<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">H. J. Kim</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The effect of statistical disclosure limitation on parameter estimation for a finite population</style></title><secondary-title><style face="normal" font="default" size="100%">J. Survey Statistics and Methodology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">Submitted</style></year></dates><volume><style face="normal" font="default" size="100%">to appear</style></volume><pages><style face="normal" font="default" size="100%">to appear</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sartore, L.</style></author><author><style face="normal" font="default" size="100%">Toppin, K.</style></author><author><style face="normal" font="default" size="100%">Spiegelman, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimated Covariance Matrices Associated with Calibration</style></title><secondary-title><style face="normal" font="default" size="100%">JSM 2017</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Agriculture</style></keyword><keyword><style  face="normal" font="default" size="100%">Calibration</style></keyword><keyword><style  face="normal" font="default" size="100%">Census</style></keyword><keyword><style  face="normal" font="default" size="100%">Estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">NASS</style></keyword><keyword><style  face="normal" font="default" size="100%">Survey</style></keyword><keyword><style  face="normal" font="default" size="100%">Variance</style></keyword><keyword><style  face="normal" font="default" size="100%">Weighting</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">Submitted</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.niss.org/sites/default/files/Sartore_Variance_Estim_20170926.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Surveys often provide numerous estimates of population parameters. Some of the population values&amp;nbsp;may be known to lie within a small range of values with a high level of certainty. Calibration is used&amp;nbsp;to adjust survey weights associated with the observations within a data set. This process ensures&amp;nbsp;that the “sample” estimates for the target population totals (benchmarks) lie within the anticipated&amp;nbsp;ranges of those population values. The additional uncertainty due to the calibration process needs&amp;nbsp;to be captured. In this paper, some methods for estimating the variance of the population totals are&amp;nbsp;proposed for an algorithmic calibration process based on minimizing the L1-norm relative error.&amp;nbsp;The estimated covariance matrices for the calibration totals are produced either by linear approximations&amp;nbsp;or bootstrap techniques. Specific data structures are required to allow for the computation&amp;nbsp;of massively large covariance matrices. In particular, the implementation of the proposed algorithms&amp;nbsp;exploits sparse matrices to reduce the computational burden and memory usage. The computational&amp;nbsp;efficiency is shown by a simulation study.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abernethy, J.</style></author><author><style face="normal" font="default" size="100%">Sartore, L.</style></author><author><style face="normal" font="default" size="100%">Benecha, H.</style></author><author><style face="normal" font="default" size="100%">Spiegelman, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimation of Capture Probabilities by Accounting for Sample Designs</style></title><secondary-title><style face="normal" font="default" size="100%">JSM 2017</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Agriculture</style></keyword><keyword><style  face="normal" font="default" size="100%">CaptureRecapture</style></keyword><keyword><style  face="normal" font="default" size="100%">Estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">government</style></keyword><keyword><style  face="normal" font="default" size="100%">NASS</style></keyword><keyword><style  face="normal" font="default" size="100%">Research</style></keyword><keyword><style  face="normal" font="default" size="100%">SampleDesigns</style></keyword><keyword><style  face="normal" font="default" size="100%">Weights</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">Submitted</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.niss.org/sites/default/files/Abernethy_Capture_Probs_20170920.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) conducts the Census of Agriculture every five years to estimate the number of U.S. farms, as well as other agriculturally related population totals. NASS applies a Dual-System Estimation (DSE) methodology on data collected from the Census and the June Area Survey (JAS) to estimate the number of farms in the U.S.. Traditional multinomial-based capture-recapture methodology requires a model to estimate the probability of capture for every captured operation on either survey. Of course, the selection probabilities associated with the JAS area frame design are different from those associated with the Census. Such a difference makes it difficult to compute the exact JAS selection probabilities for farm records captured only by the Census. For this reason, we propose and compare three methods for estimating the overall capture probability. The first two methods involve approximating the JAS selection probabilities and the third conditions them out. We compare these three techniques to investigate their precision through a simulation study.&lt;/p&gt;
