Board of Parole Performance Report, FY2012

Agency Performance Report

Department of Commerce Iowa Utilities Board, Performance Report, FY2013

This report highlights the services the IUB provided to Iowans, along with results achieved to ensure reliability, and to improve and expand utility service infrastructure in Iowa. This information is provided in accordance with the State of Iowa Accountable Government Act, Iowa Code chapter 8E. The two basic business functions of the IUB are utility regulation and compliance, and resource management. This report covers performance information for both of these areas.

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.

Iowa Board of Parole Strategic Plan 2012-2013

This report outlines the strategic plan for Iowa Board of Parole Strategic Plan including,goals and mission.

Iowa Board of Parole Performance Plan, FY2014

Agency Performance Plan, Iowa Board of Parole

Public Employment Relations Board Performance Plan, FY2014

Agency Performance Plan, Public Employment Relations Board

Iowa Utilities Board, Strategic Plan, 2012

This report outlines the strategic plan for Iowa Utilities Board including, goals and mission.

Iowa Highway Research Board FY 2013 Annual Report

This report, entitled Iowa Highway Research and Development Activities FY2013 is submitted in compliance with Iowa Code sections 310.36 and 312.3A, which directs the submission of a report of the Secondary Road Research Fund and the Street Research Fund, respectively. It is a report of the status of research and development projects in progress on June 30, 2013. It is also a report on projects completed during the fiscal year beginning July 1, 2012 and ending June 30, 2013. Detailed information on each of the research and development projects mentioned in this report is available from the office of Research and Analytics, Performance and Technology Division, Iowa Department of Transportation. All approved reports are also online for viewing at: www.iowadot.gov/operationsresearch/reports.aspx.

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.

Iowa connections : get on board with passenger rail!

Iowa's rail network offers a great opportunity for passenger rail service, connecting Iowa's largest urban areas to Chicago and potential other Midwest metropolitan centers. The Iowa Department of Transportation (DOT), working with the state of Illinois, Iowa cities, planning organizations and advocacy groups, has created a bold new vision called Iowa Connections. The vision will create a passenger rail network that connects Iowans to each other and the country, and makes Iowa a more attractive place to live, work and visit.

Iowa Board of Educational Examiners Licensure Handbook, January 15, 2013

Iowa Board of Educational Examiners Licensure Handbook

Field Evaluation of Compaction Monitoring Technology: Phase II Volumes 1 and 2, TR-495, 2006

This report documents an extensive field program carried out to identify the relationships between soil engineering properties, as measured by various in situ devices, and the results of machine compaction monitoring using prototype compaction monitoring technology developed by Caterpillar Inc. Primary research tasks for this study include the following: (1) experimental testing and statistical analyses to evaluate machine power in terms of the engineering properties of the compacted soil (e.g., density, strength, stiffness) and (2) recommendations for using the compaction monitoring technology in practice. The compaction monitoring technology includes sensors that monitor the power consumption used to move the compaction machine, an on-board computer and display screen, and a GPS system to map the spatial location of the machine. In situ soil density, strength, and stiffness data characterized the soil at various stages of compaction. For each test strip or test area, in situ soil properties were compared directly to machine power values to establish statistical relationships. Statistical models were developed to predict soil density, strength, and stiffness from the machine power values. Field data for multiple test strips were evaluated. The R2 correlation coefficient was generally used to assess the quality of the regressions. Strong correlations were observed between averaged machine power and field measurement data. The relationships are based on the compaction model derived from laboratory data. Correlation coefficients (R2) were consistently higher for thicker lifts than for thin lifts, indicating that the depth influencing machine power response exceeds the representative lift thickness encountered under field conditions. Caterpillar Inc. compaction monitoring technology also identified localized areas of an earthwork project with weak or poorly compacted soil. The soil properties at these locations were verified using in situ test devices. This report also documents the steps required to implement the compaction monitoring technology evaluated.

Iowa Board of Nursing Newsletter, August-September-October 2009, Vol. 28, no. 3

Newsletter produced by the Iowa Board of Nursing for nurses to keep them informed to CE classes, renewals, board meetings, etc. Produced 4 times a year. Also known as Nursing Newsletter.