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Ricardo J. Fernandes

Researcher at University of Porto

Publications -  384
Citations -  6976

Ricardo J. Fernandes is an academic researcher from University of Porto. The author has contributed to research in topics: Front crawl & Medicine. The author has an hindex of 35, co-authored 311 publications receiving 5330 citations. Previous affiliations of Ricardo J. Fernandes include Institute of Molecular Pathology and Immunology of the University of Porto & University of Western Ontario.

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Archaeological assessment reveals Earth’s early transformation through land use

Lucas Stephens, +119 more
- 30 Aug 2019 - 
TL;DR: An empirical global assessment of land use from 10,000 years before the present (yr B.P.) to 1850 CE reveals a planet largely transformed by hunter-gatherers, farmers, and pastoralists by 3000 years ago, considerably earlier than the dates in the land-use reconstructions commonly used by Earth scientists.
Journal ArticleDOI

Food reconstruction using isotopic transferred signals (FRUITS): a Bayesian model for diet reconstruction.

TL;DR: The Bayesian mixing model FRUITS (Food Reconstruction Using Isotopic Transferred Signals) accurately predicted dietary intakes, and more precise estimates were obtained for dietary scenarios in which expert prior information was included.
Journal ArticleDOI

Macronutrient-based model for dietary carbon routing in bone collagen and bioapatite

TL;DR: The model suggests that δ13Ccollagen signal contributions originate from surprisingly consistent proportions of protein and energy macronutrients, and possible biochemical mechanisms explaining these empirical results are discussed.
Proceedings ArticleDOI

Self-organized traffic control

TL;DR: A virtual traffic light protocol that can dynamically optimize the flow of traffic in road intersections without requiring any roadside infrastructure is designed that renders signalized control of intersections truly ubiquitous.
Journal ArticleDOI

Decision trees for mining data streams

TL;DR: The problem of constructing accurate decision tree models from data streams is studied with respect to drift, noise, the order of examples, and the initial parameters in different problems and VFDTc is extended with the ability to deal with concept drift.