Elastodynamics of radially inhomogeneous spherically anisotropic elastic materials in the Stroh formalism

A method is presented for solving elastodynamic problems in radially inhomogeneous elastic materials with spherical anisotropy, i.e.\ materials such that $c_{ijkl}= c_{ijkl}(r)$ in a spherical coordinate system ${r,\theta,\phi}$. The time harmonic displacement field $\mathbf{u}(r,\theta ,\phi)$ is expanded in a separation of variables form with dependence on $\theta,\phi$ described by vector spherical harmonics with $r$-dependent amplitudes. It is proved that such separation of variables solution is generally possible only if the spherical anisotropy is restricted to transverse isotropy with the principal axis in the radial direction, in which case the amplitudes are determined by a first-order ordinary differential system. Restricted forms of the displacement field, such as $\mathbf{u}(r,\theta)$, admit this type of separation of variables solutions for certain lower material symmetries. These results extend the Stroh formalism of elastodynamics in rectangular and cylindrical systems to spherical coordinates.Comment: 15 page

Microfactory – Blend – Compression - Performance test

Aims and objectives - API’s with acicular habits are commonplace and present processing and handling challenges due to poor flow. This is traditionally addressed by wet granulation processes during formulation. Currently continuous direct compression (CDC) is gaining favour as a simplified formulation and dose formation process. However, poor flow properties limit CDC. This work aims to enable CDC by spherical agglomeration in the primary process and develop underpinning modelling approaches to allow formulations to be explored in-silico (i.e. digital twin) - Here at CMAC an integrated crystalisation-spherical agglomeration-drying-blending-compression process is being developed (microfactory) to be used to parameterise and develop modelling tools on the g-formulate package - This work presents some of the activities on the compression component to parameterise and develop a suitable model to enable the process to be explored (i.e. digital twin

Spatially Resolved Mapping of Local Polarization Dynamics in an Ergodic Phase of Ferroelectric Relaxor

Spatial variability of polarization relaxation kinetics in relaxor ferroelectric 0.9Pb(Mg1/3Nb2/3)O3-0.1PbTiO3 is studied using time-resolved Piezoresponse Force Microscopy. Local relaxation attributed to the reorientation of polar nanoregions is shown to follow stretched exponential dependence, exp(-(t/tau)^beta), with beta~~0.4, much larger than the macroscopic value determined from dielectric spectra (beta~~0.09). The spatial inhomogeneity of relaxation time distributions with the presence of 100-200 nm "fast" and "slow" regions is observed. The results are analyzed to map the Vogel-Fulcher temperatures on the nanoscale.Comment: 23 pages, 4 figures, supplementary materials attached; to be submitted to Phys. Rev. Let

Electron-hadron shower discrimination in a liquid argon time projection chamber

By exploiting structural differences between electromagnetic and hadronic showers in a multivariate analysis we present an efficient Electron-Hadron discrimination algorithm for liquid argon time projection chambers, validated using Geant4 simulated data

Coarse-grained dynamics of an activity bump in a neural field model

We study a stochastic nonlocal PDE, arising in the context of modelling spatially distributed neural activity, which is capable of sustaining stationary and moving spatially-localized ``activity bumps''. This system is known to undergo a pitchfork bifurcation in bump speed as a parameter (the strength of adaptation) is changed; yet increasing the noise intensity effectively slowed the motion of the bump. Here we revisit the system from the point of view of describing the high-dimensional stochastic dynamics in terms of the effective dynamics of a single scalar "coarse" variable. We show that such a reduced description in the form of an effective Langevin equation characterized by a double-well potential is quantitatively successful. The effective potential can be extracted using short, appropriately-initialized bursts of direct simulation. We demonstrate this approach in terms of (a) an experience-based "intelligent" choice of the coarse observable and (b) an observable obtained through data-mining direct simulation results, using a diffusion map approach.Comment: Corrected aknowledgement

Statistical analysis of reinforced concrete bridges in Estonia

This paper introduces a possible way to use a multivariate methodology, called principal component analysis, to reduce the dimensionality of condition state database of bridge elements, collected during visual inspections. Attention is paid to the condition assessment of bridges in Estonian national roads and collected data, which plays an important role in the selection of correct statistical technique and obtaining reliable results. Additionally, detailed overview of typical road bridges and examples of collected information is provided. Statistical analysis is carried out by most natural reinforced concrete bridges in Estonia and comparison is made among different typologies. The introduced multivariate technique algorithms are presented and collated in two different formulations, with contrast on unevenness in variables and taking into account the missing data. Principal components and weighing factors, which are calculated for bridges with different typology, also have differences in results and element groups where variation is retainedTU1406 – Quality Specifications for Roadway Bridges, standardiza- tion at a European level (BridgeSpec), a COST Action sup- ported by EU Framework Programme Horizon 2020info:eu-repo/semantics/publishedVersio

