The St. Chad Gospels: Diachronic Manuscript Registration and Visualization

This paper presents a software framework for the registration and visualization of layered image sets. To demonstrate the utility of these tools, we apply them to the St. Chad Gospels manuscript, relying on images of each page of the document as it appeared over time. An automated pipeline is used to perform non-rigid registration on each series of images. To visualize the differences between copies of the same page, a registered image viewer is constructed that enables direct comparisons of registered images. The registration pipeline and viewer for the resulting aligned images are generalized for use with other data sets

EduceLab-Scrolls: Verifiable Recovery of Text from Herculaneum Papyri using X-ray CT

We present a complete software pipeline for revealing the hidden texts of the Herculaneum papyri using X-ray CT images. This enhanced virtual unwrapping pipeline combines machine learning with a novel geometric framework linking 3D and 2D images. We also present EduceLab-Scrolls, a comprehensive open dataset representing two decades of research effort on this problem. EduceLab-Scrolls contains a set of volumetric X-ray CT images of both small fragments and intact, rolled scrolls. The dataset also contains 2D image labels that are used in the supervised training of an ink detection model. Labeling is enabled by aligning spectral photography of scroll fragments with X-ray CT images of the same fragments, thus creating a machine-learnable mapping between image spaces and modalities. This alignment permits supervised learning for the detection of "invisible" carbon ink in X-ray CT, a task that is "impossible" even for human expert labelers. To our knowledge, this is the first aligned dataset of its kind and is the largest dataset ever released in the heritage domain. Our method is capable of revealing accurate lines of text on scroll fragments with known ground truth. Revealed text is verified using visual confirmation, quantitative image metrics, and scholarly review. EduceLab-Scrolls has also enabled the discovery, for the first time, of hidden texts from the Herculaneum papyri, which we present here. We anticipate that the EduceLab-Scrolls dataset will generate more textual discovery as research continues

Intra-Ethnic Diversity in Hispanic Child Mortality, 1890-1910

The recent demography of the Hispanic population of the United States has received considerable attention, but historical perspective is more elusive partly due to data limitations. A nationally representative sample of the Hispanic population of the United States, based on the manuscripts of the 1910 census, now exists that includes 71,500 Hispanic-origin persons plus another 24,000 of their non-Hispanic neighbors. We estimate childhood mortality for 1890 to 1910, using indirect demographic methods of estimation and find infant and child mortality in the Hispanic population that was higher than for the non-Hispanic whites but slightly lower than for nonwhite, non-Hispanics (mostly African Americans). Hispanic rural, farm populations in California, Texas, and Arizona did the best, though still experiencing high mortality. The usual advantage of rural residence at the turn of the century holds outside of New Mexico and Florida.

Molecular simulation of hydrogen storage and transport in cellulose

In this work we describe a computational workflow to model the sorption and transport of molecular hydrogen in cellulose frameworks. The work demonstrates the value of the molecular dynamics code, DL_POLY and Monte Carlo code, DL_MONTE sharing common input formats to enhance the compatibility of the codes, being supported by DL_FIELD. Structures generated using cellulose-builder were processed by DL_FIELD to generate input files for DL_POLY using the OPLS_2005 force field. After relaxation in molecular dynamics, structures were used for GCMC simulations in DL_MONTE before passing back to DL_POLY to evaluate transport properties at different levels of sorption. While no hydrogen sorption was seen in pure crystalline cellulose, increasing separation between layers did allow sorption. When slit-pores were sufficiently wide, interactions with the cellulose led to the volumetric density of adsorbed hydrogen exceeding vacuum density at accessible partial pressures as well as allowing diffusion through the system. These model systems can give useful insight into the behaviour of amorphous cellulose in future simulation and experiment

ElAM: A computer program for the analysis and representation of anisotropic elastic properties

