CFD-DEM simulation of three-dimensional aeolian sand movement

A three-dimensional CFD-DEM model is proposed to investigate the aeolian sand movement. The results show that the mean particle horizontal velocity can be expressed by a power function of heights. The probability distribution of the impact and lift-off velocities of particles can be described by a log-normal function, and that of the impact and lift-off angles can be expressed by an exponential function. The probability distribution of particle horizontal velocity at different heights can be described as a lognormal function, while the probability distribution of longitudinal and vertical velocity can be described as a normal function. The comparison with previous two-dimensional calculations shows that the variations of mean particle horizontal velocity along the heights in two-dimensional and three-dimensional models are similar. However, the mean particle density of the two-dimensional model is larger than that in reality, which will result in the overestimation of sand transportation rate in the two-dimensional calculation. The study also shows that the predicted probability distributions of particle velocities are in good agreement with the experimental results.

"Dual-Parallel-Channel" Shape-Gradient Surfaces: Toward Oriented and Reversible Movement of Water Droplets

In this paper, we prepared "dual-parallel-channel" shape-gradient surfaces, on which water droplets can reversibly and orientedly move between two adjacent pools under the guidance of an external voltage. Furthermore, it is found that the motion speed is governed by several parameters, including bath condition, gradient angle, and the working voltage. In this self-transportation process of water droplets, the external voltage works like a traffic light, which can give "moving", "stopping", "turning" and "straight-going" signals to the Water droplets.

Generation of internal and surface waves by seafloor movement in a two layer fluid system

Internal and surface waves generated by the deformations of the solid bed in a two layer fluid system of infinite lateral extent and uniform depth are investigated. An integral solution is developed for an arbitrary bed displacement on the basis of a linear approximation of the complete description of wave motion using a transform method (Laplace in time and Fourier in space) analogous to that used to study the generation of tsunamis by many researchers. The theoretical solutions are presented for three interesting specific deformations of the seafloor; the spatial variation of each seafloor displacement consists of a block section of the seafloor moving vertically either up or down while the time-displacement history of the block section is varied. The generation process and the profiles of the internal and surface waves for the case of the exponential bed movement are numerically illustrated, and the effects of the deformation parameters, densities and depths of the two layers on the solutions are discussed. As expected, the solutions derived from the present work include as special cases that obtained by Kervella et al. [Theor Comput Fluid Dyn 21:245-269, 2007] for tsunamis cased by an instantaneous seabed deformation and those presented by Hammack [J Fluid Mech 60:769-799, 1973] for the exponential and the half-sine bed displacements when the density of the upper fluid is taken as zero.

THE VARIATION AND MOVEMENT OF A KIND OF EDDIES IN SHELF SLOPE WATER

Considering the characteristics of the time and space scales of the eddies we established a quasi-static and quasi-geostrophic model to describe their variation and movement in shelf slope water. The analytical solution revealed the main properties of the variation: slow expansion and fast stagnation processes and the law of the eddy motion affected under the background field. All theoretical results are proved by satellite image measurements.

Typically Anti-American? The Labour movement, America and Broadcasting in Britain from Beveridge to Pilkington, 1949-62

O'Malley, T. (2007). Typically Anti-American?: The Labour movement, America and Broadcasting in Britain from Beveridge to Pilkington, 1949-62. In J. Wiener and M. Hampton (Eds.), Anglo- American Media Interactions,1850-2000 (pp.234-253). Basingstoke: Palgrave Macmillan. RAE2008

The game jam movement: disruption, performance and artwork

This paper explores the current conventions and intentions of the game jam - contemporary events that encourage the rapid, collaborative creation of game design prototypes. Game jams are often renowned for their capacity to encourage creativity and the development of alternative, innovative game designs. However, there is a growing necessity for game jams to continue to challenge traditional development practices through evolving new formats and perspectives to maintain the game jam as a disruptive, refreshing aspect of game development culture. As in other creative jam style events, a game jam is not only a process but also, an outcome. Through a discussion of the literature this paper establishes a theoretical basis with which to analyse game jams as disruptive, performative processes that result in original creative artefacts. In support of this, case study analysis of Development Cultures: a series of workshops that centred on innovation and new forms of practice through play, chance, and experimentation, is presented. The findings indicate that game jams can be considered as processes that inspire creativity within a community and that the resulting performances can be considered as a form of creative artefact, thus parallels can be drawn between game jams and performative and interactive art.

