Comparison of two parallel/series flow turbofan propulsion concepts for supersonic V/STOL

The thrust, specific fuel consumption, and relative merits of the tandem fan and the dual reverse flow front fan propulsion systems for a supersonic V/STOL aircraft are discussed. Consideration is given to: fan pressure ratio, fan air burning, and variable core supercharging. The special propulsion system components required are described, namely: the deflecting front inlet/nozzle, the aft subsonic inlet, the reverse pitch fan, the variable core supercharger and the low pressure forward burner. The potential benefits for these unconventional systems are indicated

Operating characteristics of the primary flow loop of a conceptual nuclear Brayton space powerplant

Steady state and transient operating characteristics of lithium cooled primary flow loop of nuclear Brayton space power plan

Investigation of the Adenosine A(2A) Receptor on the Enhanced Rewarding Effects of Nicotine and Dopamine D2 Receptor Signaling in a Novel Heritable Model of Drug Abuse Vulnerability

Investigation of the adenosine A(2A) receptor on the enhanced rewarding effects of nicotine and dopamine D2 receptor signaling in a novel heritable model of drug abuse vulnerability Seth E. Turney, Loren D. Peeters, Olivia A. Jennings, Liza J. Wills, Russell W. Brown Many years ago, our laboratory along with a collaborator established that neonatal treatment of the dopamine (DA)D2-like receptor agonist quinpirole (NQ) to rats induces an increase in DAD2 receptor sensitivity throughout the animal’s lifetime, which has validity to schizophrenia (SZ) and a number of clinical conditions. These clinical conditions, which include SZ but also bipolar disorder, obsessive-compulsive disorder, panic disorder, and major depression all demonstrate increased drug abuse vulnerability, especially to cigarette smoking. Based on this permanent change in DAD2 sensitivity, we bred NQ-treated male and female rats with their NQ-treated or neonatal saline (NS)-treated counterparts. This F1 generation also demonstrated increases in DAD2 signaling, both behaviorally as well as through DAD2 signaling mechanisms. We have shown both d enhanced behavioral responding to nicotine on the conditioned place preference (CPP) and behavioral sensitization paradigms. These F1 offspring of NQ-treated rats also demonstrated increases of G-protein dependent and G-protein independent DAD2 signaling. Interestingly, the adenosine A(2A) receptor forms a mutual inhibitory heteromer with the DAD2 receptor. Adenosine is a known neuromodulator that can increase or decrease synaptic transmission in the brain, and there exists a hypothesis that adenosine dysfunction is the primary system which is disrupted in SZ which leads to changes in the dopamine and other neurotransmitter systems. The drug CGS 21680, an A(2A) agonist which stimulates the A(2A) receptor, is known to decrease DAD2 signaling and has been shown to block nicotine behavioral sensitization. A major focus in this project is on the adenosine A(2A) receptor as a novel pharmacological treatment target, since it is known that antipsychotic drugs which are often used to treat SZ and these other clinical conditions which have increased DAD2 signaling produce deleterious side effects, and novel medications are needed. Results here revealed that a 0.09 mg/kg dose of CGS 21680 was effective to block enhanced nicotine CPP and changes in DAD2 G-protein independent signaling in F1 generation rats. Interestingly, CGS 21680 did not affect G-protein dependent signaling in F1 generation animals, suggesting that the mechanism through which it is working may not be through the traditional DAD2 signaling mechanism. Future work is designed to analyze underlying mechanisms of this effect

SHCal13 Southern Hemisphere calibration, 0–50,000 years cal BP

The Southern Hemisphere SHCal04 radiocarbon calibration curve has been updated with the addition of new data sets extending measurements to 2145 cal BP and including the ANSTO Younger Dryas Huon pine data set. Outside the range of measured data, the curve is based upon the Northern Hemisphere data sets as presented in IntCal13, with an interhemispheric offset averaging 43 ± 23 yr modeled by an autoregressive process to represent the short-term correlations in the offset

The influence of calibration curve construction and composition on the accuracy and precision of radiocarbon wiggle-matching of tree rings, illustrated by Southern Hemisphere atmospheric data sets from ad 1500–1950

This research investigates two factors influencing the ability of tree-ring data to provide accurate 14C calibration information: the fitness and rigor of the statistical model used to combine the data into a curve; and the accuracy, precision and reproducibility of the component 14C data sets. It presents a new Bayesian spline method for calibration curve construction and tests it on extant and new Southern Hemisphere (SH) data sets (also examining their dendrochronology and pretreatment) for the post-Little Ice Age (LIA) interval AD 1500–1950. The new method of construction allows calculation of component data offsets, permitting identification of laboratory and geographic biases. Application of the new method to the 10 suitable SH 14C data sets suggests that individual offset ranges for component data sets appear to be in the region of ± 10 yr. Data sets with individual offsets larger than this need to be carefully assessed before selection for calibration purposes. We identify a potential geographical offset associated with the Southern Ocean (high latitude) Campbell Island data. We test the new methodology for wiggle-matching short tree-ring sequences and use an OxCal simulation to assess the likely precision obtainable by wiggle-matching in the post-LIA interval

