Surface flow profiles for dry and wet granular materials by Particle Tracking Velocimetry; the effect of wall roughness

Two-dimensional Particle Tracking Velocimetry (PTV) is a promising technique to study the behaviour of granular flows. The aim is to experimentally determine the free surface width and position of the shear band from the velocity profile to validate simulations in a split-bottom shear cell geometry. The position and velocities of scattered tracer particles are tracked as they move with the bulk flow by analyzing images. We then use a new technique to extract the continuum velocity field, applying coarse-graining with the postprocessing toolbox MercuryCG on the discrete experimental PTV data. For intermediate filling heights, the dependence of the shear (or angular) velocity on the radial coordinate at the free surface is well fitted by an error function. From the error function, we get the width and the centre position of the shear band. We investigate the dependence of these shear band properties on filling height and rotation frequencies of the shear cell for dry glass beads for rough and smooth wall surfaces. For rough surfaces, the data agrees with the existing experimental results and theoretical scaling predictions. For smooth surfaces, particle-wall slippage is significant and the data deviates from the predictions. We further study the effect of cohesion on the shear band properties by using small amount of silicon oil and glycerol as interstitial liquids with the glass beads. While silicon oil does not lead to big changes, glycerol changes the shear band properties considerably. The shear band gets wider and is situated further inward with increasing liquid saturation, due to the correspondingly increasing trend of particles to stick together

Prediction of the outcome of a Twenty-20 Cricket Match : A Machine Learning Approach

Twenty20 cricket, sometimes written Twenty-20, and often abbreviated to T20, is a short form of cricket. In a Twenty20 game the two teams of 11 players have a single innings each, which is restricted to a maximum of 20 overs. This version of cricket is especially unpredictable and is one of the reasons it has gained popularity over recent times. However, in this paper we try four different machine learning approaches for predicting the results of T20 Cricket Matches. Specifically we take in to account: previous performance statistics of the players involved in the competing teams, ratings of players obtained from reputed cricket statistics websites, clustering the players' with similar performance statistics and propose a novel method using an ELO based approach to rate players. We compare the performances of each of these feature engineering approaches by using different ML algorithms, including logistic regression, support vector machines, bayes network, decision tree, random forest.Comment: Machine Learning Applications, Sports, Cricket Outcome Predictio

Shape matters: Competing mechanisms of particle shape segregation

It is well-known that granular mixtures that differ in size or shape segregate when sheared. In the past, two mechanisms have been proposed to describe this effect, and it is unclear if both exist. To settle this question, we consider a bidisperse mixture of spheroids of equal volume in a rotating drum, where the two mechanisms are predicted to act in opposite directions. We present the first evidence that there are two distinct segregation mechanisms driven by relative over-stress. Additionally, we showed that for non-spherical particles, these two mechanisms can act in different directions leading to a competition between the effects of the two. As a result, the segregation intensity varies nonmonotonically as a function of AR, and at specific points, the segregation direction changes for both prolate and oblate spheroids, explaining the surprising segregation reversal previously reported. Consistent with previous results, we found that the kinetic mechanism is dominant for (almost) spherical particles. Furthermore, for moderate aspect ratios, the kinetic mechanism is responsible for the spherical particles segregation to the periphery of the drum, and the gravity mechanism plays only a minor role. Whereas, at the extreme values of AR, the gravity mechanism notably increases and overtakes its kinetic counterpart

Yoga of Immortals Intervention Reduces Symptoms of Depression, Insomnia and Anxiety

Background: Depression, anxiety, and disordered sleep are some common symptoms associated with sub-optimal mental health. During the COVID-19 pandemic, mental health issues have grown increasingly more prevalent in the population. Due to social distancing and other limitations during the pandemic, there is a need for home-based, flexible interventions that can improve mental health. The Yoga of Immortals (YOI) mobile application provides a structured intervention that can be used on any mobile device and applied from the user's home.Methods: A total of 1,505 participants were enrolled in the study and used the YOI app for an 8-week period. Participants were asked to fill out three questionnaires: The Patient Health Questionnaire, 8 items (PHQ-8), the Generalized Anxiety Disorder questionnaire (GAD-7) and the Insomnia Severity Index (ISI). These three items were completed by 1,297 participants a total of four times: before starting YOI, two more times during use, and a fourth time after the 8-week usage period. Changes in PHQ8, GAD7 and ISI in participants were compared to a control group, who did not use the YOI app but completed all questionnaires (590 controls finished all questionnaires).Results: Participants reported significant decreases in depression and anxiety-related symptoms. Compared to baseline, PHQ-8 scores decreased 50% on average after the 8-week period. GAD-7 scores also decreased by 40–50% on average, and ISI scores decreased by 50%. These changes were significantly greater (p < 0.05) than that observed in the control group. Participants who reported a previous diagnosis of depression and generalized anxiety reported significantly larger decreases in PHQ-8 and GAD-7 as compared to participants with no prior diagnosis (p < 0.05).Conclusions: Regular use of the YOI intervention over an 8-week period led to significant decreases in symptoms of both depression and anxiety, as well as alleviation of insomnia

