A novel approach to Isoscaling: the role of the order parameter m = (N-Z)/A

Isoscaling is derived within a recently proposed modified Fisher model where the free energy near the critical point is described by the Landau O(m^6) theory. In this model m = (N-Z)/A is the order parameter, a consequence of (one of) the symmetries of the nuclear Hamiltonian. Within this framework we show that isoscaling depends mainly on this order parameter through the 'external (conjugate) field' H. The external field is just given by the difference in chemical potentials of the neutrons and protons of the two sources. To distinguish from previously employed isoscaling relationships, this approach is dubbed: m - scaling. We discuss the relationship between this framework and the standard isoscaling formalism and point out some substantial differences in interpretation of experimental results which might result. These should be investigated further both theoretically and experimentally.Comment: 14 pages, 5 figure

Temperature and density of hot decaying 40Ca and 28Si

By means of quantum-fluctuation analysis techniques, temperatures and local partial densities of bosonic and fermionic fragments produced in the decay of hot 40Ca and 28Si projectile-like sources produced in mid-peripheral collisionsat sub-Fermi energies have been obtained. The used method treats bosonic and fermionic fragments differently. The purpose of such treatment is to trace important quantum effects such as fermion quenching or Bose-Einstein Condensation (BEC) in nuclei

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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