U.S. State Building and the Second Amendment

This dissertation used a comparative case study strategy employing a mixed methods thematic content analysis approach1 to explore U.S. government support for Second Amendment freedoms as compared to other freedoms in the U.S. Bill of Rights in American-led state-building projects in Cuba (1898-1901), Germany (1945-1949), and Iraq (2003-2005). The dissertation tested for Republican and Democratic political party support regarding Second Amendment freedoms in U.S. state-building projects. Findings from the three case studies showed that the American government did not support individual arms rights in its state-building efforts as it did with the other nine Bill of Rights freedoms. Findings showed support by the Republican and Democratic parties for all Bill of Rights freedoms with the exception of Second Amendment freedoms. ________________ 1Researchers have used other names, such as summative content analysis of text, for the identification of themes or major ideas in documents; see John Creswell, Research Design: Qualitative, Quantitative and Mixed-Methods Approaches, 3rd ed. (Thousand Oaks, CA: Sage Publications: 2009); John Creswell and Vicki Clark, Designing and Conducting Mixed Methods Research (Thousand Oaks, CA: Sage, 2007), 67- 69, for more information on the nested or embedded approach and page 12 for more information on defining mixed methods using content analysis. Although this dissertation is focusing on the qualitative nature of the evidence, quantitative steps of data collection and analysis, which is historically linked to content analysis, will be included. The quantitative data for this dissertation will be descriptive in nature with no regression analysis

A depletable pool of adenosine in area CA1 of the rat hippocampus

Adenosine plays a major modulatory and neuroprotective role in the mammalian CNS. During cerebral metabolic stress, such as hypoxia or ischemia, the increase in extracellular adenosine inhibits excitatory synaptic transmission onto vulnerable neurons via presynaptic adenosine A1 receptors, thereby reducing the activation of postsynaptic glutamate receptors. Using a combination of extracellular and whole-cell recordings in the CA1 region of hippocampal slices from 12- to 24-d-old rats, we have found that this protective depression of synaptic transmission weakens with repeated exposure to hypoxia, thereby allowing potentially damaging excitation to both persist for longer during oxygen deprivation and recover more rapidly on reoxygenation. This phenomenon is unlikely to involve A1 receptor desensitization or impaired nucleoside transport. Instead, by using the selective A1 antagonist 8-cyclopentyl-1,3-dipropylxanthine and a novel adenosine sensor, we demonstrate that adenosine production is reduced with repeated episodes of hypoxia. Furthermore, this adenosine depletion can be reversed at least partially either by the application of exogenous adenosine, but not by a stable A1 agonist, N6-cyclopentyladenosine, or by endogenous means by prolonged (2 hr) recovery between hypoxic episodes. Given the vital neuroprotective role of adenosine, these findings suggest that depletion of adenosine may underlie the increased neuronal vulnerability to repetitive or secondary hypoxia/ischemia in cerebrovascular disease and head injury

Assessment of Evolving Conjunction Risk for Small Satellite Missions

This study presents an assessment of evolving conjunction risk for small satellite missions (5U or smaller) by using the suite of LeoLabs\u27 products. The aim is to (1) quantify the growth of small satellites population in the low Earth orbit (LEO), (2) assess the impact of on-orbit break-up events and small debris (sub-10 cm) objects on small satellite missions, and (3) present an optimal risk mitigation timeline for small satellite missions, based on conjunction alerts issued in 2023. The global network of S-band radars built and operated by LeoLabs provides a 24/7 data feed to power this assessment and help identify the evolution of this risk. The ability to access this enhances operational safety. Thus, a statistical assessment of the risk posed and quantification of the evolution of this risk over mission timeline is important. Further, understanding the optimal risk mitigation timeline for small satellite missions is critical as these missions have limited on-board resources and hence, knowing the severity of the risk and taking appropriate and timely mitigative action (attitude change or thrusting \u27n\u27 days before time of closest approach, i.e., TCA) is paramount. Although, the mitigative action (the level and duration of thrusting or the amount of attitude change) itself is not studied as these specifics often vary based on the event type, the optimal timeline (as in how many days before TCA?) of this mitigative action is reviewed by studying the conjunction events encountered by small satellites

Multiple Time Scales in Diffraction Measurements of Diffusive Surface Relaxation

We grew SrTiO3 on SrTiO3 (001) by pulsed laser deposition, using x-ray scattering to monitor the growth in real time. The time-resolved small angle scattering exhibits a well-defined length scale associated with the spacing between unit cell high surface features. This length scale imposes a discrete spectrum of Fourier components and rate constants upon the diffusion equation solution, evident in multiple exponential relaxation of the "anti-Bragg" diffracted intensity. An Arrhenius analysis of measured rate constants confirms that they originate from a single activation energy.Comment: 4 pages, 3 figure