Copyright © 2010 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Computer Physics Communications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Physics Communications, Volume 181, Issue 12 (2010), DOI: 10.1016/j.cpc.2010.08.033The continuum theory of elasticity has been used for more than a century and has applications in many fields of science and engineering. It is very robust, well understood and mathematically elegant. In the isotropic case elastic properties are easily represented, but for non-isotropic materials, even in the simple cubic symmetry, it can be difficult to visualise how properties such as Young's modulus or Poisson's ratio vary with stress/strain orientation. The ElAM (Elastic Anisotropy Measures) code carries out the required tensorial operations (inversion, rotation, diagonalisation) and creates 3D models of an elastic property's anisotropy. It can also produce 2D cuts in any given plane, compute averages following diverse schemes and query a database of elastic constants to support meta-analyses. Program summary Program title: ElAM1.0 Catalogue identifier: AEHB_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHB_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 43 848 No. of bytes in distributed program, including test data, etc.: 2 498 882 Distribution format: tar.gz Programming language: Fortran90 Computer: Any Operating system: Linux, Windows (XP, Vista) RAM: Depends chiefly on the size of the arrays representing elastic properties in 3D Classification: 7.7 Nature of problem: Representation of elastic moduli and ratios, and of wave velocities, in 3D; automatic discovery of unusual elastic properties. Solution method: Stiffness matrix (6×6)(6×6) inversion and conversion to compliance tensor (3×3×3×3)(3×3×3×3), tensor rotation, dynamic matrix diagonalisation, simple optimisation, postscript and VRML output preparation. Running time: Dependent on angular accuracy and size of elastic constant database (from a few seconds to a few hours). The tests provided take from a few seconds for test0 to approximately 1 hour for test4

Effect of Personalized Incentives on Dietary Quality of Groceries Purchased A Randomized Crossover Trial

Importance Many factors are associated with food choice. Personalized interventions could help improve dietary intake by using individual purchasing preferences to promote healthier grocery purchases. Objective To test whether a healthy food incentive intervention using an algorithm incorporating customer preferences, purchase history, and baseline diet quality improves grocery purchase dietary quality and spending on healthy foods. Design, Setting, and Participants This was a 9-month randomized clinical crossover trial (AB–BA) with a 2- to 4-week washout period between 3-month intervention periods. Participants included 224 loyalty program members at an independent Rhode Island supermarket who completed baseline questionnaires and were randomized from July to September 2018 to group 1 (AB) or group 2 (BA). Data analysis was performed from September 2019 to May 2020. Intervention Participants received personalized weekly coupons with nutrition education during the intervention period (A) and occasional generic coupons with nutrition education during the control period (B). An automated study algorithm used customer data to allocate personalized healthy food incentives to participant loyalty cards. All participants received a 5% grocery discount. Main Outcomes and Measures Grocery Purchase Quality Index–2016 (GPQI-16) scores (range, 0-75, with higher scores denoting healthier purchases) and percentage spending on targeted foods were calculated from cumulative purchasing data. Participants in the top and bottom 1% of spending were excluded. Paired t tests examined between-group differences. Results The analytical sample included 209 participants (104 in group 1 and 105 in group 2), with a mean (SD) age of 55.4 (14.0) years. They were predominantly non-Hispanic White (193 of 206 participants [94.1%]) and female (187 of 207 participants [90.3%]). Of 161 participants with income data, 81 (50.3%) had annual household incomes greater than or equal to $100 000. Paired t tests showed that the intervention increased GPQI-16 scores (between-group difference, 1.06; 95% CI, 0.27-1.86; P = .01) and percentage spending on targeted foods (between-group difference, 1.38%; 95% CI, 0.08%-2.69%; P = .04). During the initial intervention period, group 1 (AB) and group 2 (BA) had similar mean (SD) GPQI-16 scores (41.2 [6.6] vs 41.0 [7.5]) and mean (SD) percentage spending on targeted healthy foods (32.0% [10.8%] vs 31.0% [10.5%]). During the crossover intervention period, group 2 had a higher mean (SD) GPQI-16 score than group 1 (42.9 [7.7] vs 41.0 [6.8]) and mean (SD) percentage spending on targeted foods (34.0% [12.1%] vs 32.0% [13.1%]). Conclusions and Relevance This pilot trial demonstrated preliminary evidence for the effectiveness of a novel personalized healthy food incentive algorithm to improve grocery purchase dietary quality. Trial Registration ClinicalTrials.gov Identifier: NCT0374805