Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular Neural Ring (MNR)

Applications of simulation technique on debris-flow hazard zone delineation: a case study in Hualien County, Taiwan

Debris flows pose severe hazards to communities in mountainous areas, often resulting in the loss of life and property. Helping debris-flow-prone communities delineate potential hazard zones provides local authorities with useful information for developing emergency plans and disaster management policies. In 2003, the Soil and Water Conservation Bureau of Taiwan proposed an empirical model to delineate hazard zones for all creeks (1420 in total) with potential of debris flows and utilized the model to help establish a hazard prevention system. However, the model does not fully consider hydrologic and physiographical conditions for a given creek in simulation. The objective of this study is to propose new approaches that can improve hazard zone delineation accuracy and simulate hazard zones in response to different rainfall intensity. In this study, a two-dimensional commercial model FLO-2D, physically based and taking into account the momentum and energy conservation of flow, was used to simulate debris-flow inundated areas. <br><br> Sensitivity analysis with the model was conducted to determine the main influence parameters which affect debris flow simulation. Results indicate that the roughness coefficient, yield stress and volumetric sediment concentration dominate the computed results. To improve accuracy of the model, the study examined the performance of the rainfall-runoff model of FLO-2D as compared with that of the HSPF (Hydrological Simulation Program Fortran) model, and then the proper values of the significant parameters were evaluated through the calibration process. Results reveal that the HSPF model has a better performance than the FLO-2D model at peak flow and flow recession period, and the volumetric sediment concentration and yield stress can be estimated by the channel slope. The validation of the model for simulating debris-flow hazard zones has been confirmed by a comparison of field evidence from historical debris-flow disaster data. The model can successfully replicate the influence zone of the debris-flow disaster event with an acceptable error and demonstrate a better result than the empirical model adopted by the Soil and Water Conservation Bureau of Taiwan

The effect of local lattice distortion on physical properties of hexagonal rubidium tungsten bronze Rb0.23WOy

[[abstract]]Superconducting transition temperature Tc and normal-state resistivity as a function of oxygen content for hexagonal tungsten bronze Rb0.23WOy with 2.90 < y < 3.05 were obtained from transport measurements. It is remarkably interesting that Tc enhances about 50% and room-temperature resistivity increases about three orders of magnitude as oxygen content varies from 2.90 to 3.05. The low-temperature specific heat data indicate that the Einstein-like mode associated with Rb vibration has a dimensionality crossover from 3D to quasi-2D as oxygen content increases from 2.90 to 3.05. W L3-edge x-ray absorption spectra further show that W-O bond intensity gradually weakens as oxygen content increases, indicative of more oxygen disorder present in the oxygen-rich samples. The observed results strongly suggest that the local lattice distortion induced by oxygen disorder not only modulates Rb vibration, possibly coupled to electron-phonon interaction responsible for superconductivity, and also reduces the charge transfer between O 2p and W 5d orbital in the vicinity of y = 3.00. This scenario can possibly account for significant increases of Tc and normal-state resistivity of Rb0.23WOy as oxygen content slightly changes from 2.90 to 3.05.[[incitationindex]]SCI[[booktype]]電子

Nitrogen-Functionalized Graphene Nanoflakes (GNFs:N): Tunable Photoluminescence and Electronic Structures

This study investigates the strong photoluminescence (PL) and X-ray excited optical luminescence observed in nitrogen-functionalized 2D graphene nanoflakes (GNFs:N), which arise from the significantly enhanced density of states in the region of {\pi} states and the gap between {\pi} and {\pi}* states. The increase in the number of the sp2 clusters in the form of pyridine-like N-C, graphite-N-like, and the C=O bonding and the resonant energy transfer from the N and O atoms to the sp2 clusters were found to be responsible for the blue shift and the enhancement of the main PL emission feature. The enhanced PL is strongly related to the induced changes of the electronic structures and bonding properties, which were revealed by the X-ray absorption near-edge structure, X-ray emission spectroscopy, and resonance inelastic X-ray scattering. The study demonstrates that PL emission can be tailored through appropriate tuning of the nitrogen and oxygen contents in GNFs and pave the way for new optoelectronic devices.Comment: 8 pages, 6 figures (including toc figure

The Supersonic Project: The Early Evolutionary Path of Supersonically Induced Gas Objects

Supersonically induced gas objects (SIGOs) are a class of early universe objects that have gained attention as a potential formation route for globular clusters. SIGOs have recently begun to be studied in the context of molecular hydrogen cooling, which is key to characterizing their structure and evolution. Studying the population-level properties of SIGOs with molecular cooling is important for understanding their potential for collapse and star formation, and for addressing whether SIGOs can survive to the present epoch. Here, we investigate the evolution of SIGOs before they form stars, using a combination of numerical and analytical analysis. We study timescales important to the evolution of SIGOs at a population level in the presence of molecular cooling. Revising the previous formulation for the critical density of collapse for SIGOs allows us to show that their prolateness tends to act as an inhibiting factor to collapse. We find that simulated SIGOs are limited by artificial two-body relaxation effects that tend to disperse them. We expect that SIGOs in nature will be longer lived compared to our simulations. Further, the fall-back timescale on which SIGOs fall into nearby dark matter halos, potentially producing a globular-cluster-like system, is frequently longer than their cooling timescale and the collapse timescale on which they shrink through gravity. Therefore, some SIGOs have time to cool and collapse outside of halos despite initially failing to exceed the critical density. From this analysis we conclude that SIGOs should form stars outside of halos in nonnegligible stream velocity patches in the universe

Demonstration of a stimulatory protein for virus-specified DNA polymerase in phorbol ester-treated Epstein-Barr virus-carrying cells.

A heat-labile Epstein-Barr virus-specific DNA polymerase stimulatory protein having a molecular mass of 45 kDa was purified from phorbol 12-myristate 13-acetate-treated P3HR-1 cells by column chromatography. The virus DNA polymerase stimulatory protein was precipitated by sera from patients with nasopharyngeal carcinoma but not by sera from healthy donors. The interaction of the stimulatory protein with DNA polymerase was stoichiometric. Furthermore, this protein stimulated Epstein-Barr virus DNA polymerase but not herpes simplex virus type 1 or type 2 or human DNA polymerase alpha. The stimulatory protein did not alter the Km value of dTTP or DNA but did increase the Vmax of DNA polymerase. Salt concentrations between 100 mM and 150 mM KCl were optimal for this protein-induced stimulation of Epstein-Barr virus DNA polymerase activity. The presence of the stimulatory protein in the reaction mixture enhanced the sensitivity of virus DNA polymerase to phosphonoformate