A general framework for integrating environmental time series into stock assessment models: model description, simulation testing, and example

We present a method to integrate environmental time series into stock assessment models and to test the significance of correlations between population processes and the environmental time series. Parameters that relate the environmental time series to population processes are included in the stock assessment model, and likelihood ratio tests are used to determine if the parameters improve the fit to the data significantly. Two approaches are considered to integrate the environmental relationship. In the environmental model, the population dynamics process (e.g. recruitment) is proportional to the environmental variable, whereas in the environmental model with process error it is proportional to the environmental variable, but the model allows an additional temporal variation (process error) constrained by a log-normal distribution. The methods are tested by using simulation analysis and compared to the traditional method of correlating model estimates with environmental variables outside the estimation procedure. In the traditional method, the estimates of recruitment were provided by a model that allowed the recruitment only to have a temporal variation constrained by a log-normal distribution. We illustrate the methods by applying them to test the statistical significance of the correlation between sea-surface temperature (SST) and recruitment to the snapper (Pagrus auratus) stock in the Hauraki Gulf–Bay of Plenty, New Zealand. Simulation analyses indicated that the integrated approach with additional process error is superior to the traditional method of correlating model estimates with environmental variables outside the estimation procedure. The results suggest that, for the snapper stock, recruitment is positively correlated with SST at the time of spawning.

Modified Kanerva model. Results for real time word recognition

This paper describes results obtained using the modified Kanerva model to perform word recognition in continuous speech after being trained on the multi-speaker Alvey 'Hotel' speech corpus. Theoretical discoveries have recently enabled us to increase the speed of execution of part of the model by two orders of magnitude over that previously reported by Prager & Fallside. The memory required for the operation of the model has been similarly reduced. The recognition accuracy reaches 95% without syntactic constraints when tested on different data from seven trained speakers. Real time simulation of a model with 9,734 active units is now possible in both training and recognition modes using the Alvey PARSIFAL transputer array. The modified Kanerva model is a static network consisting of a fixed nonlinear mapping (location matching) followed by a single layer of conventional adaptive links. A section of preprocessed speech is transformed by the non-linear mapping to a high dimensional representation. From this intermediate representation a simple linear mapping is able to perform complex pattern discrimination to form the output, indicating the nature of the speech features present in the input window.

Application of the fixed point method for solution in time stepping finite element analysis using the inverse vector Jiles-Atherton model

An implementation of the inverse vector Jiles-Atherton model for the solution of non-linear hysteretic finite element problems is presented. The implementation applies the fixed point method with differential reluctivity values obtained from the Jiles-Atherton model. Differential reluctivities are usually computed using numerical differentiation, which is ill-posed and amplifies small perturbations causing large sudden increases or decreases of differential reluctivity values, which may cause numerical problems. A rule based algorithm for conditioning differential reluctivity values is presented. Unwanted perturbations on the computed differential reluctivity values are eliminated or reduced with the aim to guarantee convergence. Details of the algorithm are presented together with an evaluation of the algorithm by a numerical example. The algorithm is shown to guarantee convergence, although the rate of convergence depends on the choice of algorithm parameters. © 2011 IEEE.

Dynamic Covariance Models for Multivariate Financial Time Series

The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data. However, some of the most popular models suffer from a) overfitting problems and multiple local optima, b) failure to capture shifts in market conditions and c) large computational costs. To address these problems we introduce a novel dynamic model for time-changing covariances. Over-fitting and local optima are avoided by following a Bayesian approach instead of computing point estimates. Changes in market conditions are captured by assuming a diffusion process in parameter values, and finally computationally efficient and scalable inference is performed using particle filters. Experiments with financial data show excellent performance of the proposed method with respect to current standard models.

A time-varying controllable fault-tolerant field associative memory model and its simulation

A time-varying controllable fault-tolerant field associative memory model and the realization algorithms are proposed. On the one hand, this model simulates the time-dependent changeability character of the fault-tolerant field of human brain's associative memory. On the other hand, fault-tolerant fields of the memory samples of the model can be controlled, and we can design proper fault-tolerant fields for memory samples at different time according to the essentiality of memory samples. Moreover, the model has realized the nonlinear association of infinite value pattern from n dimension space to m dimension space. And the fault-tolerant fields of the memory samples are full of the whole real space R-n. The simulation shows that the model has the above characters and the speed of associative memory about the model is faster.

