Network evolution by nonlinear preferential rewiring of edges

NASA Astrophysics Data System (ADS)

Xu, Xin-Jian; Hu, Xiao-Ming; Zhang, Li-Jie

2011-06-01

The mathematical framework for small-world networks proposed in a seminal paper by Watts and Strogatz sparked a widespread interest in modeling complex networks in the past decade. However, most of research contributing to static models is in contrast to real-world dynamic networks, such as social and biological networks, which are characterized by rearrangements of connections among agents. In this paper, we study dynamic networks evolved by nonlinear preferential rewiring of edges. The total numbers of vertices and edges of the network are conserved, but edges are continuously rewired according to the nonlinear preference. Assuming power-law kernels with exponents α and β, the network structures in stationary states display a distinct behavior, depending only on β. For β>1, the network is highly heterogeneous with the emergence of starlike structures. For β<1, the network is widely homogeneous with a typical connectivity. At β=1, the network is scale free with an exponential cutoff.

Laser ignition - Spark plug development and application in reciprocating engines

NASA Astrophysics Data System (ADS)

Pavel, Nicolaie; Bärwinkel, Mark; Heinz, Peter; Brüggemann, Dieter; Dearden, Geoff; Croitoru, Gabriela; Grigore, Oana Valeria

2018-03-01

Combustion is one of the most dominant energy conversion processes used in all areas of human life, but global concerns over exhaust gas pollution and greenhouse gas emission have stimulated further development of the process. Lean combustion and exhaust gas recirculation are approaches to improve the efficiency and to reduce pollutant emissions; however, such measures impede reliable ignition when applied to conventional ignition systems. Therefore, alternative ignition systems are a focus of scientific research. Amongst others, laser induced ignition seems an attractive method to improve the combustion process. In comparison with conventional ignition by electric spark plugs, laser ignition offers a number of potential benefits. Those most often discussed are: no quenching of the combustion flame kernel; the ability to deliver (laser) energy to any location of interest in the combustion chamber; the possibility of delivering the beam simultaneously to different positions, and the temporal control of ignition. If these advantages can be exploited in practice, the engine efficiency may be improved and reliable operation at lean air-fuel mixtures can be achieved, making feasible savings in fuel consumption and reduction in emission of exhaust gasses. Therefore, laser ignition can enable important new approaches to address global concerns about the environmental impact of continued use of reciprocating engines in vehicles and power plants, with the aim of diminishing pollutant levels in the atmosphere. The technology can also support increased use of electrification in powered transport, through its application to ignition of hybrid (electric-gas) engines, and the efficient combustion of advanced fuels. In this work, we review the progress made over the last years in laser ignition research, in particular that aimed towards realizing laser sources (or laser spark plugs) with dimensions and properties suitable for operating directly on an engine. The main envisaged solutions for positioning of the laser spark plug, i.e. placing it apart from or directly on the engine, are introduced. The path taken from the first solution proposed, to build a compact laser suitable for ignition, to the practical realization of a laser spark plug is described. Results obtained by ignition of automobile test engines, with laser devices that resemble classical spark plugs, are specifically discussed. It is emphasized that technological advances have brought this method of laser ignition close to the application and installation in automobiles powered by gasoline engines. Achievements made in the laser ignition of natural gas engines are outlined, as well as the utilization of laser ignition in other applications. Scientific and technical advances have allowed realization of laser devices with multiple (up to four) beam outputs, but many other important aspects (such as integration, thermal endurance or vibration strength) are still to be solved. Recent results of multi-beam ignition of a single-cylinder engine in a test bench set-up are encouraging and have led to increased research interest in this direction. A fundamental understanding of the processes involved in laser ignition is crucial in order to exploit the technology's full potential. Therefore, several measurement techniques, primarily optical types, used to characterize the laser ignition process are reviewed in this work.

Computational study of arc discharges: Spark plug and railplug ignitors

NASA Astrophysics Data System (ADS)

Ekici, Ozgur

A theoretical study of electrical arc discharges that focuses on the discharge processes in spark plug and railplug ignitors is presented. The aim of the study is to gain a better understanding of the dynamics of electrical discharges, more specifically the transfer of electrical energy into the gas and the effect of this energy transfer on the flow physics. Different levels of computational models are presented to investigate the types of arc discharges seen in spark plugs and railplugs (i.e., stationary and moving arc discharges). Better understanding of discharge physics is important for a number of applications. For example, improved fuel economy under the constraint of stricter emissions standards and improved plug durability are important objectives of current internal combustion engine designs. These goals can be achieved by improving the existing systems (spark plug) and introducing more sophisticated ignition systems (railplug). In spite of the fact spark plug and railplug ignitors are the focus of this work, the methods presented in this work can be extended to study the discharges found in other applications such as plasma torches, laser sparks, and circuit breakers. The system of equations describing the physical processes in an air plasma is solved using computational fluid dynamics codes to simulate thermal and flow fields. The evolution of the shock front, temperature, pressure, density, and flow of a plasma kernel were investigated for both stationary and moving arcs. Arc propagation between the electrodes under the effects of gas dynamics and electromagnetic processes was studied for moving arcs. The air plasma is regarded as a continuum, single substance material in local thermal equilibrium. Thermophysical properties of high temperature air are used to take into consideration the important processes such as dissociation and ionization. The different mechanisms and the relative importance of several assumptions in gas discharges and thermal plasma modeling were investigated. Considering the complex nature of the studied problem, the computational models aid in analyzing the analytical theory and serve as relatively inexpensive tools when compared to experiments in design process.

Large eddy simulation of forced ignition of an annular bluff-body burner

DOE Office of Scientific and Technical Information (OSTI.GOV)

Subramanian, V.; Domingo, P.; Vervisch, L.

2010-03-15

The optimization of the ignition process is a crucial issue in the design of many combustion systems. Large eddy simulation (LES) of a conical shaped bluff-body turbulent nonpremixed burner has been performed to study the impact of spark location on ignition success. This burner was experimentally investigated by Ahmed et al. [Combust. Flame 151 (2007) 366-385]. The present work focuses on the case without swirl, for which detailed measurements are available. First, cold-flow measurements of velocities and mixture fractions are compared with their LES counterparts, to assess the prediction capabilities of simulations in terms of flow and turbulent mixing. Timemore » histories of velocities and mixture fractions are recorded at selected spots, to probe the resolved probability density function (pdf) of flow variables, in an attempt to reproduce, from the knowledge of LES-resolved instantaneous flow conditions, the experimentally observed reasons for success or failure of spark ignition. A flammability map is also constructed from the resolved mixture fraction pdf and compared with its experimental counterpart. LES of forced ignition is then performed using flamelet fully detailed tabulated chemistry combined with presumed pdfs. Various scenarios of flame kernel development are analyzed and correlated with typical flow conditions observed in this burner. The correlations between, velocities and mixture fraction values at the sparking time and the success or failure of ignition, are then further discussed and analyzed. (author)« less

Turbulent flame spreading mechanisms after spark ignition

NASA Astrophysics Data System (ADS)

Subramanian, V.; Domingo, Pascale; Vervisch, Luc

2009-12-01

Numerical simulation of forced ignition is performed in the framework of Large-Eddy Simulation (LES) combined with a tabulated detailed chemistry approach. The objective is to reproduce the flame properties observed in a recent experimental work reporting probability of ignition in a laboratory-scale burner operating with Methane/air non premixed mixture [1]. The smallest scales of chemical phenomena, which are unresolved by the LES grid, are approximated with a flamelet model combined with presumed probability density functions, to account for the unresolved part of turbulent fluctuations of species and temperature. Mono-dimensional flamelets are simulated using GRI-3.0 [2] and tabulated under a set of parameters describing the local mixing and progress of reaction. A non reacting case was simulated at first, to study the unsteady velocity and mixture fields. The time averaged velocity and mixture fraction, and their respective turbulent fluctuations, are compared against the experimental measurements, in order to estimate the prediction capabilities of LES. The time history of axial and radial components of velocity and mixture fraction is cumulated and analysed for different burner regimes. Based on this information, spark ignition is mimicked on selected ignition spots and the dynamics of kernel development analyzed to be compared against the experimental observations. The possible link between the success or failure of the ignition and the flow conditions (in terms of velocity and composition) at the sparking time are then explored.

Accelerated dynamic cardiac MRI exploiting sparse-Kalman-smoother self-calibration and reconstruction (k  -  t SPARKS)

NASA Astrophysics Data System (ADS)

Park, Suhyung; Park, Jaeseok

2015-05-01

Accelerated dynamic MRI, which exploits spatiotemporal redundancies in k  -  t space and coil dimension, has been widely used to reduce the number of signal encoding and thus increase imaging efficiency with minimal loss of image quality. Nonetheless, particularly in cardiac MRI it still suffers from artifacts and amplified noise in the presence of time-drifting coil sensitivity due to relative motion between coil and subject (e.g. free breathing). Furthermore, a substantial number of additional calibrating signals is to be acquired to warrant accurate calibration of coil sensitivity. In this work, we propose a novel, accelerated dynamic cardiac MRI with sparse-Kalman-smoother self-calibration and reconstruction (k  -  t SPARKS), which is robust to time-varying coil sensitivity even with a small number of calibrating signals. The proposed k  -  t SPARKS incorporates Kalman-smoother self-calibration in k  -  t space and sparse signal recovery in x  -   f space into a single optimization problem, leading to iterative, joint estimation of time-varying convolution kernels and missing signals in k  -  t space. In the Kalman-smoother calibration, motion-induced uncertainties over the entire time frames were included in modeling state transition while a coil-dependent noise statistic in describing measurement process. The sparse signal recovery iteratively alternates with the self-calibration to tackle the ill-conditioning problem potentially resulting from insufficient calibrating signals. Simulations and experiments were performed using both the proposed and conventional methods for comparison, revealing that the proposed k  -  t SPARKS yields higher signal-to-error ratio and superior temporal fidelity in both breath-hold and free-breathing cardiac applications over all reduction factors.

Accelerated dynamic cardiac MRI exploiting sparse-Kalman-smoother self-calibration and reconstruction (k  -  t SPARKS).

PubMed

Park, Suhyung; Park, Jaeseok

2015-05-07

Accelerated dynamic MRI, which exploits spatiotemporal redundancies in k  -  t space and coil dimension, has been widely used to reduce the number of signal encoding and thus increase imaging efficiency with minimal loss of image quality. Nonetheless, particularly in cardiac MRI it still suffers from artifacts and amplified noise in the presence of time-drifting coil sensitivity due to relative motion between coil and subject (e.g. free breathing). Furthermore, a substantial number of additional calibrating signals is to be acquired to warrant accurate calibration of coil sensitivity. In this work, we propose a novel, accelerated dynamic cardiac MRI with sparse-Kalman-smoother self-calibration and reconstruction (k  -  t SPARKS), which is robust to time-varying coil sensitivity even with a small number of calibrating signals. The proposed k  -  t SPARKS incorporates Kalman-smoother self-calibration in k  -  t space and sparse signal recovery in x  -   f space into a single optimization problem, leading to iterative, joint estimation of time-varying convolution kernels and missing signals in k  -  t space. In the Kalman-smoother calibration, motion-induced uncertainties over the entire time frames were included in modeling state transition while a coil-dependent noise statistic in describing measurement process. The sparse signal recovery iteratively alternates with the self-calibration to tackle the ill-conditioning problem potentially resulting from insufficient calibrating signals. Simulations and experiments were performed using both the proposed and conventional methods for comparison, revealing that the proposed k  -  t SPARKS yields higher signal-to-error ratio and superior temporal fidelity in both breath-hold and free-breathing cardiac applications over all reduction factors.

The role of spray-enhanced swirl flow for combustion stabilization in a stratified-charge DISI engine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zeng, Wei; Sjöberg, Magnus; Reuss, David L.

Implementing spray-guided stratified-charge direct-injection spark-ignited (DISI) engines is inhibited by the occurrence of misfire and partial burns. Engine-performance tests demonstrate that increasing engine speed induces combustion instability, but this deterioration can be prevented by generating swirling flow during the intake stroke. In-cylinder pressure-based heat-release analysis reveals that the appearance of poor-burn cycles is not solely dependent on the variability of early flame-kernel growth. Moreover, cycles can experience burning-rate regression during later combustion stages and may or may not recover before the end of the cycle. Thermodynamic analysis and optical diagnostics are used here to clarify why swirl improves the combustionmore » repeatability from cycle to cycle. The fluid dynamics of swirl/spray interaction was previously demonstrated using high-speed PIV measurements of in-cylinder motored flow. It was found that the sprays of the multi-hole injector redistribute the intake-generated swirl flow momentum, thereby creating a better-centered higher angular-momentum vortex with reduced variability. The engine operation with high swirl was found to have significant improvement in cycle-to-cycle variations of both flow pattern and flow momentum. This paper is an extension of the previous work. Here, PIV measurements and flame imaging are applied to fired operation for studying how the swirl flow affects variability of ignition and subsequent combustion phases. PIV results for fired operation are consistent with the measurements made of motored flow. They demonstrate that the spark-plasma motion is highly correlated with the direction of the gas flow in the vicinity of the spark-plug gap. Without swirl, the plasma is randomly stretched towards either side of the spark plug, causing variability in the ignition of the two spray plumes that are straddling the spark plug. Conversely, swirl flow always convects the spark plasma towards one spray plume, causing a more repeatable ignition. The swirl decreases local RMS velocity, consistent with an observed reduction of early-burn variability. Broadband flame imaging demonstrates that with swirl, the flame consistently propagates in multiple directions to consume fuel–air mixtures within the piston bowl. In contrast, operation without swirl displays higher variability of flame-spread patterns, occasionally causing the appearance of partial-burn cycles.« less

The role of spray-enhanced swirl flow for combustion stabilization in a stratified-charge DISI engine

DOE PAGES

Zeng, Wei; Sjöberg, Magnus; Reuss, David L.; ...

2016-06-01

Implementing spray-guided stratified-charge direct-injection spark-ignited (DISI) engines is inhibited by the occurrence of misfire and partial burns. Engine-performance tests demonstrate that increasing engine speed induces combustion instability, but this deterioration can be prevented by generating swirling flow during the intake stroke. In-cylinder pressure-based heat-release analysis reveals that the appearance of poor-burn cycles is not solely dependent on the variability of early flame-kernel growth. Moreover, cycles can experience burning-rate regression during later combustion stages and may or may not recover before the end of the cycle. Thermodynamic analysis and optical diagnostics are used here to clarify why swirl improves the combustionmore » repeatability from cycle to cycle. The fluid dynamics of swirl/spray interaction was previously demonstrated using high-speed PIV measurements of in-cylinder motored flow. It was found that the sprays of the multi-hole injector redistribute the intake-generated swirl flow momentum, thereby creating a better-centered higher angular-momentum vortex with reduced variability. The engine operation with high swirl was found to have significant improvement in cycle-to-cycle variations of both flow pattern and flow momentum. This paper is an extension of the previous work. Here, PIV measurements and flame imaging are applied to fired operation for studying how the swirl flow affects variability of ignition and subsequent combustion phases. PIV results for fired operation are consistent with the measurements made of motored flow. They demonstrate that the spark-plasma motion is highly correlated with the direction of the gas flow in the vicinity of the spark-plug gap. Without swirl, the plasma is randomly stretched towards either side of the spark plug, causing variability in the ignition of the two spray plumes that are straddling the spark plug. Conversely, swirl flow always convects the spark plasma towards one spray plume, causing a more repeatable ignition. The swirl decreases local RMS velocity, consistent with an observed reduction of early-burn variability. Broadband flame imaging demonstrates that with swirl, the flame consistently propagates in multiple directions to consume fuel–air mixtures within the piston bowl. In contrast, operation without swirl displays higher variability of flame-spread patterns, occasionally causing the appearance of partial-burn cycles.« less

Spark Gap Electrode Erosion

DTIC Science & Technology

1984-12-01

N~JFOSR-TR- 85-0282 o ~FINAL REPORT S SPARK GAP ELECTRODE EROSION 00i Air Force Office of Scientific Research Grant No. 84-0015- Approve", t’r p...OF MONITORING ORGANIZATION Texas Tech University IDibj Air Office of Scientific Research it- ADORESS rCat.. State and ZIP CG*, 7b. ADONESS ’CitY...spark gap was measured for various electrode, gas, and pressure combinations. A previously developed model of self breakdown voltage distribution was

Accelerating biomedical innovation: a case study of the SPARK program at Stanford University, School of Medicine.

PubMed

Kim, Esther S; Omura, Paige M C; Lo, Andrew W

2017-07-01

Translating academic medical research into new therapies is an important challenge for the biopharmaceutical industry and investment communities, which have historically favored later-stage assets with lower risk and clearer commercial value. The Stanford SPARK program is an innovative model for addressing this challenge. The program was created in 2006 to educate students and faculty about bringing academic research from bench to bedside. Every year, the program provides mentorship and funding for approximately a dozen SPARK 'scholars,' with a focus on impacting patient lives, regardless of economic factors. By reviewing the detailed structure, function and operation of SPARK we hope to provide a template for other universities and institutions interested in de-risking and facilitating the translation of biomedical research. Copyright © 2017 Elsevier Ltd. All rights reserved.

Space Projects and Research by Kids (SPARK): A Web Based Research Journal for Middle School Students

NASA Astrophysics Data System (ADS)

Limaye, S. S.; Pertzborn, R. A.

1999-05-01

Project SPARK is designed to facilitate opportunities for upper elementary and middle school students to develop the necessary skills to conduct investigations that focus on the subjects of astronomy, space exploration, and earth remote sensing. This program actively engages students in conducting their own research project to acquire increased understanding and content knowledge in the space sciences. While the development of scientific inquiry skills and content literacy is the primary focus, students also enhance their critical thinking, analytical, technological and communications skills. As in the professional science community, the web based SPARK Journal presents an avenue for students to effectively communicate the results of their investigations and work to classmates as well as the "global learning community" via the world wide web. Educational outreach staff at the Sapce Science and Engineering Center have developed active partnerships with teachers and schools throughout Wisconsin to facilitate the development of standards based curriculum and research projects focusing on current topics in the space sciences. Student research projects and activities arising from these initiatives were submitted in the Spring and Fall of 1998 for inclusion in SPARK, Volume 1. The second volume of SPARK will be published in Spring, 1999. Support for the development of this journal was provided by the NASA/IDEAS Program.

Damping Resonant Current in a Spark-Gap Trigger Circuit to Reduce Noise

DTIC Science & Technology

2009-06-01

DAMPING RESONANT CURRENT IN A SPARK- GAP TRIGGER CIRCUIT TO REDUCE NOISE E. L. Ruden Air Force Research Laboratory, Directed Energy Directorate, AFRL...REPORT TYPE N/A 3. DATES COVERED - 4. TITLE AND SUBTITLE Damping Resonant Current In A Spark- Gap Trigger Circuit To Reduce Noise 5a...thereby triggering 2 after delay 0, is 1. Each of the two rail- gaps (represented by 2) is trig- gered to close after the spark- gap (1) in the

Comparing Alternative Kernels for the Kernel Method of Test Equating: Gaussian, Logistic, and Uniform Kernels. Research Report. ETS RR-08-12

ERIC Educational Resources Information Center

Lee, Yi-Hsuan; von Davier, Alina A.

2008-01-01

The kernel equating method (von Davier, Holland, & Thayer, 2004) is based on a flexible family of equipercentile-like equating functions that use a Gaussian kernel to continuize the discrete score distributions. While the classical equipercentile, or percentile-rank, equating method carries out the continuization step by linear interpolation,…

Experimental Investigation of Augmented Spark Ignition of a LO2/LCH4 Reaction Control Engine at Altitude Conditions

NASA Technical Reports Server (NTRS)

Kleinhenz, Julie; Sarmiento, Charles; Marshall, William

2012-01-01

The use of nontoxic propellants in future exploration vehicles would enable safer, more cost-effective mission scenarios. One promising green alternative to existing hypergols is liquid methane (LCH4) with liquid oxygen (LO2). A 100 lbf LO2/LCH4 engine was developed under the NASA Propulsion and Cryogenic Advanced Development project and tested at the NASA Glenn Research Center Altitude Combustion Stand in a low pressure environment. High ignition energy is a perceived drawback of this propellant combination; so this ignition margin test program examined ignition performance versus delivered spark energy. Sensitivity of ignition to spark timing and repetition rate was also explored. Three different exciter units were used with the engine s augmented (torch) igniter. Captured waveforms indicated spark behavior in hot fire conditions was inconsistent compared to the well-behaved dry sparks. This suggests that rising pressure and flow rate increase spark impedance and may at some point compromise an exciter s ability to complete each spark. The reduced spark energies of such quenched deliveries resulted in more erratic ignitions, decreasing ignition probability. The timing of the sparks relative to the pressure/flow conditions also impacted the probability of ignition. Sparks occurring early in the flow could trigger ignition with energies as low as 1 to 6 mJ, though multiple, similarly timed sparks of 55 to 75 mJ were required for reliable ignition. Delayed spark application and reduced spark repetition rate both correlated with late and occasional failed ignitions. An optimum time interval for spark application and ignition therefore coincides with propellant introduction to the igniter.

Breakdown voltage determination of gaseous and near cryogenic fluids with application to rocket engine ignition

NASA Astrophysics Data System (ADS)

Nugent, Nicholas Jeremy

Liquid rocket engines extensively use spark-initiated torch igniters for ignition. As the focus shifts to longer missions that require multiple starts of the main engines, there exists a need to solve the significant problems associated with using spark-initiated devices. Improving the fundamental understanding of predicting the required breakdown voltage in rocket environments along with reducing electrical noise is necessary to ensure that missions can be completed successfully. To better understand spark ignition systems and add to the fundamental research on spark development in rocket applications, several parameter categories of interest were hypothesized to affect breakdown voltage: (i) fluid, (ii) electrode, and (iii) electrical. The fluid properties varied were pressure, temperature, density and mass flow rate. Electrode materials, insert electrode angle and spark gap distance were the electrode properties varied. Polarity was the electrical property investigated. Testing how breakdown voltage is affected by each parameter was conducted using three different isolated insert electrodes fabricated from copper and nickel. A spark plug commonly used in torch igniters was the other electrode. A continuous output power source connected to a large impedance source and capacitance provided the pulsing potential. Temperature, pressure and high voltage measurements were recorded for the 418 tests that were successfully completed. Nitrogen, being inert and similar to oxygen, a propellant widely used in torch igniters, was used as the fluid for the majority of testing. There were 68 tests completed with oxygen and 45 with helium. A regression of the nitrogen data produced a correction coefficient to Paschen's Law that predicts the breakdown voltage to within 3000 volts, better than 20%, compared to an over prediction on the order of 100,000 volts using Paschen's Law. The correction coefficient is based on the parameters most influencing breakdown voltage: fluid density, spark gap distance, electrode angles, electrode materials and polarity. The research added to the fundamental knowledge of spark development in rocket ignition applications by determining the parameters that most influence breakdown voltage. Some improvements to the research should include better temperature measurements near the spark gap, additional testing with oxygen and testing with fuels of interest such as hydrogen and methane.

Application of microplasma discharge in a spark gap for high repetitive switching

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rahaman, Hasibur; Nam, Sang Hoon; Nam, Jong Woo

2010-04-05

The electrical breakdown in a spark gap for repetitive switching has been a long research interest. For this purpose, microplasma discharge is implemented in the spark gap which is further integrated inside a coaxial transmission line. This work addresses important physical properties and insights of the microplasma discharge, to be optimized, such as plasma generation in a spark channel, dielectric recovery process, and residual plasma in the postspark discharge period. Although understanding the microplasma discharge is the primary goal, considerable attention has been focused on an external circuit scheme to drive the discharge system at a high repetition rate.

X-Ray Radiography Measurements of the Thermal Energy in Spark Ignition Plasma at Variable Ambient Conditions

DOE PAGES

Matusik, Katarzyna E.; Duke, Daniel J.; Kastengren, Alan L.; ...

2017-04-09

The sparking behavior in an internal combustion engine affects the fuel efficiency, engine-out emissions, and general drivability of a vehicle. As emissions regulations become progressively stringent, combustion strategies, including exhaust gas recirculation (EGR), lean-burn, and turbocharging are receiving increasing attention as models of higher efficiency advanced combustion engines with reduced emissions levels. Because these new strategies affect the working environment of the spark plug, ongoing research strives to understand the influence of external factors on the spark ignition process. Due to the short time and length scales involved and the harsh environment, experimental quantification of the deposited energy from themore » sparking event is difficult to obtain. We present the results of x-ray radiography measurements of spark ignition plasma generated by a conventional spark plug. Our measurements were performed at the 7-BM beamline of the Advanced Photon Source at Argonne National Laboratory. The synchrotron x-ray source enables time-resolved measurements of the density change due to glow discharge in the spark gap with 153 ns temporal and 5 μm spatial resolutions. We also explore the effects of charging time, EGR-relevant gas compositions, and gas pressure on the sparking behavior. We also quantify the influence of the measurement technique on the obtained results.« less

The SPARK Programs: A Public Health Model of Physical Education Research and Dissemination

ERIC Educational Resources Information Center

McKenzie, Thomas L.; Sallis, James F.; Rosengard, Paul; Ballard, Kymm

2016-01-01

SPARK [Sports, Play, and Active Recreation for Kids], in its current form, is a brand that represents a collection of exemplary, research-based, physical education and physical activity programs that emphasize a highly active curriculum, on-site staff development, and follow-up support. Given its complexity (e.g., multiple school levels, inclusion…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matusik, Katarzyna E.; Duke, Daniel J.; Kastengren, Alan L.

The sparking behavior in an internal combustion engine affects the fuel efficiency, engine-out emissions, and general drivability of a vehicle. As emissions regulations become progressively stringent, combustion strategies, including exhaust gas recirculation (EGR), lean-burn, and turbocharging are receiving increasing attention as models of higher efficiency advanced combustion engines with reduced emissions levels. Because these new strategies affect the working environment of the spark plug, ongoing research strives to understand the influence of external factors on the spark ignition process. Due to the short time and length scales involved and the harsh environment, experimental quantification of the deposited energy from themore » sparking event is difficult to obtain. We present the results of x-ray radiography measurements of spark ignition plasma generated by a conventional spark plug. Our measurements were performed at the 7-BM beamline of the Advanced Photon Source at Argonne National Laboratory. The synchrotron x-ray source enables time-resolved measurements of the density change due to glow discharge in the spark gap with 153 ns temporal and 5 μm spatial resolutions. We also explore the effects of charging time, EGR-relevant gas compositions, and gas pressure on the sparking behavior. We also quantify the influence of the measurement technique on the obtained results.« less

Performance Characteristics of a Kernel-Space Packet Capture Module

DTIC Science & Technology

2010-03-01

Defense, or the United States Government . AFIT/GCO/ENG/10-03 PERFORMANCE CHARACTERISTICS OF A KERNEL-SPACE PACKET CAPTURE MODULE THESIS Presented to the...3.1.2.3 Prototype. The proof of concept for this research is the design, development, and comparative performance analysis of a kernel level N2d capture...changes to kernel code 5. Can be used for both user-space and kernel-space capture applications in order to control comparative performance analysis to

Deep kernel learning method for SAR image target recognition

NASA Astrophysics Data System (ADS)

Chen, Xiuyuan; Peng, Xiyuan; Duan, Ran; Li, Junbao

2017-10-01

With the development of deep learning, research on image target recognition has made great progress in recent years. Remote sensing detection urgently requires target recognition for military, geographic, and other scientific research. This paper aims to solve the synthetic aperture radar image target recognition problem by combining deep and kernel learning. The model, which has a multilayer multiple kernel structure, is optimized layer by layer with the parameters of Support Vector Machine and a gradient descent algorithm. This new deep kernel learning method improves accuracy and achieves competitive recognition results compared with other learning methods.

Ultra Fast, High Rep Rate, High Voltage Spark Gap Pulser

DTIC Science & Technology

1995-07-01

current rise time. The spark gap was designed to have a coaxial geometry reducing its inductance. Provisions were made to pass flowing gas between the...ULTRA FAST, HIGH REP RATE, HIGH VOLTAGE SPARK GAP PULSER Robert A. Pastore Jr., Lawrence E. Kingsley, Kevin Fonda, Erik Lenzing Electrophysics and...Modeling Branch AMSRL-PS-EA Tel.: (908)-532-0271 FAX: (908)-542-3348 U.S. Army Research Laboratory Physical Sciences Directorate Ft. Monmouth

3DRT-MPASS

NASA Technical Reports Server (NTRS)

Lickly, Ben

2005-01-01

Data from all current JPL missions are stored in files called SPICE kernels. At present, animators who want to use data from these kernels have to either read through the kernels looking for the desired data, or write programs themselves to retrieve information about all the needed objects for their animations. In this project, methods of automating the process of importing the data from the SPICE kernels were researched. In particular, tools were developed for creating basic scenes in Maya, a 3D computer graphics software package, from SPICE kernels.

[Ttextual research of Cannabis sativa varieties and medicinal part].

PubMed

Wei, Yingfang; Wang, Huadong; Guo, Shanshan; Yan, Jie; Long, Fei

2010-07-01

To determine the medicinal part and varieties of Cannabis Sativa through herbal textual research to Provide bibliographic reference for clinical application. Herbal textual research of C. Sativa from ancient herbal works and modern data analysis. Through the herbal textual research, the plant of the C. sativa, for Fructus Cannabis used now is identical with that described in ancient herbal literatures. People did not make a sharp distinction on medicinal part of C. sativa in the early stage literatures, female inflorescence and unripe fruit, fruit and kernel of seed were all used. Since Taohongjing realized the toxicity ofpericarp, all the herbal and prescription works indicate that the pericarp shall be removed before usage and only the kernel can be used. However, in modem literatures, both fruit and kernel can be used as medicinal part. The plants for Fructus Cannabis described in modern and ancient literatures are identical. The base of the original plant is the same either in ancient or modern. And the toxicity of the fruit is more than that of the kernel. The kernel is the exact medicinal part of C. Sativa.

Diamond High Assurance Security Program: Trusted Computing Exemplar

DTIC Science & Technology

2002-09-01

computing component, the Embedded MicroKernel Prototype. A third-party evaluation of the component will be initiated during development (e.g., once...target technologies and larger projects is a topic for future research. Trusted Computing Reference Component – The Embedded MicroKernel Prototype We...Kernel The primary security function of the Embedded MicroKernel will be to enforce process and data-domain separation, while providing primitive

Time-Resolved Images of Laser-Induced Gas Ignition Using High-Speed Photographic and Spectroscopic Techniques

NASA Astrophysics Data System (ADS)

Chen, Ying-Ling; Lewis, J. W. L.; Parigger, C. G.

1997-11-01

Two-dimensional visualization of laser-induced spark ignition in atmospheric-pressure gases is reported. Laser-induced breakdown in air, O2 and combustible NH_3/O2 mixture was achieved using a 1064 nm, Nd:YAG laser of approximately 6 ns pulse width, focused at 10-mm above a 60-mm diameter flat-flame burner. An argon sheath-gas flow was used to stabilize the core flowfield. High-speed photographic techniques were applied to trace a complete sequence of kernel development of a single breakdown or ignition event. Thermochemical characteristics of the post-breakdown regime were analyzed by laser-induced fluorescence spectroscopy (LIFS). Spatial distribution of NH free radical observed by planar-LIF showed the contours of the developing flame-front. The corresponding NH temperature maps achieved by excitation LIFS and Boltzmann plot are also presented.

The Flux OSKit: A Substrate for Kernel and Language Research

DTIC Science & Technology

1997-10-01

unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 tions. Our own microkernel -based OS, Fluke [17], puts almost all of the OSKit to use...kernels distance the language from the hardware; even microkernels and other extensible kernels enforce some default policy which often conflicts with a...be particu- larly useful in these research projects. 6.1.1 The Fluke OS In 1996 we developed an entirely new microkernel - based system called Fluke

Geographically weighted regression model on poverty indicator

NASA Astrophysics Data System (ADS)

Slamet, I.; Nugroho, N. F. T. A.; Muslich

2017-12-01

In this research, we applied geographically weighted regression (GWR) for analyzing the poverty in Central Java. We consider Gaussian Kernel as weighted function. The GWR uses the diagonal matrix resulted from calculating kernel Gaussian function as a weighted function in the regression model. The kernel weights is used to handle spatial effects on the data so that a model can be obtained for each location. The purpose of this paper is to model of poverty percentage data in Central Java province using GWR with Gaussian kernel weighted function and to determine the influencing factors in each regency/city in Central Java province. Based on the research, we obtained geographically weighted regression model with Gaussian kernel weighted function on poverty percentage data in Central Java province. We found that percentage of population working as farmers, population growth rate, percentage of households with regular sanitation, and BPJS beneficiaries are the variables that affect the percentage of poverty in Central Java province. In this research, we found the determination coefficient R2 are 68.64%. There are two categories of district which are influenced by different of significance factors.

SparkText: Biomedical Text Mining on Big Data Framework.

PubMed

Ye, Zhan; Tafti, Ahmad P; He, Karen Y; Wang, Kai; He, Max M

Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment. In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers) from tens of thousands of articles downloaded from PubMed, and then employed Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression to build prediction models to mine the articles. The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%. While competing text-mining tools took more than 11 hours, SparkText mined the dataset in approximately 6 minutes. This study demonstrates the potential for mining large-scale scientific articles on a Big Data infrastructure, with real-time update from new articles published daily. SparkText can be extended to other areas of biomedical research.

SparkText: Biomedical Text Mining on Big Data Framework

PubMed Central

He, Karen Y.; Wang, Kai

2016-01-01

Background Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment. Results In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers) from tens of thousands of articles downloaded from PubMed, and then employed Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression to build prediction models to mine the articles. The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%. While competing text-mining tools took more than 11 hours, SparkText mined the dataset in approximately 6 minutes. Conclusions This study demonstrates the potential for mining large-scale scientific articles on a Big Data infrastructure, with real-time update from new articles published daily. SparkText can be extended to other areas of biomedical research. PMID:27685652

Adolescent Thriving: The Role of Sparks, Relationships, and Empowerment

ERIC Educational Resources Information Center

Scales, Peter C.; Benson, Peter L.; Roehlkepartain, Eugene C.

2011-01-01

Although most social science research on adolescence emphasizes risks and challenges, an emergent field of study focuses on adolescent thriving. The current study extends this line of inquiry by examining the additive power of identifying and nurturing young people's "sparks," giving them "voice," and providing the relationships and opportunities…

Coupling individual kernel-filling processes with source-sink interactions into GREENLAB-Maize.

PubMed

Ma, Yuntao; Chen, Youjia; Zhu, Jinyu; Meng, Lei; Guo, Yan; Li, Baoguo; Hoogenboom, Gerrit

2018-02-13

Failure to account for the variation of kernel growth in a cereal crop simulation model may cause serious deviations in the estimates of crop yield. The goal of this research was to revise the GREENLAB-Maize model to incorporate source- and sink-limited allocation approaches to simulate the dry matter accumulation of individual kernels of an ear (GREENLAB-Maize-Kernel). The model used potential individual kernel growth rates to characterize the individual potential sink demand. The remobilization of non-structural carbohydrates from reserve organs to kernels was also incorporated. Two years of field experiments were conducted to determine the model parameter values and to evaluate the model using two maize hybrids with different plant densities and pollination treatments. Detailed observations were made on the dimensions and dry weights of individual kernels and other above-ground plant organs throughout the seasons. Three basic traits characterizing an individual kernel were compared on simulated and measured individual kernels: (1) final kernel size; (2) kernel growth rate; and (3) duration of kernel filling. Simulations of individual kernel growth closely corresponded to experimental data. The model was able to reproduce the observed dry weight of plant organs well. Then, the source-sink dynamics and the remobilization of carbohydrates for kernel growth were quantified to show that remobilization processes accompanied source-sink dynamics during the kernel-filling process. We conclude that the model may be used to explore options for optimizing plant kernel yield by matching maize management to the environment, taking into account responses at the level of individual kernels. © The Author(s) 2018. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Straight-chain halocarbon forming fluids for TRISO fuel kernel production - Tests with yttria-stabilized zirconia microspheres

NASA Astrophysics Data System (ADS)

Baker, M. P.; King, J. C.; Gorman, B. P.; Braley, J. C.

2015-03-01

Current methods of TRISO fuel kernel production in the United States use a sol-gel process with trichloroethylene (TCE) as the forming fluid. After contact with radioactive materials, the spent TCE becomes a mixed hazardous waste, and high costs are associated with its recycling or disposal. Reducing or eliminating this mixed waste stream would not only benefit the environment, but would also enhance the economics of kernel production. Previous research yielded three candidates for testing as alternatives to TCE: 1-bromotetradecane, 1-chlorooctadecane, and 1-iodododecane. This study considers the production of yttria-stabilized zirconia (YSZ) kernels in silicone oil and the three chosen alternative formation fluids, with subsequent characterization of the produced kernels and used forming fluid. Kernels formed in silicone oil and bromotetradecane were comparable to those produced by previous kernel production efforts, while those produced in chlorooctadecane and iodododecane experienced gelation issues leading to poor kernel formation and geometry.

Evidence-based Kernels: Fundamental Units of Behavioral Influence

PubMed Central

Biglan, Anthony

2008-01-01

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior. PMID:18712600

Shakedown and Preliminary Calibration Tests for the Fuel Engine Evaluation System Using the KM914A Sachs Rotary Combustion Engine.

DTIC Science & Technology

1981-12-01

obtained recommendations are made to improve the system. FEES was designed to handle spark ignition and compression ignition research engines of...Thermometer T W OF Temperature Web Bulb Sling Psychrometer % Relative Humidity Psychrometric chart mm Hg Vapor Pressure Vapor Pressure chart - Correction...results obtained recommendations are made to improve the system. FEES was designed to handle spark ignition and compression ignition research engines of

Use of reinforced inorganic cement materials for spark wire and drift chamber wire frames

NASA Technical Reports Server (NTRS)

1987-01-01

The results of a survey, materials test, and analysis study directed toward the development of an inorganic glass-fiber reinforced cement material for use in the construction of space qualified spark wire frames and drift chamber frames are presented. The purpose for this research was to evaluate the feasibility of using glass fiber reinforced cement (GFRC) for large dimensioned structural frames for supporting a number of precisely located spark wires in multiple planes. A survey of the current state of the art in fiber reinforced cement materials was made; material sample mixes were made and tested to determine their laboratory performances. Tests conducted on sample materials showed that compressive and flexural strengths of this material could approach values which would enable fabrication of structural spark wire frames.

Modeling of high speed chemically reacting flow-fields

NASA Technical Reports Server (NTRS)

Drummond, J. P.; Carpenter, Mark H.; Kamath, H.

1989-01-01

The SPARK3D and SPARK3D-PNS computer programs were developed to model 3-D supersonic, chemically reacting flow-fields. The SPARK3D code is a full Navier-Stokes solver, and is suitable for use in scramjet combustors and other regions where recirculation may be present. The SPARK3D-PNS is a parabolized Navier-Stokes solver and provides an efficient means of calculating steady-state combustor far-fields and nozzles. Each code has a generalized chemistry package, making modeling of any chemically reacting flow possible. Research activities by the Langley group range from addressing fundamental theoretical issues to simulating problems of practical importance. Algorithmic development includes work on higher order and upwind spatial difference schemes. Direct numerical simulations employ these algorithms to address the fundamental issues of flow stability and transition, and the chemical reaction of supersonic mixing layers and jets. It is believed that this work will lend greater insight into phenomenological model development for simulating supersonic chemically reacting flows in practical combustors. Currently, the SPARK3D and SPARK3D-PNS codes are used to study problems of engineering interest, including various injector designs and 3-D combustor-nozzle configurations. Examples, which demonstrate the capabilities of each code are presented.

Effects of kernel vitreousness and protein level on protein molecular weight distribution, milling quality, and breadmaking quality in hard red spring wheat

USDA-ARS?s Scientific Manuscript database

Dark, hard, and vitreous kernel content is an important grading characteristic for hard red spring (HRS) wheat in the U.S. This research aimed to determine the associations of kernel vitreousness (KV) with protein molecular weight distribution (MWD) and quality traits that were not biased by quanti...

Development of Ionic Liquid Monopropellants for In-Space Propulsion

NASA Technical Reports Server (NTRS)

Blevins, John A.; Drake, Gregory W.; Osborne, Robin J.

2005-01-01

A family of new, low toxicity, high energy monopropellants is currently being evaluated at NASA Marshall Space Flight Center for in-space rocket engine applications such as reaction control engines. These ionic liquid monopropellants, developed in recent years by the Air Force Research Laboratory, could offer system simplification, less in-flight thermal management, and reduced handling precautions, while increasing propellant energy density as compared to traditional storable in-space propellants such as hydrazine and nitrogen tetroxide. However, challenges exist in identifying ignition schemes for these ionic liquid monopropellants, which are known to burn at much hotter combustion temperatures compared to traditional monopropellants such as hydrazine. The high temperature combustion of these new monopropellants make the use of typical ignition catalyst beds prohibitive since the catalyst cannot withstand the elevated temperatures. Current research efforts are focused on monopropellant ignition and burn rate characterization, parameters that are important in the fundamental understanding of the monopropellant behavior and the eventual design of a thruster. Laboratory studies will be conducted using alternative ignition techniques such as laser-induced spark ignition and hot wire ignition. Ignition delay, defined as the time between the introduction of the ignition source and the first sign of light emission from a developing flame kernel, will be measured using Schlieren visualization. An optically-accessible liquid monopropellant burner, shown schematically in Figure 1 and similar in design to apparatuses used by other researchers to study solid and liquid monopropellants, will be used to determine propellant burn rate as a function of pressure and initial propellant temperature. The burn rate will be measured via high speed imaging through the chamber s windows.

Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data

PubMed Central

2013-01-01

Background Arguably, genotypes and phenotypes may be linked in functional forms that are not well addressed by the linear additive models that are standard in quantitative genetics. Therefore, developing statistical learning models for predicting phenotypic values from all available molecular information that are capable of capturing complex genetic network architectures is of great importance. Bayesian kernel ridge regression is a non-parametric prediction model proposed for this purpose. Its essence is to create a spatial distance-based relationship matrix called a kernel. Although the set of all single nucleotide polymorphism genotype configurations on which a model is built is finite, past research has mainly used a Gaussian kernel. Results We sought to investigate the performance of a diffusion kernel, which was specifically developed to model discrete marker inputs, using Holstein cattle and wheat data. This kernel can be viewed as a discretization of the Gaussian kernel. The predictive ability of the diffusion kernel was similar to that of non-spatial distance-based additive genomic relationship kernels in the Holstein data, but outperformed the latter in the wheat data. However, the difference in performance between the diffusion and Gaussian kernels was negligible. Conclusions It is concluded that the ability of a diffusion kernel to capture the total genetic variance is not better than that of a Gaussian kernel, at least for these data. Although the diffusion kernel as a choice of basis function may have potential for use in whole-genome prediction, our results imply that embedding genetic markers into a non-Euclidean metric space has very small impact on prediction. Our results suggest that use of the black box Gaussian kernel is justified, given its connection to the diffusion kernel and its similar predictive performance. PMID:23763755

Composition analyzer for microparticles using a spark ion source

NASA Technical Reports Server (NTRS)

Auer, S.; Berg, O. E.

1975-01-01

Iron microparticles were fired onto a capacitor-type microparticle detector which responded to an impact with a spark discharge. Ion currents were extracted from the spark and analyzed in a time-of-flight mass spectrometer. The mass spectra showed the elements of both detector and particle materials. The total extracted ion current was typically 10 A within a period of 100 nsec, indicating very efficient vaporization of the particle and ionization of the vapor. Potential applications include research on cosmic dust, atmospheric aerosols and cloud droplets, particles ejected by rocket or jet engines, by machining processes or by nuclear bomb explosions.

Development of Ionic Liquid Monopropellants for In-Space Propulsion

NASA Technical Reports Server (NTRS)

Blevins, John A.; Osborne, Robin; Drake, Gregory W.

2005-01-01

A family of new, low toxicity, high energy monopropellants is currently being evaluated at NASA Marshall Space Flight Center for in-space rocket engine applications such as reaction control engines. These ionic liquid monopropellants, developed in recent years by the Air Force Research Laboratory, could offer system simplification, less in-flight thermal management, and reduced handling precautions, while increasing propellant energy density as compared to traditional storable in-space propellants such as hydrazine and nitrogen tetroxide. However, challenges exist in identifying ignition schemes for these ionic liquid monopropellants, which are known to burn at much hotter combustion temperatures compared to traditional monopropellants such as hydrazine. The high temperature combustion of these new monopropellants make the use of typical ignition catalyst beds prohibitive since the catalyst cannot withstand the elevated temperatures. Current research efforts are focused on monopropellant ignition and burn rate characterization, parameters that are important in the fundamental understanding of the monopropellant behavior and the eventual design of a thruster. Laboratory studies will be conducted using alternative ignition techniques such as laser-induced spark ignition and hot wire ignition. Ignition delay, defined as the time between the introduction of the ignition source and the first sign of light emission from a developing flame kernel, will be measured using Schlieren visualization. An optically-accessible liquid monopropellant burner will be used to determine propellant burn rate as a function of pressure and initial propellant temperature. The burn rate will be measured via high speed imaging through the chamber s windows.

Coordinated Research Program in Pulsed Power Physics.

DTIC Science & Technology

1984-02-27

storage element and the spark gap sectional area at the injected beam) which helps reduce elec- are both contained within the high pressure vessel of a...ns At the present time the continued research is aimed at duration of the first region corresponds closely to the FWHM answering various unresolved...10-ns e-beam has been used to trigger a spark gap pressurized to 3 atm of N2 . The gap voltage is close to self-breakdown voltage (Le., 0.95 Vb

A survey of kernel-type estimators for copula and their applications

NASA Astrophysics Data System (ADS)

Sumarjaya, I. W.

2017-10-01

Copulas have been widely used to model nonlinear dependence structure. Main applications of copulas include areas such as finance, insurance, hydrology, rainfall to name but a few. The flexibility of copula allows researchers to model dependence structure beyond Gaussian distribution. Basically, a copula is a function that couples multivariate distribution functions to their one-dimensional marginal distribution functions. In general, there are three methods to estimate copula. These are parametric, nonparametric, and semiparametric method. In this article we survey kernel-type estimators for copula such as mirror reflection kernel, beta kernel, transformation method and local likelihood transformation method. Then, we apply these kernel methods to three stock indexes in Asia. The results of our analysis suggest that, albeit variation in information criterion values, the local likelihood transformation method performs better than the other kernel methods.

Establishment of a Remotely Piloted Helicopter Test Flight Program for Higher Harmonic Control Research

DTIC Science & Technology

1990-06-01

amplitude of the l IC actuators are set manually with no feedback of airframe response. Closed loop contrl refers to a system which utilizes response...mixture being controlled by the all position diaphragm carburetor and fuel pump . Ignition spark is ac-.cvd using " OX I mm spark plg. 28 b. Drive

Beyond the Stucco Tower: Design, Development, and Dissemination of the SPARK Physical Education Programs

ERIC Educational Resources Information Center

McKenzie, Thomas L.; Sallis, James F.; Rosengard, Paul

2009-01-01

School physical education plays an important role in public health. Nonetheless, there are few evidence-based, health-related, physical education programs and very little is known about how to disseminate them for widespread use. This article (a) presents background information and a review of the completed research on the SPARK (Sports, Play, and…

Genetic, Genomic, and Breeding Approaches to Further Explore Kernel Composition Traits and Grain Yield in Maize

ERIC Educational Resources Information Center

Da Silva, Helena Sofia Pereira

2009-01-01

Maize ("Zea mays L.") is a model species well suited for the dissection of complex traits which are often of commercial value. The purpose of this research was to gain a deeper understanding of the genetic control of maize kernel composition traits starch, protein, and oil concentration, and also kernel weight and grain yield. Germplasm with…

An Evaluation of Kernel Equating: Parallel Equating with Classical Methods in the SAT Subject Tests[TM] Program. Research Report. ETS RR-09-06

ERIC Educational Resources Information Center

Grant, Mary C.; Zhang, Lilly; Damiano, Michele

2009-01-01

This study investigated kernel equating methods by comparing these methods to operational equatings for two tests in the SAT Subject Tests[TM] program. GENASYS (ETS, 2007) was used for all equating methods and scaled score kernel equating results were compared to Tucker, Levine observed score, chained linear, and chained equipercentile equating…

Searching for efficient Markov chain Monte Carlo proposal kernels

PubMed Central

Yang, Ziheng; Rodríguez, Carlos E.

2013-01-01

Markov chain Monte Carlo (MCMC) or the Metropolis–Hastings algorithm is a simulation algorithm that has made modern Bayesian statistical inference possible. Nevertheless, the efficiency of different Metropolis–Hastings proposal kernels has rarely been studied except for the Gaussian proposal. Here we propose a unique class of Bactrian kernels, which avoid proposing values that are very close to the current value, and compare their efficiency with a number of proposals for simulating different target distributions, with efficiency measured by the asymptotic variance of a parameter estimate. The uniform kernel is found to be more efficient than the Gaussian kernel, whereas the Bactrian kernel is even better. When optimal scales are used for both, the Bactrian kernel is at least 50% more efficient than the Gaussian. Implementation in a Bayesian program for molecular clock dating confirms the general applicability of our results to generic MCMC algorithms. Our results refute a previous claim that all proposals had nearly identical performance and will prompt further research into efficient MCMC proposals. PMID:24218600

Shell cracking strength in almond (Prunus dulcis [Mill.] D.A. Webb.) and its implication in uses as a value-added product.

PubMed

Ledbetter, C A

2008-09-01

Researchers are currently developing new value-added uses for almond shells, an abundant agricultural by-product. Almond varieties are distinguished by processors as being either hard or soft shelled, but these two broad classes of almond also exhibit varietal diversity in shell morphology and physical characters. By defining more precisely the physical and chemical characteristics of almond shells from different varieties, researchers will better understand which specific shell types are best suited for specific industrial processes. Eight diverse almond accessions were evaluated in two consecutive harvest seasons for nut and kernel weight, kernel percentage and shell cracking strength. Shell bulk density was evaluated in a separate year. Harvest year by almond accession interactions were highly significant (p0.01) for each of the analyzed variables. Significant (p0.01) correlations were noted for average nut weight with kernel weight, kernel percentage and shell cracking strength. A significant (p0.01) negative correlation for shell cracking strength with kernel percentage was noted. In some cases shell cracking strength was independent of the kernel percentage which suggests that either variety compositional differences or shell morphology affect the shell cracking strength. The varietal characterization of almond shell materials will assist in determining the best value-added uses for this abundant agricultural by-product.

Welding of titanium and nickel alloy by combination of explosive welding and spark plasma sintering technologies

NASA Astrophysics Data System (ADS)

Malyutina, Yu. N.; Bataev, A. A.; Mali, V. I.; Anisimov, A. G.; Shevtsova, L. I.

2015-10-01

A possibility of titanium and nickel-based alloys composite materials formation using combination of explosive welding and spark plasma sintering technologies was demonstrated in the current research. An employment of interlayer consisting of copper and tantalum thin plates makes possible to eliminate a contact between metallurgical incompatible titanium and nickel that are susceptible to intermetallic compounds formation during their interaction. By the following spark plasma sintering process the bonding has been received between titanium and titanium alloy VT20 through the thin powder layer of pure titanium that is distinguished by low defectiveness and fine dispersive structure.