Learning a Factor Model via Regularized PCA

We consider the problem of learning a linear factor model. We propose a regularized form of principal component analysis (PCA) and demonstrate through experiments with synthetic and real data the superiority of resulting estimates to those produced by pre-existing factor analysis approaches. We also establish theoretical results that explain how our algorithm corrects the biases induced by conventional approaches. An important feature of our algorithm is that its computational requirements are similar to those of PCA, which enjoys wide use in large part due to its efficiency

Data-adaptive harmonic spectra and multilayer Stuart-Landau models

Harmonic decompositions of multivariate time series are considered for which we adopt an integral operator approach with periodic semigroup kernels. Spectral decomposition theorems are derived that cover the important cases of two-time statistics drawn from a mixing invariant measure. The corresponding eigenvalues can be grouped per Fourier frequency, and are actually given, at each frequency, as the singular values of a cross-spectral matrix depending on the data. These eigenvalues obey furthermore a variational principle that allows us to define naturally a multidimensional power spectrum. The eigenmodes, as far as they are concerned, exhibit a data-adaptive character manifested in their phase which allows us in turn to define a multidimensional phase spectrum. The resulting data-adaptive harmonic (DAH) modes allow for reducing the data-driven modeling effort to elemental models stacked per frequency, only coupled at different frequencies by the same noise realization. In particular, the DAH decomposition extracts time-dependent coefficients stacked by Fourier frequency which can be efficiently modeled---provided the decay of temporal correlations is sufficiently well-resolved---within a class of multilayer stochastic models (MSMs) tailored here on stochastic Stuart-Landau oscillators. Applications to the Lorenz 96 model and to a stochastic heat equation driven by a space-time white noise, are considered. In both cases, the DAH decomposition allows for an extraction of spatio-temporal modes revealing key features of the dynamics in the embedded phase space. The multilayer Stuart-Landau models (MSLMs) are shown to successfully model the typical patterns of the corresponding time-evolving fields, as well as their statistics of occurrence.Comment: 26 pages, double columns; 15 figure

Effect of occupational therapy home visit discharge planning on participation after stroke: Protocol for the HOME Rehab trial

Introduction: After first stroke, the transition from rehabilitation to home can be confronting and fraught with challenges. Although stroke clinical practice guidelines recommend predischarge occupational therapy home visits to ensure safe discharge and provision of appropriate equipment, there is currently limited evidence to support this recommendation. Methods and analysis: The HOME Rehab trial is a national, multicentre, phase III randomised controlled trial with concealed allocation, blinded assessment and intention-to-treat analysis being conducted in Australia. The trial aim is to determine the effect and potential cost-effectiveness of an enhanced occupational therapy discharge planning intervention that involves pre and postdischarge home visits, goal setting and occupational therapy in the home (the HOME programme) in comparison to an in-hospital predischarge planning intervention. Stroke survivors aged ≥ 45 years, admitted to a rehabilitation ward, expected to return to a community (private) dwelling after discharge, with no significant prestroke disability will be randomly allocated 1:1 to receive a standardised discharge planning intervention and the HOME programme or the standardised discharge planning intervention alone. The primary outcome is participation measured using the Nottingham Extended Activities of Daily Living. Secondary outcome areas include hospital readmission, disability, performance of instrumental activities of daily living, health-related quality of life, quality of care transition and carer burden. Resources used/costs will be collected for the cost-effectiveness analysis and hospital readmission. Recruitment commenced in 2019. Allowing for potential attrition, 360 participants will be recruited to detect a clinically important treatment difference with 80% power at a two-tailed significance level of 0.05. Ethics and dissemination: This study is approved by the Alfred Health Human Research Ethics Committee and site-specific ethics approval has been obtained at all participating sites. Results of the main trial and the secondary endpoint of cost-effectiveness will be submitted for publication in peer-reviewed journals Trial registration number: ACTRN1261800136020

A new feature extraction method for signal classification applied to cat spinal cord signals

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods