A neural network for mining large volumes of time series data

Efficiently mining large volumes of time series data is amongst the most challenging problems that are fundamental in many fields such as industrial process monitoring, medical data analysis and business forecasting. This paper discusses a high-performance neural network for mining large time series data set and some practical issues on time series data mining. Examples of how this technology is used to search the engine data within a major UK eScience Grid project (DAME) for supporting the maintenance of Rolls-Royce aero-engine are presented

Health Care Benefits -- Creating the Optimal Design

Explores the role of benefit design and market innovations such as consumer-driven health plans and value-based insurance design to maximize beneficial coverage and offer incentives for better health and efficiency. Outlines implications for reform

Crystalline Polymers with Exceptionally Low Thermal Conductivity Studied using Molecular Dynamics

Semi-crystalline polymers have been shown to have greatly increased thermal conductivity compared to amorphous bulk polymers due to effective heat conduction along the covalent bonds of the backbone. However, the mechanisms governing the intrinsic thermal conductivity of polymers remain largely unexplored as thermal transport has been studied in relatively few polymers. Here, we use molecular dynamics simulations to study heat transport in polynorbornene, a polymer that can be synthesized in semi-crystalline form using solution processing. We find that even perfectly crystalline polynorbornene has an exceptionally low thermal conductivity near the amorphous limit due to extremely strong anharmonic scattering. Our calculations show that this scattering is sufficiently strong to prevent the formation of propagating phonons, with heat being instead carried by non-propagating, delocalized vibrational modes known as diffusons. Our results demonstrate a mechanism for achieving intrinsically low thermal conductivity even in crystalline polymers that may be useful for organic thermoelectrics

Labeling Fruits and Vegetables for Sale in Ohio

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How to Collect your Water Sample and Interpret the Results for the Poultry Analytical Package

Rapidly growing birds may consume up to twice as much water as feed (Scantling and Watkins 2013), which means a plentiful supply of clean water is crucial for poultry health and productivity. To determine the quality of your poultry’s water resources, periodic sampling and analysis is needed. Analyzing water supplies can also be a crucial tool in identifying existing or potential challenges. The Arkansas Water Resources Center (AWRC) in cooperation with the UA Cooperative Extension Service offers several analytical packages to assess the quality of your water resources. This document is intended to provide guidance to poultry producers on collecting water samples for analysis and understanding the “Poultry Water Report Form” provided by the AWRC’s Water Quality Laboratory (Lab). The information contained within this fact sheet should be used as general guidance, and the reader is encouraged to seek advice from Extension specialists regarding the interpretation of individual reports and water testing results that may be of concern

Recent Extreme Ultraviolet Solar Spectra and Spectroheliograms

Extreme ultraviolet solar spectra and spectroheliogram analyse

Solar cycle variations of stratospheric ozone and temperature in simulations of a coupled chemistry-climate model

The results from three 45-year simulations of a coupled chemistry climate model are analysed for solar cycle influences on ozone and temperature. The simulations include UV forcing at the top of the atmosphere, which includes a generic 27-day solar rotation effect as well as the observed monthly values of the solar fluxes. The results are analysed for the 27-day and 11-year cycles in temperature and ozone. In accordance with previous results, the 27-day cycle results are in good qualitative agreement with observations, particularly for ozone. However, the results show significant variations, typically a factor of two or more in sensitivity to solar flux, depending on the solar cycle. <br><br> In the lower and middle stratosphere we show good agreement also between the modelled and observed 11-year cycle results for the ozone vertical profile averaged over low latitudes. In particular, the minimum in solar response near 20 hPa is well simulated. In comparison, experiments of the model with fixed solar phase (solar maximum/solar mean) and climatological sea surface temperatures lead to a poorer simulation of the solar response in the ozone vertical profile, indicating the need for variable phase simulations in solar sensitivity experiments. The role of sea surface temperatures and tropical upwelling in simulating the ozone minimum response are also discussed

C-NNAP - A parallel processing architecture for binary neural networks

This paper describes the CNNAP machine, a MIMD implementation of an array of ADAM binary neural networks, primarily designed for image processing. CNNAP comprises an array of VME cards each containing a DSP, SCSI controller, and a new design of the SAT peripheral processor. The SAT processor is a dedicated hardware implemention that performs binary neural network computations. The SAT processor yields a potential speed-up of between 108 times to 182 times that of the current DSP with its dedicated coprocessor. CNNAP in association with the SAT provides a fast, parallel environment for performing binary neural network operations