A Model for the Propagation of Sound in Granular Materials

This paper presents a simple ball-and-spring model for the propagation of small amplitude vibrations in a granular material. In this model, the positional disorder in the sample is ignored and the particles are placed on the vertices of a square lattice. The inter-particle forces are modeled as linear springs, with the only disorder in the system coming from a random distribution of spring constants. Despite its apparent simplicity, this model is able to reproduce the complex frequency response seen in measurements of sound propagation in a granular system. In order to understand this behavior, the role of the resonance modes of the system is investigated. Finally, this simple model is generalized to include relaxation behavior in the force network -- a behavior which is also seen in real granular materials. This model gives quantitative agreement with experimental observations of relaxation.Comment: 21 pages, requires Harvard macros (9/91), 12 postscript figures not included, HLRZ preprint 6/93, (replacement has proper references included

Power-Laws in Nonlinear Granular Chain under Gravity

The signal generated by a weak impulse propagates in an oscillatory way and dispersively in a gravitationally compacted granular chain. For the power-law type contact force, we show analytically that the type of dispersion follows power-laws in depth. The power-law for grain displacement signal is given by $h^{-1/4(1-1/p)}$ where $h$ and $p$ denote depth and the exponent of contact force, and the power-law for the grain velocity is $h^{-1/4({1/3}+1/p)}$. Other depth-dependent power-laws for oscillation frequency, wavelength, and period are given by combining above two and the phase velocity power-law $h^{1/2(1-1/p)}$. We verify above power-laws by comparing with the data obtained by numerical simulations.Comment: 12 pages, 3 figures; Changed conten

Force Distribution in a Granular Medium

We report on systematic measurements of the distribution of normal forces exerted by granular material under uniaxial compression onto the interior surfaces of a confining vessel. Our experiments on three-dimensional, random packings of monodisperse glass beads show that this distribution is nearly uniform for forces below the mean force and decays exponentially for forces greater than the mean. The shape of the distribution and the value of the exponential decay constant are unaffected by changes in the system preparation history or in the boundary conditions. An empirical functional form for the distribution is proposed that provides an excellent fit over the whole force range measured and is also consistent with recent computer simulation data.Comment: 6 pages. For more information, see http://mrsec.uchicago.edu/granula

Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification

Diabetic Retinopathy (DR) is one of the microvascular complications of Diabetes Mellitus, which remains as one of the leading causes of blindness worldwide. Computational models based on Convolutional Neural Networks represent the state of the art for the automatic detection of DR using eye fundus images. Most of the current work address this problem as a binary classification task. However, including the grade estimation and quantification of predictions uncertainty can potentially increase the robustness of the model. In this paper, a hybrid Deep Learning-Gaussian process method for DR diagnosis and uncertainty quantification is presented. This method combines the representational power of deep learning, with the ability to generalize from small datasets of Gaussian process models. The results show that uncertainty quantification in the predictions improves the interpretability of the method as a diagnostic support tool. The source code to replicate the experiments is publicly available at https://github.com/stoledoc/DLGP-DR-Diagnosis

Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System

Recent advancement in predictive machine learning has led to its application in various use cases in manufacturing. Most research focused on maximising predictive accuracy without addressing the uncertainty associated with it. While accuracy is important, focusing primarily on it poses an overfitting danger, exposing manufacturers to risk, ultimately hindering the adoption of these techniques. In this paper, we determine the sources of uncertainty in machine learning and establish the success criteria of a machine learning system to function well under uncertainty in a cyber-physical manufacturing system (CPMS) scenario. Then, we propose a multi-agent system architecture which leverages probabilistic machine learning as a means of achieving such criteria. We propose possible scenarios for which our architecture is useful and discuss future work. Experimentally, we implement Bayesian Neural Networks for multi-tasks classification on a public dataset for the real-time condition monitoring of a hydraulic system and demonstrate the usefulness of the system by evaluating the probability of a prediction being accurate given its uncertainty. We deploy these models using our proposed agent-based framework and integrate web visualisation to demonstrate its real-time feasibility

Cell Wall Antibiotics Provoke Accumulation of Anchored mCherry in the Cross Wall of Staphylococcus aureus

A fluorescence microscopy method to directly follow the localization of defined proteins in Staphylococcus was hampered by the unstable fluorescence of fluorescent proteins. Here, we constructed plasmid (pCX) encoded red fluorescence (RF) mCherry (mCh) hybrids, namely mCh-cyto (no signal peptide and no sorting sequence), mCh-sec (with signal peptide), and mCh-cw (with signal peptide and cell wall sorting sequence). The S. aureus clones targeted mCh-fusion proteins into the cytosol, the supernatant and the cell envelope respectively; in all cases mCherry exhibited bright fluorescence. In staphylococci two types of signal peptides (SP) can be distinguished: the +YSIRK motif SPlip and the −YSIRK motif SPsasF. mCh-hybrids supplied with the +YSIRK motif SPlip were always expressed higher than those with −YSIRK motif SPsasF. To study the location of the anchoring process and also the influence of SP type, mCh-cw was supplied on the one hand with +YSIRK motif (mCh-cw1) and the other hand with -YSIRK motif (mCh-cw2). MCh-cw1 preferentially localized at the cross wall, while mCh-cw2 preferentially localized at the peripheral wall. Interestingly, when treated with sub-lethal concentrations of penicillin or moenomycin, both mCh-cw1 and mCh-cw2 were concentrated at the cross wall. The shift from the peripheral wall to the cross wall required Sortase A (SrtA), as in the srtA mutant this effect was blunted. The effect is most likely due to antibiotic mediated increase of free anchoring sites (Lipid II) at the cross wall, the substrate of SrtA, leading to a preferential incorporation of anchored proteins at the cross wall

Bioavailability of Macro and Micronutrients Across Global Topsoils: Main Drivers and Global Change Impacts

Understanding the chemical composition of our planet\u27s crust was one of the biggest questions of the 20th century. More than 100 years later, we are still far from understanding the global patterns in the bioavailability and spatial coupling of elements in topsoils worldwide, despite their importance for the productivity and functioning of terrestrial ecosystems. Here, we measured the bioavailability and coupling of thirteen macro- and micronutrients and phytotoxic elements in topsoils (3–8 cm) from a range of terrestrial ecosystems across all continents (∼10,000 observations) and in response to global change manipulations (∼5,000 observations). For this, we incubated between 1 and 4 pairs of anionic and cationic exchange membranes per site for a mean period of 53 days. The most bioavailable elements (Ca, Mg, and K) were also amongst the most abundant in the crust. Patterns of bioavailability were biome-dependent and controlled by soil properties such as pH, organic matter content and texture, plant cover, and climate. However, global change simulations resulted in important alterations in the bioavailability of elements. Elements were highly coupled, and coupling was predictable by the atomic properties of elements, particularly mass, mass to charge ratio, and second ionization energy. Deviations from the predictable coupling-atomic mass relationship were attributed to global change and agriculture. Our work illustrates the tight links between the bioavailability and coupling of topsoil elements and environmental context, human activities, and atomic properties of elements, thus deeply enhancing our integrated understanding of the biogeochemical connections that underlie the productivity and functioning of terrestrial ecosystems in a changing world