Graph model analysis

Good, A.C. and Cheney, D.L Analysis and optimization of stmcture-based virtual screening protocols (1) exploration of ligand conformational sampling techniques. /. Mol. Graph. Model. 2003, 22, 23-30. 

Spellmeyee, D.C., Wong, A.K., Bower, M.J., and Bianey, J.M. Conformational analysis using distance geometry methods./. Mol. Graph. Model. 1997, 35, 18-36. 

Verkhivker GM (2004) Computational analysis of ligand binding dynamics at the intermolecular hot spots with the aid of simulated tempering and binding free energy calculations, Mol J Graph Model, 22(5) 335-348... 

A systematic semiempirical study of the core-level photoemission spectra of a wide range of 3d transition-metal compounds has been carried out (Bocquet et al., 1992,1996). The values for U and A obtained from a simplified Cl cluster model analysis are demonstrated in Figure 7.2. As can be inferred from the graphs, the heavier 3d transition metal compounds shown in the figure are expected to be charge-transfer insulators, whereas the compounds of the fighter metals are generally expected to be of the Mott-Hubbard type. 

Cherkasov, A.R., Jonsson, M. and Galkin, V.I. (1999) A novel approach to the analysis of substituent effects quantitative description of ionization energies and gas basicity of amines./. Mol Graph. Model., 17, 28 2. 

Gao, H., Lajiness, M.S. and Van Drie, J. (2002) Enhancement of binary QSAR analysis by a GA-based variable selection method. /. Mol. Graph. Model, 20, 259-268. 

Godden, J.W. and Bajorath, J. (2000) Shannon entropy a novel concept in molecular descriptor and diversity analysis. J. Mol. Graph. Model., 18, 73-76. 

Mattioni, B.E. and Jurs, P.C. (2003) Prediction of dihydrofolate reductase inhibition and selectivity using computational neural networks and linear discriminant analysis. /. Mol. Graph. Model., 21, 391-419. 

Taha MO, Bustanji Y, Al-Bakri AG et al (2007) Discovery of new potent human protein tyrosine phosphatase inhibitors via pharmacophore and QSAR. analysis followed by in sUico screening. J Mol Graph Model 25 870-884... 

Figure 7.1. A causal graph for risk analysis. The model depicted in this figure can be formalized using a Bayesian network (Ricci et al. 2006) A probabilistic framework interprets the model described in this figure as a Bayesian belief network or causal graph model. Each variable with inward-pointing arrows is interpreted as a random variable with a conditional probability distribution that depends only on the values of the variables that point into it. The essence of this approach to modeling and evaluating uncertain risks is to sample successively from the (often conditional) distribution of each variable, given the values of its predecessors. Algorithms exist to identify and validate possible causal structures.

J. Bajorath, W. J. Metzler, P. S. Linsley. Molecular modeling of CD28 and three-dimensional analysis of residue conservation in the CD28/CD152 family. J Mol Graph Model. 1997, 15, 135-139, 108-111. 

Harpsoe K, Liljefors T, Balle T (2008) Prediction of the binding mode of biarylpropylsulfo-namide allosteric AMPA receptor modulators based on docking, GRID molecular interaction fields and 3D-QSAR analysis. J Mol Graph Model 26 874-883... 

Barnard JM, Downs GM, von Scholley-Pfab A, Brown R. Use of Markush structure analysis techniques for descriptor generation and clustering of large combinatorial libraries. J Mol Graph Model 2000 18 452-463. 

The graph method is also useful for theoretical analysis of the models. We will present some theoretical results obtained by using the graph model and applying results from graph theory. In particular, we give a method to determine a minimal set of flow rates in a model and we derive a necessary and sufficient condition for certain systems to be minimal. 

Another application of the graph model is to derive equivalence for certain parts of the model. In analysis and simulation of the model, it is often desirable to combine several components to an equivalent single component. The overall reduction of a model to a minimal system will be of great value not only for computations but also for theoretical study of essential characteristics of the system and comparison to the traditional compartment modeling. The graph model will facilitate the development of such equivalence transformations. 

Beyond model development for the purpose of analysis and simulation of the dynamic or the frequency domain behaviour, bond graphs have also proven useful as a tool for model-based FDI in engineering systems represented by continuous time models. Only recently, bond graph model-based FDI has also been used for systems described by a hybrid model. Following common terminology a system will be called a hybrid system for short if its dynamic behaviour is appropriately described by a hybrid model. In Chap. 2.1 of [1] Kowalewski rightly remarks ... 

Buisson, J.,Cormerais,H., Richard, P. Y. (2002). Analysis ofthe bond graph model of hybrid physical systems with ideal switches. Proceedings of the Institution cf Mechanical Engineers Part I Systems and Control Engineering, 216 ), 47-63. 

Chapter 3 introduces a special decomposition of bond graph elements in a part with nominal parameters and one with uncertain parameters. The resulting bond graph model of a bond graph element is called linear fractional transformation (LFT) model. In case of linear models, bond graphs with elements replaced by their LFT model enable the derivation of state space and output equations in LFT form as used for stability analysis and control law synthesis based on /r-analysis. 

Diagnosis of uncertain systems has been the subject of several recent research works [1-6]. This interest is reflected by the fact that physical systems are complex and non-stationary and require more security and performance. The bond graph model in LFT form allows the generation of analytical redundancy relations (ARRs) composed of two completely separated parts a nominal part, which represents the residuals, and an uncertain part which serves for both the calculation of adaptive thresholds and sensitivity analysis. 

The presented FDI method allows by using a bond graph model in LFT form, to generate residuals and adaptive thresholds. To improve and monitor the performances of the diagnosis, a method of residual sensitivity analysis is proposed to estimate the detectable values of the faults. 

Abstract Fuel cells are environmentally friendly futuristic power sources. They involve multiple energy domains and hence bond graph method is suitable for their modelling. A true bond graph model of a solid oxide fuel cell is presented in this chapter. This model is based on the concepts of network thermodynamics, in which the couplings between the various energy domains are represented in a unified manner. The simulations indicate that the model captures all the essential dynamics of the fuel cell and therefore is useful for control theoretic analysis. 

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