Multiple parasite infections are widespread in the developing world and understanding their geographical distribution is important for spatial targeting of differing intervention packages. We investigated the spatial epidemiology of mono- and co-infection with helminth parasites in East Africa and developed a geostatistical model to predict infection risk. The data used for the analysis were ta...

[1] The influence of aquifer property correlation on multiphase fluid migration and entrapment was explored through the use of correlated and uncorrelated porosity, permeability, and capillary pressure-saturation (Pc-Sat) parameter fields in a crosssectional numerical multiphase flow model. Data collected from core samples in a nonuniform sandy aquifer were used to generate three-dimensional aq...

This paper describes a geostatistical approach for environmental data modeling based on value descriptive analysis, variographic analysis, estimation method and validation. It shows an application related to atmospheric pollutant dispersion (ozone) detected by active biomonitoring in the province of Livorno (Italy). This work proposes a two-stage approach based on: the computation of an index o...

This paper aims at presenting the preparation for Schema Theory, including language and cognition, construal, categorization. Also, it shows definition history of schema, highlighting role schema building as a powerful approach maximizing International English Language Testing System listening teaching, expounding benefits challenges theory in IELTS teaching well making recommendations teachers...

Seismic site effects are influenced mainly by geospatial uncertainties corresponding to geological or geotechnical spatial variance. Therefore, the development of a geospatial database is essential to characterize site-specific geotechnical information in multiscale areas and to optimize geospatial zonation methods with potentially high degrees of spatial variability based on trial-and-error ge...

Meteorological data are used in many studies, especially in planning, disaster management, water resources management, hydrology, agriculture and environment. Analyzing changes in meteorological variables is very important to understand a climate system and minimize the adverse effects of the climate changes. One of the main issues in meteorological analysis is the interpolation of spatial data...

In this report the main focuses are geostatistical Gaussian Markov random field (GMRF) models and parallel exact sampling of GMRFs. There are also brief overviews of parallel computing and Markov chain Monte Carlo (MCMC) methods, and a literature review of parallel MCMC. The geostatistical GMRF models are constructed by discretising the domain region using a lattice. Instead of giving this latt...

Reliable prediction of subsurface flow and contaminant transport depends on the accuracy with which the values and spatial distribution of process-relevant model parameters can be identified. Successful characterization methods for complex soil systems are based on (1) an adequate parameterization of the subsurface, capable of capturing both random and structured aspects of the heterogeneous sy...