Estimating Willingness to Pay for River Amenities and Safety Measures Associated with Shale Gas Extraction

This paper utilizes a Contingent Valuation Method survey of a random sample of residents to estimate that households are willing to pay an average of $12.00 per month for public projects designed to improve river access and $10.46 per month for additional safety measures that would eliminate risks to local watersheds from drilling for natural gas from underground shale formations. These estimates can be compared to the costs of providing each of these two amenities to help foster the formation of efficient policy decisions.

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.

The Meanings of Work for Older Workers

Several elements influence the meanings of work: the basic psychological processes of aging; the cohort or generation of the worker; the ecology of the work itself; and the larger social context of managing the risks of aging. This article discusses the meaning of work across the lifespan, and then reviews each of these elements to describe the meanings of work for older workers. The authors summarize data from multiple sources to answer several related questions: Why do older workers continue to work—beyond the solely monetary motivation? How do older workers' meanings of work vary by financial, health, job satisfaction, familial, or workplace concerns? What are the implications of these findings for employers and employees?