Audit report on the Iowa Agricultural Development Authority for the year ended June 30, 2012

Audit report on the Iowa Agricultural Development Authority for the year ended June 30, 2012

Audit report on America’s Agricultural Industrial Heritage Landscape, Inc., d/b/a Silos and Smokestacks National Heritage Area and Silos and Smokestacks Natural Heritage Area Foundation in Waterloo, Iowa for the years ended December 31, 2012 and 2011

Audit report on America’s Agricultural Industrial Heritage Landscape, Inc., d/b/a Silos and Smokestacks National Heritage Area and Silos and Smokestacks Natural Heritage Area Foundation in Waterloo, Iowa for the years ended December 31, 2012 and 2011

Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.

Web-based reference databases and subject gateways to locate current research information in agricultural and food sciences : a Finnish perspective

Selostus: Maatalous- ja elintarviketieteiden www-pohjaiset viitetietokannat ja aihehakemistot - suomalaisen tiedonetsijän näkökulma

Audit report on the Muscatine Agricultural Learning Center for the year ended December 31, 2012

Audit report on the Muscatine Agricultural Learning Center for the year ended December 31, 2012

Audit report on the Iowa Agricultural Development Authority for the year ended June 30, 2013

Audit report on the Iowa Agricultural Development Authority for the year ended June 30, 2013

Cold hardiness research on agricultural and horticultural crops in Finland

Selostus: Pelto- ja puutarhakasvien kylmänkestävyystutkimus Suomessa

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

Response of Iowa Pavements to a Tracked Agricultural Vehicle, HR-1075, Phase II, 2000

The overall objective of the work summarized in this report and in the interim report was to study the effects of targeted implement-of-husbandry loads. This report is to complement phase I of this work, which was summarized in the interim report, entitled Response of Iowa Pavements to Heavy Agricultural Loads (December 1999). The response of newly constructed Portland cement concrete (PCC) and asphalt cement concrete (ACC) pavements under semitruck, single-axle single-tire grain wagon, single-axle dual-tire grain wagon, tandem and tridem tank wagons were summarized in the interim report. Phase II of this project, presented herein, was to complete the study in terms of how tracked agricultural vehicles relate to the reference 20,000-pound single-axle semi-truck. In this report the response of these two pavements under a tracked grain wagon is documented.

Response of Iowa Pavements to Heavy Agricultural Loads, HR-1075, 1999

Iowa's county road system includes several thousands of miles of paved roads which consist of Portland cement concrete (PCC) surfaces, asphalt cement concrete (ACC) surfaces, and combinations of thin surface treatments such as seal coats and slurries. These pavements are relatively thin pavements when compared to the state road system and therefore are more susceptible to damage from heavy loads for which they were not designed. As the size of the average farm in Iowa has increased, so have the size and weights of implements of husbandry. These implements typically have fewer axles than a truck hauling the same weight would be required to have; in other words, some farm implements have significantly higher axle weights than would be legal for semi-trailers. Since stresses induced in pavements are related to a vehicle's axle weight, concerns have been raised among county and state engineers regarding the possible damage to roadway surfaces that could result from some of these large implements of husbandry. Implements of husbandry on Iowa's highway system have traditionally not been required to comply with posted weight embargo on bridges or with regulations regarding axle-weight limitations on roadways. In 1999, with House File 651, the Iowa General Assembly initiated a phased program of weight restrictions for implements of husbandry. To help county and state engineers and the Iowa legislature understand the effects of implements of husbandry on Iowa's county roads, the following study was conducted. The study investigated the effects of variously configured grain carts, tank wagons, and fence-line feeders on Iowa's roadways, as well as the possible mitigating effects of flotation tires and tracks on the transfer of axle weights to the roadway. The study was accomplished by conducting limited experimental and analytical research under static loading conditions

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

Why microalgal biofuels won't save the internal combustion machine

Proponents of microalgae biofuel technologies often claim that the world demand of liquid fuels, about 5 trillion liters per year, could be supplied by microalgae cultivated on only a few tens of millions of hectares. This perspective reviews this subject and points out that such projections are greatly exaggerated, because (1) the pro- ductivities achieved in large-scale commercial microalgae production systems, operated year-round, do not surpass those of irrigated tropical crops; (2) cultivating, harvesting and processing microalgae solely for the production of biofuels is simply too expensive using current or prospective technology; and (3) currently available (limited) data suggest that the energy balance of algal biofuels is very poor. Thus, microalgal biofuels are no panacea for depleting oil or global warming, and are unlikely to save the internal combustion machine.