Competing Interactions among Supramolecular Structures on Surfaces

A simple model was constructed to describe the polar ordering of non-centrosymmetric supramolecular aggregates formed by self assembling triblock rodcoil polymers. The aggregates are modeled as dipoles in a lattice with an Ising-like penalty associated with reversing the orientation of nearest neighbor dipoles. The choice of the potentials is based on experimental results and structural features of the supramolecular objects. For films of finite thickness, we find a periodic structure along an arbitrary direction perpendicular to the substrate normal, where the repeat unit is composed of two equal width domains with dipole up and dipole down configuration. When a short range interaction between the surface and the dipoles is included the balance between the up and down dipole domains is broken. Our results suggest that due to surface effects, films of finite thickness have a none zero macroscopic polarization, and that the polarization per unit volume appears to be a function of film thickness.Comment: 3 pages, 3 eps figure

Robust nonlinear system identification: Bayesian mixture of experts using the t-distribution

A novel variational Bayesian mixture of experts model for robust regression of bifurcating and piece-wise continuous processes is introduced. The mixture of experts model is a powerful model which probabilistically splits the input space allowing different models to operate in the separate regions. However, current methods have no fail-safe against outliers. In this paper, a robust mixture of experts model is proposed which consists of Student-t mixture models at the gates and Student-t distributed experts, trained via Bayesian inference. The Student-t distribution has heavier tails than the Gaussian distribution, and so it is more robust to outliers, noise and nonnormality in the data. Using both simulated data and real data obtained from the Z24 bridge this robust mixture of experts performs better than its Gaussian counterpart when outliers are present. In particular, it provides robustness to outliers in two forms: unbiased parameter regression models, and robustness to overfitting/complex models

A New Class of Materials Based on Nanoporous High Entropy Alloys with Outstanding Properties

Nanoporous metals with a random, bicontinuous structure of both pores and ligaments exhibit many unique mechanical properties, but their technical applications are often limited by their intrinsic brittleness under tensile strain triggered by fracture of the weakest ligaments. Here, we use molecular dynamics simulations to study the mechanical behavior and thermal stability of two different bicontinuous nanoporous high entropy alloys, Al0.1CoCrFeNi and NbMoTaW. To isolate the properties related to the nanoporous nature of our samples, we also studied the corresponding bulk and nanocrystalline systems. The results demonstrate that the specific modulus of nanoporous HEAs are 2 to 3 times greater than that of single element nanoporous materials with specific strength reaching values 5 to 10 times higher, comparable to bulk metals with the highest specific strength. Bicontinuous HEAs also displayed excellent resistance to thermal degradation as evidenced by the absence of coarsening ligaments up to temperatures of 1273 K which ensures the durability and reliability in high-temperature applications. The findings uncover unprecedented mechanical and thermal properties of bicontinuous nanoporous high entropy alloys, paving the way for their promising utilization in advanced engineering and structural applications

Experimental studies on impact damage location in composite aerospace structures using genetic algorithms and neural networks

Impact damage detection in composite structures has gained a considerable interest in many engineering areas. The capability to detect damage at the early stages reduces any risk of catastrophic failure. This paper compares two advanced signal processing methods for impact location in composite aircraft structures. The first method is based on a modified triangulation procedure and Genetic Algorithms whereas the second technique applies Artificial Neural Networks. A series of impacts is performed experimentally on a composite aircraft wing�box structure instrumented with low-profile, bonded piezoceramic sensors. The strain data are used for learning in the Neural Network approach. The triangulation procedure utilises the same data to establish impact velocities for various angles of strain wave propagation. The study demonstrates that both approaches are capable of good impact location estimates in this complex structure

The Mg 2 h and k lines in a sample of dMe and dM stars

Both Mg II h and k line fluxes are presented for a sample of 4 dMe and 3 dM stars obtained with the IUE satellite in the long wavelength, low dispersion mode. The observed fluxes are converted to stellar surface flux units and the importance of chromospheric non radiative heating in this sample of M dwarf stars is intercompared. In addition, the net chromospheric radiative losses due to the Ca II H and K lines in those stars in the sample for which calibrated Ca II H and K line data exist are compared. Active region filling factors which likely give rise to the observed optical and ultraviolet chromospheric emission are estimated. The implications of the results for homogeneous, single component stellar model chromospheres analyses are discussed

Some Recent Developments in SHM Based on Nonstationary Time Series Analysis

Many of the algorithms used for structural health monitoring (SHM) are based on, or motivated by, time series analysis. Quite often, detection methods are variants of approaches developed within the statistical process control (SPC) community. Many of the algorithms used represent mature theory and have a rigorous probabilistic or mathematical basis. However, one of the main issues facing SHM practitioners is that the structures of interest rarely respect the assumptions inherent in deriving algorithms. In the case of time series data, SPC-based approaches usually require the data to be stationary and, unfortunately, SHM data are often nonstationary because of benign variations in the environment of the structure of interest, or because of deliberate operational changes in the use of the structure. This nonstationarity can manifest itself as slowly varying trends on the data or in abrupt switches between regimes. Recent work in nonstationary time series methods for SHM has made considerable progress in accommodating nonstationarity and some of that work is discussed within this paper: in terms of understanding slowly varying trends, the cointegration algorithm from econometrics is presented; for understanding abrupt switches, Bayesian mixtures of experts are presented. Another issue in time series analysis is indirectly related to the assumption of linear behavior of structures and the impact of this assumption is briefly considered in terms of its effects on detection thresholds in SPC-like methods; again, progress has been made recently. Some issues still remain, and these are discussed also

Sedimentary controls on modern sand grain coat formation

Clay coated quartz grains can influence reservoir quality evolution during sandstone diagenesis. Porosity can be reduced and fluid flow restricted where grain coats encroach into pore space. Conversely pore-lining grain coats can restrict the growth of pore-filling quartz cement in deeply buried sandstones, and thus can result in unusually high porosity in deeply buried sandstones. Being able to predict the distribution of clay coated sand grains within petroleum reservoirs is thus important to help find good reservoir quality. Here we report a modern analogue study of 12 sediment cores from the Anllóns Estuary, Galicia, NW Spain, collected from a range of sub-environments, to help develop an understanding of the occurrence and distribution of clay coated grains. The cores were described for grain size, bioturbation and sedimentary structures, and then sub-sampled for electron and light microscopy, laser granulometry, and X-ray diffraction analysis. The Anllóns Estuary is sand-dominated with intertidal sand flats and saltmarsh environments at the margins; there is a shallowing/fining-upwards trend in the estuary-fill succession. Grain coats are present in nearly every sample analysed; they are between 1 μm and 100 μm thick and typically lack internal organisation. The extent of grain coat coverage can exceed 25% in some samples with coverage highest in the top 20 cm of cores. Samples from muddy intertidal flat and the muddy saltmarsh environments, close to the margins of the estuary, have the highest coat coverage (mean coat coverage of 20.2% and 21.3%, respectively). The lowest mean coat coverage occurs in the sandy saltmarsh (10.4%), beyond the upper tidal limit and sandy intertidal flat environments (8.4%), close to the main estuary channel. Mean coat coverage correlates with the concentration of clay fraction. The primary controls on the distribution of fine-grained sediment, and therefore grain coat distribution, are primary sediment transport and deposition processes that concentrate the clay fraction in the sediment towards the margins of the estuary. Bioturbation and clay illuviation/mechanical infiltration are secondary processes that may redistribute fine-grained sediment and produce grain coats. Here we have shown that detrital grain coats are more likely in marginal environments of ancient estuary-fills, which are typically found in the fining-upward part of progradational successions