A wavelet transform algorithm for peak detection and application to powder x-ray diffraction data

Peak detection is ubiquitous in the analysis of spectral data. While many noise-filtering algorithms and peak identification algorithms have been developed, recent work [P. Du, W. Kibbe, and S. Lin, Bioinformatics 22, 2059 (2006); A. Wee, D. Grayden, Y. Zhu, K. Petkovic-Duran, and D. Smith, Electrophoresis 29, 4215 (2008)] has demonstrated that both of these tasks are efficiently performed through analysis of the wavelet transform of the data. In this paper, we present a wavelet-based peak detection algorithm with user-defined parameters that can be readily applied to the application of any spectral data. Particular attention is given to the algorithm's resolution of overlapping peaks. The algorithm is implemented for the analysis of powder diffraction data, and successful detection of Bragg peaks is demonstrated for both low signal-to-noise data from theta–theta diffraction of nanoparticles and combinatorial x-ray diffraction data from a composition spread thin film. These datasets have different types of background signals which are effectively removed in the wavelet-based method, and the results demonstrate that the algorithm provides a robust method for automated peak detection

A wavelet transform algorithm for peak detection and application to powder x-ray diffraction data

Peak detection is ubiquitous in the analysis of spectral data. While many noise-filtering algorithms and peak identification algorithms have been developed, recent work [P. Du, W. Kibbe, and S. Lin, Bioinformatics 22, 2059 (2006); A. Wee, D. Grayden, Y. Zhu, K. Petkovic-Duran, and D. Smith, Electrophoresis 29, 4215 (2008)] has demonstrated that both of these tasks are efficiently performed through analysis of the wavelet transform of the data. In this paper, we present a wavelet-based peak detection algorithm with user-defined parameters that can be readily applied to the application of any spectral data. Particular attention is given to the algorithm's resolution of overlapping peaks. The algorithm is implemented for the analysis of powder diffraction data, and successful detection of Bragg peaks is demonstrated for both low signal-to-noise data from theta–theta diffraction of nanoparticles and combinatorial x-ray diffraction data from a composition spread thin film. These datasets have different types of background signals which are effectively removed in the wavelet-based method, and the results demonstrate that the algorithm provides a robust method for automated peak detection

Observed Effects of a Changing Step-Edge Density on Thin-Film Growth Dynamics

We grew SrTiO3 on SrTiO3 [001] by pulsed laser deposition, while observing x-ray diffraction at the (0 0 .5) position. The drop dI in the x-ray intensity following a laser pulse contains information about plume-surface interactions. Kinematic theory predicts dI/I = -4sigma(1-sigma), so that dI/I depends only on the amount of deposited material sigma. In contrast, we observed experimentally that |dI/I| < 4sigma(1-sigma), and that dI/I depends on the phase of x-ray growth oscillations. The combined results suggest a fast smoothing mechanism that depends on surface step-edge density.Comment: 4 figure

X-ray scattering from surfaces: discrete and continuous components of roughness

Incoherent surface scattering yields a statistical description of the surface, due to the ensemble averaging over many independently sampled volumes. Depending on the state of the surface and direction of the scattering vector relative to the surface normal, the height distribution is discrete, continuous, or a combination of the two. We present a treatment for the influence of multimodal surface height distributions on Crystal Truncation Rod scattering. The effects of a multimodal height distribution are especially evident during in situ monitoring of layer-by-layer thin-film growth via Pulsed Laser Deposition. We model the total height distribution as a convolution of discrete and continuous components, resulting in a broadly applicable parameterization of surface roughness which can be applied to other scattering probes, such as electrons and neutrons. Convolution of such distributions could potentially be applied to interface or chemical scattering. Here we find that this analysis describes accurately our experimental studies of SrTiO3 annealing and homoepitaxial growth.Comment: 15 pages, 7 figure

Scanning AC nanocalorimetry combined with in-situ x-ray diffraction

Micromachined nanocalorimetry sensors have shown excellent performance for high-temperature and high-scanning rate calorimetry measurements. Here, we combine scanning AC nanocalorimetry with in-situ x-ray diffraction (XRD) to facilitate interpretation of the calorimetry measurements. Time-resolved XRD during in-situ operation of nanocalorimetry sensors using intense, high-energy synchrotron radiation allows unprecedented characterization of thermal and structural material properties. We demonstrate this experiment with detailed characterization of the melting and solidification of elemental Bi, In, and Sn thin-film samples, using heating and cooling rates up to 300 K/s. Our experiments show that the solidification process is distinctly different for each of the three samples. The experiments are performed using a combinatorial device that contains an array of individually addressable nanocalorimetry sensors. Combined with XRD, this device creates a new platform for high-throughput mapping of the composition dependence of solid-state reactions and phase transformations