A Network of Portable, Low-Cost, X-Band Radars

Radar is a unique tool to get an overview on the weather situation, given its high spatio- temporal resolution. Over 60 years, researchers have been investigating ways for obtaining the best use of radar. As a result we often find assurances on how much radar is a useful tool, and it is! After this initial statement, however, regularly comes a long list on how to increase the accuracy of radar or in what direction to move for improving it. Perhaps we should rather ask: is the resulting data good enough for our application? The answers are often more complicated than desired. At first, some people expect miracles. Then, when their wishes are disappointed, they discard radar as a tool: both attitudes are wrong; radar is a unique tool to obtain an excellent overview on what is happening: when and where it is happening. At short ranges, we may even get good quantitative data. But at longer ranges it may be impossible to obtain the desired precision, e.g. the precision needed to alert people living in small catchments in mountainous terrain. We would have to set the critical limit for an alert so low that this limit would lead to an unacceptable rate of false alarm

Correcting Publication Bias In Meta-Analysis: A Truncation Approach

Meta-analyses are increasingly used to support national policy decision making. The practical implications of publications bias in meta-analysis are discussed. Standard approaches to correct for publication bias require knowledge of the selection mechanism that leads to publication. In this study, an alternative approach is proposed based on Cohen’s corrections for a truncated normal. The approach makes less assumptions, is easy to implement, and performs well in simulations with small samples. The approach is illustrated with two published meta-analyses

When, where and how to perform efficiency estimation

In this paper we compare two flexible estimators of technical efficiency in a cross-sectional setting: the nonparametric kernel SFA estimator of Fan, Li and Weersink (1996) to the nonparametric bias corrected DEA estimator of Kneip, Simar andWilson (2008). We assess the finite sample performance of each estimator via Monte Carlo simulations and empirical examples. We find that the reliability of efficiency scores critically hinges upon the ratio of the variation in efficiency to the variation in noise. These results should be a valuable resource to both academic researchers and practitioners.Bootstrap; Nonparametric kernel; Technical efficiency

When, Where and How to Perform Efficiency Estimation

In this paper we compare two flexible estimators of technical efficiency in a cross-sectional setting: the nonparametric kernel SFA estimator of Fan, Li and Weersink (1996) to the nonparametric bias corrected DEA estimator of Kneip, Simar and Wilson (2008). We assess the finite sample performance of each estimator via Monte Carlo simulations and empirical examples. We find that the reliability of efficiency scores critically hinges upon the ratio of the variation in efficiency to the variation in noise. These results should be a valuable resource to both academic researchers and practitioners.nonparametric kernel, technical efficiency, bootstrap

When, where and how to perform efficiency estimation

In this paper we compare two flexible estimators of technical efficiency in a cross-sectional setting: the nonparametric kernel SFA estimator of Fan, Li and Weersink (1996) to the nonparametric bias corrected DEA estimator of Kneip, Simar and Wilson (2008). We assess the finite sample performance of each estimator via Monte Carlo simulations and empirical examples. We find that the reliability of efficiency scores critically hinges upon the ratio of the variation in efficiency to the variation in noise. These results should be a valuable resource to both academic researchers and practitioners.Bootstrap, Nonparametric Kernel, Technical Efficiency

Repeat Maltreatment in Alaska: Assessment and Exploration of Alternative Measures

Most deaths and serious injuries among children who are abused or neglected are preceded by multiple reported instances of maltreatment. The Office of Children Services (OCS), Alaska’s child protection agency, is very concerned about repeat maltreatment. It’s extremely damaging to children and demoralizing to everyone who tries to help prevent it. Over the last several years, Alaska has consistently had among the highest rates in the country of repeat child maltreatment, as reported by the Children’s Bureau of the U.S. Department of Health and Human Services. Those federal figures measure the percentage of children who were the victims of at least two substantiated reports—that is, confirmed reports—of maltreatment within six months. In 2009, nearly 10% of children who were the subjects of investigation by OCS were reported as suffering repeat maltreatment, compared with less than 6% nationwide. By 2013, the share in Alaska was at nearly 13%, compared with a national rate of less than 5.5% (Figure S-1). But even those grim federal statistics don’t provide a complete picture of repeat child maltreatment in Alaska. Many analysts believe that not all cases where maltreatment may have occurred are substantiated, and that maltreatment of a child may be reported a number of times, over a longer period, before it is substantiated. Also, for various reasons, many reports of maltreatment are not investigated at all, in Alaska and other states, and only a small share of those that are investigated are substantiated. For example, in Alaska in 2013, 42% of reports in an average month were not investigated, and only 12% of reports were substantiatedExecutive Summary / Introduction / Definition of repeat maltreatment: some challenges / Data / Findings / Varying the indicator / Varying time periods / Demographics of repeatedly maltreated children / Discussion / Limitations and suggestions for future research / Conclusion / Reference

Complete characterization of sink-strengths for 1D to 3D mobilities of defect clusters.II. Bridging between limiting cases with effective sink-strengths calculations

In a companion paper, we proposed new analytical expressions of cluster sink-strengths (CSS) indispensable to any complete parameterization of rate equations cluster dynamics accounting for reaction between defect clusters populations having a 1D-mobility. In this second paper, we first establish simulation setup rules for truly converged estimates of effective CSS by Kinetic Monte-Carlo, and then we grid on a wide set of radii, rotation energies, diffusion coefficients and concentrations of both reaction partners. Symmetric roles of some parameters are used to infer a generic form for a semi-analytical expression of CSS depending on all these interaction parameters: it is composed of the various analytical limiting cases established and fitted transition functions that allow a gradual switching between them. The analysis of the residuals shows that the overall agreement is reasonably good: it is only in the transition zones that discrepancies are located and this is due to the asymmetry of the actual transition functions. The expression can be easily extended to temperatures at least a few hundred degrees around the reference. But further extending the CSS evaluations to much smaller diffusion coefficients ratios, we see that the domain for 1D-1D mobility is very extended: for a $10^{-3}$ ratio the computed CSS is still not correctly described by the 1D-CSS with respect to a fixed sink (1D-0), but rather by the established 1D-1D expression. For our typical sets of conditions, it is only when approaching a ratio of $10^{-6}$ that the 1D-0 CSS starts to become more relevant. This highlights the counter-intuitive fact that the growth kinetics of moderately trapped 1D mobile loops, whose effective mobility is greatly reduced, may not be described by 1D-0 kinetics but rather by appropriately corrected 1D-1D CSS which have completely different order of magnitude and kinetic orders.Comment: 21 pages, 12 figure

Behavioral Economics: Past, Present, Future

Behavioral economics increases the explanatory power of economics by providing it with more realistic psychological foundations. This book consists of representative recent articles in behavioral economics. This chapter is intended to provide an introduction to the approach and methods of behavioral economics, and to some of its major findings, applications, and promising new directions. It also seeks to fill some unavoidable gaps in the chapters’ coverage of topics