Equivalent diffusion coefficient of clay-rich geological formations: comparison between numerical and analytical estimates

Laboratory experiments in rock samples collected from clay-rich formations indicate that the effective molecular diffusion coefficient (D) is a heterogeneous and anisotropic property. Since laboratory measurements of D are representative of a very small volume, upscaling is necessary in order to incorporate these data in large-scale numerical models of diffusive transport. In this work we address the problem of the estimating the equivalent diffusion coefficient (D _eq ), in terms of total diffusive flux, in a three-dimensional domain characterized by a heterogeneous and anisotropic spatial distribution of D. D _eq was estimated from the results of steady-state diffusive transport simulations through several realizations of the D field. The ensemble averages of D _eq from fields with different degrees of heterogeneity and anisotropy were then compared with estimates from analytical upscaling expressions based on stochastic as well as power-averaging approaches. These expressions are largely based on similar expressions developed for calculating the effective hydraulic conductivity in heterogeneous and anisotropic domains. Comparisons showed that stochastic expressions provide accurate estimates of D _eq only for fields characterized by low heterogeneity. Within the range of heterogeneity and anisotropy considered, our results showed that a power-averaging expression is very accurate in predicting D _eq especially when the parameter p _i is estimated through fitting of the numerical results. Nonetheless, the relationship between this parameter and the anisotropy ratio is linear.     

Assessment of NeQuick ionospheric model for Galileo single-frequency users

The ionosphere is the main error source in GNSS measurements and in extreme cases can degrade the positioning significantly, with errors exceeding 100 m; therefore, modelling and predicting of this type of error is crucial and critical. The ionospheric effect can be reduced using different techniques, such as dual-frequency receiver or suitable augmentation system (DGPS, SBAS); the aforesaid approaches involve the use of expensive devices and/or complex architectures. Single frequency stand-alone receivers are the cheapest and most widespread GNSS devices; they can estimate and partially correct the error due to the ionosphere, through adequate algorithms, which use parameters broadcasted by the navigation message. The aim of this paper is performance assessment of the ionospheric model NeQuick, adopted by the European GNSS Galileo for single frequency receivers. The analysis is performed in measurements domain and the data are collected in different geographical locations and in various geomagnetic conditions.     

Contextual neural gas for spatial clustering and analysis

This study aims to introduce contextual Neural Gas (CNG), a variant of the Neural Gas algorithm, which explicitly accounts for spatial dependencies within spatial data. The main idea of the CNG is to map spatially close observations to neurons, which are close with respect to their rank distance. Thus, spatial dependency is incorporated independently from the attribute values of the data. To discuss and compare the performance of the CNG and GeoSOM, this study draws from a series of experiments, which are based on two artificial and one real-world dataset. The experimental results of the artificial datasets show that the CNG produces more homogenous clusters, a better ratio of positional accuracy, and a lower quantization error than the GeoSOM. The results of the real-world dataset illustrate that the resulting patterns of the CNG are theoretically more sound and coherent than that of the GeoSOM, which emphasizes its applicability for geographic analysis tasks.     

Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study

The majority of cities are rapidly growing. This makes the monitoring and modeling of urban change’s spatial patterns critical to urban planners, decision makers, and environment protection activists. Although a wide range of methods exists for modeling and simulating urban growth, machine learning (ML) techniques have received less attention despite their potential for producing highly accurate predictions of future urban extents. The aim of this study is to investigate two ML techniques, namely radial basis function network (RBFN) and multi-layer perceptron (MLP) networks, for modeling urban change. By predicting urban change for 2010, the models’ performance is evaluated by comparing results with a reference map and by using a set of pertinent statistical measures, such as average spatial distance deviation and figure of merit. The application of these techniques employs the case study area of Mumbai, India. The results show that both models, which were tested using the same explanatory variables, produced promising results in terms of predicting the size and extent of future urban areas. Although a close match between RBFN and MLP is observed, RBFN demonstrates higher spatial accuracy of prediction. Accordingly, RBFN was utilized to simulate urban change for 2020 and 2030. Overall, the study provides evidence that RBFN is a robust and efficient ML technique and can therefore be recommended for land use change modeling.     

Microplate motions in the hotspot reference frame

We have computed motions of the major plates (seven large plates and seven medium‐sized plates) and 38 microplates relative to the hotspot reference frame, and present velocities of these 52 plates. Moreover, using updated plate boundaries for the present, we have computed new geometrical factors for plates and microplates, useful for kinematic calculations and to obtain the net‐rotation of the lithosphere and plate velocities in the mean‐lithosphere reference frame. Instead of a continuum or gradational distribution of the plates by size, the plates clearly partition into three groups each having their own characteristics. For the seven large plates, rotation poles generally lie in high latitudes; the seven medium‐sized plates have rotation poles in a restricted equatorial area; the 38 small plates show the greatest scatter. Moreover subsets of the 52 plates reveal differing fractal behaviour: the large, middle and small groupings each have a characteristic fractal dimension, suggestive of microplate clustering. The highest angular velocities occur for some of the smallest plates, with the location of their rotation poles closeby. Terra Nova, 18, 276–281, 2006     

Long term spectral variability in the soft gamma-ray repeater SGR 1900+14

We present a systematic analysis of all the BeppoSAX data of SGR1900+14. The observations spanning five years show that the source was brighter than usual on two occasions: ～20 days after the August 1998 giant flare and during the 10⁵?s long X-ray afterglow following the April 2001 intermediate flare. In the latter case, we explore the possibility of describing the observed short term spectral evolution only with a change of the temperature of the blackbody component. In the only BeppoSAX observation performed before the giant flare, the spectrum of the SGR1900+14 persistent emission was significantly harder and detected also above 10 keV with the PDS instrument. In the last BeppoSAX observation (April 2002) the flux was at least a factor 1.2 below the historical level, suggesting that the source was entering a quiescent period.     

A Geometric Interpretation of Integrable Motions

Integrability, one of the classic issues in galactic dynamics and in general in celestial mechanics, is here revisited in a Riemannian geometric framework, where Newtonian motions are seen as geodesics of suitable -mechanical- manifolds. The existence of constants of motion that entail integrability is associated with the existence of Killing tensor fields on the mechanical manifolds. Such tensor fields correspond to hidden symmetries of non-Noetherian kind. Explicit expressions for Killing tensor fields are given for the N = 2 Toda model, and for a modified Hénon-Heiles model, recovering the already known analytic expressions of the second conserved quantity besides energy for each model respectively.