</style></abstract><custom1><style face="normal" font="default" size="100%">&lt;p&gt;In Proceedings of the Government Statistics Section, JSM 2017. Download&amp;nbsp;https://www.niss.org/sites/default/files/Abernethy_Capture_Probs_20170920.pdf&lt;/p&gt;
</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Benecha, H.</style></author><author><style face="normal" font="default" size="100%">Abreu, D.</style></author><author><style face="normal" font="default" size="100%">Abernethy, J.</style></author><author><style face="normal" font="default" size="100%">Sartore, L.</style></author><author><style face="normal" font="default" size="100%">Young, L. Y.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%"> Evaluation of a New Approach for Estimating the Number of U.S. Farms</style></title><secondary-title><style face="normal" font="default" size="100%">JSM 2017</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Agriculture</style></keyword><keyword><style  face="normal" font="default" size="100%">Area-frame</style></keyword><keyword><style  face="normal" font="default" size="100%">BigData</style></keyword><keyword><style  face="normal" font="default" size="100%">Capture-Recapture</style></keyword><keyword><style  face="normal" font="default" size="100%">List Frame</style></keyword><keyword><style  face="normal" font="default" size="100%">Logistic Regression</style></keyword><keyword><style  face="normal" font="default" size="100%">Misclassification Error</style></keyword><keyword><style  face="normal" font="default" size="100%">NASS</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">Submitted</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.niss.org/sites/default/files/Benecha_Estim_Farms_20170929.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;USDA’s National Agricultural Statistics Service (NASS) employs the June Area Survey (JAS) to produce annual&amp;nbsp;estimates of U.S. farm numbers. The JAS is an area-frame-based survey conducted every year during the first two&amp;nbsp;weeks of June. NASS also publishes an independent estimate of the number of farms from the quinquennial Census&amp;nbsp;of Agriculture. Studies conducted by NASS have shown that farm number estimates from the JAS can be biased,&amp;nbsp;mainly due to misclassification of agricultural tracts during the pre-screening and data collection processes. To adjust&amp;nbsp;for the bias, NASS has developed a capture-recapture model that uses NASS’s list frame as the second sample, where&amp;nbsp;estimation is performed based on records in the JAS with matches in the list frame. In the current paper, we describe&amp;nbsp;an alternative capture-recapture approach that uses all available data from the JAS and the Census of Agriculture to&amp;nbsp;correct for biases due to misclassification and to produce more stable farm number estimates.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bellow M.E.</style></author><author><style face="normal" font="default" size="100%">Daniel K.</style></author><author><style face="normal" font="default" size="100%">Gorsak M.</style></author><author><style face="normal" font="default" size="100%">Erciulescu A.L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluating Record Linkage Software for Agricultural Surveys</style></title><secondary-title><style face="normal" font="default" size="100%">JSM Proceedings. Survey Research Methods Section. Alexandria, VA: American Statistical Association.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ww2.amstat.org/MembersOnly/proceedings/2016/data/assets/pdf/389754.pdf.</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">3225-3235</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lawrence H. Cox</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Enabling statistical analysis of suppressed tabular data, in Privacy in Statistical Databases</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Heidelberg</style></pub-location><volume><style face="normal" font="default" size="100%">8744</style></volume><pages><style face="normal" font="default" size="100%">1-10</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fogel, P.</style></author><author><style face="normal" font="default" size="100%">Gobinet, C.</style></author><author><style face="normal" font="default" size="100%">Young, S.S.</style></author><author><style face="normal" font="default" size="100%">Zugaj, D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluation of unmixing methods for the separation of Quantum Dot sources</style></title><secondary-title><style face="normal" font="default" size="100%">Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009. WHISPERS ’09. First Workshop on</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bayesian methods</style></keyword><keyword><style  face="normal" font="default" size="100%">Bayesian positive source separation</style></keyword><keyword><style  face="normal" font="default" size="100%">BPSS</style></keyword><keyword><style  face="normal" font="default" size="100%">cadmium compounds</style></keyword><keyword><style  face="normal" font="default" size="100%">CdSe</style></keyword><keyword><style  