Genetic algorithms for local controller network construction

Local Controller Networks (LCNs) provide nonlinear control by interpolating between a set of locally valid, subcontrollers covering the operating range of the plant. Constructing such networks typically requires knowledge of valid local models. This paper describes a new genetic learning approach to the construction of LCNs directly from the dynamic equations of the plant, or from modelling data. The advantage is that a priori knowledge about valid local models is not needed. In addition to allowing simultaneous optimisation of both the controller and validation function parameters, the approach aids transparency by ensuring that each local controller acts independently of the rest at its operating point. It thus is valuable for simultaneous design of the LCNs and identification of the operating regimes of an unknown plant. Application results from a highly nonlinear pH neutralisation process and its associated neural network representation are utilised to illustrate these issues.

A keystone effect for parasites in intraguild predation?

Intraguild predation (IGP) is common in communities, yet theory suggests it should not often persist and coexistence of participating species should be rare. As parasitism can play keystone roles in interactions between competitors, and between predators and prey, here we examine the role of parasites in maintaining IGP. We used numerical exploration of population dynamic equations to determine coexistence and exclusion zones for two species engaged in IGP with shared parasitism. We demonstrate that parasitism increases the range of conditions leading to coexistence when the parasite exerts a greater deleterious effect on the 'stronger' species in terms of the combined effects of competition and predation. Such a parasite can enable an inferior competitor that is also the less predatory to persist, and may actually lead to numerical dominance of this species.

A dielectric continuum molecular dynamics method

We introduce a novel method to simulate hydrated macromolecules with a dielectric continuum representation of the surrounding solvent. In our approach, the interaction between the solvent and the molecular degrees of freedom is described by means of a polarization density free energy functional which is minimum at electrostatic equilibrium. After a pseudospectral expansion of the polarization and a discretization of the functional, we construct the equations of motion for the system based on a Car-Parrinello technique. In the limit of the adiabatic evolution of the polarization field variables, our method provides the solution of the dielectric continuum problem "on the fly," while the molecular coordinates are propagated. In this first study, we show how our dielectric continuum molecular dynamics method can be successfully applied to hydrated biomolecules, with low cost compared to free energy simulations with explicit solvent. To our knowledge, this is the first time that stable and conservative molecular dynamic simulations of solutes can be performed for a dielectric continuum model of the solvent. (C) 2001 American Institute of Physics.

Modeling Multivariate Interest Rates Using Time-Varying Copulas and Reducible Nonlinear Stochastic Differential Equations

We propose a new approach for modeling nonlinear multivariate interest rate processes based on time-varying copulas and reducible stochastic differential equations (SDEs). In the modeling of the marginal processes, we consider a class of nonlinear SDEs that are reducible to Ornstein--Uhlenbeck (OU) process or Cox, Ingersoll, and Ross (1985) (CIR) process. The reducibility is achieved via a nonlinear transformation function. The main advantage of this approach is that these SDEs can account for nonlinear features, observed in short-term interest rate series, while at the same time leading to exact discretization and closed-form likelihood functions. Although a rich set of specifications may be entertained, our exposition focuses on a couple of nonlinear constant elasticity volatility (CEV) processes, denoted as OU-CEV and CIR-CEV, respectively. These two processes encompass a number of existing models that have closed-form likelihood functions. The transition density, the conditional distribution function, and the steady-state density function are derived in closed form as well as the conditional and unconditional moments for both processes. In order to obtain a more flexible functional form over time, we allow the transformation function to be time varying. Results from our study of U.S. and UK short-term interest rates suggest that the new models outperform existing parametric models with closed-form likelihood functions. We also find the time-varying effects in the transformation functions statistically significant. To examine the joint behavior of interest rate series, we propose flexible nonlinear multivariate models by joining univariate nonlinear processes via appropriate copulas. We study the conditional dependence structure of the two rates using Patton (2006a) time-varying symmetrized Joe--Clayton copula. We find evidence of asymmetric dependence between the two rates, and that the level of dependence is positively related to the level of the two rates. (JEL: C13, C32, G12) Copyright The Author 2010. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org, Oxford University Press.

Evaluation of a multi-functional nanocarrier for targeted breast cancer iNOS gene therapy.