World-wide evangelization the urgent business of the church; addresses delivered before the fourth international convention of the Student volunteer movement for foreign missions, Toronto, Canada, February 26-March 2, 1902.

http://www.archive.org/details/worldwideevangel00unknuoft

Learning and Production of Movement Sequences: Behavioral, Neurophysiological, and Modeling Perspectives

A growing wave of behavioral studies, using a wide variety of paradigms that were introduced or greatly refined in recent years, has generated a new wealth of parametric observations about serial order behavior. What was a mere trickle of neurophysiological studies has grown to a more steady stream of probes of neural sites and mechanisms underlying sequential behavior. Moreover, simulation models of serial behavior generation have begun to open a channel to link cellular dynamics with cognitive and behavioral dynamics. Here we summarize the major results from prominent sequence learning and performance tasks, namely immediate serial recall, typing, 2XN, discrete sequence production, and serial reaction time. These populate a continuum from higher to lower degrees of internal control of sequential organization. The main movement classes covered are speech and keypressing, both involving small amplitude movements that are very amenable to parametric study. A brief synopsis of classes of serial order models, vis-à-vis the detailing of major effects found in the behavioral data, leads to a focus on competitive queuing (CQ) models. Recently, the many behavioral predictive successes of CQ models have been joined by successful prediction of distinctively patterend electrophysiological recordings in prefrontal cortex, wherein parallel activation dynamics of multiple neural ensembles strikingly matches the parallel dynamics predicted by CQ theory. An extended CQ simulation model-the N-STREAMS neural network model-is then examined to highlight issues in ongoing attemptes to accomodate a broader range of behavioral and neurophysiological data within a CQ-consistent theory. Important contemporary issues such as the nature of working memory representations for sequential behavior, and the development and role of chunks in hierarchial control are prominent throughout.

Neural Dynamics of Saccadic and Smooth Pursuit Eye Movement Coordination during Visual Tracking of Unpredictably Moving Targets

How does the brain use eye movements to track objects that move in unpredictable directions and speeds? Saccadic eye movements rapidly foveate peripheral visual or auditory targets and smooth pursuit eye movements keep the fovea pointed toward an attended moving target. Analyses of tracking data in monkeys and humans reveal systematic deviations from predictions of the simplest model of saccade-pursuit interactions, which would use no interactions other than common target selection and recruitment of shared motoneurons. Instead, saccadic and smooth pursuit movements cooperate to cancel errors of gaze position and velocity, and thus to maximize target visibility through time. How are these two systems coordinated to promote visual localization and identification of moving targets? How are saccades calibrated to correctly foveate a target despite its continued motion during the saccade? A neural model proposes answers to such questions. The modeled interactions encompass motion processing areas MT, MST, FPA, DLPN and NRTP; saccade planning and execution areas FEF and SC; the saccadic generator in the brain stem; and the cerebellum. Simulations illustrate the model’s ability to functionally explain and quantitatively simulate anatomical, neurophysiological and behavioral data about SAC-SPEM tracking.

Target Selection by Frontal Cortex During Coordinated Saccadic and Smooth Pursuit Eye Movement

Oculomotor tracking of moving objects is an important component of visually based cognition and planning. Such tracking is achieved by a combination of saccades and smooth pursuit eye movements. In particular, the saccadic and smooth pursuit systems interact to often choose the same target, and to maximize its visibility through time. How do multiple brain regions interact, including frontal cortical areas, to decide the choice of a target among several competing moving stimuli? How is target selection information that is created by a bias (e.g., electrical stimulation) transferred from one movement system to another? These saccade-pursuit interactions are clarified by a new computational neural model, which describes interactions among motion processing areas MT, MST, FPA, DLPN; saccade specification, selection, and planning areas LIP, FEF, SNr, SC; the saccadic generator in the brain stem; and the cerebellum. Model simulations explain a broad range of neuroanatomical and neurophysiological data. These results are in contrast with the simplest parallel model with no interactions between saccades and pursuit than common-target selection and recruitment of shared motoneurons. Actual tracking episodes in primates reveal multiple systematic deviations from predictions of the simplest parallel model, which are explained by the current model.

The Hippocampus and Cerebellum in Adaptively Timed Learning, Recognition, and Movement

The concepts of declarative memory and procedural memory have been used to distinguish two basic types of learning. A neural network model suggests how such memory processes work together as recognition learning, reinforcement learning, and sensory-motor learning take place during adaptive behaviors. To coordinate these processes, the hippocampal formation and cerebellum each contain circuits that learn to adaptively time their outputs. Within the model, hippocampal timing helps to maintain attention on motivationally salient goal objects during variable task-related delays, and cerebellar timing controls the release of conditioned responses. This property is part of the model's description of how cognitive-emotional interactions focus attention on motivationally valued cues, and how this process breaks down due to hippocampal ablation. The model suggests that the hippocampal mechanisms that help to rapidly draw attention to salient cues could prematurely release motor commands were not the release of these commands adaptively timed by the cerebellum. The model hippocampal system modulates cortical recognition learning without actually encoding the representational information that the cortex encodes. These properties avoid the difficulties faced by several models that propose a direct hippocampal role in recognition learning. Learning within the model hippocampal system controls adaptive timing and spatial orientation. Model properties hereby clarify how hippocampal ablations cause amnesic symptoms and difficulties with tasks which combine task delays, novelty detection, and attention towards goal objects amid distractions. When these model recognition, reinforcement, sensory-motor, and timing processes work together, they suggest how the brain can accomplish conditioning of multiple sensory events to delayed rewards, as during serial compound conditioning.

Vector Associative Maps: Unsupervised Real-time Error-based Learning and Control of Movement Trajectories

This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.