors, informatics editors, journal editors, consumers), we are hopeful of rapid endorsement and implementation of RIVA-C.</p
Conversion of 10 min Rain Rate Time Series into 1 min Time Series: Theory, Experimental Results, and Application in Satellite Communications
Full text linkWe propose a semi-empirical method-based on a filtered Markov process-to convert 10 min rain rate time series into 1 min time series, i.e., quasi-instantaneous rainfall-the latter to be used as input to the synthetic storm technique, which is a very reliable tool for calculating rain attenuation time series in satellite communication systems or for estimating runoff, erosion, pollutant transport, and other applications in hydrology. To develop the method, we used a very large data bank of 1 min rain rate time series collected in several sites with different climatic conditions. The experimental and simulated 1 min rain rate time series agree very well. Afterward, we used them to simulate rain attenuation time series at 20.7 GHz, in 35.5 degrees slant paths to geostationary satellites. The two simulated annual rain attenuation probability distributions show very small differences. We conclude that the rain rate conversion method is very reliable
Global Formulation of the Synthetic Storm Technique Oriented to Satellite Link–Budget Design
Full text linkWe have updated the global Synthetic Storm Technique (referred to as the global SST) by reformulating it according to a larger database of rain rate time series collected in several sites in different climatic regions. For each site, the average annual probability distribution of rain attenuation obtained with the global SST, PSST,glo (A), in a slant path, was compared with that given by the full SST, PSST (A), which we have considered as experimental data. The test was performed for frequency ranging from 10 to 100 GHz, for elevation angle θ ranging from 20° to 60° and for annual probabilities 10% to 0.01%. The global SST tends to underestimate the attenuation by approximately 10% for elevation angle θ ≤ 30° and about 20% for 30° < θ < 60° in the probability range 10% to 0.1%, and approximately 15% in the probability range 0.1% to 0.01%. For any probability, the error is zero for θ = 90◦ because at the zenith, the global SST coincides with the full SST
Raccontare l’imprevedibile: i sindaci italiani su Facebook durante la pandemia da COVID-19
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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