Excitation of Na D-line radiation in collisions of sodium atoms with internally excited H2, D2, and N2

Excitation of D-line radiation in collisions of Na atoms with vibrationally excited N2, H2 and D2 was studied in two modulated crossed beam experiments. In both experiments, the vibrational excitation of the molecules was provided by heating the molecular beam source to temperatures in the range of 2000 to 3000 K, which was assumed to give populations according to the Boltzmann expression. In the first experiment, a total rate coefficient was measured as a function of molecular beam temperature, with absolute calibration of the photon detector being made using the black body radiation from the heated molecular beam source. Since heating affects both the internal energy and the collisional kinetic energy, the first experiment could not determine the relative contributions of internal energy transfer versus collisional excitation. The second experiment achieved partial separation of internal versus kinetic energy transfer effects by using a velocity-selected molecular beam. Using two simple models for the kinetic energy dependence of the transfer cross section for a given change in vibrational quantum number, the data from both experiments were used to determine parameters in the models

The atom-molecule reaction D plus H2 yields HD plus H studied by molecular beams

Collisions between deuterium atoms and hydrogen molecules were studied in a modulated crossed beam experiment. The relative signal intensity and the signal phase for the product HD from reactive collisions permitted determination of both the angular distribution and HD mean velocity as a function of angle. From these a relative differential reactive scattering cross section in center-of-mass coordinates was deduced. The experiment indicates that reactively formed HD which has little or no internal excitation departs from the collision anisotropically, with maximum amplitude 180 deg from the direction of the incident D beam in center-of-mass coordinates, which shows that the D-H-H reacting configuration is short-lived compared to its rotation time. Non reactive scattering of D by H2 was used to assign absolute values to the differential reactive scattering cross sections

Transfer of excitation energy from nitrogen molecules to sodium atoms

Transfer of excitation energy from nitrogen molecules to sodium atom

The polarization of Lyman alpha radiation produced by direct excitation of hydrogen atoms by proton impact

Lyman alpha radiation measurement in collision between protons and hydrogen atom

The polarization of Lyman alpha radiation produced in charge transfer collisions between protons and the inert gases

Polarization of Lyman alpha radiation in proton collisions with helium, argon, and neon atom

The Role of Hostile Attributions in the Associations between Child Maltreatment and Reactive and Proactive Aggression

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Aggression, Maltreatment and Trauma on October 8th, 2016, available online: http://www.tandfonline.com/10.1080/10926771.2016.1231148.The present study examined the relations between child maltreatment and reactive and proactive functions of aggression, and whether hostile attribution biases partially accounted for these associations in a sample of 339 college students (mean age = 19; 51% male). Child maltreatment was associated with reactive, but not proactive, aggression, and instrumental hostile attribution biases accounted for this association. Relational hostile attributions were correlated with both reactive and proactive aggression, but did not play a role in the link between child maltreatment and reactive aggression

Links Between Stressful Life Events and Proactive and Reactive Functions of Aggression

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Aggression, Maltreatment and Trauma on June 01, 2017, available online: http://www.tandfonline.com/10.1080/10926771.2017.1322658.Recently, more attention has been devoted to understanding how stressful life events might relate to proactive and reactive aggression. Findings suggest that stressful life events are more strongly linked to reactive, than proactive, aggression; however, it is unclear whether the impact of stressful life events on proactive and reactive aggression might vary as a function of the level of exposure to or type of stressful life event. The current study examined how level of exposure to stressful life events (i.e., witnessed, experienced, and learned about) and stressful life event types (i.e., war zone exposure, sexual victimization, interpersonal violence, and other trauma exposure) related to proactive and reactive aggression. The sample was comprised of 500 undergraduate students (M = 18.96, SD = 1.22, 49.6% male) recruited from a Midwestern university. Findings indicated that all three levels of stressful life event exposure (i.e., experienced, witnesses, and learned) were associated with reactive aggression; however, only witnessed stressful life events were associated with proactive aggression. Clinical implications and future directions are discussed

Machine Learning vs Conventional Analysis Techniques for the Earth’s Magnetic Field Study

Abstract. Current techniques for calculating and generating models used for analyzing the Earth’s magnetic field are laborious and time-consuming. We assert that machine learning can have a significant impact on building magnetic field models more quickly and on various levels of complexity, specifically as it pertains to data cleansing and sorting. Our approach to this problem uses a reverse iterative multi-phase process for data cleansing, in which, initially, the CHAOS-6 model data is examined to determine if machine learning can be used to differentiate between useful data components for spherical harmonics, versus data noise. During this phase, six different machine learning techniques are used and compared: two classification techniques (Convolutional Neural Network (CNN) and Support Vector Classification (SVC)) and four regression techniques (Random Forest Regression (RFR), Support Vector Regression (SVR), Logistic Regression, and Linear Regression). During this initial phase, the focus is on understanding the accuracy of machine learning for model selection and uses relatively clean data. Future phases should include machine learning relevance as it pertains to the massive volume of data received from satellites. Exploring the machine learning capabilities for magnetic field datasets accomplishes 1) faster and more efficient computation when there are millions of rows of data in any given 30-day period, and 2) lowers the propagation of errors that cause some data to be useless in the spherical harmonics computations used in the model generation

Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture

Comparison of Computational-Model and Experimental-Example Trained Neural Networks for Processing Speckled Fringe Patterns

The responses of artificial neural networks to experimental and model-generated inputs are compared for detection of damage in twisted fan blades using electronic holography. The training-set inputs, for this work, are experimentally generated characteristic patterns of the vibrating blades. The outputs are damage-flag indicators or second derivatives of the sensitivity-vector-projected displacement vectors from a finite element model. Artificial neural networks have been trained in the past with computational-model-generated training sets. This approach avoids the difficult inverse calculations traditionally used to compare interference fringes with the models. But the high modeling standards are hard to achieve, even with fan-blade finite-element models