Galactic Dark Matter: a Dynamical Consequence of Cosmological Expansion

This work wants to show how standard General Relativity (GR) is able to explain galactic rotation curves without the need for dark matter, this starting from the idea that when Einstein's equations are applied to the dynamics of a galaxy embedded in an expanding universe they do not reduce to Poisson's equation but a generalisation of it taking cosmological expansion into account. A non-linear scheme to perturb Einstein's field equations around the Robertson-Walker (R-W) metric is devised in order to find their non-relativistic limit without losing their characteristic non-linearities. The resulting equation is used to numerically study the gravitational potential of a cosmological perturbation and applied to a simple galactic model with an exponentially decreasing baryonic matter distribution. The non-relativistic limit of GR in a R-W space-time produces a generalised Poisson equation for the gravitational potential which is non-linear, parabolic and heat-like. It is shown how its non-linearities generate an effective "dark matter" distribution caused by both cosmological expansion and the dynamics of the perturbation's gravitational potential. It is also shown how this dynamical effect gets completely lost during a linearisation of Einstein's equations. The equation is then used to successfully fit real galactic rotation curves numerically using a matter distribution following the shape of a simple S\'ersic luminosity profile, common to most galaxies, thus without recourse to dark matter. A relation for the dark to luminous matter ratio is found, explaining the domination of dark matter in low-mass galaxies. A few rotation curves with a faster than Newtonian decrease are also presented and successfully fitted, opening the way to a new possible interpretation of these phenomena in terms of an effective "anti-gravitational" dark matter distribution, purely geometrical in origin.Comment: 8 pages, 18 figures, Research Pape

Resource requirements and speed versus geometry of unconditionally secure physical key exchanges

The imperative need for unconditional secure key exchange is expounded by the increasing connectivity of networks and by the increasing number and level of sophistication of cyberattacks. Two concepts that are information theoretically secure are quantum key distribution (QKD) and Kirchoff-law-Johnson-noise (KLJN). However, these concepts require a dedicated connection between hosts in peer-to-peer (P2P) networks which can be impractical and or cost prohibitive. A practical and cost effective method is to have each host share their respective cable(s) with other hosts such that two remote hosts can realize a secure key exchange without the need of an additional cable or key exchanger. In this article we analyze the cost complexities of cable, key exchangers, and time required in the star network. We mentioned the reliability of the star network and compare it with other network geometries. We also conceived a protocol and equation for the number of secure bit exchange periods needed in a star network. We then outline other network geometries and trade-off possibilities that seem interesting to explore.Comment: 13 pages, 7 figures, MDPI Entrop

Ageing of Natural Rubber under Stress

We report a dynamical-mechanical study of stress relaxation at small deformation in a natural (polyisoprene) rubber well above its glass transition temperature Tg. We find that an almost complete relaxation of stress takes place over very long periods of time, even though the elastic network integrity is fully retained. The relaxation rate and the long-time equilibrium modulus are sensitive functions of temperature which do not follow time-temperature superposition. Many characteristic features of non-ergodic ageing response are apparent at both short and very long times. We interpret the observed behaviour in terms of the properties of rubber crosslinks, capable of isomerisation under stress, and relate the results to recent models of soft glassy rheology.Comment: Latex 2e (EPJ style), 5 EPS figure

Dual descriptions of spin two massive particles in D=2+1D=2+1 via master actions

In the first part of this work we show the decoupling (up to contact terms) of redundant degrees of freedom which appear in the covariant description of spin two massive particles in $D=2+1$. We make use of a master action which interpolates, without solving any constraints, between a first, second and third order (in derivatives) self-dual model. An explicit dual map between those models is derived. In our approach the absence of ghosts in the third order self-dual model, which corresponds to a quadratic truncation of topologically massive gravity, is due to the triviality (no particle content) of the Einstein-Hilbert action in $D=2+1$. In the second part of the work, also in $D=2+1$, we prove the quantum equivalence of the gauge invariant sector of a couple of self-dual models of opposite helicities (+2 and -2) and masses $m_+$ and $m_-$ to a generalized self-dual model which contains a quadratic Einstein-Hilbert action, a Chern-Simons term of first order and a Fierz-Pauli mass term. The use of a first order Chern-Simons term instead of a third order one avoids conflicts with the sign of the Einstein-Hilbert action.Comment: title and abstract slightly modified, 3 references added, comments on interactions include

StochSoCs: High performance biocomputing simulations for large scale Systems Biology

The stochastic simulation of large-scale biochemical reaction networks is of great importance for systems biology since it enables the study of inherently stochastic biological mechanisms at the whole cell scale. Stochastic Simulation Algorithms (SSA) allow us to simulate the dynamic behavior of complex kinetic models, but their high computational cost makes them very slow for many realistic size problems. We present a pilot service, named WebStoch, developed in the context of our StochSoCs research project, allowing life scientists with no high-performance computing expertise to perform over the internet stochastic simulations of large-scale biological network models described in the SBML standard format. Biomodels submitted to the service are parsed automatically and then placed for parallel execution on distributed worker nodes. The workers are implemented using multi-core and many-core processors, or FPGA accelerators that can handle the simulation of thousands of stochastic repetitions of complex biomodels, with possibly thousands of reactions and interacting species. Using benchmark LCSE biomodels, whose workload can be scaled on demand, we demonstrate linear speedup and more than two orders of magnitude higher throughput than existing serial simulators.Comment: The 2017 International Conference on High Performance Computing & Simulation (HPCS 2017), 8 page