Advances in the theory of III-V Nanowire Growth Dynamics

Nanowire (NW) crystal growth via the vapour_liquid_solid mechanism is a complex dynamic process involving interactions between many atoms of various thermodynamic states. With increasing speed over the last few decades many works have reported on various aspects of the growth mechanisms, both experimentally and theoretically. We will here propose a general continuum formalism for growth kinetics based on thermodynamic parameters and transition state kinetics. We use the formalism together with key elements of recent research to present a more overall treatment of III_V NW growth, which can serve as a basis to model and understand the dynamical mechanisms in terms of the basic control parameters, temperature and pressures/beam fluxes. Self-catalysed GaAs NW growth on Si substrates by molecular beam epitaxy is used as a model system.Comment: 63 pages, 25 figures and 4 tables. Some details are explained more carefully in this version aswell as a new figure is added illustrating various facets of a WZ crysta

On the estimation of normal copula discrete regression models using the continuous extension and simulated likelihood

The continuous extension of a discrete random variable is amongst the computational methods used for estimation of multivariate normal copula-based models with discrete margins. Its advantage is that the likelihood can be derived conveniently under the theory for copula models with continuous margins, but there has not been a clear analysis of the adequacy of this method. We investigate the asymptotic and small-sample efficiency of two variants of the method for estimating the multivariate normal copula with univariate binary, Poisson, and negative binomial regressions, and show that they lead to biased estimates for the latent correlations, and the univariate marginal parameters that are not regression coefficients. We implement a maximum simulated likelihood method, which is based on evaluating the multidimensional integrals of the likelihood with randomized quasi Monte Carlo methods. Asymptotic and small-sample efficiency calculations show that our method is nearly as efficient as maximum likelihood for fully specified multivariate normal copula-based models. An illustrative example is given to show the use of our simulated likelihood method

Creating Landscapes of Practice through Sequential Learning - A New Vision for PBL

In the current conceptualisations of Problem Based Learning and how we practice it, the students are expected to possess the necessary academic competencies in order to study through PBL. However, a desk research reveals that students in many cases don't have the necessary understanding or conceptual comprehension of disciplines such as problem formulation, analysis, exploration, literature review etc., which prevents them from unfolding an explorative approach to their professional practice. This article thus discusses Dewey's concepts of sequential inquiry processes to create new forms of learning designs to bolden further students ability to work problem-based. The article discusses through the development of iterative learning design how structured sequences of activities can provide a descriptive language to qualify a methodology for PBL. The study is based on Educational Design Research (EDR) as the overarching framework where the methods of Design thinking inform the design activities through iterative processes. Through a period of two years a total of 400 students at the education of ATCM, at University College of Northern Denmark has participated. The data collection included results from observation, reflective portfolios and sound recordings from the students' group work in combination with sketches, drawing and artefacts from the iterative design process.

Parallelization of the PC Algorithm

This paper describes a parallel version of the PC algorithm for learning the structure of a Bayesian network from data. The PC algorithm is a constraint-based algorithm consisting of fi ve steps where the first step is to perform a set of (conditional) independence tests while the remaining four steps relate to identifying the structure of the Bayesian network using the results of the (conditional) independence tests. In this paper, we describe a new approach to parallelization of the (conditional) independence testing as experiments illustrate that this is by far the most time consuming step. The proposed parallel PC algorithm is evaluated on data sets generated at random from five different real- world Bayesian networks. The results demonstrate that signi cant time performance improvements are possible using the proposed algorithm