face="normal" font="default" size="100%">consensus nonnegative matrix factorization</style></keyword><keyword><style  face="normal" font="default" size="100%">Fluorescence</style></keyword><keyword><style  face="normal" font="default" size="100%">hyperspectral images</style></keyword><keyword><style  face="normal" font="default" size="100%">Hyperspectral imaging</style></keyword><keyword><style  face="normal" font="default" size="100%">hyperspectral system</style></keyword><keyword><style  face="normal" font="default" size="100%">ICA</style></keyword><keyword><style  face="normal" font="default" size="100%">II-VI semiconductors</style></keyword><keyword><style  face="normal" font="default" size="100%">independent component analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Nanobioscience</style></keyword><keyword><style  face="normal" font="default" size="100%">Nanocrystals</style></keyword><keyword><style  face="normal" font="default" size="100%">nanometer dimensions</style></keyword><keyword><style  face="normal" font="default" size="100%">NMF</style></keyword><keyword><style  face="normal" font="default" size="100%">Photonic crystals</style></keyword><keyword><style  face="normal" font="default" size="100%">Probes</style></keyword><keyword><style  face="normal" font="default" size="100%">quantum dot sources</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantum dots</style></keyword><keyword><style  face="normal" font="default" size="100%">semiconductor crystals</style></keyword><keyword><style  face="normal" font="default" size="100%">semiconductor quantum dots</style></keyword><keyword><style  face="normal" font="default" size="100%">Source separation</style></keyword><keyword><style  face="normal" font="default" size="100%">spatial localization</style></keyword><keyword><style  face="normal" font="default" size="100%">ultraviolet spectra</style></keyword><keyword><style  face="normal" font="default" size="100%">unmixing methods</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pages><style face="normal" font="default" size="100%">1-4</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4244-4686-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Quantum Dots (QDs) are semiconductor crystals with nanometer dimensions, which have fluorescence properties that can be adjusted through controlling their diameter. Under ultraviolet light excitation, these nanocrystals re-emit photons in the visible spectrum, with a wavelength ranging from red to blue as their size diminishes. We created an experiment to evaluate unmixing methods for hyperspectral images. The wells of a matrix [3 times 3] were filled with individual or up to three of five QDs. The matrix was imaged by a hyperspectral system (Photon Etc., Montreal, QC, CA) and a data ldquocuberdquo of 512 rows times 512 columns times 63 wavelengths was generated. For unmixing, we tested three approaches: independent component analysis (ICA), Bayesian positive source separation (BPSS) and our new consensus non-negative matrix factorization (CNFM) method. For each of these methods, we assessed the ability to separate the different sources from both spectral and spatial localization points of view. In this situation, we showed that BPSS and CNMF model estimates were very close to the original design of our experiment and were better than the ICA results. However, the time needed for the BPSS model to converge is substantially higher than CNMF. In addition, we show how the CNMF coefficients can be used to provide reasonable bounds for the number of sources, a key issue for unmixing methods, and allow for an effective segmentation of the spatial signal.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mi-ja Woo</style></author><author><style face="normal" font="default" size="100%">Jerome Reiter</style></author><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimation of propensity scores using generalized additive models</style></title><secondary-title><style face="normal" font="default" size="100%">Statisics in Medicine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">3806-3816</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wang, X. S.</style></author><author><style face="normal" font="default" size="100%">Salloum, G.A.</style></author><author><style face="normal" font="default" size="100%">Chipman, H.A.</style></author><author><style face="normal" font="default" size="100%">Welch, W.J.</style></author><author><style face="normal" font="default" size="100%">Young, S.S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploration of cluster structure-activity relationship analysis in efficient high-throughput screening</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemical Information and Modeling</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">47</style></volume><pages><style face="normal" font="default" size="100%">1206-1214</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Sequential screening has become increasingly popular in drug discovery. It iteratively builds quantitative structure-activity relationship (QSAR) models from successive high-throughput screens, making screening more effective and efficient. We compare cluster structure-activity relationship analysis (CSARA) as a QSAR method with recursive partitioning (RP), by designing three strategies for sequential collection and analysis of screening data. Various descriptor sets are used in the QSAR models to characterize chemical structure, including high-dimensional sets and some that by design have many variables not related to activity. The results show that CSARA outperforms RP. We also extend the CSARA method to deal with a continuous assay measurement.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hawkins, D.M.</style></author><author><style face="normal" font="default" size="100%">Wolfinger, R.D.</style></author><author><style face="normal" font="default" size="100%">L. Liu</style></author><author><style face="normal" font="default" size="100%">Young. S.S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploring blood spectra for signs of ovarian cancer</style></title><secondary-title><style face="normal" font="default" size="100%">Chance</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">19-23</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bruce E Ankenman</style></author><author><style face="normal" font="default" size="100%">Hui Liu</style></author><author><style face="normal" font="default" size="100%">Alan F. Karr</style></author><author><style face="normal" font="default" size="100%">Jeffrey D. Picka</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Experimental designs for estimating a response surface and variance components</style></title><secondary-title><style face="normal" font="default" size="100%">Technometrics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">44</style></volume><pages><style face="normal" font="default" size="100%">45-54</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">C.-M. Aldea</style></author><author><style face="normal" font="default" size="100%">M. Ghandehari</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimation of water flow through cracked concrete under load</style></title><secondary-title><style face="normal" font="default" size="100%">ACI Materials Journal</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">97</style></volume><pages><style face="normal" font="default" size="100%">567?575</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This research studied the relationship between cracking and water permeability of normal-strength concrete under load and compared the experimental results with theoretical models. A feedback-controlled wedge splitting test was used to generate width-controlled cracks. Speckle interferometry was used to record the cracking history. Water permeability of the loaded specimens was evaluated by a low-pressure water permeability test at the designed crack mouth opening displacements (CMODs). Water permeability results were compared with those previously obtained for unloaded specimens for which cracks were induced by a feedback-controlled splitting tension test. The experimental results indicate that water permeability of cracked material significantly increases with increasing crack width. The flow for the same cracking level is repeatable regardless of the procedure used for inducing the cracks. No direct relationship between water flow and crack length was observed, whereas clear relationships existed between CMOD or crack area and flow characteristics. Experimentally measured flow was compared with theoretical models of flow through cracked rocks with parallel walls and a correction factor accounting for the tortuosity of the crack was determined. Calculated flow through cracks induced by a wedge-splitting test provided an acceptable approximation of the measured flow.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">C.-M. Aldea</style></author><author><style face="normal" font="default" size="100%">J.D. Picka</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author><author><style face="normal" font="default" size="100%">S.S. Jaiswal</style></author><author><style face="normal" font="default" size="100%">T. Igusa</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Experimental and statistical study of chloride permeability of cracked high strength concrete</style></title><secondary-title><style face="normal" font="default" size="100%">ASTM Cement, Concrete and Aggregates</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year><pub-dates><date><style  face="normal" font="default" size="100%">December</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">000-000</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Within any cast cylinder of concrete, the coarse aggregate will tend to be inhomogeneously distributed. This variability may arise as a result of segregation caused by gravity or as a result of the wall effect that is caused by the inability of the aggregate to penetrate the walls of the mold. Using methods from image analysis, stereology, and statistics, local estimates of aggregate inhomogeniety are defined that quantify phenomena that have been qualitatively described in the past. These methods involve modification of the two-dimensional images to prepare them for analysis, as well as simple diagnostic statistics for determining the presence of a wall effect. While the techniques presented herein are developed specifically for cast cylinders, they can be generalized to other cast or cored concrete specimens.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr.