This paper reports on a rough set approach to classifying meteorological volumetric radar data used to detect storm events responsible for summer severe weather. The classification of storm cells is a difficult problem due to the complex evolution of storm cells, the high dimensionality of the weather data, and the imprecision and incompleteness of the data. A rough set approach is used to clas...

This study aims to develop a forecasting model by assessing the weather variability associated with seasonal fluctuation of Aedes aegypti oviposition dynamic at a city level in Orán, in northwestern Argentina. Oviposition dynamics were assessed by weekly monitoring of 90 ovitraps in the urban area during 2005-2007. Correlations were performed between the number of eggs collected weekly and weat...

PURPOSE The 4D-CT data used for comparing a patient's ventilation distributions before and after lung radiotherapy are acquired at different times. As a result, an additional variable--the tidal volume (TV)--can alter the results. Therefore, in this paper we propose to normalize the ventilation to the same TV to eliminate that uncertainty. METHODS Absolute ventilation (AV) data were generated...

Objective: Multiple Sclerosis (MS) is a neurological condition which widely affects people 50-60 years of age. While clinical presentations MS are highly heterogeneous, mobility limitations one the most frequent symptoms. This study examines machine learning (ML) framework for identifying through spatiotemporal and kinetic gait features. Methods: In this study, data during self-paced walking on...

The occurrence of various meteorological phenomena, such as fog or low cloud cover, has significant impact on many human activities as air or ship transport operations. The management of air traffic at the airports was the main reason to design effective mechanisms for timely prediction of these phenomena. In both these cases meteorologists already use some physical models based on differential...

Parametric statistical models of continuous or discrete valued data are often not properly normalized, that is, they do not integrate or sum to unity. The normalization is essential for maximum likelihood estimation. While in principle, models can always be normalized by dividing them by their integral or sum (their partition function), this can in practice be extremely difficult. We have been ...

Introduction: Diffusion tensor (DT) templates of the human brain are commonly used as a reference for inter-subject spatial normalization, which is a critical step in voxelwise comparisons of neuronal microstructural integrity and brain connectivity across populations. The recently published IIT2 DT template [1] is shown to be more representative of single-subject human brain DT data, is charac...