795 research outputs found

    The Econometrics of Unobservables

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    Models of wave-function collapse, underlying theories, and experimental tests

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    We describe the state of the art in preparing, manipulating and detecting coherent molecular matter. We focus on experimental methods for handling the quantum motion of compound systems from diatomic molecules to clusters or biomolecules.Molecular quantum optics offers many challenges and innovative prospects: already the combination of two atoms into one molecule takes several well-established methods from atomic physics, such as for instance laser cooling, to their limits. The enormous internal complexity that arises when hundreds or thousands of atoms are bound in a single organic molecule, cluster or nanocrystal provides a richness that can only be tackled by combining methods from atomic physics, chemistry, cluster physics, nanotechnology and the life sciences.We review various molecular beam sources and their suitability for matter-wave experiments. We discuss numerous molecular detection schemes and give an overview over diffraction and interference experiments that have already been performed with molecules or clusters.Applications of de Broglie studies with composite systems range from fundamental tests of physics up to quantum-enhanced metrology in physical chemistry, biophysics and the surface sciences.Nanoparticle quantum optics is a growing field, which will intrigue researchers still for many years to come. This review can, therefore, only be a snapshot of a very dynamical process

    Do Race and Ethnicity Influence Turnover Intention in Newly Licensed Registered Nurses?

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    Background: Demand for health care services is rising while newly licensed nurses vacate employment positions at alarming rates. Healthcare leadership has called for an increased diversification of the healthcare workforce, but the workplace experience of nonwhite nurses in the first years has not been assessed. Methods: This study utilized a quantitative cross-sectional survey design. The sample was limited to newly licensed nurses with no prior experience as a nurse. Linear regression models were constructed to determine which personal and structural attributes are associated with turnover intention, stratified by race and ethnicity. Hierarchical, backwards stepwise selection was used to build the final model. Results: The majority of respondents were white English-speaking females, never married, holding a BSN and working in an acute care hospital. Nurses who speak a language other than English at home are treated more poorly than primary English speakers. Hispanics are most likely to report a negative work environment, a hostile climate, general incivility and inappropriate jokes. Turnover intention was associated with months at the current job, a negative work environment including experiencing incivility, not having enough time to do the things that must be done, and confidence in the ability to do one’s job list. Blacks are likely to report a high turnover intention but remain in the current job while acknowledging a hostile environment and general incivility. Many nurses employed in non-acute care settings are nonwhite, report higher workload scores and high patient assignments. Conclusions: Nonwhite nurses report negative work environments and high intention to leave but remain in their jobs. Among the full sample of newly licensed nurses, months at the current job, a negative work environment, including experiencing incivility, not having enough time to do the things that must be done, and confidence in the ability to do one’s job were associated with turnover intention. Efforts to diversify the workforce must include education to prepare minority nurses for the environment they may encounter, including uncivil behavior and high workloads. Policy initiatives must address the treatment of new nurses and support new nurses as they transition to the professional role

    Model predictive satisficing fuzzy logic control,”

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    Abstract-Model-predictive control, which is an alternative to conventional optimal control, provides controller solutions to many constrained and nonlinear control problems. However, even when a good model is available, it may be necessary for an expert to specify the relationship between local model predictions and global system performance. We present a satisficing fuzzy logic controller that is based on a receding control horizon, but which employs a fuzzy description of system consequences via model predictions. This controller considers the gains and losses associated with each control action, is compatible with robust design objectives, and permits flexible defuzzifier design. We demonstrate the controller's application to representative problems from the control of uncertain nonlinear systems

    Examining the effect of health behaviors on wages and healthcare utilization in models with endogeneity

