BACTERIOLOGICAL STANDARDS AND FOOD QUALITY/SAFETY

Food Consumption/Nutrition/Food Safety,

Mayflower, Renova Basin, and South Boulder Creek Areas

In Montana at the turn of the century a great many men sought the riches buried in the earth\u27s crust. Prospectors fanning out from Butte and other early Montana mining areas located veins at the Mayflower, Renova, and Gold Hill areas

A Rural and Urban Study of the Food Environment in Select Nevada Geographies

National and county level examination of urban and rural food environments has taken place, but primary validation of venue existence and in-venue data are lacking. The literature show disparities in access to healthy foods through low density of large grocers/supermarkets, low variety and quality of produce and higher prices for a healthy diet in rural versus urban geographies. Rural areas are of unique concern due to poor health indicators such as higher obesity rates, higher diabetes rates, lower incomes and lower educations than urban residents. The goal of this study was to explore differences in the community and consumer level food environments between two Nevada geographies. Evidence was sought to test the hypotheses of venue density, the availability and quality of produce, and the price difference between standard and healthy items between rural and urban Nevada geographies. A cross-sectional study took place through direct quantitative and descriptive data collection in two rural Nevada counties and two urban census tracts in Las Vegas, NV. A Kruskal–Wallis, Mann–Whitney and a Bonferroni correction were performed on the quantitative store audit data to compare produce availability and quality scores, and the price differences between standard and healthy items between geographies. Primary data show a greater per-capita density of grocery and convenience venues in the rural area than the urban. A greater per-capita density of fast-food venues was found in the urban sample area. The variety and quality of produce in the rural grocer venues were lower than the urban supermarkets. No significant or functional price difference was found between the standard and healthier versions of select items within commonly purchased food groups, between rural and urban geographies

Geology of the Lost Creek-Modesty Creek Area Deerlodge County, Montana

In 1915 the United States Geological Survey published a folio by Calkins and Emmons on the geology of the Philipsburg Quadrangle, which ad­joins the area now under study to the west. Geology of this portion of Montana had not been mapped previously; consequently the purpose of this thesis is to make a geological reconnais­sance of the stratigraphy and structure of the area

New recommendation to predict export value using big data and machine learning technique

Official statistics on monthly export values have a publicity lag between the current period and the published publication. None of the previous researchers estimated the value of exports for the monthly period. This circumstance is due to limitations in obtaining supporting data that can predict the criteria for the current export value of goods. AIS data is one type of big data that can provide solutions in producing the latest indicators to forecast export values. Statistical Methods and Conventional Machine Learning are implemented as forecasting methods. Seasonal ARIMA and Artificial Neural Network (ANN) methods are both used in research to forecast the value of Indonesia’s exports. However, ANN has a weakness that requires high computational costs to obtain optimal parameters. Genetic Algorithm (GA) is effective in increasing ANN accuracy. Based on these backgrounds, this paper aims to develop and select an AIS indicator to predict the monthly export value in Indonesia and optimize ANN performance by combining the ANN algorithm with the genetic algorithm (GA-ANN). The research successfully established five indicators that can be used as predictors in the forecasting model. According to the model evaluation results, the genetic algorithm has succeeded in improving the performance of the ANN model as indicated by the resulting RMSE GA-ANN value, which is smaller than the RMSE of the ANN model