Report of recommendations to the Public Employment Relations Board for the year ending, June 30, 2012

Audit report of the Public Employment Relations Board for the year ended June 30, 2012

Report on recommendations to the Iowa Ethics and Campaign Disclosure Board for the year ended June 30, 2012

Audit report of the Iowa Ethics and Campaign Disclosure Board for the year ended June 30, 2012

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.

2012 Annual Report of the Iowa Utilities Board

The IUB Annual Report contains summaries for IUB dockets that were active during the calendar year as well as IUB background information, IUB work section highlights, descriptions of IUB court cases and participation in federal proceedings, listings of IUB assessments to jurisdictional utilities, and the IUB fiscal year budget.

Report of recommendations to the Public Employment Relations Board for the year ending June 30, 2005

Report of recommendations to the Public Employment Relations Board for the year ending June 30, 2005

Iowa Board of Nursing Annual Report, July 1, 2012-June 30, 2013

The annual report of the Iowa Board of Nursing includes information on legislation, administrative rules, nursing education, nursing practice, continuing education, licensing, enforcement, administration, financial report, statistics and general nursing demographics.

Iowa Board of Nursing Annual Report, July 1, 2012-June 30, 2013

The annual report of the Iowa Board of Nursing includes information on legislation, administrative rules, nursing education, nursing practice, continuing education, licensing, enforcement, administration, financial report, statistics and general nursing demographics.

Report on recommendations to the Iowa Ethics and Campaign Disclosure Board for the year ended June 30, 2005

Report on recommendations to the Iowa Ethics and Campaign Disclosure Board for the year ended June 30

Report on recommendations to the Iowa Ethics and Campaign Disclosure Board for the year ended June 30, 2009

Report on recommendations to the Iowa Ethics and Campaign Disclosure Board for the year ended June 30, 2009

Report on recommendations to the Iowa Ethics and Campaign Disclosure Board for the year ended June 30, 2011

Report on recommendations to the Iowa Ethics and Campaign Disclosure Board for the year ended June 30

Audit report on the Iowa Petroleum Underground Storage Tank Board for the year ended June 30, 2012

Audit report on the Iowa Petroleum Underground Storage Tank Board for the year ended June 30, 2012

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.

Audit report on the Iowa Corn Promotion Board for the years ended August 31, 2013 and 2012

Audit report on the Iowa Corn Promotion Board for the years ended August 31, 2013 and 2012

Iowa Board of Parole Annual Report, 2013

Annual Report from the Iowa Board of Parole