1,623 research outputs found
Simulação da Produção de Cupuaçu (Theobroma grandiflorum) e Pupunha (Bactris gasipaes) em Sistemas Agroflorestais Utilizando Modelos de Dinâmica de Sistemas.
bitstream/item/64869/1/cot32-2009-safs-marcelo.pd
Finding Evidence for Massive Neutrinos using 3D Weak Lensing
In this paper we investigate the potential of 3D cosmic shear to constrain
massive neutrino parameters. We find that if the total mass is substantial
(near the upper limits from LSS, but setting aside the Ly alpha limit for now),
then 3D cosmic shear + Planck is very sensitive to neutrino mass and one may
expect that a next generation photometric redshift survey could constrain the
number of neutrinos N_nu and the sum of their masses m_nu to an accuracy of
dN_nu ~ 0.08 and dm_nu ~ 0.03 eV respectively. If in fact the masses are close
to zero, then the errors weaken to dN_nu ~ 0.10 and dm_nu~0.07 eV. In either
case there is a factor 4 improvement over Planck alone. We use a Bayesian
evidence method to predict joint expected evidence for N_nu and m_nu. We find
that 3D cosmic shear combined with a Planck prior could provide `substantial'
evidence for massive neutrinos and be able to distinguish `decisively' between
many competing massive neutrino models. This technique should `decisively'
distinguish between models in which there are no massive neutrinos and models
in which there are massive neutrinos with |N_nu-3| > 0.35 and m_nu > 0.25 eV.
We introduce the notion of marginalised and conditional evidence when
considering evidence for individual parameter values within a multi-parameter
model.Comment: 9 pages, 2 Figures, 2 Tables, submitted to Physical Review
The potential of AI in health higher education to increase the students’ learning outcomes
The main goal of this article is to understand the potential learning applications based on AI technologies for health higher education students. We employed a Systematic Literature Review, contributing to explore to what extent AI
technologies are currently influencing the Health learning processes in higher education and the skills
developed during the learning path. The intent is to contribute to a more profound understanding of
learning contexts, methodologies, technologies, and pedagogical processes with the application of AI
technologies. The literature emphasizes that AI can be used to potentiate the learning process and the learning
outcomes, especially in laboratory classes, and such contexts are still largely unstudied. To fulfil this gap,
some practical applications based on AI technologies applied to health higher education studies were
identified, highlighting AI's innovations and possible opportunities for health higher education.info:eu-repo/semantics/publishedVersio
Statistical methods in cosmology
The advent of large data-set in cosmology has meant that in the past 10 or 20
years our knowledge and understanding of the Universe has changed not only
quantitatively but also, and most importantly, qualitatively. Cosmologists rely
on data where a host of useful information is enclosed, but is encoded in a
non-trivial way. The challenges in extracting this information must be overcome
to make the most of a large experimental effort. Even after having converged to
a standard cosmological model (the LCDM model) we should keep in mind that this
model is described by 10 or more physical parameters and if we want to study
deviations from it, the number of parameters is even larger. Dealing with such
a high dimensional parameter space and finding parameters constraints is a
challenge on itself. Cosmologists want to be able to compare and combine
different data sets both for testing for possible disagreements (which could
indicate new physics) and for improving parameter determinations. Finally,
cosmologists in many cases want to find out, before actually doing the
experiment, how much one would be able to learn from it. For all these reasons,
sophisiticated statistical techniques are being employed in cosmology, and it
has become crucial to know some statistical background to understand recent
literature in the field. I will introduce some statistical tools that any
cosmologist should know about in order to be able to understand recently
published results from the analysis of cosmological data sets. I will not
present a complete and rigorous introduction to statistics as there are several
good books which are reported in the references. The reader should refer to
those.Comment: 31, pages, 6 figures, notes from 2nd Trans-Regio Winter school in
Passo del Tonale. To appear in Lectures Notes in Physics, "Lectures on
cosmology: Accelerated expansion of the universe" Feb 201
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