1,721,003 research outputs found
Choroid plexus compression of glossopharyngeal nerve in patients with glossopharyngeal neuralgia
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Inhibiting action human glioblastoma extract on haemolytic complement of human and xenogeneic sera
No full textPosturography in mild traumatic brain injury patients as a new method in outcome evaluation
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respiratory distress caused by migration of ventriculoperitoneal shunt catheter into the chest cavity report of a case and review of the literature
No full textMachine learning models for river flow forecasting in small catchments
No full textIn consideration of ongoing climate changes, it has been necessary to provide new tools capable of mitigating hydrogeological risks. These effects will be more marked in small catchments, where the geological and environmental contexts do not require long warning times to implement risk mitigation measures. In this context, deep learning models can be an effective tool for local authorities to have solid forecasts of outflows and to make correct choices during the alarm phase. In this study, we investigate the use of deep learning models able to forecast hydrometric height in very fast hydrographic basins. The errors of the models are very small and about a few centimetres, with several forecasting hours. The models allow a prediction of extreme events with also 4–6 h (RMSE of about 10–30 cm, with a forecasting time of 6 h) in hydrographic basins characterized by rapid changes in the river flow rates. However, to reduce the uncertainties of the predictions with the increase in forecasting time, the system performs better when using a machine learning model able to provide a confidence interval of the prediction based on the last observed river flow rate. By testing models based on different input datasets, the results indicate that a combination of models can provide a set of predictions allowing for a more comprehensive description of the possible future evolutions of river flows. Once the deep learning models have been trained, their application is purely objective and very rapid, permitting the development of simple software that can be used even by lower skilled individuals
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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