Welding of titanium and nickel alloy by combination of explosive welding and spark plasma sintering technologies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Malyutina, Yu. N., E-mail: iuliiamaliutina@gmail.com; Bataev, A. A., E-mail: bataev@adm.nstu.ru; Shevtsova, L. I., E-mail: edeliya2010@mail.ru

A possibility of titanium and nickel-based alloys composite materials formation using combination of explosive welding and spark plasma sintering technologies was demonstrated in the current research. An employment of interlayer consisting of copper and tantalum thin plates makes possible to eliminate a contact between metallurgical incompatible titanium and nickel that are susceptible to intermetallic compounds formation during their interaction. By the following spark plasma sintering process the bonding has been received between titanium and titanium alloy VT20 through the thin powder layer of pure titanium that is distinguished by low defectiveness and fine dispersive structure.

A composition analyzer for microparticles using a spark ion source. [using time of flight spectrometers

NASA Technical Reports Server (NTRS)

Auer, S. O.; Berg, O. E.

1975-01-01

Iron microparticles were fired onto a capacitor-type microparticle detector which responded to an impact with a spark discharge. Ion currents were extracted from the spark and analyzed in a time-of-flight mass spectrometer. The mass spectra showed the element of both detector and particle materials. The total extracted ion currents was typically 10A within a period of 100ns, indicating very efficient vaporization of the particle and ionization of the vapor. Potential applications include research on cosmic dust, atmospheric aerosols and cloud droplets, particles ejected by rocket or jet engines, by machining processes, or by nuclear bomb explosions.

Research on retailer data clustering algorithm based on Spark

NASA Astrophysics Data System (ADS)

Huang, Qiuman; Zhou, Feng

2017-03-01

Big data analysis is a hot topic in the IT field now. Spark is a high-reliability and high-performance distributed parallel computing framework for big data sets. K-means algorithm is one of the classical partition methods in clustering algorithm. In this paper, we study the k-means clustering algorithm on Spark. Firstly, the principle of the algorithm is analyzed, and then the clustering analysis is carried out on the supermarket customers through the experiment to find out the different shopping patterns. At the same time, this paper proposes the parallelization of k-means algorithm and the distributed computing framework of Spark, and gives the concrete design scheme and implementation scheme. This paper uses the two-year sales data of a supermarket to validate the proposed clustering algorithm and achieve the goal of subdividing customers, and then analyze the clustering results to help enterprises to take different marketing strategies for different customer groups to improve sales performance.

Structure and characteristics of functional powder composite materials obtained by spark plasma sintering

NASA Astrophysics Data System (ADS)

Oglezneva, S. A.; Kachenyuk, M. N.; Kulmeteva, V. B.; Ogleznev, N. B.

2017-07-01

The article describes the results of spark plasma sintering of ceramic materials based on titanium carbide, titanium carbosilicide, ceramic composite materials based on zirconium oxide, strengthened by carbon nanostructures and composite materials of electrotechnical purpose based on copper with addition of carbon structures and titanium carbosilicide. The research shows that the spark plasma sintering can achieve relative density of the material up to 98%. The effect of sintering temperature on the phase composition, density and porosity of the final product has been studied. It was found that with addition of carbon nanostructures the relative density and hardness decrease, but the fracture strength of ZrO2 increases up to times 2. The relative erosion resistance of the electrodes made of composite copper-based powder materials, obtained by spark plasma sintering during electroerosion treatment of tool steel exceeds that parameter of pure copper up to times 15.

Alternative Fuels DISI Engine Research ? Autoignition Metrics.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sjoberg, Carl Magnus Goran; Vuilleumier, David

Improved engine efficiency is required to comply with future fuel economy standards. Alternative fuels have the potential to enable more efficient engines while addressing concerns about energy security. This project contributes to the science base needed by industry to develop highly efficient direct injection spark igniton (DISI) engines that also beneficially exploit the different properties of alternative fuels. Here, the emphasis is on quantifying autoignition behavior for a range of spark-ignited engine conditions, including directly injected boosted conditions. The efficiency of stoichiometrically operated spark ignition engines is often limited by fuel-oxidizer end-gas autoignition, which can result in engine knock. Amore » fuel’s knock resistance is assessed empirically by the Research Octane Number (RON) and Motor Octane Number (MON) tests. By clarifying how these two tests relate to the autoignition behavior of conventional and alternative fuel formulations, fuel design guidelines for enhanced engine efficiency can be developed.« less

Sparse Event Modeling with Hierarchical Bayesian Kernel Methods

DTIC Science & Technology

2016-01-05

SECURITY CLASSIFICATION OF: The research objective of this proposal was to develop a predictive Bayesian kernel approach to model count data based on...several predictive variables. Such an approach, which we refer to as the Poisson Bayesian kernel model , is able to model the rate of occurrence of...which adds specificity to the model and can make nonlinear data more manageable. Early results show that the 1. REPORT DATE (DD-MM-YYYY) 4. TITLE

Experimental investigations of argon spark gap recovery times by developing a high voltage double pulse generator.

PubMed

Reddy, C S; Patel, A S; Naresh, P; Sharma, Archana; Mittal, K C

2014-06-01

The voltage recovery in a spark gap for repetitive switching has been a long research interest. A two-pulse technique is used to determine the voltage recovery times of gas spark gap switch with argon gas. First pulse is applied to the spark gap to over-volt the gap and initiate the breakdown and second pulse is used to determine the recovery voltage of the gap. A pulse transformer based double pulse generator capable of generating 40 kV peak pulses with rise time of 300 ns and 1.5 μs FWHM and with a delay of 10 μs-1 s was developed. A matrix transformer topology is used to get fast rise times by reducing L(l)C(d) product in the circuit. Recovery Experiments have been conducted for 2 mm, 3 mm, and 4 mm gap length with 0-2 bars pressure for argon gas. Electrodes of a sparkgap chamber are of rogowsky profile type, made up of stainless steel material, and thickness of 15 mm are used in the recovery study. The variation in the distance and pressure effects the recovery rate of the spark gap. An intermediate plateu is observed in the spark gap recovery curves. Recovery time decreases with increase in pressure and shorter gaps in length are recovering faster than longer gaps.

a Comparison Study of Different Kernel Functions for Svm-Based Classification of Multi-Temporal Polarimetry SAR Data

NASA Astrophysics Data System (ADS)

Yekkehkhany, B.; Safari, A.; Homayouni, S.; Hasanlou, M.

2014-10-01

In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

Effects of spark plug configuration on combustion and emission characteristics of a LPG fuelled lean burn SI engine

NASA Astrophysics Data System (ADS)

Ravi, K.; Khan, Manazir Ahmed; Pradeep Bhasker, J.; Porpatham, E.

2017-11-01

Introduction of technological innovation in automotive engines in reducing pollution and increasing efficiency have been under contemplation. Gaseous fuels have proved to be a promising way to reduce emissions in Spark Ignition (SI) engines. In particular, LPG settled to be a favourable fuel for SI engines because of their higher hydrogen to carbon ratio, octane rating and lower emissions. Wide ignition limits and efficient combustion characteristics make LPG suitable for lean burn operation. But lean combustion technology has certain drawbacks like poor flame propagation, cyclic variations etc. Based on copious research it was found that location, types and number of spark plug significantly influence in reducing cyclic variations. In this work the influence of single and dual spark plugs of conventional and surface discharge electrode type were analysed. Dual surface discharge electrode spark plug enhanced the brake thermal efficiency and greatly reduced the cyclic variations. The experimental results show that rate of heat release and pressure rise was more and combustion duration was shortened in this configuration. On the emissions front, the NOx emission has increased whereas HC and CO emissions were reduced under lean condition.

The Effects of Personality and Simulated Negotiation on Negotiation Effectiveness

DTIC Science & Technology

1978-12-01

A THE GALVANOMETER CASE ROLE of LARRY LYON , DIRECTOR OF MARKETING, SPARK ELECTRONICS CO. Your company manufactures, among other major products, galva...weighted guide- lines. Thus, with your negotiation position clearly in mind, you await the arrival of Larry Lyon , Director of Marketing, Spark Elec- A...E. T. Paine, and J. J. Ivancevich , "The Relative Effectiveness of Training Methods -- Expert Opinion and Research," Personnel Psychology, Autumn

Genetic Analysis of Kernel Traits in Maize-Teosinte Introgression Populations.

PubMed

Liu, Zhengbin; Garcia, Arturo; McMullen, Michael D; Flint-Garcia, Sherry A

2016-08-09

Seed traits have been targeted by human selection during the domestication of crop species as a way to increase the caloric and nutritional content of food during the transition from hunter-gather to early farming societies. The primary seed trait under selection was likely seed size/weight as it is most directly related to overall grain yield. Additional seed traits involved in seed shape may have also contributed to larger grain. Maize (Zea mays ssp. mays) kernel weight has increased more than 10-fold in the 9000 years since domestication from its wild ancestor, teosinte (Z. mays ssp. parviglumis). In order to study how size and shape affect kernel weight, we analyzed kernel morphometric traits in a set of 10 maize-teosinte introgression populations using digital imaging software. We identified quantitative trait loci (QTL) for kernel area and length with moderate allelic effects that colocalize with kernel weight QTL. Several genomic regions with strong effects during maize domestication were detected, and a genetic framework for kernel traits was characterized by complex pleiotropic interactions. Our results both confirm prior reports of kernel domestication loci and identify previously uncharacterized QTL with a range of allelic effects, enabling future research into the genetic basis of these traits. Copyright © 2016 Liu et al.

Genetic Analysis of Kernel Traits in Maize-Teosinte Introgression Populations

PubMed Central

Liu, Zhengbin; Garcia, Arturo; McMullen, Michael D.; Flint-Garcia, Sherry A.

2016-01-01

Seed traits have been targeted by human selection during the domestication of crop species as a way to increase the caloric and nutritional content of food during the transition from hunter-gather to early farming societies. The primary seed trait under selection was likely seed size/weight as it is most directly related to overall grain yield. Additional seed traits involved in seed shape may have also contributed to larger grain. Maize (Zea mays ssp. mays) kernel weight has increased more than 10-fold in the 9000 years since domestication from its wild ancestor, teosinte (Z. mays ssp. parviglumis). In order to study how size and shape affect kernel weight, we analyzed kernel morphometric traits in a set of 10 maize-teosinte introgression populations using digital imaging software. We identified quantitative trait loci (QTL) for kernel area and length with moderate allelic effects that colocalize with kernel weight QTL. Several genomic regions with strong effects during maize domestication were detected, and a genetic framework for kernel traits was characterized by complex pleiotropic interactions. Our results both confirm prior reports of kernel domestication loci and identify previously uncharacterized QTL with a range of allelic effects, enabling future research into the genetic basis of these traits. PMID:27317774

Optimizing R with SparkR on a commodity cluster for biomedical research.

PubMed

Sedlmayr, Martin; Würfl, Tobias; Maier, Christian; Häberle, Lothar; Fasching, Peter; Prokosch, Hans-Ulrich; Christoph, Jan

2016-12-01

Medical researchers are challenged today by the enormous amount of data collected in healthcare. Analysis methods such as genome-wide association studies (GWAS) are often computationally intensive and thus require enormous resources to be performed in a reasonable amount of time. While dedicated clusters and public clouds may deliver the desired performance, their use requires upfront financial efforts or anonymous data, which is often not possible for preliminary or occasional tasks. We explored the possibilities to build a private, flexible cluster for processing scripts in R based on commodity, non-dedicated hardware of our department. For this, a GWAS-calculation in R on a single desktop computer, a Message Passing Interface (MPI)-cluster, and a SparkR-cluster were compared with regards to the performance, scalability, quality, and simplicity. The original script had a projected runtime of three years on a single desktop computer. Optimizing the script in R already yielded a significant reduction in computing time (2 weeks). By using R-MPI and SparkR, we were able to parallelize the computation and reduce the time to less than three hours (2.6 h) on already available, standard office computers. While MPI is a proven approach in high-performance clusters, it requires rather static, dedicated nodes. SparkR and its Hadoop siblings allow for a dynamic, elastic environment with automated failure handling. SparkR also scales better with the number of nodes in the cluster than MPI due to optimized data communication. R is a popular environment for clinical data analysis. The new SparkR solution offers elastic resources and allows supporting big data analysis using R even on non-dedicated resources with minimal change to the original code. To unleash the full potential, additional efforts should be invested to customize and improve the algorithms, especially with regards to data distribution. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Research in Parallel Computing: 1987-1990

DTIC Science & Technology

1994-08-05

emulation, we layered UNIX BSD 4.3 functionality above the kernel primitives, but packaged both as a monolithic unit running in privileged state. This...further, so that only a "pure kernel " or " microkernel " runs in privileged mode, while the other components of the environment execute as one or more client... kernel DTIC TAB 24 2.2.2 Nectar’s communication software Unannounced 0 25 2.2.3 A Nectar programming interface Justification 25 2.3 System evaluation 26

The SPARK Tool to prioritise questions for systematic reviews in health policy and systems research: development and initial validation.

PubMed

Akl, Elie A; Fadlallah, Racha; Ghandour, Lilian; Kdouh, Ola; Langlois, Etienne; Lavis, John N; Schünemann, Holger; El-Jardali, Fadi

2017-09-04

Groups or institutions funding or conducting systematic reviews in health policy and systems research (HPSR) should prioritise topics according to the needs of policymakers and stakeholders. The aim of this study was to develop and validate a tool to prioritise questions for systematic reviews in HPSR. We developed the tool following a four-step approach consisting of (1) the definition of the purpose and scope of tool, (2) item generation and reduction, (3) testing for content and face validity, (4) and pilot testing of the tool. The research team involved international experts in HPSR, systematic review methodology and tool development, led by the Center for Systematic Reviews on Health Policy and Systems Research (SPARK). We followed an inclusive approach in determining the final selection of items to allow customisation to the user's needs. The purpose of the SPARK tool was to prioritise questions in HPSR in order to address them in systematic reviews. In the item generation and reduction phase, an extensive literature search yielded 40 relevant articles, which were reviewed by the research team to create a preliminary list of 19 candidate items for inclusion in the tool. As part of testing for content and face validity, input from international experts led to the refining, changing, merging and addition of new items, and to organisation of the tool into two modules. Following pilot testing, we finalised the tool, with 22 items organised in two modules - the first module including 13 items to be rated by policymakers and stakeholders, and the second including 9 items to be rated by systematic review teams. Users can customise the tool to their needs, by omitting items that may not be applicable to their settings. We also developed a user manual that provides guidance on how to use the SPARK tool, along with signaling questions. We have developed and conducted initial validation of the SPARK tool to prioritise questions for systematic reviews in HPSR, along with a user manual. By aligning systematic review production to policy priorities, the tool will help support evidence-informed policymaking and reduce research waste. We invite others to contribute with additional real-life implementation of the tool.

A Temperature Compensation Method for Piezo-Resistive Pressure Sensor Utilizing Chaotic Ions Motion Algorithm Optimized Hybrid Kernel LSSVM.

PubMed

Li, Ji; Hu, Guoqing; Zhou, Yonghong; Zou, Chong; Peng, Wei; Alam Sm, Jahangir

2016-10-14

A piezo-resistive pressure sensor is made of silicon, the nature of which is considerably influenced by ambient temperature. The effect of temperature should be eliminated during the working period in expectation of linear output. To deal with this issue, an approach consists of a hybrid kernel Least Squares Support Vector Machine (LSSVM) optimized by a chaotic ions motion algorithm presented. To achieve the learning and generalization for excellent performance, a hybrid kernel function, constructed by a local kernel as Radial Basis Function (RBF) kernel, and a global kernel as polynomial kernel is incorporated into the Least Squares Support Vector Machine. The chaotic ions motion algorithm is introduced to find the best hyper-parameters of the Least Squares Support Vector Machine. The temperature data from a calibration experiment is conducted to validate the proposed method. With attention on algorithm robustness and engineering applications, the compensation result shows the proposed scheme outperforms other compared methods on several performance measures as maximum absolute relative error, minimum absolute relative error mean and variance of the averaged value on fifty runs. Furthermore, the proposed temperature compensation approach lays a foundation for more extensive research.

ClimateSpark: An In-memory Distributed Computing Framework for Big Climate Data Analytics

NASA Astrophysics Data System (ADS)

Hu, F.; Yang, C. P.; Duffy, D.; Schnase, J. L.; Li, Z.

2016-12-01

Massive array-based climate data is being generated from global surveillance systems and model simulations. They are widely used to analyze the environment problems, such as climate changes, natural hazards, and public health. However, knowing the underlying information from these big climate datasets is challenging due to both data- and computing- intensive issues in data processing and analyzing. To tackle the challenges, this paper proposes ClimateSpark, an in-memory distributed computing framework to support big climate data processing. In ClimateSpark, the spatiotemporal index is developed to enable Apache Spark to treat the array-based climate data (e.g. netCDF4, HDF4) as native formats, which are stored in Hadoop Distributed File System (HDFS) without any preprocessing. Based on the index, the spatiotemporal query services are provided to retrieve dataset according to a defined geospatial and temporal bounding box. The data subsets will be read out, and a data partition strategy will be applied to equally split the queried data to each computing node, and store them in memory as climateRDDs for processing. By leveraging Spark SQL and User Defined Function (UDFs), the climate data analysis operations can be conducted by the intuitive SQL language. ClimateSpark is evaluated by two use cases using the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) climate reanalysis dataset. One use case is to conduct the spatiotemporal query and visualize the subset results in animation; the other one is to compare different climate model outputs using Taylor-diagram service. Experimental results show that ClimateSpark can significantly accelerate data query and processing, and enable the complex analysis services served in the SQL-style fashion.

SciDB versus Spark: A Preliminary Comparison Based on an Earth Science Use Case

NASA Astrophysics Data System (ADS)

Clune, T.; Kuo, K. S.; Doan, K.; Oloso, A.

2015-12-01

We compare two Big Data technologies, SciDB and Spark, for performance, usability, and extensibility, when applied to a representative Earth science use case. SciDB is a new-generation parallel distributed database management system (DBMS) based on the array data model that is capable of handling multidimensional arrays efficiently but requires lengthy data ingest prior to analysis, whereas Spark is a fast and general engine for large scale data processing that can immediately process raw data files and thereby avoid the ingest process. Once data have been ingested, SciDB is very efficient in database operations such as subsetting. Spark, on the other hand, provides greater flexibility by supporting a wide variety of high-level tools including DBMS's. For the performance aspect of this preliminary comparison, we configure Spark to operate directly on text or binary data files and thereby limit the need for additional tools. Arguably, a more appropriate comparison would involve exploring other configurations of Spark which exploit supported high-level tools, but that is beyond our current resources. To make the comparison as "fair" as possible, we export the arrays produced by SciDB into text files (or converting them to binary files) for the intake by Spark and thereby avoid any additional file processing penalties. The Earth science use case selected for this comparison is the identification and tracking of snowstorms in the NASA Modern Era Retrospective-analysis for Research and Applications (MERRA) reanalysis data. The identification portion of the use case is to flag all grid cells of the MERRA high-resolution hourly data that satisfies our criteria for snowstorm, whereas the tracking portion connects flagged cells adjacent in time and space to form a snowstorm episode. We will report the results of our comparisons at this presentation.

Research on offense and defense technology for iOS kernel security mechanism

NASA Astrophysics Data System (ADS)

Chu, Sijun; Wu, Hao

2018-04-01

iOS is a strong and widely used mobile device system. It's annual profits make up about 90% of the total profits of all mobile phone brands. Though it is famous for its security, there have been many attacks on the iOS operating system, such as the Trident apt attack in 2016. So it is important to research the iOS security mechanism and understand its weaknesses and put forward targeted protection and security check framework. By studying these attacks and previous jailbreak tools, we can see that an attacker could only run a ROP code and gain kernel read and write permissions based on the ROP after exploiting kernel and user layer vulnerabilities. However, the iOS operating system is still protected by the code signing mechanism, the sandbox mechanism, and the not-writable mechanism of the system's disk area. This is far from the steady, long-lasting control that attackers expect. Before iOS 9, breaking these security mechanisms was usually done by modifying the kernel's important data structures and security mechanism code logic. However, after iOS 9, the kernel integrity protection mechanism was added to the 64-bit operating system and none of the previous methods were adapted to the new versions of iOS [1]. But this does not mean that attackers can not break through. Therefore, based on the analysis of the vulnerability of KPP security mechanism, this paper implements two possible breakthrough methods for kernel security mechanism for iOS9 and iOS10. Meanwhile, we propose a defense method based on kernel integrity detection and sensitive API call detection to defense breakthrough method mentioned above. And we make experiments to prove that this method can prevent and detect attack attempts or invaders effectively and timely.

The effects of focusing power on TEA CO2 laser-induced gas breakdown and the consequent pulse shaping effects

NASA Astrophysics Data System (ADS)

Beheshtipour, Saleheh; Safari, Ebrahim; Majdabadi, Abbas; Silakhori, Kaveh

2018-02-01

Transversely Excited Atmospheric (TEA) CO2 laser pulses were used in order to generate an optical breakdown in a variety of mono- and polyatomic molecules using different focusing powers. The dependence of the spark kernel geometry and the transmitted pulse shapes on the focusing power as well as the pressure, molecular weight, and ionization energy of the gases was investigated in detail. Partial removal of the transmitted pulse tail in the 0.05-2.6 μs range together with shortened spikes in the 10-60 ns range has been observed by applying a 2.5 cm focal length lens for all the gases. At higher focal lengths, this effect is only incompletely observed for He gas. Spatial-temporal analyses of the laser beams and the relevant plasma plumes indicate that this behavior is due to the drop in the plasma density below the critical level, before the laser pulse tail is completed.

The NAS kernel benchmark program

NASA Technical Reports Server (NTRS)

Bailey, D. H.; Barton, J. T.

1985-01-01

A collection of benchmark test kernels that measure supercomputer performance has been developed for the use of the NAS (Numerical Aerodynamic Simulation) program at the NASA Ames Research Center. This benchmark program is described in detail and the specific ground rules are given for running the program as a performance test.

Development of a SPARK Training Dataset

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sayre, Amanda M.; Olson, Jarrod R.

2015-03-01

In its first five years, the National Nuclear Security Administration’s (NNSA) Next Generation Safeguards Initiative (NGSI) sponsored more than 400 undergraduate, graduate, and post-doctoral students in internships and research positions (Wyse 2012). In the past seven years, the NGSI program has, and continues to produce a large body of scientific, technical, and policy work in targeted core safeguards capabilities and human capital development activities. Not only does the NGSI program carry out activities across multiple disciplines, but also across all U.S. Department of Energy (DOE)/NNSA locations in the United States. However, products are not readily shared among disciplines and acrossmore » locations, nor are they archived in a comprehensive library. Rather, knowledge of NGSI-produced literature is localized to the researchers, clients, and internal laboratory/facility publication systems such as the Electronic Records and Information Capture Architecture (ERICA) at the Pacific Northwest National Laboratory (PNNL). There is also no incorporated way of analyzing existing NGSI literature to determine whether the larger NGSI program is achieving its core safeguards capabilities and activities. A complete library of NGSI literature could prove beneficial to a cohesive, sustainable, and more economical NGSI program. The Safeguards Platform for Automated Retrieval of Knowledge (SPARK) has been developed to be a knowledge storage, retrieval, and analysis capability to capture safeguards knowledge to exist beyond the lifespan of NGSI. During the development process, it was necessary to build a SPARK training dataset (a corpus of documents) for initial entry into the system and for demonstration purposes. We manipulated these data to gain new information about the breadth of NGSI publications, and they evaluated the science-policy interface at PNNL as a practical demonstration of SPARK’s intended analysis capability. The analysis demonstration sought to answer the question, “Who leads research and development at PNNL, scientists or policy researchers?” The analysis was inconclusive as to whether policy researchers or scientists are primary drivers for research at PNNL. However, the dataset development and analysis activity did demonstrate the utility and usability of the SPARK dataset. After the initiation of the NGSI program there is a clear increase in the number of publications of safeguards products. Employing the natural language analysis tool IN SPIRE™ showed the presence of vocation- and topic-specific vernacular within NGSI sub-topics. The methodology developed to define the scope of the dataset was useful in describing safeguards applications, but may be applicable for research on other topics beyond safeguards. The analysis emphasized the need for an expanded dataset to fully understand the scope of safeguards publications and research both nationally and internationally. As the SPARK dataset grows to include publications outside PNNL, topics crosscutting disciplines and DOE/NNSA locations should become more apparent. NGSI was established in 2008 to cultivate the next generation of safeguards professionals and support the development of core safeguards capabilities (NNSA 2012). Now a robust system to preserve and share institutional memory such as SPARK is needed to inspire and equip the next generation of safeguards experts, technologies, and policies.« less

Change Detection of Mobile LIDAR Data Using Cloud Computing

NASA Astrophysics Data System (ADS)

Liu, Kun; Boehm, Jan; Alis, Christian

2016-06-01

Change detection has long been a challenging problem although a lot of research has been conducted in different fields such as remote sensing and photogrammetry, computer vision, and robotics. In this paper, we blend voxel grid and Apache Spark together to propose an efficient method to address the problem in the context of big data. Voxel grid is a regular geometry representation consisting of the voxels with the same size, which fairly suites parallel computation. Apache Spark is a popular distributed parallel computing platform which allows fault tolerance and memory cache. These features can significantly enhance the performance of Apache Spark and results in an efficient and robust implementation. In our experiments, both synthetic and real point cloud data are employed to demonstrate the quality of our method.

Fractional quantum integral operator with general kernels and applications

NASA Astrophysics Data System (ADS)

Babakhani, Azizollah; Neamaty, Abdolali; Yadollahzadeh, Milad; Agahi, Hamzeh

In this paper, we first introduce the concept of fractional quantum integral with general kernels, which generalizes several types of fractional integrals known from the literature. Then we give more general versions of some integral inequalities for this operator, thus generalizing some previous results obtained by many researchers.2,8,25,29,30,36

Effect of Protein Molecular Weight Distribution on Kernel and Baking Characteristics and Intra-varietal Variation in Hard Spring Wheats

USDA-ARS?s Scientific Manuscript database

Specific wheat protein fractions are known to have distinct associations with wheat quality traits. Research was conducted on 10 hard spring wheat cultivars grown at two North Dakota locations to identify protein fractions that affected wheat kernel characteristics and breadmaking quality. SDS ext...

Visible and near-infrared instruments for detection and quantification of individual sprouted wheat kernels

USDA-ARS?s Scientific Manuscript database

Pre-harvest sprouting of wheat kernels within the grain head presents serious problems as it can greatly affect end use quality. Functional properties of wheat flour made from sprouted wheat result in poor dough and bread-making quality. This research examined the ability of two instruments to estim...

A WPS Based Architecture for Climate Data Analytic Services (CDAS) at NASA

NASA Astrophysics Data System (ADS)

Maxwell, T. P.; McInerney, M.; Duffy, D.; Carriere, L.; Potter, G. L.; Doutriaux, C.

2015-12-01

Faced with unprecedented growth in the Big Data domain of climate science, NASA has developed the Climate Data Analytic Services (CDAS) framework. This framework enables scientists to execute trusted and tested analysis operations in a high performance environment close to the massive data stores at NASA. The data is accessed in standard (NetCDF, HDF, etc.) formats in a POSIX file system and processed using trusted climate data analysis tools (ESMF, CDAT, NCO, etc.). The framework is structured as a set of interacting modules allowing maximal flexibility in deployment choices. The current set of module managers include: Staging Manager: Runs the computation locally on the WPS server or remotely using tools such as celery or SLURM. Compute Engine Manager: Runs the computation serially or distributed over nodes using a parallelization framework such as celery or spark. Decomposition Manger: Manages strategies for distributing the data over nodes. Data Manager: Handles the import of domain data from long term storage and manages the in-memory and disk-based caching architectures. Kernel manager: A kernel is an encapsulated computational unit which executes a processor's compute task. Each kernel is implemented in python exploiting existing analysis packages (e.g. CDAT) and is compatible with all CDAS compute engines and decompositions. CDAS services are accessed via a WPS API being developed in collaboration with the ESGF Compute Working Team to support server-side analytics for ESGF. The API can be executed using either direct web service calls, a python script or application, or a javascript-based web application. Client packages in python or javascript contain everything needed to make CDAS requests. The CDAS architecture brings together the tools, data storage, and high-performance computing required for timely analysis of large-scale data sets, where the data resides, to ultimately produce societal benefits. It is is currently deployed at NASA in support of the Collaborative REAnalysis Technical Environment (CREATE) project, which centralizes numerous global reanalysis datasets onto a single advanced data analytics platform. This service permits decision makers to investigate climate changes around the globe, inspect model trends, compare multiple reanalysis datasets, and variability.

Mars Spark Source Prototype Developed

NASA Technical Reports Server (NTRS)

Eichenberg, Dennis J.; Lindamood, Glenn R.; VanderWal, Randall L.; Weiland, Karen J.

2000-01-01

The Mars Spark Source Prototype (MSSP) hardware was developed as part of a proof of concept system for the detection of trace metals such as lead, cadmium, and arsenic in Martian dusts and soils. A spark discharge produces plasma from a soil sample, and detectors measure the optical emission from metals in the plasma to identify and quantify them. Trace metal measurements are vital in assessing whether or not the Martian environment will be toxic to human explorers. The current method of x-ray fluorescence can yield concentrations of major species only. Other instruments are incompatible with the volume, weight, and power constraints for a Mars mission. The new instrument will be developed primarily for use in the Martian environment, but it would be adaptable for terrestrial use in environmental monitoring. The NASA Glenn Research Center at Lewis Field initiated the development of the MSSP as part of Glenn's Director's Discretionary Fund project for the Spark Analysis Detection of Trace Metal Species in Martian Dusts and Soils. The objective of this project is to develop and demonstrate a compact, sensitive optical instrument for the detection of trace hazardous metals in Martian dusts and soils.

Weighted Feature Gaussian Kernel SVM for Emotion Recognition

PubMed Central

Jia, Qingxuan

2016-01-01

Emotion recognition with weighted feature based on facial expression is a challenging research topic and has attracted great attention in the past few years. This paper presents a novel method, utilizing subregion recognition rate to weight kernel function. First, we divide the facial expression image into some uniform subregions and calculate corresponding recognition rate and weight. Then, we get a weighted feature Gaussian kernel function and construct a classifier based on Support Vector Machine (SVM). At last, the experimental results suggest that the approach based on weighted feature Gaussian kernel function has good performance on the correct rate in emotion recognition. The experiments on the extended Cohn-Kanade (CK+) dataset show that our method has achieved encouraging recognition results compared to the state-of-the-art methods. PMID:27807443

[Research on the methods for multi-class kernel CSP-based feature extraction].

PubMed

Wang, Jinjia; Zhang, Lingzhi; Hu, Bei

2012-04-01

To relax the presumption of strictly linear patterns in the common spatial patterns (CSP), we studied the kernel CSP (KCSP). A new multi-class KCSP (MKCSP) approach was proposed in this paper, which combines the kernel approach with multi-class CSP technique. In this approach, we used kernel spatial patterns for each class against all others, and extracted signal components specific to one condition from EEG data sets of multiple conditions. Then we performed classification using the Logistic linear classifier. Brain computer interface (BCI) competition III_3a was used in the experiment. Through the experiment, it can be proved that this approach could decompose the raw EEG singles into spatial patterns extracted from multi-class of single trial EEG, and could obtain good classification results.

Effects of sample size on KERNEL home range estimates

USGS Publications Warehouse

Seaman, D.E.; Millspaugh, J.J.; Kernohan, Brian J.; Brundige, Gary C.; Raedeke, Kenneth J.; Gitzen, Robert A.

1999-01-01

Kernel methods for estimating home range are being used increasingly in wildlife research, but the effect of sample size on their accuracy is not known. We used computer simulations of 10-200 points/home range and compared accuracy of home range estimates produced by fixed and adaptive kernels with the reference (REF) and least-squares cross-validation (LSCV) methods for determining the amount of smoothing. Simulated home ranges varied from simple to complex shapes created by mixing bivariate normal distributions. We used the size of the 95% home range area and the relative mean squared error of the surface fit to assess the accuracy of the kernel home range estimates. For both measures, the bias and variance approached an asymptote at about 50 observations/home range. The fixed kernel with smoothing selected by LSCV provided the least-biased estimates of the 95% home range area. All kernel methods produced similar surface fit for most simulations, but the fixed kernel with LSCV had the lowest frequency and magnitude of very poor estimates. We reviewed 101 papers published in The Journal of Wildlife Management (JWM) between 1980 and 1997 that estimated animal home ranges. A minority of these papers used nonparametric utilization distribution (UD) estimators, and most did not adequately report sample sizes. We recommend that home range studies using kernel estimates use LSCV to determine the amount of smoothing, obtain a minimum of 30 observations per animal (but preferably a?Y50), and report sample sizes in published results.

Mapping and validation of major quantitative trait loci for kernel length in wild barley (Hordeum vulgare ssp. spontaneum).

PubMed

Zhou, Hong; Liu, Shihang; Liu, Yujiao; Liu, Yaxi; You, Jing; Deng, Mei; Ma, Jian; Chen, Guangdeng; Wei, Yuming; Liu, Chunji; Zheng, Youliang

2016-09-13

Kernel length is an important target trait in barley (Hordeum vulgare L.) breeding programs. However, the number of known quantitative trait loci (QTLs) controlling kernel length is limited. In the present study, we aimed to identify major QTLs for kernel length, as well as putative candidate genes that might influence kernel length in wild barley. A recombinant inbred line (RIL) population derived from the barley cultivar Baudin (H. vulgare ssp. vulgare) and the long-kernel wild barley genotype Awcs276 (H.vulgare ssp. spontaneum) was evaluated at one location over three years. A high-density genetic linkage map was constructed using 1,832 genome-wide diversity array technology (DArT) markers, spanning a total of 927.07 cM with an average interval of approximately 0.49 cM. Two major QTLs for kernel length, LEN-3H and LEN-4H, were detected across environments and further validated in a second RIL population derived from Fleet (H. vulgare ssp. vulgare) and Awcs276. In addition, a systematic search of public databases identified four candidate genes and four categories of proteins related to LEN-3H and LEN-4H. This study establishes a fundamental research platform for genomic studies and marker-assisted selection, since LEN-3H and LEN-4H could be used for accelerating progress in barley breeding programs that aim to improve kernel length.

Ignition of Combustible Dust Clouds by Strong Capacitive Electric Sparks of Short Discharge Times

NASA Astrophysics Data System (ADS)

Eckhoff, Rolf K.

2017-10-01

It has been known for more than half a century that the discharge times of capacitive electric sparks can influence the minimum ignition energies of dust clouds substantially. Experiments by various workers have shown that net electric-spark energies for igniting explosive dust clouds in air were reduced by a factor of the order of 100 when spark discharge times were increased from a few μs to 0.1-1 ms. Experiments have also shown that the disturbance of the dust cloud by the shock/blast wave emitted by "short" spark discharges is a likely reason for this. The disturbance increases with increasing spark energy. In this paper a hitherto unpublished comprehensive study of this problem is presented. The work was performed about 50 years ago, using sparks of comparatively high energies (strong sparks). Lycopodium was used as test dust. The experiments were conducted in a brass vessel of 1 L volume. A transient dust cloud was generated in the vessel by a blast of compressed air. Synchronization of appearance of dust cloud and spark discharge was obtained by breaking the spark gap down by the dust cloud itself. This may in fact also be one possible synchronization mechanism in accidental industrial dust explosions initiated by electrostatic sparks. The experimental results for various spark energies were expressed as the probability of ignition, based on 100 replicate experiments, as a function of the nominal dust concentration. All probabilities obtained were 0%

Fruit position within the canopy affects kernel lipid composition of hazelnuts.

PubMed

Pannico, Antonio; Cirillo, Chiara; Giaccone, Matteo; Scognamiglio, Pasquale; Romano, Raffaele; Caporaso, Nicola; Sacchi, Raffaele; Basile, Boris

2017-11-01

The aim of this research was to study the variability in kernel composition within the canopy of hazelnut trees. Kernel fresh and dry weight increased linearly with fruit height above the ground. Fat content decreased, while protein and ash content increased, from the bottom to the top layers of the canopy. The level of unsaturation of fatty acids decreased from the bottom to the top of the canopy. Thus, the kernels located in the bottom layers of the canopy appear to be more interesting from a nutritional point of view, but their lipids may be more exposed to oxidation. The content of different phytosterols increased progressively from bottom to top canopy layers. Most of these effects correlated with the pattern in light distribution inside the canopy. The results of this study indicate that fruit position within the canopy is an important factor in determining hazelnut kernel growth and composition. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Novel Laser Ignition Technique Using Dual-Pulse Pre-Ionization

NASA Astrophysics Data System (ADS)

Dumitrache, Ciprian

Recent advances in the development of compact high power laser sources and fiber optic delivery of giant pulses have generated a renewed interest in laser ignition. The non-intrusive nature of laser ignition gives it a set of unique characteristics over the well-established capacitive discharge devices (or spark plugs) that are currently used as ignition sources in engines. Overall, the use of laser ignition has been shown to have a positive impact on engine operation leading to a reduction in NOx emission, fuel saving and an increased operational envelope of current engines. Conventionally, laser ignition is achieved by tightly focusing a high-power q-switched laser pulse until the optical intensity at the focus is high enough to breakdown the gas molecules. This leads to the formation of a spark that serves as the ignition source in engines. However, there are certain disadvantages associated with this ignition method. This ionization approach is energetically inefficient as the medium is transparent to the laser radiation until the laser intensity is high enough to cause gas breakdown. As a consequence, very high energies are required for ignition (about an order of magnitude higher energy than capacitive plugs at stoichiometric conditions). Additionally, the fluid flow induced during the plasma recombination generates high vorticity leading to high rates of flame stretching. In this work, we are addressing some of the aforementioned disadvantages of laser ignition by developing a novel approach based on a dual-pulse pre-ionization scheme. The new technique works by decoupling the effect of the two ionization mechanisms governing plasma formation: multiphoton ionization (MPI) and electron avalanche ionization (EAI). An UV nanosecond pulse (lambda = 266 nm) is used to generate initial ionization through MPI. This is followed by an overlapped NIR nanosecond pulse (lambda = 1064 nm) that adds energy into the pre-ionized mixture into a controlled manner until the gas temperature is suitable for combustion (T=2000-3000 K). This technique is demonstrated by attempting ignition of various mixtures of propane-air and it is shown to have distinct advantages when compared to the classical approach: lower ignition energy for given stoichiometry than conventional laser ignition ( 20% lower), extension of the lean limit ( 15% leaner) and improvement in combustion efficiency. Moreover, it is demonstrated that careful alignment of the two pulses influences the fluid dynamics of the early flame kernel growth. This finding has a number of implications for practical uses as it demonstrates that the flame kernel dynamics can be tailored using various combinations of laser pulses and opens the door for implementing such a technique to applications such as: flame holding and flame stabilization in high speed flow combustors (such as ramjet and scramjet engines), reducing flame stretching in highly turbulent combustion devices and increasing combustion efficiency for stationary natural gas engines. As such, the work presented in this dissertation should be of interest to a broad audience including those interested in combustion research, engine operation, chemically reacting flows, plasma dynamics and laser diagnostics.

Optimization of the acceptance of prebiotic beverage made from cashew nut kernels and passion fruit juice.

PubMed

Rebouças, Marina Cabral; Rodrigues, Maria do Carmo Passos; Afonso, Marcos Rodrigues Amorim

2014-07-01

The aim of this research was to develop a prebiotic beverage from a hydrosoluble extract of broken cashew nut kernels and passion fruit juice using response surface methodology in order to optimize acceptance of its sensory attributes. A 2(2) central composite rotatable design was used, which produced 9 formulations, which were then evaluated using different concentrations of hydrosoluble cashew nut kernel, passion fruit juice, oligofructose, and 3% sugar. The use of response surface methodology to interpret the sensory data made it possible to obtain a formulation with satisfactory acceptance which met the criteria of bifidogenic action and use of hydrosoluble cashew nut kernels by using 14% oligofructose and 33% passion fruit juice. As a result of this study, it was possible to obtain a new functional prebiotic product, which combined the nutritional and functional properties of cashew nut kernels and oligofructose with the sensory properties of passion fruit juice in a beverage with satisfactory sensory acceptance. This new product emerges as a new alternative for the industrial processing of broken cashew nut kernels, which have very low market value, enabling this sector to increase its profits. © 2014 Institute of Food Technologists®

Validity and reliability of a structured interview for early detection and risk assessment of parenting and developmental problems in young children: a cross-sectional study

PubMed Central

2012-01-01

Background Preventive child health care is well suited for the early detection of parenting and developmental problems. However, as far as the younger age group is concerned, there are no validated early detection instruments which cover both the child and its environment. Therefore, we have developed a broad-scope structured interview which assesses parents’ concerns and their need for support, using both the parental perspective and the experience of the child health care nurse: the Structured Problem Analysis of Raising Kids (SPARK). This study reports the psychometric characteristics of the SPARK. Method A cross-sectional study of 2012 18-month-old children, living in Zeeland, a province of the Netherlands. Inter-rater reliability was assessed in 67 children. Convergent validity was assessed by comparing SPARK-domains with domains in self-report questionnaires on child development and parenting stress. Discriminative validity was assessed by comparing different outcomes of the SPARK between groups with different levels of socio-economic status and by performing an extreme-groups comparison. The user experience of both parents and nurses was assessed with the aid of an online survey. Results The response rate was 92.1% for the SPARK. Self-report questionnaires were returned in the case of 66.9% of the remaining 1721 children. There was selective non-reporting: 33.1% of the questionnaires were not returned, covering 65.2% of the children with a high-risk label according to the SPARK (p < 0.001). Inter-rater reliability was good to excellent with intraclass correlations between 0.85 and 1.0 for physical topics; between 0.61 and 0.8 for social-emotional topics and 0.92 for the overall risk assessment. Convergent validity was unexpectedly low (all correlations ≤0.3) although the pattern was as expected. Discriminative validity was good. Users were satisfied with the SPARK and identified some topics for improvement. Conclusion The SPARK discriminates between children with a high, increased and low risk of parenting and developmental problems. It does so in a reliable way, but more research is needed on aspects of validity and in other populations. PMID:22697218

A robust, high-throughput method for computing maize ear, cob, and kernel attributes automatically from images.

PubMed

Miller, Nathan D; Haase, Nicholas J; Lee, Jonghyun; Kaeppler, Shawn M; de Leon, Natalia; Spalding, Edgar P

2017-01-01

Grain yield of the maize plant depends on the sizes, shapes, and numbers of ears and the kernels they bear. An automated pipeline that can measure these components of yield from easily-obtained digital images is needed to advance our understanding of this globally important crop. Here we present three custom algorithms designed to compute such yield components automatically from digital images acquired by a low-cost platform. One algorithm determines the average space each kernel occupies along the cob axis using a sliding-window Fourier transform analysis of image intensity features. A second counts individual kernels removed from ears, including those in clusters. A third measures each kernel's major and minor axis after a Bayesian analysis of contour points identifies the kernel tip. Dimensionless ear and kernel shape traits that may interrelate yield components are measured by principal components analysis of contour point sets. Increased objectivity and speed compared to typical manual methods are achieved without loss of accuracy as evidenced by high correlations with ground truth measurements and simulated data. Millimeter-scale differences among ear, cob, and kernel traits that ranged more than 2.5-fold across a diverse group of inbred maize lines were resolved. This system for measuring maize ear, cob, and kernel attributes is being used by multiple research groups as an automated Web service running on community high-throughput computing and distributed data storage infrastructure. Users may create their own workflow using the source code that is staged for download on a public repository. © 2016 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.

Apparatus and method for tuned unsteady flow purging of high pulse rate spark gaps

DOEpatents

Thayer, III, William J.

1990-01-01

A spark gap switch apparatus is disclosed which is capable of operating at a high pulse rate which comprises an insulated housing; a pair of spaced apart electrodes each having one end thereof within a first bore formed in the housing and defining a spark gap therebetween; a pressure wave reflector in the first bore in the housing and spaced from the spark gap and capable of admitting purge flow; and a second enlarged bore contiguous with the first bore and spaced from the opposite side of the spark gap; whereby pressure waves generated during discharge of a spark across the spark gap will reflect off the wave reflector and back from the enlarged bore to the spark gap to clear from the spark gap hot gases residues generated during the discharge and simultaneously restore the gas density and pressure in the spark gap to its initial value.

Measurements of some parameters of thermal sparks with respect to their ability to ignite aviation fuel/air mixtures

NASA Technical Reports Server (NTRS)

Haigh, S. J.; Hardwick, C. J.; Baldwin, R. E.

1991-01-01

A method used to generate thermal sparks for experimental purposes and methods by which parameters of the sparks, such as speed, size, and temperature, were measured are described. Values are given of the range of such parameters within these spark showers. Titanium sparks were used almost exclusively, since it is particles of this metal which are found to be ejected during simulation tests to carbon fiber composite (CFC) joints. Tests were then carried out in which titanium sparks and spark showers were injected into JP4/(AVTAG F40) mixtures with air. Single large sparks and dense showers of small sparks were found to be capable of causing ignition. Tests were then repeated using ethylene/air mixtures, which were found to be more easily ignited by thermal sparks than the JP4/ air mixtures.

Kernel and Traditional Equipercentile Equating with Degrees of Presmoothing. Research Report. ETS RR-07-15

ERIC Educational Resources Information Center

Moses, Tim; Holland, Paul

2007-01-01

The purpose of this study was to empirically evaluate the impact of loglinear presmoothing accuracy on equating bias and variability across chained and post-stratification equating methods, kernel and percentile-rank continuization methods, and sample sizes. The results of evaluating presmoothing on equating accuracy generally agreed with those of…

Effects of Fuel Composition on EGR Dilution Tolerance in Spark Ignited Engines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Szybist, James P

2016-01-01

Fuel-specific differences in exhaust gas recirculation (EGR) dilution tolerance are studied in a modern, direct-injection single-cylinder research engine. A total of 6 model fuel blends are examined at a constant research octane number (RON) of 95 using n-heptane, iso-octane, toluene, and ethanol. Laminar flame speeds for these mixtures, which were calculated two different methods (an energy fraction mixing rule and a detailed kinetic simulation), spanned a range of about 6 cm/s. A constant fueling nominal load of 350 kPa IMEPg at 2000 rpm was operated with varying CA50 from 8-20 CAD aTDCf, and with EGR increasing until a COV ofmore » IMEP of 5% is reached. The results illustrate that flame speed affects EGR dilution tolerance; fuels with increased flame speeds increase EGR tolerance. Specifically, flame speed correlates most closely to the initial flame kernel growth, measured as the time of ignition to 5% mass fraction burned. The effect of the latent heat of vaporization on the flame speed is taken into account for the ethanol-containing fuels. At a 30 vol% blend level, the increased enthalpy of vaporization of ethanol compared to conventional hydrocarbons can decrease the temperature at the time of ignition by a maximum of 15 C, which can account for up to a 3.5 cm/s decrease in flame speed. The ethanol-containing fuels, however, still exhibit a flame speed advantage, and a dilution tolerance advantage over the slower flame-speed fuels. The fuel-specific differences in dilution tolerance are significant at the condition examined, allowing for a 50% relative increase in EGR (4% absolute difference in EGR) at a constant COV of IMEP of 3%.« less

Monte-Carlo computation of turbulent premixed methane/air ignition

NASA Astrophysics Data System (ADS)

Carmen, Christina Lieselotte

The present work describes the results obtained by a time dependent numerical technique that simulates the early flame development of a spark-ignited premixed, lean, gaseous methane/air mixture with the unsteady spherical flame propagating in homogeneous and isotropic turbulence. The algorithm described is based upon a sub-model developed by an international automobile research and manufacturing corporation in order to analyze turbulence conditions within internal combustion engines. Several developments and modifications to the original algorithm have been implemented including a revised chemical reaction scheme and the evaluation and calculation of various turbulent flame properties. Solution of the complete set of Navier-Stokes governing equations for a turbulent reactive flow is avoided by reducing the equations to a single transport equation. The transport equation is derived from the Navier-Stokes equations for a joint probability density function, thus requiring no closure assumptions for the Reynolds stresses. A Monte-Carlo method is also utilized to simulate phenomena represented by the probability density function transport equation by use of the method of fractional steps. Gaussian distributions of fluctuating velocity and fuel concentration are prescribed. Attention is focused on the evaluation of the three primary parameters that influence the initial flame kernel growth-the ignition system characteristics, the mixture composition, and the nature of the flow field. Efforts are concentrated on the effects of moderate to intense turbulence on flames within the distributed reaction zone. Results are presented for lean conditions with the fuel equivalence ratio varying from 0.6 to 0.9. The present computational results, including flame regime analysis and the calculation of various flame speeds, provide excellent agreement with results obtained by other experimental and numerical researchers.

Evaluation of Biosynthesis, Accumulation and Antioxidant Activityof Vitamin E in Sweet Corn (Zea mays L.) during Kernel Development

PubMed Central

Xie, Lihua; Yu, Yongtao; Mao, Jihua; Liu, Haiying; Hu, Jian Guang; Li, Tong; Guo, Xinbo; Liu, Rui Hai

2017-01-01

Sweet corn kernels were used in this research to study the dynamics of vitamin E, by evaluatingthe expression levels of genes involved in vitamin E synthesis, the accumulation of vitamin E, and the antioxidant activity during the different stage of kernel development. Results showed that expression levels of ZmHPT and ZmTC genes increased, whereas ZmTMT gene dramatically decreased during kernel development. The contents of all the types of vitamin E in sweet corn had a significant upward increase during kernel development, and reached the highest level at 30 days after pollination (DAP). Amongst the eight isomers of vitamin E, the content of γ-tocotrienol was the highest, and increased by 14.9 folds, followed by α-tocopherolwith an increase of 22 folds, and thecontents of isomers γ-tocopherol, α-tocotrienol, δ-tocopherol,δ-tocotrienol, and β-tocopherol were also followed during kernel development. The antioxidant activity of sweet corn during kernel development was increased, and was up to 101.8 ± 22.3 μmol of α-tocopherol equivlent/100 g in fresh weight (FW) at 30 DAP. There was a positive correlation between vitamin E contents and antioxidant activity in sweet corn during the kernel development, and a negative correlation between the expressions of ZmTMT gene and vitamin E contents. These results revealed the relations amongst the content of vitamin E isomers and the gene expression, vitamin E accumulation, and antioxidant activity. The study can provide a harvesting strategy for vitamin E bio-fortification in sweet corn. PMID:29261149

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

NASA Astrophysics Data System (ADS)

Tehrany, Mahyat Shafapour; Pradhan, Biswajeet; Jebur, Mustafa Neamah

2014-05-01

Flood is one of the most devastating natural disasters that occur frequently in Terengganu, Malaysia. Recently, ensemble based techniques are getting extremely popular in flood modeling. In this paper, weights-of-evidence (WoE) model was utilized first, to assess the impact of classes of each conditioning factor on flooding through bivariate statistical analysis (BSA). Then, these factors were reclassified using the acquired weights and entered into the support vector machine (SVM) model to evaluate the correlation between flood occurrence and each conditioning factor. Through this integration, the weak point of WoE can be solved and the performance of the SVM will be enhanced. The spatial database included flood inventory, slope, stream power index (SPI), topographic wetness index (TWI), altitude, curvature, distance from the river, geology, rainfall, land use/cover (LULC), and soil type. Four kernel types of SVM (linear kernel (LN), polynomial kernel (PL), radial basis function kernel (RBF), and sigmoid kernel (SIG)) were used to investigate the performance of each kernel type. The efficiency of the new ensemble WoE and SVM method was tested using area under curve (AUC) which measured the prediction and success rates. The validation results proved the strength and efficiency of the ensemble method over the individual methods. The best results were obtained from RBF kernel when compared with the other kernel types. Success rate and prediction rate for ensemble WoE and RBF-SVM method were 96.48% and 95.67% respectively. The proposed ensemble flood susceptibility mapping method could assist researchers and local governments in flood mitigation strategies.

