Optimal estimation of parameters of dynamical systems by neural network collocation method

In this paper we propose a new method to estimate parameters of a dynamical system from observation data on the basis of a neural network collocation method. We construct an object function consisting of squared residuals of dynamical model equations at collocation points and squared deviations of the observations from their corresponding computed values. The neural network is then trained by optimizing the object function.The proposed method is demonstrated by performing several numerical experiments for the optimal estimates of parameters for two different nonlinear systems. Firstly, we consider the weakly and highly nonlinear cases of the Lorenz model and apply the method to estimate the optimum values of parameters for the two cases under various conditions. Then we apply it to estimate the parameters of one-dimensional oscillator with nonlinear damping and restoring terms representing the nonlinear ship roll motion under various conditions. Satisfactory results have been obtained for both the problems.     

Accurate and simple synthesis formulas for coplanar waveguides

Simple and accurate closed‐form formulas obtained by using a differential evolution algorithm are presented for the synthesis of coplanar waveguides (CPW). The results of the synthesis formulas proposed in this article are compared with those of the quasi‐static analysis, the synthesis formulas reported by the other researchers and also the experimental works available in the literature. The accuracy of the proposed synthesis formulas is found to be better than 0.75% for 9256 CPWs samples. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2008.     

Reconstruction of reflectance spectra using weighted principal component analysis

The weighted principal component analysis technique is employed for reconstruction of reflectance spectra of surface colors from the related tristimulus values. A dynamic eigenvector subspace based on applying certain weights to reflectance data of Munsell color chips has been formed for each particular sample and the color difference value between the target, and Munsell dataset is chosen as a criterion for determination of weighting factors. Implementation of this method enables one to increase the influence of samples which are closer to target on extracted principal eigenvectors and subsequently diminish the effect of those samples which benefit from higher amount of color difference. The performance of the suggested method is evaluated in spectral reflectance reconstruction of three different collections of colored samples by the use of the first three Munsell bases. The resulting spectra show considerable improvements in terms of root mean square error between the actual and reconstructed reflectance curves as well as CIELAB color difference under illuminant A in comparison to those obtained from the standard PCA method. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 360–371, 2008     

Genetic evidence for the role of isocytochrome c2 in photosynthetic growth of Rhodobacter sphaeroides Spd mutants

In Rhodobacter sphaeroides, cytochrome c2 (cyt c2)-deficient mutants are photosynthetically incompetent (PS-). However, mutations which suppress the photosynthetic deficiency (spd mutations) of cyt c2 mutants increase the levels of a cyt c2 isoform, isocyt c2. To determine whether isocyt c2 was required for photosynthetic growth of Spd mutants, we used Tn5 mutagenesis to generate a PS- mutant (TP39) that lacks both cyt c2 and isocyt c2. DNA sequence analysis of wild-type DNA that restores isocyt c2 production and photosynthetic growth to TP39 indicates that it encodes the isocyt c2 structural gene, cycI. The Tn5 insertion in TP39 is approximately 1.5 kb upstream of cycI, and our results show that it is polar onto cycI. The cycI gene has been physically mapped to a region of chromosome I that is approximately 700 kb from the R. sphaeroides photosynthetic gene cluster. Construction of a defined cycI null mutant and complementation of several mutants with the cycI gene under the control of the cyt c2 promoter region indicate that an increase in the levels of isocyt c2 alone is necessary and sufficient for photosynthetic growth in the absence of cyt c2. The data are discussed in terms of the obligate role of isocyt c2 in cyt c2-independent photosynthesis of R. sphaeroides.     

An extended minimax disparity to determine the OWA operator weights

This paper contributes to extend the minimax disparity to determine the ordered weighted averaging (OWA) model based on linear programming. It introduces the minimax disparity approach between any distinct pairs of the weights and uses the duality of linear programming to prove the feasibility of the extended OWA operator weights model. The paper finishes with an open problem.     

Threshold cryptography based on Asmuth-Bloom secret sharing

In this paper, we investigate how threshold cryptography can be conducted with the Asmuth-Bloom secret sharing scheme and present three novel function sharing schemes for RSA, ElGamal and Paillier cryptosystems. To the best of our knowledge, these are the first provably secure threshold cryptosystems realized using the Asmuth-Bloom secret sharing. Proposed schemes are comparable in performance to earlier proposals in threshold cryptography.     

A comparison between reliability analyses based primarily on disjointness or statistical independence: The case of the generalized indra network

A switching expression is readily convertible to a reliability expression if (a) all ORed terms are disjoint, and (b) all ANDed sums are statistically independent. The usual approach of system reliability analysis makes a primary use of (a) and a secondary use of (b). An alternative approach reverses the roles of (a) and (b). Symbolic reliability expressions for the source-to-terminal reliability of a generalized Indra network (GIN) with nonidentical components are derived by the two approaches. For this particular case, the second approach leads to a shorter, more elegant derivation and simpler novel results. Typical plots of the GIN reliability functions are presented and their properties are discussed.     

Screening for diabetic retinopathy: the utility of nonmydriatic retinal photography in Egyptian adults

BACKGROUND AND OBJECTIVE: Drug resistance has become a major cause of treatment failure in patients with acute leukemia. P-glycoprotein (Pgp), which is associated with the multidrug resistance (MDR) phenotype, has been reported to be an important predictor of treatment outcome. The aim of this study was to analyze the value of Pgp expression in bone marrow or peripheral blood as a predictor of the response to remission induction chemotherapy as well as the duration of remission in patients with de novo acute myeloid leukemia (AML). DESIGN AND METHODS: We examined the expression of Pgp in 82 patients with de novo AML using an immunocytochemical assay with the C219 monoclonal antibody. RESULTS: Twenty-seven of the 82 patients (33%) were C219-positive in from 1% to 100% of their cells. Thirteen cases (16%) showed a positive reaction in more than 50% of the leukemic cells. Only hyperleukocytosis was significantly associated with higher expression of Pgp. Although 8 of the 13 cases (62%) with more than 50% of cells having Pgp expression were CD34-positive, this association was not statistically significant. A univariate analysis of resistance to induction therapy showed a significantly higher resistance rate in patients with increased Pgp expression (P = 0.01) as well as in those patients with decreased reactivity to myeloperoxidase. The multivariate analysis revealed the independent prognostic value of Pgp expression. C219 reactivity did not have an influence on remission duration. INTERPRETATION AND CONCLUSIONS: Our data indicate that P-glycoprotein expression is a reliable marker of resistance to induction treatment in patients with de novo AML.     

Uniform modeling of parameter dependent nonlinear systems

This paper addresses the problem of approximating parameter dependent nonlinear systems in a unified framework. This modeling has been presented for the first time in the form of parameter dependent piecewise affine systems. In this model, the matrices and vectors defining piecewise affine systems are affine functions of parameters. Modeling of the system is done based on distinct spaces of state and parameter, and the operating regions are partitioned into the sections that we call ’multiplied simplices’. It is proven that this method of partitioning leads to less complexity of the approximated model compared with the few existing methods for modeling of parameter dependent nonlinear systems. It is also proven that the approximation is continuous for continuous functions and can be arbitrarily close to the original one. Next, the approximation error is calculated for a special class of parameter dependent nonlinear systems. For this class of systems, by solving an optimization problem, the operating regions can be partitioned into the minimum number of hyper-rectangles such that the modeling error does not exceed a specified value. This modeling method can be the first step towards analyzing the parameter dependent nonlinear systems with a uniform method.