Equivalence conditions for classes of linear and non-linear distributed parameter systems

Equivalence of certain classes of second-order non-linear distributed parameter systems and corresponding linear third-order systems is established through a differential transformation technique. As linear systems are amenable to analysis through existing techniques, this study is expected to offer a method of tackling certain classes of non-linear problems which may otherwise prove to be formidable in nature.

Genetic characterization of Indian type O FMD virus 3A region in context with host cell preference

The 3A region of foot-and-mouth disease virus has been implicated in host range and virulence. For example, amino acid deletions in the porcinophilic strain (O/TAW/97) at 93-102 aa of the 153 codons long 3A protein have been recognized as the determinant of species specificity. In the present study, 18 type 0 FMDV isolates from India were adapted in different cell culture systems and the 3A sequence was analyzed. These isolates had complete 3A coding sequence (153 aa) and did not exhibit growth restriction in cells based on species of origin. The 3A region was found to be highly conserved at N-terminal half (1-75 aa) but exhibited variability or substitutions towards C-terminal region (80-153). Moreover the amino acid substitutions were more frequent in recent Indian buffalo isolates but none of the Indian isolates showed deletion in 3A protein, which may be the reason for the absence of host specificity in vitro. Further inclusive analysis of 3A region will reveal interesting facts about the variability of FMD virus 3A region in an endemic environment. (C) 2010 Elsevier B.V. All rights reserved.

Genetic algorithm for satellite customer assignment

The problem of assigning customers to satellite channels is considered. Finding an optimal allocation of customers to satellite channels is a difficult combinatorial optimization problem and is shown to be NP-complete in an earlier study. We propose a genetic algorithm (GA) approach to search for the best/optimal assignment of customers to satellite channels. Various issues related to genetic algorithms such as solution representation, selection methods, genetic operators and repair of invalid solutions are presented. A comparison of this approach with the standard optimization method is presented to show the advantages of this approach in terms of computation time

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Genetic Heterogeneity in Wild Isolates of Cellular Slime Mold Social Groups

This study addresses the issues of spatial distribution, dispersal, and genetic heterogeneity in social groups of the cellular slime molds (CSMs). The CSMs are soil amoebae with an unusual life cycle that consists of alternating solitary and social phases. Because the social phase involves division of labor with what appears to be an extreme form of "altruism", the CSMs raise interesting evolutionary questions regarding the origin and maintenance of sociality. Knowledge of the genetic structure of social groups in the wild is necessary for answering these questions. We confirm that CSMs are widespread in undisturbed forest soil from South India. They are dispersed over long distances via the dung of a variety of large mammals. Consistent with this mode of dispersal, most social groups in the two species examined for detailed study, Dictyostelium giganteum and Dictyostelium purpureum, are multi-clonal.

Optimal reservoir operation for irrigation of multiple crops using genetic algorithms

This paper presents a genetic algorithm (GA) model for obtaining an optimal operating policy and optimal crop water allocations from an irrigation reservoir. The objective is to maximize the sum of the relative yields from all crops in the irrigated area. The model takes into account reservoir inflow, rainfall on the irrigated area, intraseasonal competition for water among multiple crops, the soil moisture dynamics in each cropped area, the heterogeneous nature of soils. and crop response to the level of irrigation applied. The model is applied to the Malaprabha single-purpose irrigation reservoir in Karnataka State, India. The optimal operating policy obtained using the GA is similar to that obtained by linear programming. This model can be used for optimal utilization of the available water resources of any reservoir system to obtain maximum benefits.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

Thermodynamic consistency of the interaction parameter formalism

The apparent contradiction between the exact nature of the interaction parameter formalism as presented by Lupis and Elliott and the inconsistencies discussed recently by Pelton and Bale arise from the truncation of the Maclaurin series in the latter treatment. The truncation removes the exactness of the expression for the logarithm of the activity coefficient of a solute in a multi-component system. The integrals are therefore path dependent. Formulae for integration along paths of constant Xi,or X i/Xj are presented. The expression for In γsolvent given by Pelton and Bale is valid only in the limit that the mole fraction of solvent tends to one. The truncation also destroys the general relations between interaction parameters derived by Lupis and Elliott. For each specific choice of parameters special relationships are obtained between interaction parameters.

