Low-temperature thermochronology and thermokinematic modeling of deformation, exhumation, and development of topography in the central Southern Alps, New Zealand

Apatite and zircon (U-Th)/He and fission track ages were obtained from ridge transects across the central Southern Alps, New Zealand. Interpretation of local profiles is difficult because relationships between ages and topography or local faults are complex and the data contain large uncertainties, with poor reproducibility between sample duplicates. Data do form regional patterns, however, consistent with theoretical systematics and corroborating previous observations: young Neogene ages occur immediately southeast of the Alpine Fault (the main plate boundary structure on which rocks are exhumed); partially reset ages occur in the central Southern Alps; and older Mesozoic ages occur further toward the southeast. Zircon apparent ages are older than apatite apparent ages for the equivalent method. Three-dimensional thermokinematic modeling of plate convergence incorporates advection of the upper Pacific plate along a low-angle detachment then up an Alpine Fault ramp, adopting a generally accepted tectonic scenario for the Southern Alps. The modeling incorporates heat flow, evolving topography, and the detailed kinetics of different thermochronometric systems and explains both complex local variations and regional patterns. Inclusion of the effects of radiation damage on He diffusion in detrital apatite is shown to have dramatic effects on results. Geometric and velocity parameters are tuned to fit model ages to observed data. Best fit is achieved at 9 mm a−1 plate convergence, with Pacific plate delamination on a gentle 10°SE dipping detachment and more rapid uplift on a 45–60° dipping Alpine Fault ramp from 15 km depth. Thermokinematic modeling suggests dip-slip motion on reverse faults within the Southern Alps should be highest ∼22 km from the Alpine Fault and much lower toward the southeast

Engineering design applications of surrogate-assisted optimization techniques

The construction of models aimed at learning the behaviour of a system whose responses to inputs are expensive to measure is a branch of statistical science that has been around for a very long time. Geostatistics has pioneered a drive over the last half century towards a better understanding of the accuracy of such ‘surrogate’ models of the expensive function. Of particular interest to us here are some of the even more recent advances related to exploiting such formulations in an optimization context. While the classic goal of the modelling process has been to achieve a uniform prediction accuracy across the domain, an economical optimization process may aim to bias the distribution of the learning budget towards promising basins of attraction. This can only happen, of course, at the expense of the global exploration of the space and thus finding the best balance may be viewed as an optimization problem in itself. We examine here a selection of the state of-the-art solutions to this type of balancing exercise through the prism of several simple, illustrative problems, followed by two ‘real world’ applications: the design of a regional airliner wing and the multi-objective search for a low environmental impact hous

Self-organizing input space for control of structures

We propose a novel type of neural networks for structural control, which comprises an adaptive input space. This feature is purposefully designed for sequential input selection during adaptive identification and control of nonlinear systems, which allows the input space to be organized dynamically, while the excitation is occurring. The neural network has the main advantages of (1) automating the input selection process for time series that are not known a priori; (2) adapting the representation to nonstationarities; and (3) using limited observations. The algorithm designed for the adaptive input space assumes local quasi-stationarity of the time series, and embeds local maps sequentially in a delay vector using the embedding theorem. The input space of the representation, which in our case is a wavelet neural network, is subsequently updated. We demonstrate that the neural net has the potential to significantly improve convergence of a black-box model in adaptive tracking of a nonlinear system. Its performance is further assessed in a full-scale simulation of an existing civil structure subjected to nonstationary excitations (wind and earthquakes), and shows the superiority of the proposed method

Ms2lda.org: web-based topic modelling for substructure discovery in mass spectrometry

Motivation: We recently published MS2LDA, a method for the decomposition of sets of molecular fragment data derived from large metabolomics experiments. To make the method more widely available to the community, here we present ms2lda.org, a web application that allows users to upload their data, run MS2LDA analyses and explore the results through interactive visualisations. Results: Ms2lda.org takes tandem mass spectrometry data in many standard formats and allows the user to infer the sets of fragment and neutral loss features that co-occur together (Mass2Motifs). As an alternative workflow, the user can also decompose a dataset onto predefined Mass2Motifs. This is accomplished through the web interface or programmatically from our web service

Air gap influence on the vibro-acoustic response of Solar Arrays during launch

One of the primary elements on the space missions is the electrical power subsystem, for which the critical component is the solar array. The behaviour of these elements during the ascent phase of the launch is critical for avoiding damages on the solar panels, which are the primary source of energy for the satellite in its final configuration. The vibro-acoustic response to the sound pressure depends on the solar array size, mass, stiffness and gap thickness. The stowed configuration of the solar array consists of a multiple system composed of structural elements and the air layers between panels. The effect of the air between panels on the behaviour of the system affects the frequency response of the system not only modifying the natural frequencies of the wings but also as interaction path between the wings of the array. The usual methods to analyze the vibro-acoustic response of structures are the FE and BE methods for the low frequency range and the SEA formulation for the high frequency range. The main issue in the latter method is, on one hand, selecting the appropriate subsystems, and, on the other, identifying the parameters of the energetic system: the internal and coupling loss factors. From the experimental point of view, the subsystems parameters can be identified by exciting each subsystem and measuring the energy of all the subsystems composing the Solar Array. Although theoretically possible, in practice it is difficult to apply loads on the air gaps. To analyse this situation, two different approaches can be studied depending on whether the air gaps between the panels are included explicitly in the problem or not. For a particular case of a solar array of three wings in stowed configuration both modelling philosophies are compared. This stowed configuration of a three wing solar arrays in stowed configuration has been tested in an acoustic chamber. The measured data on the solar wings allows, in general, determining the loss factors of the configuration. The paper presents a test description and measurements on the structure, in terms of the acceleration power spectral density. Finally, the performance of each modelling technique has been evaluated by comparison between simulations with experimental results on a spacecraft solar array and the influence on the apparent properties of the system in terms of the SEA loss factors has been analyse

Multivariate Methods for Monitoring Structural Change

Detection of structural change is a critical empirical activity, but continuous 'monitoring' of series, for structural changes in real time, raises well-known econometric issues that have been explored in a single series context. If multiple series co-break then it is possible that simultaneous examination of a set of series helps identify changes with higher probability or more rapidly than when series are examined on a case-by-case basis. Some asymptotic theory is developed for maximum and average CUSUM detection tests. Monte Carlo experiments suggest that these both provide an improvement in detection relative to a univariate detector over a wide range of experimental parameters, given a sufficiently large number of co-breaking series. This is robust to a cross-sectional correlation in the errors (a factor structure) and heterogeneity in the break dates. We apply the test to a panel of UK price indices.Monitoring, Structural change, Panel, CUSUM, Fluctuation test

Incidental computed tomography diagnosis of a rare triad consisting of absence of coronary sinus, persistent left superior vena cava, and scimitar syndrome

We report a case of an unusual congenital triad consisting of absence of coronary sinus, persistent left superior vena cava and scimitar syndrome incidentally found in a CT-scan performed on a female complaining of exertional dyspnea

Ground State Energy of the One-Dimensional Discrete Random Schr\"{o}dinger Operator with Bernoulli Potential

In this paper, we show the that the ground state energy of the one dimensional Discrete Random Schroedinger Operator with Bernoulli Potential is controlled asymptotically as the system size N goes to infinity by the random variable \ell_N, the length the longest consecutive sequence of sites on the lattice with potential equal to zero. Specifically, we will show that for almost every realization of the potential the ground state energy behaves asymptotically as $\frac{\pi^2}{\ell_N+1)^2}$ in the sense that the ratio of the quantities goes to one

The financial benefits of various catastrophic failure prevention strategies in a wind farm: Two market studies (UK-Spain)

Operation of wind farms is driven by the overall aim of minimising costs while maximising energy sales. However, in certain circumstances investments are required to guarantee safe operation and survival of an asset. In this paper, we discuss the merits of various catastrophic failure prevention strategies in a Spanish wind farm. The wind farm operator was required to replace blades in two phases: temporary and final repair. We analyse the power performance of the turbine in the different states and investigate four scenarios with different timing of temporary and final repair during one year. The financial consequences of the scenarios are compared with a baseline by using a discounted cash flow analysis that considers the wholesale electricity market selling prices and interest rates. A comparison with the UK electricity market is conducted to highlight differences in the rate of return in the two countries

Statistical evaluation of SCADA data for wind turbine condition monitoring and farm assessment

Operational data from wind farms is crucial for wind turbine condition monitoring and performance assessment. In this paper, we analyse three wind farms with the aim to monitor environmental and operational conditions that might result in underperformance or failures. The assessment includes a simple wind speed characterisation and wake analysis. The evolution of statistical parameters is used to identify anomalous turbine behaviour. In total, 88 turbines and 12 failures are analysed, covering different component failures. Notwithstanding the short period of data available, several operational parameters are found to deviate from the farm trend in some turbines affected by failures. As a result, some parameters show better monitoring capabilities than others, for the detection of certain failures. However, the limitations of SCADA statistics are also shown as not all failures showed anomalies in the observed parameters