The REFLEX Galaxy Cluster Survey VII: Omega_m and sigma_8 from cluster abundance and large-scale clustering

For the first time the large-scale clustering and the mean abundance of galaxy clusters are analysed simultaneously to get precise constraints on the normalized cosmic matter density $\Omega_m$ and the linear theory RMS fluctuations in mass $\sigma_8$. A self-consistent likelihood analysis is described which combines, in a natural and optimal manner, a battery of sensitive cosmological tests where observational data are represented by the (Karhunen-Lo\'{e}ve) eigenvectors of the sample correlation matrix. This method breaks the degeneracy between $\Omega_m$ and $\sigma_8$. The cosmological tests are performed with the ROSAT ESO Flux-Limited X-ray (REFLEX) cluster sample. The computations assume cosmologically flat geometries and a non-evolving cluster population mainly over the redshift range $0<z<0.3$. The REFLEX sample gives the cosmological constraints and their $1\sigma$ random errors of $\Omega_m = 0.341 ^{+0.031}_{-0.029}$ and $\sigma_8 = 0.711 ^{+0.039}_{-0.031}$. Possible systematic errors are evaluated by estimating the effects of uncertainties in the value of the Hubble constant, the baryon density, the spectral slope of the initial scalar fluctuations, the mass/X-ray luminosity relation and its intrinsic scatter, the biasing scheme, and the cluster mass density profile. All these contributions sum up to total systematic errors of $\sigma_{\Omega_m}=^{+0.087}_{-0.071}$ and $\sigma_{\sigma_8}=^{+0.120}_{-0.162}$.Comment: 10 pages, 7 figures, accepted for publication in Astronomy and Astrophysic

Essays in time series econometrics

This cumulative dissertation consists of three self-contained papers all contributing to the cointegrating regression literature. The first chapter is devoted to classical linear cointegrating regressions, i.e., regressions that contain integrated processes as regressors. It combines traditional and self-normalized Wald-type test statistics with a vector autoregressive sieve bootstrap to reduce size distortions of hypothesis tests on the cointegrating vector. The second chapter focuses on panels of cointegrating polynomial regressions, i.e., panels of regressions that include an integrated process and its powers as regressors. It derives the asymptotic properties of a group-mean fully modified OLS estimator and hypothesis tests based upon it in a fixed cross-section and large time series dimension. The third chapter is devoted to testing for a cointegrating relationship between a fixed number of integrated processes. In particular, it derives asymptotic theory for an existing nonparametric variance ratio unit root test (originally proposed to test for an unit root in an observed univariate time series) when applied to regression residuals

Experimental and Analytical Investigation on the Nonlinear Behaviors of Glulam Moment-Resisting Joints Composed of Inclined Self-Tapping Screws with Steel Side Plates

Glulam moment-resisting joint composed of inclined self-tapping-screws (STS) with steel side plates were designed and its nonlinear moment-rotational skeleton curve was predicted by taking nonlinear load(P)-deformation(u) relationships of all moment-resisting components into considerations within step-wise linear calculation process. P-u relationships of all moment-resisting components were estimated by the fundamental shear joint tests or appropriate empirical relationships and they were approximated by the tetra polygonal-line curves or bi-linear curves. The extended Normalized Characteristic Loop (NCL) model, which was originally developed for RC construction, was applied to describe the hysteresis loops. For predicting failure load, the design equations for a mechanical joint loaded with inclination to the grain direction were applied. Three replications of T-shaped beam-column joint specimens were fabricated using Canadian spruce glulam beam and column. Connections of steel plates to glulam members were all composed of full-threaded inclined-STS. Static push-pull cyclic loading tests were conducted and observed behaviors were compared with step-wise linear calculation results. Agreements between predicted nonlinear behaviors and observed ones were good on the whole

Accurate and (almost) tuning parameter free inference in cointegrating regressions

Tuning parameter choices complicate statistical inference in cointegrating regressions and affect finite sample distributions of test statistics. As commonly used asymptotic theory fails to capture these effects, tests often suffer from severe size distortions. We propose a novel self-normalized test statistic for general linear hypotheses, which avoids the choice of tuning parameters. Its limiting null distributions is nonstandard, but simulating asymptotically valid critical values is straightforward. To further improve the performance of the test in small to medium samples, we employ the vector autoregressive sieve bootstrap to construct critical values. To show its consistency, we establish a bootstrap invariance principle result under conditions that go beyond the assumptions commonly imposed in the literature. Simulation results demonstrate that our new test outperforms competing approaches, as it has good power properties and is considerably less prone to size distortions

Comparison Between Single and Combined Clinical Postural Stability Tests in Individuals With and Without Chronic Ankle Instability

Objective: To determine if a single or/and combined clinical tests match group membership based on self-reported ankle function. Design: Cross-sectional. Setting: Biomechanics Laboratory. Participants: From participants, 58 meeting inclusion/exclusion criteria were divided into a chronic ankle instability (CAI) group (n = 25) who reported ≤25 on the Cumberland Ankle Instability Tool (CAIT) and a history of moderate–severe ankle sprain(s) and a control group (n = 33) who reported ≥29 on the CAIT and no history of ankle sprain(s). Interventions: Participants completed the following clinical tests: Foot Lift Test (FLT), the Star Excursion Balance Test (SEBT), the Single-Leg Hop Test (SLHT), and the Time in Balance Test (TIB) in a randomized order. A linear regression model was applied to determine measures that matched ankle group membership. Main Outcome Measures: The mean of SEBT reach distance was normalized to percentage leg length. The mean of number of errors in the FLT was recorded. The SLHT and TIB were reported as time in seconds, and the means were calculated. Results: The most parsimonious combination of tests (SLHT and SEBT) resulted in correctly matching 70.69% (41/58) of participants into groups, which was significantly better than chance. The multiple correlation coefficients (R value) for combining the SLHT and SEBT was 0.39. Conclusions: Using SLHT and SEBT resulted in improved recognition of participants designated into the CAI or control groups. Self-report perception of ankle function provides limited information for clinicians and researchers. Using multiple clinical function tests may be more helpful in determining deficits and intervention effectiveness

Modeling of asphalt durability and self-healing with discrete particles method

Asphalt is an important road paving material. Besides an acceptable price, durability, surface conditions (like roughening and evenness), age-, weather- and traffic-induced failures and degradation are relevant aspects. In the professional road-engineering branch empirical models are used to describe the mechanical behaviour of the material and to address large-scale problems for road distress phenomena like rutting, ravelling, cracking and roughness. The mesoscopic granular nature of asphalt and the mechanics of the bitumen layer between the particles are only partly involved in this kind of approach. The discrete particle method is a modern tool that allows for arbitrary (self- )organization of the asphalt meso-structure and for rearrangements due to compaction and cyclic loading. This is of utmost importance for asphalt during the construction phase and the usage period, in forecasting the relevant distress phenomena and understand their origin on the grain-, contact-, or molecular scales. Contact models that involve viscoelasticity, plasticity, friction and roughness are state-of-the art in fields like particle technology and can now be modified for asphalt and validated experimentally on small samples. The ultimate goal is then to derive micro- and meso-based constitutive models that can be applied to model behaviour of asphalt pavements on the larger macroscale. Using the new contact models, damage and crack formation in asphalt and their propagation can be modelled, as well as compaction. Furthermore, the possibility to trigger self-healing in the material can be investigated from a micro-mechanical point of view