Organizational politics and multisource feedbacklh[electronic resource]

The current research examined the effects of perceptions of organizational politics, understanding of organizational processes, and control over organizational events on rater attitudes (i.e., acceptance, cost-benefit evaluations) toward multisource feedback systems (MSFS). Six-hundred-and-three employees were surveyed concerning their perceptions of organizational politics, understanding, control, and rater attitudes toward MSFS. The present research demonstrated that (a) understanding interacted with organizational politics perceptions in the prediction of rater acceptance of MSFS, (b) control moderated the relationship between understanding and rater attitudes toward peer feedback, (c) perceptions of organizational politics were consistently negatively related to the favorability of rater attitudes toward MSFS, (d) participants reported the most positive attitudes for providing supervisor feedback, followed by subordinate feedback, followed by peer feedback, and (e) individuals with prior experience with MSFS reported more positive attitudes toward MSFS than did individuals without prior experience with these systems. Contributions, limitations, and potential avenues for future research are discussed

Pulsar Search Using Supervised Machine Learning

Pulsars are rapidly rotating neutron stars which emit a strong beam of energy through mechanisms that are not entirely clear to physicists. These very dense stars are used by astrophysicists to study many basic physical phenomena, such as the behavior of plasmas in extremely dense environments, behavior of pulsar-black hole pairs, and tests of general relativity. Many of these tasks require information to answer the scientific questions posed by physicists. In order to provide more pulsars to study, there are several large-scale pulsar surveys underway, which are generating a huge backlog of unprocessed data. Searching for pulsars is a very labor-intensive process, currently requiring skilled people to examine and interpret plots of data output by analysis programs. An automated system for screening the plots will speed up the search for pulsars by a very large factor. Research to date on using machine learning and pattern recognition has not yielded a completely satisfactory system, as systems with the desired near 100% recall have false positive rates that are higher than desired, causing more manual labor in the classification of pulsars. This work proposed to research, identify, propose and develop methods to overcome the barriers to building an improved classification system with a false positive rate of less than 1% and a recall of near 100% that will be useful for the current and next generation of large pulsar surveys. The results show that it is possible to generate classifiers that perform as needed from the available training data. While a false positive rate of 1% was not reached, recall of over 99% was achieved with a false positive rate of less than 2%. Methods of mitigating the imbalanced training and test data were explored and found to be highly effective in enhancing classification accuracy

Elastic Differential Cross Sections for Space Radiation Applications

The eikonal, partial wave (PW) Lippmann-Schwinger, and three-dimensional Lippmann- Schwinger (LS3D) methods are compared for nuclear reactions that are relevant for space radiation applications. Numerical convergence of the eikonal method is readily achieved when exact formulas of the optical potential are used for light nuclei (A $\le$ 16), and the momentum-space representation of the optical potential is used for heavier nuclei. The PW solution method is known to be numerically unstable for systems that require a large number of partial waves, and, as a result, the LS3D method is employed. The effect of relativistic kinematics is studied with the PW and LS3D methods and is compared to eikonal results. It is recommended that the LS3D method be used for high energy nucleon-nucleus reactions and nucleus-nucleus reactions at all energies because of its rapid numerical convergence and stability

Robust designs for Poisson regression models

We consider the problem of how to construct robust designs for Poisson regression models. An analytical expression is derived for robust designs for first-order Poisson regression models where uncertainty exists in the prior parameter estimates. Given certain constraints in the methodology, it may be necessary to extend the robust designs for implementation in practical experiments. With these extensions, our methodology constructs designs which perform similarly, in terms of estimation, to current techniques, and offers the solution in a more timely manner. We further apply this analytic result to cases where uncertainty exists in the linear predictor. The application of this methodology to practical design problems such as screening experiments is explored. Given the minimal prior knowledge that is usually available when conducting such experiments, it is recommended to derive designs robust across a variety of systems. However, incorporating such uncertainty into the design process can be a computationally intense exercise. Hence, our analytic approach is explored as an alternative

A Man For All Seasons : A Tribute to Dean J. Martin Burke

A Man For All Seasons : A tribute to Dean J. Martin Burk