Criminal History Records: Additional Actions Could Enhance the Completeness of Records Used Employment-Related Background Checks for Employment-Related Background Checks

Authorized employers use information from FBI criminal history record checks to assess a person’s suitability for employment or to obtain a license. States create criminal records and the FBI facilitates access to these records by other states for nationwide checks. GAO was asked to assess efforts to address concerns about incomplete records, among other things. This report addresses to what extent (1) states conduct FBI record checks for selected employment sectors and face any challenges; (2) states have improved the completeness of records, and remaining challenges that federal agencies can help mitigate; and (3) private companies conduct criminal record checks, the benefits those checks provide to employers, and any related challenges. GAO analyzed laws and regulations used to conduct criminal record checks and assessed the completeness of records; conducted a nationwide survey, which generated responses from 47 states and the District of Columbia; and interviewed officials that manage checks from the FBI and 4 states (California, Florida, Idaho, and Washington). GAO selected states based on geographic location and other factors

Polarization of superfluid turbulence

We show that normal fluid eddies in turbulent helium II polarize the tangle of quantized vortex lines present in the flow, thus inducing superfluid vorticity patterns similar to the driving normal fluid eddies. We also show that the polarization is effective over the entire inertial range. The results help explain the surprising analogies between classical and superfluid turbulence which have been observed recently.Comment: 3 figure

Differential Dynamic Microscopy to characterize Brownian motion and bacteria motility

We have developed a lab work module where we teach undergraduate students how to quantify the dynamics of a suspension of microscopic particles, measuring and analyzing the motion of those particles at the individual level or as a group. Differential Dynamic Microscopy (DDM) is a relatively recent technique that precisely does that and constitutes an alternative method to more classical techniques such as dynamics light scattering (DLS) or video particle tracking (VPT). DDM consists in imaging a particle dispersion with a standard light microscope and a camera. The image analysis requires the students to code and relies on digital Fourier transform to obtain the intermediate scattering function, an autocorrelation function that characterizes the dynamics of the dispersion. We first illustrate DDM on the textbook case of colloids where we measure the diffusion coefficient. Then we show that DDM is a pertinent tool to characterize biologic systems such as motile bacteria i.e.bacteria that can self propel, where we not only determine the diffusion coefficient but also the velocity and the fraction of motile bacteria. Finally, so that our paper can be used as a tutorial to the DDM technique, we have joined to this article movies of the colloidal and bacterial suspensions and the DDM algorithm in both Matlab and Python to analyze the movies

Evaporation of a packet of quantized vorticity

A recent experiment has confirmed the existence of quantized turbulence in superfluid He3-B and suggested that turbulence is inhomogenous and spreads away from the region around the vibrating wire where it is created. To interpret the experiment we study numerically the diffusion of a packet of quantized vortex lines which is initially confined inside a small region of space. We find that reconnections fragment the packet into a gas of small vortex loops which fly away. We determine the time scale of the process and find that it is in order of magnitude agreement with the experiment.Comment: figure 1a,b,c and d, figure2, figure

The Key Role of Heavy Precipitation Events in Climate Model Disagreements of Future Annual Precipitation Changes in California

Climate model simulations disagree on whether future precipitation will increase or decrease over California, which has impeded efforts to anticipate and adapt to human-induced climate change. This disagreement is explored in terms of daily precipitation frequency and intensity. It is found that divergent model projections of changes in the incidence of rare heavy (\u3e60 mm day−1) daily precipitation events explain much of the model disagreement on annual time scales, yet represent only 0.3% of precipitating days and 9% of annual precipitation volume. Of the 25 downscaled model projections examined here, 21 agree that precipitation frequency will decrease by the 2060s, with a mean reduction of 6–14 days yr−1. This reduces California\u27s mean annual precipitation by about 5.7%. Partly offsetting this, 16 of the 25 projections agree that daily precipitation intensity will increase, which accounts for a model average 5.3% increase in annual precipitation. Between these conflicting tendencies, 12 projections show drier annual conditions by the 2060s and 13 show wetter. These results are obtained from 16 global general circulation models downscaled with different combinations of dynamical methods [Weather Research and Forecasting (WRF), Regional Spectral Model (RSM), and version 3 of the Regional Climate Model (RegCM3)] and statistical methods [bias correction with spatial disaggregation (BCSD) and bias correction with constructed analogs (BCCA)], although not all downscaling methods were applied to each global model. Model disagreements in the projected change in occurrence of the heaviest precipitation days (\u3e60 mm day−1) account for the majority of disagreement in the projected change in annual precipitation, and occur preferentially over the Sierra Nevada and Northern California. When such events are excluded, nearly twice as many projections show drier future conditions

Probabilistic estimates of future changes in California temperature and precipitation usingstatistical and dynamical downscaling

Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling

Improved Bias Correction Techniques for Hydrological Simulations of Climate Change

Global climate model (GCM) output typically needs to be bias corrected before it can be used for climate change impact studies. Three existing bias correction methods, and a new one developed here, are applied to daily maximum temperature and precipitation from 21 GCMs to investigate how different methods alter the climate change signal of the GCM. The quantile mapping (QM) and cumulative distribution function transform (CDF-t) bias correction methods can significantly alter the GCM’s mean climate change signal, with differences of up to 2°C and 30% points for monthly mean temperature and precipitation, respectively. Equidistant quantile matching (EDCDFm) bias correction preserves GCM changes in mean daily maximum temperature but not precipitation. An extension to EDCDFm termed PresRat is introduced, which generally preserves the GCM changes in mean precipitation. Another problem is that GCMs can have difficulty simulating variance as a function of frequency. To address this, a frequency-dependent bias correction method is introduced that is twice as effective as standard bias correction in reducing errors in the models’ simulation of variance as a function of frequency, and it does so without making any locations worse, unlike standard bias correction. Last, a preconditioning technique is introduced that improves the simulation of the annual cycle while still allowing the bias correction to take account of an entire season’s values at once

The Claims Culture: A Taxonomy of Industry Attitudes

This paper presents an analysis of a familiar aspect of construction industry culture that we have dubbed 'the claims culture'. This is a culture of contract administration that lays a strong emphasis on the planning and management of claims. The principal elements of the analysis are two sets of distinctions. The first comprises economic and occupational orders, referring to two kinds of control that are exercised over the construction process; predicated respectively on economic ownership and occupational competence. The second refers to contrasting attitudes towards relationships and problem solving within these orders: respectively 'distributive' and 'integrative'. The concepts of economic and occupational order entail further sub-categories. The various attitudes associated with these categories and sub-categories are described. They are assessed as to their consequences for change initiatives in the industry

Time-separated entangled light pulses from a single-atom emitter

The controlled interaction between a single, trapped, laser-driven atom and the mode of a high-finesse optical cavity allows for the generation of temporally separated, entangled light pulses. Entanglement between the photon-number fluctuations of the pulses is created and mediated via the atomic center-of-mass motion, which is interfaced with light through the mechanical effect of atom-photon interaction. By means of a quantum noise analysis we determine the correlation matrix which characterizes the entanglement, as a function of the system parameters. The scheme is feasible in experimentally accessible parameter regimes. It may be easily extended to the generation of entangled pulses at different frequencies, even at vastly different wavelengths.Comment: 17 pages, 5 figures. Modified version, to appear in the New Journal of Physic