A-1 - Monthly Public Assistance Statistical Report Family Investment Program, November 2015

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

Effectiveness of Dynamic Messaging on Driver Behavior for Late Merge Lane Road Closures, 2009

Efforts to improve safety and traffic flow through merge areas on high volume/high speed roadways have included early merge and late merge concepts and several studies of the effectiveness of these concepts, many using Intelligent Transportation Systems for implementation. The Iowa Department of Transportation (Iowa DOT) planned to employ a system of dynamic message signs (DMS) to enhance standard temporary traffic control for lane closures and traffic merges at two bridge construction projects in western Iowa (Adair County and Cass County counties) on I-80 during the 2008 construction season. To evaluate the DMS system’s effectiveness for impacting driver merging actions, the Iowa DOT contracted with Iowa State University’s Center for Transportation Research and Education to perform the evaluation and make recommendations for future use of this system based on the results. Data were collected over four weekends, beginning August 1–4 and ending October 16–20, 2008. Two weekends yielded sufficient data for evaluation, one of transition traffic flow and the other with a period of congestion. For both of these periods, a statistical review of collected data did not indicate a significant impact on driver merging actions when the DMS messaging was activated as compared to free flow conditions with no messaging. Collection of relevant project data proved to be problematic for several reasons. In addition to personnel safety issues associated with the placement and retrieval of counting devices on a high speed roadway, unsatisfactory equipment performance and insufficient congestion to activate the DMS messaging hampered efforts. A review of the data that was collected revealed different results taken by the tube counters compared to the older model plate counters. Although variations were not significant from a practical standpoint, a statistical evaluation showed that the data, including volumes, speeds, and classifications from the two sources were not comparable at a 95% level of confidence. Comparison of data from the Iowa DOT’s automated traffic recorders (ATRs) in the area also suggested variations in results from these data collection systems. Additional comparison studies were recommended.

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, November 2015

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, December 2015

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, January 2016

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

Statistical significance of quantitative PCR.

BACKGROUND: PCR has the potential to detect and precisely quantify specific DNA sequences, but it is not yet often used as a fully quantitative method. A number of data collection and processing strategies have been described for the implementation of quantitative PCR. However, they can be experimentally cumbersome, their relative performances have not been evaluated systematically, and they often remain poorly validated statistically and/or experimentally. In this study, we evaluated the performance of known methods, and compared them with newly developed data processing strategies in terms of resolution, precision and robustness. RESULTS: Our results indicate that simple methods that do not rely on the estimation of the efficiency of the PCR amplification may provide reproducible and sensitive data, but that they do not quantify DNA with precision. Other evaluated methods based on sigmoidal or exponential curve fitting were generally of both poor resolution and precision. A statistical analysis of the parameters that influence efficiency indicated that it depends mostly on the selected amplicon and to a lesser extent on the particular biological sample analyzed. Thus, we devised various strategies based on individual or averaged efficiency values, which were used to assess the regulated expression of several genes in response to a growth factor. CONCLUSION: Overall, qPCR data analysis methods differ significantly in their performance, and this analysis identifies methods that provide DNA quantification estimates of high precision, robustness and reliability. These methods allow reliable estimations of relative expression ratio of two-fold or higher, and our analysis provides an estimation of the number of biological samples that have to be analyzed to achieve a given precision.

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, February 2016

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, March 2016

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

Statistical Summaries of Selected Iowa Streamflow Data Through September 2013, TR-669

This report is the final product of a two-year study that began October 1, 2013. In addition to the funding provided for this study by the Iowa Highway Research Board and the Iowa Department of Transportation (TR-669), the project was also funded by the U.S. Army Corps of Engineers and the U.S. Geological Survey. The report was published as an online report on January 4, 2016. The report is available online at http://dx.doi.org/10.3133/ofr20151214 . The main body of the report provides a description of the statistics presented for the streamgages and an explanation of the streamgage summaries, also included is a discussion of the USGS streamgage network in Iowa. Individual streamgage summaries are available as links listed in table 1, or all 184 streamgage summaries are available in a zipped file named “Streamgage Summaries.”

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, April 2016

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

Development of Evaluation, Rehabilitation, and Strengthening Concepts for Low Volume Bridges, Phase I Progress Report, HR-323, 1990

The primary objective of this project is to develop a design manual that would aid the county or municipal engineer in making structurally sound bridge strengthening or replacement decisions. The contents of this progress report are related only to Phase I of the study and deal primarily with defining the extent of the bridge problem in Iowa. In addition, the types of bridges to which the manual should be directed have been defined.

Statistical methods for river runoff prediction

Methods used to analyze one type of nonstationary stochastic processes?the periodically correlated process?are considered. Two methods of one-step-forward prediction of periodically correlated time series are examined. One-step-forward predictions made in accordance with an autoregression model and a model of an artificial neural network with one latent neuron layer and with an adaptation mechanism of network parameters in a moving time window were compared in terms of efficiency. The comparison showed that, in the case of prediction for one time step for time series of mean monthly water discharge, the simpler autoregression model is more efficient.

Repurposing Social Tagging Data for Extraction of Domain-level Concepts

The World Wide Web, the world¿s largest resource for information, has evolved from organizing information using controlled, top-down taxonomies to a bottom up approach that emphasizes assigning meaning to data via mechanisms such as the Social Web (Web 2.0). Tagging adds meta-data, (weak semantics) to the content available on the web. This research investigates the potential for repurposing this layer of meta-data. We propose a multi-phase approach that exploits user-defined tags to identify and extract domain-level concepts. We operationalize this approach and assess its feasibility by application to a publicly available tag repository. The paper describes insights gained from implementing and applying the heuristics contained in the approach, as well as challenges and implications of repurposing tags for extraction of domain-level concepts.