Evaluation of Biosynthesis, Accumulation and Antioxidant Activityof Vitamin E in Sweet Corn (Zea mays L.) during Kernel Development.

PubMed

Xie, Lihua; Yu, Yongtao; Mao, Jihua; Liu, Haiying; Hu, Jian Guang; Li, Tong; Guo, Xinbo; Liu, Rui Hai

2017-12-20

Sweet corn kernels were used in this research to study the dynamics of vitamin E, by evaluatingthe expression levels of genes involved in vitamin E synthesis, the accumulation of vitamin E, and the antioxidant activity during the different stage of kernel development. Results showed that expression levels of Zm HPT and Zm TC genes increased, whereas Zm TMT gene dramatically decreased during kernel development. The contents of all the types of vitamin E in sweet corn had a significant upward increase during kernel development, and reached the highest level at 30 days after pollination (DAP). Amongst the eight isomers of vitamin E, the content of γ-tocotrienol was the highest, and increased by 14.9 folds, followed by α-tocopherolwith an increase of 22 folds, and thecontents of isomers γ-tocopherol, α-tocotrienol, δ-tocopherol,δ-tocotrienol, and β-tocopherol were also followed during kernel development. The antioxidant activity of sweet corn during kernel development was increased, and was up to 101.8 ± 22.3 μmol of α-tocopherol equivlent/100 g in fresh weight (FW) at 30 DAP. There was a positive correlation between vitamin E contents and antioxidant activity in sweet corn during the kernel development, and a negative correlation between the expressions of Zm TMT gene and vitamin E contents. These results revealed the relations amongst the content of vitamin E isomers and the gene expression, vitamin E accumulation, and antioxidant activity. The study can provide a harvesting strategy for vitamin E bio-fortification in sweet corn.

Optical diagnostics integrated with laser spark delivery system

DOEpatents

Yalin, Azer [Fort Collins, CO; Willson, Bryan [Fort Collins, CO; Defoort, Morgan [Fort Collins, CO; Joshi, Sachin [Fort Collins, CO; Reynolds, Adam [Fort Collins, CO

2008-09-02

A spark delivery system for generating a spark using a laser beam is provided, and includes a laser light source and a laser delivery assembly. The laser delivery assembly includes a hollow fiber and a launch assembly comprising launch focusing optics to input the laser beam in the hollow fiber. The laser delivery assembly further includes exit focusing optics that demagnify an exit beam of laser light from the hollow fiber, thereby increasing the intensity of the laser beam and creating a spark. Other embodiments use a fiber laser to generate a spark. Embodiments of the present invention may be used to create a spark in an engine. Yet other embodiments include collecting light from the spark or a flame resulting from the spark and conveying the light for diagnostics. Methods of using the spark delivery systems and diagnostic systems are provided.

Fiber laser coupled optical spark delivery system

DOEpatents

Yalin, Azer [Fort Collins, CO; Willson, Bryan [Fort Collins, CO; Defoort, Morgan [Fort Collins, CO; Joshi, Sachin [Fort Collins, CO; Reynolds, Adam [Fort Collins, CO

2008-03-04

A spark delivery system for generating a spark using a laser beam is provided, and includes a laser light source and a laser delivery assembly. The laser delivery assembly includes a hollow fiber and a launch assembly comprising launch focusing optics to input the laser beam in the hollow fiber. The laser delivery assembly further includes exit focusing optics that demagnify an exit beam of laser light from the hollow fiber, thereby increasing the intensity of the laser beam and creating a spark. Other embodiments use a fiber laser to generate a spark. Embodiments of the present invention may be used to create a spark in an engine. Yet other embodiments include collecting light from the spark or a flame resulting from the spark and conveying the light for diagnostics. Methods of using the spark delivery systems and diagnostic systems are provided.

DECISION-MAKING SPARK CHAMBERS,

DTIC Science & Technology

of scattering of a particle and coplanarity of two particles. Decision - making spark chambers are used to trigger an optical spark chamber of two...the position of a spark and the separation of two sparks. Many other kinds of spatial decisions can be made with these devices such as the recognition

On the thermodynamic properties of thermal plasma in the flame kernel of hydrocarbon/air premixed gases

NASA Astrophysics Data System (ADS)

Askari, Omid; Beretta, Gian Paolo; Eisazadeh-Far, Kian; Metghalchi, Hameed

2016-07-01

Thermodynamic properties of hydrocarbon/air plasma mixtures at ultra-high temperatures must be precisely calculated due to important influence on the flame kernel formation and propagation in combusting flows and spark discharge applications. A new algorithm based on the complete chemical equilibrium assumption is developed to calculate the ultra-high temperature plasma composition and thermodynamic properties, including enthalpy, entropy, Gibbs free energy, specific heat at constant pressure, specific heat ratio, speed of sound, mean molar mass, and degree of ionization. The method is applied to compute the thermodynamic properties of H2/air and CH4/air plasma mixtures for different temperatures (1000-100 000 K), different pressures (10-6-100 atm), and different fuel/air equivalence ratios within flammability limit. In calculating the individual thermodynamic properties of the atomic species needed to compute the complete equilibrium composition, the Debye-Huckel cutoff criterion has been used for terminating the series expression of the electronic partition function so as to capture the reduction of the ionization potential due to pressure and the intense connection between the electronic partition function and the thermodynamic properties of the atomic species and the number of energy levels taken into account. Partition functions have been calculated using tabulated data for available atomic energy levels. The Rydberg and Ritz extrapolation and interpolation laws have been used for energy levels which are not observed. The calculated plasma properties are then presented as functions of temperature, pressure and equivalence ratio, in terms of a new set of thermodynamically self-consistent correlations that are shown to provide very accurate fits suitable for efficient use in CFD simulations. Comparisons with existing data for air plasma show excellent agreement.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heroux, Michael Allen; Marker, Bryan

This report summarizes the progress made as part of a one year lab-directed research and development (LDRD) project to fund the research efforts of Bryan Marker at the University of Texas at Austin. The goal of the project was to develop new techniques for automatically tuning the performance of dense linear algebra kernels. These kernels often represent the majority of computational time in an application. The primary outcome from this work is a demonstration of the value of model driven engineering as an approach to accurately predict and study performance trade-offs for dense linear algebra computations.

Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction.

PubMed

Liu, Ruiming; Liu, Erqi; Yang, Jie; Zeng, Yong; Wang, Fanglin; Cao, Yuan

2007-11-01

Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction

NASA Astrophysics Data System (ADS)

Liu, Ruiming; Liu, Erqi; Yang, Jie; Zeng, Yong; Wang, Fanglin; Cao, Yuan

2007-11-01

Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

Geographically Weighted Regression Model with Kernel Bisquare and Tricube Weighted Function on Poverty Percentage Data in Central Java Province

NASA Astrophysics Data System (ADS)

Nugroho, N. F. T. A.; Slamet, I.

2018-05-01

Poverty is a socio-economic condition of a person or group of people who can not fulfil their basic need to maintain and develop a dignified life. This problem still cannot be solved completely in Central Java Province. Currently, the percentage of poverty in Central Java is 13.32% which is higher than the national poverty rate which is 11.13%. In this research, data of percentage of poor people in Central Java Province has been analyzed through geographically weighted regression (GWR). The aim of this research is therefore to model poverty percentage data in Central Java Province using GWR with weighted function of kernel bisquare, and tricube. As the results, we obtained GWR model with bisquare and tricube kernel weighted function on poverty percentage data in Central Java province. From the GWR model, there are three categories of region which are influenced by different of significance factors.

An overview of NASA research on positive displacement general-aviation engines

NASA Technical Reports Server (NTRS)

Kempke, E. E., Jr.

1980-01-01

The research and technology program related to improved and advanced general aviation engines is described. Current research is directed at the near-term improvement of conventional air-cooled spark-ignition piston engines and at future alternative engine systems based on all-new spark-ignition piston engines, lightweight diesels, and rotary combustion engines that show potential for meeting program goals in the midterm and long-term future. The conventional piston engine activities involve efforts on applying existing technology to improve fuel economy, investigation of key processes to permit leaner operation and reduce drag, and the development of cost effective technology to permit flight at high-altitudes where fuel economy and safety are improved. The advanced engine concepts activities include engine conceptual design studies and enabling technology efforts on the critical or key technology items.

Notes on a storage manager for the Clouds kernel

NASA Technical Reports Server (NTRS)

Pitts, David V.; Spafford, Eugene H.

1986-01-01

The Clouds project is research directed towards producing a reliable distributed computing system. The initial goal is to produce a kernel which provides a reliable environment with which a distributed operating system can be built. The Clouds kernal consists of a set of replicated subkernels, each of which runs on a machine in the Clouds system. Each subkernel is responsible for the management of resources on its machine; the subkernal components communicate to provide the cooperation necessary to meld the various machines into one kernel. The implementation of a kernel-level storage manager that supports reliability is documented. The storage manager is a part of each subkernel and maintains the secondary storage residing at each machine in the distributed system. In addition to providing the usual data transfer services, the storage manager ensures that data being stored survives machine and system crashes, and that the secondary storage of a failed machine is recovered (made consistent) automatically when the machine is restarted. Since the storage manager is part of the Clouds kernel, efficiency of operation is also a concern.

The Dispersal and Persistence of Invasive Marine Species

NASA Astrophysics Data System (ADS)

Glick, E. R.; Pringle, J.

2007-12-01

The spread of invasive marine species is a continuing problem throughout the world, though not entirely understood. Why do some species invade more easily than the rest? How are the range limits of these species set? Recent research (Byers & Pringle 2006, Pringle & Wares 2007) has produced retention criteria that determine whether a coastal species with a benthic adult stage and planktonic larvae can be retained within its range and invade in the direction opposite that of the mean current experienced by the larvae (i.e. upstream). These results however, are only accurate for Gaussian dispersal kernels. For kernels whose kurtosis differs from a Gaussian's, the retention criteria becomes increasingly inaccurate as the mean current increases. Using recent results of Lutscher (2006), we find an improved retention criterion which is much more accurate for non- Gaussian dispersal kernels. The importance of considering non-Gaussian kernels is illustrated for a number of commonly used dispersal kernels, and the relevance of these calculations is illustrated by considering the northward limit of invasion of Hemigrapsus sanguineus, an important invader in the Gulf of Maine.

Researches on Preliminary Chemical Reactions in Spark-Ignition Engines

NASA Technical Reports Server (NTRS)

Muehlner, E.

1943-01-01

Chemical reactions can demonstrably occur in a fuel-air mixture compressed in the working cylinder of an Otto-cycle (spark ignition) internal-combustion engine even before the charge is ignited by the flame proceeding from the sparking plug. These are the so-called "prelinminary reactions" ("pre-flame" combustion or oxidation), and an exact knowledge of their characteristic development is of great importance for a correct appreciation of the phenomena of engine-knock (detonation), and consequently for its avoidance. Such reactions can be studied either in a working engine cylinder or in a combustion bomb. The first method necessitates a complicated experimental technique, while the second has the disadvantage of enabling only a single reaction to be studied at one time. Consequently, a new series of experiments was inaugurated, conducted in a motored (externally-driven) experimental engine of mixture-compression type, without ignition, the resulting preliminary reactions being detectable and measurable thermometrically.

EOS: A project to investigate the design and construction of real-time distributed embedded operating systems

NASA Technical Reports Server (NTRS)

Campbell, R. H.; Essick, R. B.; Grass, J.; Johnston, G.; Kenny, K.; Russo, V.

1986-01-01

The EOS project is investigating the design and construction of a family of real-time distributed embedded operating systems for reliable, distributed aerospace applications. Using the real-time programming techniques developed in co-operation with NASA in earlier research, the project staff is building a kernel for a multiple processor networked system. The first six months of the grant included a study of scheduling in an object-oriented system, the design philosophy of the kernel, and the architectural overview of the operating system. In this report, the operating system and kernel concepts are described. An environment for the experiments has been built and several of the key concepts of the system have been prototyped. The kernel and operating system is intended to support future experimental studies in multiprocessing, load-balancing, routing, software fault-tolerance, distributed data base design, and real-time processing.

High-voltage spark atomic emission detector for gas chromatography

NASA Technical Reports Server (NTRS)

Calkin, C. L.; Koeplin, S. M.; Crouch, S. R.

1982-01-01

A dc-powered, double-gap, miniature nanosecond spark source for emission spectrochemical analysis of gas chromatographic effluents is described. The spark is formed between two thoriated tungsten electrodes by the discharge of a coaxial capacitor. The spark detector is coupled to the gas chromatograph by a heated transfer line. The gas chromatographic effluent is introduced into the heated spark chamber where atomization and excitation of the effluent occurs upon breakdown of the analytical gap. A microcomputer-controlled data acquisition system allows the implementation of time-resolution techniques to distinguish between the analyte emission and the background continuum produced by the spark discharge. Multiple sparks are computer averaged to improve the signal-to-noise ratio. The application of the spark detector for element-selective detection of metals and nonmetals is reported.

Ethics in Field-Based Research: Contractual and Relational Responsibilities.

ERIC Educational Resources Information Center

Brickhouse, Nancy W.

The desire to abolish the gap between research theory and classroom practice has sparked an increasing interest in field-based research among science educators. Although most researchers are aware of the standard meanings of informed consent and confidentiality, and there are some codes of ethical principles published by such groups as the…

Spark-Timing Control Based on Correlation of Maximum-Economy Spark Timing, Flame-front Travel, and Cylinder-Pressure Rise

NASA Technical Reports Server (NTRS)

Cook, Harvey A; Heinicke, Orville H; Haynie, William H

1947-01-01

An investigation was conducted on a full-scale air-cooled cylinder in order to establish an effective means of maintaining maximum-economy spark timing with varying engine operating conditions. Variable fuel-air-ratio runs were conducted in which relations were determined between the spark travel, and cylinder-pressure rise. An instrument for controlling spark timing was developed that automatically maintained maximum-economy spark timing with varying engine operating conditions. The instrument also indicated the occurrence of preignition.

Effect of dark, hard, and vitreous kernel content on protein molecular weight distribution and on milling and breadmaking quality characteristics for hard spring wheat samples from diverse growing regions

USDA-ARS?s Scientific Manuscript database

Kernel vitreousness is an important grading characteristic for segregation of sub-classes of hard red spring (HRS) wheat in the U.S. This research investigated the protein molecular weight distribution (MWD), and flour and baking quality characteristics of different HRS wheat market sub-classes. T...

An Evaluation of the Kernel Equating Method: A Special Study with Pseudotests Constructed from Real Test Data. Research Report. ETS RR-06-02

ERIC Educational Resources Information Center

von Davier, Alina A.; Holland, Paul W.; Livingston, Samuel A.; Casabianca, Jodi; Grant, Mary C.; Martin, Kathleen

2006-01-01

This study examines how closely the kernel equating (KE) method (von Davier, Holland, & Thayer, 2004a) approximates the results of other observed-score equating methods--equipercentile and linear equatings. The study used pseudotests constructed of item responses from a real test to simulate three equating designs: an equivalent groups (EG)…

Sparking Connections: Toward Better Linkages Between Research and Human Health Policy — An Example with Multiwalled Carbon Nanotubes

EPA Science Inventory

Risk assessment and subsequent risk management of environmental contaminants can benefit from early collaboration among researchers, risk assessors and risk managers. The benefits of collaboration in research planning are particularly evident in light of 1) increasing calls to ex...

The effect of CT technical factors on quantification of lung fissure integrity

NASA Astrophysics Data System (ADS)

Chong, D.; Brown, M. S.; Ochs, R.; Abtin, F.; Brown, M.; Ordookhani, A.; Shaw, G.; Kim, H. J.; Gjertson, D.; Goldin, J. G.

2009-02-01

A new emphysema treatment uses endobronchial valves to perform lobar volume reduction. The degree of fissure completeness may predict treatment efficacy. This study investigated the behavior of a semiautomated algorithm for quantifying lung fissure integrity in CT with respect to reconstruction kernel and dose. Raw CT data was obtained for six asymptomatic patients from a high-risk population for lung cancer. The patients were scanned on either a Siemens Sensation 16 or 64, using a low-dose protocol of 120 kVp, 25 mAs. Images were reconstructed using kernels ranging from smooth to sharp (B10f, B30f, B50f, B70f). Research software was used to simulate an even lower-dose acquisition of 15 mAs, and images were generated at the same kernels resulting in 8 series per patient. The left major fissure was manually contoured axially at regular intervals, yielding 37 contours across all patients. These contours were read into an image analysis and pattern classification system which computed a Fissure Integrity Score (FIS) for each kernel and dose. FIS values were analyzed using a mixed-effects model with kernel and dose as fixed effects and patient as random effect to test for difference due to kernel and dose. Analysis revealed no difference in FIS between the smooth kernels (B10f, B30f) nor between sharp kernels (B50f, B70f), but there was a significant difference between the sharp and smooth groups (p = 0.020). There was no significant difference in FIS between the two low-dose reconstructions (p = 0.882). Using a cutoff of 90%, the number of incomplete fissures increased from 5 to 10 when the imaging protocol changed from B50f to B30f. Reconstruction kernel has a significant effect on quantification of fissure integrity in CT. This has potential implications when selecting patients for endobronchial valve therapy.

Rotating arc spark plug

DOEpatents

Whealton, John H.; Tsai, Chin-Chi

2003-05-27

A spark plug device includes a structure for modification of an arc, the modification including arc rotation. The spark plug can be used in a combustion engine to reduce emissions and/or improve fuel economy. A method for operating a spark plug and a combustion engine having the spark plug device includes the step of modifying an arc, the modifying including rotating the arc.

Spark Ignition Characteristics of a L02/LCH4 Engine at Altitude Conditions

NASA Technical Reports Server (NTRS)

Kleinhenz, Julie; Sarmiento, Charles; Marshall, William

2012-01-01

The use of non-toxic propellants in future exploration vehicles would enable safer, more cost effective mission scenarios. One promising "green" alternative to existing hypergols is liquid methane/liquid oxygen. To demonstrate performance and prove feasibility of this propellant combination, a 100lbf LO2/LCH4 engine was developed and tested under the NASA Propulsion and Cryogenic Advanced Development (PCAD) project. Since high ignition energy is a perceived drawback of this propellant combination, a test program was performed to explore ignition performance and reliability versus delivered spark energy. The sensitivity of ignition to spark timing and repetition rate was also examined. Three different exciter units were used with the engine s augmented (torch) igniter. Propellant temperature was also varied within the liquid range. Captured waveforms indicated spark behavior in hot fire conditions was inconsistent compared to the well-behaved dry sparks (in quiescent, room air). The escalating pressure and flow environment increases spark impedance and may at some point compromise an exciter s ability to deliver a spark. Reduced spark energies of these sparks result in more erratic ignitions and adversely affect ignition probability. The timing of the sparks relative to the pressure/flow conditions also impacted the probability of ignition. Sparks occurring early in the flow could trigger ignition with energies as low as 1-6mJ, though multiple, similarly timed sparks of 55-75mJ were required for reliable ignition. An optimum time interval for spark application and ignition coincided with propellant introduction to the igniter and engine. Shifts of ignition timing were manifested by changes in the characteristics of the resulting ignition.

Spark Ignition Characteristics of a LO2/LCH4 Engine at Altitude Conditions

NASA Technical Reports Server (NTRS)

Kleinhenz, Julie; Sarmiento, Charles; Marshall, William

2012-01-01

The use of non-toxic propellants in future exploration vehicles would enable safer, more cost effective mission scenarios. One promising "green" alternative to existing hypergols is liquid methane/liquid oxygen. To demonstrate performance and prove feasibility of this propellant combination, a 100lbf LO2/LCH4 engine was developed and tested under the NASA Propulsion and Cryogenic Advanced Development (PCAD) project. Since high ignition energy is a perceived drawback of this propellant combination, a test program was performed to explore ignition performance and reliability versus delivered spark energy. The sensitivity of ignition to spark timing and repetition rate was also examined. Three different exciter units were used with the engine's augmented (torch) igniter. Propellant temperature was also varied within the liquid range. Captured waveforms indicated spark behavior in hot fire conditions was inconsistent compared to the well-behaved dry sparks (in quiescent, room air). The escalating pressure and flow environment increases spark impedance and may at some point compromise an exciter.s ability to deliver a spark. Reduced spark energies of these sparks result in more erratic ignitions and adversely affect ignition probability. The timing of the sparks relative to the pressure/flow conditions also impacted the probability of ignition. Sparks occurring early in the flow could trigger ignition with energies as low as 1-6mJ, though multiple, similarly timed sparks of 55-75mJ were required for reliable ignition. An optimum time interval for spark application and ignition coincided with propellant introduction to the igniter and engine. Shifts of ignition timing were manifested by changes in the characteristics of the resulting ignition.

Properties of Ca2+ sparks evoked by action potentials in mouse ventricular myocytes

PubMed Central

Bridge, John H B; Ershler, Philip R; Cannell, Mark B

1999-01-01

Calcium sparks were examined in enzymatically dissociated mouse cardiac ventricular cells using the calcium indicator fluo-3 and confocal microscopy. The properties of the mouse cardiac calcium spark are generally similar to those reported for other species.Examination of the temporal relationship between the action potential and the time course of calcium spark production showed that calcium sparks are more likely to occur during the initial repolarization phase of the action potential. The latency of their occurrence varied by less than 1·4 ms (s.d.) and this low variability may be explained by the interaction of the gating of L-type calcium channels with the changes in driving force for calcium entry during the action potential.When fixed sites within the cell are examined, calcium sparks have relatively constant amplitude but the amplitude of the sparks was variable among sites. The low variability of the amplitude of the calcium sparks suggests that more than one sarcoplasmic reticulum (SR) release channel must be involved in their genesis. Noise analysis (with the assumption of independent gating) suggests that > 18 SR calcium release channels may be involved in the generation of the calcium spark. At a fixed site, the response is close to ‘all-or-none’ behaviour which suggests that calcium sparks are indeed elementary events underlying cardiac excitation-contraction coupling.A method for selecting spark sites for signal averaging is presented which allows the time course of the spark to be examined with high temporal and spatial resolution. Using this method we show the development of the calcium spark at high signal-to-noise levels. PMID:10381593

Fiber coupled optical spark delivery system

DOEpatents

Yalin, Azer; Willson, Bryan; Defoort, Morgan

2008-08-12

A spark delivery system for generating a spark using a laser beam is provided, the spark delivery system including a laser light source and a laser delivery assembly. The laser delivery assembly includes a hollow fiber and a launch assembly comprising launch focusing optics to input the laser beam in the hollow fiber. In addition, the laser delivery assembly includes exit focusing optics that demagnify an exit beam of laser light from the hollow fiber, thereby increasing the intensity of the laser beam and creating a spark. In accordance with embodiments of the present invention, the assembly may be used to create a spark in a combustion engine. In accordance with other embodiments of the present invention, a method of using the spark delivery system is provided. In addition, a method of choosing an appropriate fiber for creating a spark using a laser beam is also presented.

Spark gap with low breakdown voltage jitter

DOEpatents

Rohwein, G.J.; Roose, L.D.

1996-04-23

Novel spark gap devices and electrodes are disclosed. The novel spark gap devices and electrodes are suitable for use in a variety of spark gap device applications. The shape of the electrodes gives rise to local field enhancements and reduces breakdown voltage jitter. Breakdown voltage jitter of approximately 5% has been measured in spark gaps according the invention. Novel electrode geometries and materials are disclosed. 13 figs.

An Exploration of Kernel Equating Using SAT® Data: Equating to a Similar Population and to a Distant Population. Research Report. ETS RR-07-17

ERIC Educational Resources Information Center

Liu, Jinghua; Low, Albert C.

2007-01-01

This study applied kernel equating (KE) in two scenarios: equating to a very similar population and equating to a very different population, referred to as a distant population, using SAT® data. The KE results were compared to the results obtained from analogous classical equating methods in both scenarios. The results indicate that KE results are…

Kernel-based whole-genome prediction of complex traits: a review.

PubMed

Morota, Gota; Gianola, Daniel

2014-01-01

Prediction of genetic values has been a focus of applied quantitative genetics since the beginning of the 20th century, with renewed interest following the advent of the era of whole genome-enabled prediction. Opportunities offered by the emergence of high-dimensional genomic data fueled by post-Sanger sequencing technologies, especially molecular markers, have driven researchers to extend Ronald Fisher and Sewall Wright's models to confront new challenges. In particular, kernel methods are gaining consideration as a regression method of choice for genome-enabled prediction. Complex traits are presumably influenced by many genomic regions working in concert with others (clearly so when considering pathways), thus generating interactions. Motivated by this view, a growing number of statistical approaches based on kernels attempt to capture non-additive effects, either parametrically or non-parametrically. This review centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants. We discuss various kernel-based approaches tailored to capturing total genetic variation, with the aim of arriving at an enhanced predictive performance in the light of available genome annotation information. Connections between prediction machines born in animal breeding, statistics, and machine learning are revisited, and their empirical prediction performance is discussed. Overall, while some encouraging results have been obtained with non-parametric kernels, recovering non-additive genetic variation in a validation dataset remains a challenge in quantitative genetics.

SVM and SVM Ensembles in Breast Cancer Prediction.

PubMed

Huang, Min-Wei; Chen, Chih-Wen; Lin, Wei-Chao; Ke, Shih-Wen; Tsai, Chih-Fong

2017-01-01

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.

SVM and SVM Ensembles in Breast Cancer Prediction

PubMed Central

Huang, Min-Wei; Chen, Chih-Wen; Lin, Wei-Chao; Ke, Shih-Wen; Tsai, Chih-Fong

2017-01-01

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers. PMID:28060807

Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery.

PubMed

Speicher, Nora K; Pfeifer, Nico

2015-06-15

Despite ongoing cancer research, available therapies are still limited in quantity and effectiveness, and making treatment decisions for individual patients remains a hard problem. Established subtypes, which help guide these decisions, are mainly based on individual data types. However, the analysis of multidimensional patient data involving the measurements of various molecular features could reveal intrinsic characteristics of the tumor. Large-scale projects accumulate this kind of data for various cancer types, but we still lack the computational methods to reliably integrate this information in a meaningful manner. Therefore, we apply and extend current multiple kernel learning for dimensionality reduction approaches. On the one hand, we add a regularization term to avoid overfitting during the optimization procedure, and on the other hand, we show that one can even use several kernels per data type and thereby alleviate the user from having to choose the best kernel functions and kernel parameters for each data type beforehand. We have identified biologically meaningful subgroups for five different cancer types. Survival analysis has revealed significant differences between the survival times of the identified subtypes, with P values comparable or even better than state-of-the-art methods. Moreover, our resulting subtypes reflect combined patterns from the different data sources, and we demonstrate that input kernel matrices with only little information have less impact on the integrated kernel matrix. Our subtypes show different responses to specific therapies, which could eventually assist in treatment decision making. An executable is available upon request. © The Author 2015. Published by Oxford University Press.

Laser Induced Aluminum Surface Breakdown Model

NASA Technical Reports Server (NTRS)

Chen, Yen-Sen; Liu, Jiwen; Zhang, Sijun; Wang, Ten-See (Technical Monitor)

2002-01-01

Laser powered propulsion systems involve complex fluid dynamics, thermodynamics and radiative transfer processes. Based on an unstructured grid, pressure-based computational aerothermodynamics; platform, several sub-models describing such underlying physics as laser ray tracing and focusing, thermal non-equilibrium, plasma radiation and air spark ignition have been developed. This proposed work shall extend the numerical platform and existing sub-models to include the aluminum wall surface Inverse Bremsstrahlung (IB) effect from which surface ablation and free-electron generation can be initiated without relying on the air spark ignition sub-model. The following tasks will be performed to accomplish the research objectives.

University reforms spark mass protests

NASA Astrophysics Data System (ADS)

Cartlidge, Edwin

2011-02-01

A controversial bill designed to modernize Italy's underperforming universities has been signed into law despite critics warning that it will cut university resources, damage researchers' career prospects and reduce universities' autonomy.

Fire prevention on airplanes. Part II

NASA Technical Reports Server (NTRS)

Sabatier, J

1929-01-01

This part of the report presents a detailed examination of spark prevention, fire extinguishers, and fuel tank location and design. A continued program of investigations and research is also proposed.

Predicting drug-target interactions by dual-network integrated logistic matrix factorization

NASA Astrophysics Data System (ADS)

Hao, Ming; Bryant, Stephen H.; Wang, Yanli

2017-01-01

In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.

A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram.

PubMed

Wu, Chung Kit; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei

2016-05-09

Globally, 1.2 million people die and 50 million people are injured annually due to traffic accidents. These traffic accidents cost $500 billion dollars. Drunk drivers are found in 40% of the traffic crashes. Existing drunk driving detection (DDD) systems do not provide accurate detection and pre-warning concurrently. Electrocardiogram (ECG) is a proven biosignal that accurately and simultaneously reflects human's biological status. In this letter, a classifier for DDD based on ECG is investigated in an attempt to reduce traffic accidents caused by drunk drivers. At this point, it appears that there is no known research or literature found on ECG classifier for DDD. To identify drunk syndromes, the ECG signals from drunk drivers are studied and analyzed. As such, a precise ECG-based DDD (ECG-DDD) using a weighted kernel is developed. From the measurements, 10 key features of ECG signals were identified. To incorporate the important features, the feature vectors are weighted in the customization of kernel functions. Four commonly adopted kernel functions are studied. Results reveal that weighted feature vectors improve the accuracy by 11% compared to the computation using the prime kernel. Evaluation shows that ECG-DDD improved the accuracy by 8% to 18% compared to prevailing methods.

Super-resolution fusion of complementary panoramic images based on cross-selection kernel regression interpolation.

PubMed

Chen, Lidong; Basu, Anup; Zhang, Maojun; Wang, Wei; Liu, Yu

2014-03-20

A complementary catadioptric imaging technique was proposed to solve the problem of low and nonuniform resolution in omnidirectional imaging. To enhance this research, our paper focuses on how to generate a high-resolution panoramic image from the captured omnidirectional image. To avoid the interference between the inner and outer images while fusing the two complementary views, a cross-selection kernel regression method is proposed. First, in view of the complementarity of sampling resolution in the tangential and radial directions between the inner and the outer images, respectively, the horizontal gradients in the expected panoramic image are estimated based on the scattered neighboring pixels mapped from the outer, while the vertical gradients are estimated using the inner image. Then, the size and shape of the regression kernel are adaptively steered based on the local gradients. Furthermore, the neighboring pixels in the next interpolation step of kernel regression are also selected based on the comparison between the horizontal and vertical gradients. In simulation and real-image experiments, the proposed method outperforms existing kernel regression methods and our previous wavelet-based fusion method in terms of both visual quality and objective evaluation.

Sparking Conversations about Graduate Programs in Geoscience Education Research

ERIC Educational Resources Information Center

McNeal, Karen S.; Petcovic, Heather L.

2017-01-01

The calls for a college-educated science and technology workforce, as well as a scientifically literate citizenry, have led to a demand for higher education faculty prepared in discipline-based education research (DBER). These faculty members conduct research on teaching and learning in the context of a specific discipline, including the…

Use of the mathematical model of the ignition system to analyze the spark discharge, including the destruction of spark plug electrodes

NASA Astrophysics Data System (ADS)

Różowicz, Sebastian

2018-03-01

The paper presents the results of analytical and experimental studies concerning the influence of different kinds of fuel additives on the quality of the spark discharge for different configurations of the ignition system. The wear of the spark plug electrode and the value of spark discharge were determined for various impurities and configurations of the air-fuel mixture.

Remediation to restoration to revitalization: A path forward for AOCs progress report

EPA Science Inventory

At the 2016 Great Lakes Areas of Concern (AOC) Conference, researchers from the USEPA Office of Research and Development (ORD) sparked conversation about community revitalization and the different states of progress throughout the basin. The conversation was meant to provide AOC ...

Dual Enrollment: The Missing Link to College Readiness

ERIC Educational Resources Information Center

Swanson, Joni

2010-01-01

Dual enrollment programs have sparked the interest of educational researchers and practitioners who want to determine whether offering college courses to high school students might positively affect their persistence in college or other postsecondary education. Current research suggests that participating in dual enrollment programs improves…

Detonating apparatus

DOEpatents

Johnston, Lawrence H.

1976-01-01

1. Apparatus for detonation of high explosive in uniform timing comprising in combination, an outer case, spark gap electrodes insulatedly supported in spaced relationship within said case to form a spark gap, high explosive of the class consisting of pentaerythritol tetranitrate and trimethylene trinitramine substantially free from material sensitive to detonation by impact compressed in surrounding relation to said electrodes including said spark gap under a pressure from about 100 psi to about 500 psi, said spark gap with said compressed explosive therein requiring at least 1000 volts for sparking, and means for impressing at least 1000 volts on said spark gap.

The effect of electrode temperature on the sparking voltage of short spark gaps

NASA Technical Reports Server (NTRS)

Silsbee, F B

1924-01-01

This report presents the results of an investigation to determine what effect the temperature of spark plug electrodes might have on the voltage at which a spark occurred. A spark gap was set up so that one electrode could be heated to temperatures up to 700 degrees C., while the other electrode and the air in the gap were maintained at room temperature. The sparking voltages were measured both with direct voltage and with voltage impulse from ignition coil. It was found that the sparking voltage of the gap decreased materially with increase of temperature. This change was more marked when the hot electrode was of negative polarity. The phenomena observed can be explained by the ionic theory of gaseous conduction, and serve to account for certain hitherto unexplained actions in the operation of internal combustion engines. These results indicate that the ignition spark will pass more readily when the spark-plug design is such as to make the electrodes run hot. This possible gain is, however, very closely limited by the danger of producing preignition. These experiments also show that sparking is somewhat easier when the hot electrode (which is almost always the central electrode) is negative than when the polarity is reversed.

A Comparative Study of Cycle Variability of Laser Plug Ignition vs Classical Spark Plug Ignition in Combustion Engines

NASA Astrophysics Data System (ADS)

Done, Bogdan

2017-10-01

Over the past 30 years numerous studies and laboratory experiments have researched the use of laser energy to ignite gas and fuel-air mixtures. The actual implementation of this laser application has still to be fully achieved in a commercial automotive application. Laser Plug Ignition as a replacement for Spark Plug Ignition in the internal combustion engines of automotive vehicles, offers several potential benefits such as extending lean burn capability, reducing the cyclic variability between combustion cycles and decreasing the total amount of ignition costs, and implicitly weight and energy requirements. The paper presents preliminary results of cycle variability study carried on a SI Engine equipped with laser Plug Ignition system. Versus classic ignition system, the use of the laser Plug Ignition system assures the reduction of the combustion process variability, reflected in the lower values of the coefficient of variability evaluated for indicated mean effective pressure, maximum pressure, maximum pressure angle and maximum pressure rise rate. The laser plug ignition system was mounted on an experimental spark ignition engine and tested at the regime of 90% load and 2800 rev/min, at dosage of λ=1.1. Compared to conventional spark plug, laser ignition assures the efficiency at lean dosage.

Big Data Analytics with Datalog Queries on Spark.

PubMed

Shkapsky, Alexander; Yang, Mohan; Interlandi, Matteo; Chiu, Hsuan; Condie, Tyson; Zaniolo, Carlo

2016-01-01

There is great interest in exploiting the opportunity provided by cloud computing platforms for large-scale analytics. Among these platforms, Apache Spark is growing in popularity for machine learning and graph analytics. Developing efficient complex analytics in Spark requires deep understanding of both the algorithm at hand and the Spark API or subsystem APIs (e.g., Spark SQL, GraphX). Our BigDatalog system addresses the problem by providing concise declarative specification of complex queries amenable to efficient evaluation. Towards this goal, we propose compilation and optimization techniques that tackle the important problem of efficiently supporting recursion in Spark. We perform an experimental comparison with other state-of-the-art large-scale Datalog systems and verify the efficacy of our techniques and effectiveness of Spark in supporting Datalog-based analytics.

Big Data Analytics with Datalog Queries on Spark

PubMed Central

Shkapsky, Alexander; Yang, Mohan; Interlandi, Matteo; Chiu, Hsuan; Condie, Tyson; Zaniolo, Carlo

2017-01-01

There is great interest in exploiting the opportunity provided by cloud computing platforms for large-scale analytics. Among these platforms, Apache Spark is growing in popularity for machine learning and graph analytics. Developing efficient complex analytics in Spark requires deep understanding of both the algorithm at hand and the Spark API or subsystem APIs (e.g., Spark SQL, GraphX). Our BigDatalog system addresses the problem by providing concise declarative specification of complex queries amenable to efficient evaluation. Towards this goal, we propose compilation and optimization techniques that tackle the important problem of efficiently supporting recursion in Spark. We perform an experimental comparison with other state-of-the-art large-scale Datalog systems and verify the efficacy of our techniques and effectiveness of Spark in supporting Datalog-based analytics. PMID:28626296

Electron beam lithographic modeling assisted by artificial intelligence technology

NASA Astrophysics Data System (ADS)

Nakayamada, Noriaki; Nishimura, Rieko; Miura, Satoru; Nomura, Haruyuki; Kamikubo, Takashi

2017-07-01

We propose a new concept of tuning a point-spread function (a "kernel" function) in the modeling of electron beam lithography using the machine learning scheme. Normally in the work of artificial intelligence, the researchers focus on the output results from a neural network, such as success ratio in image recognition or improved production yield, etc. In this work, we put more focus on the weights connecting the nodes in a convolutional neural network, which are naturally the fractions of a point-spread function, and take out those weighted fractions after learning to be utilized as a tuned kernel. Proof-of-concept of the kernel tuning has been demonstrated using the examples of proximity effect correction with 2-layer network, and charging effect correction with 3-layer network. This type of new tuning method can be beneficial to give researchers more insights to come up with a better model, yet it might be too early to be deployed to production to give better critical dimension (CD) and positional accuracy almost instantly.

Flecainide inhibits arrhythmogenic Ca2+ waves by open state block of ryanodine receptor Ca2+ release channels and reduction of Ca2+ spark mass

PubMed Central

Hilliard, Fredrick A.; Steele, Derek S.; Laver, Derek; Yang, Zhaokang; Le Marchand, Sylvain J.; Chopra, Nagesh; Piston, David W.; Huke, Sabine; Knollmann, Björn C.

2009-01-01

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is linked to mutations in the cardiac ryanodine receptor (RyR2) or calsequestrin. We recently found that the drug flecainide inhibits RyR2 channels and prevents CPVT in mice and humans. Here we compared the effects of flecainide and tetracaine, a known RyR2 inhibitor ineffective in CPVT myocytes, on arrhythmogenic Ca2+ waves and elementary sarcoplasmic reticulum (SR) Ca2+ release events, Ca2+ sparks. In ventricular myocytes isolated from a CPVT mouse model, flecainide significantly reduced spark amplitude and spark width, resulting in a 40% reduction in spark mass. Surprisingly, flecainide significantly increased spark frequency. As a result, flecainide had no significant effect on spark-mediated SR Ca2+ leak or SR Ca2+ content. In contrast, tetracaine decreased spark frequency and spark-mediated SR Ca2+ leak, resulting in a significantly increased SR Ca2+ content. Measurements in permeabilized rat ventricular myocytes confirmed the different effects of flecainide and tetracaine on spark frequency and Ca2+ waves. In lipid bilayers, flecainide inhibited RyR2 channels by open state block, whereas tetracaine primarily prolonged RyR2 closed times. The differential effects of flecainide and tetracaine on sparks and RyR2 gating can explain why flecainide, unlike tetracaine, does not change the balance of SR Ca2+ fluxes. We suggest that the smaller spark mass contributes to flecainide's antiarrhythmic action by reducing the probability of saltatory wave propagation between adjacent Ca2+ release units. Our results indicate that inhibition of the RyR2 open state provides a new therapeutic strategy to prevent diastolic Ca2+ waves resulting in triggered arrhythmias, such as CPVT. PMID:19835880

Power measurements of spark discharge experiments.

PubMed

Navarro-Gonzalez, R; Romero, A; Honda, Y

1998-04-01

An accurate and precise knowledge of the amount of energy introduced into prebiotic discharge experiments is important to understand the relative roles of different energy sources in the synthesis of organic compounds in the primitive Earth's atmosphere and other planetary atmospheres. Two methods widely used to determine the power of spark discharges were evaluated, namely calorimetric and oscilloscopic, using a chemically inert gas. The power dissipated by the spark in argon at 500 Torr was determined to be 2.4 (+12%/-17%) J s-1 by calorimetry and 5.3 (+/- 15%) J s-1 by the oscilloscope. The difference between the two methods was attributed to (1) an incomplete conversion of the electric energy into heat, and (2) heat loss from the spark channel to the connecting cables through the electrodes. The latter contribution leads to an unwanted effect in the spark channel by lowering the spark product yields as the spark channel cools by mixing with surrounding air and by losing heat to the electrodes. Once the concentrations of the spark products have frozen at the freeze-out temperature, any additional loss of heat from the spark channel to the electrodes has no consequence in product yields. Therefore, neither methods accurately determines the net energy transferred to the system. With a lack of a quantitative knowledge of the amount of heat loss from the spark channel during the interval from ignition of the spark to when the freeze-out temperature is reached, it is recommended to derive the energy yields of the spark products from the mean value of the two methods with the uncertainty being their standard deviation. For the case of argon at 500 Torr, this would be 3.8 (+/-50%) J s-1.

Nutritional value of high fiber co-products from the copra, palm kernel, and rice industries in diets fed to pigs.

PubMed

Stein, Hans Henrik; Casas, Gloria Amparo; Abelilla, Jerubella Jerusalem; Liu, Yanhong; Sulabo, Rommel Casilda

2015-01-01

High fiber co-products from the copra and palm kernel industries are by-products of the production of coconut oil and palm kernel oil. The co-products include copra meal, copra expellers, palm kernel meal, and palm kernel expellers. All 4 ingredients are very high in fiber and the energy value is relatively low when fed to pigs. The protein concentration is between 14 and 22 % and the protein has a low biological value and a very high Arg:Lys ratio. Digestibility of most amino acids is less than in soybean meal but close to that in corn. However, the digestibility of Lys is sometimes low due to Maillard reactions that are initiated due to overheating during drying. Copra and palm kernel ingredients contain 0.5 to 0.6 % P. Most of the P in palm kernel meal and palm kernel expellers is bound to phytate, but in copra products less than one third of the P is bound to phytate. The digestibility of P is, therefore, greater in copra meal and copra expellers than in palm kernel ingredients. Inclusion of copra meal should be less than 15 % in diets fed to weanling pigs and less than 25 % in diets for growing-finishing pigs. Palm kernel meal may be included by 15 % in diets for weanling pigs and 25 % in diets for growing and finishing pigs. Rice bran contains the pericarp and aleurone layers of brown rice that is removed before polished rice is produced. Rice bran contains approximately 25 % neutral detergent fiber and 25 to 30 % starch. Rice bran has a greater concentration of P than most other plant ingredients, but 75 to 90 % of the P is bound in phytate. Inclusion of microbial phytase in the diets is, therefore, necessary if rice bran is used. Rice bran may contain 15 to 24 % fat, but it may also have been defatted in which case the fat concentration is less than 5 %. Concentrations of digestible energy (DE) and metabolizable energy (ME) are slightly less in full fat rice bran than in corn, but defatted rice bran contains less than 75 % of the DE and ME in corn. The concentration of crude protein is 15 to 18 % in rice bran and the protein has a high biological value and most amino acids are well digested by pigs. Inclusion of rice bran in diets fed to pigs has yielded variable results and based on current research it is recommended that inclusion levels are less than 25 to 30 % in diets for growing-finishing pigs, and less than 20 % in diets for weanling pigs. However, there is a need for additional research to determine the inclusion rates that may be used for both full fat and defatted rice bran.

Nitrogen spark denoxer

DOEpatents

Ng, Henry K.; Novick, Vincent J.; Sekar, Ramanujam R.

1997-01-01

A NO.sub.X control system for an internal combustion engine includes an oxygen enrichment device that produces oxygen and nitrogen enriched air. The nitrogen enriched air contains molecular nitrogen that is provided to a spark plug that is mounted in an exhaust outlet of an internal combustion engine. As the nitrogen enriched air is expelled at the spark gap of the spark plug, the nitrogen enriched air is exposed to a pulsating spark that is generated across the spark gap of the spark plug. The spark gap is elongated so that a sufficient amount of atomic nitrogen is produced and is injected into the exhaust of the internal combustion engine. The injection of the atomic nitrogen into the exhaust of the internal combustion engine causes the oxides of nitrogen to be reduced into nitrogen and oxygen such that the emissions from the engine will have acceptable levels of NO.sub.X. The oxygen enrichment device that produces both the oxygen and nitrogen enriched air can include a selectively permeable membrane.

A Response from the Field: Perspectives on Translating Neuroscience to Clinical Practice

ERIC Educational Resources Information Center

Sprang, Ginny; Kaak, H. Otto; Staton-Tindall, Michele; Clark, James J.; Hubbard, Kay; Whitt-Woosley, Adrienne; Mau, Aimee; Combs, Angela; Risk, Heather

2009-01-01

The scientific meeting "From Neuroscience to Social Practice: Translational Research on Violence Against Children" sparked a dialogue between clinicians, researchers, and policy makers about the applicability and relevance of neuroscientific discoveries regarding the impact of violence towards children on contemporary behavioral health…

Visual Research Methods: Image, Society, and Representation

ERIC Educational Resources Information Center

Stanczak, Gregory C., Ed.

2007-01-01

Visual research is reemerging across the social sciences as a significant, underutilized resource producing unique lines of inquiry and sparking innovative pedagogies. Stanczak's edited volume crisscrosses disciplines in ways that highlights the multiple manifestations of this newer interdisciplinary trend. As such, this volume will be useful as…

Construction of Chained True Score Equipercentile Equatings under the Kernel Equating (KE) Framework and Their Relationship to Levine True Score Equating. Research Report. ETS RR-09-24

ERIC Educational Resources Information Center

Chen, Haiwen; Holland, Paul

2009-01-01

In this paper, we develop a new chained equipercentile equating procedure for the nonequivalent groups with anchor test (NEAT) design under the assumptions of the classical test theory model. This new equating is named chained true score equipercentile equating. We also apply the kernel equating framework to this equating design, resulting in a…

A scalable kernel-based semisupervised metric learning algorithm with out-of-sample generalization ability.

PubMed

Yeung, Dit-Yan; Chang, Hong; Dai, Guang

2008-11-01

In recent years, metric learning in the semisupervised setting has aroused a lot of research interest. One type of semisupervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale well with the data set size. In this letter, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.

Knock detection system to improve petrol engine performance, using microphone sensor

NASA Astrophysics Data System (ADS)

Sujono, Agus; Santoso, Budi; Juwana, Wibawa Endra

2017-01-01

An increase of power and efficiency of spark ignition engines (petrol engines) are always faced with the problem of knock. Even the characteristics of the engine itself are always determined from the occurrence of knock. Until today, this knocking problem has not been solved completely. Knock is caused by principal factors that are influenced by the engine rotation, the load or opening the throttle and spark advance (ignition timing). In this research, the engine is mounted on the engine test bed (ETB) which is equipped with the necessary sensors. Knock detection using a new method, which is based on pattern recognition, which through the knock sound detection by using a microphone sensor, active filter, the regression of the normalized envelope function, and the calculation of the Euclidean distance is used for identifying knock. This system is implemented with a microcontroller which uses fuzzy logic controller ignition (FLIC), which aims to set proper spark advance, in accordance with operating conditions. This system can improve the engine performance for approximately 15%.

Effects of solution volume on hydrogen production by pulsed spark discharge in ethanol solution

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xin, Y. B.; Sun, B., E-mail: sunb88@dlmu.edu.cn; Zhu, X. M.

2016-07-15

Hydrogen production from ethanol solution (ethanol/water) by pulsed spark discharge was optimized by varying the volume of ethanol solution (liquid volume). Hydrogen yield was initially increased and then decreased with the increase in solution volume, which achieved 1.5 l/min with a solution volume of 500 ml. The characteristics of pulsed spark discharge were studied in this work; the results showed that the intensity of peak current, the rate of current rise, and energy efficiency of hydrogen production can be changed by varying the volume of ethanol solution. Meanwhile, the mechanism analysis of hydrogen production was accomplished by monitoring the process of hydrogenmore » production and the state of free radicals. The analysis showed that decreasing the retention time of gas production and properly increasing the volume of ethanol solution can enhance the hydrogen yield. Through this research, a high-yield and large-scale method of hydrogen production can be achieved, which is more suitable for industrial application.« less

Alternative Fuels Data Center: Partnerships Spark Biodiesel Success for

Science.gov Websites

Essential Baking Company Partnerships Spark Biodiesel Success for Essential Baking Company to Baking Company on Facebook Tweet about Alternative Fuels Data Center: Partnerships Spark Biodiesel Success for Essential Baking Company on Twitter Bookmark Alternative Fuels Data Center: Partnerships Spark

With NSF Support, Research Moves into Science Labs of 2-Year Colleges

ERIC Educational Resources Information Center

Berrett, Dan

2012-01-01

Original research in biology, which is thought to spark student interest and bolster majors, makes its way to the associate-degree level. Through a grant from the National Science Foundation, students of biology in community colleges will have the chance to do research on open-ended, real-world questions with no predetermined answers--and…

Preventing Problems vs. Promoting the Positive: What Do We Want for Our Children? Child Trends Research Brief.

ERIC Educational Resources Information Center

Moore, Kristin A.; Halle, Tamara G.

Noting that there is little focus in research literature, in popular discussions, or in policymaking regarding how to promote positive youth development, this research brief presents a preliminary set of constructs that might comprise positive youth development in order to spark productive conversations that will lead to a better conceptualization…

Spark channel propagation in a microbubble liquid

DOE Office of Scientific and Technical Information (OSTI.GOV)

Panov, V. A.; Vasilyak, L. M., E-mail: vasilyak@ihed.ras.ru; Vetchinin, S. P.

Experimental study on the development of the spark channel from the anode needle under pulsed electrical breakdown of isopropyl alcohol solution in water with air microbubbles has been performed. The presence of the microbubbles increases the velocity of the spark channel propagation and increases the current in the discharge gap circuit. The observed rate of spark channel propagation in microbubble liquid ranges from 4 to 12 m/s, indicating the thermal mechanism of the spark channel development in a microbubble liquid.

Osteoarthritis Severity Determination using Self Organizing Map Based Gabor Kernel

NASA Astrophysics Data System (ADS)

Anifah, L.; Purnomo, M. H.; Mengko, T. L. R.; Purnama, I. K. E.

2018-02-01

The number of osteoarthritis patients in Indonesia is enormous, so early action is needed in order for this disease to be handled. The aim of this paper to determine osteoarthritis severity based on x-ray image template based on gabor kernel. This research is divided into 3 stages, the first step is image processing that is using gabor kernel. The second stage is the learning stage, and the third stage is the testing phase. The image processing stage is by normalizing the image dimension to be template to 50 □ 200 image. Learning stage is done with parameters initial learning rate of 0.5 and the total number of iterations of 1000. The testing stage is performed using the weights generated at the learning stage. The testing phase has been done and the results were obtained. The result shows KL-Grade 0 has an accuracy of 36.21%, accuracy for KL-Grade 2 is 40,52%, while accuracy for KL-Grade 2 and KL-Grade 3 are 15,52%, and 25,86%. The implication of this research is expected that this research as decision support system for medical practitioners in determining KL-Grade on X-ray images of knee osteoarthritis.