Influence of Inter-Fiber Spacing and Interfacial Adhesion on Failure of Multi-Fiber Model Composites: Experiment and Numerical Analysis

The uniaxial tension experiments on glass-fiber-reinforced epoxy matrix composites reveal that the fragmentations of fibers display vertically aligned fracture, clustered fracture, coordinated fracture, and random fracture with the increase of inter-fiber spacing. The finite element analysis indicates that the fragmentations of fibers displaying different phenomena are due to the stress concentration as well as the inherent randomness of fiber defects, which is the dominant factor. The experimental results show that matrices adjacent to the fiber breakpoints all exhibit birefringent-whitening patterns for the composites with different interfacial adhesion strengths. The larger the extent of the interfacial debonding, the less the domain of the birefringent-whitening patterns. The numerical analysis indicates that the orientation of the matrix adjacent to a fiber breakpoint is caused by the interfacial shear stress, resulting in the birefringent-whitening patterns. The area of shear stress concentrations decides on the domain of the birefringent-whitening patterns.

Analysis of micro-failure behaviors in hybrid fiber model composites

Micro-failure modes and statistical fragment lengths in the hybrid fiber and non-hybrid reference composites in the uniaxial tension were investigated. Similiar to the reference experiments, fibers in hybrid strong interface/medium interface fiber composites display a decrease in aspect ratio and an increase in interfacial shear stress (IFSS) with the increase of inter-fiber spacing. While for the fibers with weak interfaces in the hybrid strong interface/weak interface fiber composites, the aspect ratio increases and IFSS decreases with enlargement of inter-fiber spacing, which is contrary to other systems. Finite element numerical analysis was used to interpret the special phenomena.

Relaxation behavior of polymer blends with complex morphologies: Palierne emulsion model for uncompatibilized and compatibilized PP/PA6 blends

This paper deals with the dynamic rheological behavior of polypropylene/polyamide6 (PP/PA6) uncompatibilized blends and those compatibilized with a maleic anhydride grafted PP (PP/PP-g-MAH/PA6). The terminal relaxation times of the blends predicted by the Palierne emulsion model were compared with those obtained from experimental relaxation time spectra. The Palierne model succeeded well in describing PP/PA6 uncompatibilized blends with relatively low dispersed phase contents (10 wt%) and failed doing so for those of which the dispersed contents were high (30 wt%). It also failed for the compatibilized ones, irrespective of the dispersed phase content (10 or 30 wt%) and whether or not interface relaxation was taken into consideration. In the case of the uncompatibilized blend with high dispersed-phase content, interconnections among inclusions of the dispersed phase were responsible for the failure of the Palierne model. As for the compatiblized blends, in addition to particle interconnections, the existence of emulsion-in-emulsion (EE) structures was another factor responsible for the failure of Palieme model.

Temperature dependence of the distribution of the first passage time: Results from discontinuous molecular dynamics simulations of an all-atom model of the second beta-hairpin fragment of protein G

More than 22 000 folding kinetic simulations were performed to study the temperature dependence of the distribution of first passage time (FPT) for the folding of an all-atom Go-like model of the second beta-hairpin fragment of protein G. We find that the mean FPT (MFPT) for folding has a U (or V)-shaped dependence on the temperature with a minimum at a characteristic optimal folding temperature T-opt*. The optimal folding temperature T-opt* is located between the thermodynamic folding transition temperature and the solidification temperature based on the Lindemann criterion for the solid. Both the T-opt* and the MFPT decrease when the energy bias gap against nonnative contacts increases. The high-order moments are nearly constant when the temperature is higher than T-opt* and start to diverge when the temperature is lower than T-opt*. The distribution of FPT is close to a log-normal-like distribution at T* greater than or equal to T-opt*. At even lower temperatures, the distribution starts to develop long power-law-like tails, indicating the non-self-averaging intermittent behavior of the folding dynamics. It is demonstrated that the distribution of FPT can also be calculated reliably from the derivative of the fraction not folded (or fraction folded), a measurable quantity by routine ensemble-averaged experimental techniques at dilute protein concentrations.

Real-Time Decision Making and Aggressive Behavior in Youth: A Heuristic Model of Response Evaluation and Decision (RED).

Considerable scientific and intervention attention has been paid to judgment and decision-making systems associated with aggressive behavior in youth. However, most empirical studies have investigated social-cognitive correlates of stable child and adolescent aggressiveness, and less is known about real-time decision making to engage in aggressive behavior. A model of real-time decision making must incorporate both impulsive actions and rational thought. The present paper advances a process model (response evaluation and decision; RED) of real-time behavioral judgments and decision making in aggressive youths with mathematic representations that may be used to quantify response strength. These components are a heuristic to describe decision making, though it is doubtful that individuals always mentally complete these steps. RED represents an organization of social-cognitive operations believed to be active during the response decision step of social information processing. The model posits that RED processes can be circumvented through impulsive responding. This article provides a description and integration of thoughtful, rational decision making and nonrational impulsivity in aggressive behavioral interactions.