The Right to Learn: Intellectual Honesty and the First Amendment

Science education is one of the most hotly contested issues in public debate. Even after decades of jurisprudence and scholarly analysis, politicians still ignite public passions by suggesting that creationism or intelligent design theory be taught alongside of evolution in public school science classrooms. Despite political rhetoric, the Establishment Clause has been steadfastly used to prevent religion masquerading as science from entering the science classroom. However, public officials have launched attacks recently on other scientific theories, such as climate change, that are not religiously motivated. Students are left in these instances without resort to the Establishment Clause and are potentially forced to learn propaganda and psuedo-science in science classrooms. This Article confronts the vexing problem that students are protected from psuedoscience that is religiously based and not psuedo-science that is politically motivated, even though they are both equally damaging to the students\u27 education. There is no intellectual or constitutional basis for banning some kinds of pseudo-science and allowing others within a compulsory public science class. This Article is the first to argue that students in public school science classrooms are entitled to an accurate and honest rendering of the scientific facts free from propaganda, proselytizing and politics under the Free Speech Clause of the First Amendment. Simply because a scientific theory is attacked for political motivations does not render it constitutionally permissible. So long as students are required by compulsory education laws to attend science class, the state is constitutionally required to protect the students\u27 right to learn. This right to learn is rooted in a long and distinguished line of cases. Not only does it have a judicial pedigree, but the right to learn rooted in the Free Speech Clause shifts the constitutional analysis from the difficult task of determining motivations to the pursuit of scientific accuracy

On Toroidal Horizons in Binary Black Hole Inspirals

We examine the structure of the event horizon for numerical simulations of two black holes that begin in a quasicircular orbit, inspiral, and finally merge. We find that the spatial cross section of the merged event horizon has spherical topology (to the limit of our resolution), despite the expectation that generic binary black hole mergers in the absence of symmetries should result in an event horizon that briefly has a toroidal cross section. Using insight gained from our numerical simulations, we investigate how the choice of time slicing affects both the spatial cross section of the event horizon and the locus of points at which generators of the event horizon cross. To ensure the robustness of our conclusions, our results are checked at multiple numerical resolutions. 3D visualization data for these resolutions are available for public access online. We find that the structure of the horizon generators in our simulations is consistent with expectations, and the lack of toroidal horizons in our simulations is due to our choice of time slicing.Comment: Submitted to Phys. Rev.

Demonstration of Active Combustion Control

The primary objective of this effort was to demonstrate active control of combustion instabilities in a direct-injection gas turbine combustor that accurately simulates engine operating conditions and reproduces an engine-type instability. This report documents the second phase of a two-phase effort. The first phase involved the analysis of an instability observed in a developmental aeroengine and the design of a single-nozzle test rig to replicate that phenomenon. This was successfully completed in 2001 and is documented in the Phase I report. This second phase was directed toward demonstration of active control strategies to mitigate this instability and thereby demonstrate the viability of active control for aircraft engine combustors. This involved development of high-speed actuator technology, testing and analysis of how the actuation system was integrated with the combustion system, control algorithm development, and demonstration testing in the single-nozzle test rig. A 30 percent reduction in the amplitude of the high-frequency (570 Hz) instability was achieved using actuation systems and control algorithms developed within this effort. Even larger reductions were shown with a low-frequency (270 Hz) instability. This represents a unique achievement in the development and practical demonstration of active combustion control systems for gas turbine applications

Prevention for a Healthier California: Investments in Disease Prevention Yield Significant Savings, Stronger Communities

Estimates how much the state and the nation could save in healthcare costs by investing in disease prevention through community programs that increase physical activity, improve nutrition, and reduce tobacco use. Provides examples of prevention efforts

Mixer assembly for a gas turbine engine having a pilot mixer with a corner flame stabilizing recirculation zone

A mixer assembly for a gas turbine engine is provided, including a main mixer, and a pilot mixer having an annular housing in which a corner is formed between an aft portion of the housing and a bulkhead wall in which a corner recirculation zone is located to stabilize and anchor the flame of the pilot mixer. The pilot mixer can further include features to cool the annular housing, including in the area of the corner recirculation zone

A critical edition of the Baba rabbah section of the Samaritan chronicle no. II: with translation and commentary

Summary available: p.iii-v

Estimating the proportion of guilty suspects and posterior probability of guilt in lineups using signal-detection models

Background The majority of eyewitness lineup studies are laboratory-based. How well the conclusions of these studies, including the relationship between confidence and accuracy, generalize to real-world police lineups is an open question. Signal detection theory (SDT) has emerged as a powerful framework for analyzing lineups that allows comparison of witnesses’ memory accuracy under different types of identification procedures. Because the guilt or innocence of a real-world suspect is generally not known, however, it is further unknown precisely how the identification of a suspect should change our belief in their guilt. The probability of guilt after the suspect has been identified, the posterior probability of guilt (PPG), can only be meaningfully estimated if we know the proportion of lineups that include a guilty suspect, P(guilty). Recent work used SDT to estimate P(guilty) on a single empirical data set that shared an important property with real-world data; that is, no information about the guilt or innocence of the suspects was provided. Here we test the ability of the SDT model to recover P(guilty) on a wide range of pre-existing empirical data from more than 10,000 identification decisions. We then use simulations of the SDT model to determine the conditions under which the model succeeds and, where applicable, why it fails. Results For both empirical and simulated studies, the model was able to accurately estimate P(guilty) when the lineups were fair (the guilty and innocent suspects did not stand out) and identifications of both suspects and fillers occurred with a range of confidence levels. Simulations showed that the model can accurately recover P(guilty) given data that matches the model assumptions. The model failed to accurately estimate P(guilty) under conditions that violated its assumptions; for example, when the effective size of the lineup was reduced, either because the fillers were selected to be poor matches to the suspect or because the innocent suspect was more familiar than the guilty suspect. The model also underestimated P(guilty) when a weapon was shown. Conclusions Depending on lineup quality, estimation of P(guilty) and, relatedly, PPG, from the SDT model can range from poor to excellent. These results highlight the need to carefully consider how the similarity relations between fillers and suspects influence identifications

Combustor Assembly for a Turbine Engine

A combustor assembly includes a first wall, a second wall, a bulkhead and a plurality of fuel injectors. The bulkhead forms a combustion chamber with the first and the second walls. The fuel injectors are configured with the first wall in a unique and/or a fluctuating pattern

Strategies for Using a Spatial Method to Promote Active Learning of Probability Concepts

We developed and tested strategies for using spatial representations to help students understand core probability concepts, including the multiplication rule for computing a joint probability from a marginal and conditional probability, interpreting an odds value as the ratio of two probabilities, and Bayesian inference. The general goal of these strategies is to promote active learning by introducing concepts in an intuitive spatial format and then encouraging students to try to discover the explicit equations associated with the spatial representations. We assessed the viability of the proposed active-learning approach with two exercises that tested undergraduates’ ability to specify mathematical equations after learning to use the spatial solution method. A majority of students succeeded in independently discovering fundamental mathematical concepts underlying probabilistic reasoning. For example, in the second exercise, 76% of students correctly multiplied marginal and conditional probabilities to find joint probabilities, 86% correctly divided joint probabilities to get an odds value, and 69% did both to achieve full Bayesian inference. Thus, we conclude that the spatial method is an effective way to promote active learning of probability equations