Latent degradation indicators estimation and prediction : a Monte Carlo approach

Asset health inspections can produce two types of indicators: (1) direct indicators (e.g. the thickness of a brake pad, and the crack depth on a gear) which directly relate to a failure mechanism; and (2) indirect indicators (e.g. the indicators extracted from vibration signals and oil analysis data) which can only partially reveal a failure mechanism. While direct indicators enable more precise references to asset health condition, they are often more difficult to obtain than indirect indicators. The state space model provides an efficient approach to estimating direct indicators by using indirect indicators. However, existing state space models to estimate direct indicators largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires fixed inspection intervals. The discrete state assumption entails discretising continuous degradation indicators, which often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This paper proposes a state space model without these assumptions. Monte Carlo-based algorithms are developed to estimate the model parameters and the remaining useful life. These algorithms are evaluated for performance using numerical simulations through MATLAB. The result shows that both the parameters and the remaining useful life are estimated accurately. Finally, the new state space model is used to process vibration and crack depth data from an accelerated test of a gearbox. During this application, the new state space model shows a better fitness result than the state space model with linear and Gaussian assumption.

Remote monitoring of outdoor performance of low scale dye sensitized solar cells for nanosensors nodes

This is the first outdoor test of small-scale dye sensitized solar cells (DSC) powering a stand-alone nanosensor node. A solar cell test station (SCTS) has been developed using standard DSC to power a gas nanosensor, a radio transmitter, and the control electronics (CE) for battery charging. The station is remotely monitored through wired (Ethernet cable) or wireless connection (radio transmitter) in order to evaluate in real time the performance of the solar cells and devices under different weather conditions. The 408 cm2 active surface module produces enough energy to power a gas nanosensor and a radio transmitter during the day and part of the night. Also, by using a programmable load we keep the system working on the maximum power point (MPP) quantifying the total energy generated and stored in a battery. These experiments provide useful data for future outdoor applications such as nanosensor networks.

Poly[di-mu-aqua-bis(mu-2-amino-4-nitrobenzoato)dicaesium]

In the structure of title compound [Cs2(C7H5N2O4)2(H2O)2]n the asymmetric unit comprises two independent and different Cs centres, one nine-coordinate, the other seven coordinate, with both having irregular stereochemistry. The CsO9 coordination comprises oxygen donors from three bridging water molecules, one of which is doubly bridging, three from carboxylate groups, and three from nitro groups, of which two are bidentate chelate bridging. The CsO6N coordination comprises the two bridging water molecules, one amine N donor, one carboxyl O donor and four O donors from nitro groups (two from the chelate bridges). The extension of the dimeric unit gives a two-dimensional polymeric structure which is stabilized by both intra- and intermolecular amine N-H...O and water O-H...O hydrogen bonds to carboxyl O acceptors, as well as inter-ring pi-pi interactions [minimum ring centroid separation, 3.4172(15)A].

A Python package for Bayesian estimation using Markov Chain Monte Carlo

Markov chain Monte Carlo (MCMC) estimation provides a solution to the complex integration problems that are faced in the Bayesian analysis of statistical problems. The implementation of MCMC algorithms is, however, code intensive and time consuming. We have developed a Python package, which is called PyMCMC, that aids in the construction of MCMC samplers and helps to substantially reduce the likelihood of coding error, as well as aid in the minimisation of repetitive code. PyMCMC contains classes for Gibbs, Metropolis Hastings, independent Metropolis Hastings, random walk Metropolis Hastings, orientational bias Monte Carlo and slice samplers as well as specific modules for common models such as a module for Bayesian regression analysis. PyMCMC is straightforward to optimise, taking advantage of the Python libraries Numpy and Scipy, as well as being readily extensible with C or Fortran.

[P,P-Di-tert-butyl-N-trimethylsilyl- P-(trimethylsilylamino)phosphineimidato-N,N]bis(pyridine-N)- lithium(I)

In the title compound, [Li(C14H36N2PSi2)(C5H5N)2], the bulky chelating monoanionic P,P-di-tert-butyl-N-trimethylsilyl-P-(trimethylsilylamino)phosphine imidate ligand and two pyridine ligands bind to Li in a pseudo-tetrahedral arrangement with twofold symmetry. The Li-N [phosphine]distance is 2.048 (5) Å, while the LiP distance is 2.520 (6) Å

Stochastic simulation for spatial modelling of dynamic process in a living cell

One of the fundamental motivations underlying computational cell biology is to gain insight into the complicated dynamical processes taking place, for example, on the plasma membrane or in the cytosol of a cell. These processes are often so complicated that purely temporal mathematical models cannot adequately capture the complex chemical kinetics and transport processes of, for example, proteins or vesicles. On the other hand, spatial models such as Monte Carlo approaches can have very large computational overheads. This chapter gives an overview of the state of the art in the development of stochastic simulation techniques for the spatial modelling of dynamic processes in a living cell.

A sequential Monte Carlo approach to design for population pharmacokinetics studies

Here we present a sequential Monte Carlo approach that can be used to find optimal designs. Our focus is on the design of phase III clinical trials where the derivation of sampling windows is required, along with the optimal sampling schedule. The search is conducted via a particle filter which traverses a sequence of target distributions artificially constructed via an annealed utility. The algorithm derives a catalogue of highly efficient designs which, not only contain the optimal, but can also be used to derive sampling windows. We demonstrate our approach by designing a hypothetical phase III clinical trial.

Mechanism of interaction of hydrocalumites (Ca/Al-LDH) with methyl orange and acidic scarlet GR

The development of new materials for water purification is of universal importance. Among these types of materials are layered double hydroxides (LDHs). Non-ionic materials pose a significant problem as pollutants. The interaction of methyl orange (MO) and acidic scarlet GR (GR) adsorption on hydrocalumite (Ca/Al-LDH-Cl) were studied by X-ray diffraction (XRD), infrared spectroscopy (MIR), scanning electron microscope (SEM) and near-infrared spectroscopy (NIR). The XRD results revealed that the basal spacing of Ca/Al-LDH-MO was expanded to 2.45 nm, and the MO molecules were intercalated with a inter-penetrating bilayer model in the gallery of LDH, with 49o tilting angle. Yet Ca/Al-LDH-GR was kept the same d-value as Ca/Al-LDH-Cl. The NIR spectrum for Ca/Al-LDH-MO showed a prominent band around 5994 cm-1, assigned to the combination result of the N-H stretching vibrations, which was considered as a mark to assess MO- ion intercalation into Ca/Al-LDH-Cl interlayers. From SEM images, the particle morphology of Ca/Al-LDH-MO mainly changed to irregular platelets, with a “honey-comb” like structure. Yet the Ca/Al-LDH-GR maintained regular hexagons platelets, which was similar to that of Ca/Al-LDH-Cl. All results indicated that MO- ion was intercalated into Ca/Al-LDH-Cl interlayers, and acidic scarlet GR was only adsorped upon Ca/Al-LDH-Cl surfaces.