University of Bath

About: University of Bath is a education organization based out in Bath, Bath and North East Somerset, United Kingdom. It is known for research contribution in the topics: Population & Photonic-crystal fiber. The organization has 15830 authors who have published 39608 publications receiving 1358769 citations. The organization is also known as: Bath University.

Metallic Conductivity in a Two-Dimensional Cobalt Dithiolene Metal–Organic Framework

Abstract: Two-dimensional (2D) metal–organic frameworks (MOFs) have received a great deal of attention due to their relatively high charge carrier mobility and low resistivity. Here we report on the temperature-dependent charge transport properties of a 2D cobalt 2,3,6,7,10,11-triphenylenehexathiolate framework. Variable temperature resistivity studies reveal a transition from a semiconducting to a metallic phase with decreasing temperature, which is unprecedented in MOFs. We find this transition to be highly dependent on the film thickness and the amount of solvent trapped in the pores, with density functional theory calculations of the electronic-structure supporting the complex metallic conductivity of the material. These results identify the first experimentally observed MOF that exhibits band-like metallic conductivity.

Generalized additive models for large data sets

Abstract: Summary. We consider an application in electricity grid load prediction, where generalized additive models are appropriate, but where the data set’s size can make their use practically intractable with existing methods. We therefore develop practical generalized additive model fitting methods for large data sets in the case in which the smooth terms in the model are represented by using penalized regression splines. The methods use iterative update schemes to obtain factors of the model matrix while requiring only subblocks of the model matrix to be computed at any one time.We show that efficient smoothing parameter estimation can be carried out in a well-justified manner. The grid load prediction problem requires updates of the model fit, as new data become available, and some means for dealing with residual auto-correlation in grid load. Methods are provided for these problems and parallel implementation is covered. The methods allow estimation of generalized additive models for large data sets by using modest computer hardware, and the grid load prediction problem illustrates the utility of reduced rank spline smoothing methods for dealing with complex modelling problems.

The Challenge of the Individualistic Mycelium

Abstract: The mycelium of higher fungi is portrayed as a developmentally versatile collective in which an initially dendritic pattern of branching is converted, by hyphal anastomosis, into a communication network. Spatial and functional patterns in the mycelium depend on the establishment of gradients allowing flow of protoplasmic resources and organelles via radial or tangential routes between source and sink regions. (...)