No abstract

The introduction of multidimensional NMR spectroscopy was a breakthrough in biological NMR methodology because it allowed the unequivocal correlation of different spin states of the system. The introduction of large pressure perturbations in the corresponding radio frequency (RF) pulse sequences adds an extra structural dimension into these experiments. We have developed a microprocessor-controlled pressure jump unit that is able to introduce fast, strong pressure changes at any point in the pulse sequences. Repetitive pressure changes of 80 MPa in the sample tube are thus feasible in less than 30 ms. Two general forms of these experiments are proposed here, the pressure perturbation transient state spectroscopy (PPTSS) and the pressure perturbation state correlation spectroscopy (PPSCS). PPTSS can be used to measure the rate constants and the activation energies and activation volumes for the transition between different conformational states including the folded and unfolded state of proteins, for polymerization–depolymerization processes, and for ligand binding at atomic resolution. PPSCS spectroscopy correlates the NMR parameters of different pressure-induced states of the system, thus allowing the measurement of properties of a given pressure induced state such as a folding intermediate in a different state, for example, the folded state. Selected examples for PPTSS and PPSCS spectroscopy are presented in this Article.

Nonparametric Bayesian inference has seen a rapid growth over the last decade but only few nonparametric Bayesian approaches to time series analysis have been developed. Most existing approaches use Whittle's likelihood for Bayesian modelling of the spectral density as the main nonparametric characteristic of stationary time series. It is known that the loss of efficiency using Whittle's likelihood can be substantial. On the other hand, parametric methods are more powerful than nonparametric methods if the observed time series is close to the considered model class but fail if the model is misspecified. Therefore, we suggest a nonparametric correction of a parametric likelihood that takes advantage of the efficiency of parametric models while mitigating sensitivities through a nonparametric amendment. We use a nonparametric Bernstein polynomial prior on the spectral density with weights induced by a Dirichlet process and prove posterior consistency for Gaussian stationary time series. Bayesian posterior computations are implemented via an MH-within-Gibbs sampler and the performance of the nonparametrically corrected likelihood for Gaussian time series is illustrated in a simulation study and in three astronomy applications, including estimating the spectral density of gravitational wave data from the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO).

The background-oriented schlieren technique was used to visualize the blade-tip vortices of a Eurocopter AS532UL Cougar helicopter in maneuvering flight. The test program covered a large part of the flight envelope, including maneuvers such as hover flight, fast forward flight, flare maneuvers, and high-speed turns. For selected flight conditions, the aerodynamic results are presented here. It is shown that, with the reference-free background-oriented schlieren method, the detection of vortex filaments up to vortex ages of ψ v 540 deg is possible. The visualization of the vortex system is used to detect aerodynamic phenomena such as blade-vortex interactions, vortex-airframe interactions, and the occurrence of smooth sinuous disturbances. A detailed description of the applied reference-free background-oriented schlieren setup is given, and the suitability of different natural backgrounds for the background-oriented schlieren method is analyzed. Nomenclatureof vortex above tip path plane, m I = intensity count K = Gladstone-Dale constant, m 3 · kg −1 M = optical magnification factor Ma = Mach number n = refractive index of air N b = number of rotor blades R = rotor radius, m T = rotor thrust, N t = time, s u, v, Δy = image displacements, m x, y, z = coordinates, m Z B= distance background -camera lens, m Z D = distance background-phase object, m Z i = distance camera lens-camera sensor, m ϵ y = deflection angle, rad ρ = density of air, kg · m −3 σ = solidity; N b · c · πR −1 Ψ = azimuth; Ωt, rad ψ v = vortex age, rad Ω = rotor rotational frequency, rad · s −1 ω z = rotation of the image displacement; ∂v∕∂x − ∂u∕∂y

Time series data, i.e., temporally ordered data, is routinely collected and analysed in in many fields of natural science, economy, technology and medicine, where it is of importance to verify the assumption of stochastic stationarity prior to modeling the data. Nonstationarities in the data are often attributed to structural changes with segments between adjacent change-points being approximately stationary. A particularly important, and thus widely studied, problem in statistics and signal processing is to detect changes in the mean at unknown time points. In this paper, we present the R package mosum, which implements elegant and mathematically well-justified procedures for the multiple mean change problem using the moving sum statistics.