Research Says/Curiosity Is Fleeting, but Teachable

ERIC Educational Resources Information Center

Goodwin, Bryan

2014-01-01

Psychologists and researchers have long puzzled over questions regarding "curiosity" and have more or less settled on a two-pronged definition as: (1) trait curiosity (an intrinsic drive for exploration and learning); and (2) state curiosity (an interest sparked by external conditions). Many studies have shown that human beings are…

Examining Teachers' Conception of and Needs on Action Research

ERIC Educational Resources Information Center

Morales, Marie Paz E.; Abulon, Edna Luz R.; Soriano, Portia R.; David, Adonis P.; Hermosisima, Ma. Victoria C.; Gerundio, Maribel G.

2016-01-01

Action research is viewed as a path towards better student achievement. This track may be attained through the reflective nature instilled in the teacher that sparks initiatives to promote better classroom practices in the aspects of pedagogy, assessment, and parental involvement. This descriptive survey explores Filipino teachers' conceptions of…

SOME ENGINEERING PROPERTIES OF SHELLED AND KERNEL TEA (Camellia sinensis) SEEDS.

PubMed

Altuntas, Ebubekir; Yildiz, Merve

2017-01-01

Camellia sinensis is the source of tea leaves and it is an economic crop now grown around the World. Tea seed oil has been used for cooking in China and other Asian countries for more than a thousand years. Tea is the most widely consumed beverages after water in the world. It is mainly produced in Asia, central Africa, and exported throughout the World. Some engineering properties (size dimensions, sphericity, volume, bulk and true densities, friction coefficient, colour characteristics and mechanical behaviour as rupture force of shelled and kernel tea ( Camellia sinensis ) seeds were determined in this study. This research was carried out for shelled and kernel tea seeds. The shelled tea seeds used in this study were obtained from East-Black Sea Tea Cooperative Institution in Rize city of Turkey. Shelled and kernel tea seeds were characterized as large and small sizes. The average geometric mean diameter and seed mass of the shelled tea seeds were 15.8 mm, 10.7 mm (large size); 1.47 g, 0.49 g (small size); while the average geometric mean diameter and seed mass of the kernel tea seeds were 11.8 mm, 8 mm for large size; 0.97 g, 0.31 g for small size, respectively. The sphericity, surface area and volume values were found to be higher in a larger size than small size for the shelled and kernel tea samples. The shelled tea seed's colour intensity (Chroma) were found between 59.31 and 64.22 for large size, while the kernel tea seed's chroma values were found between 56.04 68.34 for large size, respectively. The rupture force values of kernel tea seeds were higher than shelled tea seeds for the large size along X axis; whereas, the rupture force values of along X axis were higher than Y axis for large size of shelled tea seeds. The static coefficients of friction of shelled and kernel tea seeds for the large and small sizes higher values for rubber than the other friction surfaces. Some engineering properties, such as geometric mean diameter, sphericity, volume, bulk and true densities, the coefficient of friction, L*, a*, b* colour characteristics and rupture force of shelled and kernel tea ( Camellia sinensis ) seeds will serve to design the equipment used in postharvest treatments.

Learning a peptide-protein binding affinity predictor with kernel ridge regression

PubMed Central

2013-01-01

Background The cellular function of a vast majority of proteins is performed through physical interactions with other biomolecules, which, most of the time, are other proteins. Peptides represent templates of choice for mimicking a secondary structure in order to modulate protein-protein interaction. They are thus an interesting class of therapeutics since they also display strong activity, high selectivity, low toxicity and few drug-drug interactions. Furthermore, predicting peptides that would bind to a specific MHC alleles would be of tremendous benefit to improve vaccine based therapy and possibly generate antibodies with greater affinity. Modern computational methods have the potential to accelerate and lower the cost of drug and vaccine discovery by selecting potential compounds for testing in silico prior to biological validation. Results We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalizes eight kernels, comprised of the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it’s approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of predicting the binding affinity of any peptide to any protein with reasonable accuracy. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. Conclusion On all benchmarks, our method significantly (p-value ≤ 0.057) outperforms the current state-of-the-art methods at predicting peptide-protein binding affinities. The proposed approach is flexible and can be applied to predict any quantitative biological activity. Moreover, generating reliable peptide-protein binding affinities will also improve system biology modelling of interaction pathways. Lastly, the method should be of value to a large segment of the research community with the potential to accelerate the discovery of peptide-based drugs and facilitate vaccine development. The proposed kernel is freely available at http://graal.ift.ulaval.ca/downloads/gs-kernel/. PMID:23497081

Alternative Fuels Data Center: New Mexico Utility Sparks Change with Fleet

Science.gov Websites

Electrification New Mexico Utility Sparks Change with Fleet Electrification to someone by E -mail Share Alternative Fuels Data Center: New Mexico Utility Sparks Change with Fleet Electrification on Facebook Tweet about Alternative Fuels Data Center: New Mexico Utility Sparks Change with Fleet

46 CFR 30.10-63 - Spark arrester-TB/ALL.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 46 Shipping 1 2014-10-01 2014-10-01 false Spark arrester-TB/ALL. 30.10-63 Section 30.10-63 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY TANK VESSELS GENERAL PROVISIONS Definitions § 30.10-63 Spark arrester—TB/ALL. The term spark arrester means any device, assembly, or method of a...

46 CFR 30.10-63 - Spark arrester-TB/ALL.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 1 2010-10-01 2010-10-01 false Spark arrester-TB/ALL. 30.10-63 Section 30.10-63 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY TANK VESSELS GENERAL PROVISIONS Definitions § 30.10-63 Spark arrester—TB/ALL. The term spark arrester means any device, assembly, or method of a...

46 CFR 30.10-63 - Spark arrester-TB/ALL.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 46 Shipping 1 2012-10-01 2012-10-01 false Spark arrester-TB/ALL. 30.10-63 Section 30.10-63 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY TANK VESSELS GENERAL PROVISIONS Definitions § 30.10-63 Spark arrester—TB/ALL. The term spark arrester means any device, assembly, or method of a...

46 CFR 30.10-63 - Spark arrester-TB/ALL.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 46 Shipping 1 2013-10-01 2013-10-01 false Spark arrester-TB/ALL. 30.10-63 Section 30.10-63 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY TANK VESSELS GENERAL PROVISIONS Definitions § 30.10-63 Spark arrester—TB/ALL. The term spark arrester means any device, assembly, or method of a...

46 CFR 30.10-63 - Spark arrester-TB/ALL.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 46 Shipping 1 2011-10-01 2011-10-01 false Spark arrester-TB/ALL. 30.10-63 Section 30.10-63 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY TANK VESSELS GENERAL PROVISIONS Definitions § 30.10-63 Spark arrester—TB/ALL. The term spark arrester means any device, assembly, or method of a...

Extending lean operating limit and reducing emissions of methane spark-ignited engines using a microwave-assisted spark plug

DOE PAGES

Rapp, Vi H.; DeFilippo, Anthony; Saxena, Samveg; ...

2012-01-01

Amore » microwave-assisted spark plug was used to extend the lean operating limit (lean limit) and reduce emissions of an engine burning methane-air. In-cylinder pressure data were collected at normalized air-fuel ratios of λ = 1.46, λ = 1.51, λ = 1.57, λ = 1.68, and λ = 1.75. For each λ , microwave energy (power supplied to the magnetron per engine cycle) was varied from 0 mJ (spark discharge alone) to 1600 mJ. At lean conditions, the results showed adding microwave energy to a standard spark plug discharge increased the number of complete combustion cycles, improving engine stability as compared to spark-only operation. Addition of microwave energy also increased the indicated thermal efficiency by 4% at λ = 1.68. At λ = 1.75, the spark discharge alone was unable to consistently ignite the air-fuel mixture, resulting in frequent misfires. Although microwave energy produced more consistent ignition than spark discharge alone at λ = 1.75, 59% of the cycles only partially burned. Overall, the microwave-assisted spark plug increased engine performance under lean operating conditions (λ = 1.68) but did not affect operation at conditions closer to stoichiometric.« less

Automated qualification and analysis of protective spark gaps for DC accelerators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Banerjee, Srutarshi; Rajan, Rehim N.; Dewangan, S.

2014-07-01

Protective spark gaps are used in the high voltage multiplier column of a 3 MeV DC Accelerator to prevent excessive voltage build-ups. Precise gap of 5 mm is maintained between the electrodes in these spark gaps for obtaining 120 kV± 5 kV in 6 kg/cm{sup 2} SF{sub 6} environment which is the dielectric medium. There are 74 such spark gaps used in the multiplier. Each spark gap has to be qualified for electrical performance before fitting in the accelerator to ensure reliable operation. As the breakdown voltage stabilizes after a large number of sparks between the electrodes, the qualification processmore » becomes time consuming and cumbersome. For qualifying large number of spark gaps an automatic breakdown analysis setup has been developed. This setup operates in air, a dielectric medium. The setup consists of a flyback topology based high voltage power supply with maximum rating of 25 kV. This setup works in conjunction with spark detection and automated shutdown circuit. The breakdown voltage is sensed using a peak detector circuit. The voltage breakdown data is recorded and statistical distribution of the breakdown voltage has been analyzed. This paper describes details of the diagnostics and the spark gap qualification process based on the experimental data. (author)« less

Kernel abortion in maize : I. Carbohydrate concentration patterns and Acid invertase activity of maize kernels induced to abort in vitro.

PubMed

Hanft, J M; Jones, R J

1986-06-01

Kernels cultured in vitro were induced to abort by high temperature (35 degrees C) and by culturing six kernels/cob piece. Aborting kernels failed to enter a linear phase of dry mass accumulation and had a final mass that was less than 6% of nonaborting field-grown kernels. Kernels induced to abort by high temperature failed to synthesize starch in the endosperm and had elevated sucrose concentrations and low fructose and glucose concentrations in the pedicel during early growth compared to nonaborting kernels. Kernels induced to abort by high temperature also had much lower pedicel soluble acid invertase activities than did nonaborting kernels. These results suggest that high temperature during the lag phase of kernel growth may impair the process of sucrose unloading in the pedicel by indirectly inhibiting soluble acid invertase activity and prevent starch synthesis in the endosperm. Kernels induced to abort by culturing six kernels/cob piece had reduced pedicel fructose, glucose, and sucrose concentrations compared to kernels from field-grown ears. These aborting kernels also had a lower pedicel soluble acid invertase activity compared to nonaborting kernels from the same cob piece and from field-grown ears. The low invertase activity in pedicel tissue of the aborting kernels was probably caused by a lack of substrate (sucrose) for the invertase to cleave due to the intense competition for available assimilates. In contrast to kernels cultured at 35 degrees C, aborting kernels from cob pieces containing all six kernels accumulated starch in a linear fashion. These results indicate that kernels cultured six/cob piece abort because of an inadequate supply of sugar and are similar to apical kernels from field-grown ears that often abort prior to the onset of linear growth.

SciSpark: In-Memory Map-Reduce for Earth Science Algorithms

NASA Astrophysics Data System (ADS)

Ramirez, P.; Wilson, B. D.; Whitehall, K. D.; Palamuttam, R. S.; Mattmann, C. A.; Shah, S.; Goodman, A.; Burke, W.

2016-12-01

We are developing a lightning fast Big Data technology called SciSpark based on ApacheTM Spark under a NASA AIST grant (PI Mattmann). Spark implements the map-reduce paradigm for parallel computing on a cluster, but emphasizes in-memory computation, "spilling" to disk only as needed, and so outperforms the disk-based Apache Hadoop by 100x in memory and by 10x on disk. SciSpark extends Spark to support Earth Science use in three ways: Efficient ingest of N-dimensional geo-located arrays (physical variables) from netCDF3/4, HDF4/5, and/or OPeNDAP URLS; Array operations for dense arrays in scala and Java using the ND4S/ND4J or Breeze libraries; Operations to "split" datasets across a Spark cluster by time or space or both. For example, a decade-long time-series of geo-variables can be split across time to enable parallel "speedups" of analysis by day, month, or season. Similarly, very high-resolution climate grids can be partitioned into spatial tiles for parallel operations across rows, columns, or blocks. In addition, using Spark's gateway into python, PySpark, one can utilize the entire ecosystem of numpy, scipy, etc. Finally, SciSpark Notebooks provide a modern eNotebook technology in which scala, python, or spark-sql codes are entered into cells in the Notebook and executed on the cluster, with results, plots, or graph visualizations displayed in "live widgets". We have exercised SciSpark by implementing three complex Use Cases: discovery and evolution of Mesoscale Convective Complexes (MCCs) in storms, yielding a graph of connected components; PDF Clustering of atmospheric state using parallel K-Means; and statistical "rollups" of geo-variables or model-to-obs. differences (i.e. mean, stddev, skewness, & kurtosis) by day, month, season, year, and multi-year. Geo-variables are ingested and split across the cluster using methods on the sciSparkContext object including netCDFVariables() for spatial decomposition and wholeNetCDFVariables() for time-series. The presentation will cover the architecture of SciSpark, the design of the scientific RDD (sRDD) data structures for N-dim. arrays, results from the three science Use Cases, example Notebooks, lessons learned from the algorithm implementations, and parallel performance metrics.

Classification of Microarray Data Using Kernel Fuzzy Inference System

PubMed Central

Kumar Rath, Santanu

2014-01-01

The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function ϕ through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function. PMID:27433543

7 CFR 810.602 - Definition of other terms.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) Damaged kernels. Kernels and pieces of flaxseed kernels that are badly ground-damaged, badly weather... instructions. Also, underdeveloped, shriveled, and small pieces of flaxseed kernels removed in properly... recleaning. (c) Heat-damaged kernels. Kernels and pieces of flaxseed kernels that are materially discolored...

Kernel Abortion in Maize 1

PubMed Central

Hanft, Jonathan M.; Jones, Robert J.

1986-01-01

Kernels cultured in vitro were induced to abort by high temperature (35°C) and by culturing six kernels/cob piece. Aborting kernels failed to enter a linear phase of dry mass accumulation and had a final mass that was less than 6% of nonaborting field-grown kernels. Kernels induced to abort by high temperature failed to synthesize starch in the endosperm and had elevated sucrose concentrations and low fructose and glucose concentrations in the pedicel during early growth compared to nonaborting kernels. Kernels induced to abort by high temperature also had much lower pedicel soluble acid invertase activities than did nonaborting kernels. These results suggest that high temperature during the lag phase of kernel growth may impair the process of sucrose unloading in the pedicel by indirectly inhibiting soluble acid invertase activity and prevent starch synthesis in the endosperm. Kernels induced to abort by culturing six kernels/cob piece had reduced pedicel fructose, glucose, and sucrose concentrations compared to kernels from field-grown ears. These aborting kernels also had a lower pedicel soluble acid invertase activity compared to nonaborting kernels from the same cob piece and from field-grown ears. The low invertase activity in pedicel tissue of the aborting kernels was probably caused by a lack of substrate (sucrose) for the invertase to cleave due to the intense competition for available assimilates. In contrast to kernels cultured at 35°C, aborting kernels from cob pieces containing all six kernels accumulated starch in a linear fashion. These results indicate that kernels cultured six/cob piece abort because of an inadequate supply of sugar and are similar to apical kernels from field-grown ears that often abort prior to the onset of linear growth. PMID:16664846

Multiple Voices in Health, Sport, Recreation, and Physical Education Research: Revealing Unfamiliar Spaces in a Polyvocal Review of Qualitative Research Genres

ERIC Educational Resources Information Center

Hopper, Tim F.; Madill, Leanna E.; Bratseth, Chris D.; Cameron, Kathi A.; Coble, James D.; Nimmon, Laura E.

2008-01-01

The purpose of this article is to outline the potential genres of qualitative research that can be used to research the domains of health, sport, recreation, and physical education. Drawing on Denzin and Lincoln (2000) and Sparkes (2002a), and connecting to the work of six researchers, this article will present five genres of qualitative research…

Out-of-Sample Extensions for Non-Parametric Kernel Methods.

PubMed

Pan, Binbin; Chen, Wen-Sheng; Chen, Bo; Xu, Chen; Lai, Jianhuang

2017-02-01

Choosing suitable kernels plays an important role in the performance of kernel methods. Recently, a number of studies were devoted to developing nonparametric kernels. Without assuming any parametric form of the target kernel, nonparametric kernel learning offers a flexible scheme to utilize the information of the data, which may potentially characterize the data similarity better. The kernel methods using nonparametric kernels are referred to as nonparametric kernel methods. However, many nonparametric kernel methods are restricted to transductive learning, where the prediction function is defined only over the data points given beforehand. They have no straightforward extension for the out-of-sample data points, and thus cannot be applied to inductive learning. In this paper, we show how to make the nonparametric kernel methods applicable to inductive learning. The key problem of out-of-sample extension is how to extend the nonparametric kernel matrix to the corresponding kernel function. A regression approach in the hyper reproducing kernel Hilbert space is proposed to solve this problem. Empirical results indicate that the out-of-sample performance is comparable to the in-sample performance in most cases. Experiments on face recognition demonstrate the superiority of our nonparametric kernel method over the state-of-the-art parametric kernel methods.

40 CFR 90.7 - Reference materials.

Code of Federal Regulations, 2013 CFR

2013-07-01

... EMISSIONS FROM NONROAD SPARK-IGNITION ENGINES AT OR BELOW 19 KILOWATTS General § 90.7 Reference materials... Fuels by the Research Method Appendix A to subpart D, Table 3. ASTM D2700-92: Standard Test Method for...

40 CFR 90.7 - Reference materials.

Code of Federal Regulations, 2014 CFR

2014-07-01

... EMISSIONS FROM NONROAD SPARK-IGNITION ENGINES AT OR BELOW 19 KILOWATTS General § 90.7 Reference materials... Fuels by the Research Method Appendix A to subpart D, Table 3. ASTM D2700-92: Standard Test Method for...

40 CFR 90.7 - Reference materials.

Code of Federal Regulations, 2012 CFR

2012-07-01

... EMISSIONS FROM NONROAD SPARK-IGNITION ENGINES AT OR BELOW 19 KILOWATTS General § 90.7 Reference materials... Fuels by the Research Method Appendix A to subpart D, Table 3. ASTM D2700-92: Standard Test Method for...

7 CFR 810.1202 - Definition of other terms.

Code of Federal Regulations, 2010 CFR

2010-01-01

... kernels. Kernels, pieces of rye kernels, and other grains that are badly ground-damaged, badly weather.... Also, underdeveloped, shriveled, and small pieces of rye kernels removed in properly separating the...-damaged kernels. Kernels, pieces of rye kernels, and other grains that are materially discolored and...

The Genetic Basis of Natural Variation in Kernel Size and Related Traits Using a Four-Way Cross Population in Maize.

PubMed

Chen, Jiafa; Zhang, Luyan; Liu, Songtao; Li, Zhimin; Huang, Rongrong; Li, Yongming; Cheng, Hongliang; Li, Xiantang; Zhou, Bo; Wu, Suowei; Chen, Wei; Wu, Jianyu; Ding, Junqiang

2016-01-01

Kernel size is an important component of grain yield in maize breeding programs. To extend the understanding on the genetic basis of kernel size traits (i.e., kernel length, kernel width and kernel thickness), we developed a set of four-way cross mapping population derived from four maize inbred lines with varied kernel sizes. In the present study, we investigated the genetic basis of natural variation in seed size and other components of maize yield (e.g., hundred kernel weight, number of rows per ear, number of kernels per row). In total, ten QTL affecting kernel size were identified, three of which (two for kernel length and one for kernel width) had stable expression in other components of maize yield. The possible genetic mechanism behind the trade-off of kernel size and yield components was discussed.

The Genetic Basis of Natural Variation in Kernel Size and Related Traits Using a Four-Way Cross Population in Maize

PubMed Central

Liu, Songtao; Li, Zhimin; Huang, Rongrong; Li, Yongming; Cheng, Hongliang; Li, Xiantang; Zhou, Bo; Wu, Suowei; Chen, Wei; Wu, Jianyu; Ding, Junqiang

2016-01-01

Kernel size is an important component of grain yield in maize breeding programs. To extend the understanding on the genetic basis of kernel size traits (i.e., kernel length, kernel width and kernel thickness), we developed a set of four-way cross mapping population derived from four maize inbred lines with varied kernel sizes. In the present study, we investigated the genetic basis of natural variation in seed size and other components of maize yield (e.g., hundred kernel weight, number of rows per ear, number of kernels per row). In total, ten QTL affecting kernel size were identified, three of which (two for kernel length and one for kernel width) had stable expression in other components of maize yield. The possible genetic mechanism behind the trade-off of kernel size and yield components was discussed. PMID:27070143

Somatic Cells Become Cancer’s “Starter Dough” | Center for Cancer Research

Cancer.gov

Cancer stem cells (CSCs) is a term that sparks animated differences of opinions among researchers in the oncology community.  Much of the disagreement comes from the difficulty involved in isolating these cells and manipulating them ex vivo. When putative CSCs are isolated from clinical samples, researchers are unable to retrospectively identify the cell type that suffered the

7 CFR 810.802 - Definition of other terms.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) Damaged kernels. Kernels and pieces of grain kernels for which standards have been established under the.... (d) Heat-damaged kernels. Kernels and pieces of grain kernels for which standards have been...

The Results of a Randomized Control Trial Evaluation of the SPARK Literacy Program

ERIC Educational Resources Information Center

Jones, Curtis J.; Christian, Michael; Rice, Andrew

2016-01-01

The purpose of this report is to present the results of a two-year randomized control trial evaluation of the SPARK literacy program. SPARK is an early grade literacy program developed by Boys & Girls Clubs of Greater Milwaukee. In 2010, SPARK was awarded an Investing in Innovation (i3) Department of Education grant to further develop the…

The Interest-Driven Pursuits of 15 Year Olds: "Sparks" and Their Association with Caring Relationships and Developmental Outcomes

ERIC Educational Resources Information Center

Ben-Eliyahu, Adar; Rhodes, Jean E.; Scales, Peter

2014-01-01

In this study, we examined the characteristics of adolescents' deep interests or "sparks," the role of relationships in supporting the development of sparks, and whether having a spark was associated with positive developmental outcomes. Participants included 1,860 15 years olds from across the United States who participated in the…

Effect of the SPARK Program on Physical Activity, Cardiorespiratory Endurance, and Motivation in Middle-School Students.

PubMed

Fu, You; Gao, Zan; Hannon, James C; Burns, Ryan D; Brusseau, Timothy A

2016-05-01

This study aimed to examine the effect of a 9-week SPARK program on physical activity (PA), cardiorespiratory endurance (Progressive Aerobic Cardiovascular Endurance Run; PACER), and motivation in middle-school students. 174 students attended baseline and posttests and change scores computed for each outcome. A MANOVA was employed to examine change score differences using follow-up ANOVA and Bonferroni post hoc tests. MANOVA yielded a significant interaction for Grade × Gender × Group (Wilks's Λ = 0.89, P < .001). ANOVA for PA revealed significant differences between SPARK grades 6 and 7 (Mean Δ = 8.11, P < .01) and Traditional grades 6 and 8 (Mean Δ = -6.96, P < .01). ANOVA also revealed greater PACER change for Traditional boys in grade 8 (P < .01) and SPARK girls in grade 8 (P < .01). There were significant interactions with perceived competence differences between SPARK grades 6 and 8 (Mean Δ = 0.38, P < .05), Enjoyment differences between SPARK grades 6 and 7 (Mean Δ = 0.67, P < .001), and SPARK grades 6 and 8 (Mean Δ = 0.81, P < .001). Following the intervention, SPARK displayed greater increases on PA and motivation measures in younger students compared with the Traditional program.

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2014 CFR

2014-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2011 CFR

2011-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2012 CFR

2012-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2013 CFR

2013-01-01

... kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as... purposes of determining inedible kernels, pieces, or particles of almond kernels. [59 FR 39419, Aug. 3...

Salient Feature Identification and Analysis using Kernel-Based Classification Techniques for Synthetic Aperture Radar Automatic Target Recognition

DTIC Science & Technology

2014-03-27

and machine learning for a range of research including such topics as medical imaging [10] and handwriting recognition [11]. The type of feature...1989. [11] C. Bahlmann, B. Haasdonk, and H. Burkhardt, “Online handwriting recognition with support vector machines-a kernel approach,” in Eighth...International Workshop on Frontiers in Handwriting Recognition, pp. 49–54, IEEE, 2002. [12] C. Cortes and V. Vapnik, “Support-vector networks,” Machine

Perspectives on Effective Teaching and the Cooperative Classroom. Analysis and Action Series.

ERIC Educational Resources Information Center

Reinhartz, Judy, Ed.

This collection of 7 articles focuses on the themes of 10 workshops that comprise an inservice training program, Effective Teaching, and the Cooperative Classroom. In "Research in Teacher Effectiveness," Georgea M. Sparks traces the research findings on effective teaching practices and educational outcomes during the last 15 years, and…

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

PubMed

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-06-19

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

Presumed PDF Modeling of Early Flame Propagation in Moderate to Intense Turbulence Environments

NASA Technical Reports Server (NTRS)

Carmen, Christina; Feikema, Douglas A.

2003-01-01

The present paper describes the results obtained from a one-dimensional time dependent numerical technique that simulates early flame propagation in a moderate to intense turbulent environment. Attention is focused on the development of a spark-ignited, premixed, lean methane/air mixture with the unsteady spherical flame propagating in homogeneous and isotropic turbulence. A Monte-Carlo particle tracking method, based upon the method of fractional steps, is utilized to simulate the phenomena represented by a probability density function (PDF) transport equation. Gaussian distributions of fluctuating velocity and fuel concentration are prescribed. Attention is focused on three primary parameters that influence the initial flame kernel growth: the detailed ignition system characteristics, the mixture composition, and the nature of the flow field. The computational results of moderate and intense isotropic turbulence suggests that flames within the distributed reaction zone are not as vulnerable, as traditionally believed, to the adverse effects of increased turbulence intensity. It is also shown that the magnitude of the flame front thickness significantly impacts the turbulent consumption flame speed. Flame conditions studied have fuel equivalence ratio s in the range phi = 0.6 to 0.9 at standard temperature and pressure.

A trial of ignition innovation of gasoline engine by nanosecond pulsed low temperature plasma ignition

NASA Astrophysics Data System (ADS)

Shiraishi, Taisuke; Urushihara, Tomonori; Gundersen, Martin

2009-07-01

Application of nanosecond pulsed low temperature plasma as an ignition technique for automotive gasoline engines, which require a discharge under conditions of high back pressure, has been studied experimentally using a single-cylinder engine. The nanosecond pulsed plasma refers to the transient (non-equilibrated) phase of a plasma before the formation of an arc discharge; it was obtained by applying a high voltage with a nanosecond pulse (FWHM of approximately 80 or 25 ns) between coaxial cylindrical electrodes. It was confirmed that nanosecond pulsed plasma can form a volumetric multi-channel streamer discharge at an energy consumption of 60 mJ cycle-1 under a high back pressure of 1400 kPa. It was found that the initial combustion period was shortened compared with the conventional spark ignition. The initial flame visualization suggested that the nanosecond pulsed plasma ignition results in the formation of a spatially dispersed initial flame kernel at a position of high electric field strength around the central electrode. It was observed that the electric field strength in the air gap between the coaxial cylindrical electrodes was increased further by applying a shorter pulse. It was also clarified that the shorter pulse improved ignitability even further.

Detection of Splice Sites Using Support Vector Machine

NASA Astrophysics Data System (ADS)

Varadwaj, Pritish; Purohit, Neetesh; Arora, Bhumika

Automatic identification and annotation of exon and intron region of gene, from DNA sequences has been an important research area in field of computational biology. Several approaches viz. Hidden Markov Model (HMM), Artificial Intelligence (AI) based machine learning and Digital Signal Processing (DSP) techniques have extensively and independently been used by various researchers to cater this challenging task. In this work, we propose a Support Vector Machine based kernel learning approach for detection of splice sites (the exon-intron boundary) in a gene. Electron-Ion Interaction Potential (EIIP) values of nucleotides have been used for mapping character sequences to corresponding numeric sequences. Radial Basis Function (RBF) SVM kernel is trained using EIIP numeric sequences. Furthermore this was tested on test gene dataset for detection of splice site by window (of 12 residues) shifting. Optimum values of window size, various important parameters of SVM kernel have been optimized for a better accuracy. Receiver Operating Characteristic (ROC) curves have been utilized for displaying the sensitivity rate of the classifier and results showed 94.82% accuracy for splice site detection on test dataset.

The effect of smelting time and composition of palm kernel shell charcoal reductant toward extractive Pomalaa nickel laterite ore in mini electric arc furnace

NASA Astrophysics Data System (ADS)

Sihotang, Iqbal Huda; Supriyatna, Yayat Iman; Ismail, Ika; Sulistijono

2018-04-01

Indonesia is a country that is rich in natural resources. Being a third country which has a nickel laterite ore in the world after New Caledonia and Philippines. However, the processing of nickel laterite ore to increase its levels in Indonesia is still lacking. In the processing of nickel laterite ore into metal, it can be processed by pyrometallurgy method that typically use coal as a reductant. However, coal is a non-renewable energy and have high enough levels of pollution. One potentially replace is the biomass, that is a renewable energy. Palm kernel shell are biomass that can be used as a reductant because it has a fairly high fix carbon content. This research aims to make nickel laterite ores become metal using palm kernel shell charcoal as reductant in mini electric arc furnace. The result show that the best smelting time of this research is 60 minutes with the best composition of the reductant is 2,000 gram.

An Overview of NASA Research on Positive Displacement Type General Aviation Engines

NASA Technical Reports Server (NTRS)

Kempke, E. E.; Willis, E. A.

1979-01-01

The general aviation positive displacement engine program encompassing conventional, lightweight diesel, and rotary combustion engines is described. Lean operation of current production type spark ignition engines and advanced alternative engine concepts are emphasized.

Classification With Truncated Distance Kernel.

PubMed

Huang, Xiaolin; Suykens, Johan A K; Wang, Shuning; Hornegger, Joachim; Maier, Andreas

2018-05-01

This brief proposes a truncated distance (TL1) kernel, which results in a classifier that is nonlinear in the global region but is linear in each subregion. With this kernel, the subregion structure can be trained using all the training data and local linear classifiers can be established simultaneously. The TL1 kernel has good adaptiveness to nonlinearity and is suitable for problems which require different nonlinearities in different areas. Though the TL1 kernel is not positive semidefinite, some classical kernel learning methods are still applicable which means that the TL1 kernel can be directly used in standard toolboxes by replacing the kernel evaluation. In numerical experiments, the TL1 kernel with a pregiven parameter achieves similar or better performance than the radial basis function kernel with the parameter tuned by cross validation, implying the TL1 kernel a promising nonlinear kernel for classification tasks.

Plasma spark discharge reactor and durable electrode

DOEpatents

Cho, Young I.; Cho, Daniel J.; Fridman, Alexander; Kim, Hyoungsup

2017-01-10

A plasma spark discharge reactor for treating water. The plasma spark discharge reactor comprises a HV electrode with a head and ground electrode that surrounds at least a portion of the HV electrode. A passage for gas may pass through the reactor to a location proximate to the head to provide controlled formation of gas bubbles in order to facilitate the plasma spark discharge in a liquid environment.

40 CFR Table 1b to Subpart Zzzz of... - Operating Limitations for Existing, New, and Reconstructed Spark Ignition 4SRB Stationary RICE...

Code of Federal Regulations, 2012 CFR

2012-07-01

..., and Reconstructed Spark Ignition 4SRB Stationary RICE >500 HP Located at a Major Source of HAP Emissions and Existing Spark Ignition 4SRB Stationary RICE >500 HP Located at an Area Source of HAP... Limitations for Existing, New, and Reconstructed Spark Ignition 4SRB Stationary RICE >500 HP Located at a...

40 CFR Table 1b to Subpart Zzzz of... - Operating Limitations for Existing, New, and Reconstructed Spark Ignition 4SRB Stationary RICE...

Code of Federal Regulations, 2011 CFR

2011-07-01

..., New, and Reconstructed Spark Ignition 4SRB Stationary RICE >500 HP Located at a Major Source of HAP Emissions and Existing Spark Ignition 4SRB Stationary RICE >500 HP Located at an Area Source of HAP... Limitations for Existing, New, and Reconstructed Spark Ignition 4SRB Stationary RICE >500 HP Located at a...

Modeling of Spark Gap Performance

DTIC Science & Technology

1983-06-01

MODELING OF SPARK GAP PERFORMANCE* A. L. Donaldson, R. Ness, M. Hagler, M. Kristiansen Department of Electrical Engineering and L. L. Hatfield...gas pressure, and chaJ:ging rate on the voltage stability of high energy spark gaps is discussed. Implications of the model include changes in...an extremely useful, and physically reasonable framework, from which the properties of spark gaps under a wide variety of experimental conditions

Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

PubMed

Hussain, Lal

2018-06-01

Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

Underwater spark discharge with long transmission line for cleaning horizontal wells

NASA Astrophysics Data System (ADS)

Lee, Kern; Chung, Kyoung-Jae; Hwang, Y. S.; Kim, C. Y.

2017-06-01

A transmission line is discussed for application in an underwater spark-discharge technique in the cleaning of a horizontal well by incorporating a power-transmission model into the simulation. The pulsed-spark-discharge technique has been proposed for clogged-well rehabilitation, because it removes incrustations that are attached to well screens by using strong pressure waves that are generated by the rapid expansion of a spark channel. To apply the pulsed-spark-discharge technique to the cleaning of horizontal wells, the coaxial cable between the pulsed power supply and the spark gap as a load needs to be extended to a few hundred meters. Prior to field application, pulsed-spark-discharge experiments were conducted and the role of the transmission line was examined using an improved simulation model. In the model, a non-linear interaction of the spark channel and the capacitor bank is described by the pulse-forming action of the coaxial cable. Based on the accurate physical properties of the water plasma, such as the equation of state and electrical conductivity within the region of interest, the amount of energy contributed to the development of a shock wave was evaluated. The simulation shows that if the initial conditions of the spark channel are the same, no further reduction in strength of the pressure wave occurs, even if the cable length is increased above 50 m. Hence, the degraded peak pressure that was observed in the experiments using the longer cable is attributed to a change in the initial condition of the spark channel. The parametric study suggests that the low initial charging voltage, the high ambient water pressure, and the long cable length yield the low initial spark-channel density, which results in a reduced peak pressure. The simulation of line charging is presented to discuss the principle of disturbing the pre-breakdown process by an extended cable.

SciSpark's SRDD : A Scientific Resilient Distributed Dataset for Multidimensional Data

NASA Astrophysics Data System (ADS)

Palamuttam, R. S.; Wilson, B. D.; Mogrovejo, R. M.; Whitehall, K. D.; Mattmann, C. A.; McGibbney, L. J.; Ramirez, P.

2015-12-01

Remote sensing data and climate model output are multi-dimensional arrays of massive sizes locked away in heterogeneous file formats (HDF5/4, NetCDF 3/4) and metadata models (HDF-EOS, CF) making it difficult to perform multi-stage, iterative science processing since each stage requires writing and reading data to and from disk. We have developed SciSpark, a robust Big Data framework, that extends ApacheTM Spark for scaling scientific computations. Apache Spark improves the map-reduce implementation in ApacheTM Hadoop for parallel computing on a cluster, by emphasizing in-memory computation, "spilling" to disk only as needed, and relying on lazy evaluation. Central to Spark is the Resilient Distributed Dataset (RDD), an in-memory distributed data structure that extends the functional paradigm provided by the Scala programming language. However, RDDs are ideal for tabular or unstructured data, and not for highly dimensional data. The SciSpark project introduces the Scientific Resilient Distributed Dataset (sRDD), a distributed-computing array structure which supports iterative scientific algorithms for multidimensional data. SciSpark processes data stored in NetCDF and HDF files by partitioning them across time or space and distributing the partitions among a cluster of compute nodes. We show usability and extensibility of SciSpark by implementing distributed algorithms for geospatial operations on large collections of multi-dimensional grids. In particular we address the problem of scaling an automated method for finding Mesoscale Convective Complexes. SciSpark provides a tensor interface to support the pluggability of different matrix libraries. We evaluate performance of the various matrix libraries in distributed pipelines, such as Nd4jTM and BreezeTM. We detail the architecture and design of SciSpark, our efforts to integrate climate science algorithms, parallel ingest and partitioning (sharding) of A-Train satellite observations from model grids. These solutions are encompassed in SciSpark, an open-source software framework for distributed computing on scientific data.

Large-scale seismic waveform quality metric calculation using Hadoop

NASA Astrophysics Data System (ADS)

Magana-Zook, S.; Gaylord, J. M.; Knapp, D. R.; Dodge, D. A.; Ruppert, S. D.

2016-09-01

In this work we investigated the suitability of Hadoop MapReduce and Apache Spark for large-scale computation of seismic waveform quality metrics by comparing their performance with that of a traditional distributed implementation. The Incorporated Research Institutions for Seismology (IRIS) Data Management Center (DMC) provided 43 terabytes of broadband waveform data of which 5.1 TB of data were processed with the traditional architecture, and the full 43 TB were processed using MapReduce and Spark. Maximum performance of 0.56 terabytes per hour was achieved using all 5 nodes of the traditional implementation. We noted that I/O dominated processing, and that I/O performance was deteriorating with the addition of the 5th node. Data collected from this experiment provided the baseline against which the Hadoop results were compared. Next, we processed the full 43 TB dataset using both MapReduce and Apache Spark on our 18-node Hadoop cluster. These experiments were conducted multiple times with various subsets of the data so that we could build models to predict performance as a function of dataset size. We found that both MapReduce and Spark significantly outperformed the traditional reference implementation. At a dataset size of 5.1 terabytes, both Spark and MapReduce were about 15 times faster than the reference implementation. Furthermore, our performance models predict that for a dataset of 350 terabytes, Spark running on a 100-node cluster would be about 265 times faster than the reference implementation. We do not expect that the reference implementation deployed on a 100-node cluster would perform significantly better than on the 5-node cluster because the I/O performance cannot be made to scale. Finally, we note that although Big Data technologies clearly provide a way to process seismic waveform datasets in a high-performance and scalable manner, the technology is still rapidly changing, requires a high degree of investment in personnel, and will likely require significant changes in other parts of our infrastructure. Nevertheless, we anticipate that as the technology matures and third-party tool vendors make it easier to manage and operate clusters, Hadoop (or a successor) will play a large role in our seismic data processing.

Fundamental Studies of Ignition Process in Large Natural Gas Engines Using Laser Spark Ignition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Azer Yalin; Bryan Willson

Past research has shown that laser ignition provides a potential means to reduce emissions and improve engine efficiency of gas-fired engines to meet longer-term DOE ARES (Advanced Reciprocating Engine Systems) targets. Despite the potential advantages of laser ignition, the technology is not seeing practical or commercial use. A major impediment in this regard has been the 'open-path' beam delivery used in much of the past research. This mode of delivery is not considered industrially practical owing to safety factors, as well as susceptibility to vibrations, thermal effects etc. The overall goal of our project has been to develop technologies andmore » approaches for practical laser ignition systems. To this end, we are pursuing fiber optically coupled laser ignition system and multiplexing methods for multiple cylinder engine operation. This report summarizes our progress in this regard. A partial summary of our progress includes: development of a figure of merit to guide fiber selection, identification of hollow-core fibers as a potential means of fiber delivery, demonstration of bench-top sparking through hollow-core fibers, single-cylinder engine operation with fiber delivered laser ignition, demonstration of bench-top multiplexing, dual-cylinder engine operation via multiplexed fiber delivered laser ignition, and sparking with fiber lasers. To the best of our knowledge, each of these accomplishments was a first.« less

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

PubMed Central

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-01-01

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification. PMID:28629202

Gabor-based kernel PCA with fractional power polynomial models for face recognition.

PubMed

Liu, Chengjun

2004-05-01

This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.

Improved lifetime high voltage switch electrode

NASA Astrophysics Data System (ADS)

Halverson, W.

1985-06-01

In this Phase 1 Small Business Innovation Research (SBIR) program, preliminary tests of ion implantation to increase the lifetime of spark switch electrodes have indicated that a 185 keV carbon ion implant into a tungsten-copper composite has reduced electrode erosion by a factor of two to four. Apparently, the thin layer of tungsten carbide (WC) has better thermal properties than pure tungsten; the WC may have penetrated into the unimplanted body of the electrode by liquid and/or solid phase diffusion during erosion testing. These encouraging results should provide the basis for a Phase 2 SBIR program to investigate further the physical and chemical effects of ion implantation on spark gap electrodes and to optimize the technique for applications.

Research on personalized recommendation algorithm based on spark

NASA Astrophysics Data System (ADS)

Li, Zeng; Liu, Yu

2018-04-01

With the increasing amount of data in the past years, the traditional recommendation algorithm has been unable to meet people's needs. Therefore, how to better recommend their products to users of interest, become the opportunities and challenges of the era of big data development. At present, each platform enterprise has its own recommendation algorithm, but how to make efficient and accurate push information is still an urgent problem for personalized recommendation system. In this paper, a hybrid algorithm based on user collaborative filtering and content-based recommendation algorithm is proposed on Spark to improve the efficiency and accuracy of recommendation by weighted processing. The experiment shows that the recommendation under this scheme is more efficient and accurate.

Big Data Approaches for the Analysis of Large-Scale fMRI Data Using Apache Spark and GPU Processing: A Demonstration on Resting-State fMRI Data from the Human Connectome Project

PubMed Central

Boubela, Roland N.; Kalcher, Klaudius; Huf, Wolfgang; Našel, Christian; Moser, Ewald

2016-01-01

Technologies for scalable analysis of very large datasets have emerged in the domain of internet computing, but are still rarely used in neuroimaging despite the existence of data and research questions in need of efficient computation tools especially in fMRI. In this work, we present software tools for the application of Apache Spark and Graphics Processing Units (GPUs) to neuroimaging datasets, in particular providing distributed file input for 4D NIfTI fMRI datasets in Scala for use in an Apache Spark environment. Examples for using this Big Data platform in graph analysis of fMRI datasets are shown to illustrate how processing pipelines employing it can be developed. With more tools for the convenient integration of neuroimaging file formats and typical processing steps, big data technologies could find wider endorsement in the community, leading to a range of potentially useful applications especially in view of the current collaborative creation of a wealth of large data repositories including thousands of individual fMRI datasets. PMID:26778951

Injection system used into SI engines for complete combustion and reduction of exhaust emissions in the case of alcohol and petrol alcohol mixtures feed

NASA Astrophysics Data System (ADS)

Ispas, N.; Cofaru, C.; Aleonte, M.

2017-10-01

Internal combustion engines still play a major role in today transportation but increasing the fuel efficiency and decreasing chemical emissions remain a great goal of the researchers. Direct injection and air assisted injection system can improve combustion and can reduce the concentration of the exhaust gas pollutes. Advanced air-to-fuel and combustion air-to-fuel injection system for mixtures, derivatives and alcohol gasoline blends represent a major asset in reducing pollutant emissions and controlling combustion processes in spark-ignition engines. The use of these biofuel and biofuel blending systems for gasoline results in better control of spark ignition engine processes, making combustion as complete as possible, as well as lower levels of concentrations of pollutants in exhaust gases. The main purpose of this paper was to provide most suitable tools for ensure the proven increase in the efficiency of spark ignition engines, making them more environmentally friendly. The conclusions of the paper allow to highlight the paths leading to a better use of alcohols (biofuels) in internal combustion engines of modern transport units.

Advanced Light-Duty SI Engine Fuels Research: Multiple Optical Diagnostics of Well-mixed and Stratified Operation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sjoberg, Carl Magnus Goran; Vuilleumier, David

Ever tighter fuel economy standards and concerns about energy security motivate efforts to improve engine efficiency and to develop alternative fuels. This project contributes to the science base needed by industry to develop highly efficient direct injection spark ignition (DISI) engines that also beneficially exploit the different properties of alternative fuels. Here, the emphasis is on lean operation, which can provide higher efficiencies than traditional non-dilute stoichiometric operation. Since lean operation can lead to issues with ignition stability, slow flame propagation and low combustion efficiency, the focus is on techniques that can overcome these challenges. Specifically, fuel stratification is usedmore » to ensure ignition and completeness of combustion but this technique has soot and NOx emissions challenges. For ultra-lean well-mixed operation, turbulent deflagration can be combined with controlled end-gas autoignition to render mixed-mode combustion for sufficiently fast heat release. However, such mixed-mode combustion requires very stable inflammation, motivating studies on the effects of near-spark flow and turbulence, and the use of small amounts of fuel stratification near the spark plug.« less

A multi-label learning based kernel automatic recommendation method for support vector machine.

PubMed

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine

PubMed Central

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance. PMID:25893896

Cavitation Erosion of Electro Spark Deposited Nitinol vs. Stellite Alloy on Stainless Steel Substrate

DTIC Science & Technology

2015-07-15

EROSION OF ELECTRO SPARK DEPOSITED NITINOL VS. STELLITE® ALLOY ON STAINLESS STEEL SUBSTRATE Theresa A. Hoffard Lean-Miguel San Pedro Mikhail...SUBTITLE 5a. CONTRACT NUMBER CAVITATION EROSION TESTING OF ELECTRO SPARK DEPOSITED NITINOL VS STELLITE® ALLOY ON STAINLESS STEEL SUBTRATE 5b. GRANT...of combining Nitinol (NiTi) superelastic metal alloy with ElectroSpark Deposition (ESD) technology to increase the cavitation erosion resistance of

Reactive Spark Plasma Sintering (SPS) of Nitride Reinforced Titanium Alloy Composites (Postprint)

DTIC Science & Technology

2014-08-15

AFRL-RX-WP-JA-2014-0177 REACTIVE SPARK PLASMA SINTERING (SPS) OF NITRIDE REINFORCED TITANIUM ALLOY COMPOSITES (POSTPRINT) Jaimie S...titanium–vanadium alloys, has been achieved by introducing reactive nitrogen gas during the spark plasma sintering (SPS) of blended titanium and...lcomReactive spark plasma sintering (SPS) of nitride reinforced titanium alloy compositeshttp://dx.doi.org/10.1016/j.jallcom.2014.08.049 0925-8388

Type 1 Inositol (1,4,5)-Trisphosphate Receptor Activates Ryanodine Receptor 1 to Mediate Calcium Spark Signaling in Adult Mammalian Skeletal Muscle*♦

PubMed Central

Tjondrokoesoemo, Andoria; Li, Na; Lin, Pei-Hui; Pan, Zui; Ferrante, Christopher J.; Shirokova, Natalia; Brotto, Marco; Weisleder, Noah; Ma, Jianjie

2013-01-01

Functional coupling between inositol (1,4,5)-trisphosphate receptor (IP3R) and ryanodine receptor (RyR) represents a critical component of intracellular Ca2+ signaling in many excitable cells; however, the role of this mechanism in skeletal muscle remains elusive. In skeletal muscle, RyR-mediated Ca2+ sparks are suppressed in resting conditions, whereas application of transient osmotic stress can trigger activation of Ca2+ sparks that are restricted to the periphery of the fiber. Here we show that onset of these spatially confined Ca2+ sparks involves interaction between activation of IP3R and RyR near the sarcolemmal membrane. Pharmacological prevention of IP3 production or inhibition of IP3R channel activity abolishes stress-induced Ca2+ sparks in skeletal muscle. Although genetic ablation of the type 2 IP3R does not appear to affect Ca2+ sparks in skeletal muscle, specific silencing of the type 1 IP3R leads to ablation of stress-induced Ca2+ sparks. Our data indicate that membrane-delimited signaling involving cross-talk between IP3R1 and RyR1 contributes to Ca2+ spark activation in skeletal muscle. PMID:23223241

Scanning Apollo Flight Films and Reconstructing CSM Trajectories

NASA Astrophysics Data System (ADS)

Speyerer, E.; Robinson, M. S.; Grunsfeld, J. M.; Locke, S. D.; White, M.

2006-12-01

Over thirty years ago, the astronauts of the Apollo program made the journey from the Earth to the Moon and back. To record their historic voyages and collect scientific observations many thousands of photographs were acquired with handheld and automated cameras. After returning to Earth, these films were developed and stored at the film archive at Johnson Space Center (JSC), where they still reside. Due to the historical significance of the original flight films typically only duplicate (2nd or 3rd generation) film products are studied and used to make prints. To allow full access to the original flight films for both researchers and the general public, JSC and Arizona State University are scanning and creating an online digital archive. A Leica photogrammetric scanner is being used to insure geometric and radiometric fidelity. Scanning resolution will preserve the grain of the film. Color frames are being scanned and archived as 48 bit pixels to insure capture of the full dynamic range of the film (16 bit for BW). The raw scans will consist of 70 Terabytes of data (10,000 BW Hasselblad, 10,000 color Hasselblad, 10,000 Metric frames, 4500 Pan frames, 620 35mm frames counts; are estimates). All the scanned films will be made available for download through a searchable database. Special tools are being developed to locate images based on various search parameters. To geolocate metric and panoramic frames acquired during Apollos 15\\-17, prototype SPICE kernels are being generated from existing photographic support data by entering state vectors and timestamps from multiple points throughout each orbit into the NAIF toolkit to create a type 9 Spacecraft and Planet Ephemeris Kernel (SPK), a nadir pointing C\\- matrix Kernel (CK), and a Spacecraft Clock Kernel (SCLK). These SPICE kernels, in addition to the Instrument Kernel (IK) and Frames Kernel (FK) that also under development, will be archived along with the scanned images. From the generated kernels, several IDL programs have been designed to display orbital tracks, produce footprint plots, and create image projections. Using the output from these SPICE based programs enables accurate geolocating of SIM bay photography as well as providing potential data from lunar gravitational studies.

Utilizing fluorescence hyperspectral imaging to differentiate corn inoculated with toxigenic and atoxigenic fungal strains

NASA Astrophysics Data System (ADS)

Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Brown, Robert L.; Bhatnagar, Deepak; Cleveland, Thomas E.

2012-05-01

Naturally occurring Aspergillus flavus strains can be either toxigenic or atoxigenic, indicating their ability to produce aflatoxin or not, under specific conditions. Corn contaminated with toxigenic strains of A. flavus can result in great losses to the agricultural industry and pose threats to public health. Past research showed that fluorescence hyperspectral imaging could be a potential tool for rapid and non-invasive detection of aflatoxin contaminated corn. The objective of the current study was to assess, with the use of a hyperspectral sensor, the difference in fluorescence emission between corn kernels inoculated with toxigenic and atoxigenic inoculums of A. flavus. Corn ears were inoculated with AF13, a toxigenic strain of A. flavus, and AF38, an atoxigenic strain of A. flavus, at dough stage of development and harvested 8 weeks after inoculation. After harvest, single corn kernels were divided into three groups prior to imaging: control, adjacent, and glowing. Both sides of the kernel, germplasm and endosperm, were imaged separately using a fluorescence hyperspectral imaging system. It was found that the classification accuracies of the three manually designated groups were not promising. However, the separation of corn kernels based on different fungal inoculums yielded better results. The best result was achieved with the germplasm side of the corn kernels. Results are expected to enhance the potential of fluorescence hyperspectral imaging for the detection of aflatoxin contaminated corn.

Assessing pigmented pericarp of maize kernels as possible source of resistance to fusarium ear rot, Fusarium spp. infection and fumonisin accumulation.

