A-1 - Monthly Public Assistance Statistical Report Family Investment Program, January 2013

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

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, February 2013

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

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, March 2013

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

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, April 2013

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

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, May 2013

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

Importance of a three-dimensional measure of humeral head subluxation in osteoarthritic shoulders.

HYPOTHESIS: During total shoulder arthroplasty (TSA), humeral head subluxation may be difficult to manage. Furthermore, there is a risk for postoperative recurrence of subluxation, affecting the outcome of TSA. An accurate evaluation of the subluxation is necessary to evaluate this risk. Currently, subluxation is measured in 2 dimensions (2D), usually relative to the glenoid face. The goal of this study was to extend this measure to 3 dimensions (3D) to compare glenohumeral and scapulohumeral subluxation and to evaluate the association of subluxation with the glenoid version. MATERIALS AND METHODS: The study analyzed 112 computed tomography scans of osteoarthritic shoulders. We extended the usual 2D definition of glenohumeral subluxation, scapulohumeral subluxation, and glenoid version by measuring their orientation in 3D relative to the scapular plane and the scapular axis. We evaluated statistical associations between subluxation and version in 2D and 3D. RESULTS: Orientation of subluxation and version covered all sectors of the glenoid surface. Scapulohumeral subluxation and glenoid version were highly correlated in amplitude (R(2) = 0.71; P < .01) and in orientation (R(2) = 0.86; P < .01). Approximately every degree of glenoid version induced 1% of scapulohumeral subluxation in the same orientation of the version. Conversely, glenohumeral subluxation was not correlated to glenoid version in 2D or in 3D. CONCLUSIONS: Orientation of the humeral subluxation is rarely within the arbitrary computed tomography plane and should therefore be measured in 3D to detect out-of-plane subluxation. Scapulohumeral subluxation and glenoid version measured in 3D could bring valuable information for decision making during TSA.

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, June 2013

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

An Experimental Validation of a Statistical-Based Damage Detection Approach, January 2011

In this work, a previously-developed, statistical-based, damage-detection approach was validated for its ability to autonomously detect damage in bridges. The damage-detection approach uses statistical differences in the actual and predicted behavior of the bridge caused under a subset of ambient trucks. The predicted behavior is derived from a statistics-based model trained with field data from the undamaged bridge (not a finite element model). The differences between actual and predicted responses, called residuals, are then used to construct control charts, which compare undamaged and damaged structure data. Validation of the damage-detection approach was achieved by using sacrificial specimens that were mounted to the bridge and exposed to ambient traffic loads and which simulated actual damage-sensitive locations. Different damage types and levels were introduced to the sacrificial specimens to study the sensitivity and applicability. The damage-detection algorithm was able to identify damage, but it also had a high false-positive rate. An evaluation of the sub-components of the damage-detection methodology and methods was completed for the purpose of improving the approach. Several of the underlying assumptions within the algorithm were being violated, which was the source of the false-positives. Furthermore, the lack of an automatic evaluation process was thought to potentially be an impediment to widespread use. Recommendations for the improvement of the methodology were developed and preliminarily evaluated. These recommendations are believed to improve the efficacy of the damage-detection approach.

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, July 2013

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

The InterActive Choice Aid: A new approach to supporting online consumer decision making

Interactive Choice Aid (ICA) is a decision aid, introduced in this paper, that systematically assists consumers with online purchase decisions. ICA integrates aspects from prescriptive decision theory, insights from descriptive decision research, and practical considerations; thereby combining pre-existing best practices with novel features. Instead of imposing an objectively ideal but unnatural decision procedure on the user, ICA assists the natural process of human decision-making by providing explicit support for the execution of the user's decision strategies. The application contains an innovative feature for in-depth comparisons of alternatives through which users' importance ratings are elicited interactively and in a playful way. The usability and general acceptance of the choice aid was studied; results show that ICA is a promising contribution and provides insights that may further improve its usability.

Statistical Modeling of the Eye for Multimodal Treatment Planning for External Beam Radiation Therapy of Intraocular Tumors.

PURPOSE: Ocular anatomy and radiation-associated toxicities provide unique challenges for external beam radiation therapy. For treatment planning, precise modeling of organs at risk and tumor volume are crucial. Development of a precise eye model and automatic adaptation of this model to patients' anatomy remain problematic because of organ shape variability. This work introduces the application of a 3-dimensional (3D) statistical shape model as a novel method for precise eye modeling for external beam radiation therapy of intraocular tumors. METHODS AND MATERIALS: Manual and automatic segmentations were compared for 17 patients, based on head computed tomography (CT) volume scans. A 3D statistical shape model of the cornea, lens, and sclera as well as of the optic disc position was developed. Furthermore, an active shape model was built to enable automatic fitting of the eye model to CT slice stacks. Cross-validation was performed based on leave-one-out tests for all training shapes by measuring dice coefficients and mean segmentation errors between automatic segmentation and manual segmentation by an expert. RESULTS: Cross-validation revealed a dice similarity of 95% ± 2% for the sclera and cornea and 91% ± 2% for the lens. Overall, mean segmentation error was found to be 0.3 ± 0.1 mm. Average segmentation time was 14 ± 2 s on a standard personal computer. CONCLUSIONS: Our results show that the solution presented outperforms state-of-the-art methods in terms of accuracy, reliability, and robustness. Moreover, the eye model shape as well as its variability is learned from a training set rather than by making shape assumptions (eg, as with the spherical or elliptical model). Therefore, the model appears to be capable of modeling nonspherically and nonelliptically shaped eyes.