Hierarchical decomposition of variance with applications in environmental mapping based on satellite images

A quadtree-based image segmentation procedure (HQ) is presented to map complex environmental conditions. It applies a hierarchical nested analysis of variance within the framework of multiresolution wavelet approximation. The procedure leads to an optimal solution for determining mapping units based on spatial variability with constraints on the arrangement and shape of the units. Linkages to geostatisiics are pointed out, but the HQ decomposition algorithm does not require any homogeneity criteria. The computer implementation can be parameterized by either the number of required mapping units or the maximum within-unit variance, or it can provide a spectrum of significances of nested ANOVA. The detailed mathematical background and methodology is illustrated by a salt-affected grassland mapping study (Hortobágy, Hungary), where heterogeneous environmental characteristics have been sampled and predicted based on remotely sensed images using these principles.     

Once more on quasar periodicities

In an earlier paper Arp, Bi, Chu and Zhu investigated various quasar samples and recognized a periodicity of remarkable degree in them. Their conclusion was that the existence of such a periodicity is a counterevidence against the cosmological origin of quasar redshifts. Since this question is of great importance in a cosmological context, here we reanalyze some of the samples together with a galaxy sample of the pencil-beam survey of Broadhurstet al. Our result is that on the plane of cosmological parameters (₀, ₀) there is a non-negligible region where two quasar samples and the galaxy sample are simultaneously fairly periodic. Pure periodicity is still compatible with cosmological principles, at least on length scales much longer than the period length. So the regularities can fit into a cosmological context.     

Adria,the African promontory,in mesozoic Mediterranean palaeogeography

The orogenic belts encircling the present-day Adriatic Sea are the deformed Mesozoic continental margin of an area known as Adria, the outline of which began to take shape during Middle Triassic continental rifting. Early Jurassic oceanic rifting was usually close to, but not coincident with, sites of earlier continental rifting. The Triassic rifted zones were usually incorporated into the continental margin of Adria, profoundly influencing its subsequent development. The Mesozoic platform/basin morphology of this margin can be correlated along the length of the belt.Palaeomagnetic data from autochthonous outcrops of the foreland of Adria do not indicate relative rotation and moreover suggest that this foreland has moved in coordination with Africa since the Early Mesozoic. Seismic soundings indicate that thick Mesozoic sedimentary sequences which can be correlated with sections on the African platform are continuous beneath the eastern Mediterranean seas. The concept of Adria as having behaved as a promontory of the African plate is tested by correlation of the main tectonic events in the belt with the spreading history of the Atlantic. The simplest model which adequately accounts for available data comprises a continuous Mesozoic continental margin from the Magrebids of Tunisia, through the Apennines, Alps, Dinarides and Hellenides to the alpine belt of Turkey. This margin was the southern margin of the Mesozoic Tethys and its foreland was more or less continuous with the African platform. Some structural and geochemical features of the double ophiolitic belt on the eastern side of Adria may be explained in terms of more external oceanic branches giving a more diversified continental margin of Adria. The present undulations of the Periadriatic belt are mainly a product of Late Cretaceous to recent deformation, which severely modified the shape of this margin by continental collision and by subsequent development of back-arc features.     

Novel photosensors for neutrino detectors and telescopes

The volume and the photosensitive area of next generation detectors of the numerous rarely occurring phenomena will greatly exceed the sizes of the current experiments. These phenomena include cosmic neutrinos, atmospheric neutrinos, long-baseline neutrino beams from accelerators, geo-neutrinos, geo-reactor neutrinos, and hypothetic proton decays. Similar requirements hold for a new type of a large scanning device for homeland security and nuclear proliferation control, and for the future widely accessible medical imaging devices. Photon detectors are the most important component of such detectors. Existing photosensors are based on vacuum tubes and dynode electron multipliers that are essentially hand-made, expensive and nearly impossible to produce in large enough quantities. Silicon detectors are too small for experiments requiring a very large photosensitive area. Our laboratory is developing novel detectors with a large photosensitive area that can be mass-produced, similar to large flat panel TV displays.     

A hybrid artificial neural network-numerical model for ground water problems

Numerical models constitute the most advanced physical-based methods for modeling complex ground water systems. Spatial and/or temporal variability of aquifer parameters, boundary conditions, and initial conditions (for transient simulations) can be assigned across the numerical model domain. While this constitutes a powerful modeling advantage, it also presents the formidable challenge of overcoming parameter uncertainty, which, to date, has not been satisfactorily resolved, inevitably producing model prediction errors. In previous research, artificial neural networks (ANNs), developed with more accessible field data, have achieved excellent predictive accuracy over discrete stress periods at site-specific field locations in complex ground water systems. In an effort to combine the relative advantages of numerical models and ANNs, a new modeling paradigm is presented. The ANN models generate accurate predictions for a limited number of field locations. Appending them to a numerical model produces an overdetermined system of equations, which can be solved using a variety of mathematical techniques, potentially yielding more accurate numerical predictions. Mathematical theory and a simple two-dimensional example are presented to overview relevant mathematical and modeling issues. Two of the three methods for solving the overdetermined system achieved an overall improvement in numerical model accuracy for various levels of synthetic ANN errors using relatively few constrained head values (i.e., cells), which, while demonstrating promise, requires further research. This hybrid approach is not limited to ANN technology; it can be used with other approaches for improving numerical model predictions, such as regression or support vector machines (SVMs).     

Dispersion modeling of air pollutants in the atmosphere: a review

Modeling of dispersion of air pollutants in the atmosphere is one of the most important and challenging scientific problems. There are several natural and anthropogenic events where passive or chemically active compounds are emitted into the atmosphere. The effect of these chemical species can have serious impacts on our environment and human health. Modeling the dispersion of air pollutants can predict this effect. Therefore, development of various model strategies is a key element for the governmental and scientific communities. We provide here a brief review on the mathematical modeling of the dispersion of air pollutants in the atmosphere. We discuss the advantages and drawbacks of several model tools and strategies, namely Gaussian, Lagrangian, Eulerian and CFD models. We especially focus on several recent advances in this multidisciplinary research field, like parallel computing using graphical processing units, or adaptive mesh refinement.     

Low velocity shock-cloud encounters I.

Shocks propagating in the interstellar medium (ISM) play an important role in the life of molecular clouds. Through a theoretical study of interaction between clouds and shocks we can understand, for example, the density distribution of observed molecular clouds and the first steps of star formation. The only way to study of interaction in detail is via a numerical hydrodynamical simulation. In this paper we present the first results of a hydrocode which is able to follow the processes after the collision between the cloud and shock front.Our main theoretical result is that the chemical processes (e.g. H₂ dissociation) can affect the dynamical processes significantly. Global parameters of the cloud are calculated for the comparision of the simulation and the observations.     

Optimal sequencing for a multipurpose water supply system

The optimal sequencing of a multipurpose water supply system in the Hajduhátság region of Hungary is determined by dynamic programming. The goal function minimizes the present value of capital costs, operation costs, and economic losses due to water shortages. Future water requirements are considered to be random variables because of natural and forecasting uncertainties. The nonlinear optimization problem at each stage is equivalent to a readily solved game theoretical problem, the solution of which is straightforward. Sensitivity analysis performed with respect to economic losses, water requirements and discount rate, showed that optimal development and sequencing depend largely on the economic losses and the discount rate.