A three-dimensional nonlinear reduced-order predictive joint model

Mechanical joints can have significant effects on the dynamics of assembled structures. However, the lack of efficacious predictive dynamic models for joints hinders accurate prediction of their dynamic behavior. The goal of our work is to develop physics-based, reduced-order, finite element models that are capable of replicating the effects of joints on vibrating structures. The authors recently developed the so-called two-dimensional adjusted Iwan beam element (2-D AIBE) to simulate the hysteretic behavior of bolted joints in 2-D beam structures. In this paper, 2-D AIBE is extended to three-dimensional cases by formulating a three-dimensional adjusted Iwan beam element (3-D AIBE). Impulsive loading experiments are applied to a jointed frame structure and a beam structure containing the same joint. The frame is subjected to excitation out of plane so that the joint is under rotation and single axis bending. By assuming that the rotation in the joint is linear elastic, the parameters of the joint associated with bending in the frame are identified from acceleration responses of the jointed beam structure, using a multi-layer feed-forward neural network (MLFF). Numerical simulation is then performed on the frame structure using the identified parameters. The good agreement between the simulated and experimental impulsive acceleration responses of the frame structure validates the efficacy of the presented 3-D AIBE, and indicates that the model can potentially be applied to more complex structural systems with joint parameters identified from a relatively simple structure.     

Estimation of heterogeneous aquifer parameters from piezometric data using ridge functions and neural networks

Inverse modeling aims to estimate transmissivity and other parameters needed by distributed aquifer models, using piezometric measurements. While these parameters are highly variable in space, the two-dimensional aquifer area is essentially empty of measurements (curse of dimensionality). To address this problem, a representation of the two-dimensional transmissivity map based on ridge functions and neural networks is introduced and applied to inverse aquifer modeling. The proposed representation has good expressive power, i.e. it is concise and convergences quickly as the number of parameters are increased, and it is expected to express complex transmissivity variations with relatively few parameters which can be estimated from the piezometric measurements. A simple regularization that can dampen erratic high frequency terms in the estimated parameters is suggested. Several examples indicate that the proposed parameterization can handle diverse types of transmissivity variations while it is particularly suited when the true transmissivity map exhibits specific sorts of heterogeneity with large anisotropies or abrupt changes along lines.     

Social Network Approach to Understand the Ethnic Economy: A Theoretical Discourse<Superscript>1</Superscript>

In this paper we suggest how social network analysis, in contrast to looking at physical space, can be used to trace the social and economic location of ethnic enclaves. Taking skilled workers immigrating to Canada from China as an example, we analyze critically how split labor market theories describe materialist and structural factors that determine immigrants’ limited options. Cultural theories play up immigrants’ interest in using their cultural resources to pull themselves ahead. We propose that social network analysis as a single framework can bring together elements from materialist–structural and cultural theories. The position of people and firms in these networks gives us a view of the kinds of jobs immigrants get and the businesses they set up. To understand the ethnic economy, we discuss how networks of social and economic relations intersect each other. By seeing the ethnic economy embedded in social networks, we can provide a more general explanation of the social space of the ethnic economy in contrast to its physical location. We use three cases of ethnic entrepreneurs to illustrate how the social and economic relations locate their businesses in the enclave and how they are also linked to the mainstream economy. ¹This paper has benefitted from the critical clarifications of Chiu Luk and an anonymous reviewer, and the talented editing of Allen Sutterfield. Lynn Xu Liping helped on an earlier draft.     

Tenseurs de perméabilité équivalente d'un domaine hétérogène fini

In an earlier paper, it has been shown that for a heterogeneous block of finite size, under linear boundary pressure conditions, a symmetric and positive definite tensor, denoted here by ${\mathbf{K}}^{\mathrm{p}}$, exists, which relates the mean flux to the mean gradient of pressure in the block. In this paper, the conditions of constant boundary flux are first defined. Then it is shown that, under these conditions also, a symmetric and positive definite tensor, denoted ${\mathbf{K}}^{\mathrm{q}}$, exists, which relates the mean flux to the mean pressure gradient. It is then shown that ${\mathbf{K}}^{\mathrm{q}}$ and ${\mathbf{K}}^{\mathrm{p}}$ respectively give the equivalent permeability in the direction of mean gradient and of mean flux. The directional permeability values given by ${\mathbf{K}}^{\mathrm{q}}$ are shown to be smaller than ${\mathbf{K}}^{\mathrm{p}}$. To cite this article: A. Pouya, C. R. Geoscience 337 (2005).     

BITNET: A TOOL FOR COMMUNICATIONS AMONG GEOGRAPHERS*

BITNET is a telecommunications network for higher education. The network's general characteristics, services, the availability of BITNET to academic geographers, and their use of the system are examined. Although more than 80% of geography faculty in the United States and Canada are at BITNET-supported institutions, a survey of AAG Specialty Group chairs suggests that geographers' actual use of BITNET is relatively low.     

