Modeling segregation of bidisperse granular materials using physical control parameters in the quasi‐2D bounded heap

Quantitatively predicting segregation of size‐disperse granular materials is of potential value in many industrial applications. We consider granular segregation of size‐bidisperse particles in quasi‐2D bounded heaps, a canonical granular flow, using an advection‐diffusion transport equation with an additional term to account for particle segregation. The equation is characterized by two dimensionless parameters that are functions of control parameters (flow rate, system size, and particle sizes) and kinematic parameters (flowing layer depth, diffusion coefficient, and percolation length scale). As the kinematic parameters are usually difficult to measure in practice, their dependence on the control parameters is determined directly from discrete element method simulations. Using these relationships, it is possible to determine which values of the control parameters result in a mixed or segregated heap. The approach used here is broadly applicable to a wide range of other flow geometries and particle systems. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1524–1534, 2015     

Lattice Boltzmann Modeling of Thermal Explosion in Natural Convection Conditions

Lattice Boltzmann (LB) computational code developed by the authors is used to simulate the development of thermal explosion in reactive mixtures subjected to convection. The work demonstrates an LB modeling application to reactive flows by considering practically important combustion and fire research problem. The problem of convection-affected thermal explosion is of particular interest from both theoretical and practical points of view. Critical conditions for convection-affected thermal explosion are found in the range of Rayleigh numbers 10³–10⁸.     

Textile Technology for Soft Robotic and Autonomous Garments

Textiles have emerged as a promising class of materials for developing wearable robots that move and feel like everyday clothing. Textiles represent a favorable material platform for wearable robots due to their flexibility, low weight, breathability, and soft hand-feel. Textiles also offer a unique level of programmability because of their inherent hierarchical nature, enabling researchers to modify and tune properties at several interdependent material scales. With these advantages and capabilities in mind, roboticists have begun to use textiles, not simply as substrates, but as functional components that program actuation and sensing. In parallel, materials scientists are developing new materials that respond to thermal, electrical, and hygroscopic stimuli by leveraging textile structures for function. Although textiles are one of humankind's oldest technologies, materials scientists and roboticists are just beginning to tap into their potential. This review provides a textile-centric survey of the current state of the art in wearable robotic garments and highlights metrics that will guide materials development. Recent advances in textile materials for robotic components (i.e., as sensors, actuators, and integration components) are described with a focus on how these materials and technologies set the stage for wearable robots programmed at the material level.     

Intelligent Sensing for Citizen Science

Interest in Citizen Science has grown significantly over the last decade. Much of this interest can be traced to the provision of sophisticated platforms that enable seamless collaboration, cooperation and coordination between professional and amateur scientists. In terms of field research, smart-phones have been widely adopted, automating data collection and enriching observations with photographs, sound recordings and GPS coordinates using embedded sensors hosted on the device itself. Interaction with external sensor platforms such as those normally used in the environmental monitoring domain is practically null-existent. Remedying this deficiency would have positive ramifications for both the professional and citizen science communities. To illustrate the relevant issues, this paper considers a common problem, that of data collection in sparse sensor networks, and proposes a practical solution that would enable citizen scientists act as Human Relays thus facilitating the collection of data from such networks. Broader issues necessary for enabling intelligent sensing using common smart-phones and embedded sensing technologies are then discussed.     

A biosocial developmental model of borderline personality: Elaborating and extending linehan’s theory.

Over the past several decades, research has focused increasingly on developmental precursors to psychological disorders that were previously assumed to emerge only in adulthood. This change in focus follows from the recognition that complex transactions between biological vulnerabilities and psychosocial risk factors shape emotional and behavioral development beginning at conception. To date, however, empirical research on the development of borderline personality is extremely limited. Indeed, in the decade since M. M. Linehan initially proposed a biosocial model of the development of borderline personality disorder, there have been few attempts to test the model among at-risk youth. In this review, diverse literatures are reviewed that can inform understanding of the ontogenesis of borderline pathology, and testable hypotheses are proposed to guide future research with at-risk children and adolescents. One probable pathway is identified that leads to borderline personality disorder; it begins with early vulnerability, expressed initially as impulsivity and followed by heightened emotional sensitivity. These vulnerabilities are potentiated across development by environmental risk factors that give rise to more extreme emotional, behavioral, and cognitive dysregulation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Prediction of sensory textural properties from rheological analysis for process cheeses varying in emulsifying salt,protein and moisture contents

Textural characteristics of process cheeses varying in emulsifying salt (disodium phosphate), protein and moisture contents were evaluated by rheological compression using texture profile analysis and by sensory evaluation. The primary objective of this study was to predict sensory textural parameters using instrumental rheological parameters. All sensory parameters correlated with one or more instrumental parameters, e.g. rheological firmness versus sensory firmness (R = 0.98, P < 0.001), rheological chewiness versus sensory rubbery (R = 0.92, P < 0.001) and rheological chewiness versus sensory chewy (R = 0.86, P < 0.001). Partial least squares calibration models were developed for each of nine sensory parameters using instrumental parameters. Principal component analysis of instrumental and sensory parameters illustrated relationships among parameters. It was shown that instrumental parameters could be used to supplement sensory evaluation of process cheese texture. Increasing emulsifying salt content increased firmness, springiness and chewiness and decreased adhesiveness, mouthcoating and mass formation. Increasing protein content resulted in increased fracture strain and stress and chewiness and decreased melting. Increasing moisture content increased cohesiveness and decreased firmness and chewiness. Copyright © 2007 Society of Chemical Industry     

Framework for Assessing Robotic Dexterity within Flexible Manufacturing

With growing demand for flexibility in manufacturing processes, interest in dexterous industrial robots is increasing. To facilitate benchmarking, and to assess the suitability of these robots for flexible manufacturing tasks, there is a need to develop new methods of capturing the relevant performance characteristics of industrial robots. This research aims to show that the Boothroyd-Dewhurst (B-D) Design-For-Assembly method, an established method for optimizing manufacturing processes, can be effectively adopted to form the basis of a comprehensive robotic dexterity assessment within flexible manufacturing. A comparative study is conducted which shows that the B-D classification tables offer the most comprehensive solution due to the range of operations and artifacts considered. Building on these tables, a framework is developed for determining the suitability of a robot system within flexible manufacturing operations. In a sample test-case scenario involving a pick-and-place operation, the framework is shown to produce an accurate estimate of robot performance that can be easily compared to human data. The framework establishes a link between manufacturing operations and robot performance metrics, which addresses the current difficulty in robot integration and highlights the framework’s potential for adoption within flexible manufacturing.