Australian Government

The notion of defects in crystalline phases of matter has been extremely powerful for understanding crystal growth, deformation and melting. Many of these discontinuities in the periodic order of crystals are well described by the Burgers vector, derived from the particle displacements, which encapsulates the direction and magnitude of slip relative to the undeformed state. Since the reference structure of the crystal is known a priori, the Burgers vector can be determined experimentally using both imaging and diffraction methods to measure the final lattice distortion, and thus infer the particle displacements. Glasses have structures that lack the periodicity of crystals, and thus a well-defined reference state. Yet, measurable structural parameters can still be obtained from diffraction from a glass. Here we examine the usefulness of these parameters to probe deformation in glasses. We find that co-ordinated transformations in the centrosymmetry of local particle arrangements are a strong marker of plastic events. For a glass, determining the local distortions corresponding to these plastic events requires measurements before and after deformation. We investigate two geometric indicators that can be derived from these distortions, namely the continuous Burgers vector and the quadrupolar strain. We find that the Burgers vector again emerges as a robust and sensitive metric for understanding local structural transformations due to mechanical deformation, even in disordered glasses.

This article investigates factor-augmented sparse MIDAS (Mixed Data Sampling) regressions for high-dimensional time series data, which may be observed at different frequencies. Our novel approach integrates sparse and dense dimensionality reduction techniques. We derive the convergence rate of our estimator under misspecification due to the MIDAS approximation error, tau -mixing dependence, and polynomial tails. Our method's finite sample performance is assessed via Monte Carlo simulations. We apply the methodology to nowcasting U.S. GDP growth and demonstrate that it outperforms both sparse regression and standard factor-augmented regression during the COVID-19 pandemic. These findings indicate that the growth through this period was influenced by both idiosyncratic (sparse) and common (dense) shocks. The approach is implemented in the midasml R package, available on CRAN.

Directed Energy Deposition-Arc (DED-Arc) processes create residual stresses in the material due to their nonuniform thermal gradients. These residual stresses interact with the in-service loads and could negatively impact the material integrity. To understand and mitigate the negative effects of these stresses, in this study, a thin-walled structure out of a high strength low alloy (HSLA) steel was manufactured by DED-Arc process. The formation of residual stresses was studied with neutron diffraction at the centerline of the structure. To incorporate the effects of martensitic phase transformation on the residual stress formation, the microstructure of the material was studied using electron and optical microscopy. Also, the phase fractions were calculated using image segmentation methods. Furthermore, thermodynamics calculations were performed to understand the kinetics of the phase changes. The results show that the structure follows a tensile-compressive-tensile-compressive residual stress regime in the travel direction by moving from the lower side of the substrate to the top side of the thin-walled structure. The main influencing factors in the formation of these stresses are the volume expansion due to martensitic and bainitic phase transformations, contraction due to cooling of the hot material, and bending of the structure due to the interaction between tensile and compressive stresses at different heights of the part.

Explicit results are obtained using simple and exact methods for the joint queue-length distribution of the M/M/c queue with an arbitrary number of non-preemptive priority levels. This work is the first to provide explicit results for the joint probability generating function and joint probability mass function for a general number of priority levels. A fixed-point iteration is developed for the stationary balance equations, which enables direct computation of the joint queue-length distribution. A multi-variate probability generating function is also derived, from which the joint probability mass function can be computed by means of a multi-dimensional fast Fourier transform method.

mRNA‐loaded lipid nanoparticles (LNPs) offer significant therapeutic potential for various diseases, yet their structural characteristics and component distribution remain incompletely understood. This study utilizes small‐angle neutron scattering (SANS) to explore the internal architecture of KC2 LNPs, employing deuterated lipid substitutions in contrast‐varied media. Our analyses reveal that KC2 LNPs have diameters of 50–60 nm, consistent with cryogenic transmission electron microscopy (cryoTEM) and dynamic light scattering. Core–shell modeling indicates a core radius of 16–17 nm and a shell thickness of 6–8 nm, details often overlooked by cryogenic electron microscopy. Interestingly, results demonstrated that LNPs contain 48%–55% water (total particle volume), which is highly exchangeable with the dispersing medium. This water exchange is crucial during structural monitoring under conditions simulating endosomal environments, as acidification leads to an internal structural reorganization, involving solvent influx into the LNPs and potentially reorganization of components within and across the core and shell phases. Invariant analysis further confirms the high water content of LNPs, validating the model fitting results, affirming KC2 LNPs’ compositional complexity, which are pivotal for understanding LNP stability and responsiveness. These insights can guide the future design of mRNA nanotherapeutics, enhancing their therapeutic efficacy.