Discrete element modelling of granular column collapse tests with industrial applications

Describing the behaviour of granular materials is a challenging issue for the industry. Our work concerns packaging industries where packing equipment is designed to handle a wide range of powders and bulk solids with varying physical and mechanical properties. While packaging, a variety of material conveying techniques are used ranging from air fluidisation to discharge of material through a hopper. Thereby even a small improvement in their efficiency can lead to significant benefits, both financial and environmental. Flowability of powders and bulk solids is often experimentally investigated using granular column collapse, as this test provides deep insights into the kinematics of granular flow both at particle and bulk levels [1]. Here, we consider a quasi-two-dimensional set-up with a reservoir containing the granular pile which is instantaneously released onto a channel where run-out takes place. Instead of experiments, we use discrete particle simulations allowing us to quantitatively link bulk-level observations to particle-level properties of the materials, besides enabling inverse analysis leading to indirect measures of micro-scale parameters. We present a simulation strategy aimed at controlling several particle parameters influencing the run-out: - Polydispersity in size, using different particle size distributions; and also in shape, comparing the use of spherical and non-spherical particles, namely cylinders and ellipsoids. - Mechanical properties of the contacts, comprising normal stiffness and dissipation, as well as sliding, rolling and torsion coefficients. Specifically, hygroscopic behaviour of bulk materials is inspected modifying the contact law parameters. Additionally at the bulk level, air fluidisation of the columns before release is studied through the initial packing state by changing the volume fraction of the piles. Numerical simulations are implemented with the open-source code MercuryDPMPostprint (published version

Discrete element modelling of granular column collapse tests with industrial applications

The effect of particle size distribution on dry granular flows of spherical particles has been numerically investigated. A quasi-two-dimensional granular column collapse set-up has been modelled using the Discrete Element Method (DEM). Systems formed by monodisperse particles of radius R = 0.01 m and polydisperse particles of the same average radius and coefficient of uniformity Cu = 1.9 have been studied for initial granular columns aspect ratios of 1.1 and 2.2. The results using monodisperse and narrow particle size distributions show similar evolution of the run-out profiles, the final run-out distance being reached in less than one second in every configuration. Averaged velocity fields have been obtained, from which peak values of longitudinal and vertical components of velocity have been found

On the Robustness of Explanations of Deep Neural Network Models: A Survey

Explainability has been widely stated as a cornerstone of the responsible and trustworthy use of machine learning models. With the ubiquitous use of Deep Neural Network (DNN) models expanding to risk-sensitive and safety-critical domains, many methods have been proposed to explain the decisions of these models. Recent years have also seen concerted efforts that have shown how such explanations can be distorted (attacked) by minor input perturbations. While there have been many surveys that review explainability methods themselves, there has been no effort hitherto to assimilate the different methods and metrics proposed to study the robustness of explanations of DNN models. In this work, we present a comprehensive survey of methods that study, understand, attack, and defend explanations of DNN models. We also present a detailed review of different metrics used to evaluate explanation methods, as well as describe attributional attack and defense methods. We conclude with lessons and take-aways for the community towards ensuring robust explanations of DNN model predictions.Comment: Under Review ACM Computing Surveys "Special Issue on Trustworthy AI

Mercury-DPM: Fast particle simulations in complex geometries

Mercury-DPM is a code for performing discrete particle simulations. That is to say, it simulates the motion of particles, or atoms, by applying forces and torques that stem either from external body forces, (e.g. gravity, magnetic fields, etc…) or from particle interactions. For granular particles, these are typically contact forces (elastic, viscous, frictional, plastic, cohesive), while for molecular simulations, forces typically stem from interaction potentials (e.g. Lennard-Jones). Often the method used in these packages is referred to as the discrete element method (DEM), which was originally designed for geotechnical applications. However, as Mercury-DPM is designed for simulating particles with emphasis on contact models, optimized contact detection for highly different particle sizes, and in-code coarse graining (in contrast to post-processing), we prefer the more general name discrete particle simulation. The code was originally developed for granular chute flows, and has since been extended to many other granular applications, including the geophysical modeling of cinder cone creation. Despite its granular heritage it is designed in a flexible way so it can be adapted to include other features such as long-range interactions and non-spherical particles, etc