Speeding Up Image Reconstruction Methods in Coded Mask γ Cameras Using Neural Networks: Application to the EM Algorithm

When using γ-ray coded-mask cameras, one does not get a direct image as in classical optical cameras but the correlation of the mask response with the source. Therefore the data must be mathematically treated in order to reconstruct the original sky sources. Generally this reconstruction is based on linear methods, such as correlating the detector plane with a reconstruction array, or non-linear ones such as iterative or maximization methods (i.e. the EM algorithm). The latter have a better performance but they increase the computational complexity by taking a lot of time to reconstruct an image. Here we present a method for speeding up such kind of algorithms by making use of a neural network with a back-propagation learning rule. This revised version was published online in July 2006 with corrections to the Cover Date.     

Automatic picking of P and S phases using a neural tree

The large amount of digital data recorded by permanent and temporary seismic networks makes automatic analysis of seismograms and automatic wave onset time picking schemes of great importance for timely and accurate event locations. We propose a fast and efficient P- and S-wave onset time, automatic detection method based on neural networks. The neural networks adopted here are particular neural trees, called IUANT2, characterized by a high generalization capability. Comparison between neural network automatic onset picking and standard, manual methods, shows that the technique presented here is generally robust and that it is capable to correctly identify phase-types while providing estimates of their accuracies. In addition, the automatic post processing method applied here can remove the ambiguity deriving from the incorrect association of events occurring closely in time. We have tested the methodology against standard STA/LTA phase picks and found that this neural approach performs better especially for low signal-to-noise ratios. We adopt the recall, precision and accuracy estimators to appraise objectively the results and compare them with those obtained with other methodologies.Tests of the proposed method are presented for 342 earthquakes recorded by 23 different stations (about 5000 traces). Our results show that the distribution of the differences between manual and automatic picking has a standard deviation of 0.064 s and 0.11 s for the P and the S waves, respectively. Our results show also that the number of false alarms deriving from incorrect detection is small and, thus, that the method is inherently robust.This paper has not been submitted elsewhere in identical or similar form, nor will it be during the first three months after its submission to Journal of Seismology.     

Adaptive neuro-fuzzy inference system for prediction of water level in reservoir

Accurate prediction of the water level in a reservoir is crucial to optimizing the management of water resources. A neuro-fuzzy hybrid approach was used to construct a water level forecasting system during flood periods. In particular, we used the adaptive network-based fuzzy inference system (ANFIS) to build a prediction model for reservoir management. To illustrate the applicability and capability of the ANFIS, the Shihmen reservoir, Taiwan, was used as a case study. A large number (132) of typhoon and heavy rainfall events with 8640 hourly data sets collected in past 31 years were used. To investigate whether this neuro-fuzzy model can be cleverer (accurate) if human knowledge, i.e. current reservoir operation outflow, is provided, we developed two ANFIS models: one with human decision as input, another without. The results demonstrate that the ANFIS can be applied successfully and provide high accuracy and reliability for reservoir water level forecasting in the next three hours. Furthermore, the model with human decision as input variable has consistently superior performance with regard to all used indexes than the model without this input.     

Critiquing environmental management in indigenous Australia: two case studies

The notion of community participation in sustainable environmental management is well documented, but applications between environmental scientists and indigenous Australians are less known. Many indigenous Australians are socially and culturally marginalized, and agencies debate over appropriate designs for collaborative, cross-cultural environmental programmes that will alleviate marginalization. This paper presents two case studies from northern Australia, where indigenous people participated in an environmental project exploring the commercial potential of the local sea cucumber resource. A framework with principles of participation and knowledge exchange guided project implementation at both localities. Participant observation data that related to these principles suggests that abuse of power imbalances and exploitation (institutional racism) may be more easily disguised within formalized governance structures than in the informal networks and structures that can be supported through less formalized representation. Linking informal and formal governance structures and canvassing a range of governance modalities will ultimately connect the human behaviours and governance structures needed to progress indigenous environmental management in Australia.     

Self-organized complexity in geomorphology: Observations and models

The focus of this paper is to relate fundamental statistical properties of landforms and drainage networks to models that have been developed in statistical physics. Relevant properties and models are reviewed and a general overview is presented. Landforms and drainage networks are clearly complex, but well-defined scaling laws are found. Coastlines, topography contours, and lakes are classic self-similar fractals. The height of topography along a linear track is well approximated as a Brownian walk, a self-affine fractal. This type of behavior has also been found in surface physics, for example the surface roughness of a fracture. An applicable model is the Langevin equation, the heat equation with a stochastic white-noise driver. This model also reproduces the statistics of sediment deposition. Drainage networks were one of the original examples of self-similar fractal trees. An important advance in quantifying the structure of drainage networks is the application of the Tokunaga fractal side-branching statistics. A classic problem in statistical physics is the diffusion-limited aggregation. The resulting tree like structures have been shown to also satisfy the Tokunaga statistics. A modified version of the diffusion-limited aggregation model reproduces the statistics of drainage networks. It is concluded that the models developed in statistical physics have direct applicability to the fundamental problems in geomorphology.