</style></author><author><style face="normal" font="default" size="100%">C.-M. Aldea</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Effect of cracking on water and chloride permeability of concrete</style></title><secondary-title><style face="normal" font="default" size="100%">ACSE Journal of Materials in Civil Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">181?187</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The goal of this research was to study the relationship between cracking and concrete permeability and to support accounting for permeability and cracking resistance to other factors besides strength, as criteria to be considered in mix design to achieve a durable concrete. The effect of material composition [normal-strength concrete (NSC) and high-strength concrete (HSC) with two different mix designs] and crack width (ranging from 50 to 400 ?m) on water and chloride permeability were examined. Cracks of designed widths were induced in the concrete specimens using a feedback-controlled splitting tensile test. Chloride permeability of the cracked samples was evaluated using a rapid chloride permeability test and the water permeability of cracked concrete was then evaluated by a low-pressure water permeability test. Uncracked HSC was less water permeable than NSC, as expected, but cracking changed the material behavior in terms of permeability. Both NSC and HSC were affected by cracking, and the water permeability of cracked samples increased with increasing crack width. Among the tested materials, only HSC with a very low water-to-cement ratio chloride permeability was sensitive with respect to cracking. Results indicate that the water permeability is significantly more sensitive than the chloride permeability with respect to the crack widths used in this study.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">C.-M. Aldea</style></author><author><style face="normal" font="default" size="100%">S. P. Shah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Effect of microcracking on durability of high strength concrete</style></title><secondary-title><style face="normal" font="default" size="100%">Transportation Research Record</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><volume><style face="normal" font="default" size="100%">1668</style></volume><pages><style face="normal" font="default" size="100%">86-90</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The relationship between cracking and chloride and water permeability of high-strength concrete (HSC) was studied. Two different mix designs were used: HSC_1 (w/b = 0.31) and HSC_2 (w/b = 0.25). The effects of crack width and sample thickness on permeability were examined. Cracks of designed widths were induced in the concrete specimens using the feedback-controlled splitting tensile test. Chloride permeability of the cracked samples was evaluated by using a rapid chloride permeability test. The water permeability of cracked concrete was then evaluated by a low-pressure water permeability test. Among the materials tested, only high-strength concrete with a very low water-to-cement ratio conductivity is sensitive with respect to cracking. The water permeability of cracked HSC significantly increases with increasing crack width. Among the parameters considered, crack parameters significantly affect water permeability, and there is little thickness effect. The results indicate that the water permeability is significantly more sensitive than conductivity with respect to the crack width used.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sen, Ashish</style></author><author><style face="normal" font="default" size="100%">P. Metaxatos</style></author><author><style face="normal" font="default" size="100%">Sööt, Siim</style></author><author><style face="normal" font="default" size="100%">Piyushimita Thakuriah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimation of Demand due to Welfare Reform</style></title><secondary-title><style face="normal" font="default" size="100%">In Papers in Regional Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><volume><style face="normal" font="default" size="100%">78</style></volume><pages><style face="normal" font="default" size="100%">195 – 211</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Todd L. Graves</style></author><author><style face="normal" font="default" size="100%">Harrold, Mary Jean</style></author><author><style face="normal" font="default" size="100%">Kim, Jung-Min</style></author><author><style face="normal" font="default" size="100%">Adam Porter</style></author><author><style face="normal" font="default" size="100%">Rothermel, Gregg</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Empirical Study of Regression Test Selection Techniques</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 20th International Conference on Software Engineering</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">ICSE ’98</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dl.acm.org/citation.cfm?id=302163.302182</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE Computer Society</style></publisher><pub-location><style face="normal" font="default" size="100%">Washington, DC, USA</style></pub-location><pages><style face="normal" font="default" size="100%">188–197</style></pages><isbn><style