Dissociation of CH4 by electron impact: Production of metastable hydrogen and carbon fragments

Metastable fragments produced by electron impact excitation of CH4 have been investigated for incident electron energies from threshold to 300 eV. Only metastable hydrogen and carbon atoms were observed. Onset energies for the production of metastable hydrogen atoms were observed at electron impact energies of 22.0 + or - .5 eV, 25.5 + or - .6 eV, 36.7 + or - .6 eV and 66 + or - 3 eV, and at 26.6 + or - .6 eV for the production of metastable carbon atoms. Most of the fragments appear to have been formed in high-lying Rydberg states. The total metastable hydrogen cross section reaches a maximum value of approximately 1 X 10 to the minus 18th power sq cm at 100 eV. At the same energy, the metastable carbon cross section is 2 x 10 to the minus 19th power sq cm

Wetting of Curved Surfaces

As a first step towards a microscopic understanding of the effective interaction between colloidal particles suspended in a solvent we study the wetting behavior of one-component fluids at spheres and fibers. We describe these phenomena within density functional theory which keeps track of the microscopic interaction potentials governing these systems. In particular we properly take into account the power-law decay of both the fluid-fluid interaction potentials and the substrate potentials. The thicknesses of the wetting films as a function of temperature and chemical potential as well as the wetting phase diagrams are determined by minimizing an effective interface potential which we obtain by applying a sharp-kink approximation to the density functional. We compare our results with previous approaches to this problem.Comment: 54 pages, 17 figures, accepted for publication in Physica

Investigating the Avoidability of Hospitalizations of Long Stay Nursing Home Residents: Opportunities for Improvement

Background and Objectives: To examine the relationship between hospital diagnoses associated with hospital transfers of long stay nursing home residents, ratings of avoidability of transfer, and RN-identified quality improvement opportunities. Research Design and Methods: Prospective clinical demonstration project, named OPTIMISTIC, with trained RNs embedded in nursing homes that performed root cause analyses for 1,931 transfers to the hospital between November 2014 and July 2016. OPTIMISTIC RNs also rated whether transfers were avoidable, identified quality improvement opportunities, and recorded hospital diagnoses. Resident characteristics were obtained from Minimum Data Set assessments. Relationships between six hospital diagnoses commonly considered "potentially avoidable" and OPTIMISTIC RN root cause analysis findings were examined. Facilities were participating in the OPTIMISTIC demonstration project designed to reduce hospital transfers during the study period. Results: Twenty-five percent of acute transfers associated with six common diagnoses were considered definitely or probably avoidable by project RNs versus 22% of transfers associated with other diagnoses. The most common quality improvement opportunity identified for transfers rated as avoidable was that the condition could have been managed safely if appropriate resources were available, a factor cited in 45% of transfers associated with any of the six diagnoses. Problems with communication among stakeholders were the most commonly noted area for improvement (48%) for transfers associated with other diagnoses. Many other areas for quality improvement were noted, including earlier detection of change in status and the need for understanding patient preferences or a palliative care plan. Discussion and Implications: Although some nursing home transfers may later be deemed potentially avoidable based on post-transfer hospital diagnosis from Medicare claims data, OPTIMISTIC nurses caring for these residents at time of transfer categorized the majority of these transfers as unavoidable irrespective of the hospital diagnosis. Multiple quality improvement opportunities were identified associated with these hospital transfers, whether the transfer was considered potentially avoidable or unavoidable

Patterns of Emergency Department Use Among Long-Stay Nursing Home Residents With Differing Levels of Dementia Severity

OBJECTIVES: To describe emergency department (ED) utilization among long-stay nursing home residents with different levels of dementia severity. DESIGN: Retrospective cohort study. SETTING: Public Health System. PARTICIPANTS: A total of 4491 older adults (age 65 years and older) who were long-stay nursing home residents. MEASUREMENTS: Patient demographics, dementia severity, comorbidities, ED visits, ED disposition decisions, and discharge diagnoses. RESULTS: Forty-seven percent of all long-stay nursing home residents experienced at least 1 transfer to the ED over the course of a year. At their first ED transfer, 36.4% of the participants were admitted to the hospital, whereas 63.1% of those who visited the ED were not. The median time to first ED visit for the participants with advanced stage dementia was 258 days, whereas it was 250 days for the participants with early to moderate stage dementia and 202 days for the participants with no dementia (P = .0034). Multivariate proportional hazard modeling showed that age, race, number of comorbidities, number of hospitalizations in the year prior, and do not resuscitate status all significantly influenced participants' time to first ED visit (P < .05 for all). After accounting for these effects, dementia severity (P = .66), years in nursing home before qualification (P = .46), and gender (P = .36) lost their significance. CONCLUSIONS: This study confirms high rates of transfer of long-stay nursing home residents, with nearly one-half of the participants experiencing at least 1 ED visit over the course of a year. Although dementia severity is not a predictor of time to ED use in our analyses, other factors that influence ED use are readily identifiable. Nursing home providers should be aware of these factors when developing strategies that meet patient care goals and avoid transfer from the nursing home to the ED

The Complexity of Determining Whether a Nursing Home Transfer Is Avoidable at Time of Transfer

