A new high-speed solar radio spectrograph for meter and decameter wavelengths

The design and characteristics of a high resolution, digital solar spectrograph are discussed. The spectrometer operates in the 10 to 80 MHz range. The primary considerations in the design of the spectrograph were: (1) optimun sensitivity, (2) wide dynamic range, (3) flexibility in time and frequency resolution, and (4) modern data handling techniques with a simple computer interface

Materials for Advanced Turbine Engines

An attempt was made to improve methods for producing powder metallurgy aircraft gas turbine engine parts from the nickel base superalloy known as Rene 95. The parts produced were the high pressure turbine aft shaft for the CF6-50 engine and the stages 5 through 9 compressor disk forgings for the CFM56/F101 engines. A 50% cost reduction was achieved as compared to conventional cast and wrought processing practices. An integrated effort involving several powder producers and a major forging source were included

FLOX/methane pump-fed engine study

FLOX and methane rocket propellant for use with modified RL-10-A-1 engin

Chapter 18. DAYCENT Simulated Effects of Land Use and Climate on County Level N Loss Vectors in the USA

We describe the nitrogen (N) gas (NH3, NOx, N2O, N2) emission and NO3 leaching submodels used in the DAYCENT ecosystem model and demonstrate the ability of DAYCENT to simulate observed N2O emission and NO3 leaching rates for various sites representing different climate regimes, soil types, and land uses. DAYCENT simulated seven major crops, grazing lands, and potential native vegetation at the county level for the United States. At the national scale, NO3 leaching was the major loss vector, accounting for 86%, 66%, and 56% of total N losses for cropped soils, grazed lands, and native vegetation, respectively. NH3 volatilization + NOx emissions made up the majority of national N gas losses, accounting for 58%, 89%, and 86% of N gas losses from cropped soils, grazed lands, and native vegetation, respectively. However, there was considerable spatial variability in the N loss vectors, with leaching accounting for less than 20% of total N losses and NOx + NH3 emissions accounting for less than 50% of N gas losses in some counties. Land use area weighted mean annual N losses were 43.9 (SD = 26.8) and 12.3 (SD = 22.2)kg N/ha for cropped/grazed and native systems, respectively. Area weighted mean annual N gas losses were 11.8 (SD = 4.8) and 5.4 (SD = 2.1)kg N/ha for cropped/grazed and native systems, respectively. Total N losses and NO3 leaching tended to increase as N inputs and precipitation increased, and as soils became coarser textured. Total N gas losses also increased with N inputs and as soils became coarser textured, but N2O and N2 made up a larger portion of N gas losses as soils became finer textured and as precipitation increased

Machine-Part cell formation through visual decipherable clustering of Self Organizing Map

Machine-part cell formation is used in cellular manufacturing in order to process a large variety, quality, lower work in process levels, reducing manufacturing lead-time and customer response time while retaining flexibility for new products. This paper presents a new and novel approach for obtaining machine cells and part families. In the cellular manufacturing the fundamental problem is the formation of part families and machine cells. The present paper deals with the Self Organising Map (SOM) method an unsupervised learning algorithm in Artificial Intelligence, and has been used as a visually decipherable clustering tool of machine-part cell formation. The objective of the paper is to cluster the binary machine-part matrix through visually decipherable cluster of SOM color-coding and labelling via the SOM map nodes in such a way that the part families are processed in that machine cells. The Umatrix, component plane, principal component projection, scatter plot and histogram of SOM have been reported in the present work for the successful visualization of the machine-part cell formation. Computational result with the proposed algorithm on a set of group technology problems available in the literature is also presented. The proposed SOM approach produced solutions with a grouping efficacy that is at least as good as any results earlier reported in the literature and improved the grouping efficacy for 70% of the problems and found immensely useful to both industry practitioners and researchers.Comment: 18 pages,3 table, 4 figure

A Study of Brain Networks Associated with Swallowing Using Graph-Theoretical Approaches

Functional connectivity between brain regions during swallowing tasks is still not well understood. Understanding these complex interactions is of great interest from both a scientific and a clinical perspective. In this study, functional magnetic resonance imaging (fMRI) was utilized to study brain functional networks during voluntary saliva swallowing in twenty-two adult healthy subjects (all females, 23.1±1.52 years of age). To construct these functional connections, we computed mean partial correlation matrices over ninety brain regions for each participant. Two regions were determined to be functionally connected if their correlation was above a certain threshold. These correlation matrices were then analyzed using graph-theoretical approaches. In particular, we considered several network measures for the whole brain and for swallowing-related brain regions. The results have shown that significant pairwise functional connections were, mostly, either local and intra-hemispheric or symmetrically inter-hemispheric. Furthermore, we showed that all human brain functional network, although varying in some degree, had typical small-world properties as compared to regular networks and random networks. These properties allow information transfer within the network at a relatively high efficiency. Swallowing-related brain regions also had higher values for some of the network measures in comparison to when these measures were calculated for the whole brain. The current results warrant further investigation of graph-theoretical approaches as a potential tool for understanding the neural basis of dysphagia. © 2013 Luan et al

Communication Biophysics

Contains reports on five research projects.National Institutes of Health (Grant 5 RO1 NB-05462-02)National Aeronautics and Space Administration (Grant NsG-496)National Science Foundation (Grant GP-2495)National Institutes of Health (Grant MH-04737-05

Dopamine Beta Hydroxylase Genotype Identifies Individuals Less Susceptible to Bias in Computer-Assisted Decision Making

Computerized aiding systems can assist human decision makers in complex tasks but can impair performance when they provide incorrect advice that humans erroneously follow, a phenomenon known as “automation bias.” The extent to which people exhibit automation bias varies significantly and may reflect inter-individual variation in the capacity of working memory and the efficiency of executive function, both of which are highly heritable and under dopaminergic and noradrenergic control in prefrontal cortex. The dopamine beta hydroxylase (DBH) gene is thought to regulate the differential availability of dopamine and norepinephrine in prefrontal cortex. We therefore examined decision-making performance under imperfect computer aiding in 100 participants performing a simulated command and control task. Based on two single nucleotide polymorphism (SNPs) of the DBH gene, −1041 C/T (rs1611115) and 444 G/A (rs1108580), participants were divided into groups of low and high DBH enzyme activity, where low enzyme activity is associated with greater dopamine relative to norepinephrine levels in cortex. Compared to those in the high DBH enzyme activity group, individuals in the low DBH enzyme activity group were more accurate and speedier in their decisions when incorrect advice was given and verified automation recommendations more frequently. These results indicate that a gene that regulates relative prefrontal cortex dopamine availability, DBH, can identify those individuals who are less susceptible to bias in using computerized decision-aiding systems