An Electrically Detonated Downhole Seismic Gun

An electrically detonated downhole seismic gun (EDG) that will fire blank 8-guage shells underwater has been constructed and tested to 80m depth (hydrostatic pressures of 130 psi or 8.9 x 105 Pa). Although other engineering seismic guns which fire blank or projectile sources are available, they are for near-surface shots and are not meant to be used for downhold seismic surveys in water-filled boreholes. The EDG was designed primarily for checkshot surveys (well travel-time surveys) and high-quality reflection/refraction tests, but potential applications include shallow vertical seismic profiling and borehole to borehole or borehole to surface tomography, as well as optimum offset and common midpoint seismic reflection surveys. The EDG consists off four steel subassemblies: (1) chamber; (2) breech; (3) pipe; and (4) hanger. A blank 8-gauge electrical shell is held by the chamber and is detonated by an electrode located inside the breech. O-ring seals prevent water from entering the breech and causing short circuits. The breech is screwed into a pipe which is also fitted with o-ring seals to keep the internal wiring dry. A hanger subassembly provides a convenient attaching point for the hoist cable. Arming of the EDG with an explosives blaster occurs only after lowering to operating depth. The EDG has been tested with various size black powder loads up to 750 grains. Frequency bandwidth and repeatability tests were carried out under saturated conditions in a fluvial and lacustrine sedimentary section. These preliminary tests show significant frequency content in the 25-200 Hz band (peak near 100 Hz) for reflections from depths of 150-300 m, and acceptable waveform repeatability for different shot records obtained with identical geometry and acquisition parameters

Gross Anatomy for Physical Therapy course

The study of anatomy supported with human dissection is foundational to training of health science professionals. For a student, cadaver dissection offers an active learning experience. For the training of physical therapists, we created a manual that efficiently instructs the dissection of the whole body over the course of 30 laboratory periods. In the spirit of academic collegiality, this letter provides health science educators a direct download link (https://hdl.handle.net/11299/218174) to the dissection manual hosted by the University of Minnesota (USA) Digital Conservancy. No registration is required, the download is free, and the PDF file of the dissection manual can be reproduced or adapted for any educational use. &nbsp

An Empirical Analysis of Incentive Clauses and Performance Among Head Coaches at NCAA Football Bowl Series Public Institutions

The trend of increased compensation among college football head coaches has been well documented. (Brady, E., Berkowitz, S., & Upton, J., 2012). Along with increased compensation have come elaborate contract dynamics and incentive clauses that reward coaches for academic and athletic performance. (Reynolds, 2012). The purpose of this study was to quantify the prevalence of incentive clauses in coaching contracts and determine incentive clauses' effect on athletic and academic performance. This study analyzed 747 contract years from 78 public, FBS member institutions from 2002-2012. Contract data was compared to athletic and academic performance information to determine whether incentive presence and/or size significantly affected performance outcomes. The majority of incentive conditions did not produce significantly different results when present in contracts. Similarly, for most incentive types, the size of incentives did not significantly affect performance outcomes. This study suggests that most incentive practices do not significantly correspond to improved performance.Master of Scienc

“Why Should I Go Vote Without Understanding What I Am Going to Vote For?” The Impact of First Generation Voting Barriers on Alaska Natives

This article explores the many forms of discrimination that have persisted in Alaska, the resulting first generation voting barriers faced by Alaska Native voters, and the two contested lawsuits it took to attain a measure of equality for those voters in four regions of Alaska: Nick v. Bethel and Toyukak v. Treadwell. In the end, the court’s decision in Toyukak came down to a comparison of just two pieces of evidence: (1) the Official Election Pamphlet that English-speaking voters received that was often more than 100 pages long; and (2) the single sheet of paper that Alaska Native language speakers received, containing only the date, time, and location of the election, along with a notice that they could request language assistance. Those two pieces of evidence, when set side by side, showed the fundamental unequal access to the ballot. The lessons learned from Nick and Toyukak detailed below are similarly simple: (1) first generation voting barriers still exist in the United States; and (2) Section 203 of the VRA does not permit American Indian and Alaska Native language speaking voters to receive less information than their English-speaking counterparts. The voters in these cases had been entitled to equality for 40 years, but they had to fight for nearly a decade in two federal court cases to get it

Elimination of the Conduct Requirement in Government Monopolization Cases

On July 12, 1978, Professor John Flynn of the University of Utah Law School urged the National Commission for the Review of Antitrust Laws and Procedures (National Commission or NCRALP) to consider recommending that Congress amend section 2 of the Sherman Act to permit the government to challenge persistent and substantial monopoly power without showing that the monopoly power was acquired or maintained through objectionable conduct. In Professor Flynn\u27s view, eliminating the prevailing conduct requirement in certain government monopolization cases would expedite litigation and produce more effective remedies, two of the National Commission\u27s central objectives. First, this article explains that a no-conduct standard would lead to faster, more efficient proceedings. Second, the article maintains that eliminating the conduct requirement should produce more effective remedies. In the remainder of the article the authors present in greater detail the reasons underlying these two particular conclusions, and in the process address the key issues highlighted by the National Commission

Noise-injected neural networks show promise for use on small-sample expression data

BACKGROUND: Overfitting the data is a salient issue for classifier design in small-sample settings. This is why selecting a classifier from a constrained family of classifiers, ones that do not possess the potential to too finely partition the feature space, is typically preferable. But overfitting is not merely a consequence of the classifier family; it is highly dependent on the classification rule used to design a classifier from the sample data. Thus, it is possible to consider families that are rather complex but for which there are classification rules that perform well for small samples. Such classification rules can be advantageous because they facilitate satisfactory classification when the class-conditional distributions are not easily separated and the sample is not large. Here we consider neural networks, from the perspectives of classical design based solely on the sample data and from noise-injection-based design. RESULTS: This paper provides an extensive simulation-based comparative study of noise-injected neural-network design. It considers a number of different feature-label models across various small sample sizes using varying amounts of noise injection. Besides comparing noise-injected neural-network design to classical neural-network design, the paper compares it to a number of other classification rules. Our particular interest is with the use of microarray data for expression-based classification for diagnosis and prognosis. To that end, we consider noise-injected neural-network design as it relates to a study of survivability of breast cancer patients. CONCLUSION: The conclusion is that in many instances noise-injected neural network design is superior to the other tested methods, and in almost all cases it does not perform substantially worse than the best of the other methods. Since the amount of noise injected is consequential, the effect of differing amounts of injected noise must be considered