The nonlinear dynamic equations introduced by Biot to model porous media have not been implemented to describe nonlinear acoustic waves in such media. In this work the equations are revised and a mathematical model depicting the physical nonlinearity is established. A perturbation technique is then applied to find solutions to the nonlinear Biot equations. An important feature of the developed model is the introduction of the dependence of the structural parameters of the medium on its porosity. The model establishes a correlation between the measurable effective nonlinear parameter and structural parameters of the porous medium. This suggests employing nonlinear measurements as a diagnostic tool for porous media.

The Biot model is widely used to model poroelastic media. Several authors have studied its applicability to cancellous bone. In this article the feasibility of determining the Biot parameters of cancellous bone by acoustic interrogation using frequencies in the 5-15 kHz range is studied. It is found that the porosity of the specimen can be determined with a high degree of accuracy. The degree to which other parameters can be determined accurately depends upon porosity.

We propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of stock returns from high‐frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non‐stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non‐decimated wavelet‐based multi‐resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one‐, three‐ and five step‐ahead horizons was achieved by the proposed model. The procedure used to build the neuro‐wavelet model is reusable and can be applied to any high‐frequency financial series to specify the model characteristics associated with that particular series. Copyright © 2013 John Wiley & Sons, Ltd.

This article addresses the stock market as a complex system. The complexity of the stock market arises from the structure of the environment, agent heterogeneity, interactions among agents, and interactions with market regulators. We develop the idea of a meta‐model, which is a model of models represented in an agent‐based model that allows us to investigate this type of market complexity. The novelty of this article is the incorporation of various complexities captured by network theoretical models or induced by investment behavior. The model considers agents heterogeneous in terms of their strategies and investment behavior. Four investment strategies are included in the model: zero‐intelligence, fundamental strategy, momentum (trend followers), and adaptive trading strategy using the artificial neural network algorithm. In terms of behavior, the agents can be risk averse or loss occupied with overconfidence or conservative biases. The agents may interact with each other by sharing market sentiments through a structured scale‐free network. The market regulator controls the market through various control tools such as the risk‐free rate and taxation. Parameters are calibrated to the S&P500. The calibration is implemented using a scatter search heuristic approach. The model is validated using various stylized facts of stock return patterns such as excess kurtosis, auto‐correlation, and ARCH effect phenomena. Analysis at the macro and micro level of the market was performed by measuring the sensitivity of volatility and market capital and investigating the wealth distributions of the agents. We found that volatility is more sensitive to the model parameters than to market capital, and thus, the level of volatility does not affect market capital. In addition, the findings suggest that the efficient market hypothesis holds at the macro level but not at the micro level. © 2016 Wiley Periodicals, Inc. Complexity 21: 530–554, 2016

Systems engineers have always been concerned with the problems surrounding complex systems. An evolutionary class of complex systems called Complex Adaptive Systems (CAS) is even more relevant today as we struggle to understand and cope with the often undesirable emergence effects of some of these systems, evidenced by recurring financial and sociopolitical crises. A key characteristic of CAS is a large, diverse, and sometimes unknown set of interactions both internally and across the system boundary. For the purpose of managing this complexity we present in this paper the concept that a principles orientation as compared to rules orientation of a complex system is an important and useful characteristic in need of further study. The US financial system is used as a CAS exemplar in support of this inquiry. ©2012 Wiley Periodicals, Inc. Syst Eng 15