Objectives To describe the relationship between nursing facility resident risk conditions and signs and symptoms at time of acute transfer and diagnosis of conditions associated with potentially avoidable acute transfers (pneumonia, urinary tract infection, congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD) or asthma, dehydration, pressure sores). Design As part of a demonstration project to reduce potentially avoidable hospital transfers, Optimizing Patient Transfers, Impacting Medical Quality, Improving Symptoms: Transforming Institutional Care (OPTIMISTIC) project clinical staff collected data on residents who transferred to the emergency department (ED) or hospital. Cross‐tabulations were used to identify associations between risk conditions or symptoms and hospital diagnoses or death. Mixed‐effects logistic regression models were used to describe the significance of risk conditions, signs, or symptoms as predictors of potentially avoidable hospital diagnoses or death. Setting Indiana nursing facilities (N=19). Participants Long‐stay nursing facility residents (N=1,174), who experienced 1,931 acute transfers from November 2014 to July 2016. Measurements Participant symptoms, transfers, risk factors, and hospital diagnoses. Results We found that 44% of acute transfers were associated with 1 of 6 potentially avoidable diagnoses. Symptoms before transfer did not discriminate well among hospital diagnoses. Symptoms mapped into multiple diagnoses and most hospital diagnoses had multiple associated symptoms. For example, more than two‐thirds of acute transfers of residents with a history of CHF and COPD were for reasons other than exacerbations of those two conditions. Conclusion Although it is widely recognized that many transfers of nursing facility residents are potentially avoidable, determining “avoidability” at time of transfer is complex. Symptoms and risk conditions were only weakly predictive of hospital diagnoses

A recurrent neural network with ever changing synapses

A recurrent neural network with noisy input is studied analytically, on the basis of a Discrete Time Master Equation. The latter is derived from a biologically realizable learning rule for the weights of the connections. In a numerical study it is found that the fixed points of the dynamics of the net are time dependent, implying that the representation in the brain of a fixed piece of information (e.g., a word to be recognized) is not fixed in time.Comment: 17 pages, LaTeX, 4 figure

Full-Scale Ab Initio Simulation of Magic-Angle-Spinning Dynamic Nuclear Polarization

Theoretical models aimed at describing magic-angle-spinning (MAS) dynamic nuclear polarization (DNP) NMR typically face a trade-off between the scientific rigor obtained with a strict quantum mechanical description, and the need for using realistically large spin systems, for instance using phenomenological models. Thus far, neither approach has accurately reproduced experimental results, let alone achieved the generality required to act as a reliable predictive tool. Here, we show that the use of aggressive state-space restrictions and an optimization strategy allows full-scale ab initio MAS-DNP simulations of spin systems containing thousands of nuclei. Our simulations are the first ever to achieve quantitative reproduction of experimental DNP enhancements and their MAS rate dependence for both frozen solutions and solid materials. They also revealed the importance of a previously unrecognized structural feature found in some polarizing agents that helps minimize the sensitivity losses imposed by the spin diffusion barrier

Structure of ternary additive hard-sphere fluid mixtures

Monte Carlo simulations on the structural properties of ternary fluid mixtures of additive hard spheres are reported. The results are compared with those obtained from a recent analytical approximation [S. B. Yuste, A. Santos, and M. Lopez de Haro, J. Chem. Phys. 108, 3683 (1998)] to the radial distribution functions of hard-sphere mixtures and with the results derived from the solution of the Ornstein-Zernike integral equation with both the Martynov-Sarkisov and the Percus-Yevick closures. Very good agreement between the results of the first two approaches and simulation is observed, with a noticeable improvement over the Percus-Yevick predictions especially near contact.Comment: 11 pages, including 8 figures; A minor change; accepted for publication in PR

Phase transitions in a ferrofluid at magnetic field induced microphase separation

In the presence of a magnetic field applied perpendicular to a thin sample layer, a suspension of magnetic colloidal particles (ferrofluid) can form spatially modulated phases with a characteristic length determined by the competition between dipolar forces and short-range forces opposing density variations. We introduce models for thin-film ferrofluids in which magnetization and particle density are viewed as independent variables and in which the non-magnetic properties of the colloidal particles are described either by a lattice-gas entropy or by the Carnahan-Starling free energy. Our description is particularly well suited to the low-particle density regions studied in many experiments. Within mean-field theory, we find isotropic, hexagonal and stripe phases, separated in general by first-order phase boundaries.Comment: 12 pages, RevTex, to appear in PR

New avenue to the Parton Distribution Functions: Self-Organizing Maps

Neural network algorithms have been recently applied to construct Parton Distribution Function (PDF) parametrizations which provide an alternative to standard global fitting procedures. We propose a technique based on an interactive neural network algorithm using Self-Organizing Maps (SOMs). SOMs are a class of clustering algorithms based on competitive learning among spatially-ordered neurons. Our SOMs are trained on selections of stochastically generated PDF samples. The selection criterion for every optimization iteration is based on the features of the clustered PDFs. Our main goal is to provide a fitting procedure that, at variance with the standard neural network approaches, allows for an increased control of the systematic bias by enabling user interaction in the various stages of the process.Comment: 34 pages, 17 figures, minor revisions, 2 figures update