An Obstruction to Quantization of the Sphere

In the standard example of strict deformation quantization of the symplectic sphere $S^2$, the set of allowed values of the quantization parameter $\hbar$ is not connected; indeed, it is almost discrete. Li recently constructed a class of examples (including $S^2$) in which $\hbar$ can take any value in an interval, but these examples are badly behaved. Here, I identify a natural additional axiom for strict deformation quantization and prove that it implies that the parameter set for quantizing $S^2$ is never connected.Comment: 23 page. v2: changed sign conventio

Stress, Motivation and Professional Satisfaction among Health Care Workers in HIV/AIDS Care and Treatment Centers in Urban Tanzania: A Cross-Sectional Study.

Shortages of health care workers (HCWs) represents a serious challenge to ensuring effective HIV care in resource-limited settings (RLS). Stress, motivation, and job satisfaction have been linked with HCW retention and are important in addressing HCW shortages. In this cross-sectional study HCW stress, motivation and perceived ability to meet patient needs were assessed in PEPFAR-supported urban HIV care and treatment clinics (CTCs) in Tanzania. A self-administered questionnaire measuring motivation, stress, and perceived ability to and meet patient needs was given to HCWs at 16 CTCs. Scales measuring HCW satisfaction, motivation, and stress were developed using principle components analysis. Hierarchical linear models were used to explore the association of HCW and site characteristics with reported satisfaction, stress, motivation, and ability to meet patients' needs.\ud Seventy-three percent (279) of HCWs completed the questionnaire. Most (73%) HCWs reported minimal/no work-related stress, with 48% reporting good/excellent motivation, but 41% also reporting feeling emotionally drained. Almost all (98%) reported feeling able to help their patients, with 68% reporting work as rewarding. Most reported receipt of training and supervision, with good availability of resources. In the multivariate model, direct clinical providers reported lower motivation than management (p < 0.05) and HCWs at medium-sized sites reported higher motivation than HCWs at larger sites (p < 0.05). HCWs at small and medium sites were more likely to feel able to help patients than those from larger sites (p < 0.05 and p < 0.001 respectively). Despite significant patient loads, HCWs in these PEPFAR-supported CTCs reported high levels of motivation, job satisfaction, ability to meet patients' needs, low levels of stress but significant emotional toll. Understanding the relationship between support systems such as strong supervision and training and these outcomes is critical in designing interventions to improve motivation, reduce stress and increase retention of HCWs

Accelerating and enabling convergence of nonlinear solvers for Navier-Stokes equations by continuous data assimilation

This paper considers improving the Picard and Newton iterative solvers for the Navier-Stokes equations in the setting where data measurements or solution observations are available. We construct adapted iterations that use continuous data assimilation (CDA) style nudging to incorporate the known solution data into the solvers. For CDA-Picard, we prove the method has an improved convergence rate compared to usual Picard, and the rate improves as more measurement data is incorporated. We also prove that CDA-Picard is contractive for larger Reynolds numbers than usual Picard, and the more measurement data that is incorporated the larger the Reynolds number can be with CDA-Picard still being contractive. For CDA-Newton, we prove that the domain of convergence, with respect to both the initial guess and the Reynolds number, increases as as the amount of measurement data is increased. Additionally, for both methods we show that CDA can be implemented as direct enforcement of measurement data into the solution. Numerical results for common benchmark Navier-Stokes tests illustrate the theory

Assessing Cognitive Processing and Human Factors Challenges in NextGen Air Traffic Control Tower Team Operations

Previous research of Terminal Radar Control Facilities and Standard Terminal Automation Replacement Systems interactions by the authors examined how combined NextGen digitized technology affects air traffic controller functions. Applying their updated SHELL model, human factors implications on the Tower Team before and after implementing NextGen technology were examined, focusing on cognitive loading and automated functions affecting each team member. A survey examined where cognitive difficulties occur when controllers are responsible for multiple screen views, remote airfields or helipads, and digitized cameras and blind spots. Scanning challenges were identified where local traffic, ground operations, and data converge onto one screen, and when attention is diverted to distant screens. Also studied were automatic aircraft handoffs and potential for missed handoffs, and, assessing changes from voice communication to text messaging for human error. Findings indicated a necessity for controllers to manage balanced tasking, vigilance pacing, and resource management

Realising Māori Potential within the Youth Guarantee — An Evaluation of the Youth Guarantee Programme with a Focus on Māori Learners

Māori students’ educational success is critical to Aotearoa New Zealand’s success. The New Zealand Government is committed to supporting Māori learners explore and achieve full potential as Māori. To fulfil this commitment, the Ministry of Education released Ka Hikitia – Managing for Success: The Māori Education Strategy 2008–2012 in April 2008, which sets the direction for improving education outcomes for and with Māori learners. The Youth Guarantee (YG) programme is one of the initiatives which aim to increase the educational achievement of 16 and 17 year olds by making the education system more responsive to their needs. The Ministry of Education is currently undertaking a research project to evaluate the YG with a focus on improving these programmes to better meet the needs of Māori learners. This report offers a snapshot of the achievements of the YG Māori learners at Wintec and the challenges they face

Palaeomagnetic field intensity measurements from the 2.6 Ga Yandinilling dyke swarm (Western Australia)

Precambrian palaeointensity measurements provide fundamental constraints on the evolution of the deep Earth. Core evolution models predict trends in dipole moment on billion-year timescales that can be tested by palaeomagnetic records. Here, we report new palaeointensity results from the recently identified ∼2.62 Ga Yandinilling dyke swarm of the Yilgarn Craton, Western Australia, and consider them alongside published measurements spanning 500 Myr across the late Archaean to earliest Proterozoic. Rock magnetic and scanning electron microscopy analysis confirm that the magnetic mineralogy is fine-grained magnetite, appearing mostly as exsolved lamellae with ilmenite. Six sites produced acceptable palaeointensity estimates from thermal and microwave IZZI protocol Thellier experiments and from double-heating technique Shaw experiments. These site mean values of 9-26 μT translate to virtual dipole moments of 11-44 ZAm2 that are considerably lower than today's dipole moment of ∼80 ZAm2 and the value predicted for this time period by some thermal evolution models. Their average (median = 41 ZAm2) is, however, similar to the long-term average during both of the intervals 2300-2800 Ma (median = 44 ZAm2; N = 103) and 10-500 Ma (median 41 ZAm2; N = 997). While there is little evidence for a substantial net change in average dipole moment between the late Archaean and Phanerozoic, there is preliminary evidence that its variance has increased between the two intervals. This lower variance more than two billion years ago supports the idea that the geodynamo, even while not producing a stronger magnetic field, was more stable on average at the Archaean-Proterozoic transition than it is today

Electrochemical probing of selective haemoglobin binding in hydrogel-based molecularly imprinted polymers

An electrochemical method has been developed for the probing of hydrogel-based molecularly imprinted polymers (HydroMIPs) on the surface of a glassy carbon electrode. HydroMIPs designed for bovine haemoglobin selectivity were electrochemically characterised and their rebinding properties were monitored using cyclic voltammetry. The electrochemical reduction of bovine oxyhaemoglobin (BHb) in solution was observed to occur at ?0.460 V vs (Ag/AgCl) in 150 mM phosphate buffer solution (PBS). When the protein was selectively bound to the MIP, the electrochemical reduction of oxyhaemoglobin could be observed at a similar peak potential of ?0.480 V vs (Ag/AgCl). When analysing the non-imprinted control polymer (NIP) interfaced at the electrode, which contained no protein, the peak reduction potential corresponded to that observed for dissolved oxygen in solution (?0.65 V vs (Ag/AgCl)). MIP and NIP (in the absence of protein) were interfaced at the electrode and protein allowed to diffuse through the polymers from the bulk solution end to the electrode. It was observed that whereas NIP exhibited a protein response within 10 min of protein exposure, up to 45 min of exposure time was required in the case of the MIP before a protein response could be obtained. Our results suggest that due to the selective nature of the MIP, BHb arrival at the electrode via diffusion is delayed by the MIP due to attractive selective interactions with exposed cavities, but not the NIP which is devoid of selective cavities

A Fast Algorithm for Robust Regression with Penalised Trimmed Squares

The presence of groups containing high leverage outliers makes linear regression a difficult problem due to the masking effect. The available high breakdown estimators based on Least Trimmed Squares often do not succeed in detecting masked high leverage outliers in finite samples. An alternative to the LTS estimator, called Penalised Trimmed Squares (PTS) estimator, was introduced by the authors in \cite{ZiouAv:05,ZiAvPi:07} and it appears to be less sensitive to the masking problem. This estimator is defined by a Quadratic Mixed Integer Programming (QMIP) problem, where in the objective function a penalty cost for each observation is included which serves as an upper bound on the residual error for any feasible regression line. Since the PTS does not require presetting the number of outliers to delete from the data set, it has better efficiency with respect to other estimators. However, due to the high computational complexity of the resulting QMIP problem, exact solutions for moderately large regression problems is infeasible. In this paper we further establish the theoretical properties of the PTS estimator, such as high breakdown and efficiency, and propose an approximate algorithm called Fast-PTS to compute the PTS estimator for large data sets efficiently. Extensive computational experiments on sets of benchmark instances with varying degrees of outlier contamination, indicate that the proposed algorithm performs well in identifying groups of high leverage outliers in reasonable computational time.Comment: 27 page

Improving Inference of Gaussian Mixtures Using Auxiliary Variables

Expanding a lower-dimensional problem to a higher-dimensional space and then projecting back is often beneficial. This article rigorously investigates this perspective in the context of finite mixture models, namely how to improve inference for mixture models by using auxiliary variables. Despite the large literature in mixture models and several empirical examples, there is no previous work that gives general theoretical justification for including auxiliary variables in mixture models, even for special cases. We provide a theoretical basis for comparing inference for mixture multivariate models with the corresponding inference for marginal univariate mixture models. Analytical results for several special cases are established. We show that the probability of correctly allocating mixture memberships and the information number for the means of the primary outcome in a bivariate model with two Gaussian mixtures are generally larger than those in each univariate model. Simulations under a range of scenarios, including misspecified models, are conducted to examine the improvement. The method is illustrated by two real applications in ecology and causal inference

The distribution of stellar mass in the low-redshift Universe

We use a complete and uniform sample of almost half a million galaxies from the Sloan Digital Sky Survey to characterise the distribution of stellar mass in the low-redshift Universe. Galaxy abundances are well determined over almost four orders of magnitude in stellar mass, and are reasonably but not perfectly fit by a Schechter function with characteristic stellar mass m* = 6.7 x 10^10 M_sun and with faint-end slope \alpha = -1.155. For a standard cosmology and a standard stellar Initial Mass Function, only 3.5% of the baryons in the low-redshift Universe are locked up in stars. The projected autocorrelation function of stellar mass is robustly and precisely determined for r_p < 30 Mpc/h. Over the range 10 kpc/kpc < r_p < 10 Mpc/h it is extremely well represented by a power law. The corresponding three-dimensional autocorrelation function is \xi*(r) = (r/6.1 Mpc/h)^{-1.84}. Relative to the dark matter, the bias of the stellar mass distribution is approximately constant on large scales, but varies by a factor of five for r_p < 1 Mpc/h. This behaviour is approximately but not perfectly reproduced by current models for galaxy formation in the concordance LCDM cosmology. Detailed comparison suggests that a fluctuation amplitude \sigma_8 ~ 0.8 is preferred to the somewhat larger value adopted in the Millennium Simulation models with which we compare our data. This comparison also suggests that observations of stellar mass autocorrelations as a function of redshift might provide a powerful test for the nature of Dark Energy.Comment: 12 pages, 11 figures, accepted for publication in Monthly Notices, two appendices added to explore possible systematic biases due to the stellar mass definition and surface density limit