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).     

The Water Framework Directive: Can more information be extracted from groundwater data? A case study of Seewinkel,Burgenland, eastern Austria

Water protection is one of the most important goals in environmental protection. The Clean Water Act in the USA and the Water Framework Directive (WFD) in Europe are the legal frameworks to facilitate the achievement of this goal. The question is raised of whether more information can be extracted from WFD-related groundwater data. To answer it, a methodology has been developed that is easy to use and could be implemented into official practice. A case study is presented in which the groundwater data of a sodic area in Austria (Seewinkel) is assessed. Eighteen parameters in groundwater sampled from 23 wells (1991–2011) were analyzed. With basic statistics, trend-, cluster-, Wilks’ λ and spatial sampling density analysis, local phosphorus and boron phenomena were described, along with the determining role of sulphate, groundwater flow, and the oxygen gradient in the area. As a final step, the spatial sampling density was determined. Regarding the current set of parameters, all the sampling sites are necessary and only in the case of certain parameters (Ca²⁺, Mg²⁺, K⁺, NO₃ ^?, pH) could one sampling site be abandoned. The methodology applied brings a new perspective to exploring groundwater data collected according to the requirements of the WFD.     

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.     

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.     

Period analysis at high noise level

Analytical expressions are derived for the variances of some types of the periodograms due to normal-distributed noise present in the data. The equivalence of the Jurkevich and the Warner and Robinson methods is proved. The optimum phase cell number of the Warner and Robinson method is given; this number depends on the data length, signal form and noise level. The results are illustrated by numerical examples.