Glidewell (United States)

This study aims to assess and compare the shrinkage, accuracy, and accuracy stability of a novel 3D-printing model resin and eight commercially available 3D-printing model resin materials. The experimental model resin was developed by our 3D-printing proprietary resin technology. Eight commercially available 3D-printing model resins were included for comparison. The AcuVol video imaging technique was used to test volumetric shrinkage. Full-arch tooth models were printed for each model resin via digital light processing (DLP) technology. The 3D average distance between the scanned model and the designed CAD digital file was applied to determine the dimensional accuracy of the 3D-printed full-arch tooth models. One-way ANOVA and Tukey's post hoc test (p < 0.05) were utilized to analyze the average values of volumetric shrinkage and 3D average distance (dimensional accuracy). The experimental model resin showed significantly lower volumetric shrinkage (7.28%) and significantly higher or higher accuracy and accuracy stability (11.66-13.77 µm from the initial day to four weeks) than the other commercially available model resins (7.66-11.2%, 14.03-41.14 µm from the initial day to four weeks). A strong correlation was observed between volumetric shrinkage and dimensional accuracy (Pearson correlation coefficient R = 0.7485). For clinically successful modelling applications in restorations, orthodontics, implants, and so on, the new 3D-printing model resin is a promising option.

This new edition continues on its journey of being an introduction to the wide variety of conveyor types and application methods available. It discusses conveyor and system design and in its new edition, expands on information about sorters and motorized roller conveyors, new information on robotic interfaces, as well as some of the trade-offs made when conveyor design rules are intentionally broken. Each chapter now also includes questions and problems to help reinforce the material covered. Conveyors: Application, Selection, and Integration includes a pictorial of various installations of conveyors in detail that the reader can model after. It presents common application mistakes made and discusses how the reader can avoid them in the future during the integration process. The book focuses on conveyor application information that is offered in just this book and discusses the effect that extreme temperatures have while providing real-life examples of conveyor system design that may at times break common design rules. This book is a resource for engineers, managers, and executives who want to gain a better understanding of conveyors and their application. It is also for colleges and universities that offer industrial and/or manufacturing engineering curricula.

BackgroundThe Centers for Disease Control and Prevention's Surveillance of Congenital Heart Defects Across the Lifespan project uses large clinical and administrative databases at sites throughout the United States to understand population‐based congenital heart defect (CHD) epidemiology and outcomes. These individual databases are also relied upon for accurate coding of CHD to estimate population prevalence.Methods and ResultsThis validation project assessed a sample of 774 cases from 4 surveillance sites to determine the positive predictive value (PPV) for identifying a true CHD case and classifying CHD anatomic group accurately based on 57 International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes. Chi‐square tests assessed differences in PPV by CHD severity and age. Overall, PPV was 76.36% (591/774 [95% CI, 73.20–79.31]) for all sites and all CHD‐related ICD‐9‐CM codes. Of patients with a code for complex CHD, 89.85% (177/197 [95% CI, 84.76–93.69]) had CHD; corresponding PPV estimates were 86.73% (170/196 [95% CI, 81.17–91.15]) for shunt, 82.99% (161/194 [95% CI, 76.95–87.99]) for valve, and 44.39% (83/187 [95% CI, 84.76–93.69]) for “Other” CHD anatomic group (X 2=142.16, P<0.0001). ICD‐9‐CM codes had higher PPVs for having CHD in the 3 younger age groups compared with those >64 years of age, (X 2=4.23, P<0.0001).ConclusionsWhile CHD ICD‐9‐CM codes had acceptable PPV (86.54%) (508/587 [95% CI, 83.51–89.20]) for identifying whether a patient has CHD when excluding patients with ICD‐9‐CM codes for “Other” CHD and code 745.5, further evaluation and algorithm development may help inform and improve accurate identification of CHD in data sets across the CHD ICD‐9‐CM code groups.

This chapter contains sections titled: Using This Troubleshooting Guide Troubleshooting Checklist and Pre-Work Baseline Problems Baseline Drift or Wander Noisy Baseline Spikes in the Baseline Peak Shape Problems Tailing Peaks Fronting Peaks Extremely Broad or Rounded Peaks Flat Top Peaks Split Peaks Negative Peaks Excessively Broad Solvent Front Loss of Resolution Retention Changes Retention Time (tr) Change Only Retention Factor (k) Change Peak Size Problems No Peaks All Peaks Change in Size Some Peaks Change in Size or Missing Peaks Extra or Ghost Peaks (Carryover) Rapid Column Deterioration Quantitation Problems

This chapter contains sections titled: Why Bother? Peak Shapes Peak Width (W) Peak Symmetry Retention Retention Time (tr) Adjusted Retention Time (tr′) Retention Factor (k) Retention Index (I) Phase Ratio (β) Distribution Constant (KC) KC and Column Dimensions KC and Column Temperature Column Efficiency Number of Theoretical Plates (N) Height Equivalent to a Theoretical Plate (H) Effective Theoretical Plates (Neff) and Effective Plate Heights (Heff) Precautions When Using Theoretical Plates Utilization of Theoretical Efficiency (UTE%) Separation Factor (α) Resolution (R) Trennzahl (TZ) Column Capacity

This chapter contains sections titled: Carrier Gas and Capillary Columns Linear Velocity versus Flow Rate Controlling the Linear Velocity and Flow Rate Van Deemter Curves Carrier Gas Measurements Average Linear Velocity (©) Column Flow Rate Carrier Gas Selection Nitrogen Helium Hydrogen Recommended Average Linear Velocities Gas Purities Common Carrier Gas Problems

This appendix contains sections titled: Introduction Column Considerations Carrier Gas Considerations Injector Considerations Detector and Data System Considerations GC Oven Considerations Sample Considerations An Example of High Speed GC Using a Small Diameter Column High Speed GC Summary