Cellular-Automata Model for Dense-Snow Avalanches

This paper introduces a three-dimensional model for simulating dense-snow avalanches, based on the numerical method of cellular automata. This method allows one to study the complex behavior of the avalanche by dividing it into small elements, whose interaction is described by simple laws, obtaining a reduction of the computational power needed to perform a three-dimensional simulation. Similar models by several authors have been used to model rock avalanches, mud and lava flows, and debris avalanches. A peculiar aspect of avalanche dynamics, i.e., the mechanisms of erosion of the snowpack and deposition of material from the avalanche is taken into account in the model. The capability of the proposed approach has been illustrated by modeling three documented avalanches that occurred in Susa Valley (Western Italian Alps). Despite the qualitative observations used for calibration, the proposed method is able to reproduce the correct three-dimensional avalanche path, using a digital terrain model, and the order of magnitude of the avalanche deposit volume.     

Off-Site Exposure to Respirable Aerosols Produced during the Disk-Incorporation of Class B Biosolids

Field experiments were conducted at a Class B biosolids land application site in central Arizona to measure, model, and source-track the off-site transport of aerosols emitted when biosolids were disk-incorporated into soils. Real-time PM10 monitoring provided time-resolved aerosol information sufficient for verifying both off-site concentration and off-site exposure time model results. Under the conditions considered and at a distance of 165?m from the aerosol source, biosolids disk-incorporation resulted in an intermittent exposure to biosolids-derived aerosol concentration between 15 and 40?μg/m3 and an inhalable biosolids dose between 2 and 8?μg. Transport modeling predicted that these doses will decrease with increasing wind speed. In addition, three DNA sequence-based biosolids source tracking methods were applied to aerosol samples and confirmed the presence of biosolids in aerosols at 5, 65, and 165?m from the aerosol source. Field measurements and modeling indicate that the nature of biosolids-derived aerosol exposure is a series of intermittent high concentration puffs, rather than a continuous low concentration.     

Practical Equivalent Continuum Model for Simulation of Jointed Rock Mass Using FLAC3D

A simple practical equivalent continuum numerical model for simulating the behavior of jointed rock mass has been extended to three-dimensional using FLAC3D. This model estimates the properties of jointed rock mass from the properties of intact rock and a joint factor (Jf), which is the integration of the properties of joints to take care of the effects of frequency, orientation, and strength of joint. A new FISH function has been written in FLAC3D specifically for modeling jointed rocks using the Duncan and Chang hyperbolic model. This model has been validated first with simple element tests at different confining pressures for different rocks with different joint configurations. Explicit modeling of the joints has also been done in element tests and results obtained compare well with the results of equivalent continuum model and also with experimental results. Further, this has been applied for a case study of a large underground power house cavern in the Himalayas. The analysis has been done under various stages of excavation, assigning a null model available in FLAC3D for simulating the excavation.     

Model Selection in Applied Science and Engineering: A Decision-Theoretic Approach

Mathematical models are developed and used to study the properties of complex systems in just about every area of applied science and engineering. Information on the system being modeled is, in general, incomplete, so that there may be two or more models consistent with the available information. The collection of these models is called the class of candidate models. A decision-theoretic method is developed for selecting the optimal member from the collection. The optimal model depends on the available information, the class of candidate models, and the model use. The candidate models may be deterministic or random. Classical methods for model selection, including the method of maximum likelihood and Bayesian methods, are briefly reviewed. These methods ignore model use and require data to be available. In addition, examples are used to show that classical methods for model selection can be unreliable in the sense that they can deliver unsatisfactory models when data is limited. The proposed decision-theoretic method for model selection does not have these limitations. The method accounts for model use via a utility function. This feature is especially important when modeling high-risk systems where the consequences of using an inappropriate model for the system can be disastrous.     

Two-Dimensional Physical and Numerical Modeling of a Pile-Supported Earth Platform over Soft Soil

This paper focuses on the mechanisms occurring in a granular earth platform over soft ground improved by rigid piles. Two-dimensional physical model experiments were performed using the Schneebeli’s analogical soil to investigate the load transfer mechanisms by arching and the settlement reduction and homogenization. Experimental outputs are compared to results obtained on a numerical model using a plane strain continuum approach. The impact of the constitutive model complexity to simulate the platform material behavior was first assessed, but no large difference was recorded. As far as the proposed model, which takes the main features of the observed behavior satisfactorily into account, the numerical procedure could be validated and the parametric studies extended numerically. Both approaches of this study underlined the main geometrical and geotechnical parameters which should inevitably be taken into account in a simplified design method, namely the capping ratio, the platform height, and the platform material shear strength.     

A fuzzy clustering algorithm enhancing local model interpretability

In this work, simple modifications on the cost index of particular local-model fuzzy clustering algorithms are proposed in order to improve the readability of the resulting models. The final goal is simultaneously providing local linear models (reasonably close to the plant’s Jacobian) and clustering in the input space so that desirable characteristics (regarding final model accuracy, and convexity and smoothness of the cluster membership functions) are improved with respect to other proposals in literature. Some examples illustrate the proposed approach.     

Modeling language acquisition in atypical phenotypes.

An increasing number of connectionist models have been proposed to explain behavioral deficits in developmental disorders. These simulations motivate serious consideration of the theoretical implications of the claim that a developmental disorder fits within the parameter space of a particular computational model of normal development. The authors examine these issues in depth with respect to a series of new simulations investigating past-tense formation in Williams syndrome. This syndrome and the past-tense domain are highly relevant because both have been used to make strong theoretical claims about the processes underlying normal language acquisition. The authors conclude that computational models have great potential to advance psychologists' understanding of developmental deficits because they focus on the developmental process itself as a pivotal causal factor in producing atypical phenotypic outcomes. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Why is science difficult to learn? Things are seldom what they seem

Abstract The difficulties of learning science are related to the nature of science itself and to the methods by which science is customarily taught without regard to what is known about children's learning. An information processing model is proposed to guide thinking and research in this area.     

Wave propagation in two-phase flow of magnetic fluid under a nonuniform magnetic field

The effect of nonuniform magnetic field on the linear and nonlinear wave propagation phenomena in two-phase pipe flow of magnetic fluid is investigated theoretically to realize the effective energy conversion system using boiling two-phase flow of magnetic fluid. Firstly, the governing equations of two-phase flow based on the unsteady thermal nonequilibrium two-fluid model are presented and the linear void wave propagation phenomena in boiling two-phase flow are numerically analyzed by using the finite volume method. Next, the nonlinear pressure wave propagation in gas-liquid two-phase flow is numerically analyzed by using the finite different method. According to these theoretical studies on the wave propagation phenomena in two-phase flow of magnetic fluid, it seems to be a reasonable proposal that the precise control of the wave propagation in two-phase flow is possible by effective use of the magnetic force.     

Seeking the truth about ad hoc join costs

In this paper, we re-examine the results of prior work on methods for computing ad hoc joins. We develop a detailed cost model for predicting join algorithm performance, and we use the model to develop cost formulas for the major ad hoc join methods found in the relational database literature. We show that various pieces of “common wisdom” about join algorithm performance fail to hold up when analyzed carefully, and we use our detailed cost model to derive op timal buffer allocation schemes for each of the join methods examined here. We show that optimizing their buffer allocations can lead to large performance improvements, e.g., as much as a 400% improvement in some cases. We also validate our cost model's predictions by measuring an actual implementation of each join algorithm considered. The results of this work should be directly useful to implementors of relational query optimizers and query processing systems. Edited by M. Adiba. Received May 1993 / Accepted April 1996