The present study determines whether the novel designer biomimetic vector (DBV) can condense anddeliver the cytotoxic iNOS gene to breast cancer cells to achieve a therapeutic effect. We have previouslyshown the benefits of iNOS for cancer gene therapy but the stumbling block to future development hasbeen the delivery system.The DBV was expressed, purified and complexed with the iNOS gene. The particle size and chargewere determined via dynamic light scattering techniques. The toxicity of the DBV/iNOS nanoparticleswas quantified using the cell toxicity and clonogenic assays. Over expression of iNOS was confirmed viaWestern blotting and Griess test.The DBV delivery system fully condensed the iNOS gene with nanoparticles less than 100 nm. Transfectionwith the DBV/iNOS nanoparticles resulted in a maximum of 62% cell killing and less than 20%clonogenicity. INOS overexpression was confirmed and total nitrite levels were in the range of 18M.We report for the first time that the DBV can successfully deliver iNOS and achieve a therapeuticeffect. There is significant cytotoxicity coupled with evidence of a bystander effect. We concludethat the success of the DBV fusion protein in the delivery of iNOS in vitro is worthy of future in vivo experiments.

The C-Terminal Variable Region Specifies the Dynamic Properties of Arabidopsis Microtubule-Associated Protein MAP65 Isotypes

The microtubule-associated protein, MAP65, is a member of a family of divergent microtubule-associated proteins from different organisms generally involved in maintaining the integrity of the central spindle in mitosis. The dicotyledon Arabidopsis thaliana and the monocotyledon rice (Oryza sativa) genomes contain 9 and 11 MAP65 genes, respectively. In this work, we show that the majority of these proteins fall into five phylogenetic clades, with the greatest variation between clades being in the C-terminal random coil domain. At least one Arabidopsis and one rice isotype is within each clade, indicating a functional specification for the C terminus. In At MAP65-1, the C-terminal domain is a microtubule binding region (MTB2) harboring the phosphorylation sites that control its activity. The At MAP65 isotypes show differential localization to microtubule arrays and promote microtubule polymerization with variable efficiency in a MTB2-dependent manner. In vivo studies demonstrate that the dynamics of the association and dissociation of different MAP65 isotypes with microtubules can vary up to 10-fold and that this correlates with their ability to promote microtubule polymerization. Our data demonstrate that the C-terminal variable region, MTB2, determines the dynamic properties of individual isotypes and suggest that slower turnover is conditional for more efficient microtubule polymerization.

Interfacing cellular networks of S. cerevisiae and E. coli: Connecting dynamic and genetic information

Background: In recent years, various types of cellular networks have penetrated biology and are nowadays used omnipresently for studying eukaryote and prokaryote organisms. Still, the relation and the biological overlap among phenomenological and inferential gene networks, e.g., between the protein interaction network and the gene regulatory network inferred from large-scale transcriptomic data, is largely unexplored.

Results: We provide in this study an in-depth analysis of the structural, functional and chromosomal relationship between a protein-protein network, a transcriptional regulatory network and an inferred gene regulatory network, for S. cerevisiae and E. coli. Further, we study global and local aspects of these networks and their biological information overlap by comparing, e.g., the functional co-occurrence of Gene Ontology terms by exploiting the available interaction structure among the genes.

Conclusions: Although the individual networks represent different levels of cellular interactions with global structural and functional dissimilarities, we observe crucial functions of their network interfaces for the assembly of protein complexes, proteolysis, transcription, translation, metabolic and regulatory interactions. Overall, our results shed light on the integrability of these networks and their interfacing biological processes.

Dynamic identification of building structures: new strategies:Meccanica dei materiali e delle strutture

In this paper the evolution of a time domain dynamic identification technique based on a statistical moment approach is presented. This technique can be used in the case of structures under base random excitations in the linear state and in the non linear one. By applying Itoˆ stochastic calculus, special algebraic equations can be obtained depending on the statistical moments of the response of the system to be identified. Such equations can be used for the dynamic identification of the mechanical parameters and of the input. The above equations, differently from many techniques in the literature, show the possibility of obtaining the identification of the dissipation characteristics independently from the input. Through the paper the first formulation of this technique, applicable to non linear systems, based on the use of a restricted class of the potential models, is presented. Further a second formulation of the technique in object, applicable to each kind of linear systems and based on the use of a class of linear models, characterized by a mass proportional damping matrix, is described.