PubMed

Venturini, Giovanni; Babazadeh, Laleh; Casati, Paola; Pilu, Roberto; Salomoni, Daiana; Toffolatti, Silvia L

2016-06-16

One of the purposes of maize genetic improvement is the research of genotypes resistant to fusarium ear rot (FER) and fumonisin accumulation. Flavonoids in the pericarp of the kernels are considered particularly able to reduce the fumonisin accumulation (FUM). The aim of this field study was to assess the effect of flavonoids, associated with anti-insect protection and Fusarium verticillioides inoculation, on FER symptoms and fumonisin contamination in maize kernels. Two isogenic hybrids, one having pigmentation in the pericarp (P1-rr) and the other without it (P1-wr), were compared. P1-rr showed lower values of FER symptoms and FUM contamination than P1-wr only if the anti-insect protection and the F. verticillioides inoculations were applied in combination. Fusarium spp. kernel infection was not influenced by the presence of flavonoids in the pericarp. Artificial F. verticillioides inoculation was more effective than anti-insect protection in enhancing the inhibition activity of flavonoids toward FUM contamination. The interactions between FUM contamination levels and FER ratings were better modeled in the pigmented hybrid than in the unpigmented one. The variable role that the pigment played in kernel defense against FER and FUM indicates that flavonoids alone may not be completely effective in the resistance of fumonisin contamination in maize. Copyright © 2016 Elsevier B.V. All rights reserved.

SparkJet Efficiency

NASA Technical Reports Server (NTRS)

Golbabaei-Asl, Mona; Knight, Doyle; Anderson, Kellie; Wilkinson, Stephen

2013-01-01

A novel method for determining the thermal efficiency of the SparkJet is proposed. A SparkJet is attached to the end of a pendulum. The motion of the pendulum subsequent to a single spark discharge is measured using a laser displacement sensor. The measured displacement vs time is compared with the predictions of a theoretical perfect gas model to estimate the fraction of the spark discharge energy which results in heating the gas (i.e., increasing the translational-rotational temperature). The results from multiple runs for different capacitances of c = 3, 5, 10, 20, and 40 micro-F show that the thermal efficiency decreases with higher capacitive discharges.

7 CFR 981.7 - Edible kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Edible kernel. 981.7 Section 981.7 Agriculture... Regulating Handling Definitions § 981.7 Edible kernel. Edible kernel means a kernel, piece, or particle of almond kernel that is not inedible. [41 FR 26852, June 30, 1976] ...

Kernel K-Means Sampling for Nyström Approximation.

PubMed

He, Li; Zhang, Hong

2018-05-01

A fundamental problem in Nyström-based kernel matrix approximation is the sampling method by which training set is built. In this paper, we suggest to use kernel -means sampling, which is shown in our works to minimize the upper bound of a matrix approximation error. We first propose a unified kernel matrix approximation framework, which is able to describe most existing Nyström approximations under many popular kernels, including Gaussian kernel and polynomial kernel. We then show that, the matrix approximation error upper bound, in terms of the Frobenius norm, is equal to the -means error of data points in kernel space plus a constant. Thus, the -means centers of data in kernel space, or the kernel -means centers, are the optimal representative points with respect to the Frobenius norm error upper bound. Experimental results, with both Gaussian kernel and polynomial kernel, on real-world data sets and image segmentation tasks show the superiority of the proposed method over the state-of-the-art methods.

Spark ignition of flowing gases I : energies to ignite propane-air mixtures in pressure range of 2 to 4 inches mercury absolute

NASA Technical Reports Server (NTRS)

Swett, Clyde C , Jr

1949-01-01

Ignition studies of flowing gases were made to obtain information applicable to ignition problems in gas-turbine and ram-jet aircraft propulsion systems operating at altitude conditions.Spark energies required for ignition of a flowing propane-air mixture were determined for pressure of 2 to 4 inches mercury absolute, gas velocities of 5.0 to 54.2 feet per second, fuel-air ratios of 0.0607 to 0.1245, and spark durations of 1.5 to 24,400 microseconds. The results showed that at a pressure of 3 inches mercury absolute the minimum energy required for ignition occurred at fuel-air ratios of 0.08 to 0.095. The energy required for ignition increased almost linearly with increasing gas velocity. Shortening the spark duration from approximately 25,000 to 125 microseconds decreased the amount of energy required for ignition. A spark produced by the discharge of a condenser directly into the spark gap and having a duration of 1.5 microseconds required ignition energies larger than most of the long-duration sparks.

Exploiting graph kernels for high performance biomedical relation extraction.

PubMed

Panyam, Nagesh C; Verspoor, Karin; Cohn, Trevor; Ramamohanarao, Kotagiri

2018-01-30

Relation extraction from biomedical publications is an important task in the area of semantic mining of text. Kernel methods for supervised relation extraction are often preferred over manual feature engineering methods, when classifying highly ordered structures such as trees and graphs obtained from syntactic parsing of a sentence. Tree kernels such as the Subset Tree Kernel and Partial Tree Kernel have been shown to be effective for classifying constituency parse trees and basic dependency parse graphs of a sentence. Graph kernels such as the All Path Graph kernel (APG) and Approximate Subgraph Matching (ASM) kernel have been shown to be suitable for classifying general graphs with cycles, such as the enhanced dependency parse graph of a sentence. In this work, we present a high performance Chemical-Induced Disease (CID) relation extraction system. We present a comparative study of kernel methods for the CID task and also extend our study to the Protein-Protein Interaction (PPI) extraction task, an important biomedical relation extraction task. We discuss novel modifications to the ASM kernel to boost its performance and a method to apply graph kernels for extracting relations expressed in multiple sentences. Our system for CID relation extraction attains an F-score of 60%, without using external knowledge sources or task specific heuristic or rules. In comparison, the state of the art Chemical-Disease Relation Extraction system achieves an F-score of 56% using an ensemble of multiple machine learning methods, which is then boosted to 61% with a rule based system employing task specific post processing rules. For the CID task, graph kernels outperform tree kernels substantially, and the best performance is obtained with APG kernel that attains an F-score of 60%, followed by the ASM kernel at 57%. The performance difference between the ASM and APG kernels for CID sentence level relation extraction is not significant. In our evaluation of ASM for the PPI task, ASM performed better than APG kernel for the BioInfer dataset, in the Area Under Curve (AUC) measure (74% vs 69%). However, for all the other PPI datasets, namely AIMed, HPRD50, IEPA and LLL, ASM is substantially outperformed by the APG kernel in F-score and AUC measures. We demonstrate a high performance Chemical Induced Disease relation extraction, without employing external knowledge sources or task specific heuristics. Our work shows that graph kernels are effective in extracting relations that are expressed in multiple sentences. We also show that the graph kernels, namely the ASM and APG kernels, substantially outperform the tree kernels. Among the graph kernels, we showed the ASM kernel as effective for biomedical relation extraction, with comparable performance to the APG kernel for datasets such as the CID-sentence level relation extraction and BioInfer in PPI. Overall, the APG kernel is shown to be significantly more accurate than the ASM kernel, achieving better performance on most datasets.

Expand and Regularize Federal Funding for Human Pluripotent Stem Cell Research

ERIC Educational Resources Information Center

Owen-Smith, Jason; Scott, Christopher Thomas; McCormick, Jennifer B.

2012-01-01

Human embryonic stem cell (hESC) research has sparked incredible scientific and public excitement, as well as significant controversy. hESCs are pluripotent, which means, in theory, that they can be differentiated into any type of cell found in the human body. Thus, they evoke great enthusiasm about potential clinical applications. They are…

Entrepreneurial Education: Creating a Usable Economic Community Base. Rural Research Report. Volume 15, Issue 8, Spring 2004

ERIC Educational Resources Information Center

Williams, Lori E.

2004-01-01

If small businesses are the engines of our economy, then entrepreneurs are the sparks that ignite those engines. This research report will explain how entrepreneurship education programs can help students create and establish their own business. The paper defines entrepreneurship education, outlines the major components of entrepreneurship…

Alternatives to Animal Use in Research, Testing, and Education. Summary.

ERIC Educational Resources Information Center

Congress of the U.S., Washington, DC. Office of Technology Assessment.

With an estimated 17-22 million animals used in laboratories annually in the United States, public interest in animal welfare has sparked an often emotional debate over such uses of animals. Concerns focus on balancing societal needs for continued progress in biomedical and behavioral research, for toxicity testing to safeguard the public, and for…

Ignition of Nanocomposite Thermites by Electric Spark and Shock Wave

DTIC Science & Technology

2014-04-30

Acknowledgments The research described here was based on work supported by the US Army Research Office under awards W911NG-13-0217 ( DDD ) and W911NF-12-1...0161 (ELD), and the US Defense Threat Reduc- tion Agency (DTRA) under award HDTRA1-12-1-0011 ( DDD ). William L. Shaw acknowledges support from the

Grounding Our Vision: Brain Research and Strategic Vision

ERIC Educational Resources Information Center

Walker, Mike

2011-01-01

While recognizing the value of "vision," it could be argued that vision alone--at least in schools--is not enough to rally the financial and emotional support required to translate an idea into reality. A compelling vision needs to reflect substantive, research-based knowledge if it is to spark the kind of strategic thinking and insight…

Research Linking Teacher Preparation and Student Performance: Teacher Education Yearbook XII

ERIC Educational Resources Information Center

Guyton, Edith M., Ed.; Dangel, Julie Rainer, Ed.

2004-01-01

The case studies, research, and projects presented here strike at the very heart of effective teaching. The specific practices to improve student performance will spark classroom discussion and improve teacher practice. After a foreword by Frances van Tassell and an introduction by Edith M. Guyton and Julie Rainer Dangel, this book is divided into…

An implementation of support vector machine on sentiment classification of movie reviews

NASA Astrophysics Data System (ADS)

Yulietha, I. M.; Faraby, S. A.; Adiwijaya; Widyaningtyas, W. C.

2018-03-01

With technological advances, all information about movie is available on the internet. If the information is processed properly, it will get the quality of the information. This research proposes to the classify sentiments on movie review documents. This research uses Support Vector Machine (SVM) method because it can classify high dimensional data in accordance with the data used in this research in the form of text. Support Vector Machine is a popular machine learning technique for text classification because it can classify by learning from a collection of documents that have been classified previously and can provide good result. Based on number of datasets, the 90-10 composition has the best result that is 85.6%. Based on SVM kernel, kernel linear with constant 1 has the best result that is 84.9%

7 CFR 810.2202 - Definition of other terms.

Code of Federal Regulations, 2014 CFR

2014-01-01

... kernels, foreign material, and shrunken and broken kernels. The sum of these three factors may not exceed... the removal of dockage and shrunken and broken kernels. (g) Heat-damaged kernels. Kernels, pieces of... sample after the removal of dockage and shrunken and broken kernels. (h) Other grains. Barley, corn...

7 CFR 981.8 - Inedible kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Inedible kernel. 981.8 Section 981.8 Agriculture... Regulating Handling Definitions § 981.8 Inedible kernel. Inedible kernel means a kernel, piece, or particle of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel, or...

7 CFR 51.1415 - Inedible kernels.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Inedible kernels. 51.1415 Section 51.1415 Agriculture... Standards for Grades of Pecans in the Shell 1 Definitions § 51.1415 Inedible kernels. Inedible kernels means that the kernel or pieces of kernels are rancid, moldy, decayed, injured by insects or otherwise...

An Approximate Approach to Automatic Kernel Selection.

PubMed

Ding, Lizhong; Liao, Shizhong

2016-02-02

Kernel selection is a fundamental problem of kernel-based learning algorithms. In this paper, we propose an approximate approach to automatic kernel selection for regression from the perspective of kernel matrix approximation. We first introduce multilevel circulant matrices into automatic kernel selection, and develop two approximate kernel selection algorithms by exploiting the computational virtues of multilevel circulant matrices. The complexity of the proposed algorithms is quasi-linear in the number of data points. Then, we prove an approximation error bound to measure the effect of the approximation in kernel matrices by multilevel circulant matrices on the hypothesis and further show that the approximate hypothesis produced with multilevel circulant matrices converges to the accurate hypothesis produced with kernel matrices. Experimental evaluations on benchmark datasets demonstrate the effectiveness of approximate kernel selection.

Potential of Spark Ignition Engine, 1979 Summary Source Document

DOT National Transportation Integrated Search

1980-03-01

This report provides an assessment of the potential for spark ignition engines passenger cars and light trucks. The assessment includes: tradeoffs between fuel economy and emissions; improvements in spark ignition engine efficiency; improvements in e...

Liquid-Arc/Spark-Excitation Atomic-Emission Spectroscopy

NASA Technical Reports Server (NTRS)

Schlagen, Kenneth J.

1992-01-01

Constituents of solutions identified in situ. Liquid-arc/spark-excitation atomic-emission spectroscopy (LAES) is experimental variant of atomic-emission spectroscopy in which electric arc or spark established in liquid and spectrum of light from arc or spark analyzed to identify chemical elements in liquid. Observations encourage development of LAES equipment for online monitoring of process streams in such industries as metal plating, electronics, and steel, and for online monitoring of streams affecting environment.

Simultaneous dual mode combustion engine operating on spark ignition and homogenous charge compression ignition

DOEpatents

Fiveland, Scott B.; Wiggers, Timothy E.

2004-06-22

An engine particularly suited to single speed operation environments, such as stationary power generators. The engine includes a plurality of combustion cylinders operable under homogenous charge compression ignition, and at least one combustion cylinder operable on spark ignition concepts. The cylinder operable on spark ignition concepts can be convertible to operate under homogenous charge compression ignition. The engine is started using the cylinders operable under spark ignition concepts.

Energy loss in spark gap switches

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oreshkin, V. I., E-mail: oreshkin@ovpe.hcei.tsc.ru; Lavrinovich, I. V.; National Research Tomsk Polytechnic University, Lenin Avenue 30, 634050 Tomsk

2014-04-15

The paper reports on numerical study of the energy loss in spark gap switches. The operation of the switches is analyzed using the Braginsky model which allows calculation of the time dependence of the spark channel resistance. The Braginsky equation is solved simultaneously with generator circuit equations for different load types. Based on the numerical solutions, expressions which determine both the energy released in a spark gap switch and the switching time are derived.

ObsPy: A Python toolbox for seismology - Current state, applications, and ecosystem around it

NASA Astrophysics Data System (ADS)

Lecocq, Thomas; Megies, Tobias; Krischer, Lion; Sales de Andrade, Elliott; Barsch, Robert; Beyreuther, Moritz

2016-04-01

ObsPy (http://www.obspy.org) is a community-driven, open-source project offering a bridge for seismology into the scientific Python ecosystem. It provides * read and write support for essentially all commonly used waveform, station, and event metadata formats with a unified interface, * a comprehensive signal processing toolbox tuned to the needs of seismologists, * integrated access to all large data centers, web services and databases, and * convenient wrappers to third party codes like libmseed and evalresp. Python, in contrast to many other languages and tools, is simple enough to enable an exploratory and interactive coding style desired by many scientists. At the same time it is a full-fledged programming language usable by software engineers to build complex and large programs. This combination makes it very suitable for use in seismology where research code often has to be translated to stable and production ready environments. It furthermore offers many freely available high quality scientific modules covering most needs in developing scientific software. ObsPy has been in constant development for more than 5 years and nowadays enjoys a large rate of adoption in the community with thousands of users. Successful applications include time-dependent and rotational seismology, big data processing, event relocations, and synthetic studies about attenuation kernels and full-waveform inversions to name a few examples. Additionally it sparked the development of several more specialized packages slowly building a modern seismological ecosystem around it. This contribution will give a short introduction and overview of ObsPy and highlight a number of use cases and software built around it. We will furthermore discuss the issue of sustainability of scientific software.

ObsPy: A Python toolbox for seismology - Current state, applications, and ecosystem around it

NASA Astrophysics Data System (ADS)

Krischer, L.; Megies, T.; Sales de Andrade, E.; Barsch, R.; Beyreuther, M.

2015-12-01

ObsPy (http://www.obspy.org) is a community-driven, open-source project offering a bridge for seismology into the scientific Python ecosystem. It provides read and write support for essentially all commonly used waveform, station, and event metadata formats with a unified interface, a comprehensive signal processing toolbox tuned to the needs of seismologists, integrated access to all large data centers, web services and databases, and convenient wrappers to third party codes like libmseed and evalresp. Python, in contrast to many other languages and tools, is simple enough to enable an exploratory and interactive coding style desired by many scientists. At the same time it is a full-fledged programming language usable by software engineers to build complex and large programs. This combination makes it very suitable for use in seismology where research code often has to be translated to stable and production ready environments. It furthermore offers many freely available high quality scientific modules covering most needs in developing scientific software.ObsPy has been in constant development for more than 5 years and nowadays enjoys a large rate of adoption in the community with thousands of users. Successful applications include time-dependent and rotational seismology, big data processing, event relocations, and synthetic studies about attenuation kernels and full-waveform inversions to name a few examples. Additionally it sparked the development of several more specialized packages slowly building a modern seismological ecosystem around it.This contribution will give a short introduction and overview of ObsPy and highlight a number of us cases and software built around it. We will furthermore discuss the issue of sustainability of scientific software.

Unconventional protein sources: apricot seed kernels.

PubMed

Gabrial, G N; El-Nahry, F I; Awadalla, M Z; Girgis, S M

1981-09-01

Hamawy apricot seed kernels (sweet), Amar apricot seed kernels (bitter) and treated Amar apricot kernels (bitterness removed) were evaluated biochemically. All kernels were found to be high in fat (42.2--50.91%), protein (23.74--25.70%) and fiber (15.08--18.02%). Phosphorus, calcium, and iron were determined in all experimental samples. The three different apricot seed kernels were used for extensive study including the qualitative determination of the amino acid constituents by acid hydrolysis, quantitative determination of some amino acids, and biological evaluation of the kernel proteins in order to use them as new protein sources. Weanling albino rats failed to grow on diets containing the Amar apricot seed kernels due to low food consumption because of its bitterness. There was no loss in weight in that case. The Protein Efficiency Ratio data and blood analysis results showed the Hamawy apricot seed kernels to be higher in biological value than treated apricot seed kernels. The Net Protein Ratio data which accounts for both weight, maintenance and growth showed the treated apricot seed kernels to be higher in biological value than both Hamawy and Amar kernels. The Net Protein Ratio for the last two kernels were nearly equal.

An introduction to kernel-based learning algorithms.

PubMed

Müller, K R; Mika, S; Rätsch, G; Tsuda, K; Schölkopf, B

2001-01-01

This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by discussing applications such as optical character recognition and DNA analysis.

7 CFR 981.408 - Inedible kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Inedible kernel. 981.408 Section 981.408 Agriculture... Administrative Rules and Regulations § 981.408 Inedible kernel. Pursuant to § 981.8, the definition of inedible kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as...

Design of CT reconstruction kernel specifically for clinical lung imaging

NASA Astrophysics Data System (ADS)

Cody, Dianna D.; Hsieh, Jiang; Gladish, Gregory W.

2005-04-01

In this study we developed a new reconstruction kernel specifically for chest CT imaging. An experimental flat-panel CT scanner was used on large dogs to produce 'ground-truth" reference chest CT images. These dogs were also examined using a clinical 16-slice CT scanner. We concluded from the dog images acquired on the clinical scanner that the loss of subtle lung structures was due mostly to the presence of the background noise texture when using currently available reconstruction kernels. This qualitative evaluation of the dog CT images prompted the design of a new recon kernel. This new kernel consisted of the combination of a low-pass and a high-pass kernel to produce a new reconstruction kernel, called the 'Hybrid" kernel. The performance of this Hybrid kernel fell between the two kernels on which it was based, as expected. This Hybrid kernel was also applied to a set of 50 patient data sets; the analysis of these clinical images is underway. We are hopeful that this Hybrid kernel will produce clinical images with an acceptable tradeoff of lung detail, reliable HU, and image noise.

Quality changes in macadamia kernel between harvest and farm-gate.

PubMed

Walton, David A; Wallace, Helen M

2011-02-01

Macadamia integrifolia, Macadamia tetraphylla and their hybrids are cultivated for their edible kernels. After harvest, nuts-in-shell are partially dried on-farm and sorted to eliminate poor-quality kernels before consignment to a processor. During these operations, kernel quality may be lost. In this study, macadamia nuts-in-shell were sampled at five points of an on-farm postharvest handling chain from dehusking to the final storage silo to assess quality loss prior to consignment. Shoulder damage, weight of pieces and unsound kernel were assessed for raw kernels, and colour, mottled colour and surface damage for roasted kernels. Shoulder damage, weight of pieces and unsound kernel for raw kernels increased significantly between the dehusker and the final silo. Roasted kernels displayed a significant increase in dark colour, mottled colour and surface damage during on-farm handling. Significant loss of macadamia kernel quality occurred on a commercial farm during sorting and storage of nuts-in-shell before nuts were consigned to a processor. Nuts-in-shell should be dried as quickly as possible and on-farm handling minimised to maintain optimum kernel quality. 2010 Society of Chemical Industry.

Dual Spark Plugs For Stratified-Charge Rotary Engine

NASA Technical Reports Server (NTRS)

Abraham, John; Bracco, Frediano V.

1996-01-01

Fuel efficiency of stratified-charge, rotary, internal-combustion engine increased by improved design featuring dual spark plugs. Second spark plug ignites fuel on upstream side of main fuel injector; enabling faster burning and more nearly complete utilization of fuel.

An anomalous subdiffusion model for calcium spark in cardiac myocytes

NASA Astrophysics Data System (ADS)

Tan, Wenchang; Fu, Chaoqi; Fu, Ceji; Xie, Wenjun; Cheng, Heping

2007-10-01

The elementary events of excitation-contraction coupling in heart muscle are Ca2+ sparks, which arise from ryanodine receptors in the sarcoplasmic reticulum (SR). Here, an anomalous subdiffusion model is developed to explore Ca2+ spark formation in cardiac myocytes. Numerical simulations reproduce the brightness, the time course, and spatial size of a typical cardiac Ca2+ spark. It is suggested that the diffusion of Ca2+ spark in the cytoplasm may no longer obey Fickian second law, but the anomalous space subdiffusion. The physical reason is perhaps due to the effects of the electric field of the calcium ions and the viscoelasticity of the cytoplasm and its complex structures.

A fast and objective multidimensional kernel density estimation method: fastKDE

DOE PAGES

O'Brien, Travis A.; Kashinath, Karthik; Cavanaugh, Nicholas R.; ...

2016-03-07

Numerous facets of scientific research implicitly or explicitly call for the estimation of probability densities. Histograms and kernel density estimates (KDEs) are two commonly used techniques for estimating such information, with the KDE generally providing a higher fidelity representation of the probability density function (PDF). Both methods require specification of either a bin width or a kernel bandwidth. While techniques exist for choosing the kernel bandwidth optimally and objectively, they are computationally intensive, since they require repeated calculation of the KDE. A solution for objectively and optimally choosing both the kernel shape and width has recently been developed by Bernacchiamore » and Pigolotti (2011). While this solution theoretically applies to multidimensional KDEs, it has not been clear how to practically do so. A method for practically extending the Bernacchia-Pigolotti KDE to multidimensions is introduced. This multidimensional extension is combined with a recently-developed computational improvement to their method that makes it computationally efficient: a 2D KDE on 10 5 samples only takes 1 s on a modern workstation. This fast and objective KDE method, called the fastKDE method, retains the excellent statistical convergence properties that have been demonstrated for univariate samples. The fastKDE method exhibits statistical accuracy that is comparable to state-of-the-science KDE methods publicly available in R, and it produces kernel density estimates several orders of magnitude faster. The fastKDE method does an excellent job of encoding covariance information for bivariate samples. This property allows for direct calculation of conditional PDFs with fastKDE. It is demonstrated how this capability might be leveraged for detecting non-trivial relationships between quantities in physical systems, such as transitional behavior.« less

A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.

PubMed

Marceau, Rachel; Lu, Wenbin; Holloway, Shannon; Sale, Michèle M; Worrall, Bradford B; Williams, Stephen R; Hsu, Fang-Chi; Tzeng, Jung-Ying

2015-09-01

Kernel machine (KM) models are a powerful tool for exploring associations between sets of genetic variants and complex traits. Although most KM methods use a single kernel function to assess the marginal effect of a variable set, KM analyses involving multiple kernels have become increasingly popular. Multikernel analysis allows researchers to study more complex problems, such as assessing gene-gene or gene-environment interactions, incorporating variance-component based methods for population substructure into rare-variant association testing, and assessing the conditional effects of a variable set adjusting for other variable sets. The KM framework is robust, powerful, and provides efficient dimension reduction for multifactor analyses, but requires the estimation of high dimensional nuisance parameters. Traditional estimation techniques, including regularization and the "expectation-maximization (EM)" algorithm, have a large computational cost and are not scalable to large sample sizes needed for rare variant analysis. Therefore, under the context of gene-environment interaction, we propose a computationally efficient and statistically rigorous "fastKM" algorithm for multikernel analysis that is based on a low-rank approximation to the nuisance effect kernel matrices. Our algorithm is applicable to various trait types (e.g., continuous, binary, and survival traits) and can be implemented using any existing single-kernel analysis software. Through extensive simulation studies, we show that our algorithm has similar performance to an EM-based KM approach for quantitative traits while running much faster. We also apply our method to the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, examining gene-by-vitamin effects on recurrent stroke risk and gene-by-age effects on change in homocysteine level. © 2015 WILEY PERIODICALS, INC.

Multiple Kernel Learning with Random Effects for Predicting Longitudinal Outcomes and Data Integration

PubMed Central

Chen, Tianle; Zeng, Donglin

2015-01-01

Summary Predicting disease risk and progression is one of the main goals in many clinical research studies. Cohort studies on the natural history and etiology of chronic diseases span years and data are collected at multiple visits. Although kernel-based statistical learning methods are proven to be powerful for a wide range of disease prediction problems, these methods are only well studied for independent data but not for longitudinal data. It is thus important to develop time-sensitive prediction rules that make use of the longitudinal nature of the data. In this paper, we develop a novel statistical learning method for longitudinal data by introducing subject-specific short-term and long-term latent effects through a designed kernel to account for within-subject correlation of longitudinal measurements. Since the presence of multiple sources of data is increasingly common, we embed our method in a multiple kernel learning framework and propose a regularized multiple kernel statistical learning with random effects to construct effective nonparametric prediction rules. Our method allows easy integration of various heterogeneous data sources and takes advantage of correlation among longitudinal measures to increase prediction power. We use different kernels for each data source taking advantage of the distinctive feature of each data modality, and then optimally combine data across modalities. We apply the developed methods to two large epidemiological studies, one on Huntington's disease and the other on Alzheimer's Disease (Alzheimer's Disease Neuroimaging Initiative, ADNI) where we explore a unique opportunity to combine imaging and genetic data to study prediction of mild cognitive impairment, and show a substantial gain in performance while accounting for the longitudinal aspect of the data. PMID:26177419

A new discriminative kernel from probabilistic models.

PubMed

Tsuda, Koji; Kawanabe, Motoaki; Rätsch, Gunnar; Sonnenburg, Sören; Müller, Klaus-Robert

2002-10-01

Recently, Jaakkola and Haussler (1999) proposed a method for constructing kernel functions from probabilistic models. Their so-called Fisher kernel has been combined with discriminative classifiers such as support vector machines and applied successfully in, for example, DNA and protein analysis. Whereas the Fisher kernel is calculated from the marginal log-likelihood, we propose the TOP kernel derived; from tangent vectors of posterior log-odds. Furthermore, we develop a theoretical framework on feature extractors from probabilistic models and use it for analyzing the TOP kernel. In experiments, our new discriminative TOP kernel compares favorably to the Fisher kernel.

Particle models for discrete element modeling of bulk grain properties of wheat kernels

USDA-ARS?s Scientific Manuscript database

Recent research has shown the potential of discrete element method (DEM) in simulating grain flow in bulk handling systems. Research has also revealed that simulation of grain flow with DEM requires establishment of appropriate particle models for each grain type. This research completes the three-p...

Implementing Kernel Methods Incrementally by Incremental Nonlinear Projection Trick.

PubMed

Kwak, Nojun

2016-05-20

Recently, the nonlinear projection trick (NPT) was introduced enabling direct computation of coordinates of samples in a reproducing kernel Hilbert space. With NPT, any machine learning algorithm can be extended to a kernel version without relying on the so called kernel trick. However, NPT is inherently difficult to be implemented incrementally because an ever increasing kernel matrix should be treated as additional training samples are introduced. In this paper, an incremental version of the NPT (INPT) is proposed based on the observation that the centerization step in NPT is unnecessary. Because the proposed INPT does not change the coordinates of the old data, the coordinates obtained by INPT can directly be used in any incremental methods to implement a kernel version of the incremental methods. The effectiveness of the INPT is shown by applying it to implement incremental versions of kernel methods such as, kernel singular value decomposition, kernel principal component analysis, and kernel discriminant analysis which are utilized for problems of kernel matrix reconstruction, letter classification, and face image retrieval, respectively.

Combustion and operating characteristics of spark-ignition engines

NASA Technical Reports Server (NTRS)

Heywood, J. B.; Keck, J. C.; Beretta, G. P.; Watts, P. A.

1980-01-01

The spark-ignition engine turbulent flame propagation process was investigated. Then, using a spark-ignition engine cycle simulation and combustion model, the impact of turbocharging and heat transfer variations or engine power, efficiency, and NO sub x emissions was examined.

75 FR 19368 - Foreign-Trade Zone 126-Reno, NV; Site Renumbering Notice

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-14

... at 728 Spice Island Drive, Sparks; Site 2 (9 acres)--located at 450-475 Lillard Drive, Sparks; Site 3..., 700 South Rock Boulevard, Reno; Site 14 (0.4 acres)--located at 1095 Spice Island Drive, Sparks; Site...

Increasing accuracy of dispersal kernels in grid-based population models

USGS Publications Warehouse

Slone, D.H.

2011-01-01

Dispersal kernels in grid-based population models specify the proportion, distance and direction of movements within the model landscape. Spatial errors in dispersal kernels can have large compounding effects on model accuracy. Circular Gaussian and Laplacian dispersal kernels at a range of spatial resolutions were investigated, and methods for minimizing errors caused by the discretizing process were explored. Kernels of progressively smaller sizes relative to the landscape grid size were calculated using cell-integration and cell-center methods. These kernels were convolved repeatedly, and the final distribution was compared with a reference analytical solution. For large Gaussian kernels (σ > 10 cells), the total kernel error was <10 &sup-11; compared to analytical results. Using an invasion model that tracked the time a population took to reach a defined goal, the discrete model results were comparable to the analytical reference. With Gaussian kernels that had σ ≤ 0.12 using the cell integration method, or σ ≤ 0.22 using the cell center method, the kernel error was greater than 10%, which resulted in invasion times that were orders of magnitude different than theoretical results. A goal-seeking routine was developed to adjust the kernels to minimize overall error. With this, corrections for small kernels were found that decreased overall kernel error to <10-11 and invasion time error to <5%.

Properties of low power spark ablation in aqueous solution for dissolution of precious metals and alloys

NASA Astrophysics Data System (ADS)

Goltz, Douglas; Boileau, Michael; Plews, Ian; Charleton, Kimberly; Hinds, Michael W.

2006-07-01

Spark ablation or electric dispersion of metal samples in aqueous solution can be a useful approach for sample preparation. The ablated metal forms a stable suspension that has been described as colloidal, which is easily dissolved with a small amount of concentrated (16 M) HNO 3. In this study, we have examined some of the properties of the spark ablation process for a variety of metals (Rh and Au) and alloys (stainless steel) using a low power spark (100-300 W). Particle size distributions and conductivity measurements were carried out on selected metals to characterize the stable suspensions. A LASER diffraction particle size analyzer was useful for showing that ablated particles varied in size from 1 to 30 μm for both the silver and the nickel alloy, Inconel. In terms of weight percent most of the particles were between 10 and 30 μm. Conductivity of the spark ablation solution was found to increase linearly for approximately 3 min before leveling off at approximately 300 S cm 3. These measurements suggest that a significant portion of the ablated metal is also ionic in nature. Scanning electron microscope measurements revealed that a low power spark is much less damaging to the metal surface than a high power spark. Crater formation of the low power spark was found in a wider area than expected with the highest concentration where the spark was directed. The feasibility of using spark ablation for metal dissolution of a valuable artifact such as gold was also performed. Determinations of Ag (4-12%) and Cu (1-3%) in Bullion Reference Material (BRM) gave results that were in very good agreement with the certified values. The precision was ± 0.27% for Ag at 4.15% (RSD = 6.5%) and ± 0.09% for Cu at 1% (RSD = 9.0%).

Anthraquinones isolated from the browned Chinese chestnut kernels (Castanea mollissima blume)

NASA Astrophysics Data System (ADS)

Zhang, Y. L.; Qi, J. H.; Qin, L.; Wang, F.; Pang, M. X.

2016-08-01

Anthraquinones (AQS) represent a group of secondary metallic products in plants. AQS are often naturally occurring in plants and microorganisms. In a previous study, we found that AQS were produced by enzymatic browning reaction in Chinese chestnut kernels. To find out whether non-enzymatic browning reaction in the kernels could produce AQS too, AQS were extracted from three groups of chestnut kernels: fresh kernels, non-enzymatic browned kernels, and browned kernels, and the contents of AQS were determined. High performance liquid chromatography (HPLC) and nuclear magnetic resonance (NMR) methods were used to identify two compounds of AQS, rehein(1) and emodin(2). AQS were barely exists in the fresh kernels, while both browned kernel groups sample contained a high amount of AQS. Thus, we comfirmed that AQS could be produced during both enzymatic and non-enzymatic browning process. Rhein and emodin were the main components of AQS in the browned kernels.

Effects of rogue ryanodine receptors on Ca2+ sparks in cardiac myocytes

PubMed Central

Chen, Xudong; Feng, Yundi; Tan, Wenchang

2018-01-01

Ca2+ sparks and Ca2+ quarks, arising from clustered and rogue ryanodine receptors (RyRs), are significant Ca2+ release events from the junctional sarcoplasmic reticulum (JSR). Based on the anomalous subdiffusion of Ca2+ in the cytoplasm, a mathematical model was developed to investigate the effects of rogue RyRs on Ca2+ sparks in cardiac myocytes. Ca2+ quarks and sparks from the stochastic opening of rogue and clustered RyRs are numerically reproduced and agree with experimental measurements. It is found that the stochastic opening Ca2+ release units (CRUs) of clustered RyRs are regulated by free Ca2+ concentration in the JSR lumen (i.e. [Ca2+]lumen). The frequency of spontaneous Ca2+ sparks is remarkably increased by the rogue RyRs opening at high [Ca2+]lumen, but not at low [Ca2+]lumen. Hence, the opening of rogue RyRs contributes to the formation of Ca2+ sparks at high [Ca2+]lumen. The interplay of Ca2+ sparks and Ca2+ quarks has been discussed in detail. This work is of significance to provide insight into understanding Ca2+ release mechanisms in cardiac myocytes. PMID:29515864

Effects of rogue ryanodine receptors on Ca2+ sparks in cardiac myocytes.

PubMed

Chen, Xudong; Feng, Yundi; Huo, Yunlong; Tan, Wenchang

2018-02-01

Ca 2+ sparks and Ca 2+ quarks, arising from clustered and rogue ryanodine receptors (RyRs), are significant Ca 2+ release events from the junctional sarcoplasmic reticulum (JSR). Based on the anomalous subdiffusion of Ca 2+ in the cytoplasm, a mathematical model was developed to investigate the effects of rogue RyRs on Ca 2+ sparks in cardiac myocytes. Ca 2+ quarks and sparks from the stochastic opening of rogue and clustered RyRs are numerically reproduced and agree with experimental measurements. It is found that the stochastic opening Ca 2+ release units (CRUs) of clustered RyRs are regulated by free Ca 2+ concentration in the JSR lumen (i.e. [Ca 2+ ] lumen ). The frequency of spontaneous Ca 2+ sparks is remarkably increased by the rogue RyRs opening at high [Ca 2+ ] lumen , but not at low [Ca 2+ ] lumen . Hence, the opening of rogue RyRs contributes to the formation of Ca 2+ sparks at high [Ca 2+ ] lumen . The interplay of Ca 2+ sparks and Ca 2+ quarks has been discussed in detail. This work is of significance to provide insight into understanding Ca 2+ release mechanisms in cardiac myocytes.

Broken rice kernels and the kinetics of rice hydration and texture during cooking.

PubMed

Saleh, Mohammed; Meullenet, Jean-Francois

2013-05-01

During rice milling and processing, broken kernels are inevitably present, although to date it has been unclear as to how the presence of broken kernels affects rice hydration and cooked rice texture. Therefore, this work intended to study the effect of broken kernels in a rice sample on rice hydration and texture during cooking. Two medium-grain and two long-grain rice cultivars were harvested, dried and milled, and the broken kernels were separated from unbroken kernels. Broken rice kernels were subsequently combined with unbroken rice kernels forming treatments of 0, 40, 150, 350 or 1000 g kg(-1) broken kernels ratio. Rice samples were then cooked and the moisture content of the cooked rice, the moisture uptake rate, and rice hardness and stickiness were measured. As the amount of broken rice kernels increased, rice sample texture became increasingly softer (P < 0.05) but the unbroken kernels became significantly harder. Moisture content and moisture uptake rate were positively correlated, and cooked rice hardness was negatively correlated to the percentage of broken kernels in rice samples. Differences in the proportions of broken rice in a milled rice sample play a major role in determining the texture properties of cooked rice. Variations in the moisture migration kinetics between broken and unbroken kernels caused faster hydration of the cores of broken rice kernels, with greater starch leach-out during cooking affecting the texture of the cooked rice. The texture of cooked rice can be controlled, to some extent, by varying the proportion of broken kernels in milled rice. © 2012 Society of Chemical Industry.

Oversampling the Minority Class in the Feature Space.

PubMed

Perez-Ortiz, Maria; Gutierrez, Pedro Antonio; Tino, Peter; Hervas-Martinez, Cesar

2016-09-01

The imbalanced nature of some real-world data is one of the current challenges for machine learning researchers. One common approach oversamples the minority class through convex combination of its patterns. We explore the general idea of synthetic oversampling in the feature space induced by a kernel function (as opposed to input space). If the kernel function matches the underlying problem, the classes will be linearly separable and synthetically generated patterns will lie on the minority class region. Since the feature space is not directly accessible, we use the empirical feature space (EFS) (a Euclidean space isomorphic to the feature space) for oversampling purposes. The proposed method is framed in the context of support vector machines, where the imbalanced data sets can pose a serious hindrance. The idea is investigated in three scenarios: 1) oversampling in the full and reduced-rank EFSs; 2) a kernel learning technique maximizing the data class separation to study the influence of the feature space structure (implicitly defined by the kernel function); and 3) a unified framework for preferential oversampling that spans some of the previous approaches in the literature. We support our investigation with extensive experiments over 50 imbalanced data sets.

Tool grinding and spark testing

NASA Technical Reports Server (NTRS)

Widener, Edward L.

1993-01-01

The objectives were the following: (1) to revive the neglected art of metal-sparking; (2) to promote quality-assurance in the workplace; (3) to avoid spark-ignited explosions of dusts or volatiles; (4) to facilitate the salvage of scrap metals; and (5) to summarize important references.

Bandlimited computerized improvements in characterization of nonlinear systems with memory

NASA Astrophysics Data System (ADS)

Nuttall, Albert H.; Katz, Richard A.; Hughes, Derke R.; Koch, Robert M.

2016-05-01

The present article discusses some inroads in nonlinear signal processing made by the prime algorithm developer, Dr. Albert H. Nuttall and co-authors, a consortium of research scientists from the Naval Undersea Warfare Center Division, Newport, RI. The algorithm, called the Nuttall-Wiener-Volterra 'NWV' algorithm is named for its principal contributors [1], [2],[ 3] over many years of developmental research. The NWV algorithm significantly reduces the computational workload for characterizing nonlinear systems with memory. Following this formulation, two measurement waveforms on the system are required in order to characterize a specified nonlinear system under consideration: (1) an excitation input waveform, x(t) (the transmitted signal); and, (2) a response output waveform, z(t) (the received signal). Given these two measurement waveforms for a given propagation channel, a 'kernel' or 'channel response', h= [h0,h1,h2,h3] between the two measurement points, is computed via a least squares approach that optimizes modeled kernel values by performing a best fit between measured response z(t) and a modeled response y(t). New techniques significantly diminish the exponential growth of the number of computed kernel coefficients at second and third order in order to combat and reasonably alleviate the curse of dimensionality.

40 CFR 90.905 - Testing exemption.

Code of Federal Regulations, 2011 CFR

2011-07-01

...) CONTROL OF EMISSIONS FROM NONROAD SPARK-IGNITION ENGINES AT OR BELOW 19 KILOWATTS Exclusion and Exemption of Nonroad Engines from Regulations § 90.905 Testing exemption. (a) Any person requesting a testing... proposed test program, an appropriate purpose would be research, investigations, studies, demonstrations...

40 CFR 90.905 - Testing exemption.

Code of Federal Regulations, 2014 CFR

2014-07-01

...) CONTROL OF EMISSIONS FROM NONROAD SPARK-IGNITION ENGINES AT OR BELOW 19 KILOWATTS Exclusion and Exemption of Nonroad Engines from Regulations § 90.905 Testing exemption. (a) Any person requesting a testing... proposed test program, an appropriate purpose would be research, investigations, studies, demonstrations...

40 CFR 90.905 - Testing exemption.

Code of Federal Regulations, 2012 CFR

2012-07-01

...) CONTROL OF EMISSIONS FROM NONROAD SPARK-IGNITION ENGINES AT OR BELOW 19 KILOWATTS Exclusion and Exemption of Nonroad Engines from Regulations § 90.905 Testing exemption. (a) Any person requesting a testing... proposed test program, an appropriate purpose would be research, investigations, studies, demonstrations...

40 CFR 90.905 - Testing exemption.

Code of Federal Regulations, 2010 CFR

2010-07-01

...) CONTROL OF EMISSIONS FROM NONROAD SPARK-IGNITION ENGINES AT OR BELOW 19 KILOWATTS Exclusion and Exemption of Nonroad Engines from Regulations § 90.905 Testing exemption. (a) Any person requesting a testing... proposed test program, an appropriate purpose would be research, investigations, studies, demonstrations...

40 CFR 90.905 - Testing exemption.

Code of Federal Regulations, 2013 CFR

2013-07-01

...) CONTROL OF EMISSIONS FROM NONROAD SPARK-IGNITION ENGINES AT OR BELOW 19 KILOWATTS Exclusion and Exemption of Nonroad Engines from Regulations § 90.905 Testing exemption. (a) Any person requesting a testing... proposed test program, an appropriate purpose would be research, investigations, studies, demonstrations...

Development of a Dynamic Traffic Assignment Model for Northern Nevada

DOT National Transportation Integrated Search

2014-06-01

The objective of this research is to build and calibrate a DTA model for Northern Nevada (RenoSparks Area) based on the network profile and travel demand information updated to date. The critical procedures include development of consistent and readi...

Evaluating the Impact of Data Placement to Spark and SciDB with an Earth Science Use Case

NASA Technical Reports Server (NTRS)

Doan, Khoa; Oloso, Amidu; Kuo, Kwo-Sen; Clune, Thomas; Yu, Hongfeng; Nelson, Brian; Zhang, Jian

2016-01-01

We investigate the impact of data placement for two Big Data technologies, Spark and SciDB, with a use case from Earth Science where data arrays are multidimensional. Simultaneously, this investigation provides an opportunity to evaluate the performance of the technologies involved. Two datastores, HDFS and Cassandra, are used with Spark for our comparison. It is found that Spark with Cassandra performs better than with HDFS, but SciDB performs better yet than Spark with either datastore. The investigation also underscores the value of having data aligned for the most common analysis scenarios in advance on a shared nothing architecture. Otherwise, repartitioning needs to be carried out on the fly, degrading overall performance.

Dependency, Partiality and the Generation of Research Questions in Refugee Education

ERIC Educational Resources Information Center

Turner, Marianne; Fozdar, Farida

2010-01-01

From the late 1990s to the mid 2000s refugees who gained entry to Australia came mainly from the African region, in particular Sudan. This contrasts with earlier intakes from Asia, the Middle East and the former Yugoslavia. The change in regional focus sparked research into the resettlement of African refugees in Australia, but it is a new field…

Educators' Perceptions of the Effectiveness of the Desired Results System: A Study of the California Department of Education Accountability Initiative

ERIC Educational Resources Information Center

Khaleghi, Farahnaz

2010-01-01

The deluge of early child development research over the past 20 years has sparked an unprecedented public interest in the first years of children's lives. Research indicates that quality early education promotes the overall development of a healthy child. The Desired Results System, a standards-based accountability initiative of the California…

DESIGN OF A HIGH COMPRESSION, DIRECT INJECTION, SPARK-IGNITION, METHANOL FUELED RESEARCH ENGINE WITH AN INTEGRAL INJECTOR-IGNITION SOURCE INSERT, SAE PAPER 2001-01-3651

EPA Science Inventory

A stratified charge research engine and test stand were designed and built for this work. The primary goal of this project was to evaluate the feasibility of using a removal integral injector ignition source insert which allows a convenient method of charging the relative locat...

Nonlinear Deep Kernel Learning for Image Annotation.

PubMed

Jiu, Mingyuan; Sahbi, Hichem

2017-02-08

Multiple kernel learning (MKL) is a widely used technique for kernel design. Its principle consists in learning, for a given support vector classifier, the most suitable convex (or sparse) linear combination of standard elementary kernels. However, these combinations are shallow and often powerless to capture the actual similarity between highly semantic data, especially for challenging classification tasks such as image annotation. In this paper, we redefine multiple kernels using deep multi-layer networks. In this new contribution, a deep multiple kernel is recursively defined as a multi-layered combination of nonlinear activation functions, each one involves a combination of several elementary or intermediate kernels, and results into a positive semi-definite deep kernel. We propose four different frameworks in order to learn the weights of these networks: supervised, unsupervised, kernel-based semisupervised and Laplacian-based semi-supervised. When plugged into support vector machines (SVMs), the resulting deep kernel networks show clear gain, compared to several shallow kernels for the task of image annotation. Extensive experiments and analysis on the challenging ImageCLEF photo annotation benchmark, the COREL5k database and the Banana dataset validate the effectiveness of the proposed method.

Multineuron spike train analysis with R-convolution linear combination kernel.

PubMed

Tezuka, Taro

2018-06-01

A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Study on Energy Productivity Ratio (EPR) at palm kernel oil processing factory: case study on PT-X at Sumatera Utara Plantation

NASA Astrophysics Data System (ADS)

Haryanto, B.; Bukit, R. Br; Situmeang, E. M.; Christina, E. P.; Pandiangan, F.

2018-02-01

The purpose of this study was to determine the performance, productivity and feasibility of the operation of palm kernel processing plant based on Energy Productivity Ratio (EPR). EPR is expressed as the ratio of output to input energy and by-product. Palm Kernel plan is process in palm kernel to become palm kernel oil. The procedure started from collecting data needed as energy input such as: palm kernel prices, energy demand and depreciation of the factory. The energy output and its by-product comprise the whole production price such as: palm kernel oil price and the remaining products such as shells and pulp price. Calculation the equality of energy of palm kernel oil is to analyze the value of Energy Productivity Ratio (EPR) bases on processing capacity per year. The investigation has been done in Kernel Oil Processing Plant PT-X at Sumatera Utara plantation. The value of EPR was 1.54 (EPR > 1), which indicated that the processing of palm kernel into palm kernel oil is feasible to be operated based on the energy productivity.

Spark alloying of VK8 and T15K6 hard alloys

NASA Astrophysics Data System (ADS)

Kuptsov, S. G.; Fominykh, M. V.; Mukhinov, D. V.; Magomedova, R. S.; Nikonenko, E. A.; Pleshchev, V. P.

2015-08-01

A method is developed to restore the service properties of VK hard alloy plates using preliminary carburizing followed by spark alloying with a VT1-0 alloy. The phase composition is studied as a function of the spark treatment time.

75 FR 21594 - Foreign-Trade Zone 126-Reno, NV, Application for Reorganization/Expansion Under Alternative Site...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-26

... 1 (13.9 acres)--728 Spice Island Drive, Sparks; Site 2 (9 acres)--450-475 Lillard Drive, Sparks... 700 South Rock Boulevard, Reno; Site 14 (0.4 acres)--1095 Spice Island Drive, Sparks; Site 15 (0.7...

Laser spark distribution and ignition system

DOEpatents

Woodruff, Steven [Morgantown, WV; McIntyre, Dustin L [Morgantown, WV

2008-09-02

A laser spark distribution and ignition system that reduces the high power optical requirements for use in a laser ignition and distribution system allowing for the use of optical fibers for delivering the low peak energy pumping pulses to a laser amplifier or laser oscillator. An optical distributor distributes and delivers optical pumping energy from an optical pumping source to multiple combustion chambers incorporating laser oscillators or laser amplifiers for inducing a laser spark within a combustion chamber. The optical distributor preferably includes a single rotating mirror or lens which deflects the optical pumping energy from the axis of rotation and into a plurality of distinct optical fibers each connected to a respective laser media or amplifier coupled to an associated combustion chamber. The laser spark generators preferably produce a high peak power laser spark, from a single low power pulse. The laser spark distribution and ignition system has application in natural gas fueled reciprocating engines, turbine combustors, explosives and laser induced breakdown spectroscopy diagnostic sensors.

The fast algorithm of spark in compressive sensing

NASA Astrophysics Data System (ADS)

Xie, Meihua; Yan, Fengxia

2017-01-01

Compressed Sensing (CS) is an advanced theory on signal sampling and reconstruction. In CS theory, the reconstruction condition of signal is an important theory problem, and spark is a good index to study this problem. But the computation of spark is NP hard. In this paper, we study the problem of computing spark. For some special matrixes, for example, the Gaussian random matrix and 0-1 random matrix, we obtain some conclusions. Furthermore, for Gaussian random matrix with fewer rows than columns, we prove that its spark equals to the number of its rows plus one with probability 1. For general matrix, two methods are given to compute its spark. One is the method of directly searching and the other is the method of dual-tree searching. By simulating 24 Gaussian random matrixes and 18 0-1 random matrixes, we tested the computation time of these two methods. Numerical results showed that the dual-tree searching method had higher efficiency than directly searching, especially for those matrixes which has as much as rows and columns.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Womeldorff, Geoffrey Alan; Payne, Joshua Estes; Bergen, Benjamin Karl

These are slides for a presentation on PARTISN Research and FleCSI Updates. The following topics are covered: SNAP vs PARTISN, Background Research, Production Code (structural design and changes, kernel design and implementation, lessons learned), NuT IMC Proxy, FleCSI Update (design and lessons learned). It can all be summarized in the following manner: Kokkos was shown to be effective in FY15 in implementing a C++ version of SNAP's kernel. This same methodology was applied to a production IC code, PARTISN. This was a much more complex endeavour than in FY15 for many reasons; a C++ kernel embedded in Fortran, overloading Fortranmore » memory allocations, general language interoperability, and a fully fleshed out production code versus a simplified proxy code. Lessons learned are Legion. In no particular order: Interoperability between Fortran and C++ was really not that hard, and a useful engineering effort. Tracking down all necessary memory allocations for a kernel in a production code is pretty hard. Modifying a production code to work for more than a handful of use cases is also pretty hard. Figuring out the toolchain that will allow a successful implementation of design decisions is quite hard, if making use of "bleeding edge" design choices. In terms of performance, production code concurrency architecture can be a virtual showstopper; being too complex to easily rewrite and test in a short period of time, or depending on tool features which do not exist yet. Ultimately, while the tools used in this work were not successful in speeding up the production code, they helped to identify how work would be done, and provide requirements to tools.« less

Nanosecond repetitively pulsed discharges in air at atmospheric pressure—the spark regime

NASA Astrophysics Data System (ADS)

Pai, David Z.; Lacoste, Deanna A.; Laux, Christophe O.

