Using entanglement to discern phases in the disordered one-dimensional Bose-Hubbard model

We perform a matrix product state based density matrix renormalisation group analysis of the phases for the disordered one-dimensional Bose-Hubbard model. For particle densities N/L = 1, 1/2 and 2 we show that it is possible to obtain a full phase diagram using only the entanglement properties, which come "for free" when performing an update. We confirm the presence of Mott insulating, superfluid and Bose glass phases when N/L = 1 and 1/2 (without the Mott insulator) as found in previous studies. For the N/L = 2 system we find a double lobed superfluid phase with possible reentrance.Comment: 6 pages, 4 figure

Implementation of Safe Patient Toileting to Decrease Patient Falls on Medical-Surgical Unit

BACKGROUND: Patient falls are a serious safety concern in the hospital setting throughout the country. Falls are one of the most challenging patient safety events to prevent, as there are many contributing factors with toileting activities producing the highest incidence. Fall prevention bundles are used to minimize and reduce these such events although multifaceted. The project was conducted with an academic medical center on an acute inpatient medical-surgical unit primarily housing burn wound patients. Nursing leaders and front-line nursing staff participated. METHODS: Literature review to determine the gap in knowledge of interventions to prevent acute inpatient falls was completed. Concepts from purposeful rounding were used to identify a single intervention surrounding safe toileting activities. Staff actively self-reported via audit tool supervised patient toileting activities. Leadership support to develop increased engagement and satisfaction with the intervention was present. INTERVENTION: Purposeful toileting rounds utilizing acquired knowledge and skills to encourage patient’s participation in safe patient toileting activities. A daily shift self-reporting nursing staff auditing tool was deployed and utilized to track staff participation in supervised toileting bringing awareness to safe patient toileting. Lippitt’s and Lewin’s change theories were used to drive change with in the nursing unit and staff adoption of this workflow. RESULTS: The post intervention staff survey demonstrated staff engagement and improvement in supervised safe toileting patient activities. Staff results showed 23% overall improvement in satisfaction with time spent with patients, a 24% improvement in not feeling satisfied with unsupervised patient toileting, 31% combined positive satisfaction with providing privacy with toileting and 62% combined rating for satisfaction with safe toileting activities on the unit. Nurse pre- survey satisfaction scores of very dissatisfied were eliminated in the appropriate questions and increased in the one question regarding leaving patients unsupervised. The primary goal to reduce or eliminate falls was achieved with staff engagement. There were no patient falls during the project and continued without falls post implementation. CONCLUSION: The deployment of a single focused fall prevention intervention can successfully prevent patient falls with engagement and support of front-line nursing staff. Keywords: toileting, patient falls, fall preventio

Leaf-to-leaf distances and their moments in finite and infinite m-ary tree graphs

We study the leaf-to-leaf distances on full and complete m-ary graphs using a recursive approach. In our formulation, leaves are ordered along a line. We find explicit analytical formulae for the sum of all paths for arbitrary leaf-to-leaf distance r as well as the average path lengths and the moments thereof. We show that the resulting explicit expressions can be recast in terms of Hurwitz-Lerch transcendants. Results for periodic trees are also given. For incomplete random binary trees, we provide first results by numerical techniques; we find a rapid drop of leaf-to-leaf distances for large r.Comment: 10 pages, 7 figure

Preliminary Archeological Investigations at the Callawassie Island Burial Mound (38BU19), Beaufort County, South Carolina

https://scholarcommons.sc.edu/archanth_books/1176/thumbnail.jp

Remote environmental monitoring units : An autonomous vehicle for characterizing coastal environments

Author Posting. © American Meteorological Society 2005. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Atmospheric and Oceanic Technology 22 (2005): 1797–1808, doi:10.1175/JTECH1809.1.In oceanography, there has been a growing emphasis on coastal regions, partially because of their inherent complexity, as well as the increasing acknowledgment of anthropogenic impacts. To improve understanding and characterization of coastal dynamics, there has been significant effort devoted to the development of autonomous systems that sample the ocean on relevant scales. Autonomous underwater vehicles (AUVs) are especially well suited for studies of the coastal ocean because they are able to provide near-synoptic spatial observations. These sampling platforms are beginning to transition from the engineering groups that developed and continue to improve them to the science user. With this transition comes novel applications of these vehicles to address new questions in coastal oceanography. Here, the relatively mature Remote Environmental Monitoring Units (REMUS) AUV system is described and assessed. Analysis of data, based on 37 missions and nearly 800 km of in-water operation, shows that the vehicle’s navigational error estimates were consistently less than 10 m, and error estimates of mission duration, distance, velocity, and power usage, once the vehicle was properly ballasted, were below 10%. An example of the transition to science is demonstrated in an experiment conducted in 2002 in Monterey Bay, California, where the vehicle was used to quantify critical horizontal length scales of variability. Length scales on the order of tens to hundreds of meters were found for the region within 25 km of the coastline, which has significant implications for designing proper sampling approaches and parameterizing model domains. Results also demonstrate the overall utility of the REMUS vehicle for use by coastal oceanographers.This work was supported by the Office of Naval Research (N00014-00-1- 0570 and N00014-03-1-0341 to M. Moline)

Integration of datasets for individual prediction of DNA methylation-based biomarkers

BACKGROUND: Epigenetic scores (EpiScores) can provide biomarkers of lifestyle and disease risk. Projecting new datasets onto a reference panel is challenging due to separation of technical and biological variation with array data. Normalisation can standardise data distributions but may also remove population-level biological variation.RESULTS: We compare two birth cohorts (Lothian Birth Cohorts of 1921 and 1936 - nLBC1921 = 387 and nLBC1936 = 498) with blood-based DNA methylation assessed at the same chronological age (79 years) and processed in the same lab but in different years and experimental batches. We examine the effect of 16 normalisation methods on a novel BMI EpiScore (trained in an external cohort, n = 18,413), and Horvath's pan-tissue DNA methylation age, when the cohorts are normalised separately and together. The BMI EpiScore explains a maximum variance of R2=24.5% in BMI in LBC1936 (SWAN normalisation). Although there are cross-cohort R2 differences, the normalisation method makes a minimal difference to within-cohort estimates. Conversely, a range of absolute differences are seen for individual-level EpiScore estimates for BMI and age when cohorts are normalised separately versus together. While within-array methods result in identical EpiScores whether a cohort is normalised on its own or together with the second dataset, a range of differences is observed for between-array methods.CONCLUSIONS: Normalisation methods returning similar EpiScores, whether cohorts are analysed separately or together, will minimise technical variation when projecting new data onto a reference panel. These methods are important for cases where raw data is unavailable and joint normalisation of cohorts is computationally expensive.</p