Nano-porosity in GaSb induced by swift heavy ion irradiation

Nano-porous structures form in GaSb after ion irradiation with 185 MeV Au ions. The porous layer formation is governed by the dominant electronic energy loss at this energy regime. The porous layer morphology differs significantly from that previously reported for low-energy, ion-irradiated GaSb. Prior to the onset of porosity, positron annihilation lifetime spectroscopy indicates the formation of small vacancy clusters in single ion impacts, while transmission electron microscopy reveals fragmentation of the GaSb into nanocrystallites embedded in an amorphous matrix. Following this fragmentation process, macroscopic porosity forms, presumably within the amorphous phase.The authors thank the Australian Research Council for support and the staff at the ANU Heavy Ion Accelerator Facility for their continued technical assistance. R.C.E. acknowledges the support from the Office of Basic Energy Sciences of the U.S. DOE (Grant No. DE-FG02-97ER45656)

Qualitative study in Loop Quantum Cosmology

This work contains a detailed qualitative analysis, in General Relativity and in Loop Quantum Cosmology, of the dynamics in the associated phase space of a scalar field minimally coupled with gravity, whose potential mimics the dynamics of a perfect fluid with a linear Equation of State (EoS). Dealing with the orbits (solutions) of the system, we will see that there are analytic ones, which lead to the same dynamics as the perfect fluid, and our goal is to check their stability, depending on the value of the EoS parameter, i.e., to show whether the other orbits converge or diverge to these analytic solutions at early and late times.Comment: 12 pages, 7 figures. Version accepted for publication in CQ

Self-organized critical neural networks

A mechanism for self-organization of the degree of connectivity in model neural networks is studied. Network connectivity is regulated locally on the basis of an order parameter of the global dynamics which is estimated from an observable at the single synapse level. This principle is studied in a two-dimensional neural network with randomly wired asymmetric weights. In this class of networks, network connectivity is closely related to a phase transition between ordered and disordered dynamics. A slow topology change is imposed on the network through a local rewiring rule motivated by activity-dependent synaptic development: Neighbor neurons whose activity is correlated, on average develop a new connection while uncorrelated neighbors tend to disconnect. As a result, robust self-organization of the network towards the order disorder transition occurs. Convergence is independent of initial conditions, robust against thermal noise, and does not require fine tuning of parameters.Comment: 5 pages RevTeX, 7 figures PostScrip

Interactions of asbestos-activated macrophages with an experimental fibrosarcoma

Supernatants from in vivo asbestos-activated macrophages failed to show any cytostatic activity against a syngeneic fibrosarcoma cell line in vitro. UICC chrysotile-induced peritoneal exudate cells also failed to demonstrate any growth inhibitory effect on the same cells in Winn assays of tumor growth. Mixing UICC crocidolite with inoculated tumor cells resulted in a dose-dependent inhibition of tumor growth; this could, however, be explained by a direct cytostatic effect on the tumor cells of high doses of crocidolite, which was observed in vitro

Brayton-cycle radioisotope heat source design study. Phase I - /Conceptual design/ report

Conceptual designs for radioisotope heat source systems to provide 25 kW thermal power to Brayton cycle power conversion system for space application

Recovering the state sequence of hidden Markov models using mean-field approximations

Inferring the sequence of states from observations is one of the most fundamental problems in Hidden Markov Models. In statistical physics language, this problem is equivalent to computing the marginals of a one-dimensional model with a random external field. While this task can be accomplished through transfer matrix methods, it becomes quickly intractable when the underlying state space is large. This paper develops several low-complexity approximate algorithms to address this inference problem when the state space becomes large. The new algorithms are based on various mean-field approximations of the transfer matrix. Their performances are studied in detail on a simple realistic model for DNA pyrosequencing.Comment: 43 pages, 41 figure