CSNL: A cost-sensitive non-linear decision tree algorithm

This article presents a new decision tree learning algorithm called CSNL that induces Cost-Sensitive Non-Linear decision trees. The algorithm is based on the hypothesis that nonlinear decision nodes provide a better basis than axis-parallel decision nodes and utilizes discriminant analysis to construct nonlinear decision trees that take account of costs of misclassification. The performance of the algorithm is evaluated by applying it to seventeen datasets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the datasets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using nonlinear decision nodes. The performance of the algorithm is evaluated by applying it to seventeen data sets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the data sets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using non-linear decision nodes

Inducing safer oblique trees without costs

Decision tree induction has been widely studied and applied. In safety applications, such as determining whether a chemical process is safe or whether a person has a medical condition, the cost of misclassification in one of the classes is significantly higher than in the other class. Several authors have tackled this problem by developing cost-sensitive decision tree learning algorithms or have suggested ways of changing the distribution of training examples to bias the decision tree learning process so as to take account of costs. A prerequisite for applying such algorithms is the availability of costs of misclassification. Although this may be possible for some applications, obtaining reasonable estimates of costs of misclassification is not easy in the area of safety. This paper presents a new algorithm for applications where the cost of misclassifications cannot be quantified, although the cost of misclassification in one class is known to be significantly higher than in another class. The algorithm utilizes linear discriminant analysis to identify oblique relationships between continuous attributes and then carries out an appropriate modification to ensure that the resulting tree errs on the side of safety. The algorithm is evaluated with respect to one of the best known cost-sensitive algorithms (ICET), a well-known oblique decision tree algorithm (OC1) and an algorithm that utilizes robust linear programming

IntCal09 and Marine09 radiocarbon age calibration curves, 0-50,000yeats cal BP

The IntCal04 and Marine04 radiocarbon calibration curves have been updated from 12 cal kBP (cal kBP is here defined as thousands of calibrated years before AD 1950), and extended to 50 cal kBP, utilizing newly available data sets that meet the IntCal Working Group criteria for pristine corals and other carbonates and for quantification of uncertainty in both the 14C and calendar timescales as established in 2002. No change was made to the curves from 0–12 cal kBP. The curves were constructed using a Markov chain Monte Carlo (MCMC) implementation of the random walk model used for IntCal04 and Marine04. The new curves were ratified at the 20th International Radiocarbon Conference in June 2009 and are available in the Supplemental Material at www.radiocarbon.org

Incremental dimension reduction of tensors with random index

We present an incremental, scalable and efficient dimension reduction technique for tensors that is based on sparse random linear coding. Data is stored in a compactified representation with fixed size, which makes memory requirements low and predictable. Component encoding and decoding are performed on-line without computationally expensive re-analysis of the data set. The range of tensor indices can be extended dynamically without modifying the component representation. This idea originates from a mathematical model of semantic memory and a method known as random indexing in natural language processing. We generalize the random-indexing algorithm to tensors and present signal-to-noise-ratio simulations for representations of vectors and matrices. We present also a mathematical analysis of the approximate orthogonality of high-dimensional ternary vectors, which is a property that underpins this and other similar random-coding approaches to dimension reduction. To further demonstrate the properties of random indexing we present results of a synonym identification task. The method presented here has some similarities with random projection and Tucker decomposition, but it performs well at high dimensionality only (n>10^3). Random indexing is useful for a range of complex practical problems, e.g., in natural language processing, data mining, pattern recognition, event detection, graph searching and search engines. Prototype software is provided. It supports encoding and decoding of tensors of order >= 1 in a unified framework, i.e., vectors, matrices and higher order tensors.Comment: 36 pages, 9 figure

A complete remediation process for a uranium-contaminated site and application to other sites

During the summer of 1996 the authors were able to test, at the pilot scale, the concept of leaching uranium (U) from contaminated soils. The results of this pilot scale operation showed that the system they previously had developed at the laboratory scale is applicable at the pilot scale. The paper discusses these results, together with laboratory scale results using soil from the Fernald Environmental Management Project (FEMP), Ohio. These FEMP results show how, with suitable adaptations, the process is widely applicable to other sites. The purpose of this paper is to describe results that demonstrate remediation of uranium-contaminated soils may be accomplished through a leach scheme using sodium bicarbonate