Nonlinear electromagnetic wave equations for superdense magnetized plasmas

By using the quantum hydrodynamic and Maxwell equations, we derive the generalized nonlinear electron magnetohydrodynamic, the generalized nonlinear Hall-MHD (HMHD), and the generalized nonlinear dust HMHD equations in a self-gravitating dense magnetoplasma. Our nonlinear equations include the self-gravitating, the electromagnetic, the quantum statistical electron pressure, as well as the quantum electron tunneling and electron spin forces. They are useful for investigating a number of wave phenomena including linear and nonlinear electromagnetic waves, as well as three-dimensional electromagnetic wave turbulence spectra and structures arising from mode coupling processes at nanoscales in dense quantum magnetoplasmas

Using network centrality measures to manage landscape connectivity

We use a graph-theoretical landscape modeling approach to investigate how to identify central patches in the landscape as well as how these central patches influence (1) organism movement within the local neighborhood, and (2) the dispersal of organisms beyond the local neighborhood. Organism movements were theoretically estimated based on the spatial configuration of the habitat patches in the studied landscape. We find that centrality depends on the way the graph-theoretical model of habitat patches is constructed, although even the simplest network representation, not taking strength and directionality of potential organisms flows into account, still provides a coarse-grained assessment of the most important patches according to their contribution to landscape connectivity. Moreover, we identify (at least) two general classes of centrality. One accounts for the local flow of organisms in the neighborhood of a patch and the other for the ability to maintain connectivity beyond the scale of the local neighborhood. Finally, we study how habitat patches with high scores on different network centrality measures are distributed in a fragmented agricultural landscape in Madagascar. Results show that patches with high degree-, and betweenness centrality are widely spread, while patches with high subgraph- and closeness centrality are clumped together in dense clusters. This finding may enable multi-species analyses of single-species network models