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Institution

University of Florence

EducationFlorence, Toscana, Italy
About: University of Florence is a education organization based out in Florence, Toscana, Italy. It is known for research contribution in the topics: Population & Carbonic anhydrase. The organization has 27292 authors who have published 79599 publications receiving 2341684 citations. The organization is also known as: Università degli studi di Firenze & Universita degli studi di Firenze.


Papers
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Journal ArticleDOI
TL;DR: In this article, the authors analyzed the temporal dynamics of the coronavirus disease 2019 outbreak in China, Italy and France in the time window 22 / 01 − 15 / 03 / 2020.
Abstract: In this note we analyze the temporal dynamics of the coronavirus disease 2019 outbreak in China, Italy and France in the time window 22 / 01 − 15 / 03 / 2020 . A first analysis of simple day-lag maps points to some universality in the epidemic spreading, suggesting that simple mean-field models can be meaningfully used to gather a quantitative picture of the epidemic spreading, and notably the height and time of the peak of confirmed infected individuals. The analysis of the same data within a simple susceptible-infected-recovered-deaths model indicates that the kinetic parameter that describes the rate of recovery seems to be the same, irrespective of the country, while the infection and death rates appear to be more variable. The model places the peak in Italy around March 21st 2020, with a peak number of infected individuals of about 26000 (not including recovered and dead) and a number of deaths at the end of the epidemics of about 18,000. Since the confirmed cases are believed to be between 10 and 20% of the real number of individuals who eventually get infected, the apparent mortality rate of COVID-19 falls between 4% and 8% in Italy, while it appears substantially lower, between 1% and 3% in China. Based on our calculations, we estimate that 2500 ventilation units should represent a fair figure for the peak requirement to be considered by health authorities in Italy for their strategic planning. Finally, a simulation of the effects of drastic containment measures on the outbreak in Italy indicates that a reduction of the infection rate indeed causes a quench of the epidemic peak. However, it is also seen that the infection rate needs to be cut down drastically and quickly to observe an appreciable decrease of the epidemic peak and mortality rate. This appears only possible through a concerted and disciplined, albeit painful, effort of the population as a whole.

720 citations

Journal ArticleDOI
Monika Böhm1, Ben Collen1, Jonathan E. M. Baillie1, Philip Bowles2  +240 moreInstitutions (95)
TL;DR: The results provide the first analysis of the global conservation status and distribution patterns of reptiles and the threats affecting them, highlighting conservation priorities and knowledge gaps which need to be addressed urgently to ensure the continued survival of the world’s reptiles.

720 citations

Journal ArticleDOI
Albert M. Sirunyan, Armen Tumasyan, Wolfgang Adam1, Ece Aşılar1  +2212 moreInstitutions (157)
TL;DR: A fully-fledged particle-flow reconstruction algorithm tuned to the CMS detector was developed and has been consistently used in physics analyses for the first time at a hadron collider as mentioned in this paper.
Abstract: The CMS apparatus was identified, a few years before the start of the LHC operation at CERN, to feature properties well suited to particle-flow (PF) reconstruction: a highly-segmented tracker, a fine-grained electromagnetic calorimeter, a hermetic hadron calorimeter, a strong magnetic field, and an excellent muon spectrometer. A fully-fledged PF reconstruction algorithm tuned to the CMS detector was therefore developed and has been consistently used in physics analyses for the first time at a hadron collider. For each collision, the comprehensive list of final-state particles identified and reconstructed by the algorithm provides a global event description that leads to unprecedented CMS performance for jet and hadronic τ decay reconstruction, missing transverse momentum determination, and electron and muon identification. This approach also allows particles from pileup interactions to be identified and enables efficient pileup mitigation methods. The data collected by CMS at a centre-of-mass energy of 8\TeV show excellent agreement with the simulation and confirm the superior PF performance at least up to an average of 20 pileup interactions.

719 citations

Journal ArticleDOI
19 Aug 1994-Science
TL;DR: Clusters of metal ions are a class of compounds actively investigated for their magnetic properties, which should gradually change from those of simple paramagnets to those of bulk magnets.
Abstract: Clusters of metal ions are a class of compounds actively investigated for their magnetic properties, which should gradually change from those of simple paramagnets to those of bulk magnets. However, their interest lies in a number of different disciplines: chemistry, which seeks new synthetic strategies to make larger and larger clusters in a controlled manner; physics, which can test the validity of quantum mechanical approaches at the nanometer scale; and biology, which can use them as models of biomineralization of magnetic particles.

718 citations

Journal ArticleDOI
TL;DR: The pre-print version of the Published Article can be accessed from the link below - Copyright @ 2010 Springer Verlag as discussed by the authors, which can be viewed as a preprint of the published article.
Abstract: This is the pre-print version of the Published Article, which can be accessed from the link below - Copyright @ 2010 Springer Verlag

717 citations


Authors

Showing all 27699 results

NameH-indexPapersCitations
Charles A. Dinarello1901058139668
D. M. Strom1763167194314
Gregory Y.H. Lip1693159171742
Christopher M. Dobson1501008105475
Dirk Inzé14964774468
Thomas Hebbeker1481984114004
Marco Zanetti1451439104610
Richard B. Devereux144962116403
Gunther Roland1411471100681
Markus Klute1391447104196
Tariq Aziz138164696586
Guido Tonelli138145897248
Giorgio Trinchieri13843378028
Christof Roland137130896632
Christoph Paus1371585100801
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023244
2022631
20215,298
20205,251
20194,652
20184,147