Bubble memory module

Design, fabrication and test of partially populated prototype recorder using 100 kilobit serial chips is described. Electrical interface, operating modes, and mechanical design of several module configurations are discussed. Fabrication and test of the module demonstrated the practicality of multiplexing resulting in lower power, weight, and volume. This effort resulted in the completion of a module consisting of a fully engineered printed circuit storage board populated with 5 of 8 possible cells and a wire wrapped electronics board. Interface of the module is 16 bits parallel at a maximum of 1.33 megabits per second data rate on either of two interface buses

The 10 to the 8th power bit solid state spacecraft data recorder

The results are summarized of a program to demonstrate the feasibility of Bubble Domain Memory Technology as a mass memory medium for spacecraft applications. The design, fabrication and test of a partially populated 10 to the 8th power Bit Data Recorder using 100 Kbit serial bubble memory chips is described. Design tradeoffs, design approach and performance are discussed. This effort resulted in a 10 to the 8th power bit recorder with a volume of 858.6 cu in and a weight of 47.2 pounds. The recorder is plug reconfigurable, having the capability of operating as one, two or four independent serial channel recorders or as a single sixteen bit byte parallel input recorder. Data rates up to 1.2 Mb/s in a serial mode and 2.4 Mb/s in a parallel mode may be supported. Fabrication and test of the recorder demonstrated the basic feasibility of Bubble Domain Memory technology for such applications. Test results indicate the need for improvement in memory element operating temperature range and detector performance

Nurse staffing levels, missed vital signs observations and mortality in hospital wards: modelling the consequences and costs of variations in nurse staffing and skill mix. Retrospective observational study using routinely collected data.

Background: Low nurse staffing levels are associated with adverse patient outcomes from hospital care, but the causal relationship is unclear. Limited capacity to observe patients has been hypothesised as a causal mechanism. Objectives: This study determines whether or not adverse outcomes are more likely to occur after patients experience low nurse staffing levels, and whether or not missed vital signs observations mediate any relationship. Design: Retrospective longitudinal observational study. Multilevel/hierarchical mixed-effects regression models were used to explore the association between registered nurse (RN) and health-care assistant (HCA) staffing levels and outcomes, controlling for ward and patient factors. Setting and participants: A total of 138,133 admissions to 32 general adult wards of an acute hospital from 2012 to 2015. Main outcomes: Death in hospital, adverse event (death, cardiac arrest or unplanned intensive care unit admission), length of stay and missed vital signs observations. Data sources: Patient administration system, cardiac arrest database, eRoster, temporary staff bookings and the Vitalpac system (System C Healthcare Ltd, Maidstone, Kent; formerly The Learning Clinic Limited) for observations. Results: Over the first 5 days of stay, each additional hour of RN care was associated with a 3% reduction in the hazard of death [hazard ratio (HR) 0.97, 95% confidence interval (CI) 0.94 to 1.0]. Days on which the HCA staffing level fell below the mean were associated with an increased hazard of death (HR 1.04, 95% CI 1.02 to 1.07), but the hazard of death increased as cumulative staffing exposures varied from the mean in either direction. Higher levels of temporary staffing were associated with increased mortality. Adverse events and length of stay were reduced with higher RN staffing. Overall, 16% of observations were missed. Higher RN staffing was associated with fewer missed observations in high-acuity patients (incidence rate ratio 0.98, 95% CI 0.97 to 0.99), whereas the overall rate of missed observations was related to overall care hours (RN + HCA) but not to skill mix. The relationship between low RN staffing and mortality was mediated by missed observations, but other relationships between staffing and mortality were not. Changing average skill mix and staffing levels to the levels planned by the Trust, involving an increase of 0.32 RN hours per patient day (HPPD) and a similar decrease in HCA HPPD, would be associated with reduced mortality, an increase in staffing costs of £28 per patient and a saving of £0.52 per patient per hospital stay, after accounting for the value of reduced stays. Limitations: This was an observational study in a single site. Evidence of cause is not definitive. Variation in staffing could be influenced by variation in the assessed need for staff. Our economic analysis did not consider quality or length of life. Conclusions: Higher RN staffing levels are associated with lower mortality, and this study provides evidence of a causal mechanism. There may be several causal pathways and the absolute rate of missed observations cannot be used to guide staffing decisions. Increases in nursing skill mix may be cost-effective for improving patient safety. Future work: More evidence is required to validate approaches to setting staffing levels. Other aspects of missed nursing care should be explored using objective data. The implications of findings about both costs and temporary staffing need further exploratio

Nurses' 12-hour shifts and missed or delayed vital signs observations on hospital wards: retrospective observational study

Objectives 12-hour shifts worked by nurses on acute hospital wards have been associated with increased rates of missed care reported by nurses. This study aimed to measure the association between nurses working shifts of at least 12 hours and an objective measure of missed care: vital signs observations taken on time according to an acuity-based surveillance protocol. Design A retrospective observational study using routinely collected data from March 2012 to March 2015. Setting 32 general inpatient wards at a large acute hospital in England. Participants 658 628 nursing shifts nested in 24 069 ward days. Outcome measures The rate of daily delayed and missed vital signs observations. We focused on situations where vital signs observations were required at least every 4 hours and measured the number of instances where observations were delayed or missed, per 24-hour period. For each ward and each day, shift patterns were characterised in terms of proportion of care hours per patient day deriving from 'long' shifts (>= 12 hours) for both registered nurses and healthcare assistants. Results On 99 043 occasions (53%), observations were significantly delayed, and on 81 568 occasions (44%), observations were missed. Observations were more likely to be delayed when a higher proportion of the hours worked by healthcare assistants were part of long shifts (IRR=1.05; 95% CI 1.00 to 1.10). No significant association was found in relation to the proportion of hours registered nurses worked as long shifts. Conclusion On days when a higher proportion of hours worked by healthcare assistants are from long shifts, the risk of delaying vital signs observations is higher, suggesting lower job performance. While longer shifts are thought to require fewer staff resources to maintain nurse-to-patient ratios, any benefits may be lost if staff become less productive

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Relationship between perceived body weight and body mass index based on self- reported height and weight among university students: a cross-sectional study in seven European countries

Mikolajczyk RT, Maxwell AE, El Ansari W, Stock C, Petkeviciene J, Guillen-Grima F. Relationship between perceived body weight and body mass index based on self- reported height and weight among university students: a cross-sectional study in seven European countries. BMC Public Health. 2010;10(1): 40.Background Despite low rates of obesity, many university students perceive themselves as overweight, especially women. This is of concern, because inappropriate weight perceptions can lead to unhealthy behaviours including eating disorders. Methods We used the database from the Cross National Student Health Survey (CNSHS), consisting of 5,900 records of university students from Bulgaria, Denmark, Germany, Lithuania, Poland, Spain and Turkey to analyse differences in perceived weight status based on the question: "Do you consider yourself much too thin, a little too thin, just right, a little too fat or much too fat?". The association between perceived weight and body mass index (BMI) calculated from self-reported weight and height was assessed with generalized non-parametric regression in R library gam. Results Although the majority of students reported a normal BMI (72-84% of males, 65-83% of females), only 32% to 68% of students considered their weight "just right". Around 20% of females with BMI of 20 kg/m2 considered themselves "a little too fat" or "too fat", and the percentages increased to 60% for a BMI of 22.5 kg/m2. Male students rarely felt "a little too fat" or "too fat" below BMI of 22.5 kg/m2, but most felt too thin with a BMI of 20 kg/m2. Conclusions Weight ideals are rather uniform across the European countries, with female students being more likely to perceive themselves as "too fat" at a normal BMI, while male students being more likely to perceive themselves as "too thin". Programs to prevent unhealthy behaviours to achieve ill-advised weight ideals may benefit students

TRY plant trait database - enhanced coverage and open access

This article has 730 authors, of which I have only listed the lead author and myself as a representative of University of HelsinkiPlant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.Peer reviewe

Nonparametric maximum likelihood estimation of population size based on the counting distribution

The paper discusses the estimation of an unknown population size "n". Suppose that an identification mechanism can identify "n" obs cases. The Horvitz-Thompson estimator of "n" adjusts this number by the inverse of 1 - "p" 0, where the latter is the probability of not identifying a case. When repeated counts of identifying the same case are available, we can use the counting distribution for estimating "p" 0 to solve the problem. Frequently, the Poisson distribution is used and, more recently, mixtures of Poisson distributions. Maximum likelihood estimation is discussed by means of the EM algorithm. For truncated Poisson mixtures, a nested EM algorithm is suggested and illustrated for several application cases. The algorithmic principles are used to show an inequality, stating that the Horvitz-Thompson estimator of "n" by using the mixed Poisson model is always at least as large as the estimator by using a homogeneous Poisson model. In turn, if the homogeneous Poisson model is misspecified it will, potentially strongly, underestimate the true population size. Examples from various areas illustrate this finding. Copyright 2005 Royal Statistical Society.