Deploying the Maintenance Decision Support System (MDSS) in Iowa, 2003

Adverse weather conditions dramatically affect the nation’s surface transportation system. The development of a prototype winter Maintenance Decision Support System (MDSS) is part of the Federal Highway Administration’s effort to produce a prototype tool for decision support to winter road maintenance managers to help make the highways safer for the traveling public. The MDSS is based on leading diagnostic and prognostic weather research capabilities and road condition algorithms, which are being developed at national research centers. In 2003, the Iowa Department of Transportation was chosen as a field test bed for the continuing development of this important research program. The Center for Transportation Research and Education assisted the Iowa Department of Transportation by collecting and analyzing surface condition data. The Federal Highway Administration also selected five national research centers to participate in the development of the prototype MDSS. It is anticipated that components of the prototype MDSS system developed by this project will ultimately be deployed by road operating agencies, including state departments of transportation, and generally supplied by private vendors.

Remote Sensing Change Analysis Methodology to Support Traffic Monitoring Programs, 2003

The Federal Highway Administration mandates that states collect traffic count information at specified intervals to meet the needs of the Highway Performance Monitoring System (HPMS). A manual land use change detection method was employed to determine the effects of land use change on traffic for Black Hawk County, Iowa, from 1994 to 2002. Results from land use change detection could enable redirecting traffic count activities and related data management resources to areas that are experiencing the greatest changes in land use and related traffic volume. Including a manual land use change detection process in the Iowa Department of Transportation’s traffic count program has the potential to improve efficiency by focusing monitoring activities in areas more likely to experience significant increase in traffic.

Remote Sensing Change Analysis Methodology to Support Traffic Monitoring Programs: Phase 2, 2004

The Center for Transportation Research and Education (CTRE) issued a report in July 2003, based on a sample study of the application of remote sensed image land use change detection to the methodology of traffic monitoring in Blackhawk County, Iowa. In summary, the results indicated a strong correlation and a statistically significant regression coefficient between the identification of built-up land use change areas from remote sensed data and corresponding changes in traffic patterns, expressed as vehicle miles traveled (VMT). Based on these results, the Iowa Department of Transportation (Iowa DOT) requested that CTRE expand the study area to five counties in the southwest quadrant of the state. These counties are scheduled for traffic counts in 2004, and the Iowa DOT desired the data to 1) evaluate the current methodology used to place the devices; 2) potentially influence the placement of traffic counting devices in areas of high built-up land use change; and 3) determine if opportunities exist to reduce the frequency and/or density of monitoring activity in lower trafficked rural areas of the state. This project is focused on the practical application of built-up land use change data for placement of traffic count data recording devices in five southwest Iowa counties.

Multi-source composite kernels for urban image classification

This letter presents advanced classification methods for very high resolution images. Efficient multisource information, both spectral and spatial, is exploited through the use of composite kernels in support vector machines. Weighted summations of kernels accounting for separate sources of spectral and spatial information are analyzed and compared to classical approaches such as pure spectral classification or stacked approaches using all the features in a single vector. Model selection problems are addressed, as well as the importance of the different kernels in the weighted summation.

Iowa State Plan on Aging: Balancing Long-Term Community Support Services for Aging Iowans, FY 2014–2015

This plan outlines the activities and strategies that the IDA will purse to achieve its goals, objectives, and expected outcomes in modernizing Iowa’s aging network. The goals that will move Iowa’s state plan.

Autism Support Program Annual Report, December 2015

The Autism Support Program provides funding for applied behavioral analysis services for children under the age of nine who meet certain diagnostic and financial eligibility criteria.

Family Support Subsidy and Children at Home Program SFY 2015, October 30, 2015

The Family Support Subsidy (FSS) program provides a monthly payment to help families with the cost of raising a child with a developmental disability. Parents of children with disabilities were very active in getting state and federal policy makers to look at how they could divert some of the funds going to institutional care. Families with severely disabled children wanted to raise their children at home but were met with a lot of resistance and policy barriers when they tried to get home-based support.

