Estimating the modal parameters from multiple measurement setups using a joint state space model

Computing the modal parameters of structural systems often requires processing data from multiple non-simultaneously recorded setups of sensors. These setups share some sensors in common, the so-called reference sensors, which are fixed for all measurements, while the other sensors change their position from one setup to the next. One possibility is to process the setups separately resulting in different modal parameter estimates for each setup. Then, the reference sensors are used to merge or glue the different parts of the mode shapes to obtain global mode shapes, while the natural frequencies and damping ratios are usually averaged. In this paper we present a new state space model that processes all setups at once. The result is that the global mode shapes are obtained automatically, and only a value for the natural frequency and damping ratio of each mode is estimated. We also investigate the estimation of this model using maximum likelihood and the Expectation Maximization algorithm, and apply this technique to simulated and measured data corresponding to different structures.

Measuring cosmological parameters

In this review, the status of measurements of the matter density (Ωm), the vacuum energy density or cosmological constant (ΩΛ), the Hubble constant (H0), and the ages of the oldest measured objects (t0) are summarized. Three independent types of methods for measuring the Hubble constant are considered: the measurement of time delays in multiply imaged quasars, the Sunyaev–Zel’dovich effect in clusters, and Cepheid-based extragalactic distances. Many recent independent dynamical measurements are yielding a low value for the matter density (Ωm ≈ 0.2–0.3). A wide range of Hubble constant measurements appear to be converging in the range of 60–80 km/sec per megaparsec. Areas where future improvements are likely to be made soon are highlighted—in particular, measurements of anisotropies in the cosmic microwave background. Particular attention is paid to sources of systematic error and the assumptions that underlie many of the measurement methods.

Nesting In The Clouds: Evaluating And Predicting Sea Turtle Nesting Beach Parameters From Lidar Data

Humans' desire for knowledge regarding animal species and their interactions with the natural world have spurred centuries of studies. The relatively new development of remote sensing systems using satellite or aircraft-borne sensors has opened up a wide field of research, which unfortunately largely remains dependent on coarse-scale image spatial resolution, particularly for habitat modeling. For habitat-specialized species, such data may not be sufficient to successfully capture the nuances of their preferred areas. Of particular concern are those species for which topographic feature attributes are a main limiting factor for habitat use. Coarse spatial resolution data can smooth over details that may be essential for habitat characterization. Three studies focusing on sea turtle nesting beaches were completed to serve as an example of how topography can be a main deciding factor for certain species. Light Detection and Ranging (LiDAR) data were used to illustrate that fine spatial scale data can provide information not readily captured by either field work or coarser spatial scale sources. The variables extracted from the LiDAR data could successfully model nesting density for loggerhead (Caretta caretta), green (Chelonia mydas), and leatherback (Dermochelys coriacea) sea turtle species using morphological beach characteristics, highlight beach changes over time and their correlations with nesting success, and provide comparisons for nesting density models across large geographic areas. Comparisons between the LiDAR dataset and other digital elevation models (DEMs) confirmed that fine spatial scale data sources provide more similar habitat information than those with coarser spatial scales. Although these studies focused solely on sea turtles, the underlying principles are applicable for many other wildlife species whose range and behavior may be influenced by topographic features.

Improvements of methods for determining pre-crash parameters from skid marks. Final report.

Mode of access: Internet.

CRASH III model improvements: derivation of new side stiffness parameters from crash tests. Volume 2. Final report.

Mode of access: Internet.

Estimation of pharmacokinetic parameters from non-compartmental variables using Microsoft Excel((R))

This study was conducted to develop a method, termed 'back analysis (BA)', for converting non-compartmental variables to compartment model dependent pharmacokinetic parameters for both one- and two-compartment models. A Microsoft Excel((R)) spreadsheet was implemented with the use of Solver((R)) and visual basic functions. The performance of the BA method in estimating pharmacokinetic parameter values was evaluated by comparing the parameter values obtained to a standard modelling software program, NONMEM, using simulated data. The results show that the BA method was reasonably precise and provided low bias in estimating fixed and random effect parameters for both one- and two-compartment models. The pharmacokinetic parameters estimated from the BA method were similar to those of NONMEM estimation.

Under-ice fauna, sea-ice concentration, sea-ice thickness and under-ice water parameters from the Eurasian central Arctic Ocean during POLARSTERN cruise ARK-XXVII/3 (PS80) in August-September 2012

Arctic sea-ice decline is expected to have a significant impact on Arctic marine ecosystems. Ice-associated fauna play a key role in this context because they constitute a unique part of Arctic biodiversity and transmit carbon from sea-ice algae into pelagic and benthic food webs. Our study presents the first regional-scale record of under-ice faunal distribution and the environmental characteristics of under-ice habitats throughout the Eurasian Basin. Sampling was conducted with a Surface and Under-Ice Trawl, equipped with a sensor array recording ice thickness and other physical parameters during trawling. We identified 2 environmental regimes, broadly coherent with the Nansen and Amundsen Basins. The Nansen Basin regime was distinguished from the Amundsen Basin regime by heavier sea-ice conditions, higher surface salinities and higher nitrate + nitrite concentrations. We found a diverse (28 species) under-ice community throughout the Eurasian Basin. Change in community structure reflected differences in the relative contribution of abundant species. Copepods (Calanus hyperboreus and C. glacialis) dominated in the Nansen Basin regime. In the Amundsen Basin regime, amphipods (Apherusa glacialis, Themisto libellula) dominated. Polar cod Boreogadus saida was present throughout the sampling area. Abrupt changes from a dominance of ice-associated amphipods at ice-covered stations to a dominance of pelagic amphipods (T. libellula) at nearby ice-free stations emphasised the decisive influence of sea ice on small-scale patterns in the surface-layer community. The observed response in community composition to different environmental regimes indicates potential long-term alterations in Arctic marine ecosystems as the Arctic Ocean continues to change.

Estimativas de parâmetros cosmológicos para o Dark Energy Survey

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)