face="normal" font="default" size="100%">0-8186-8368-6</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sen, Ashish</style></author><author><style face="normal" font="default" size="100%">Sööt, Siim</style></author><author><style face="normal" font="default" size="100%">Piyushimita Thakuriah</style></author><author><style face="normal" font="default" size="100%">Condie, Helen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimation of static travel times in a dynamic route guidance system—II</style></title><secondary-title><style face="normal" font="default" size="100%">Mathematical and Computer Modelling</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Advanced Traveler Information Systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Dynamic Route Guidance</style></keyword><keyword><style  face="normal" font="default" size="100%">Link travel times</style></keyword><keyword><style  face="normal" font="default" size="100%">Static estimates</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">67–85</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In an earlier paper a method for computing static profiles of link travel times was given. In this paper, the centrality of such profiles for ATIS is examined and the methods given in the earlier paper are applied to actual data. Except for a minor, easily correctable problem, the methods are shown to work very well under real-life conditions.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. F. Karr</style></author><author><style face="normal" font="default" size="100%">A. A. Porter</style></author><author><style face="normal" font="default" size="100%">L. G. Votta</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An empirical exploration of code evolution</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the InternationalWorkshop on Empirical Studies of Software Maintenance</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">P. Styer</style></author><author><style face="normal" font="default" size="100%">McMillan, N</style></author><author><style face="normal" font="default" size="100%">Gao, F</style></author><author><style face="normal" font="default" size="100%">Davis, J</style></author><author><style face="normal" font="default" size="100%">Jerome Sacks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Effect of outdoor airborne particulate matter on daily death count</style></title><secondary-title><style face="normal" font="default" size="100%">Environmental Health Perspectives</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><volume><style face="normal" font="default" size="100%">103</style></volume><pages><style face="normal" font="default" size="100%">490–497</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;To investigate the possible relationship between airborne particulate matter and mortality, we developed regression models of daily mortality counts using meteorological covariates and measures of outdoor PM10. Our analyses included data from Cook County, Illinois, and Salt Lake County, Utah. We found no evidence that particulate matter &amp;lt; or = 10 microns (PM10) contributes to excess mortality in Salt Lake County, Utah. In Cook County, Illinois, we found evidence of a positive PM10 effect in spring and autumn, but not in winter and summer. We conclude that the reported effects of particulates on mortality are unconfirmed.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sen, Ashish</style></author><author><style face="normal" font="default" size="100%">Piyushimita Thakuriah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimation of Static Travel Times in a Dynamic Route Guidance System</style></title><secondary-title><style face="normal" font="default" size="100%">Mathematical and Computer Modelling</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Advanced Travel Information System</style></keyword><keyword><style  face="normal" font="default" size="100%">Autonomous route guidance</style></keyword><keyword><style  face="normal" font="default" size="100%">Dynamic Route Guidance</style></keyword><keyword><style  face="normal" font="default" size="100%">Link travel time estimate</style></keyword><keyword><style  face="normal" font="default" size="100%">Link Travel Time Process</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">83–101</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In an Advanced Traveler Information System where route guidance is provided, a driver chooses a route before he/she actually traverses the links in the route. For such systems, link travel times need to be forecasted. However, information on several thousand links would take a fair amount of time to be conveyed to the driver, and very few drivers would be willing to wait very long to get route information, In the ADVANCE demonstration, to be implemented in suburban Chicago, the in-vehicle unit in each participating vehicle will be provided with the capability of accessing default travel time information, which will offer the vehicle with an autonomous navigation capability. The default estimates will be overwritten by dynamic up-to-the-minute forecasts if such forecasts are different from the default estimates. This paper describes the approach used to compute default travel times estimates.&lt;/p&gt;
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