Classification scheme and nomenclature for refractive-index distributions

A classification scheme and nomenclature for inhomogeneous refractive-index distributions is proposed which is consistent, unambiguous, and appeals to intuition. The classification is based on (1) the generic shape of the isoindicial surfaces, (2) the detailed dependence of the index on some position coordinate characterizing the isoindicial surfaces, and (3) whether the index distribution is given by N or N2 as some function of position. The scheme is illustrated by distributions which are of technological or physiological interest.     

Simplified Monthly Hydrology and Irrigation Water Use Model to Explore Sustainable Water Management Options in the Murray-Darling Basin

We aimed to provide whole-of-basin simulations of flows and diversions in the Murray-Darling Basin for economic and policy analysis. We describe a model based on a subdivision of the basin into 58 catchments. In each catchment, the monthly runoff, river flow and irrigation demand are modelled as lumped processes. This is the first single model of the whole of the Murray-Darling Basin. The model was calibrated using monthly flow and annual diversion records, both by trial and error and using an automated method. We use the model to examine the impact on the flow and diversions in the Murray-Darling Basin of proposed diversion reductions and climate change. The diversion reductions return water to the environment, increasing river flows below the main irrigation areas. However, a middle-of-the-range climate change projection may result in a 13 % decrease in total flows in 2030, which offsets the enhancement to flows gained by diversion reductions.     

Three Dimensional Conceptualisation of Hydrogeological Environment to Underpin Groundwater Management in Irrigation Area

Australian irrigated agriculture utilises about 70?% of all water used in the country, 21?% of which is derived from groundwater. Sustainability for irrigated agriculture also depends on keeping the watertables at a safe level below the rootzone to avoid salinisation and reduction in crop yields. There is a vital need to understand groundwater and aquifer systems and their roles in the sustainability of irrigated agriculture in order to manage groundwater properly. This study builds on the previous hydrogeological and groundwater investigations of the Coleambally Irrigation Area (CIA) in New South Wales of Australia. It presents a new approach which systematically characterises regional hydrogeological environment using a three-dimensional (3-D) conceptual framework developed in ArcGIS. The 3-D hydrological conceptualisation of the CIA has integrated disparate sources of data into a coherent knowledge base for a better visualisation of hydrogeological characteristics and a comprehensive analysis of groundwater flow and aquifers. As an application example, the model was used to develop cross-sectional models of the area and to estimate regional-scale net recharge. The results have provided a basis for the numerical modelling and added values to procedures which underpin irrigation system management investment decisions through improving the understanding of hydrogeology underlying the area and creating an action-oriented dialogue among stakeholders.     

Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values

The k-means algorithm is well known for its efficiency in clustering large data sets. However, working only on numeric values prohibits it from being used to cluster real world data containing categorical values. In this paper we present two algorithms which extend the k-means algorithm to categorical domains and domains with mixed numeric and categorical values. The k-modes algorithm uses a simple matching dissimilarity measure to deal with categorical objects, replaces the means of clusters with modes, and uses a frequency-based method to update modes in the clustering process to minimise the clustering cost function. With these extensions the k-modes algorithm enables the clustering of categorical data in a fashion similar to k-means. The k-prototypes algorithm, through the definition of a combined dissimilarity measure, further integrates the k-means and k-modes algorithms to allow for clustering objects described by mixed numeric and categorical attributes. We use the well known soybean disease and credit approval data sets to demonstrate the clustering performance of the two algorithms. Our experiments on two real world data sets with half a million objects each show that the two algorithms are efficient when clustering large data sets, which is critical to data mining applications.     

