71 research outputs found
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
where and denote depth and the exponent of contact
force, and the power-law for the grain velocity is . Other
depth-dependent power-laws for oscillation frequency, wavelength, and period
are given by combining above two and the phase velocity power-law
. 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
Direct polymerization of levulinic acid via Ugi multicomponent reactiont
A robust, direct and efficient approach has been developed for the utilization of levulinic acid (LevA) as a building block in the synthesis of polyamides. In this approach, there is no need for converting LevA to a cyclic monomer as the carboxylic acid and ketone groups are sufficient for incorporation into a polyamide. Optimization of reaction temperature, solvent, reactants as well as heating source have been performed for the Ugi multicomponent reaction. The obtained polyamides were characterized carefully using GPC, NMR, MALDI-ToF MS, DSC and TGA
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
Activity patterns of degenerating retinal projection neurons mapped with a CMOS multitransistorarray
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