Learning Confirmatory Patterns in Exploratory Factor Analysis Using ICOMP and Genetic Algorithm

The dissertation intends to develop a new approach to the identification of the best factor pattern structure. This new approach is a multivariate regression analysis where factor scores are regressed on original variables. The dissertation shows the versatility of information model selection criteria, Bozdogan\u27s ICOMP- type criteria in particular, in two types of modeling problems: determining the number of factors in factor analysis and working as the fitness function for Genetic Algorithm

THE N-P SCATTERING CROSS SECTION FROM 90 KEV TO 1.8 MEV

There have been very few measurements of the total cross section for n-p scattering below 500 keV. In order to differentiate among NN potential models, improved cross section data between 20 and 600 keV are required. We measured the n-p and n-C total cross sections in this energy region by transmission; a collimated neutron beam was passed through CH2 and C samples and transmitted neutrons were detected by a BC-501A deuterated liquid scintillator. Cross sections were obtained by taking the ratios of normalized neutron yields with the samples in the beam and with no sample in the beam. Both better precision and larger range between 90 keV and 1.8 MeV results are presented. The parameters resulting from fitting effective range theory to the data for n-p scattering are in good agreement with parameters determined from previous fits

Ammonia Concentrations and Emissions of Aviary Hen Houses

Ammonia (NH3) concentrations and emissions were monitored in four aviary laying-hen houses for one year. The mean indoor NH3 concentration of the four houses (mean ± SE) was 5.2 (±0.1) ppm. The NH3 concentrationswere below 25 ppm, the commonly recommended threshold, on all sampling days. Ammonia emission rate was 0.14(±0.01) g/d-hen. The NH3 concentrations and emission rateof the aviary houses were between those of manure-belt and high-rise cage houses reported in the literature

Nitrogen Losses from Laying Hen Manure in Commercial High-rise Layer Facilities

Nitrogen (N) losses from four high-rise laying hen houses, representing four commercial layer farms in Iowa, were determined from measured performance data of Hy-Line W-36 White Leghorn layers (i.e., manure production, egg production, feed intake, body weight, and mortality). Nitrogen loss was 25, 33, 37, and 41% for Farms A, B, C, and D, respectively, based on the Total Kjeldahl Nitrogen (TKN) in feed. A significant factor contributing to the difference in the N losses was the moisture content (MC, percent on an as-is basis) in manure stored in the ventilated manure storage area of the layer houses. The higher the MC, the higher the ratio of NH 3 /TKN Manure in the stored manure, and therefore, the higher the percentage of N loss. Mathematical relationships were developed. Manure handling systems played an important role in N loss by influencing MC of the manure which, in turn, affects rate of manure decomposition. Understanding the factors affecting decomposition and N loss mechanisms of layer manure could provide a means to significantly reduce ammonia emissions to the atmosphere from poultry facilities

Design and implementation of an intelligent car obstacle avoidance system based on deep learning

Through the integration of deep learning technology, from the simplest driving method to the realizatio n of the “carnetwork road” interaction, the use of STM32F103 microprocessor control chip, and through the PWM technology to achieve the speed and steering gear regulation, at the same time, the use of deep learning self-cognition technology, so that intelligent vehicles can make selfcognitive decisions like human minds , by looking for the best route to avoid some obstacles on the road surface, and the selection of the optimal forecast route, and through the tracking controller to achieve the black line function, through the anti-collision system to achieve the vehicle detection and obstacle avoidance function