Coupling model uncertainty for coupled rainfall/runoff and surface water quality models in river problems

Coupled hydrology and water quality models are an important tool today, used in the understanding and management of surface water and watershed areas. Such problems are generally subject to substantial uncertainty in parameters, process understanding, and data. Component models, drawing on different data, concepts, and structures, are affected differently by each of these uncertain elements. This paper proposes a framework wherein the response of component models to their respective uncertain elements can be quantified and assessed, using a hydrological model and water quality model as two exemplars. The resulting assessments can be used to identify model coupling strategies that permit more appropriate use and calibration of individual models, and a better overall coupled model response. One key finding was that an approximate balance of water quality and hydrological model responses can be obtained using both the QUAL2E and Mike11 water quality models. The balance point, however, does not support a particularly narrow surface response (or stringent calibration criteria) with respect to the water quality calibration data, at least in the case examined here. Additionally, it is clear from the results presented that the structural source of uncertainty is at least as significant as parameter-based uncertainties in areal models. © 2012 John Wiley & Sons, Ltd.

Quality practices and problems in free software projects

Free software and open source projects are often perceived to be of high quality. It has been suggested that the high level of quality found in some free software projects is related to the open development model which promotes peer review. While the quality of some free software projects is comparable to, if not better than, that of closed source software, not all free software projects are successful and of high quality. Even mature and successful projects face quality problems; some of these are related to the unique characteristics of free software and open source as a distributed development model led primarily by volunteers. In exploratory interviews performed with free software and open source developers, several common quality practices as well as actual quality problems have been identified. The results of these interviews are presented in this paper in order to take stock of the current status of quality in free software projects and to act as a starting point for the implementation of quality process improvement strategies.

Simple model of electronic density of states of graphite and its application to the investigation of superlattices

A model of graphite which is easy to comprehend and simple to implement for the simulation of scanning tunneling microscopy (STM) images is described. This model simulates the atomic density of graphite layers, which in turn correlates with the local density of states. The mechanism and construction of such a model is explained with all the necessary details which have not been explicitly reported before. This model is applied to the investigation of rippling fringes which have been experimentally observed on a superlattice, and it is found that the rippling fringes are not related to the superlattice itself. A superlattice with abnormal topmost layers interaction is simulated, and the result affirms the validity of the moiré rotation pattern assumption. The "odd-even" transition along the atomic rows of a superlattice is simulated, and the simulation result shows that when there is more than one rotated layer at the top, the "odd-even" transition will not be manifest. ©2005 The Japan Society of Applied Physics.

Characterization of vertical Mo/diamond Schottky barrier diode from non-ideal I-V and C-V measurements based on MIS model

In this study, we investigated non-ideal characteristics of a diamond Schottky barrier diode with Molybdenum (Mo) Schottky metal fabricated by Microwave Plasma Chemical Vapour Deposition (MPCVD) technique. Extraction from forward bias I-V and reverse bias C- 2-V measurements yields ideality factor of 1.3, Schottky barrier height of 1.872 eV, and on-resistance of 32.63 mö·cm2. The deviation of extracted Schottky barrier height from an ideal value of 2.24 eV (considering Mo workfunction of 4.53 eV) indicates Fermi level pinning at the interface. We attributed such non-ideal behavior to the existence of thin interfacial layer and interface states between metal and diamond which forms Metal-Interfacial layer-Semiconductor (MIS) structure. Oxygen surface treatment during fabrication process might have induced them. From forward bias C-V characteristics, the minimum thickness of the interfacial layer is approximately 0.248 nm. Energy distribution profile of the interface state density is then evaluated from the forward bias I-V characteristics based on the MIS model. The interface state density is found to be uniformly distributed with values around 1013 eV - 1·cm- 2. © 2013 Elsevier B.V.

Influence of nanowire density on the shape and optical properties of ternary InGaAs nanowires.

We have synthesized ternary InGaAs nanowires on (111)B GaAs surfaces by metal-organic chemical vapor deposition. Au colloidal nanoparticles were employed to catalyze nanowire growth. We observed the strong influence of nanowire density on nanowire height, tapering, and base shape specific to the nanowires with high In composition. This dependency was attributed to the large difference of diffusion length on (111)B surfaces between In and Ga reaction species, with In being the more mobile species. Energy dispersive X-ray spectroscopy analysis together with high-resolution electron microscopy study of individual InGaAs nanowires shows large In/Ga compositional variation along the nanowire supporting the present diffusion model. Photoluminescence spectra exhibit a red shift with decreasing nanowire density due to the higher degree of In incorporation in more sparsely distributed InGaAs nanowires.

Nonparametric Bayesian Density Modeling with Gaussian Processes

We present the Gaussian process density sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Samples drawn from the GPDS are consistent with exact, independent samples from a distribution defined by a density that is a transformation of a function drawn from a Gaussian process prior. Our formulation allows us to infer an unknown density from data using Markov chain Monte Carlo, which gives samples from the posterior distribution over density functions and from the predictive distribution on data space. We describe two such MCMC methods. Both methods also allow inference of the hyperparameters of the Gaussian process.

Auxiliary particle implementation of the probability hypothesis density filter

Optimal Bayesian multi-target filtering is, in general, computationally impractical owing to the high dimensionality of the multi-target state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed Sequential Monte Carlo (SMC) implementations of the PHD filter. However, these implementations are the equivalent of the Bootstrap Particle Filter, and the latter is well known to be inefficient. Drawing on ideas from the Auxiliary Particle Filter (APF), we present a SMC implementation of the PHD filter which employs auxiliary variables to enhance its efficiency. Numerical examples are presented for two scenarios, including a challenging nonlinear observation model.