On the Rovibrational Partition Function of Molecular Hydrogen at High Temperatures

We report a comparative study of the vibrational and rovibrational partition functions using several quantum and classical statistical mechanics approaches. The calculations refer to H2, but the conclusions are anticipated to be valid also for larger systems

Coulomb Correlation and Information Entropies in Confined Helium-Like Atoms

The present work studies aspects of the electronic correlation in confined H$^{-}$, He and Li$^+$ atoms in their ground states using the informational entropies. In this way, different variational wavefunctions are employed in order of better take account of Coulomb correlation. The obtained values for the $S_r$, $S_p$ and $S_t$ entropies are sensitive in relation to Coulomb correlation effects. In the strong confinement regime, the effects of the Coulomb correlation are negligible and the employment of the models of independent particle and two non-interacting electrons confined by a impenetrable spherical cage gains importance in this regime. Lastly, energy values are obtained in good agreement with the results available in the literature.Comment: Version accepted for publication in European Physical Journal

Electron Confinement study in a double quantum dot by means of Shannon Entropy Information

In this work, we use the Shannon informational entropies to study an electron confined in a double quantum dot; we mean the entropy in the space of positions, $S_r$, in the space of momentum, $S_p$, and the total entropy, $S_t = S_r+S_p$. We obtain $S_r$, $S_p$ and $S_t$ as a function of the parameters $A_2$ and $k$ which rules the height and the width, respectively, of the internal barrier of the confinement potential. We conjecture that the entropy $S_r$ maps the degeneracy of states when we vary $A_2$ and also is an indicator of the level of decoupling/coupling of the double quantum dot. We study the quantities $S_r$ and $S_p$ as measures of delocalization/localization of the probability distribution. Furthermore, we analyze the behaviors of the quantities $S_p$ and $S_t$ as a function of $A_2$ and $k$. Finally, we carried out an energy analysis and, when possible, compared our results with work published in the literature.Comment: Version accepted for publication in Physica B: Condensed Matte

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost