Employment status and income as potential mediators of educational inequalities in population mental health

We assessed whether educational inequalities in mental health may be mediated by employment status and household income. Poor mental health was assessed using General Health Questionnaire ‘caseness’ in working age adult participants (N = 48 654) of the Health Survey for England (2001–10). Relative indices of inequality by education level were calculated. Substantial inequalities were apparent, with adjustment for employment status and household income markedly reducing their magnitude. Educational inequalities in mental health were attenuated by employment status. Policy responses to economic recession (such as active labour market interventions) might reduce mental health inequalities but longitudinal research is needed to exclude reverse causation

Trends in population mental health before and after the 2008 recession: a repeat cross-sectional analysis of the 1991-2010 health surveys of England

<p>Objective: To assess short-term differences in population mental health before and after the 2008 recession and explore how and why these changes differ by gender, age and socio-economic position.</p> <p>Design: Repeat cross-sectional analysis of survey data.</p> <p>Setting: England.</p> <p>Participants: Representative samples of the working age (25–64 years) general population participating in the Health Survey for England between 1991 and 2010 inclusive.</p> <p>Main outcome measures: Prevalence of poor mental health (caseness) as measured by the general health questionnaire-12 (GHQ).</p> <p>Results: Age–sex standardised prevalence of GHQ caseness increased from 13.7% (95% CI 12.9% to 14.5%) in 2008 to 16.4% (95% CI 14.9% to 17.9%) in 2009 and 15.5% (95% CI 14.4% to 16.7%) in 2010. Women had a consistently greater prevalence since 1991 until the current recession. However, compared to 2008, men experienced an increase in age-adjusted caseness of 5.1% (95% CI 2.6% to 7.6%, p<0.001) in 2009 and 3% (95% CI 1.2% to 4.9%, p=0.001) in 2010, while no statistically significant changes were seen in women. Adjustment for differences in employment status and education level did not account for the observed increase in men nor did they explain the differential gender patterning. Over the last decade, socio-economic inequalities showed a tendency to increase but no clear evidence for an increase in inequalities associated with the recession was found. Similarly, no evidence was found for a differential effect between age groups.</p> <p>Conclusions: Population mental health in men has deteriorated within 2 years of the onset of the current recession. These changes, and their patterning by gender, could not be accounted for by differences in employment status. Further work is needed to monitor recessionary impacts on health inequalities in response to ongoing labour market and social policy changes.</p&gt

A frictional Cosserat model for the flow of granular materials through a vertical channel

A rigid-plastic Cosserat model has been used to study dense, fully developed flow of granular materials through a vertical channel. Frictional models based on the classical continuum do not predict the occurrence of shear layers, at variance with experimental observations. This feature has been attributed to the absence of a material length scale in their constitutive equations. The present model incorporates such a material length scale by treating the granular material as a Cosserat continuum. Thus localised couple stresses exist and the stress tensor is asymmetric. The velocity profiles predicted by the model are in close agreement with available experimental data. The predicted dependence of the shear layer thickness on the width of the channel is in reasonable agreement with data. In the limit of the ratio of the particle diameter to the half-width of the channel being small, the model predicts that the shear layer thickness scaled by the particle diameter grows.Comment: 17 pages, 12 PostScript figures, uses AmsLaTeX, psfrag and natbib. Accepted for publication in Acta Mechanic

Simultaneous Spin-Charge Relaxation in Double Quantum Dots

We investigate phonon-induced spin and charge relaxation mediated by spin-orbit and hyperfine interactions for a single electron confined within a double quantum dot. A simple toy model incorporating both direct decay to the ground state of the double dot and indirect decay via an intermediate excited state yields an electron spin relaxation rate that varies non-monotonically with the detuning between the dots. We confirm this model with experiments performed on a GaAs double dot, demonstrating that the relaxation rate exhibits the expected detuning dependence and can be electrically tuned over several orders of magnitude. Our analysis suggests that spin-orbit mediated relaxation via phonons serves as the dominant mechanism through which the double-dot electron spin-flip rate varies with detuning.Comment: 5 pages, 3 figures, Supplemental Material (2 pages, 2 figures

Prognostication of Unseen Objects using Zero-Shot Learning with a Complete Case Analysis

Generally, for a machine learning model to perform well, the data instances on which the model is being trained have to be relevant to the use case. In the case of relevant samples not being available, Zero-shot learning can be used to perform classification tasks. Zero-shot learning is the process of solving a problem when there are no examples of that problem in the phase of training. It lets us classify target classes on which the deep learning model has not been trained. In this article, Zero-shot learning is used to classify food dish classes through an object recognition model. First, the data is collected from Google Images and Kaggle. The image attributes are then extracted using a VGG16 model. The image attributes belonging to the training categories are then used to train a custom-built deep learning model. Various hypermeters of the model are tuned and the results are analyzed in order to get the best possible performance. The image attributes extracted from the zero-shot learning categories are used to test the model after the training process is completed. The predictions are made by comparing the vectors of the target class with the training classes in the Word2Vec space. The metric used to evaluate the model is Top-5 accuracy which indicates whether the expected result is present in the predictions. A Top-5 accuracy of 92% is achieved by implementing zero-shot learning for the classification of unseen food dish images

Discriminating Between Generalized Exponential Distribution and Some Life Test Models Based on Population Quantiles

A test statistic based on population quantiles using sample order statistics is suggested. The quantiles of the test statistics are evaluated for generalized exponential distribution. Similar test statistic based on moments of sample order statistic is referred and the proposed test formula is compared with it. Between the pairs of the above models it is established that the test formula proposed by us is more effective and useful than the formula based on the moments of order statistics as developed by Sultan (2007)