Experimental Design for the INL Sample Collection Operational Test

This document describes the test events and numbers of samples comprising the experimental design that was developed for the contamination, decontamination, and sampling of a building at the Idaho National Laboratory (INL). This study is referred to as the INL Sample Collection Operational Test. Specific objectives were developed to guide the construction of the experimental design. The main objective is to assess the relative abilities of judgmental and probabilistic sampling strategies to detect contamination in individual rooms or on a whole floor of the INL building. A second objective is to assess the use of probabilistic and Bayesian (judgmental + probabilistic) sampling strategies to make clearance statements of the form “X% confidence that at least Y% of a room (or floor of the building) is not contaminated. The experimental design described in this report includes five test events. The test events (i) vary the floor of the building on which the contaminant will be released, (ii) provide for varying or adjusting the concentration of contaminant released to obtain the ideal concentration gradient across a floor of the building, and (iii) investigate overt as well as covert release of contaminants. The ideal contaminant gradient would have high concentrations of contaminant in rooms near the release point, with concentrations decreasing to zero in rooms at the opposite end of the building floor. For each of the five test events, the specified floor of the INL building will be contaminated with BG, a stand-in for Bacillus anthracis. The BG contaminant will be disseminated from a point-release device located in the room specified in the experimental design for each test event. Then judgmental and probabilistic samples will be collected according to the pre-specified sampling plan. Judgmental samples will be selected based on professional judgment and prior information. Probabilistic samples will be selected in sufficient numbers to provide desired confidence for detecting contamination or clearing uncontaminated (or decontaminated) areas. Following sample collection for a given test event, the INL building will be decontaminated using Cl2O gas. For possibly contaminated areas (individual rooms or the whole floor of a building), the numbers of probabilistic samples were chosen to provide 95% confidence of detecting contaminated areas of specified sizes. The numbers of judgmental samples were chosen based on guidance from experts in judgmental sampling. For rooms that may be uncontaminated following a contamination event, or for whole floors after decontamination, the numbers of judgmental and probabilistic samples were chosen using a Bayesian approach that provides for combining judgmental and probabilistic samples to make a clearance statement of the form “95% confidence that at least 99% of the room (or floor) is not contaminated”. The experimental design also provides for making 95%/Y% clearance statements using only probabilistic samples, where Y < 99. For each test event, the numbers of samples were selected for a minimal plan (containing fewer samples) and a preferred plan (containing more samples). The preferred plan is recommended over the minimal plan. The preferred plan specifies a total of 1452 samples, 912 after contamination and 540 after decontamination. The minimal plan specifies a total of 1119 samples, 744 after contamination and 375 after decontamination. If the advantages of the “after decontamination” portion of the preferred plan are judged to be small compared to the “after decontamination” portion of the minimal plan, it is an option to combine the “after contamination” portion of the preferred plan (912 samples) with the “after decontamination” portion of the minimal plan (375 samples). This hybrid plan would involve a total of 1287 samples

Using light scattering to evaluate the separation of polydisperse nanoparticles

Appendix A Supplementary data The following are the supplementary data related to this article: Download Appendix A Supplementary data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.aca.2015.06.027. Abstract The analysis of natural and otherwise complex samples is challenging and yields uncertainty about the accuracy and precision of measurements. Here we present a practical tool to assess relative accuracy among separation protocols for techniques using light scattering detection. Due to the highly non-linear relationship between particle size and the intensity of scattered light, a few large particles may obfuscate greater numbers of small particles. Therefore, insufficiently separated mixtures may result in an overestimate of the average measured particle size. Complete separation of complex samples is needed to mitigate this challenge. A separation protocol can be considered improved if the average measured size is smaller than a previous separation protocol. Further, the protocol resulting in the smallest average measured particle size yields the best separation among those explored. If the differential in average measured size between protocols is less than the measurement uncertainty, then the selected protocols are of equivalent precision. As a demonstration, this assessment metric is applied to optimization of cross flow (V x ) protocols in asymmetric flow field flow fractionation (AF4) separation interfaced with online quasi-elastic light scattering (QELS) detection using mixtures of polystyrene beads spanning a large size range. Using this assessment metric, the V x parameter was modulated to improve separation until the average measured size of the mixture was in statistical agreement with the calculated average size of particles in the mixture. While we demonstrate this metric by improving AF4V x protocols, it can be applied to any given separation parameters for separation techniques that employ dynamic light scattering detectors. Graphical abstract Highlights • We present a tool to assess relative accuracy among separation protocols. • This metric can be applied to any techniques using light scattering detection. • An improved separation protocol minimizes the average measured particle size. • A protocol with the smallest average measured particle size is the best separation. • Metric is demonstrated by improving AF4 cross flow protocols for polystyrene beads

Development and validation of a visual grading scale for assessing image quality of AP pelvis radiographic images

OBJECTIVE: Apply psychometric theory to develop and validate a visual grading scale for assessing visual perception of AP pelvis digital image quality. METHODS: Psychometric theory was used to guide scale development. Seven phantom and 7 cadaver images of visually and objectively predetermined quality were used to help assess scale reliability and validity. 151 volunteers scored phantom images; 184 volunteers scored cadaver images. Factor analysis and Cronbach’s alpha were used to assess scale validity and reliability. RESULTS: A 24 item scale was produced. Aggregated mean volunteer scores for each image correlated with the rank order of the visually and objectively predetermined image qualities. Scale items had good inter-item correlation (≥0.2) and high factor loadings (≥0.3). Cronbach's alpha (reliability) revealed that the scale has acceptable levels of internal reliability for both phantom and cadaver images (α= 0.8 and 0.9, respectively). Factor analysis suggested the scale is multidimensional (assessing multiple quality themes). CONCLUSION: This study represents the first full development and validation of a visual image quality scale using psychometric theory. It is likely that this scale will have clinical, training and research applications. ADVANCES IN KNOWLEDGE: This article presents data to create and validate visual grading scales for radiographic examinations. The visual grading scale, for AP pelvis examinations, can act as a validated tool for future research, teaching and clinical evaluations of image quality

National Bureau of Standards Reports

Abstract: This manual provides DATAPLOT code solution to a variety of commonly occurring graphical problems. A line-by-line explanation of code is given, along with illustrations and general discussion