PARSEC: A Parametrized Simulation Engine for Ultra-High Energy Cosmic Ray Protons

We present a new simulation engine for fast generation of ultra-high energy cosmic ray data based on parametrizations of common assumptions of UHECR origin and propagation. Implemented are deflections in unstructured turbulent extragalactic fields, energy losses for protons due to photo-pion production and electron-pair production, as well as effects from the expansion of the universe. Additionally, a simple model to estimate propagation effects from iron nuclei is included. Deflections in galactic magnetic fields are included using a matrix approach with precalculated lenses generated from backtracked cosmic rays. The PARSEC program is based on object oriented programming paradigms enabling users to extend the implemented models and is steerable with a graphical user interface.Comment: 10 pages, 6 figures, accepted for publication in Astroparticle Physic

A survey of existing guidance services and a recommended program for Unified School District 323

Call number: LD2668 .R4 1966 W24

Filter inference: a scalable nonlinear mixed effects inference approach for snapshot time series data

Variability is an intrinsic property of biological systems and is often at the heart of their complex behaviour. Examples range from cell-to-cell variability in cell signalling pathways to variability in the response to treatment across patients. A popular approach to model and understand this variability is nonlinear mixed effects (NLME) modelling. However, estimating the parameters of NLME models from measurements quickly becomes computationally expensive as the number of measured individuals grows, making NLME inference intractable for datasets with thousands of measured individuals. This shortcoming is particularly limiting for snapshot datasets, common e.g. in cell biology, where high-throughput measurement techniques provide large numbers of single cell measurements. We introduce a novel approach for the estimation of NLME model parameters from snapshot measurements, which we call filter inference. Filter inference uses measurements of simulated individuals to define an approximate likelihood for the model parameters, avoiding the computational limitations of traditional NLME inference approaches and making efficient inferences from snapshot measurements possible. Filter inference also scales well with the number of model parameters, using state-of-the-art gradient-based MCMC algorithms such as the No-U-Turn Sampler (NUTS). We demonstrate the properties of filter inference using examples from early cancer growth modelling and from epidermal growth factor signalling pathway modelling

Cosmic ray propagation with CRPropa 3

Solving the question of the origin of ultra-high energy cosmic rays (UHECRs) requires the development of detailed simulation tools in order to interpret the experimental data and draw conclusions on the UHECR universe. CRPropa is a public Monte Carlo code for the galactic and extragalactic propagation of cosmic ray nuclei above $\sim 10^{17}$ eV, as well as their photon and neutrino secondaries. In this contribution the new algorithms and features of CRPropa 3, the next major release, are presented. CRPropa 3 introduces time-dependent scenarios to include cosmic evolution in the presence of cosmic ray deflections in magnetic fields. The usage of high resolution magnetic fields is facilitated by shared memory parallelism, modulated fields and fields with heterogeneous resolution. Galactic propagation is enabled through the implementation of galactic magnetic field models, as well as an efficient forward propagation technique through transformation matrices. To make use of the large Python ecosystem in astrophysics CRPropa 3 can be steered and extended in Python.Comment: 16th International workshop on Advanced Computing and Analysis Techniques in physics research (ACAT 2014) proceedings, 6 pages, 6 figure

CRPropa 3.0 - a Public Framework for Propagating UHE Cosmic Rays through Galactic and Extragalactic Space

The interpretation of experimental data of ultra-high energy cosmic rays (UHECRs) above 10^17 eV is still under controversial debate. The development and improvement of numerical tools to propagate UHECRs in galactic and extragalactic space is a crucial ingredient to interpret data and to draw conclusions on astrophysical parameters. In this contribution the next major release of the publicly available code CRPropa (3.0) is presented. It reflects a complete redesign of the code structure to facilitate high performance computing and comprises new physical features such as an interface for galactic propagation using lensing techniques and inclusion of cosmological effects in a three-dimensional environment. The performance is benchmarked and first applications are presented.Comment: 4 pages, 3 figures. Proceedings of the 33rd International Cosmic Ray Conference (ICRC), Rio de Janeiro, Brazil, 2-9 July 201

Learning and technological capability building in emerging economies: the case of the biomass power equipment industry in Malaysia

There is increasing recognition that the transfer of foreign technology to developing countries should be considered in light of broader processes of learning, technological capability, formation and industrial development. Previous studies that have looked at this in the context of cleantech industries in emerging economies tend to overlook firm-level specifics. This paper contributes to filling this gap by utilising in-depth qualitative firm-level data to analyse the extent to which the use of different learning mechanisms can explain differences in the accumulation of technological capabilities. This is explored via an examination of eight firms in the biomass power equipment industry in Malaysia during the period 1970-2011. The paper finds that firms relying on a combination of learning from foreign technology partners and internal learning by planned experimentation make most progress in terms of technological capability. Nevertheless, local spill-over effects were found to be important for some firms who learned principally from imitation of local competitors, although significantly, firms learning from local spillovers failed to advance beyond extra basic operating technological capabilities. Those firms who proactively pursued learning from foreign partners, on the other hand, advanced further, reaching basic innovative levels of technological capabilities. These findings are relevant for a wider range of industrial sectors in emerging economies