Fast Convergence of Regularised Region-based Mixture of Gaussians for Dynamic Background Modelling

The momentum term has long been used in machine learning algorithms, especially back-propagation, to improve their speed of convergence. In this paper, we derive an expression to prove the O(1/k2) convergence rate of the online gradient method, with momentum type updates, when the individual gradients are constrained by a growth condition. We then apply these type of updates to video background modelling by using it in the update equations of the Region-based Mixture of Gaussians algorithm. Extensive evaluations are performed on both simulated data, as well as challenging real world scenarios with dynamic backgrounds, to show that these regularised updates help the mixtures converge faster than the conventional approach and consequently improve the algorithm’s performance.

Region-based Mixture of Gaussians Modelling for Foreground Detection in Dynamic Scenes

One of the most widely used techniques in computer vision for foreground detection is to model each background pixel as a Mixture of Gaussians (MoG). While this is effective for a static camera with a fixed or a slowly varying background, it fails to handle any fast, dynamic movement in the background. In this paper, we propose a generalised framework, called region-based MoG (RMoG), that takes into consideration neighbouring pixels while generating the model of the observed scene. The model equations are derived from Expectation Maximisation theory for batch mode, and stochastic approximation is used for online mode updates. We evaluate our region-based approach against ten sequences containing dynamic backgrounds, and show that the region-based approach provides a performance improvement over the traditional single pixel MoG. For feature and region sizes that are equal, the effect of increasing the learning rate is to reduce both true and false positives. Comparison with four state-of-the art approaches shows that RMoG outperforms the others in reducing false positives whilst still maintaining reasonable foreground definition. Lastly, using the ChangeDetection (CDNet 2014) benchmark, we evaluated RMoG against numerous surveillance scenes and found it to amongst the leading performers for dynamic background scenes, whilst providing comparable performance for other commonly occurring surveillance scenes.

Blind Characterisation of Sensors with Second-Order Dynamic Response

Compensation for the dynamic response of a temperature sensor usually involves the estimation of its input on the basis of the measured output and model parameters. In the case of temperature measurement, the sensor dynamic response is strongly dependent on the measurement environment and fluid velocity. Estimation of time-varying sensor model parameters therefore requires continuous textit{in situ} identification. This can be achieved by employing two sensors with different dynamic properties, and exploiting structural redundancy to deduce the sensor models from the resulting data streams. Most existing approaches to this problem assume first-order sensor dynamics. In practice, however second-order models are more reflective of the dynamics of real temperature sensors, particularly when they are encased in a protective sheath. As such, this paper presents a novel difference equation approach to solving the blind identification problem for sensors with second-order models. The approach is based on estimating an auxiliary ARX model whose parameters are related to the desired sensor model parameters through a set of coupled non-linear algebraic equations. The ARX model can be estimated using conventional system identification techniques and the non-linear equations can be solved analytically to yield estimates of the sensor models. Simulation results are presented to demonstrate the efficiency of the proposed approach under various input and parameter conditions.

Dynamic Modeling and Interaction of Hybrid Natural Gas and Electricity Supply System in Microgrid

Natural gas (NG) network and electric network are becoming tightly integrated by microturbines in the microgrid. Interactions between these two networks are not well captured by the traditional microturbine (MT) models. To address this issue, two improved models for single-shaft MT and split-shaft MT are proposed in this paper. In addition, dynamic models of the hybrid natural gas and electricity system (HGES) are developed for the analysis of their interactions. Dynamic behaviors of natural gas in pipes are described by partial differential equations (PDEs), while the electric network is described by differential algebraic equations (DAEs). So the overall network is a typical two-time scale dynamic system. Numerical studies indicate that the two-time scale algorithm is faster and can capture the interactions between the two networks. The results also show the HGES with a single-shaft MT is a weakly coupled system in which disturbances in the two networks mainly influence the dc link voltage of the MT, while the split-shaft MT is a strongly coupled system where the impact of an event will affect both networks.

Application of Volterra Equations to Solve Unit Commitment Problem of Optimised Energy Storage and Generation

Development of reliable methods for optimised energy storage and generation is one of the most imminent challenges in modern power systems. In this paper an adaptive approach to load leveling problem using novel dynamic models based on the Volterra integral equations of the first kind with piecewise continuous kernels. These integral equations efficiently solve such inverse problem taking into account both the time dependent efficiencies and the availability of generation/storage of each energy storage technology. In this analysis a direct numerical method is employed to find the least-cost dispatch of available storages. The proposed collocation type numerical method has second order accuracy and enjoys self-regularization properties, which is associated with confidence levels of system demand. This adaptive approach is suitable for energy storage optimisation in real time. The efficiency of the proposed methodology is demonstrated on the Single Electricity Market of Republic of Ireland and Northern Ireland.