2010-12-01

Nanosecond repetitively pulsed (NRP) spark discharges have been studied in atmospheric pressure air preheated to 1000 K. Measurements of spark initiation and stability, plasma dynamics, gas temperature and current-voltage characteristics of the spark regime are presented. Using 10 ns pulses applied repetitively at 30 kHz, we find that 2-400 pulses are required to initiate the spark, depending on the applied voltage. Furthermore, about 30-50 pulses are required for the spark discharge to reach steady state, following initiation. Based on space- and time-resolved optical emission spectroscopy, the spark discharge in steady state is found to ignite homogeneously in the discharge gap, without evidence of an initial streamer. Using measured emission from the N2 (C-B) 0-0 band, it is found that the gas temperature rises by several thousand Kelvin in the span of about 30 ns following the application of the high-voltage pulse. Current-voltage measurements show that up to 20-40 A of conduction current is generated, which corresponds to an electron number density of up to 1015 cm-3 towards the end of the high-voltage pulse. The discharge dynamics, gas temperature and electron number density are consistent with a streamer-less spark that develops homogeneously through avalanche ionization in volume. This occurs because the pre-ionization electron number density of about 1011 cm-3 produced by the high frequency train of pulses is above the critical density for streamer-less discharge development, which is shown to be about 108 cm-3.

Image preprocessing study on KPCA-based face recognition

NASA Astrophysics Data System (ADS)

Li, Xuan; Li, Dehua

2015-12-01

Face recognition as an important biometric identification method, with its friendly, natural, convenient advantages, has obtained more and more attention. This paper intends to research a face recognition system including face detection, feature extraction and face recognition, mainly through researching on related theory and the key technology of various preprocessing methods in face detection process, using KPCA method, focuses on the different recognition results in different preprocessing methods. In this paper, we choose YCbCr color space for skin segmentation and choose integral projection for face location. We use erosion and dilation of the opening and closing operation and illumination compensation method to preprocess face images, and then use the face recognition method based on kernel principal component analysis method for analysis and research, and the experiments were carried out using the typical face database. The algorithms experiment on MATLAB platform. Experimental results show that integration of the kernel method based on PCA algorithm under certain conditions make the extracted features represent the original image information better for using nonlinear feature extraction method, which can obtain higher recognition rate. In the image preprocessing stage, we found that images under various operations may appear different results, so as to obtain different recognition rate in recognition stage. At the same time, in the process of the kernel principal component analysis, the value of the power of the polynomial function can affect the recognition result.

Primary Science Interview: Science Sparks

ERIC Educational Resources Information Center

Bianchi, Lynne

2016-01-01

In this "Primary Science" interview, Lynne Bianchi talks with Emma Vanstone about "Science Sparks," which is a website full of creative, fun, and exciting science activity ideas for children of primary-school age. "Science Sparks" started with the aim of inspiring more parents to do science at home with their…

7 CFR 981.9 - Kernel weight.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Kernel weight. 981.9 Section 981.9 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements... Regulating Handling Definitions § 981.9 Kernel weight. Kernel weight means the weight of kernels, including...

An SVM model with hybrid kernels for hydrological time series

NASA Astrophysics Data System (ADS)

Wang, C.; Wang, H.; Zhao, X.; Xie, Q.

2017-12-01

Support Vector Machine (SVM) models have been widely applied to the forecast of climate/weather and its impact on other environmental variables such as hydrologic response to climate/weather. When using SVM, the choice of the kernel function plays the key role. Conventional SVM models mostly use one single type of kernel function, e.g., radial basis kernel function. Provided that there are several featured kernel functions available, each having its own advantages and drawbacks, a combination of these kernel functions may give more flexibility and robustness to SVM approach, making it suitable for a wide range of application scenarios. This paper presents such a linear combination of radial basis kernel and polynomial kernel for the forecast of monthly flowrate in two gaging stations using SVM approach. The results indicate significant improvement in the accuracy of predicted series compared to the approach with either individual kernel function, thus demonstrating the feasibility and advantages of such hybrid kernel approach for SVM applications.

Approximate kernel competitive learning.

PubMed

Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang

2015-03-01

Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches. Copyright © 2014 Elsevier Ltd. All rights reserved.

Multiple kernels learning-based biological entity relationship extraction method.

PubMed

Dongliang, Xu; Jingchang, Pan; Bailing, Wang

2017-09-20

Automatic extracting protein entity interaction information from biomedical literature can help to build protein relation network and design new drugs. There are more than 20 million literature abstracts included in MEDLINE, which is the most authoritative textual database in the field of biomedicine, and follow an exponential growth over time. This frantic expansion of the biomedical literature can often be difficult to absorb or manually analyze. Thus efficient and automated search engines are necessary to efficiently explore the biomedical literature using text mining techniques. The P, R, and F value of tag graph method in Aimed corpus are 50.82, 69.76, and 58.61%, respectively. The P, R, and F value of tag graph kernel method in other four evaluation corpuses are 2-5% higher than that of all-paths graph kernel. And The P, R and F value of feature kernel and tag graph kernel fuse methods is 53.43, 71.62 and 61.30%, respectively. The P, R and F value of feature kernel and tag graph kernel fuse methods is 55.47, 70.29 and 60.37%, respectively. It indicated that the performance of the two kinds of kernel fusion methods is better than that of simple kernel. In comparison with the all-paths graph kernel method, the tag graph kernel method is superior in terms of overall performance. Experiments show that the performance of the multi-kernels method is better than that of the three separate single-kernel method and the dual-mutually fused kernel method used hereof in five corpus sets.

7 CFR 51.2295 - Half kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Half kernel. 51.2295 Section 51.2295 Agriculture... Standards for Shelled English Walnuts (Juglans Regia) Definitions § 51.2295 Half kernel. Half kernel means the separated half of a kernel with not more than one-eighth broken off. ...

7 CFR 810.206 - Grades and grade requirements for barley.

Code of Federal Regulations, 2010 CFR

2010-01-01

... weight per bushel (pounds) Sound barley (percent) Maximum Limits of— Damaged kernels 1 (percent) Heat damaged kernels (percent) Foreign material (percent) Broken kernels (percent) Thin barley (percent) U.S... or otherwise of distinctly low quality. 1 Includes heat-damaged kernels. Injured-by-frost kernels and...

40 CFR 91.1005 - Testing exemption.

Code of Federal Regulations, 2010 CFR

2010-07-01

...) CONTROL OF EMISSIONS FROM MARINE SPARK-IGNITION ENGINES Exclusion and Exemption of Marine SI Engines § 91... proposed test program, an appropriate purpose would be research, investigations, studies, demonstrations... must exhibit a duration of reasonable length and affect a reasonable number of engines. In this regard...

40 CFR 91.1005 - Testing exemption.

Code of Federal Regulations, 2011 CFR

2011-07-01

...) CONTROL OF EMISSIONS FROM MARINE SPARK-IGNITION ENGINES Exclusion and Exemption of Marine SI Engines § 91... proposed test program, an appropriate purpose would be research, investigations, studies, demonstrations... must exhibit a duration of reasonable length and affect a reasonable number of engines. In this regard...

40 CFR 91.1005 - Testing exemption.

Code of Federal Regulations, 2014 CFR

2014-07-01

...) CONTROL OF EMISSIONS FROM MARINE SPARK-IGNITION ENGINES Exclusion and Exemption of Marine SI Engines § 91... proposed test program, an appropriate purpose would be research, investigations, studies, demonstrations... must exhibit a duration of reasonable length and affect a reasonable number of engines. In this regard...

40 CFR 91.1005 - Testing exemption.

Code of Federal Regulations, 2012 CFR

2012-07-01

...) CONTROL OF EMISSIONS FROM MARINE SPARK-IGNITION ENGINES Exclusion and Exemption of Marine SI Engines § 91... proposed test program, an appropriate purpose would be research, investigations, studies, demonstrations... must exhibit a duration of reasonable length and affect a reasonable number of engines. In this regard...

40 CFR 91.1005 - Testing exemption.

Code of Federal Regulations, 2013 CFR

2013-07-01

...) CONTROL OF EMISSIONS FROM MARINE SPARK-IGNITION ENGINES Exclusion and Exemption of Marine SI Engines § 91... proposed test program, an appropriate purpose would be research, investigations, studies, demonstrations... must exhibit a duration of reasonable length and affect a reasonable number of engines. In this regard...

Intelligent Design of Metal Oxide Gas Sensor Arrays Using Reciprocal Kernel Support Vector Regression

NASA Astrophysics Data System (ADS)

Dougherty, Andrew W.

Metal oxides are a staple of the sensor industry. The combination of their sensitivity to a number of gases, and the electrical nature of their sensing mechanism, make the particularly attractive in solid state devices. The high temperature stability of the ceramic material also make them ideal for detecting combustion byproducts where exhaust temperatures can be high. However, problems do exist with metal oxide sensors. They are not very selective as they all tend to be sensitive to a number of reduction and oxidation reactions on the oxide's surface. This makes sensors with large numbers of sensors interesting to study as a method for introducing orthogonality to the system. Also, the sensors tend to suffer from long term drift for a number of reasons. In this thesis I will develop a system for intelligently modeling metal oxide sensors and determining their suitability for use in large arrays designed to analyze exhaust gas streams. It will introduce prior knowledge of the metal oxide sensors' response mechanisms in order to produce a response function for each sensor from sparse training data. The system will use the same technique to model and remove any long term drift from the sensor response. It will also provide an efficient means for determining the orthogonality of the sensor to determine whether they are useful in gas sensing arrays. The system is based on least squares support vector regression using the reciprocal kernel. The reciprocal kernel is introduced along with a method of optimizing the free parameters of the reciprocal kernel support vector machine. The reciprocal kernel is shown to be simpler and to perform better than an earlier kernel, the modified reciprocal kernel. Least squares support vector regression is chosen as it uses all of the training points and an emphasis was placed throughout this research for extracting the maximum information from very sparse data. The reciprocal kernel is shown to be effective in modeling the sensor responses in the time, gas and temperature domains, and the dual representation of the support vector regression solution is shown to provide insight into the sensor's sensitivity and potential orthogonality. Finally, the dual weights of the support vector regression solution to the sensor's response are suggested as a fitness function for a genetic algorithm, or some other method for efficiently searching large parameter spaces.

A simple high-voltage high current spark gap with subnanosecond jitter triggered by femtosecond laser filamentation

NASA Astrophysics Data System (ADS)

Arantchouk, L.; Houard, A.; Brelet, Y.; Carbonnel, J.; Larour, J.; André, Y.-B.; Mysyrowicz, A.

2013-04-01

We describe a simple, sturdy, and reliable spark gap operating with air at atmospheric pressure and able to switch currents in excess of 10 kA with sub-nanosecond jitter. The spark gap is remotely triggered by a femtosecond laser filament.

Observation of a spark channel generated in water with shock wave assistance in plate-to-plate electrode configuration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stelmashuk, V., E-mail: vitalij@ipp.cas.cz

2014-01-15

When a high voltage pulse with an amplitude of 30 kV is applied to a pair of disk electrodes at a time when a shock wave is passing between them, an electrical spark is generated. The dynamic changes in the spark morphology are studied here using a high-speed framing camera. The primary result of this work is the provision of experimental evidence of plasma instability that was observed in the channel of the electric spark.

Spark discharge trace element detection system

DOEpatents

Adler-Golden, Steven; Bernstein, Lawrence S.; Bien, Fritz

1988-01-01

A spark discharge trace element detection system is provided which includes a spark chamber including a pair of electrodes for receiving a sample of gas to be analyzed at no greater than atmospheric pressure. A voltage is provided across the electrodes for generating a spark in the sample. The intensity of the emitted radiation in at least one primary selected narrow band of the radiation is detected. Each primary band corresponds to an element to be detected in the gas. The intensity of the emission in each detected primary band is integrated during the afterglow time interval of the spark emission and a signal representative of the integrated intensity of the emission in each selected primary bond is utilized to determine the concentration of the corresponding element in the gas.

Spark discharge trace element detection system

DOEpatents

Adler-Golden, S.; Bernstein, L.S.; Bien, F.

1988-08-23

A spark discharge trace element detection system is provided which includes a spark chamber including a pair of electrodes for receiving a sample of gas to be analyzed at no greater than atmospheric pressure. A voltage is provided across the electrodes for generating a spark in the sample. The intensity of the emitted radiation in at least one primary selected narrow band of the radiation is detected. Each primary band corresponds to an element to be detected in the gas. The intensity of the emission in each detected primary band is integrated during the afterglow time interval of the spark emission and a signal representative of the integrated intensity of the emission in each selected primary bond is utilized to determine the concentration of the corresponding element in the gas. 12 figs.

Large-scale seismic waveform quality metric calculation using Hadoop

DOE PAGES

Magana-Zook, Steven; Gaylord, Jessie M.; Knapp, Douglas R.; ...

2016-05-27

Here in this work we investigated the suitability of Hadoop MapReduce and Apache Spark for large-scale computation of seismic waveform quality metrics by comparing their performance with that of a traditional distributed implementation. The Incorporated Research Institutions for Seismology (IRIS) Data Management Center (DMC) provided 43 terabytes of broadband waveform data of which 5.1 TB of data were processed with the traditional architecture, and the full 43 TB were processed using MapReduce and Spark. Maximum performance of ~0.56 terabytes per hour was achieved using all 5 nodes of the traditional implementation. We noted that I/O dominated processing, and that I/Omore » performance was deteriorating with the addition of the 5th node. Data collected from this experiment provided the baseline against which the Hadoop results were compared. Next, we processed the full 43 TB dataset using both MapReduce and Apache Spark on our 18-node Hadoop cluster. We conducted these experiments multiple times with various subsets of the data so that we could build models to predict performance as a function of dataset size. We found that both MapReduce and Spark significantly outperformed the traditional reference implementation. At a dataset size of 5.1 terabytes, both Spark and MapReduce were about 15 times faster than the reference implementation. Furthermore, our performance models predict that for a dataset of 350 terabytes, Spark running on a 100-node cluster would be about 265 times faster than the reference implementation. We do not expect that the reference implementation deployed on a 100-node cluster would perform significantly better than on the 5-node cluster because the I/O performance cannot be made to scale. Finally, we note that although Big Data technologies clearly provide a way to process seismic waveform datasets in a high-performance and scalable manner, the technology is still rapidly changing, requires a high degree of investment in personnel, and will likely require significant changes in other parts of our infrastructure. Nevertheless, we anticipate that as the technology matures and third-party tool vendors make it easier to manage and operate clusters, Hadoop (or a successor) will play a large role in our seismic data processing.« less

Large-scale seismic waveform quality metric calculation using Hadoop

DOE Office of Scientific and Technical Information (OSTI.GOV)

Magana-Zook, Steven; Gaylord, Jessie M.; Knapp, Douglas R.

Here in this work we investigated the suitability of Hadoop MapReduce and Apache Spark for large-scale computation of seismic waveform quality metrics by comparing their performance with that of a traditional distributed implementation. The Incorporated Research Institutions for Seismology (IRIS) Data Management Center (DMC) provided 43 terabytes of broadband waveform data of which 5.1 TB of data were processed with the traditional architecture, and the full 43 TB were processed using MapReduce and Spark. Maximum performance of ~0.56 terabytes per hour was achieved using all 5 nodes of the traditional implementation. We noted that I/O dominated processing, and that I/Omore » performance was deteriorating with the addition of the 5th node. Data collected from this experiment provided the baseline against which the Hadoop results were compared. Next, we processed the full 43 TB dataset using both MapReduce and Apache Spark on our 18-node Hadoop cluster. We conducted these experiments multiple times with various subsets of the data so that we could build models to predict performance as a function of dataset size. We found that both MapReduce and Spark significantly outperformed the traditional reference implementation. At a dataset size of 5.1 terabytes, both Spark and MapReduce were about 15 times faster than the reference implementation. Furthermore, our performance models predict that for a dataset of 350 terabytes, Spark running on a 100-node cluster would be about 265 times faster than the reference implementation. We do not expect that the reference implementation deployed on a 100-node cluster would perform significantly better than on the 5-node cluster because the I/O performance cannot be made to scale. Finally, we note that although Big Data technologies clearly provide a way to process seismic waveform datasets in a high-performance and scalable manner, the technology is still rapidly changing, requires a high degree of investment in personnel, and will likely require significant changes in other parts of our infrastructure. Nevertheless, we anticipate that as the technology matures and third-party tool vendors make it easier to manage and operate clusters, Hadoop (or a successor) will play a large role in our seismic data processing.« less

SPARK: A Framework for Multi-Scale Agent-Based Biomedical Modeling.

PubMed

Solovyev, Alexey; Mikheev, Maxim; Zhou, Leming; Dutta-Moscato, Joyeeta; Ziraldo, Cordelia; An, Gary; Vodovotz, Yoram; Mi, Qi

2010-01-01

Multi-scale modeling of complex biological systems remains a central challenge in the systems biology community. A method of dynamic knowledge representation known as agent-based modeling enables the study of higher level behavior emerging from discrete events performed by individual components. With the advancement of computer technology, agent-based modeling has emerged as an innovative technique to model the complexities of systems biology. In this work, the authors describe SPARK (Simple Platform for Agent-based Representation of Knowledge), a framework for agent-based modeling specifically designed for systems-level biomedical model development. SPARK is a stand-alone application written in Java. It provides a user-friendly interface, and a simple programming language for developing Agent-Based Models (ABMs). SPARK has the following features specialized for modeling biomedical systems: 1) continuous space that can simulate real physical space; 2) flexible agent size and shape that can represent the relative proportions of various cell types; 3) multiple spaces that can concurrently simulate and visualize multiple scales in biomedical models; 4) a convenient graphical user interface. Existing ABMs of diabetic foot ulcers and acute inflammation were implemented in SPARK. Models of identical complexity were run in both NetLogo and SPARK; the SPARK-based models ran two to three times faster.

Tensile Properties and Fracture Characteristics of Nanostructured Copper and Cu-SiC Nanocomposite Produced by Mechanical Milling and Spark Plasma Sintering Process

NASA Astrophysics Data System (ADS)

Akbarpour, M. R.

2018-03-01

The presence of large grains within nanometric and ultrafine grain matrix is an effective method in order to enhance strength while keeping the high ductility of metals. For this purpose, in this research, spark plasma sintering (SPS) was used to consolidate milled Cu and Cu-SiC powders. In SPS process, local sparks with high temperature between particles take place and locally lead to intense grain growth, and therefore, this method has the ability to produce bimodal grain structures in copper and copper-based composites. Microstructural and mechanical studies showed ≈ 185 and ≈ 437 nm matrix grain sizes, high tensile yield strength values of ≈ 188.4 and ≈ 296.9 MPa, and fracture strain values of 15.1 and 6.7% for sintered Cu and Cu-4 vol.% SiC nanocomposite materials, respectively. The presence of nanoparticles promoted the occurrence of static recrystallization and decreased the fraction of coarse grains in microstructure. The high tensile properties of the produced materials are attributed to fine grain size, homogenous dispersion of nanoparticles and retarded grain boundary migration during sintering.

Regulation of Ca2+ Sparks by Ca2+ and Mg2+ in Mammalian and Amphibian Muscle. An RyR Isoform-specific Role in Excitation–Contraction Coupling?

PubMed Central

Zhou, Jingsong; Launikonis, Bradley S.; Ríos, Eduardo; Brum, Gustavo

2004-01-01

Ca2+ and Mg2+ are important mediators and regulators of intracellular Ca2+ signaling in muscle. The effects of changes of cytosolic [Ca2+] or [Mg2+] on elementary Ca2+ release events were determined, as functions of concentration and time, in single fast-twitch permeabilized fibers of rat and frog. Ca2+ sparks were identified and their parameters measured in confocal images of fluo-4 fluorescence. Solutions with different [Ca2+] or [Mg2+] were rapidly exchanged while imaging. Faster and spatially homogeneous changes of [Ca2+] (reaching peaks >100 μM) were achieved by photolysing Ca NP-EGTA with laser flashes. In both species, incrementing cytosolic [Ca2+] caused a steady, nearly proportional increase in spark frequency, reversible upon [Ca2+] reduction. A greater change in spark frequency, usually transient, followed sudden increases in [Ca2+] after a lag of 100 ms or more. The nonlinearity, lag, and other features of this delayed effect suggest that it requires increase of [Ca2+] inside the SR. In the frog only, increases in cytosolic [Ca2+] often resulted, after a lag, in sparks that propagated transversally. An increase in [Mg2+] caused a fall of spark frequency, but with striking species differences. In the rat, but not the frog, sparks were observed at 4–40 mM [Mg2+]. Reducing [Mg2+] below 2 mM, which should enable the RyR channel's activation (CICR) site to bind Ca2+, caused progressive increase in spark frequency in the frog, but had no effect in the rat. Spark propagation and enhancement by sub-mM Mg2+ are hallmarks of CICR. Their absence in the rat suggests that CICR requires RyR3 para-junctional clusters, present only in the frog. The observed frequency of sparks corresponds to a channel open probability of 10−7 in the frog or 10−8 in the rat. Together with the failure of photorelease to induce activation directly, this indicates a basal inhibition of channels in situ. It is proposed that relief of this inhibition could be the mechanism by which increased SR load increases spark frequency. PMID:15452201

7 CFR 51.1449 - Damage.

Code of Federal Regulations, 2014 CFR

2014-01-01

...) Kernel which is “dark amber” or darker color; (e) Kernel having more than one dark kernel spot, or one dark kernel spot more than one-eighth inch in greatest dimension; (f) Shriveling when the surface of the kernel is very conspicuously wrinkled; (g) Internal flesh discoloration of a medium shade of gray...

7 CFR 51.1449 - Damage.

Code of Federal Regulations, 2013 CFR

2013-01-01

...) Kernel which is “dark amber” or darker color; (e) Kernel having more than one dark kernel spot, or one dark kernel spot more than one-eighth inch in greatest dimension; (f) Shriveling when the surface of the kernel is very conspicuously wrinkled; (g) Internal flesh discoloration of a medium shade of gray...

7 CFR 51.2125 - Split or broken kernels.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Split or broken kernels. 51.2125 Section 51.2125 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards... kernels. Split or broken kernels means seven-eighths or less of complete whole kernels but which will not...

7 CFR 51.2296 - Three-fourths half kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Three-fourths half kernel. 51.2296 Section 51.2296 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards...-fourths half kernel. Three-fourths half kernel means a portion of a half of a kernel which has more than...

The Classification of Diabetes Mellitus Using Kernel k-means

NASA Astrophysics Data System (ADS)

Alamsyah, M.; Nafisah, Z.; Prayitno, E.; Afida, A. M.; Imah, E. M.

2018-01-01

Diabetes Mellitus is a metabolic disorder which is characterized by chronicle hypertensive glucose. Automatics detection of diabetes mellitus is still challenging. This study detected diabetes mellitus by using kernel k-Means algorithm. Kernel k-means is an algorithm which was developed from k-means algorithm. Kernel k-means used kernel learning that is able to handle non linear separable data; where it differs with a common k-means. The performance of kernel k-means in detecting diabetes mellitus is also compared with SOM algorithms. The experiment result shows that kernel k-means has good performance and a way much better than SOM.

UNICOS Kernel Internals Application Development

NASA Technical Reports Server (NTRS)

Caredo, Nicholas; Craw, James M. (Technical Monitor)

1995-01-01

Having an understanding of UNICOS Kernel Internals is valuable information. However, having the knowledge is only half the value. The second half comes with knowing how to use this information and apply it to the development of tools. The kernel contains vast amounts of useful information that can be utilized. This paper discusses the intricacies of developing utilities that utilize kernel information. In addition, algorithms, logic, and code will be discussed for accessing kernel information. Code segments will be provided that demonstrate how to locate and read kernel structures. Types of applications that can utilize kernel information will also be discussed.

Detection of maize kernels breakage rate based on K-means clustering

NASA Astrophysics Data System (ADS)

Yang, Liang; Wang, Zhuo; Gao, Lei; Bai, Xiaoping

2017-04-01

In order to optimize the recognition accuracy of maize kernels breakage detection and improve the detection efficiency of maize kernels breakage, this paper using computer vision technology and detecting of the maize kernels breakage based on K-means clustering algorithm. First, the collected RGB images are converted into Lab images, then the original images clarity evaluation are evaluated by the energy function of Sobel 8 gradient. Finally, the detection of maize kernels breakage using different pixel acquisition equipments and different shooting angles. In this paper, the broken maize kernels are identified by the color difference between integrity kernels and broken kernels. The original images clarity evaluation and different shooting angles are taken to verify that the clarity and shooting angles of the images have a direct influence on the feature extraction. The results show that K-means clustering algorithm can distinguish the broken maize kernels effectively.

Modeling adaptive kernels from probabilistic phylogenetic trees.

PubMed

Nicotra, Luca; Micheli, Alessio

2009-01-01

Modeling phylogenetic interactions is an open issue in many computational biology problems. In the context of gene function prediction we introduce a class of kernels for structured data leveraging on a hierarchical probabilistic modeling of phylogeny among species. We derive three kernels belonging to this setting: a sufficient statistics kernel, a Fisher kernel, and a probability product kernel. The new kernels are used in the context of support vector machine learning. The kernels adaptivity is obtained through the estimation of the parameters of a tree structured model of evolution using as observed data phylogenetic profiles encoding the presence or absence of specific genes in a set of fully sequenced genomes. We report results obtained in the prediction of the functional class of the proteins of the budding yeast Saccharomyces cerevisae which favorably compare to a standard vector based kernel and to a non-adaptive tree kernel function. A further comparative analysis is performed in order to assess the impact of the different components of the proposed approach. We show that the key features of the proposed kernels are the adaptivity to the input domain and the ability to deal with structured data interpreted through a graphical model representation.

Aflatoxin and nutrient contents of peanut collected from local market and their processed foods

NASA Astrophysics Data System (ADS)

Ginting, E.; Rahmianna, A. A.; Yusnawan, E.

2018-01-01

Peanut is succeptable to aflatoxin contamination and the sources of peanut as well as processing methods considerably affect aflatoxin content of the products. Therefore, the study on aflatoxin and nutrient contents of peanut collected from local market and their processed foods were performed. Good kernels of peanut were prepared into fried peanut, pressed-fried peanut, peanut sauce, peanut press cake, fermented peanut press cake (tempe) and fried tempe, while blended kernels (good and poor kernels) were processed into peanut sauce and tempe and poor kernels were only processed into tempe. The results showed that good and blended kernels which had high number of sound/intact kernels (82,46% and 62,09%), contained 9.8-9.9 ppb of aflatoxin B1, while slightly higher level was seen in poor kernels (12.1 ppb). However, the moisture, ash, protein, and fat contents of the kernels were similar as well as the products. Peanut tempe and fried tempe showed the highest increase in protein content, while decreased fat contents were seen in all products. The increase in aflatoxin B1 of peanut tempe prepared from poor kernels > blended kernels > good kernels. However, it averagely decreased by 61.2% after deep-fried. Excluding peanut tempe and fried tempe, aflatoxin B1 levels in all products derived from good kernels were below the permitted level (15 ppb). This suggests that sorting peanut kernels as ingredients and followed by heat processing would decrease the aflatoxin content in the products.

Partial Deconvolution with Inaccurate Blur Kernel.

PubMed

Ren, Dongwei; Zuo, Wangmeng; Zhang, David; Xu, Jun; Zhang, Lei

2017-10-17

Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.

The fabrication and hydrophobic property of micro-nano patterned surface on magnesium alloy using combined sparking sculpture and etching route

NASA Astrophysics Data System (ADS)

Wu, Yunfeng; Wang, Yaming; Liu, Hao; Liu, Yan; Guo, Lixin; Jia, Dechang; Ouyang, Jiahu; Zhou, Yu

2016-12-01

Magnesium alloy with micro-nano structure roughness surface, can serve as the loading reservoirs of medicine capsule and industrial lubricating oil, or mimic 'lotus leaf' hydrophobic surface, having the potential applications in medical implants, automobile, aerospace and electronic products, etc. Herein, we propose a novel strategy to design a micro-nano structure roughness surface on magnesium alloy using combined microarc sparking sculpture and etching in CrO3 aqueous solution. A hydrophobic surface (as an applied example) was further fabricated by chemical decorating on the obtained patterned magnesium alloy surface to enhance the corrosion resistance. The results show that the combined micro-nano structure of 7-9 μm diameter big pores insetting with nano-scale fine pores was duplicated after etched the sparking sculptured 'over growth' oxide regions towards the magnesium substrate. The micro-nano structure surface was chemically decorated using AgNO3 and stearic acid, which enables the contact angle increased from 60° to 146.8°. The increasing contact angle is mainly attributed to the micro-nano structure and the chemical composition. The hydrophobic surface of magnesium alloy improved the corrosion potential from -1.521 V of the bare magnesium to -1.274 V. Generally, the sparking sculpture and then etching route demonstrates a low-cost, high-efficacy method to fabricate a micro-nano structure hydrophobic surface on magnesium alloy. Furthermore, our research on the creating of micro-nano structure roughness surface and the hydrophobic treatment can be easily extended to the other metal materials.

The Research on Linux Memory Forensics

NASA Astrophysics Data System (ADS)

Zhang, Jun; Che, ShengBing

2018-03-01

Memory forensics is a branch of computer forensics. It does not depend on the operating system API, and analyzes operating system information from binary memory data. Based on the 64-bit Linux operating system, it analyzes system process and thread information from physical memory data. Using ELF file debugging information and propose a method for locating kernel structure member variable, it can be applied to different versions of the Linux operating system. The experimental results show that the method can successfully obtain the sytem process information from physical memory data, and can be compatible with multiple versions of the Linux kernel.

KERNELHR: A program for estimating animal home ranges

USGS Publications Warehouse

Seaman, D.E.; Griffith, B.; Powell, R.A.

1998-01-01

Kernel methods are state of the art for estimating animal home-range area and utilization distribution (UD). The KERNELHR program was developed to provide researchers and managers a tool to implement this extremely flexible set of methods with many variants. KERNELHR runs interactively or from the command line on any personal computer (PC) running DOS. KERNELHR provides output of fixed and adaptive kernel home-range estimates, as well as density values in a format suitable for in-depth statistical and spatial analyses. An additional package of programs creates contour files for plotting in geographic information systems (GIS) and estimates core areas of ranges.

Experimental verification of the capillary plasma triggered long spark gap under the extremely low working coefficient in air

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, D.; Yang, L. J., E-mail: yanglj@mail.xjtu.edu.cn; Ma, J. B.

The paper has proposed a new triggering method for long spark gap based on capillary plasma ejection and conducted the experimental verification under the extremely low working coefficient, which represents that the ratio of the spark gap charging voltage to the breakdown voltage is particularly low. The quasi-neutral plasma is ejected from the capillary and develops through the axial direction of the spark gap. The electric field in the spark gap is thus changed and its breakdown is incurred. It is proved by the experiments that the capillary plasma ejection is effective in triggering the long spark gap under themore » extremely low working coefficient in air. The study also indicates that the breakdown probabilities, the breakdown delay, and the delay dispersion are all mainly determined by the characteristics of the ejected plasma, including the length of the plasma flow, the speed of the plasma ejection, and the ionization degree of the plasma. Moreover, the breakdown delay and the delay dispersion increase with the length of the long spark gap, and the polarity effect exists in the triggering process. Lastly, compared with the working patterns of the triggering device installed in the single electrode, the working pattern of the devices installed in both the two electrodes, though with the same breakdown process, achieves the ignition under longer gap distance. To be specific, at the gap length of 14 cm and the working coefficient of less than 2%, the spark gap is still ignited accurately.« less

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2012 CFR

2012-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2011 CFR

2011-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2013 CFR

2013-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2010 CFR

2010-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

7 CFR 981.401 - Adjusted kernel weight.

Code of Federal Regulations, 2014 CFR

2014-01-01

... based on the analysis of a 1,000 gram sample taken from a lot of almonds weighing 10,000 pounds with less than 95 percent kernels, and a 1,000 gram sample taken from a lot of almonds weighing 10,000... percent kernels containing the following: Edible kernels, 530 grams; inedible kernels, 120 grams; foreign...

7 CFR 51.1441 - Half-kernel.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Half-kernel. 51.1441 Section 51.1441 Agriculture... Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of the separated halves of an entire pecan kernel with not more than one-eighth of its original volume missing...

7 CFR 51.1403 - Kernel color classification.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Kernel color classification. 51.1403 Section 51.1403... STANDARDS) United States Standards for Grades of Pecans in the Shell 1 Kernel Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be described in terms of the color...

7 CFR 51.1450 - Serious damage.

Code of Federal Regulations, 2010 CFR

2010-01-01

...; (c) Decay affecting any portion of the kernel; (d) Insects, web, or frass or any distinct evidence of insect feeding on the kernel; (e) Internal discoloration which is dark gray, dark brown, or black and...) Dark kernel spots when more than three are on the kernel, or when any dark kernel spot or the aggregate...

7 CFR 51.1450 - Serious damage.

Code of Federal Regulations, 2011 CFR

2011-01-01

...; (c) Decay affecting any portion of the kernel; (d) Insects, web, or frass or any distinct evidence of insect feeding on the kernel; (e) Internal discoloration which is dark gray, dark brown, or black and...) Dark kernel spots when more than three are on the kernel, or when any dark kernel spot or the aggregate...

7 CFR 51.1450 - Serious damage.

Code of Federal Regulations, 2012 CFR

2012-01-01

...; (c) Decay affecting any portion of the kernel; (d) Insects, web, or frass or any distinct evidence of insect feeding on the kernel; (e) Internal discoloration which is dark gray, dark brown, or black and...) Dark kernel spots when more than three are on the kernel, or when any dark kernel spot or the aggregate...

Wavelet SVM in Reproducing Kernel Hilbert Space for hyperspectral remote sensing image classification

NASA Astrophysics Data System (ADS)

Du, Peijun; Tan, Kun; Xing, Xiaoshi

2010-12-01

Combining Support Vector Machine (SVM) with wavelet analysis, we constructed wavelet SVM (WSVM) classifier based on wavelet kernel functions in Reproducing Kernel Hilbert Space (RKHS). In conventional kernel theory, SVM is faced with the bottleneck of kernel parameter selection which further results in time-consuming and low classification accuracy. The wavelet kernel in RKHS is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. Implications on semiparametric estimation are proposed in this paper. Airborne Operational Modular Imaging Spectrometer II (OMIS II) hyperspectral remote sensing image with 64 bands and Reflective Optics System Imaging Spectrometer (ROSIS) data with 115 bands were used to experiment the performance and accuracy of the proposed WSVM classifier. The experimental results indicate that the WSVM classifier can obtain the highest accuracy when using the Coiflet Kernel function in wavelet transform. In contrast with some traditional classifiers, including Spectral Angle Mapping (SAM) and Minimum Distance Classification (MDC), and SVM classifier using Radial Basis Function kernel, the proposed wavelet SVM classifier using the wavelet kernel function in Reproducing Kernel Hilbert Space is capable of improving classification accuracy obviously.

A trace ratio maximization approach to multiple kernel-based dimensionality reduction.

PubMed

Jiang, Wenhao; Chung, Fu-lai

2014-01-01

Most dimensionality reduction techniques are based on one metric or one kernel, hence it is necessary to select an appropriate kernel for kernel-based dimensionality reduction. Multiple kernel learning for dimensionality reduction (MKL-DR) has been recently proposed to learn a kernel from a set of base kernels which are seen as different descriptions of data. As MKL-DR does not involve regularization, it might be ill-posed under some conditions and consequently its applications are hindered. This paper proposes a multiple kernel learning framework for dimensionality reduction based on regularized trace ratio, termed as MKL-TR. Our method aims at learning a transformation into a space of lower dimension and a corresponding kernel from the given base kernels among which some may not be suitable for the given data. The solutions for the proposed framework can be found based on trace ratio maximization. The experimental results demonstrate its effectiveness in benchmark datasets, which include text, image and sound datasets, for supervised, unsupervised as well as semi-supervised settings. Copyright © 2013 Elsevier Ltd. All rights reserved.

Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature

PubMed Central

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems. PMID:29099838

Hadamard Kernel SVM with applications for breast cancer outcome predictions.

PubMed

Jiang, Hao; Ching, Wai-Ki; Cheung, Wai-Shun; Hou, Wenpin; Yin, Hong

2017-12-21

Breast cancer is one of the leading causes of deaths for women. It is of great necessity to develop effective methods for breast cancer detection and diagnosis. Recent studies have focused on gene-based signatures for outcome predictions. Kernel SVM for its discriminative power in dealing with small sample pattern recognition problems has attracted a lot attention. But how to select or construct an appropriate kernel for a specified problem still needs further investigation. Here we propose a novel kernel (Hadamard Kernel) in conjunction with Support Vector Machines (SVMs) to address the problem of breast cancer outcome prediction using gene expression data. Hadamard Kernel outperform the classical kernels and correlation kernel in terms of Area under the ROC Curve (AUC) values where a number of real-world data sets are adopted to test the performance of different methods. Hadamard Kernel SVM is effective for breast cancer predictions, either in terms of prognosis or diagnosis. It may benefit patients by guiding therapeutic options. Apart from that, it would be a valuable addition to the current SVM kernel families. We hope it will contribute to the wider biology and related communities.

Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature.

PubMed

Murugesan, Gurusamy; Abdulkadhar, Sabenabanu; Natarajan, Jeyakumar

2017-01-01

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems.

Small-size controlled vacuum spark-gap in an external magnetic field

DOE Office of Scientific and Technical Information (OSTI.GOV)

Asyunin, V. I., E-mail: asvi@mail.ru; Davydov, S. G.; Dolgov, A. N., E-mail: alnikdolgov@mail.ru

2015-02-15

It is demonstrated that the operation of a small-size controlled spark-gap can be controlled by applying a uniform external magnetic field. It is shown that the magnetic field of such a simple configuration efficiently suppresses the effect of localization of the discharge current after multiple actuations of the spark-gap.

Military Assistance to Mexico: Use of Special Operations Forces

DTIC Science & Technology

2010-03-31

91 Donald Sparks, " Jackal Stone 2009; Special Opcmti011s Forces Multinational Training in Croatia", Tt/J of the Spear, USSCOM, Tampa FL, 92 Sparks...Data Analysis from 2001-2009" Justice in Mexico Project, Trans Border Institute, University of San Diego, January 2009. Sparks, Donald " Jackal Stone

Gas mixtures for spark gap closing switches

DOEpatents

Christophorou, L.G.; McCorkle, D.L.; Hunter, S.R.

1987-02-20

Gas mixtures for use in spark gap closing switches comprised of fluorocarbons and low molecular weight, inert buffer gases. To this can be added a third gas having a low ionization potential relative to the buffer gas. The gas mixtures presented possess properties that optimized the efficiency spark gap closing switches. 6 figs.

30 CFR 18.50 - Protection against external arcs and sparks.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Protection against external arcs and sparks. 18... and Design Requirements § 18.50 Protection against external arcs and sparks. Provision shall be made... of that of one power conductor unless a ground-fault tripping relay is used, in which case the...

30 CFR 18.50 - Protection against external arcs and sparks.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Protection against external arcs and sparks. 18... and Design Requirements § 18.50 Protection against external arcs and sparks. Provision shall be made... of that of one power conductor unless a ground-fault tripping relay is used, in which case the...

LZW-Kernel: fast kernel utilizing variable length code blocks from LZW compressors for protein sequence classification.

PubMed

Filatov, Gleb; Bauwens, Bruno; Kertész-Farkas, Attila

2018-05-07

Bioinformatics studies often rely on similarity measures between sequence pairs, which often pose a bottleneck in large-scale sequence analysis. Here, we present a new convolutional kernel function for protein sequences called the LZW-Kernel. It is based on code words identified with the Lempel-Ziv-Welch (LZW) universal text compressor. The LZW-Kernel is an alignment-free method, it is always symmetric, is positive, always provides 1.0 for self-similarity and it can directly be used with Support Vector Machines (SVMs) in classification problems, contrary to normalized compression distance (NCD), which often violates the distance metric properties in practice and requires further techniques to be used with SVMs. The LZW-Kernel is a one-pass algorithm, which makes it particularly plausible for big data applications. Our experimental studies on remote protein homology detection and protein classification tasks reveal that the LZW-Kernel closely approaches the performance of the Local Alignment Kernel (LAK) and the SVM-pairwise method combined with Smith-Waterman (SW) scoring at a fraction of the time. Moreover, the LZW-Kernel outperforms the SVM-pairwise method when combined with BLAST scores, which indicates that the LZW code words might be a better basis for similarity measures than local alignment approximations found with BLAST. In addition, the LZW-Kernel outperforms n-gram based mismatch kernels, hidden Markov model based SAM and Fisher kernel, and protein family based PSI-BLAST, among others. Further advantages include the LZW-Kernel's reliance on a simple idea, its ease of implementation, and its high speed, three times faster than BLAST and several magnitudes faster than SW or LAK in our tests. LZW-Kernel is implemented as a standalone C code and is a free open-source program distributed under GPLv3 license and can be downloaded from https://github.com/kfattila/LZW-Kernel. akerteszfarkas@hse.ru. Supplementary data are available at Bioinformatics Online.

The Case for "Living" Models

ERIC Educational Resources Information Center

Creelman, Valerie

2012-01-01

How best to teach and compose negative messages is a topic that continues to challenge and spark debate among business communication educators and researchers. Even where business communications textbooks emphasize the importance of context and audience analysis to determine whether to adopt a direct or indirect arrangement when expressing bad…

EXPOSURE TO HAZARDOUS SUBSTANCES AND MALE REPRODUCTIVE HEALTH: A RESEARCH FRAMEWORK

EPA Science Inventory

The discovery in the mid-1970s that occupational exposures to pesticides could diminish or destroy the fertility of workers sparked concern about the effects of hazardous substances on male reproductive health. More recently, there is evidence that sperm quantity and quality may ...

Assessment of the Potential Impact of Combustion Research on Internal Combustion Engine Emission and Fuel Consumption

DOT National Transportation Integrated Search

1979-01-01

A review of the present level of understanding of the basic thermodynamic, fluid dynamic, and chemical kinetic processes which affect the fuel economy and levels of pollutant exhaust products of Diesel, Stratified Charge, and Spark Ignition engines i...

Teaching Moral Reasoning through Gesture

ERIC Educational Resources Information Center

Beaudoin-Ryan, Leanne; Goldin-Meadow, Susan

2014-01-01

Stem-cell research. Euthanasia. Personhood. Marriage equality. School shootings. Gun control. Death penalty. Ethical dilemmas regularly spark fierce debate about the underlying moral fabric of societies. How do we prepare today's children to be fully informed and thoughtful citizens, capable of moral and ethical decisions? Current approaches…

Language Anxiety among Gifted Learners in Malaysia

ERIC Educational Resources Information Center

Kamarulzaman, Mohd Hasrul; Ibrahim, Noraniza; Yunus, Melor Md; Ishak, Noriah Mohd

2013-01-01

Language anxiety has significantly sparked great concern in the second and foreign language learning world. Researches have found negative correlation between language anxiety and academic achievement of English language learners; and, most of the studies focus on average school students and tertiary level students. This paper, however, explores…

Learning from Latino Families

ERIC Educational Resources Information Center

Auerbach, Susan

2011-01-01

As a researcher in parent engagement in school and former parent activist, the author shares three lessons for sparking more authentic partnerships between schools and immigrant families. First, schools need to move away from deficit thinking and validate families' cultures. In the case of Latino immigrant families, this entails understanding…

Linking Course Web Sites to Library Collections and Services

ERIC Educational Resources Information Center

Rieger, Oya Y.; Horne, Angela K.; Revels, Ira

2004-01-01

A five-month research study at Cornell University Library (CUL) confirmed the strategic importance of a library presence in faculty-created course Web sites. It sparked specific recommendations to support the seamless integration of the CUL digital library within the virtual learning environments created by faculty.

Adolescents' Evaluation of Cyberbullying Events

ERIC Educational Resources Information Center

Gomez-Garibello, Carlos; Shariff, Shaheen; McConnell, Megan; Talwar, Victoria

2012-01-01

Educators and other professionals working with adolescents have grown increasingly concerned about how technology affects social relationships given the amount of time that is spent engaging in online activities. Cyberbullying has sparked the interest of many researchers due to the tragic events reported in the media, relating to the online…

A framework for optimal kernel-based manifold embedding of medical image data.

PubMed

Zimmer, Veronika A; Lekadir, Karim; Hoogendoorn, Corné; Frangi, Alejandro F; Piella, Gemma

2015-04-01

Kernel-based dimensionality reduction is a widely used technique in medical image analysis. To fully unravel the underlying nonlinear manifold the selection of an adequate kernel function and of its free parameters is critical. In practice, however, the kernel function is generally chosen as Gaussian or polynomial and such standard kernels might not always be optimal for a given image dataset or application. In this paper, we present a study on the effect of the kernel functions in nonlinear manifold embedding of medical image data. To this end, we first carry out a literature review on existing advanced kernels developed in the statistics, machine learning, and signal processing communities. In addition, we implement kernel-based formulations of well-known nonlinear dimensional reduction techniques such as Isomap and Locally Linear Embedding, thus obtaining a unified framework for manifold embedding using kernels. Subsequently, we present a method to automatically choose a kernel function and its associated parameters from a pool of kernel candidates, with the aim to generate the most optimal manifold embeddings. Furthermore, we show how the calculated selection measures can be extended to take into account the spatial relationships in images, or used to combine several kernels to further improve the embedding results. Experiments are then carried out on various synthetic and phantom datasets for numerical assessment of the methods. Furthermore, the workflow is applied to real data that include brain manifolds and multispectral images to demonstrate the importance of the kernel selection in the analysis of high-dimensional medical images. Copyright © 2014 Elsevier Ltd. All rights reserved.

Large discharge-volume, silent discharge spark plug

DOEpatents

Kang, Michael

1995-01-01

A large discharge-volume spark plug for providing self-limiting microdischarges. The apparatus includes a generally spark plug-shaped arrangement of a pair of electrodes, where either of the two coaxial electrodes is substantially shielded by a dielectric barrier from a direct discharge from the other electrode, the unshielded electrode and the dielectric barrier forming an annular volume in which self-terminating microdischarges occur when alternating high voltage is applied to the center electrode. The large area over which the discharges occur, and the large number of possible discharges within the period of an engine cycle, make the present silent discharge plasma spark plug suitable for use as an ignition source for engines. In the situation, where a single discharge is effective in causing ignition of the combustible gases, a conventional single-polarity, single-pulse, spark plug voltage supply may be used.

LLNL small-scale static spark machine: static spark sensitivity test

DOE Office of Scientific and Technical Information (OSTI.GOV)

Foltz, M F; Simpson, L R

1999-08-23

Small-scale safety testing of explosives and other energetic materials is done in order to determine their sensitivity to various stimuli, such as friction, static spark, and impact. Typically this testing is done to discover potential handling problems that may exist for either newly synthesized materials of unknown behavior, or materials that have been stored for long periods of time. This report describes the existing ''Static Spark Test Apparatus'' at Lawrence Livermore National Laboratory (LLNL), as well as the method used to evaluate the relative static spark sensitivity of energetic materials. The basic design, originally developed by the Picatinny Arsenal inmore » New Jersey, is discussed. The accumulated data for the materials tested to date is not included here, with the exception of specific examples that have yielded interesting or unusual results during the tests.« less

Spark ignition timing control system for internal combustion engine with feature of suppression of jerking during engine acceleration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tomisawa, N.

1989-07-04

This patent describes a spark ignition timing control system for an internal combustion engine, it comprises: sensor means monitoring preselected parameters for producing a sensor signal; first means for deriving a spark ignition timing on the basis of data contained in the sensor signal; second means for detecting an engine acceleration demand for producing an accelerating condition indicative signal; and third means, responsive to the accelerating condition indicative signal, for modifying the spark ignition timing derived by the first means after expiration of a first predetermined period of time of occurence of the accelerating condition indicative signal, in such amore » manner that the spark ignition timing is advanced and retarded for suppressing cycle-to-cycle fluctuation of engine speed and for smoothly increasing engine speed.« less

SPARK: Adapting Keyword Query to Semantic Search

NASA Astrophysics Data System (ADS)

Zhou, Qi; Wang, Chong; Xiong, Miao; Wang, Haofen; Yu, Yong

Semantic search promises to provide more accurate result than present-day keyword search. However, progress with semantic search has been delayed due to the complexity of its query languages. In this paper, we explore a novel approach of adapting keywords to querying the semantic web: the approach automatically translates keyword queries into formal logic queries so that end users can use familiar keywords to perform semantic search. A prototype system named 'SPARK' has been implemented in light of this approach. Given a keyword query, SPARK outputs a ranked list of SPARQL queries as the translation result. The translation in SPARK consists of three major steps: term mapping, query graph construction and query ranking. Specifically, a probabilistic query ranking model is proposed to select the most likely SPARQL query. In the experiment, SPARK achieved an encouraging translation result.

Titian: Data Provenance Support in Spark

PubMed Central

Interlandi, Matteo; Shah, Kshitij; Tetali, Sai Deep; Gulzar, Muhammad Ali; Yoo, Seunghyun; Kim, Miryung; Millstein, Todd; Condie, Tyson

2015-01-01

Debugging data processing logic in Data-Intensive Scalable Computing (DISC) systems is a difficult and time consuming effort. Today’s DISC systems offer very little tooling for debugging programs, and as a result programmers spend countless hours collecting evidence (e.g., from log files) and performing trial and error debugging. To aid this effort, we built Titian, a library that enables data provenance—tracking data through transformations—in Apache Spark. Data scientists using the Titian Spark extension will be able to quickly identify the input data at the root cause of a potential bug or outlier result. Titian is built directly into the Spark platform and offers data provenance support at interactive speeds—orders-of-magnitude faster than alternative solutions—while minimally impacting Spark job performance; observed overheads for capturing data lineage rarely exceed 30% above the baseline job execution time. PMID:26726305

Technology of fast spark gaps

NASA Astrophysics Data System (ADS)

Standler, Ronald B.

1989-09-01

To protect electronic systems from the effects of electromagnetic pulse (EMP) form nuclear weapons and high-power microwave (HPM) weapons, it is desirable to have fast responding protection components. The gas-filled spark gap appears to be an attractive protection component, except that it can be slow to conduct under certain conditions. This report reviews the literature and presents ideas for construction of a spark gap that will conduct in less than one nanosecond. The key concept to making a fast-responding spark gap is to produce a large number of free electrons quickly. Seven different mechanisms for production of free electrons are reviewed, and several that are relevant to miniature spark gaps for protective applications are discussed in detail. These mechanisms include: inclusion of radioactive materials, photoelectric effect, secondary electrode emission from the anode, and field emission from the cathode.

Apparatus and method for the spectrochemical analysis of liquids using the laser spark

DOEpatents

Cremers, David A.; Radziemski, Leon J.; Loree, Thomas R.

