The ν=5/2\nu=5/2 Fractional Quantum Hall State in the Presence of Alloy Disorder

We report quantitative measurements of the impact of alloy disorder on the $\nu=5/2$ fractional quantum Hall state. Alloy disorder is controlled by the aluminum content $x$ in the Al$_x$Ga$_{1-x}$As channel of a quantum well. We find that the $\nu=5/2$ state is suppressed with alloy scattering. To our surprise, in samples with alloy disorder the $\nu=5/2$ state appears at significantly reduced mobilities when compared to samples in which alloy disorder is not the dominant scattering mechanism. Our results highlight the distinct roles of the different types of disorder present in these samples, such as the short-range alloy and the long-range Coulomb disorder

Di(2-ethylhexyl) Phthalate Metabolites May Alter Thyroid Hormone Levels in Men

Background: Phthalates are used extensively in many personal-care and consumer products, resulting in widespread nonoccupational human exposure through multiple routes and media. A limited number of animal studies suggest that exposure to phthalates may be associated with altered thyroid function, but human data are lacking. Methods: Concurrent samples of urine and blood were collected from 408 men. We measured urinary concentrations of mono(2-ethylhexyl) phthalate (MEHP), the hydrolytic metabolite of di(2-ethylhexyl) phthalate (DEHP), and other phthalate monoester metabolites, along with serum levels of free thyroxine (T$_4$), total triiodothyronine (T$_3$), and thyroid-stimulating hormone (TSH). Oxidative metabolites of DEHP were measured in urine from only 208 of the men. Results: We found an inverse association between MEHP urinary concentrations and free T$_4$ and T$_3$ serum levels, although the relationships did not appear to be linear when MEHP concentrations were categorized by quintiles. There was evidence of a plateau at the fourth quintile, which was associated with a 0.11 ng/dL decrease in free T$_4$ [95% confidence interval (CI), –0.18 to –0.03] and a 0.05 ng/mL decrease in T$_3$ (95% CI, –0.10 to 0.01) compared with the first (lowest) MEHP quintile. The inverse relationship between MEHP and free T$_4$ remained when we adjusted for oxidative metabolite concentrations; this simultaneously demonstrated a suggestive positive association with free T$_4$. Conclusions: Urinary MEHP concentrations may be associated with altered free T$_4$ and/or total T$_3$ levels in adult men, but additional study is needed to confirm the observed findings. Future studies must also consider oxidative DEHP metabolites relative to MEHP as a potential marker of metabolic susceptibility to DEHP exposure

Predicting Remaining Useful Life with Similarity-Based Priors

Prognostics is the area of research that is concerned with predicting the remaining useful life of machines and machine parts. The remaining useful life is the time during which a machine or part can be used, before it must be replaced or repaired. To create accurate predictions, predictive techniques must take external data into account on the operating conditions of the part and events that occurred during its lifetime. However, such data is often not available. Similarity-based techniques can help in such cases. They are based on the hypothesis that if a curve developed similarly to other curves up to a point, it will probably continue to do so. This paper presents a novel technique for similarity-based remaining useful life prediction. In particular, it combines Bayesian updating with priors that are based on similarity estimation. The paper shows that this technique outperforms other techniques on long-term predictions by a large margin, although other techniques still perform better on short-term predictions.</p

Stock market investors' use of stop losses and the disposition effect

The disposition effect is an investment bias where investors hold stocks at a loss longer than stocks at a gain. This bias is associated with poorer investment performance and exhibited to a greater extent by investors with less experience and less sophistication. A method of managing susceptibility to the bias is through use of stop losses. Using the trading records of UK stock market individual investors from 2006 to 2009, this paper shows that stop losses used as part of investment decisions are an effective tool for inoculating against the disposition effect. We also show that investors who use stop losses have less experience and that, when not using stop losses, these investors are more reluctant to realise losses than other investors

Empirical Survival Jensen-Shannon Divergence as a Goodness-of-Fit Measure for Maximum Likelihood Estimation and Curve Fitting

The coefficient of determination, known as R2, is commonly used as a goodness-of-fit criterion for fitting linear models. R2 is somewhat controversial when fitting nonlinear models, although it may be generalised on a case-by-case basis to deal with specific models such as the logistic model. Assume we are fitting a parametric distribution to a data set using, say, the maximum likelihood estimation method. A general approach to measure the goodness-of-fit of the fitted parameters, which is advocated herein, is to use a non- parametric measure for comparison between the empirical distribution, comprising the raw data, and the fitted model. In particular, for this purpose we put forward the Survi- val Jensen-Shannon divergence (SJS) and its empirical counterpart (ESJS) as a metric which is bounded, and is a natural generalisation of the Jensen-Shannon divergence. We demonstrate, via a straightforward procedure making use of the ESJS, that it can be used as part of maximum likelihood estimation or curve fitting as a measure of goodness-of-fit, including the construction of a confidence interval for the fitted parametric distribution. Furthermore, we show the validity of the proposed method with simulated data, and three empirical data sets

THE ROLE OF INTERDEPENDENCE IN THE MICRO-FOUNDATIONS OF ORGANIZATION DESIGN: TASK, GOAL, AND KNOWLEDGE INTERDEPENDENCE

Interdependence is a core concept in organization design, yet one that has remained consistently understudied. Current notions of interdependence remain rooted in seminal works, produced at a time when managers’ near-perfect understanding of the task at hand drove the organization design process. In this context, task interdependence was rightly assumed to be exogenously determined by characteristics of the work and the technology. We no longer live in that world, yet our view of interdependence has remained exceedingly task-centric and our treatment of interdependence overly deterministic. As organizations face increasingly unpredictable workstreams and workers co-design the organization alongside managers, our field requires a more comprehensive toolbox that incorporates aspects of agent-based interdependence. In this paper, we synthesize research in organization design, organizational behavior, and other related literatures to examine three types of interdependence that characterize organizations’ workflows: task, goal, and knowledge interdependence. We offer clear definitions for each construct, analyze how each arises endogenously in the design process, explore their interrelations, and pose questions to guide future research

Standard survey methods for estimating colony losses and explanatory risk factors in Apis mellifera

This chapter addresses survey methodology and questionnaire design for the collection of data pertaining to estimation of honey bee colony loss rates and identification of risk factors for colony loss. Sources of error in surveys are described. Advantages and disadvantages of different random and non-random sampling strategies and different modes of data collection are presented to enable the researcher to make an informed choice. We discuss survey and questionnaire methodology in some detail, for the purpose of raising awareness of issues to be considered during the survey design stage in order to minimise error and bias in the results. Aspects of survey design are illustrated using surveys in Scotland. Part of a standardized questionnaire is given as a further example, developed by the COLOSS working group for Monitoring and Diagnosis. Approaches to data analysis are described, focussing on estimation of loss rates. Dutch monitoring data from 2012 were used for an example of a statistical analysis with the public domain R software. We demonstrate the estimation of the overall proportion of losses and corresponding confidence interval using a quasi-binomial model to account for extra-binomial variation. We also illustrate generalized linear model fitting when incorporating a single risk factor, and derivation of relevant confidence intervals