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    This dissertation contains three essays on applied health economics. Although each essay is independent of the others, all three address the issue of estimating models where the relationship of interest is confounded by factors that are unobservable to the researcher. The first essay is an econometric simulation study while essays 2 and 3 address behavioral health topics. Essay 1 compares the accuracy and efficiency of parametric count data specifications paired with the Extended Olsen Model (EOM; Terza, 1998, 2009). The EOM is a nonlinear instrumental variables approach that allows for consistent estimation of model parameters when the data suffer from binary endogenous switching (e.g., endogenous sample selection or endogenous treatment). Count data models are ubiquitous in the health literature for estimating non-negative, discrete outcomes such as physician visits, hospital admissions, cigarettes smoked, etc. Essay 1 provides insight into the model selection process by informing practitioners which specification is likely to provide the most accurate parameter estimates under a variety of data configurations. Essay 1 also demonstrates the applicability of the Conway-Maxwell Poisson (CMP), a flexible count model developed in the field of industrial engineering that has yet to be utilized in the economic literature. In Essay 2 I apply a count version of the Extended Olsen Model to estimate the relationship between marijuana use disorder (MUD) and ER visits among US Medicaid recipients. This essay is the first in the literature to estimate the relationship between marijuana consumption and the demand for ER visits in isolation from other illicit drugs, thus providing an important addition to the ongoing policy regarding the potential relaxation of marijuana regulation. This study is also the first in the illicit substance literature to use an instrumental variables count data model to estimate the full distribution of ER visits, thus accounting for unobserved factors that may be jointly correlated between individual propensity for MUD and demand for ER visits. I fail to find a positive relationship between MUD and ER visits, instead uncovering suggestive, but inconclusive, evidence that MUD and ER visits may rather be negatively correlated. Essay 3 considers the relationship between wages and obesity. Although prior literature has firmly established a negative relationship between wages and obesity, it is equivocal with regard to the underlying pathway(s) through which obesity results in lower wages. Using firm-level data that gives me unique access to proxies for productivity and discrimination against obese individuals, I find that inputs to productivity, particularly health, are important confounders of the wage-obesity relationship. I fail to find any evidence of discrimination against obese employees, but I do find that among females the negative relationship between wages and obesity exists only among mothers

    Bounds on Collapse Models from Matter-Wave Interferometry: Calculational details

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    We present a simple derivation of the interference pattern in matter-wave interferometry as predicted by a class of master equations, by using the density matrix formalism. We apply the obtained formulae to the most relevant collapse models, namely the Ghirardi-Rimini-Weber (GRW) model, the continuous spontaneous localization (CSL) model together with its dissipative (dCSL) and non-markovian generalizations (cCSL), the quantum mechanics with universal position localization (QMUPL) and the Di\'{o}si-Penrose (DP) model. We discuss the separability of the collapse models dynamics along the 3 spatial directions, the validity of the paraxial approximation and the amplification mechanism. We obtain analytical expressions both in the far field and near field limits. These results agree with those already derived in the Wigner function formalism. We compare the theoretical predictions with the experimental data from two relevant matter-wave experiments: the 2012 far-field experiment and the 2013 Kapitza Dirac Talbot Lau (KDTL) near-field experiment of Arndt's group. We show the region of the parameter space for each collapse model, which is excluded by these experiments. We show that matter-wave experiments provide model insensitive bounds, valid for a wide family of dissipative and non-markovian generalizations.Comment: 49 pages,16 figure

    Feature extraction for image quality prediction

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    Viability and Performance of RF Source Localization Using Autocorrelation-Based Fingerprinting

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    Finding the source location of a radio-frequency (RF) transmission is a useful capability for many civilian, industrial, and military applications. This problem is particularly challenging when done “Blind,” or when the transmitter was not designed with finding its location in mind, and relatively little information is available about the signal before-hand. Typical methods for this operation utilize the time, phase, power, and frequency viewable from received signals. These features are all less predictable in indoor and urban environments, where signals undergo transformation from multiple interactions with the environment. These interactions imprint structure onto the received signal which is dependent on the transmission path, and therefore the initial location. Using a received signal, a signal characteristic known as the autocorrelation can be computed which will largely be shaped by this information. In this research, RF source localization using finger-printing (a technique involving matching to a known database) with signal autocorrelations is explored. A Gaussian-process-based method for autocorrelation based fingerprinting is proposed. Performance of this method is evaluated using a ray-tracing-based simulation of an indoor environment
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