1990-01-01

A method and apparatus for the qualitative and quantitative spectroscopic investigation of elements present in a liquid sample using the laser spark. A series of temporally closely spaced spark pairs is induced in the liquid sample utilizing pulsed electromagnetic radiation from a pair of lasers. The light pulses are not significantly absorbed by the sample so that the sparks occur inside of the liquid. The emitted light from the breakdown events is spectrally and temporally resolved, and the time period between the two laser pulses in each spark pair is adjusted to maximize the signal-to-noise ratio of the emitted signals. In comparison with the single pulse technique, a substantial reduction in the limits of detectability for many elements has been demonstrated. Narrowing of spectral features results in improved discrimination against interfering species.

Apparatus and method for the spectrochemical analysis of liquids using the laser spark

DOEpatents

Cremers, D.A.; Radziemski, L.J.; Loree, T.R.

1984-05-01

A method and apparatus are disclosed for the qualitative and quantitative spectroscopic investigation of elements present in a liquid sample using the laser spark. A series of temporally closely spaced spark pairs is induced in the liquid sample utilizing pulsed electromagnetic radiation from a pair of lasers. The light pulses are not significantly absorbed by the sample so that the sparks occur inside of the liquid. The emitted light from the breakdown events is spectrally and temporally resolved, and the time period between the two laser pulses in each spark pair is adjusted to maximize the signal-to-noise ratio of the emitted signals. In comparison with the single pulse technique, a substantial reduction in the limits of detectability for many elements has been demonstrated. Narrowing of spectral features results in improved discrimination against interfering species.

Evaluating the Gradient of the Thin Wire Kernel

NASA Technical Reports Server (NTRS)

Wilton, Donald R.; Champagne, Nathan J.

2008-01-01

Recently, a formulation for evaluating the thin wire kernel was developed that employed a change of variable to smooth the kernel integrand, canceling the singularity in the integrand. Hence, the typical expansion of the wire kernel in a series for use in the potential integrals is avoided. The new expression for the kernel is exact and may be used directly to determine the gradient of the wire kernel, which consists of components that are parallel and radial to the wire axis.

Kernel Machine SNP-set Testing under Multiple Candidate Kernels

PubMed Central

Wu, Michael C.; Maity, Arnab; Lee, Seunggeun; Simmons, Elizabeth M.; Harmon, Quaker E.; Lin, Xinyi; Engel, Stephanie M.; Molldrem, Jeffrey J.; Armistead, Paul M.

2013-01-01

Joint testing for the cumulative effect of multiple single nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large scale genetic association studies. The kernel machine (KM) testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori since this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest p-value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power versus using the best candidate kernel. PMID:23471868

Combined multi-kernel head computed tomography images optimized for depicting both brain parenchyma and bone.

PubMed

Takagi, Satoshi; Nagase, Hiroyuki; Hayashi, Tatsuya; Kita, Tamotsu; Hayashi, Katsumi; Sanada, Shigeru; Koike, Masayuki

2014-01-01

The hybrid convolution kernel technique for computed tomography (CT) is known to enable the depiction of an image set using different window settings. Our purpose was to decrease the number of artifacts in the hybrid convolution kernel technique for head CT and to determine whether our improved combined multi-kernel head CT images enabled diagnosis as a substitute for both brain (low-pass kernel-reconstructed) and bone (high-pass kernel-reconstructed) images. Forty-four patients with nondisplaced skull fractures were included. Our improved multi-kernel images were generated so that pixels of >100 Hounsfield unit in both brain and bone images were composed of CT values of bone images and other pixels were composed of CT values of brain images. Three radiologists compared the improved multi-kernel images with bone images. The improved multi-kernel images and brain images were identically displayed on the brain window settings. All three radiologists agreed that the improved multi-kernel images on the bone window settings were sufficient for diagnosing skull fractures in all patients. This improved multi-kernel technique has a simple algorithm and is practical for clinical use. Thus, simplified head CT examinations and fewer images that need to be stored can be expected.

Superresolution Modeling of Calcium Release in the Heart

PubMed Central

Walker, Mark A.; Williams, George S.B.; Kohl, Tobias; Lehnart, Stephan E.; Jafri, M. Saleet; Greenstein, Joseph L.; Lederer, W.J.; Winslow, Raimond L.

2014-01-01

Stable calcium-induced calcium release (CICR) is critical for maintaining normal cellular contraction during cardiac excitation-contraction coupling. The fundamental element of CICR in the heart is the calcium (Ca2+) spark, which arises from a cluster of ryanodine receptors (RyR). Opening of these RyR clusters is triggered to produce a local, regenerative release of Ca2+ from the sarcoplasmic reticulum (SR). The Ca2+ leak out of the SR is an important process for cellular Ca2+ management, and it is critically influenced by spark fidelity, i.e., the probability that a spontaneous RyR opening triggers a Ca2+ spark. Here, we present a detailed, three-dimensional model of a cardiac Ca2+ release unit that incorporates diffusion, intracellular buffering systems, and stochastically gated ion channels. The model exhibits realistic Ca2+ sparks and robust Ca2+ spark termination across a wide range of geometries and conditions. Furthermore, the model captures the details of Ca2+ spark and nonspark-based SR Ca2+ leak, and it produces normal excitation-contraction coupling gain. We show that SR luminal Ca2+-dependent regulation of the RyR is not critical for spark termination, but it can explain the exponential rise in the SR Ca2+ leak-load relationship demonstrated in previous experimental work. Perturbations to subspace dimensions, which have been observed in experimental models of disease, strongly alter Ca2+ spark dynamics. In addition, we find that the structure of RyR clusters also influences Ca2+ release properties due to variations in inter-RyR coupling via local subspace Ca2+ concentration ([Ca2+]ss). These results are illustrated for RyR clusters based on super-resolution stimulated emission depletion microscopy. Finally, we present a believed-novel approach by which the spark fidelity of a RyR cluster can be predicted from structural information of the cluster using the maximum eigenvalue of its adjacency matrix. These results provide critical insights into CICR dynamics in heart, under normal and pathological conditions. PMID:25517166

Formation of Equiaxed Alpha and Titanium Nitride Precipitates in Spark Plasma Sintered TiB/Ti-6Al-4V Composites (Preprint)

DTIC Science & Technology

2012-08-01

AFRL-RX-WP-TP-2012-0372 FORMATION OF EQUIAXED ALPHA AND TITANIUM NITRIDE PRECIPITATES IN SPARK PLASMA SINTERED TiB/Ti-6Al-4V COMPOSITES...ALPHA AND TITANIUM NITRIDE PRECIPITATES IN SPARK PLASMA SINTERED TiB/Ti-6Al-4V COMPOSITES (PREPRINT) 5a. CONTRACT NUMBER FA8650-08-C-5226 5b...distribution of TiN precipitates, as revealed by TEM studies. 15. SUBJECT TERMS Ti-6Al-4V; TiB; TiN; Spark Plasma Sintering ; Composite; α/β phase

The sparking voltage of spark plugs

NASA Technical Reports Server (NTRS)

Silsbee, F B

1925-01-01

This report has been prepared in order to collect and correlate into convenient and useful form the available data on this subject. The importance of the subject lies in the fact that it forms the common meeting ground for studies of the performance of spark generators and spark plugs on the one hand and of the internal combustion engines on the other hand. While much of the data presented was obtained from various earlier publications, numerous places were found where necessary data were lacking, and these have been provided by experiments in gasoline engines at the Bureau of Standards.

Formation of a spark discharge in an inhomogeneous electric field with current limitation by a large ballast Resistance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baldanov, B. B., E-mail: baibat@mail.ru

2016-01-15

Results of studies of a spark discharge initiated in argon in a point–plane electrode gap with limitation of the discharge current by a large ballast resistance are presented. It is shown that the current flowing through the plasma channel of such a low-current spark has the form of periodic pulses. It is experimentally demonstrated that, when a low-current spark transforms into a constricted glow discharge, current pulses disappear, the spatial structure of the cathode glow changes abruptly, and a brightly glowing positive plasma column forms in the gap.

7 CFR 810.202 - Definition of other terms.

Code of Federal Regulations, 2014 CFR

2014-01-01

... barley kernels, other grains, and wild oats that are badly shrunken and distinctly discolored black or... kernels. Kernels and pieces of barley kernels that are distinctly indented, immature or shrunken in...

7 CFR 810.202 - Definition of other terms.

Code of Federal Regulations, 2013 CFR

2013-01-01

... barley kernels, other grains, and wild oats that are badly shrunken and distinctly discolored black or... kernels. Kernels and pieces of barley kernels that are distinctly indented, immature or shrunken in...

7 CFR 810.202 - Definition of other terms.

Code of Federal Regulations, 2012 CFR

2012-01-01

... barley kernels, other grains, and wild oats that are badly shrunken and distinctly discolored black or... kernels. Kernels and pieces of barley kernels that are distinctly indented, immature or shrunken in...

graphkernels: R and Python packages for graph comparison

PubMed Central

Ghisu, M Elisabetta; Llinares-López, Felipe; Borgwardt, Karsten

2018-01-01

Abstract Summary Measuring the similarity of graphs is a fundamental step in the analysis of graph-structured data, which is omnipresent in computational biology. Graph kernels have been proposed as a powerful and efficient approach to this problem of graph comparison. Here we provide graphkernels, the first R and Python graph kernel libraries including baseline kernels such as label histogram based kernels, classic graph kernels such as random walk based kernels, and the state-of-the-art Weisfeiler-Lehman graph kernel. The core of all graph kernels is implemented in C ++ for efficiency. Using the kernel matrices computed by the package, we can easily perform tasks such as classification, regression and clustering on graph-structured samples. Availability and implementation The R and Python packages including source code are available at https://CRAN.R-project.org/package=graphkernels and https://pypi.python.org/pypi/graphkernels. Contact mahito@nii.ac.jp or elisabetta.ghisu@bsse.ethz.ch Supplementary information Supplementary data are available online at Bioinformatics. PMID:29028902

Aflatoxin variability in pistachios.

PubMed Central

Mahoney, N E; Rodriguez, S B

1996-01-01

Pistachio fruit components, including hulls (mesocarps and epicarps), seed coats (testas), and kernels (seeds), all contribute to variable aflatoxin content in pistachios. Fresh pistachio kernels were individually inoculated with Aspergillus flavus and incubated 7 or 10 days. Hulled, shelled kernels were either left intact or wounded prior to inoculation. Wounded kernels, with or without the seed coat, were readily colonized by A. flavus and after 10 days of incubation contained 37 times more aflatoxin than similarly treated unwounded kernels. The aflatoxin levels in the individual wounded pistachios were highly variable. Neither fungal colonization nor aflatoxin was detected in intact kernels without seed coats. Intact kernels with seed coats had limited fungal colonization and low aflatoxin concentrations compared with their wounded counterparts. Despite substantial fungal colonization of wounded hulls, aflatoxin was not detected in hulls. Aflatoxin levels were significantly lower in wounded kernels with hulls than in kernels of hulled pistachios. Both the seed coat and a water-soluble extract of hulls suppressed aflatoxin production by A. flavus. PMID:8919781

graphkernels: R and Python packages for graph comparison.

PubMed

Sugiyama, Mahito; Ghisu, M Elisabetta; Llinares-López, Felipe; Borgwardt, Karsten

2018-02-01

Measuring the similarity of graphs is a fundamental step in the analysis of graph-structured data, which is omnipresent in computational biology. Graph kernels have been proposed as a powerful and efficient approach to this problem of graph comparison. Here we provide graphkernels, the first R and Python graph kernel libraries including baseline kernels such as label histogram based kernels, classic graph kernels such as random walk based kernels, and the state-of-the-art Weisfeiler-Lehman graph kernel. The core of all graph kernels is implemented in C ++ for efficiency. Using the kernel matrices computed by the package, we can easily perform tasks such as classification, regression and clustering on graph-structured samples. The R and Python packages including source code are available at https://CRAN.R-project.org/package=graphkernels and https://pypi.python.org/pypi/graphkernels. mahito@nii.ac.jp or elisabetta.ghisu@bsse.ethz.ch. Supplementary data are available online at Bioinformatics. © The Author(s) 2017. Published by Oxford University Press.

Investigation of various energy deposition kernel refinements for the convolution/superposition method

PubMed Central

Huang, Jessie Y.; Eklund, David; Childress, Nathan L.; Howell, Rebecca M.; Mirkovic, Dragan; Followill, David S.; Kry, Stephen F.

2013-01-01

Purpose: Several simplifications used in clinical implementations of the convolution/superposition (C/S) method, specifically, density scaling of water kernels for heterogeneous media and use of a single polyenergetic kernel, lead to dose calculation inaccuracies. Although these weaknesses of the C/S method are known, it is not well known which of these simplifications has the largest effect on dose calculation accuracy in clinical situations. The purpose of this study was to generate and characterize high-resolution, polyenergetic, and material-specific energy deposition kernels (EDKs), as well as to investigate the dosimetric impact of implementing spatially variant polyenergetic and material-specific kernels in a collapsed cone C/S algorithm. Methods: High-resolution, monoenergetic water EDKs and various material-specific EDKs were simulated using the EGSnrc Monte Carlo code. Polyenergetic kernels, reflecting the primary spectrum of a clinical 6 MV photon beam at different locations in a water phantom, were calculated for different depths, field sizes, and off-axis distances. To investigate the dosimetric impact of implementing spatially variant polyenergetic kernels, depth dose curves in water were calculated using two different implementations of the collapsed cone C/S method. The first method uses a single polyenergetic kernel, while the second method fully takes into account spectral changes in the convolution calculation. To investigate the dosimetric impact of implementing material-specific kernels, depth dose curves were calculated for a simplified titanium implant geometry using both a traditional C/S implementation that performs density scaling of water kernels and a novel implementation using material-specific kernels. Results: For our high-resolution kernels, we found good agreement with the Mackie et al. kernels, with some differences near the interaction site for low photon energies (<500 keV). For our spatially variant polyenergetic kernels, we found that depth was the most dominant factor affecting the pattern of energy deposition; however, the effects of field size and off-axis distance were not negligible. For the material-specific kernels, we found that as the density of the material increased, more energy was deposited laterally by charged particles, as opposed to in the forward direction. Thus, density scaling of water kernels becomes a worse approximation as the density and the effective atomic number of the material differ more from water. Implementation of spatially variant, polyenergetic kernels increased the percent depth dose value at 25 cm depth by 2.1%–5.8% depending on the field size, while implementation of titanium kernels gave 4.9% higher dose upstream of the metal cavity (i.e., higher backscatter dose) and 8.2% lower dose downstream of the cavity. Conclusions: Of the various kernel refinements investigated, inclusion of depth-dependent and metal-specific kernels into the C/S method has the greatest potential to improve dose calculation accuracy. Implementation of spatially variant polyenergetic kernels resulted in a harder depth dose curve and thus has the potential to affect beam modeling parameters obtained in the commissioning process. For metal implants, the C/S algorithms generally underestimate the dose upstream and overestimate the dose downstream of the implant. Implementation of a metal-specific kernel mitigated both of these errors. PMID:24320507

Nutritional composition of shea products and chemical properties of shea butter: a review.

PubMed

Honfo, Fernande G; Akissoe, Noel; Linnemann, Anita R; Soumanou, Mohamed; Van Boekel, Martinus A J S

2014-01-01

Increasing demand of shea products (kernels and butter) has led to the assessment of the state-of-the-art of these products. In this review, attention has been focused on macronutrients and micronutrients of pulp, kernels, and butter of shea tree and also the physicochemical properties of shea butter. Surveying the literature revealed that the pulp is rich in vitamin C (196.1 mg/100 g); consumption of 50 g covers 332% and 98% of the recommended daily intake (RDI) of children (4-8 years old) and pregnant women, respectively. The kernels contain a high level of fat (17.4-59.1 g/100 g dry weight). Fat extraction is mainly done by traditional methods that involve roasting and pressing of the kernels, churning the obtained liquid with water, boiling, sieving, and cooling. The fat (butter) is used in food preparation and medicinal and cosmetics industries. Its biochemical properties indicate some antioxidant and anti-inflammatory activities. Large variations are observed in the reported values for the composition of shea products. Recommendations for future research are presented to improve the quality and the shelf-life of the butter. In addition, more attention should be given to the accuracy and precision in experimental analyses to obtain more reliable information about biological variation.

Unified heat kernel regression for diffusion, kernel smoothing and wavelets on manifolds and its application to mandible growth modeling in CT images.

PubMed

Chung, Moo K; Qiu, Anqi; Seo, Seongho; Vorperian, Houri K

2015-05-01

We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel method is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, the method is applied to characterize the localized growth pattern of mandible surfaces obtained in CT images between ages 0 and 20 by regressing the length of displacement vectors with respect to a surface template. Copyright © 2015 Elsevier B.V. All rights reserved.

Benchmarking NWP Kernels on Multi- and Many-core Processors

NASA Astrophysics Data System (ADS)

Michalakes, J.; Vachharajani, M.

2008-12-01

Increased computing power for weather, climate, and atmospheric science has provided direct benefits for defense, agriculture, the economy, the environment, and public welfare and convenience. Today, very large clusters with many thousands of processors are allowing scientists to move forward with simulations of unprecedented size. But time-critical applications such as real-time forecasting or climate prediction need strong scaling: faster nodes and processors, not more of them. Moreover, the need for good cost- performance has never been greater, both in terms of performance per watt and per dollar. For these reasons, the new generations of multi- and many-core processors being mass produced for commercial IT and "graphical computing" (video games) are being scrutinized for their ability to exploit the abundant fine- grain parallelism in atmospheric models. We present results of our work to date identifying key computational kernels within the dynamics and physics of a large community NWP model, the Weather Research and Forecast (WRF) model. We benchmark and optimize these kernels on several different multi- and many-core processors. The goals are to (1) characterize and model performance of the kernels in terms of computational intensity, data parallelism, memory bandwidth pressure, memory footprint, etc. (2) enumerate and classify effective strategies for coding and optimizing for these new processors, (3) assess difficulties and opportunities for tool or higher-level language support, and (4) establish a continuing set of kernel benchmarks that can be used to measure and compare effectiveness of current and future designs of multi- and many-core processors for weather and climate applications.

Performance of fly ash based geopolymer incorporating palm kernel shell for lightweight concrete

NASA Astrophysics Data System (ADS)

Razak, Rafiza Abd; Abdullah, Mohd Mustafa Al Bakri; Yahya, Zarina; Jian, Ang Zhi; Nasri, Armia

2017-09-01

A concrete which cement is totally replaced by source material such as fly ash and activated by highly alkaline solutions is known as geopolymer concrete. Fly ash is the most common source material for geopolymer because it is a by-product material, so it can get easily from all around the world. An investigation has been carried out to select the most suitable ingredients of geopolymer concrete so that the geopolymer concrete can achieve the desire compressive strength. The samples were prepared to determine the suitable percentage of palm kernel shell used in geopolymer concrete and cured for 7 days in oven. After that, other samples were prepared by using the suitable percentage of palm kernel shell and cured for 3, 14, 21 and 28 days in oven. The control sample consisting of ordinary Portland cement and palm kernel shell and cured for 28 days were prepared too. The NaOH concentration of 12M, ratio Na2SiO3 to NaOH of 2.5, ratio fly ash to alkaline activator solution of 2.0 and ratio water to geopolymer of 0.35 were fixed throughout the research. The density obtained for the samples were 1.78 kg/m3, water absorption of 20.41% and the compressive strength of 14.20 MPa. The compressive strength of geopolymer concrete is still acceptable as lightweight concrete although the compressive strength is lower than OPC concrete. Therefore, the proposed method by using fly ash mixed with 10% of palm kernel shell can be used to design geopolymer concrete.

Experimental study of the vidicon system for information recording using the wide-gap spark chamber of gamma - telescope gamma-I

NASA Technical Reports Server (NTRS)

Akimov, V. V.; Bazer-Bashv, R.; Voronov, S. A.; Galper, A. M.; Gro, M.; Kalinkin, L. F.; Kerl, P.; Kozlov, V. D.; Koten, F.; Kretol, D.

1979-01-01

The development of the gamma ray telescope is investigated. The wide gap spark chambers, used to identify the gamma quanta and to determine the directions of their arrival, are examined. Two systems of information recording with the spark chambers photographic and vidicon system are compared.

Final Rule for Phase 2 Emission Standards for New Nonroad Spark-Ignition Handheld Engines At or Below 19 Kilowatts and Minor Amendments to Emission Requirements Applicable to Small Spark-Ignition Engines and Marine Spark-Ignition Engines

EPA Pesticide Factsheets

Rule summary, rule history, CFR citations and additional resources concerning emissions standards for engines principally used in handheld lawn and garden equipment such as trimmers, leaf blowers, and chainsaws.

7 CFR 810.204 - Grades and grade requirements for Six-rowed Malting barley and Six-rowed Blue Malting barley.

Code of Federal Regulations, 2010 CFR

2010-01-01

...— Damaged kernels 1 (percent) Foreign material (percent) Other grains (percent) Skinned and broken kernels....0 10.0 15.0 1 Injured-by-frost kernels and injured-by-mold kernels are not considered damaged kernels or considered against sound barley. Notes: Malting barley shall not be infested in accordance with...

7 CFR 51.1413 - Damage.

Code of Federal Regulations, 2013 CFR

2013-01-01

... well cured; (e) Poorly developed kernels; (f) Kernels which are dark amber in color; (g) Kernel spots when more than one dark spot is present on either half of the kernel, or when any such spot is more...

7 CFR 51.1413 - Damage.

Code of Federal Regulations, 2014 CFR

2014-01-01

... well cured; (e) Poorly developed kernels; (f) Kernels which are dark amber in color; (g) Kernel spots when more than one dark spot is present on either half of the kernel, or when any such spot is more...

7 CFR 810.205 - Grades and grade requirements for Two-rowed Malting barley.

Code of Federal Regulations, 2010 CFR

2010-01-01

... (percent) Maximum limits of— Wild oats (percent) Foreign material (percent) Skinned and broken kernels... Injured-by-frost kernels and injured-by-mold kernels are not considered damaged kernels or considered...

The role of B-vitamins in preventing and treating cognitive impairment and decline

USDA-ARS?s Scientific Manuscript database

Many epidemiologic studies have considered the question of whether markers of B-vitamin status are associated with cognitive function and cognitive decline. This avenue of research was sparked by the homocysteine (Hcy) theory of cardiovascular disease (CVD), which was extended to Alzheimer’s disease...

40 CFR 1048.101 - What exhaust emission standards must my engines meet?

Code of Federal Regulations, 2013 CFR

2013-07-01

... engineering analysis of information equivalent to such in-use data, such as data from research engines or... my engines meet? 1048.101 Section 1048.101 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR POLLUTION CONTROLS CONTROL OF EMISSIONS FROM NEW, LARGE NONROAD SPARK-IGNITION ENGINES...

40 CFR 1048.101 - What exhaust emission standards must my engines meet?

Code of Federal Regulations, 2014 CFR

2014-07-01

... engineering analysis of information equivalent to such in-use data, such as data from research engines or... my engines meet? 1048.101 Section 1048.101 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR POLLUTION CONTROLS CONTROL OF EMISSIONS FROM NEW, LARGE NONROAD SPARK-IGNITION ENGINES...

40 CFR 91.6 - Reference materials.

Code of Federal Regulations, 2014 CFR

2014-07-01

... EMISSIONS FROM MARINE SPARK-IGNITION ENGINES General § 91.6 Reference materials. (a) Incorporation by... the Research Method Appendix A to Subpart D. ASTM D2700-92: Standard Test Method for Knock... 40 CFR part 91 reference SAE J1228/ISO 8665 November 1991 Small Craft-Marine Propulsion Engine and...

40 CFR 1048.101 - What exhaust emission standards must my engines meet?

Code of Federal Regulations, 2011 CFR

2011-07-01

... engineering analysis of information equivalent to such in-use data, such as data from research engines or... my engines meet? 1048.101 Section 1048.101 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR POLLUTION CONTROLS CONTROL OF EMISSIONS FROM NEW, LARGE NONROAD SPARK-IGNITION ENGINES...

40 CFR 91.6 - Reference materials.

Code of Federal Regulations, 2013 CFR

2013-07-01

... EMISSIONS FROM MARINE SPARK-IGNITION ENGINES General § 91.6 Reference materials. (a) Incorporation by... the Research Method Appendix A to Subpart D. ASTM D2700-92: Standard Test Method for Knock... 40 CFR part 91 reference SAE J1228/ISO 8665 November 1991 Small Craft-Marine Propulsion Engine and...

40 CFR 1048.101 - What exhaust emission standards must my engines meet?

Code of Federal Regulations, 2012 CFR

2012-07-01

... engineering analysis of information equivalent to such in-use data, such as data from research engines or... my engines meet? 1048.101 Section 1048.101 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR POLLUTION CONTROLS CONTROL OF EMISSIONS FROM NEW, LARGE NONROAD SPARK-IGNITION ENGINES...

40 CFR 91.6 - Reference materials.

Code of Federal Regulations, 2012 CFR

2012-07-01

... EMISSIONS FROM MARINE SPARK-IGNITION ENGINES General § 91.6 Reference materials. (a) Incorporation by... the Research Method Appendix A to Subpart D. ASTM D2700-92: Standard Test Method for Knock... 40 CFR part 91 reference SAE J1228/ISO 8665 November 1991 Small Craft-Marine Propulsion Engine and...

Global Perspectives on Resilience in Children and Youth

ERIC Educational Resources Information Center

Masten, Ann S.

2014-01-01

Global concerns about the consequences of disasters, political violence, disease, malnutrition, maltreatment, and other threats to human development and well-being have sparked a surge of international interest in resilience science. This article highlights progress and issues in research that aims to understand variations in human adaptation to…

Plant materials and methodologies for Great Basin rangelands

USDA-ARS?s Scientific Manuscript database

The Nevada Section, Society for Range Management held a winter meeting/symposium January 2017 in Sparks, Nevada. Nearly a century and half of research and experience was presented by scientists in the field of soil science, range and weed science and plant genetics. The ability of resource managers ...

Does Income Inequality Harm Health? New Cross-National Evidence

ERIC Educational Resources Information Center

Beckfield, Jason

2004-01-01

The provocative hypothesis that income inequality harms population health has sparked a large body of research, some of which has reported strong associations between income inequality and population health. Cross-national evidence is frequently cited in support of this important hypothesis, but the hypothesis remains controversial, and the…

Introduction into Sparks of the Learning Analytics Future

ERIC Educational Resources Information Center

Pechenizkiy, Mykola; Gaševic, Dragan

2014-01-01

This section offers a compilation of 16 extended abstracts summarizing research of the doctoral students who participated in the Second Learning Analytics Summer Institute (LASI 2014) held at Harvard University in July 2014. The abstracts highlight the motivation, main goals and expected contributions to the field from the ongoing learning…

From plasma to nanoparticles: optical and particle emission of a spark discharge generator.

PubMed

Kohut, A; Ludvigsson, L; Meuller, B O; Deppert, K; Messing, M E; Galbács, G; Geretovszky, Zs

2017-11-24

The increased demand for high purity nanoparticles (NPs) of defined geometry necessitates the continuous development of generation routes. One of the most promising physical techniques for producing metal, semiconductor or alloy NPs in the gas phase is spark discharge NP generation. The technique has a great potential for up-scaling without altering the particles. Despite the simplicity of the setup, the formation of NPs in a spark discharge takes place via complex multi-scale processes, which greatly hinders the investigation via conventional NP measurement techniques. In the present work, time-resolved optical emission spectroscopy (OES) was used to provide information on the species present in the spark from as early as approximately 100 ns after the initiation of the discharge. We demonstrate that operando emission spectroscopy can deliver valuable insights into NP formation. The emission spectra of the spark are used to identify, among others, the main stages of material erosion and to calculate the quenching rate of the generated metal vapour. We demonstrate that the alteration of key control parameters, that are typically used to optimize NP generation, clearly affect the emission spectra. We report for Cu and Au NPs that the intensity of spectral lines emitted by metal atoms levels off when spark energy is increased above an energy threshold, suggesting that the maximum concentration of metal vapour produced in the generator is limited. This explains the size variation of the generated NPs. We report a strong correlation between the optical and particle emission of the spark discharge generator, which demonstrate the suitability of OES as a valuable characterization tool that will allow for the more deliberate optimization of spark-based NP generation.

Sorcin modulation of Ca2+ sparks in rat vascular smooth muscle cells

PubMed Central

Rueda, Angélica; Song, Ming; Toro, Ligia; Stefani, Enrico; Valdivia, Héctor H

2006-01-01

Spontaneous, local Ca2+ release events or Ca2+ sparks by ryanodine receptors (RyRs) are important determinants of vascular tone and arteriolar resistance, but the mechanisms that modulate their properties in smooth muscle are poorly understood. Sorcin, a Ca2+-binding protein that associates with cardiac RyRs and quickly stops Ca2+ release in the heart, provides a potential mechanism to modulate Ca2+ sparks in vascular smooth muscle, but little is known about the functional role of sorcin in this tissue. In this work, we characterized the expression and intracellular location of sorcin in aorta and cerebral artery and gained mechanistic insights into its functional role as a modulator of Ca2+ sparks. Sorcin is present in endothelial and smooth muscle cells, as assessed by immunocytochemical and Western blot analyses. Smooth muscle sorcin translocates from cytosolic to membranous compartments in a Ca2+-dependent manner and associates with RyRs, as shown by coimmunoprecipitation and immunostaining experiments. Ca2+ sparks recorded in saponin-permeabilized vascular myocytes have increased frequency, duration and spatial spread but reduced amplitude with respect to Ca2+ sparks in intact cells, suggesting that permeabilization disrupts the normal organization of RyRs and releases diffusible substances that control Ca2+ spark properties. Perfusion of 2 μm sorcin onto permeabilized myocytes reduced the amplitude, duration and spatial spread of Ca2+ sparks, demonstrating that sorcin effectively regulates Ca2+ signalling in vascular smooth muscle. Together with a dense distribution in the perimeter of the cell along a pool of RyRs, these properties make sorcin a viable candidate to modulate vascular tone in smooth muscle. PMID:16931553

Spatial patterns of aflatoxin levels in relation to ear-feeding insect damage in pre-harvest corn.

PubMed

Ni, Xinzhi; Wilson, Jeffrey P; Buntin, G David; Guo, Baozhu; Krakowsky, Matthew D; Lee, R Dewey; Cottrell, Ted E; Scully, Brian T; Huffaker, Alisa; Schmelz, Eric A

2011-07-01

Key impediments to increased corn yield and quality in the southeastern US coastal plain region are damage by ear-feeding insects and aflatoxin contamination caused by infection of Aspergillus flavus. Key ear-feeding insects are corn earworm, Helicoverpa zea, fall armyworm, Spodoptera frugiperda, maize weevil, Sitophilus zeamais, and brown stink bug, Euschistus servus. In 2006 and 2007, aflatoxin contamination and insect damage were sampled before harvest in three 0.4-hectare corn fields using a grid sampling method. The feeding damage by each of ear/kernel-feeding insects (i.e., corn earworm/fall armyworm damage on the silk/cob, and discoloration of corn kernels by stink bugs), and maize weevil population were assessed at each grid point with five ears. The spatial distribution pattern of aflatoxin contamination was also assessed using the corn samples collected at each sampling point. Aflatoxin level was correlated to the number of maize weevils and stink bug-discolored kernels, but not closely correlated to either husk coverage or corn earworm damage. Contour maps of the maize weevil populations, stink bug-damaged kernels, and aflatoxin levels exhibited an aggregated distribution pattern with a strong edge effect on all three parameters. The separation of silk- and cob-feeding insects from kernel-feeding insects, as well as chewing (i.e., the corn earworm and maize weevil) and piercing-sucking insects (i.e., the stink bugs) and their damage in relation to aflatoxin accumulation is economically important. Both theoretic and applied ramifications of this study were discussed by proposing a hypothesis on the underlying mechanisms of the aggregated distribution patterns and strong edge effect of insect damage and aflatoxin contamination, and by discussing possible management tactics for aflatoxin reduction by proper management of kernel-feeding insects. Future directions on basic and applied research related to aflatoxin contamination are also discussed.

Spatial Patterns of Aflatoxin Levels in Relation to Ear-Feeding Insect Damage in Pre-Harvest Corn

PubMed Central

Ni, Xinzhi; Wilson, Jeffrey P.; Buntin, G. David; Guo, Baozhu; Krakowsky, Matthew D.; Lee, R. Dewey; Cottrell, Ted E.; Scully, Brian T.; Huffaker, Alisa; Schmelz, Eric A.

2011-01-01

Key impediments to increased corn yield and quality in the southeastern US coastal plain region are damage by ear-feeding insects and aflatoxin contamination caused by infection of Aspergillus flavus. Key ear-feeding insects are corn earworm, Helicoverpa zea, fall armyworm, Spodoptera frugiperda, maize weevil, Sitophilus zeamais, and brown stink bug, Euschistus servus. In 2006 and 2007, aflatoxin contamination and insect damage were sampled before harvest in three 0.4-hectare corn fields using a grid sampling method. The feeding damage by each of ear/kernel-feeding insects (i.e., corn earworm/fall armyworm damage on the silk/cob, and discoloration of corn kernels by stink bugs), and maize weevil population were assessed at each grid point with five ears. The spatial distribution pattern of aflatoxin contamination was also assessed using the corn samples collected at each sampling point. Aflatoxin level was correlated to the number of maize weevils and stink bug-discolored kernels, but not closely correlated to either husk coverage or corn earworm damage. Contour maps of the maize weevil populations, stink bug-damaged kernels, and aflatoxin levels exhibited an aggregated distribution pattern with a strong edge effect on all three parameters. The separation of silk- and cob-feeding insects from kernel-feeding insects, as well as chewing (i.e., the corn earworm and maize weevil) and piercing-sucking insects (i.e., the stink bugs) and their damage in relation to aflatoxin accumulation is economically important. Both theoretic and applied ramifications of this study were discussed by proposing a hypothesis on the underlying mechanisms of the aggregated distribution patterns and strong edge effect of insect damage and aflatoxin contamination, and by discussing possible management tactics for aflatoxin reduction by proper management of kernel-feeding insects. Future directions on basic and applied research related to aflatoxin contamination are also discussed. PMID:22069748

Cardiovascular effects of SPARK conducted electrical weapon in healthy subjects.

PubMed

Scherr, Carlos; de Carvalho, Antonio Carlos; Belem, Luciano Juaçaba; Loyola, Luiz Henrique; Guerra, Renata Leborato; Blanco, Fernanda; Mangia, Claudio

2016-12-15

The increasing use of conducted electronic weapons (CEW) cause concern regarding its secure application, specially regarding the implications in the cardiovascular system. The objective was to determine Spark CEW safety through cardiovascular parameters analysis of healthy volunteers subjected to its use. Volunteers over 18years without cardiovascular disease or recent use of illegal drugs were submitted, before and after being affected with Spark CEW, to clinical evaluation; blood collection for serum laboratory tests; transthoracic electrocardiography at rest, transthoracic echodopplercardiogram and 24hour Holter. All 71 patients reported being incapable of any voluntary reaction during the shock of the application time. No arrhythmia or myocardial necrosis was related to the use of non-lethal weapon SPARK. Reported adverse events were self-limited, and mostly mild. SPARK brand CEW is effective in incapacitating individuals by the shock of the application time, without causing. Copyright © 2016. Published by Elsevier Ireland Ltd.

Ignition of an automobile engine by high-peak power Nd:YAG/Cr⁴⁺:YAG laser-spark devices.

PubMed

Pavel, Nicolaie; Dascalu, Traian; Salamu, Gabriela; Dinca, Mihai; Boicea, Niculae; Birtas, Adrian

2015-12-28

Laser sparks that were built with high-peak power passively Q-switched Nd:YAG/Cr(4+):YAG lasers have been used to operate a Renault automobile engine. The design of such a laser spark igniter is discussed. The Nd:YAG/Cr(4+):YAG laser delivered pulses with energy of 4 mJ and 0.8-ns duration, corresponding to pulse peak power of 5 MW. The coefficients of variability of maximum pressure (COV(Pmax)) and of indicated mean effective pressure (COV(IMEP)) and specific emissions like hydrocarbons (HC), carbon monoxide (CO), nitrogen oxides (NO(x)) and carbon dioxide (CO2) were measured at various engine speeds and high loads. Improved engine stability in terms of COV(Pmax) and COV(Pmax) and decreased emissions of CO and HC were obtained for the engine that was run by laser sparks in comparison with classical ignition by electrical spark plugs.

Pulse-actuated fuel-injection spark plug

DOEpatents

Murray, Ian; Tatro, Clement A.

1978-01-01

A replacement spark plug for reciprocating internal combustion engines that functions as a fuel injector and as a spark plug to provide a "stratified-charge" effect. The conventional carburetor is retained to supply the main fuel-air mixture which may be very lean because of the stratified charge. The replacement plug includes a cylindrical piezoelectric ceramic which contracts to act as a pump whenever an ignition pulse is applied to a central rod through the ceramic. The rod is hollow at its upper end for receiving fuel, it is tapered along its lower length to act as a pump, and it is flattened at its lower end to act as a valve for fuel injection from the pump into the cylinder. The rod also acts as the center electrode of the plug, with the spark jumping from the plug base to the lower end of the rod to thereby provide spark ignition that has inherent proper timing with the fuel injection.

Handling Big Data in Medical Imaging: Iterative Reconstruction with Large-Scale Automated Parallel Computation

PubMed Central

Lee, Jae H.; Yao, Yushu; Shrestha, Uttam; Gullberg, Grant T.; Seo, Youngho

2014-01-01

The primary goal of this project is to implement the iterative statistical image reconstruction algorithm, in this case maximum likelihood expectation maximum (MLEM) used for dynamic cardiac single photon emission computed tomography, on Spark/GraphX. This involves porting the algorithm to run on large-scale parallel computing systems. Spark is an easy-to- program software platform that can handle large amounts of data in parallel. GraphX is a graph analytic system running on top of Spark to handle graph and sparse linear algebra operations in parallel. The main advantage of implementing MLEM algorithm in Spark/GraphX is that it allows users to parallelize such computation without any expertise in parallel computing or prior knowledge in computer science. In this paper we demonstrate a successful implementation of MLEM in Spark/GraphX and present the performance gains with the goal to eventually make it useable in clinical setting. PMID:27081299

Handling Big Data in Medical Imaging: Iterative Reconstruction with Large-Scale Automated Parallel Computation.

PubMed

Lee, Jae H; Yao, Yushu; Shrestha, Uttam; Gullberg, Grant T; Seo, Youngho

2014-11-01

The primary goal of this project is to implement the iterative statistical image reconstruction algorithm, in this case maximum likelihood expectation maximum (MLEM) used for dynamic cardiac single photon emission computed tomography, on Spark/GraphX. This involves porting the algorithm to run on large-scale parallel computing systems. Spark is an easy-to- program software platform that can handle large amounts of data in parallel. GraphX is a graph analytic system running on top of Spark to handle graph and sparse linear algebra operations in parallel. The main advantage of implementing MLEM algorithm in Spark/GraphX is that it allows users to parallelize such computation without any expertise in parallel computing or prior knowledge in computer science. In this paper we demonstrate a successful implementation of MLEM in Spark/GraphX and present the performance gains with the goal to eventually make it useable in clinical setting.

Detection of ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging

NASA Astrophysics Data System (ADS)

Senthilkumar, T.; Jayas, D. S.; White, N. D. G.; Fields, P. G.; Gräfenhan, T.

2017-03-01

Near-infrared (NIR) hyperspectral imaging system was used to detect five concentration levels of ochratoxin A (OTA) in contaminated wheat kernels. The wheat kernels artificially inoculated with two different OTA producing Penicillium verrucosum strains, two different non-toxigenic P. verrucosum strains, and sterile control wheat kernels were subjected to NIR hyperspectral imaging. The acquired three-dimensional data were reshaped into readable two-dimensional data. Principal Component Analysis (PCA) was applied to the two dimensional data to identify the key wavelengths which had greater significance in detecting OTA contamination in wheat. Statistical and histogram features extracted at the key wavelengths were used in the linear, quadratic and Mahalanobis statistical discriminant models to differentiate between sterile control, five concentration levels of OTA contamination in wheat kernels, and five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels. The classification models differentiated sterile control samples from OTA contaminated wheat kernels and non-OTA producing P. verrucosum inoculated wheat kernels with a 100% accuracy. The classification models also differentiated between five concentration levels of OTA contaminated wheat kernels and between five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels with a correct classification of more than 98%. The non-OTA producing P. verrucosum inoculated wheat kernels and OTA contaminated wheat kernels subjected to hyperspectral imaging provided different spectral patterns.

Application of kernel method in fluorescence molecular tomography

NASA Astrophysics Data System (ADS)

Zhao, Yue; Baikejiang, Reheman; Li, Changqing

2017-02-01

Reconstruction of fluorescence molecular tomography (FMT) is an ill-posed inverse problem. Anatomical guidance in the FMT reconstruction can improve FMT reconstruction efficiently. We have developed a kernel method to introduce the anatomical guidance into FMT robustly and easily. The kernel method is from machine learning for pattern analysis and is an efficient way to represent anatomical features. For the finite element method based FMT reconstruction, we calculate a kernel function for each finite element node from an anatomical image, such as a micro-CT image. Then the fluorophore concentration at each node is represented by a kernel coefficient vector and the corresponding kernel function. In the FMT forward model, we have a new system matrix by multiplying the sensitivity matrix with the kernel matrix. Thus, the kernel coefficient vector is the unknown to be reconstructed following a standard iterative reconstruction process. We convert the FMT reconstruction problem into the kernel coefficient reconstruction problem. The desired fluorophore concentration at each node can be calculated accordingly. Numerical simulation studies have demonstrated that the proposed kernel-based algorithm can improve the spatial resolution of the reconstructed FMT images. In the proposed kernel method, the anatomical guidance can be obtained directly from the anatomical image and is included in the forward modeling. One of the advantages is that we do not need to segment the anatomical image for the targets and background.

Credit scoring analysis using kernel discriminant

NASA Astrophysics Data System (ADS)

Widiharih, T.; Mukid, M. A.; Mustafid

2018-05-01

Credit scoring model is an important tool for reducing the risk of wrong decisions when granting credit facilities to applicants. This paper investigate the performance of kernel discriminant model in assessing customer credit risk. Kernel discriminant analysis is a non- parametric method which means that it does not require any assumptions about the probability distribution of the input. The main ingredient is a kernel that allows an efficient computation of Fisher discriminant. We use several kernel such as normal, epanechnikov, biweight, and triweight. The models accuracy was compared each other using data from a financial institution in Indonesia. The results show that kernel discriminant can be an alternative method that can be used to determine who is eligible for a credit loan. In the data we use, it shows that a normal kernel is relevant to be selected for credit scoring using kernel discriminant model. Sensitivity and specificity reach to 0.5556 and 0.5488 respectively.

Unified Heat Kernel Regression for Diffusion, Kernel Smoothing and Wavelets on Manifolds and Its Application to Mandible Growth Modeling in CT Images

PubMed Central

Chung, Moo K.; Qiu, Anqi; Seo, Seongho; Vorperian, Houri K.

2014-01-01

We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel regression is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. Unlike many previous partial differential equation based approaches involving diffusion, our approach represents the solution of diffusion analytically, reducing numerical inaccuracy and slow convergence. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, we have applied the method in characterizing the localized growth pattern of mandible surfaces obtained in CT images from subjects between ages 0 and 20 years by regressing the length of displacement vectors with respect to the template surface. PMID:25791435

The Feasibility of Palm Kernel Shell as a Replacement for Coarse Aggregate in Lightweight Concrete

NASA Astrophysics Data System (ADS)

Itam, Zarina; Beddu, Salmia; Liyana Mohd Kamal, Nur; Ashraful Alam, Md; Issa Ayash, Usama

2016-03-01

Implementing sustainable materials into the construction industry is fast becoming a trend nowadays. Palm Kernel Shell is a by-product of Malaysia’s palm oil industry, generating waste as much as 4 million tons per annum. As a means of producing a sustainable, environmental-friendly, and affordable alternative in the lightweight concrete industry, the exploration of the potential of Palm Kernel Shell to be used as an aggregate replacement was conducted which may give a positive impact to the Malaysian construction industry as well as worldwide concrete usage. This research investigates the feasibility of PKS as an aggregate replacement in lightweight concrete in terms of compressive strength, slump test, water absorption, and density. Results indicate that by using PKS for aggregate replacement, it increases the water absorption but decreases the concrete workability and strength. Results however, fall into the range acceptable for lightweight aggregates, hence it can be concluded that there is potential to use PKS as aggregate replacement for lightweight concrete.

A Scalable Data Integration and Analysis Architecture for Sensor Data of Pediatric Asthma.

PubMed

Stripelis, Dimitris; Ambite, José Luis; Chiang, Yao-Yi; Eckel, Sandrah P; Habre, Rima

2017-04-01

According to the Centers for Disease Control, in the United States there are 6.8 million children living with asthma. Despite the importance of the disease, the available prognostic tools are not sufficient for biomedical researchers to thoroughly investigate the potential risks of the disease at scale. To overcome these challenges we present a big data integration and analysis infrastructure developed by our Data and Software Coordination and Integration Center (DSCIC) of the NIBIB-funded Pediatric Research using Integrated Sensor Monitoring Systems (PRISMS) program. Our goal is to help biomedical researchers to efficiently predict and prevent asthma attacks. The PRISMS-DSCIC is responsible for collecting, integrating, storing, and analyzing real-time environmental, physiological and behavioral data obtained from heterogeneous sensor and traditional data sources. Our architecture is based on the Apache Kafka, Spark and Hadoop frameworks and PostgreSQL DBMS. A main contribution of this work is extending the Spark framework with a mediation layer, based on logical schema mappings and query rewriting, to facilitate data analysis over a consistent harmonized schema. The system provides both batch and stream analytic capabilities over the massive data generated by wearable and fixed sensors.

Correlation and classification of single kernel fluorescence hyperspectral data with aflatoxin concentration in corn kernels inoculated with Aspergillus flavus spores.

PubMed

Yao, H; Hruska, Z; Kincaid, R; Brown, R; Cleveland, T; Bhatnagar, D

2010-05-01

The objective of this study was to examine the relationship between fluorescence emissions of corn kernels inoculated with Aspergillus flavus and aflatoxin contamination levels within the kernels. Aflatoxin contamination in corn has been a long-standing problem plaguing the grain industry with potentially devastating consequences to corn growers. In this study, aflatoxin-contaminated corn kernels were produced through artificial inoculation of corn ears in the field with toxigenic A. flavus spores. The kernel fluorescence emission data were taken with a fluorescence hyperspectral imaging system when corn kernels were excited with ultraviolet light. Raw fluorescence image data were preprocessed and regions of interest in each image were created for all kernels. The regions of interest were used to extract spectral signatures and statistical information. The aflatoxin contamination level of single corn kernels was then chemically measured using affinity column chromatography. A fluorescence peak shift phenomenon was noted among different groups of kernels with different aflatoxin contamination levels. The fluorescence peak shift was found to move more toward the longer wavelength in the blue region for the highly contaminated kernels and toward the shorter wavelengths for the clean kernels. Highly contaminated kernels were also found to have a lower fluorescence peak magnitude compared with the less contaminated kernels. It was also noted that a general negative correlation exists between measured aflatoxin and the fluorescence image bands in the blue and green regions. The correlation coefficients of determination, r(2), was 0.72 for the multiple linear regression model. The multivariate analysis of variance found that the fluorescence means of four aflatoxin groups, <1, 1-20, 20-100, and >or=100 ng g(-1) (parts per billion), were significantly different from each other at the 0.01 level of alpha. Classification accuracy under a two-class schema ranged from 0.84 to 0.91 when a threshold of either 20 or 100 ng g(-1) was used. Overall, the results indicate that fluorescence hyperspectral imaging may be applicable in estimating aflatoxin content in individual corn kernels.

Classification of Phylogenetic Profiles for Protein Function Prediction: An SVM Approach

NASA Astrophysics Data System (ADS)

Kotaru, Appala Raju; Joshi, Ramesh C.

Predicting the function of an uncharacterized protein is a major challenge in post-genomic era due to problems complexity and scale. Having knowledge of protein function is a crucial link in the development of new drugs, better crops, and even the development of biochemicals such as biofuels. Recently numerous high-throughput experimental procedures have been invented to investigate the mechanisms leading to the accomplishment of a protein’s function and Phylogenetic profile is one of them. Phylogenetic profile is a way of representing a protein which encodes evolutionary history of proteins. In this paper we proposed a method for classification of phylogenetic profiles using supervised machine learning method, support vector machine classification along with radial basis function as kernel for identifying functionally linked proteins. We experimentally evaluated the performance of the classifier with the linear kernel, polynomial kernel and compared the results with the existing tree kernel. In our study we have used proteins of the budding yeast saccharomyces cerevisiae genome. We generated the phylogenetic profiles of 2465 yeast genes and for our study we used the functional annotations that are available in the MIPS database. Our experiments show that the performance of the radial basis kernel is similar to polynomial kernel is some functional classes together are better than linear, tree kernel and over all radial basis kernel outperformed the polynomial kernel, linear kernel and tree kernel. In analyzing these results we show that it will be feasible to make use of SVM classifier with radial basis function as kernel to predict the gene functionality using phylogenetic profiles.

Intraear Compensation of Field Corn, Zea mays, from Simulated and Naturally Occurring Injury by Ear-Feeding Larvae.

PubMed

Steckel, S; Stewart, S D

2015-06-01

Ear-feeding larvae, such as corn earworm, Helicoverpa zea Boddie (Lepidoptera: Noctuidae), can be important insect pests of field corn, Zea mays L., by feeding on kernels. Recently introduced, stacked Bacillus thuringiensis (Bt) traits provide improved protection from ear-feeding larvae. Thus, our objective was to evaluate how injury to kernels in the ear tip might affect yield when this injury was inflicted at the blister and milk stages. In 2010, simulated corn earworm injury reduced total kernel weight (i.e., yield) at both the blister and milk stage. In 2011, injury to ear tips at the milk stage affected total kernel weight. No differences in total kernel weight were found in 2013, regardless of when or how much injury was inflicted. Our data suggested that kernels within the same ear could compensate for injury to ear tips by increasing in size, but this increase was not always statistically significant or sufficient to overcome high levels of kernel injury. For naturally occurring injury observed on multiple corn hybrids during 2011 and 2012, our analyses showed either no or a minimal relationship between number of kernels injured by ear-feeding larvae and the total number of kernels per ear, total kernel weight, or the size of individual kernels. The results indicate that intraear compensation for kernel injury to ear tips can occur under at least some conditions. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

40 CFR 63.6590 - What parts of my plant does this subpart cover?

Code of Federal Regulations, 2014 CFR

2014-07-01

...) Existing spark ignition 2 stroke lean burn (2SLB) stationary RICE with a site rating of more than 500 brake HP located at a major source of HAP emissions; (ii) Existing spark ignition 4 stroke lean burn (4SLB... emissions; (4) A new or reconstructed spark ignition 4 stroke rich burn (4SRB) stationary RICE with a site...

40 CFR 63.6590 - What parts of my plant does this subpart cover?

Code of Federal Regulations, 2013 CFR

2013-07-01

...) Existing spark ignition 2 stroke lean burn (2SLB) stationary RICE with a site rating of more than 500 brake HP located at a major source of HAP emissions; (ii) Existing spark ignition 4 stroke lean burn (4SLB... emissions; (4) A new or reconstructed spark ignition 4 stroke rich burn (4SRB) stationary RICE with a site...

