The Fall Of Five.epub Download PORTABLE
This is the complete 6 Volume set of Edward Gibbon's The History of The Decline and Fall of the Roman Empire. First published in 1776, the six volumes follow Western civilisation from the apex of the Roman Empire to it's eventual fall. The first volume takes us from the Age of Trajan and the Antoninies, Roman rule under Commodus, Septimius Severus, Caracalla, Claudius, Tacitus and others, the defeat of the Goths, and the progress of Christianity. The second volume goes from Nero and Constantine to the foundation of Constantinople, the persecution of heresy, and the reigns of Julian, Jovian, and Valentinian. Volume three goes from the civil wars, and the reign of Theodosius, the destruction of Paganism to the invasion of Italy, and to the invasion of Gaul by Attila. Volume four explores the Gothic kingdom of Italy, and the reign of Justinian, and the state of Italy under the Lombards. Volume five discusses the Franks conquest of Italy, and the conquest by the Arabs. Volume six explores the crusades, the Moguls, the Ottomans, and the state of Rome from the twelfth century. The work was criticised for it's view of Christianity, and this is why is ended up being a banned book in several countries. Whilst he expected a certain amount of backlash for his work, Gibbons was taken aback by the extent of it, even writing a response to a particularly vicious criticism from Henry Edwards Davies, a young cleric.
the fall of five.epub download
Winter 2020 - Volume 4, Number 2"Nontraditional Approaches to Undergraduate Research"Table of Contents (PDF)Introduction / Patricia Ann Mabrouk, guest editor of the "Undergraduate Research during Times of Disruption" theme (download PDF or EPub)Full issue (PDF, members only); Full issue (EPub, members only)Teaching Research Skills to Vocational Learners: Teaching Chefs to Research / Willa Zhen (download PDF or EPub)Creative, Interdisciplinary Undergraduate Research: An Educational Cell Biology Video Game Designed by Students for Students / Isabelle Sperano, Ross Shaw, Robert Andruchow, Dana Cobzas, Cory Efird, Brian Brookwell, and William Deng (download PDF or EPub)Piloting an Oral History-Based CURE in a General Education Writing Course for First-Year Students / Joshua R. McConnell Parsons, Jannell C. McConnell Parsons, Kathryn Kohls, and Jim Ridolfo (download PDF or EPub)
Summer 2019 - Volume 2, Number 4"Big Data and Undergraduate Research"Table of Contents PDFFull SPUR PDF download (members only) /New! Full SPUR EPub download (for e-readers; members only)Introduction (PDF) / Rebecca M. Jones, issue editor (download EPub version for e-readers)Leveraging a Large Database to Increase Access to Undergraduate Research Experiences / (PDF)Laura A. Lukes, Katherine Ryker, Camerian Millsaps, Rowan Lockwood, Mark D. Uhen,Christian George, Callan Bentley, and Peter Berquist (download EPub version for e-readers)A Student Research Course on Data Analytics Problems from Industry: PIC Math / (PDF)Michael Dorff and Suzanne Weekes (download EPub version for e-readers)Undergraduate Research Highlights (PDF) / Download EPub version for e-readers
Full SPUR download (members only)IntroductionRebecca M. Jones, issue editorAssessing an Iterative Method for Improving Undergraduate Student Literature Reviews / Karen M. Travis and Priscilla Cooke St. ClairTransforming the STEM Learning Experience: Minority Students' Agency in Shaping Their Own Interdisciplinary Undergraduate Research / Shearon Roberts and Ross LouisBusiness in a Liberal Arts College: Undergraduate Research Experiences That Cultivate Habits of the Heart and Mind / Vicki L. Baker and John CarlsonUndergraduate Research Highlights
Although the prevalence of child stunting is falling in Latin America, socioeconomic inequalities persist. However, there is limited evidence on ethnic disparities. We aimed to describe ethnic inequalities of stunting and feeding practices in thirteen Latin American countries using recent nationally representative surveys.
In Latin America, stunting prevalence fell from 23.7% in 1990 to 13.5% in 2010, a 43% reduction, and prevalence is projected to fall to 10% by 2020 . However, overall progress may hide important within-country inequalities. In the 12 countries included in the present analyses, the median prevalence among indigenous children was 31.9%, being as high as 61.4% in Guatemala.
Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2017) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2018) were collected by the National Center for Health Statistics. In 2021, 1,898,160 new cancer cases and 608,570 cancer deaths are projected to occur in the United States. After increasing for most of the 20th century, the cancer death rate has fallen continuously from its peak in 1991 through 2018, for a total decline of 31%, because of reductions in smoking and improvements in early detection and treatment. This translates to 3.2 million fewer cancer deaths than would have occurred if peak rates had persisted. Long-term declines in mortality for the 4 leading cancers have halted for prostate cancer and slowed for breast and colorectal cancers, but accelerated for lung cancer, which accounted for almost one-half of the total mortality decline from 2014 to 2018. The pace of the annual decline in lung cancer mortality doubled from 3.1% during 2009 through 2013 to 5.5% during 2014 through 2018 in men, from 1.8% to 4.4% in women, and from 2.4% to 5% overall. This trend coincides with steady declines in incidence (2.2%-2.3%) but rapid gains in survival specifically for nonsmall cell lung cancer (NSCLC). For example, NSCLC 2-year relative survival increased from 34% for persons diagnosed during 2009 through 2010 to 42% during 2015 through 2016, including absolute increases of 5% to 6% for every stage of diagnosis; survival for small cell lung cancer remained at 14% to 15%. Improved treatment accelerated progress against lung cancer and drove a record drop in overall cancer mortality, despite slowing momentum for other common cancers.
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Multifactorial assessments and associated interventions may reduce falls, suggesting that at least some proportion of hospital falls are preventable.1 8 9 However, evidence for these approaches may be setting-dependent, with clinical trial evidence supporting only fall prevention approaches that incorporate fall risk assessment in subacute care hospital rehabilitation settings for older adults10 but not in conventional acute care hospital ward settings.11 This has caused some to question the value of current approaches to assessing fall risk and targeting fall prevention interventions in acute care hospital settings.12 A recent non-inferiority trial across 10 hospitals indicated falls did not increase when staff ceased completing multifactorial fall risk assessments and instead used their own clinical reasoning to select interventions from a clinical decision support intervention list without completing the multifactorial fall risk assessment.13 In addition to demonstrating non-inferiority, after adjusting for historical fall rates at the participating hospitals, the hospital fall incident rate ratio favoured the group that ceased fall risk assessment ratings.13
The increasing digitisation of hospital information systems in recent years, including the wider adoption of integrated electronic medical records (ieMRs), has laid a foundation for the development and adoption of more advanced approaches to fall risk prediction in hospitals. The use of additional predictors, obtained from ieMR and other digital hospital systems, has been shown to improve risk prediction performance in related clinical contexts.24 In addition, the potential to integrate high-performing prediction models nested within these systems may enable continuous risk predictions and computerised clinical decision support that is potentially desirable to clinicians25 and medical associations.26 The wide array of fields routinely and automatically recorded in ieMRs and associated systems provides an opportunity for the application of a range of advanced statistical modelling and algorithmic approaches that have potential utility in hospital settings.27 However, studies reporting newer approaches to fall risk prediction, including machine learning and its potential role in computerised clinical decision support systems intended to reduce fall risk, have not yet been systematically collated and considered alongside more conventional methods for predicting hospital falls. The quality of reporting for inpatient fall prediction models has also not been described previously. It is also unknown whether the publication of the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement28 has led to improved reporting quality in the context of inpatient fall prediction models.
Although a range of approaches to assessing fall risk in hospitals have been available for decades,29 performance is inconsistent,30 and validation studies suggest that generalisability of some approaches to fall risk prediction may be poor.31 Increasing completion rates of conventional approaches to multifactorial fall risk assessment and implementation of associated interventions do not have clinical trial evidence of effect in acute care hospital wards.32 Poor performance of conventional and non-conventional approaches to fall risk prediction in hospital environments may be influenced by the methods used to develop these fall risk prediction approaches. This scoping review will allow us to summarise methods and sources of data which have already been used in the development of published approaches to in-hospital fall risk prediction, and potentially identify underexplored methods and data sources. It is anticipated that these findings will provide insight into promising approaches for improving fall risk prediction models that have potential to be adopted in computerised decision support solutions suitable for hospital settings, including ieMR environments. Therefore, this review will aim to describe existing approaches to hospital fall prediction model development and the quality of reporting in these studies.