Pumping dynamics of cold-atom experiments in a single vacuum chamber

A nonlinear analytical model for the pressure dynamics in a vacuum chamber, pumped with a sputter ion pump (SIP), is proposed, discussed and experimentally evaluated. The model describes the physics of the pumping mechanism of SIPs in the context of a cold-atom experiment. By using this model, we fit pump-down curves of our vacuum system to extract the relevant physical parameters characterizing its pressure dynamics. The aim of this investigation is the optimization of cold-atom experiments in terms of reducing the dead time for quantum sensing using atom interferometry. We develop a calibration method to improve the precision in pressure measurements via the ion current in SIPs. Our method is based on a careful analysis of the gas conductance and pumping in order to reliably link the pressure readings at the SIP with the actual pressure in the vacuum (science) chamber. Our results are in agreement with the existence of essentially two pumping regimes determined by the pressure level in the system. In particular, we find our results in agreement with the well-known fact that for a given applied voltage, at low pressures, the discharge current efficiently sputters pumping material from the pump’s electrodes. This process sets the leading pumping mechanism in this limit. At high pressures, the discharge current drops and the pumping is mainly performed by the already sputtered material

Beewild' bundleflower, a new summer-growing perennial legume for central and south Texas and Mexico

Last updated: 10/19/201

A Relativistic Study of Rocket Motion in Empty Space

The problem motivating this study was stated as follows: Given a rocket of specified construction, to determine the performance to be expected of it on the basis of relativistic dynamics. The term “construction” was taken to include the factors exhaust velocity, initial mass, total mass-ratio, and exhaust-rate function as measured on the rocket; “performance” meant the final velocity attained by the rocket and the integrated equation of motion. The scope of the study was limited by the assumptions that the motion was executed in empty space, and that no gravitational fields were present. Only rectilinear motion was considered. The present study goes beyond previous works in this field primarily in considering the effect of the Einsteinian time-dilation upon the problem of finding an integrated equation of motion. Advisor: Herbert Jehl

SPECIFICITY OF THE LONDON-EISENSCHITZ WANG FORCE

The London force between macromolecules immersed in a liquid medium has an interesting property which may be of biological significance. For the purpose of formulating the London interaction, one may represent each macromolecule by a set of electric dipole oscillators of specified polarizability, frequency, and orientation. To consider the simplest case, one may study macromolecules of globular form not in direct contact with each other. (They might be separated by Debyeiuckel- Onsager atmospheres made up of molecules from the medium; then the equilibrium distance between the macromolecules would be regulated by concentration changes in the ionic medium.)\u27 Such a geometrical arrangement means that the dipole oscillators, which actually are distributed all over a macromolecule, can be replaced by oscillators located at this macromolecule\u27s center. The quadrupole, octupole, etc., terms (which arise when the oscillators are displaced to the molecular center) can be neglected in a crude first approximation

Building a best-in-class automated de-identification tool for electronic health records through ensemble learning

Summary: The presence of personally identifiable information (PII) in natural language portions of electronic health records (EHRs) constrains their broad reuse. Despite continuous improvements in automated detection of PII, residual identifiers require manual validation and correction. Here, we describe an automated de-identification system that employs an ensemble architecture, incorporating attention-based deep-learning models and rule-based methods, supported by heuristics for detecting PII in EHR data. Detected identifiers are then transformed into plausible, though fictional, surrogates to further obfuscate any leaked identifier. Our approach outperforms existing tools, with a recall of 0.992 and precision of 0.979 on the i2b2 2014 dataset and a recall of 0.994 and precision of 0.967 on a dataset of 10,000 notes from the Mayo Clinic. The de-identification system presented here enables the generation of de-identified patient data at the scale required for modern machine-learning applications to help accelerate medical discoveries. The bigger picture: Clinical notes in electronic health records convey rich historical information regarding disease and treatment progression. However, this unstructured text often contains personally identifiable information such as names, phone numbers, or residential addresses of patients, thereby limiting its dissemination for research purposes. The removal of patient identifiers, through the process of de-identification, enables sharing of clinical data while preserving patient privacy. Here, we present a best-in-class approach to de-identification, which automatically detects identifiers and substitutes them with fabricated ones. Our approach enables de-identification of patient data at the scale required to harness the unstructured, context-rich information in electronic health records to aid in medical research and advancement

Cancer-related fatigue in lung cancer : a research agenda : an official American Thoracic Society research statement

Background: Fatigue is the most common symptom among cancer survivors. Cancer-related fatigue (CRF) may occur at any point in the cancer care continuum. Multiple factors contribute to CRF development and severity, including cancer type, treatments, presence of other symptoms, comorbidities, and medication side effects. Clinically, increasing physical activity, enhancing sleep quality, and recognizing sleep disorders are integral to managing CRF. Unfortunately, CRF is infrequently recognized, evaluated, or treated in lung cancer survivors despite more frequent and severe symptoms than in other cancers. Therefore, increased awareness and understanding of CRF are needed to improve health-related quality of life in lung cancer survivors. Objectives: 1) To identify and prioritize knowledge and research gaps and 2) to develop and prioritize research questions to evaluate mechanistic, diagnostic, and therapeutic approaches to CRF among lung cancer survivors. Methods: We convened a multidisciplinary panel to review the available literature on CRF, focusing on the impacts of physical activity, rehabilitation, and sleep disturbances in lung cancer. We used a three-round modified Delphi process to prioritize research questions. Results: This statement identifies knowledge gaps in the 1) detection and diagnostic evaluation of CRF in lung cancer survivors; 2) timing, goals, and implementation of physical activity and rehabilitation; and 3) evaluation and treatment of sleep disturbances and disorders to reduce CRF. Finally, we present the panel’s initial 32 research questions and seven final prioritized questions. Conclusions: This statement offers a prioritized research agenda to 1) advance clinical and research efforts and 2) increase awareness of CRF in lung cancer survivors