40 CFR 63.6590 - What parts of my plant does this subpart cover?

Code of Federal Regulations, 2012 CFR

2012-07-01

... initial notification requirements: (i) Existing spark ignition 2 stroke lean burn (2SLB) stationary RICE... spark ignition 4 stroke lean burn (4SLB) stationary RICE with a site rating of more than 500 brake HP... spark ignition 4 stroke rich burn (4SRB) stationary RICE with a site rating of less than or equal to 500...

Interorganizational Relationships in the Heart and Stroke Foundation's Spark Together for Healthy Kids™: Insights from Using Network Analysis

ERIC Educational Resources Information Center

Yessis, Jennifer; Riley, Barbara; Stockton, Lisa; Brodovsky, Sharon; Von Sychowski, Shirley

2013-01-01

The Heart and Stroke Foundation's Spark Together for Healthy Kids™ (Spark) is a multiyear initiative in Ontario, Canada, that takes a population approach to obesity prevention. It focuses on creating healthy environments by improving access to healthy foods and physical activity, with an emphasis on strengthening the advocacy capacity of…

Integrating the Gradient of the Thin Wire Kernel

NASA Technical Reports Server (NTRS)

Champagne, Nathan J.; Wilton, Donald R.

2008-01-01

A formulation for integrating the gradient of the thin wire kernel is presented. This approach employs a new expression for the gradient of the thin wire kernel derived from a recent technique for numerically evaluating the exact thin wire kernel. This approach should provide essentially arbitrary accuracy and may be used with higher-order elements and basis functions using the procedure described in [4].When the source and observation points are close, the potential integrals over wire segments involving the wire kernel are split into parts to handle the singular behavior of the integrand [1]. The singularity characteristics of the gradient of the wire kernel are different than those of the wire kernel, and the axial and radial components have different singularities. The characteristics of the gradient of the wire kernel are discussed in [2]. To evaluate the near electric and magnetic fields of a wire, the integration of the gradient of the wire kernel needs to be calculated over the source wire. Since the vector bases for current have constant direction on linear wire segments, these integrals reduce to integrals of the form

Ranking Support Vector Machine with Kernel Approximation

PubMed Central

Dou, Yong

2017-01-01

Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms. PMID:28293256

Ranking Support Vector Machine with Kernel Approximation.

PubMed

Chen, Kai; Li, Rongchun; Dou, Yong; Liang, Zhengfa; Lv, Qi

2017-01-01

Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.

[Significance of various implantate localizations of Sparks prostheses, experimental studies in rats].

PubMed

Brieler, H S; Parwaresch, R; Thiede, A

1976-01-01

Our investigations show that Sparks prostheses after subcutaneous implantation are suitable for vascular grafting. At the end of the organization period the connective tissue becomes strong, and after the third and fourth weeks collagenous and elastic fibers can be seen. Ten weeks after s.c. implantation, collagenous fibers predominate. After this the Sparks prostheses can be used as a vascular graft. Intraperitoneal implantation, however, shows a histologically different picture with characteristic findings: only fat cells can be observed, a strong granulation tissue with elastic and collagenous fibers is not present. After intraperitoneal implantation Sparks prostheses are therefore unsuitable for vascular grafts.

A Spark Chamber With Thin Electrodes and a Study of the Position of the Alignment Point; KAMERA S TONKIMI ELEKTRODAMI IZUCHENIE POLOZHENIYA TOCHKI SPRYAMLENIYA ISKRY

DOE Office of Scientific and Technical Information (OSTI.GOV)

Legar, F.; Nikanorov, V.I.; Peter, G.

1964-01-01

A technique for making the foil electrodes with twosided working surface for spark chambers is described. Some characteristics of spark chambers with thin electrodes are given. The variation of the distance from the negative electrode to the alignment point of a spark with the energy of the detected particles and the angie of their passage through the charaber was studied. It is shown that with the increasing initial density of the gas ionization in the chamber the Townsend coefficient a becomes greater due to the charge interaction of avalanches. (auth)

SPARK GAP SWITCH

DOEpatents

Neal, R.B.

1957-12-17

An improved triggered spark gap switch is described, capable of precisely controllable firing time while switching very large amounts of power. The invention in general comprises three electrodes adjustably spaced and adapted to have a large potential impressed between the outer electrodes. The central electrode includes two separate elements electrically connected togetaer and spaced apart to define a pair of spark gaps between the end electrodes. Means are provided to cause the gas flow in the switch to pass towards the central electrode, through a passage in each separate element, and out an exit disposed between the two separate central electrode elements in order to withdraw ions from the spark gap.

ClimateSpark: An in-memory distributed computing framework for big climate data analytics

NASA Astrophysics Data System (ADS)

Hu, Fei; Yang, Chaowei; Schnase, John L.; Duffy, Daniel Q.; Xu, Mengchao; Bowen, Michael K.; Lee, Tsengdar; Song, Weiwei

2018-06-01

The unprecedented growth of climate data creates new opportunities for climate studies, and yet big climate data pose a grand challenge to climatologists to efficiently manage and analyze big data. The complexity of climate data content and analytical algorithms increases the difficulty of implementing algorithms on high performance computing systems. This paper proposes an in-memory, distributed computing framework, ClimateSpark, to facilitate complex big data analytics and time-consuming computational tasks. Chunking data structure improves parallel I/O efficiency, while a spatiotemporal index is built for the chunks to avoid unnecessary data reading and preprocessing. An integrated, multi-dimensional, array-based data model (ClimateRDD) and ETL operations are developed to address big climate data variety by integrating the processing components of the climate data lifecycle. ClimateSpark utilizes Spark SQL and Apache Zeppelin to develop a web portal to facilitate the interaction among climatologists, climate data, analytic operations and computing resources (e.g., using SQL query and Scala/Python notebook). Experimental results show that ClimateSpark conducts different spatiotemporal data queries/analytics with high efficiency and data locality. ClimateSpark is easily adaptable to other big multiple-dimensional, array-based datasets in various geoscience domains.

Cross-correlation spectroscopy study of the transient spark discharge in atmospheric pressure air

NASA Astrophysics Data System (ADS)

Janda, Mário; Hoder, Tomáš; Sarani, Abdollah; Brandenburg, Ronny; Machala, Zdenko

2017-05-01

A streamer-to-spark transition in a self-pulsing transient spark (TS) discharge of positive polarity in air was investigated using cross-correlation spectroscopy. The entire temporal evolution of the TS was recorded for several spectral bands and lines: the second positive system of N2 (337.1 nm), the first negative system of {{{{N}}}2}+ (391.4 nm), and atomic oxygen (777.1 nm). The results enable the visualization of the different phases of discharge development including the primary streamer, the secondary streamer, and the transition to the spark. The spatio-temporal distribution of the reduced electric field strength during the primary streamer phase of the TS was determined and discussed. The transition from the streamer to the spark proceeds very fast within about 10 ns for the TS with a current pulse repetition rate in the range 8-10 kHz. This is attributed to memory effects, leading to a low net electron attachment rate and faster propagation of the secondary streamer. Gas heating, accumulation of species such as oxygen atoms from the previous TS pulses, as well as generation of charged particles by stepwise ionization seem to play important roles contributing to this fast streamer-to-spark transition.

21 CFR 182.40 - Natural extractives (solvent-free) used in conjunction with spices, seasonings, and flavorings.

Code of Federal Regulations, 2011 CFR

2011-04-01

... source Apricot kernel (persic oil) Prunus armeniaca L. Peach kernel (persic oil) Prunus persica Sieb. et Zucc. Peanut stearine Arachis hypogaea L. Persic oil (see apricot kernel and peach kernel) Quince seed...

21 CFR 182.40 - Natural extractives (solvent-free) used in conjunction with spices, seasonings, and flavorings.

Code of Federal Regulations, 2013 CFR

2013-04-01

... source Apricot kernel (persic oil) Prunus armeniaca L. Peach kernel (persic oil) Prunus persica Sieb. et Zucc. Peanut stearine Arachis hypogaea L. Persic oil (see apricot kernel and peach kernel) Quince seed...

21 CFR 182.40 - Natural extractives (solvent-free) used in conjunction with spices, seasonings, and flavorings.

Code of Federal Regulations, 2012 CFR

2012-04-01

... source Apricot kernel (persic oil) Prunus armeniaca L. Peach kernel (persic oil) Prunus persica Sieb. et Zucc. Peanut stearine Arachis hypogaea L. Persic oil (see apricot kernel and peach kernel) Quince seed...

Wigner functions defined with Laplace transform kernels.

PubMed

Oh, Se Baek; Petruccelli, Jonathan C; Tian, Lei; Barbastathis, George

2011-10-24

We propose a new Wigner-type phase-space function using Laplace transform kernels--Laplace kernel Wigner function. Whereas momentum variables are real in the traditional Wigner function, the Laplace kernel Wigner function may have complex momentum variables. Due to the property of the Laplace transform, a broader range of signals can be represented in complex phase-space. We show that the Laplace kernel Wigner function exhibits similar properties in the marginals as the traditional Wigner function. As an example, we use the Laplace kernel Wigner function to analyze evanescent waves supported by surface plasmon polariton. © 2011 Optical Society of America

Fabrication and Characterization of Surrogate Fuel Particles Using the Spark Erosion Method

NASA Astrophysics Data System (ADS)

Metzger, Kathryn E.

In light of the disaster at the Fukushima Daiichi Nuclear Plant, the Department of Energy's Advanced Fuels Program has shifted its interest from enhanced performance fuels to enhanced accident tolerance fuels. Dispersion fuels possess higher thermal conductivities than traditional light water reactor fuel and as a result, offer improved safety margins. The benefits of a dispersion fuel are due to the presence of the secondary non-fissile phase (matrix), which serves as a barrier to fission products and improves the overall thermal performance of the fuel. However, the presence of a matrix material reduces the fuel volume, which lowers the fissile content of dispersion. This issue can be remedied through the development of higher density fuel phases or through an optimization of fuel particle size and volume loading. The latter requirement necessitates the development of fabrication methods to produce small, micron-order fuel particles. This research examines the capabilities of the spark erosion process to fabricate particles on the order of 10 μm. A custom-built spark erosion device by CT Electromechanica was used to produce stainless steel surrogate fuel particles in a deionized water dielectric. Three arc intensities were evaluated to determine the effect on particle size. Particles were filtered from the dielectric using a polycarbonate membrane filter and vacuum filtration system. Fabricated particles were characterized via field emission scanning electron microscopy (FESEM), laser light particle size analysis, energy-dispersive spectroscopy (EDS), X-ray diffraction analysis (XRD), and gas pycnometry. FESEM images reveal that the spark erosion process produces highly spherical particles on the order of 10 microns. These findings are substantiated by the results of particle size analysis. Additionally, EDS and XRD results indicate the presence of oxide phases, which suggests the dielectric reacted with the molten debris during particle formation.

Online learning control using adaptive critic designs with sparse kernel machines.

PubMed

Xu, Xin; Hou, Zhongsheng; Lian, Chuanqiang; He, Haibo

2013-05-01

In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization capability and learning efficiency of previous ACDs still need to be improved. In this paper, a novel framework of ACDs with sparse kernel machines is presented by integrating kernel methods into the critic of ACDs. To improve the generalization capability as well as the computational efficiency of kernel machines, a sparsification method based on the approximately linear dependence analysis is used. Using the sparse kernel machines, two kernel-based ACD algorithms, that is, kernel HDP (KHDP) and kernel DHP (KDHP), are proposed and their performance is analyzed both theoretically and empirically. Because of the representation learning and generalization capability of sparse kernel machines, KHDP and KDHP can obtain much better performance than previous HDP and DHP with manually designed neural networks. Simulation and experimental results of two nonlinear control problems, that is, a continuous-action inverted pendulum problem and a ball and plate control problem, demonstrate the effectiveness of the proposed kernel ACD methods.

Influence of wheat kernel physical properties on the pulverizing process.

PubMed

Dziki, Dariusz; Cacak-Pietrzak, Grażyna; Miś, Antoni; Jończyk, Krzysztof; Gawlik-Dziki, Urszula

2014-10-01

The physical properties of wheat kernel were determined and related to pulverizing performance by correlation analysis. Nineteen samples of wheat cultivars about similar level of protein content (11.2-12.8 % w.b.) and obtained from organic farming system were used for analysis. The kernel (moisture content 10 % w.b.) was pulverized by using the laboratory hammer mill equipped with round holes 1.0 mm screen. The specific grinding energy ranged from 120 kJkg(-1) to 159 kJkg(-1). On the basis of data obtained many of significant correlations (p < 0.05) were found between wheat kernel physical properties and pulverizing process of wheat kernel, especially wheat kernel hardness index (obtained on the basis of Single Kernel Characterization System) and vitreousness significantly and positively correlated with the grinding energy indices and the mass fraction of coarse particles (> 0.5 mm). Among the kernel mechanical properties determined on the basis of uniaxial compression test only the rapture force was correlated with the impact grinding results. The results showed also positive and significant relationships between kernel ash content and grinding energy requirements. On the basis of wheat physical properties the multiple linear regression was proposed for predicting the average particle size of pulverized kernel.

Bright Sparks of Our Future!

NASA Astrophysics Data System (ADS)

Riordan, Naoimh

2016-04-01

My name is Naoimh Riordan and I am the Vice Principal of Rockboro Primary School in Cork City, South of Ireland. I am a full time class primary teacher and I teach 4th class, my students are aged between 9-10 years. My passion for education has developed over the years and grown towards STEM (Science, Technology, Engineering and Mathematics) subjects. I believe these subjects are the way forward for our future. My passion and beliefs are driven by the unique after school programme that I have developed. It is titled "Sparks" coming from the term Bright Sparks. "Sparks" is an after school programme with a difference where the STEM subjects are concentrated on through lessons such as Science, Veterinary Science Computer Animation /Coding, Eco engineering, Robotics, Magical Maths, Chess and Creative Writing. All these subjects are taught through activity based learning and are one-hour long each week for a ten-week term. "Sparks" is fully inclusive and non-selective which gives all students of any level of ability an opportunity to engage into these subjects. "Sparks" is open to all primary students in County Cork. The "Sparks" after school programme is taught by tutors from the different Universities and Colleges in Cork City. It works very well because the tutor brings their knowledge, skills and specialised equipment from their respective universities and in turn the tutor gains invaluable teaching practise, can trial a pilot programme in a chosen STEM subject and gain an insight into what works in the physical classroom.

Relationship between processing score and kernel-fraction particle size in whole-plant corn silage.

PubMed

Dias Junior, G S; Ferraretto, L F; Salvati, G G S; de Resende, L C; Hoffman, P C; Pereira, M N; Shaver, R D

2016-04-01

Kernel processing increases starch digestibility in whole-plant corn silage (WPCS). Corn silage processing score (CSPS), the percentage of starch passing through a 4.75-mm sieve, is widely used to assess degree of kernel breakage in WPCS. However, the geometric mean particle size (GMPS) of the kernel-fraction that passes through the 4.75-mm sieve has not been well described. Therefore, the objectives of this study were (1) to evaluate particle size distribution and digestibility of kernels cut in varied particle sizes; (2) to propose a method to measure GMPS in WPCS kernels; and (3) to evaluate the relationship between CSPS and GMPS of the kernel fraction in WPCS. Composite samples of unfermented, dried kernels from 110 corn hybrids commonly used for silage production were kept whole (WH) or manually cut in 2, 4, 8, 16, 32 or 64 pieces (2P, 4P, 8P, 16P, 32P, and 64P, respectively). Dry sieving to determine GMPS, surface area, and particle size distribution using 9 sieves with nominal square apertures of 9.50, 6.70, 4.75, 3.35, 2.36, 1.70, 1.18, and 0.59 mm and pan, as well as ruminal in situ dry matter (DM) digestibilities were performed for each kernel particle number treatment. Incubation times were 0, 3, 6, 12, and 24 h. The ruminal in situ DM disappearance of unfermented kernels increased with the reduction in particle size of corn kernels. Kernels kept whole had the lowest ruminal DM disappearance for all time points with maximum DM disappearance of 6.9% at 24 h and the greatest disappearance was observed for 64P, followed by 32P and 16P. Samples of WPCS (n=80) from 3 studies representing varied theoretical length of cut settings and processor types and settings were also evaluated. Each WPCS sample was divided in 2 and then dried at 60 °C for 48 h. The CSPS was determined in duplicate on 1 of the split samples, whereas on the other split sample the kernel and stover fractions were separated using a hydrodynamic separation procedure. After separation, the kernel fraction was redried at 60°C for 48 h in a forced-air oven and dry sieved to determine GMPS and surface area. Linear relationships between CSPS from WPCS (n=80) and kernel fraction GMPS, surface area, and proportion passing through the 4.75-mm screen were poor. Strong quadratic relationships between proportion of kernel fraction passing through the 4.75-mm screen and kernel fraction GMPS and surface area were observed. These findings suggest that hydrodynamic separation and dry sieving of the kernel fraction may provide a better assessment of kernel breakage in WPCS than CSPS. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Classification of corn kernels contaminated with aflatoxins using fluorescence and reflectance hyperspectral images analysis

NASA Astrophysics Data System (ADS)

Zhu, Fengle; Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Brown, Robert; Bhatnagar, Deepak; Cleveland, Thomas

2015-05-01

Aflatoxins are secondary metabolites produced by certain fungal species of the Aspergillus genus. Aflatoxin contamination remains a problem in agricultural products due to its toxic and carcinogenic properties. Conventional chemical methods for aflatoxin detection are time-consuming and destructive. This study employed fluorescence and reflectance visible near-infrared (VNIR) hyperspectral images to classify aflatoxin contaminated corn kernels rapidly and non-destructively. Corn ears were artificially inoculated in the field with toxigenic A. flavus spores at the early dough stage of kernel development. After harvest, a total of 300 kernels were collected from the inoculated ears. Fluorescence hyperspectral imagery with UV excitation and reflectance hyperspectral imagery with halogen illumination were acquired on both endosperm and germ sides of kernels. All kernels were then subjected to chemical analysis individually to determine aflatoxin concentrations. A region of interest (ROI) was created for each kernel to extract averaged spectra. Compared with healthy kernels, fluorescence spectral peaks for contaminated kernels shifted to longer wavelengths with lower intensity, and reflectance values for contaminated kernels were lower with a different spectral shape in 700-800 nm region. Principal component analysis was applied for data compression before classifying kernels into contaminated and healthy based on a 20 ppb threshold utilizing the K-nearest neighbors algorithm. The best overall accuracy achieved was 92.67% for germ side in the fluorescence data analysis. The germ side generally performed better than endosperm side. Fluorescence and reflectance image data achieved similar accuracy.

Influence of Kernel Age on Fumonisin B1 Production in Maize by Fusarium moniliforme

PubMed Central

Warfield, Colleen Y.; Gilchrist, David G.

1999-01-01

Production of fumonisins by Fusarium moniliforme on naturally infected maize ears is an important food safety concern due to the toxic nature of this class of mycotoxins. Assessing the potential risk of fumonisin production in developing maize ears prior to harvest requires an understanding of the regulation of toxin biosynthesis during kernel maturation. We investigated the developmental-stage-dependent relationship between maize kernels and fumonisin B1 production by using kernels collected at the blister (R2), milk (R3), dough (R4), and dent (R5) stages following inoculation in culture at their respective field moisture contents with F. moniliforme. Highly significant differences (P ≤ 0.001) in fumonisin B1 production were found among kernels at the different developmental stages. The highest levels of fumonisin B1 were produced on the dent stage kernels, and the lowest levels were produced on the blister stage kernels. The differences in fumonisin B1 production among kernels at the different developmental stages remained significant (P ≤ 0.001) when the moisture contents of the kernels were adjusted to the same level prior to inoculation. We concluded that toxin production is affected by substrate composition as well as by moisture content. Our study also demonstrated that fumonisin B1 biosynthesis on maize kernels is influenced by factors which vary with the developmental age of the tissue. The risk of fumonisin contamination may begin early in maize ear development and increases as the kernels reach physiological maturity. PMID:10388675

Differential evolution algorithm-based kernel parameter selection for Fukunaga-Koontz Transform subspaces construction

NASA Astrophysics Data System (ADS)

Binol, Hamidullah; Bal, Abdullah; Cukur, Huseyin

2015-10-01

The performance of the kernel based techniques depends on the selection of kernel parameters. That's why; suitable parameter selection is an important problem for many kernel based techniques. This article presents a novel technique to learn the kernel parameters in kernel Fukunaga-Koontz Transform based (KFKT) classifier. The proposed approach determines the appropriate values of kernel parameters through optimizing an objective function constructed based on discrimination ability of KFKT. For this purpose we have utilized differential evolution algorithm (DEA). The new technique overcomes some disadvantages such as high time consumption existing in the traditional cross-validation method, and it can be utilized in any type of data. The experiments for target detection applications on the hyperspectral images verify the effectiveness of the proposed method.

Design of a multiple kernel learning algorithm for LS-SVM by convex programming.

PubMed

Jian, Ling; Xia, Zhonghang; Liang, Xijun; Gao, Chuanhou

2011-06-01

As a kernel based method, the performance of least squares support vector machine (LS-SVM) depends on the selection of the kernel as well as the regularization parameter (Duan, Keerthi, & Poo, 2003). Cross-validation is efficient in selecting a single kernel and the regularization parameter; however, it suffers from heavy computational cost and is not flexible to deal with multiple kernels. In this paper, we address the issue of multiple kernel learning for LS-SVM by formulating it as semidefinite programming (SDP). Furthermore, we show that the regularization parameter can be optimized in a unified framework with the kernel, which leads to an automatic process for model selection. Extensive experimental validations are performed and analyzed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Novel near-infrared sampling apparatus for single kernel analysis of oil content in maize.

PubMed

Janni, James; Weinstock, B André; Hagen, Lisa; Wright, Steve

2008-04-01

A method of rapid, nondestructive chemical and physical analysis of individual maize (Zea mays L.) kernels is needed for the development of high value food, feed, and fuel traits. Near-infrared (NIR) spectroscopy offers a robust nondestructive method of trait determination. However, traditional NIR bulk sampling techniques cannot be applied successfully to individual kernels. Obtaining optimized single kernel NIR spectra for applied chemometric predictive analysis requires a novel sampling technique that can account for the heterogeneous forms, morphologies, and opacities exhibited in individual maize kernels. In this study such a novel technique is described and compared to less effective means of single kernel NIR analysis. Results of the application of a partial least squares (PLS) derived model for predictive determination of percent oil content per individual kernel are shown.

Research of an electromagnetically actuated spark gap switch

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Tianyang; Chen, Dongqun, E-mail: csycdq@163.com; Liu, Jinliang

2013-11-15

As an important part of pulsed power systems, high-voltage and high-current triggered spark gap switch and its trigger system are expected to achieve a compact structure. In this paper, a high-voltage, high-current, and compact electromagnetically actuated spark gap switch is put forward, and it can be applied as a part of an intense electron-beam accelerator (IEBA). A 24 V DC power supply is used to trigger the switch. The characteristics of the switch were measured for N{sub 2} when the gas pressure is 0.10–0.30 MPa. The experimental results showed that the voltage/pressure (V/p) curve of the switch was linear relationship.more » The operating ranges of the switch were 21%–96%, 21%–95%, 21%–95%, 19%–95%, 17%–95%, and 16%–96% of the switch's self-breakdown voltage when the gas pressures were 0.10, 0.14, 0.18, 0.22, 0.26, and 0.30 MPa, respectively. The switch and its trigger system worked steadily and reliably with a peak voltage of 30 kV, a peak current of 60 kA in the IEBA when the pressure of N{sub 2} in the switch was 0.30 MPa.« less

Computed tomography coronary stent imaging with iterative reconstruction: a trade-off study between medium kernel and sharp kernel.

PubMed

Zhou, Qijing; Jiang, Biao; Dong, Fei; Huang, Peiyu; Liu, Hongtao; Zhang, Minming

2014-01-01

To evaluate the improvement of iterative reconstruction in image space (IRIS) technique in computed tomographic (CT) coronary stent imaging with sharp kernel, and to make a trade-off analysis. Fifty-six patients with 105 stents were examined by 128-slice dual-source CT coronary angiography (CTCA). Images were reconstructed using standard filtered back projection (FBP) and IRIS with both medium kernel and sharp kernel applied. Image noise and the stent diameter were investigated. Image noise was measured both in background vessel and in-stent lumen as objective image evaluation. Image noise score and stent score were performed as subjective image evaluation. The CTCA images reconstructed with IRIS were associated with significant noise reduction compared to that of CTCA images reconstructed using FBP technique in both of background vessel and in-stent lumen (the background noise decreased by approximately 25.4% ± 8.2% in medium kernel (P

Multiple Kernel Sparse Representation based Orthogonal Discriminative Projection and Its Cost-Sensitive Extension.

PubMed

Zhang, Guoqing; Sun, Huaijiang; Xia, Guiyu; Sun, Quansen

2016-07-07

Sparse representation based classification (SRC) has been developed and shown great potential for real-world application. Based on SRC, Yang et al. [10] devised a SRC steered discriminative projection (SRC-DP) method. However, as a linear algorithm, SRC-DP cannot handle the data with highly nonlinear distribution. Kernel sparse representation-based classifier (KSRC) is a non-linear extension of SRC and can remedy the drawback of SRC. KSRC requires the use of a predetermined kernel function and selection of the kernel function and its parameters is difficult. Recently, multiple kernel learning for SRC (MKL-SRC) [22] has been proposed to learn a kernel from a set of base kernels. However, MKL-SRC only considers the within-class reconstruction residual while ignoring the between-class relationship, when learning the kernel weights. In this paper, we propose a novel multiple kernel sparse representation-based classifier (MKSRC), and then we use it as a criterion to design a multiple kernel sparse representation based orthogonal discriminative projection method (MK-SR-ODP). The proposed algorithm aims at learning a projection matrix and a corresponding kernel from the given base kernels such that in the low dimension subspace the between-class reconstruction residual is maximized and the within-class reconstruction residual is minimized. Furthermore, to achieve a minimum overall loss by performing recognition in the learned low-dimensional subspace, we introduce cost information into the dimensionality reduction method. The solutions for the proposed method can be efficiently found based on trace ratio optimization method [33]. Extensive experimental results demonstrate the superiority of the proposed algorithm when compared with the state-of-the-art methods.

Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.

PubMed

Ruan, Peiying; Hayashida, Morihiro; Akutsu, Tatsuya; Vert, Jean-Philippe

2018-02-19

Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

Mapping QTLs controlling kernel dimensions in a wheat inter-varietal RIL mapping population.

PubMed

Cheng, Ruiru; Kong, Zhongxin; Zhang, Liwei; Xie, Quan; Jia, Haiyan; Yu, Dong; Huang, Yulong; Ma, Zhengqiang

2017-07-01

Seven kernel dimension QTLs were identified in wheat, and kernel thickness was found to be the most important dimension for grain weight improvement. Kernel morphology and weight of wheat (Triticum aestivum L.) affect both yield and quality; however, the genetic basis of these traits and their interactions has not been fully understood. In this study, to investigate the genetic factors affecting kernel morphology and the association of kernel morphology traits with kernel weight, kernel length (KL), width (KW) and thickness (KT) were evaluated, together with hundred-grain weight (HGW), in a recombinant inbred line population derived from Nanda2419 × Wangshuibai, with data from five trials (two different locations over 3 years). The results showed that HGW was more closely correlated with KT and KW than with KL. A whole genome scan revealed four QTLs for KL, one for KW and two for KT, distributed on five different chromosomes. Of them, QKl.nau-2D for KL, and QKt.nau-4B and QKt.nau-5A for KT were newly identified major QTLs for the respective traits, explaining up to 32.6 and 41.5% of the phenotypic variations, respectively. Increase of KW and KT and reduction of KL/KT and KW/KT ratios always resulted in significant higher grain weight. Lines combining the Nanda 2419 alleles of the 4B and 5A intervals had wider, thicker, rounder kernels and a 14% higher grain weight in the genotype-based analysis. A strong, negative linear relationship of the KW/KT ratio with grain weight was observed. It thus appears that kernel thickness is the most important kernel dimension factor in wheat improvement for higher yield. Mapping and marker identification of the kernel dimension-related QTLs definitely help realize the breeding goals.

Kernel learning at the first level of inference.

PubMed

Cawley, Gavin C; Talbot, Nicola L C

2014-05-01

Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coefficients of the kernel expansion being determined at the first level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However, if there are a large number of kernel parameters, as for instance in the case of automatic relevance determination (ARD), there is a substantial risk of over-fitting the model selection criterion, resulting in poor generalisation performance. In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e. parameter optimisation. The kernel parameters and the coefficients of the kernel expansion are jointly optimised at the first level of inference, minimising a training criterion with an additional regularisation term acting on the kernel parameters. The key advantage of this approach is that the values of only two regularisation parameters need be determined in model selection, substantially alleviating the problem of over-fitting the model selection criterion. The benefits of this approach are demonstrated using a suite of synthetic and real-world binary classification benchmark problems, where kernel learning at the first level of inference is shown to be statistically superior to the conventional approach, improves on our previous work (Cawley and Talbot, 2007) and is competitive with Multiple Kernel Learning approaches, but with reduced computational expense. Copyright © 2014 Elsevier Ltd. All rights reserved.

Research on Standard Errors of Equating Differences. Research Report. ETS RR-10-25

ERIC Educational Resources Information Center

Moses, Tim; Zhang, Wenmin

2010-01-01

In this paper, the "standard error of equating difference" (SEED) is described in terms of originally proposed kernel equating functions (von Davier, Holland, & Thayer, 2004) and extended to incorporate traditional linear and equipercentile functions. These derivations expand on prior developments of SEEDs and standard errors of equating and…

Adaptive kernel function using line transect sampling

NASA Astrophysics Data System (ADS)

Albadareen, Baker; Ismail, Noriszura

2018-04-01

The estimation of f(0) is crucial in the line transect method which is used for estimating population abundance in wildlife survey's. The classical kernel estimator of f(0) has a high negative bias. Our study proposes an adaptation in the kernel function which is shown to be more efficient than the usual kernel estimator. A simulation study is adopted to compare the performance of the proposed estimators with the classical kernel estimators.

Kernel Partial Least Squares for Nonlinear Regression and Discrimination

NASA Technical Reports Server (NTRS)

Rosipal, Roman; Clancy, Daniel (Technical Monitor)

2002-01-01

This paper summarizes recent results on applying the method of partial least squares (PLS) in a reproducing kernel Hilbert space (RKHS). A previously proposed kernel PLS regression model was proven to be competitive with other regularized regression methods in RKHS. The family of nonlinear kernel-based PLS models is extended by considering the kernel PLS method for discrimination. Theoretical and experimental results on a two-class discrimination problem indicate usefulness of the method.

Development of High-Strength Nanostructured Magnesium Alloys for Light-Weight Weapon Systems and Vehicles

DTIC Science & Technology

2014-01-13

strength nanocrystalline Mg-alloys via cryomilling and spark - plasma - sintering , 2) demonstrate the unveil evidence of nanotwins in nanocrystalline...Christopher Melnyk, Wei H. Kao, Jenn-Ming Yang. Cryomilling and spark plasma sintering of nanocrystalline magnesium-based alloy, Journal of Materials...accomplished several important milestones: 1) manufacture of high strength nanocrystalline Mg-alloys via cryomilling and spark plasma sintering (SPS

Ultra-high Strength Nanostructured Mg

DTIC Science & Technology

2014-03-31

27709-2211 Nanostructured Mg and Mg alloys, Mg metallic glass, Cryomilling, Powder consolidation, Spark plasma sintering , Deformation mechanisms REPORT...mechanically milled powder and high pressure on spark plasma sintering of Mg-Cu-Gd metallic glasses; (9) microstructure and mechanical behavior of Mg-10Li-3Al...pressure on spark plasma sintering of Mg– Cu–Gd metallic glasses, Acta Materialia , (07 2013): 4414. doi: Baolong Zheng, Ying Li, Weizong Xu

40 CFR Table 1b to Subpart Zzzz of... - Operating Limitations for Existing, New, and Reconstructed Spark Ignition, 4SRB Stationary RICE...

Code of Federal Regulations, 2010 CFR

2010-07-01

..., New, and Reconstructed Spark Ignition, 4SRB Stationary RICE >500 HP Located at a Major Source of HAP... Limitations for Existing, New, and Reconstructed Spark Ignition, 4SRB Stationary RICE >500 HP Located at a... following operating emission limitations for existing, new and reconstructed 4SRB stationary RICE >500 HP...

40 CFR Table 1a to Subpart Zzzz of... - Emission Limitations for Existing, New, and Reconstructed Spark Ignition, 4SRB Stationary RICE...

Code of Federal Regulations, 2011 CFR

2011-07-01

..., and Reconstructed Spark Ignition, 4SRB Stationary RICE > 500 HP Located at a Major Source of HAP... Limitations for Existing, New, and Reconstructed Spark Ignition, 4SRB Stationary RICE > 500 HP Located at a... stationary RICE >500 HP located at a major source of HAP emissions: For each . . . You must meet the...

40 CFR Table 1a to Subpart Zzzz of... - Emission Limitations for Existing, New, and Reconstructed Spark Ignition, 4SRB Stationary RICE...

Code of Federal Regulations, 2010 CFR

2010-07-01

..., and Reconstructed Spark Ignition, 4SRB Stationary RICE >500 HP Located at a Major Source of HAP... Limitations for Existing, New, and Reconstructed Spark Ignition, 4SRB Stationary RICE >500 HP Located at a... emission limitations for existing, new and reconstructed 4SRB stationary RICE at 100 percent load plus or...

Pollen source effects on growth of kernel structures and embryo chemical compounds in maize.

PubMed

Tanaka, W; Mantese, A I; Maddonni, G A

2009-08-01

Previous studies have reported effects of pollen source on the oil concentration of maize (Zea mays) kernels through modifications to both the embryo/kernel ratio and embryo oil concentration. The present study expands upon previous analyses by addressing pollen source effects on the growth of kernel structures (i.e. pericarp, endosperm and embryo), allocation of embryo chemical constituents (i.e. oil, protein, starch and soluble sugars), and the anatomy and histology of the embryos. Maize kernels with different oil concentration were obtained from pollinations with two parental genotypes of contrasting oil concentration. The dynamics of the growth of kernel structures and allocation of embryo chemical constituents were analysed during the post-flowering period. Mature kernels were dissected to study the anatomy (embryonic axis and scutellum) and histology [cell number and cell size of the scutellums, presence of sub-cellular structures in scutellum tissue (starch granules, oil and protein bodies)] of the embryos. Plants of all crosses exhibited a similar kernel number and kernel weight. Pollen source modified neither the growth period of kernel structures, nor pericarp growth rate. By contrast, pollen source determined a trade-off between embryo and endosperm growth rates, which impacted on the embryo/kernel ratio of mature kernels. Modifications to the embryo size were mediated by scutellum cell number. Pollen source also affected (P < 0.01) allocation of embryo chemical compounds. Negative correlations among embryo oil concentration and those of starch (r = 0.98, P < 0.01) and soluble sugars (r = 0.95, P < 0.05) were found. Coincidently, embryos with low oil concentration had an increased (P < 0.05-0.10) scutellum cell area occupied by starch granules and fewer oil bodies. The effects of pollen source on both embryo/kernel ratio and allocation of embryo chemicals seems to be related to the early established sink strength (i.e. sink size and sink activity) of the embryos.

7 CFR 868.254 - Broken kernels determination.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 7 2010-01-01 2010-01-01 false Broken kernels determination. 868.254 Section 868.254 Agriculture Regulations of the Department of Agriculture (Continued) GRAIN INSPECTION, PACKERS AND STOCKYARD... Governing Application of Standards § 868.254 Broken kernels determination. Broken kernels shall be...

7 CFR 51.2090 - Serious damage.

Code of Federal Regulations, 2010 CFR

2010-01-01

... defect which makes a kernel or piece of kernel unsuitable for human consumption, and includes decay...: Shriveling when the kernel is seriously withered, shrunken, leathery, tough or only partially developed: Provided, that partially developed kernels are not considered seriously damaged if more than one-fourth of...

Anisotropic hydrodynamics with a scalar collisional kernel

NASA Astrophysics Data System (ADS)

Almaalol, Dekrayat; Strickland, Michael

2018-04-01

Prior studies of nonequilibrium dynamics using anisotropic hydrodynamics have used the relativistic Anderson-Witting scattering kernel or some variant thereof. In this paper, we make the first study of the impact of using a more realistic scattering kernel. For this purpose, we consider a conformal system undergoing transversally homogenous and boost-invariant Bjorken expansion and take the collisional kernel to be given by the leading order 2 ↔2 scattering kernel in scalar λ ϕ4 . We consider both classical and quantum statistics to assess the impact of Bose enhancement on the dynamics. We also determine the anisotropic nonequilibrium attractor of a system subject to this collisional kernel. We find that, when the near-equilibrium relaxation-times in the Anderson-Witting and scalar collisional kernels are matched, the scalar kernel results in a higher degree of momentum-space anisotropy during the system's evolution, given the same initial conditions. Additionally, we find that taking into account Bose enhancement further increases the dynamically generated momentum-space anisotropy.

Ideal regularization for learning kernels from labels.

PubMed

Pan, Binbin; Lai, Jianhuang; Shen, Lixin

2014-08-01

In this paper, we propose a new form of regularization that is able to utilize the label information of a data set for learning kernels. The proposed regularization, referred to as ideal regularization, is a linear function of the kernel matrix to be learned. The ideal regularization allows us to develop efficient algorithms to exploit labels. Three applications of the ideal regularization are considered. Firstly, we use the ideal regularization to incorporate the labels into a standard kernel, making the resulting kernel more appropriate for learning tasks. Next, we employ the ideal regularization to learn a data-dependent kernel matrix from an initial kernel matrix (which contains prior similarity information, geometric structures, and labels of the data). Finally, we incorporate the ideal regularization to some state-of-the-art kernel learning problems. With this regularization, these learning problems can be formulated as simpler ones which permit more efficient solvers. Empirical results show that the ideal regularization exploits the labels effectively and efficiently. Copyright © 2014 Elsevier Ltd. All rights reserved.

The site, size, spatial stability, and energetics of an X-ray flare kernel

NASA Technical Reports Server (NTRS)

Petrasso, R.; Gerassimenko, M.; Nolte, J.

1979-01-01

The site, size evolution, and energetics of an X-ray kernel that dominated a solar flare during its rise and somewhat during its peak are investigated. The position of the kernel remained stationary to within about 3 arc sec over the 30-min interval of observations, despite pulsations in the kernel X-ray brightness in excess of a factor of 10. This suggests a tightly bound, deeply rooted magnetic structure, more plausibly associated with the near chromosphere or low corona rather than with the high corona. The H-alpha flare onset coincided with the appearance of the kernel, again suggesting a close spatial and temporal coupling between the chromospheric H-alpha event and the X-ray kernel. At the first kernel brightness peak its size was no larger than about 2 arc sec, when it accounted for about 40% of the total flare flux. In the second rise phase of the kernel, a source power input of order 2 times 10 to the 24th ergs/sec is minimally required.

The pre-image problem in kernel methods.

PubMed

Kwok, James Tin-yau; Tsang, Ivor Wai-hung

2004-11-01

In this paper, we address the problem of finding the pre-image of a feature vector in the feature space induced by a kernel. This is of central importance in some kernel applications, such as on using kernel principal component analysis (PCA) for image denoising. Unlike the traditional method which relies on nonlinear optimization, our proposed method directly finds the location of the pre-image based on distance constraints in the feature space. It is noniterative, involves only linear algebra and does not suffer from numerical instability or local minimum problems. Evaluations on performing kernel PCA and kernel clustering on the USPS data set show much improved performance.

Effects of Amygdaline from Apricot Kernel on Transplanted Tumors in Mice.

PubMed

Yamshanov, V A; Kovan'ko, E G; Pustovalov, Yu I

2016-03-01

The effects of amygdaline from apricot kernel added to fodder on the growth of transplanted LYO-1 and Ehrlich carcinoma were studied in mice. Apricot kernels inhibited the growth of both tumors. Apricot kernels, raw and after thermal processing, given 2 days before transplantation produced a pronounced antitumor effect. Heat-processed apricot kernels given in 3 days after transplantation modified the tumor growth and prolonged animal lifespan. Thermal treatment did not considerably reduce the antitumor effect of apricot kernels. It was hypothesized that the antitumor effect of amygdaline on Ehrlich carcinoma and LYO-1 lymphosarcoma was associated with the presence of bacterial genome in the tumor.

Development of a kernel function for clinical data.

PubMed

Daemen, Anneleen; De Moor, Bart

2009-01-01

For most diseases and examinations, clinical data such as age, gender and medical history guides clinical management, despite the rise of high-throughput technologies. To fully exploit such clinical information, appropriate modeling of relevant parameters is required. As the widely used linear kernel function has several disadvantages when applied to clinical data, we propose a new kernel function specifically developed for this data. This "clinical kernel function" more accurately represents similarities between patients. Evidently, three data sets were studied and significantly better performances were obtained with a Least Squares Support Vector Machine when based on the clinical kernel function compared to the linear kernel function.

Manycore Performance-Portability: Kokkos Multidimensional Array Library

DOE PAGES

Edwards, H. Carter; Sunderland, Daniel; Porter, Vicki; ...

2012-01-01

Large, complex scientific and engineering application code have a significant investment in computational kernels to implement their mathematical models. Porting these computational kernels to the collection of modern manycore accelerator devices is a major challenge in that these devices have diverse programming models, application programming interfaces (APIs), and performance requirements. The Kokkos Array programming model provides library-based approach to implement computational kernels that are performance-portable to CPU-multicore and GPGPU accelerator devices. This programming model is based upon three fundamental concepts: (1) manycore compute devices each with its own memory space, (2) data parallel kernels and (3) multidimensional arrays. Kernel executionmore » performance is, especially for NVIDIA® devices, extremely dependent on data access patterns. Optimal data access pattern can be different for different manycore devices – potentially leading to different implementations of computational kernels specialized for different devices. The Kokkos Array programming model supports performance-portable kernels by (1) separating data access patterns from computational kernels through a multidimensional array API and (2) introduce device-specific data access mappings when a kernel is compiled. An implementation of Kokkos Array is available through Trilinos [Trilinos website, http://trilinos.sandia.gov/, August 2011].« less

Protein Subcellular Localization with Gaussian Kernel Discriminant Analysis and Its Kernel Parameter Selection.

PubMed

Wang, Shunfang; Nie, Bing; Yue, Kun; Fei, Yu; Li, Wenjia; Xu, Dongshu

2017-12-15

Kernel discriminant analysis (KDA) is a dimension reduction and classification algorithm based on nonlinear kernel trick, which can be novelly used to treat high-dimensional and complex biological data before undergoing classification processes such as protein subcellular localization. Kernel parameters make a great impact on the performance of the KDA model. Specifically, for KDA with the popular Gaussian kernel, to select the scale parameter is still a challenging problem. Thus, this paper introduces the KDA method and proposes a new method for Gaussian kernel parameter selection depending on the fact that the differences between reconstruction errors of edge normal samples and those of interior normal samples should be maximized for certain suitable kernel parameters. Experiments with various standard data sets of protein subcellular localization show that the overall accuracy of protein classification prediction with KDA is much higher than that without KDA. Meanwhile, the kernel parameter of KDA has a great impact on the efficiency, and the proposed method can produce an optimum parameter, which makes the new algorithm not only perform as effectively as the traditional ones, but also reduce the computational time and thus improve efficiency.

Impact of deep learning on the normalization of reconstruction kernel effects in imaging biomarker quantification: a pilot study in CT emphysema

NASA Astrophysics Data System (ADS)

Jin, Hyeongmin; Heo, Changyong; Kim, Jong Hyo

2018-02-01

Differing reconstruction kernels are known to strongly affect the variability of imaging biomarkers and thus remain as a barrier in translating the computer aided quantification techniques into clinical practice. This study presents a deep learning application to CT kernel conversion which converts a CT image of sharp kernel to that of standard kernel and evaluates its impact on variability reduction of a pulmonary imaging biomarker, the emphysema index (EI). Forty cases of low-dose chest CT exams obtained with 120kVp, 40mAs, 1mm thickness, of 2 reconstruction kernels (B30f, B50f) were selected from the low dose lung cancer screening database of our institution. A Fully convolutional network was implemented with Keras deep learning library. The model consisted of symmetric layers to capture the context and fine structure characteristics of CT images from the standard and sharp reconstruction kernels. Pairs of the full-resolution CT data set were fed to input and output nodes to train the convolutional network to learn the appropriate filter kernels for converting the CT images of sharp kernel to standard kernel with a criterion of measuring the mean squared error between the input and target images. EIs (RA950 and Perc15) were measured with a software package (ImagePrism Pulmo, Seoul, South Korea) and compared for the data sets of B50f, B30f, and the converted B50f. The effect of kernel conversion was evaluated with the mean and standard deviation of pair-wise differences in EI. The population mean of RA950 was 27.65 +/- 7.28% for B50f data set, 10.82 +/- 6.71% for the B30f data set, and 8.87 +/- 6.20% for the converted B50f data set. The mean of pair-wise absolute differences in RA950 between B30f and B50f is reduced from 16.83% to 1.95% using kernel conversion. Our study demonstrates the feasibility of applying the deep learning technique for CT kernel conversion and reducing the kernel-induced variability of EI quantification. The deep learning model has a potential to improve the reliability of imaging biomarker, especially in evaluating the longitudinal changes of EI even when the patient CT scans were performed with different kernels.

Metabolic network prediction through pairwise rational kernels.

PubMed

Roche-Lima, Abiel; Domaratzki, Michael; Fristensky, Brian

2014-09-26

Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations. We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy values have been improved, while maintaining lower construction and execution times. The power of using kernels is that almost any sort of data can be represented using kernels. Therefore, completely disparate types of data can be combined to add power to kernel-based machine learning methods. When we compared our proposal using PRKs with other similar kernel, the execution times were decreased, with no compromise of accuracy. We also proved that by combining PRKs with other kernels that include evolutionary information, the accuracy can also also be improved. As our proposal can use any type of sequence data, genes do not need to be properly annotated, avoiding accumulation errors because of incorrect previous annotations.

Reaction Kernel Structure of a Slot Jet Diffusion Flame in Microgravity

NASA Technical Reports Server (NTRS)

Takahashi, F.; Katta, V. R.

2001-01-01

Diffusion flame stabilization in normal earth gravity (1 g) has long been a fundamental research subject in combustion. Local flame-flow phenomena, including heat and species transport and chemical reactions, around the flame base in the vicinity of condensed surfaces control flame stabilization and fire spreading processes. Therefore, gravity plays an important role in the subject topic because buoyancy induces flow in the flame zone, thus increasing the convective (and diffusive) oxygen transport into the flame zone and, in turn, reaction rates. Recent computations show that a peak reactivity (heat-release or oxygen-consumption rate) spot, or reaction kernel, is formed in the flame base by back-diffusion and reactions of radical species in the incoming oxygen-abundant flow at relatively low temperatures (about 1550 K). Quasi-linear correlations were found between the peak heat-release or oxygen-consumption rate and the velocity at the reaction kernel for cases including both jet and flat-plate diffusion flames in airflow. The reaction kernel provides a stationary ignition source to incoming reactants, sustains combustion, and thus stabilizes the trailing diffusion flame. In a quiescent microgravity environment, no buoyancy-induced flow exits and thus purely diffusive transport controls the reaction rates. Flame stabilization mechanisms in such purely diffusion-controlled regime remain largely unstudied. Therefore, it will be a rigorous test for the reaction kernel correlation if it can be extended toward zero velocity conditions in the purely diffusion-controlled regime. The objectives of this study are to reveal the structure of the flame-stabilizing region of a two-dimensional (2D) laminar jet diffusion flame in microgravity and develop a unified diffusion flame stabilization mechanism. This paper reports the recent progress in the computation and experiment performed in microgravity.

DOE Office of Scientific and Technical Information (OSTI.GOV)

O'Brien, Travis A.; Kashinath, Karthik; Cavanaugh, Nicholas R.

Numerous facets of scientific research implicitly or explicitly call for the estimation of probability densities. Histograms and kernel density estimates (KDEs) are two commonly used techniques for estimating such information, with the KDE generally providing a higher fidelity representation of the probability density function (PDF). Both methods require specification of either a bin width or a kernel bandwidth. While techniques exist for choosing the kernel bandwidth optimally and objectively, they are computationally intensive, since they require repeated calculation of the KDE. A solution for objectively and optimally choosing both the kernel shape and width has recently been developed by Bernacchiamore » and Pigolotti (2011). While this solution theoretically applies to multidimensional KDEs, it has not been clear how to practically do so. A method for practically extending the Bernacchia-Pigolotti KDE to multidimensions is introduced. This multidimensional extension is combined with a recently-developed computational improvement to their method that makes it computationally efficient: a 2D KDE on 10 5 samples only takes 1 s on a modern workstation. This fast and objective KDE method, called the fastKDE method, retains the excellent statistical convergence properties that have been demonstrated for univariate samples. The fastKDE method exhibits statistical accuracy that is comparable to state-of-the-science KDE methods publicly available in R, and it produces kernel density estimates several orders of magnitude faster. The fastKDE method does an excellent job of encoding covariance information for bivariate samples. This property allows for direct calculation of conditional PDFs with fastKDE. It is demonstrated how this capability might be leveraged for detecting non-trivial relationships between quantities in physical systems, such as transitional behavior.« less

The secular and the supernatural: madness and psychiatry in the short stories of Muriel Spark.

PubMed

Beveridge, A W

2015-01-01

Edinburgh-born Muriel Spark is one of modern Scotland's greatest writers. Examination of her work reveals that the subjects of madness and psychiatry are recurrent themes in her writing. She herself had a mental breakdown when she was a young woman and she took an interest in the world of psychiatry and psychoanalysis. In her short stories, Spark approaches the subject of madness in a variety of ways: she relates it to the supernatural; to writing fiction; and to religion. She frequently juxtaposes secular and supernatural explanations of mental disturbance. Spark adopts a sceptical and, at times, mocking view of psychiatrists and psychiatric treatment. Both psychoanalysis and pills are seen as problematic.

Games and Simulations for Climate, Weather and Earth Science Education

NASA Astrophysics Data System (ADS)

Russell, R. M.

2013-12-01

We will demonstrate several interactive, computer-based simulations, games, and other interactive multimedia. These resources were developed for weather, climate, atmospheric science, and related Earth system science education. The materials were created by education groups at NCAR/UCAR in Boulder, primarily Spark and the COMET Program. These materials have been disseminated via Spark's web site (spark.ucar.edu), webinars, online courses, teacher workshops, and large touchscreen displays in weather and Sun-Earth connections exhibits in NCAR's Mesa Lab facility. Spark has also assembled a web-based list of similar resources, especially simulations and games, from other sources that touch upon weather, climate, and atmospheric science topics. We'll briefly demonstrate this directory.

Differential metabolome analysis of field-grown maize kernels in response to drought stress

USDA-ARS?s Scientific Manuscript database

Drought stress constrains maize kernel development and can exacerbate aflatoxin contamination. In order to identify drought responsive metabolites and explore pathways involved in kernel responses, a metabolomics analysis was conducted on kernels from a drought tolerant line, Lo964, and a sensitive ...

Occurrence of 'super soft' wheat kernel texture in hexaploid and tetraploid wheats

USDA-ARS?s Scientific Manuscript database

Wheat kernel texture is a key trait that governs milling performance, flour starch damage, flour particle size, flour hydration properties, and baking quality. Kernel texture is commonly measured using the Perten Single Kernel Characterization System (SKCS). The SKCS returns texture values (Hardness...