Response Bias Using Two-Stage Data Collection: A study of elderly participants in a program.

Researchers examining the effects of programs, in this case a state-level pharmaceutical assistance program for the elderly, sometimes must rely on multiple methods of data collection. Two-stage data collection (e.g., a telephone interview followed by a mail questionnaire) was used to obtain a full range of information. Older age groups were found to participate less frequently in the telephone interview, while certain demographic factors characterized mail questionnaire nonparticipants, all of which supports past research. Results also show that those in the poorest health are less likely to participate in the mail survey. Combining the two methods did not result in high attrition, suggesting that innovation can be successfully employed. Knowledge of the bias associated with each method will aid in targeting special groups.

Structures of Cage, Prism, and Book Isomers of Water Hexamer from Broadband Rotational Spectroscopy

Theory predicts the water hexamer to be the smallest water cluster with a three-dimensional hydrogen-bonding network as its minimum energy structure. There are several possible low-energy isomers, and calculations with different methods and basis sets assign them different relative stabilities. Previous experimental work has provided evidence for the cage, book, and cyclic isomers, but no experiment has identified multiple coexisting structures. Here, we report that broadband rotational spectroscopy in a pulsed supersonic expansion unambiguously identifies all three isomers; we determined their oxygen framework structures by means of oxygen-18–substituted water (H218O). Relative isomer populations at different expansion conditions establish that the cage isomer is the minimum energy structure. Rotational spectra consistent with predicted heptamer and nonamer structures have also been identified.

Human Resource Performance Metrics: Methods & Processes That Demonstrate You Care

Purpose – The purpose of the present analysis is to show that HR systems are not always designed in ways that consider the well-being of employees. In particular, performance metric methods seem to be designed with organizational goals in mind while focusing less on what employees need and desire. Design/methodology/approach – A literature review and multiple case-study method was utilized. Findings – The analysis showed that performance metrics should be revaluated by executives and HR professionals if they seek to develop socially responsible organizational cultures which care about the well-being of employees. Originality/value – The paper exposes the fact that performance appraisal techniques can be rooted in methodologies that ignore or deemphasize the value of employee well-being. The analysis provides a context in which all HR practices can be questioned in relation to meeting the standards of a social justice agenda in the area of corporate social responsibility.

THE RESONATE-AND-FIRE NEURON: TIME DEPENDENT AND FREQUENCY SELECTIVE NEURONS IN NEURAL NETWORKS

The means through which the nervous system perceives its environment is one of the most fascinating questions in contemporary science. Our endeavors to comprehend the principles of neural science provide an instance of how biological processes may inspire novel methods in mathematical modeling and engineering. The application ofmathematical models towards understanding neural signals and systems represents a vibrant field of research that has spanned over half a century. During this period, multiple approaches to neuronal modeling have been adopted, and each approach is adept at elucidating a specific aspect of nervous system function. Thus while bio-physical models have strived to comprehend the dynamics of actual physical processes occurring within a nerve cell, the phenomenological approach has conceived models that relate the ionic properties of nerve cells to transitions in neural activity. Further-more, the field of neural networks has endeavored to explore how distributed parallel processing systems may become capable of storing memory. Through this project, we strive to explore how some of the insights gained from biophysical neuronal modeling may be incorporated within the field of neural net-works. We specifically study the capabilities of a simple neural model, the Resonate-and-Fire (RAF) neuron, whose derivation is inspired by biophysical neural modeling. While reflecting further biological plausibility, the RAF neuron is also analytically tractable, and thus may be implemented within neural networks. In the following thesis, we provide a brief overview of the different approaches that have been adopted towards comprehending the properties of nerve cells, along with the framework under which our specific neuron model relates to the field of neuronal modeling. Subsequently, we explore some of the time-dependent neurocomputational capabilities of the RAF neuron, and we utilize the model to classify logic gates, and solve the classic XOR problem. Finally we explore how the resonate-and-fire neuron may be implemented within neural networks, and how such a network could be adapted through the temporal backpropagation algorithm.