Prospective study of the barriers to nutritional support in a paediatric intensive care unit : P16

Introduction and aim: Children hospitalised in a paediatric intensive care unit (PICU) are mainly fed by nutritional support (NS) which may often be interrupted. The aims of the study were to verify the relationship between prescribed (PEI) and actual energy intake (AEI) and to identify the reasons for NS interruption. Methods: Prospective study in a PICU. PEI and AEI from day 1 to 15, type of NS (enteral, parenteral, mixed), position of the feeding tube, interruptions in NS and reasons for these were noted. Inter - ruptions were classified in categories of barriers and their frequency and duration were analysed. Results: Fifteen children (24 ± 25.2 months) were studied for 84 days. The NS was exclusively enteral (69%) or mixed (31%). PEI were significantly higher than AEI (54.7 ± 32.9 vs 49.2 ± 33.6 kcal/kg, p = 0.0011). AEI represented 93% of the PEI. Ninety-eight interruptions were noted and lasted 189 h, i.e. 9.4% of the evaluated time. The most frequent barriers were nursing procedures, respiratory physiotherapy and unavailability of intravenous access. The longest were caused by the necessity to stop NS for surgery or diagnostic studies, to treat burns or to carry out medical procedures. Conclusion: AEI in PICU were inferior by 7% to PEI, considerably lower than in adult studies. Making these results available to medical staff for greater anticipation and compensation could reduce NS interruptions. Starving protocols should be reconsidered.

Evaluation of the multicriteria approval method for timber-harvesting group decision support

Abstract

Interest rates mapping

The present study deals with the analysis and mapping of Swiss franc interest rates. Interest rates depend on time and maturity, defining term structure of the interest rate curves (IRC). In the present study IRC are considered in a two-dimensional feature space - time and maturity. Exploratory data analysis includes a variety of tools widely used in econophysics and geostatistics. Geostatistical models and machine learning algorithms (multilayer perceptron and Support Vector Machines) were applied to produce interest rate maps. IR maps can be used for the visualisation and pattern perception purposes, to develop and to explore economical hypotheses, to produce dynamic asset-liability simulations and for financial risk assessments. The feasibility of an application of interest rates mapping approach for the IRC forecasting is considered as well. (C) 2008 Elsevier B.V. All rights reserved.

Detection and chemical profiling of medicine counterfeits by Raman spectroscopy and chemometrics

Raman spectroscopy combined with chemometrics has recently become a widespread technique for the analysis of pharmaceutical solid forms. The application presented in this paper is the investigation of counterfeit medicines. This increasingly serious issue involves networks that are an integral part of industrialized organized crime. Efficient analytical tools are consequently required to fight against it. Quick and reliable authentication means are needed to allow the deployment of measures from the company and the authorities. For this purpose a method in two steps has been implemented here. The first step enables the identification of pharmaceutical tablets and capsules and the detection of their counterfeits. A nonlinear classification method, the Support Vector Machines (SVM), is computed together with a correlation with the database and the detection of Active Pharmaceutical Ingredient (API) peaks in the suspect product. If a counterfeit is detected, the second step allows its chemical profiling among former counterfeits in a forensic intelligence perspective. For this second step a classification based on Principal Component Analysis (PCA) and correlation distance measurements is applied to the Raman spectra of the counterfeits.

Mixed spectral-structural classification of very high resolution images with summation kernels

In this paper, mixed spectral-structural kernel machines are proposed for the classification of very-high resolution images. The simultaneous use of multispectral and structural features (computed using morphological filters) allows a significant increase in classification accuracy of remote sensing images. Subsequently, weighted summation kernel support vector machines are proposed and applied in order to take into account the multiscale nature of the scene considered. Such classifiers use the Mercer property of kernel matrices to compute a new kernel matrix accounting simultaneously for two scale parameters. Tests on a Zurich QuickBird image show the relevance of the proposed method : using the mixed spectral-structural features, the classification accuracy increases of about 5%, achieving a Kappa index of 0.97. The multikernel approach proposed provide an overall accuracy of 98.90% with related Kappa index of 0.985.