Choosing small sets of policy-relevant scenarios by combining vulnerability and diversity approaches

Computer simulation models can generate large numbers of scenarios, far more than can be effectively utilized in most decision support applications. How can one best select a small number of scenarios to consider? One approach calls for choosing scenarios that illuminate vulnerabilities of proposed policies. Another calls for choosing scenarios that span a diverse range of futures. This paper joins these two approaches for the first time, proposing an optimization-based method for choosing a small number of relevant scenarios that combine both vulnerability and diversity. The paper applies the method to a real case involving climate resilient infrastructure for three African river basins (Volta, Orange and Zambezi). Introducing selection criteria in a stepwise manner helps examine how different criteria influence the choice of scenarios. The results suggest that combining vulnerability- and diversity-based criteria can provide a systematic and transparent method for scenario selection.     

Coupling in Optical Fibres Determined by Improved Variational Approximation

Abstract

Analysis of optical fibre directional couplers is made much more accurate by the application of a new improved approximation. By adding another parameter to the modified Gaussian approximation, we are able to quantify optical fibre directional couplers covering the whole single-mode range in a simple and very accurate way.     

Robust recursive identification of multidimensional linear regression models

Stochastic adaptive estimation and control algorithms involving recursive prediction estimates have guaranteed convergence rates when the noise is not ‘too’ coloured, as when a positive-real condition on the noise mode is satisfied. Moreover, the whiter the noise environment the more robust are the algorithms. This paper shows that for linear regression signal models, the suitable introduction of while noise into the estimation algorithm can make it more robust without compromising on convergence rates. Indeed, there are guaranteed attractive convergence rates independent of the process noise colour. No positive-real condition is imposed on the noise model.     

Structural,elastic, phononic,optical and electronic properties investigation of two-dimensional XIS (X=Al,Ga, In) for photocatalytic water splitting

Two-dimensional (2D) materials have been widely developed due to their attractive properties. Here, by using density functional theory (DFT) calculations, for the first time, we explore potential applications of the novel XIS (X = Al, Ga, In) monolayer 2D materials on photocatalytic water splitting. A series of simulations were carried out to predict and study the structural, elastic, phononic, optical and electronic properties of 2D XIS materials. The results show that GaIS and InIS demonstrate low thermal conductivity. For optical properties, AlIS shows strong light absorption coefficients and refractive index only under ultraviolet (UV) light, while GaIS and InIS show stronger performance under both visible light and UV light with the band edge positions spanned the redox potential of water. The reasonable band positions and bandgaps make them promising photocatalysts for water splitting. This work reveals the potential applications of monolayer 2D XIS in thermal, electronic, and photocatalytic water splitting.     

Ethanol dehydration activity on hydrothermally stable LaPxOy catalysts synthesized using CTAB template

Nanocrystalline LaP_xO_y with various starting P to La ratios from 0.5 to 2.0 catalysts were prepared by a sol?Cgel method using cetyl trimethylammonium bromide (CTAB) as template. The catalysts were thoroughly characterized by N₂ physisorption, powder X-ray diffraction (XRD), temperature programmed desorption (TPD) of NH₃, solid state ³¹P and ¹H nuclear magnetic resonance (NMR), and transmission electron microscopy (TEM) techniques. XRD results indicate the presence of predominantly monazite LaPO₄ with minor amounts of (??3.0 wt%) rhabdophane LaPO₄ phase in the samples with starting P/La ratios of 1.0 and 1.5. NH₃-TPD results show an increasing trend in the total acidity with increase in P/La ratio. These catalysts were tested in the selective ethanol dehydration in the temperature range between 250 and 400?°C. The catalyst activity (??mol/h/m²) is increased with P/La ratio and the catalyst with highest P/La ratio of 2.0 exhibiting the highest ethanol dehydration activity. The ethanol conversion increased with reaction temperature, reaching 100% at 350?°C and remains unchanged at higher temperatures. On the other hand, the ethylene selectivity is also increased up to 350?°C and then decreased with further increase of reaction temperature. At a P/La ratio of 2, the CTAB templated LaP_xO_y catalyst showed higher catalytic activities compared to the LaP_xO_y by hydrothermal method without any template.