Generating artificial chromosomes with probability control in genetic algorithm for machine scheduling problems

In this paper, a novel genetic algorithm is developed by generating artificial chromosomes with probability control to solve the machine scheduling problems. Generating artificial chromosomes for Genetic Algorithm (ACGA) is closely related to Evolutionary Algorithms Based on Probabilistic Models (EAPM). The artificial chromosomes are generated by a probability model that extracts the gene information from current population. ACGA is considered as a hybrid algorithm because both the conventional genetic operators and a probability model are integrated. The ACGA proposed in this paper, further employs the ``evaporation concept'' applied in Ant Colony Optimization (ACO) to solve the permutation flowshop problem. The ``evaporation concept'' is used to reduce the effect of past experience and to explore new alternative solutions. In this paper, we propose three different methods for the probability of evaporation. This probability of evaporation is applied as soon as a job is assigned to a position in the permutation flowshop problem. Experimental results show that our ACGA with the evaporation concept gives better performance than some algorithms in the literature.

Microcycle conidiation and its genetic basis in Neurospora crassa

Some wild isolates of Neurospora show microcycle conidiation in liquid culture under continuous agitation. Macroconidia from agar-grown mycelial cultures germinated in liquid and the germlings spontaneously produced conidia with no intervening mycelial phase. Three types of microcycle conidiation were seen among progeny of N. crassa Vickramam A x N. crassa a wild-type: (1) multinucleate blastoconidia produced by apical budding and septation, (2) multinucleate arthroconidia produced by holothallic septation and disarticulation of cells, and (3) uninucleate microconidia produced directly from conidiogenous cells of the germlings. Two genes were identified which control specific patterns of microcycle conidiogenesis. A single gene mcb in linkage group VR near al-3 (3.2% recombination) controls blastoconidiation. This gene is epistatic to gene mcm located in linkage group IIL, very near ro-7 (1.4%). mcm controls both microconidiation and arthroconidiation depending on temperature. Strains of genotype mcm produce microconidia almost exclusively at 18-22 degrees C, but arthroconidia with few or no microconidia at 30 degrees C. Because they result in rapid and synchronized conidiation in liquid culture, the two genes should be useful for studies of developmental gene regulation. mcm makes it possible to obtain large quantities of pure microconidia rapidly for experimentation.

Sign Of The Order Parameter In Oriented Systems And Its Utility In The Study Of The Dynamic Properties Of Molecules

Novel one and two dimensional NMR techniques are proposed and utilized for the determination of the signs of the order parameters used for the study of the mobility of the fatty acid chains. The experiments designed to extract this information involve the use of the intensities of the side bands in the spectra of oriented systems spinning at the magic angle. Advantages of the two dimensional technique over the one dimensional method are discussed. The utility of the method in the study of the dynamic properties of membranes and model systems is pointed out.

A Genetic Study of Immunoglobulin E and Atopic Disease Based on Families Ascertained through Asthmatic Children

In order to investigate the modes of inheritance of serum immunoglobulin E (IgE) levels and atopic disease, serum IgE levels and data on allergic disease were obtained from 42 families ascertained through asthmatic children visiting an allergy clinic. Although the mean IgE levels were elevated (mean 637 U/ml), the prevalence of atopic disease in this population was surprisingly low. When the data were analyzed using complex segregation analysis, no major locus could be detected. Moreover, the polygenic heritability was unexpectedly small even though the correlation between serum IgE levels and the liability to atopic disease was around 0.4. Given this unusual set of findings, it is postulated that parasitic infections in this population have (in accordance with well-established results of parasitic disease) caused both elevated levels of serum IgE and a decreased prevalence of allergic disease with the possible masking of the various genetic components of serum IgE levels and atopic disease.

Determination of the sign of the order parameter from MAS spectra of oriented molecules

The use of the intensities of the spinning sidebands in the magic-angle spinning spectra of oriented molecules is proposed for the determination of the signs of the order parameters. The method is demonstrated for benzene and chloroform oriented in nematic phases of liquid crystals. On the basis of the theoretical expressions derived for the various order sidebands, the applicability of the method for different experimental conditions is discussed.

A near-optimal initial seed value selection in K-means means algorithm using a genetic algorithm

The K-means algorithm for clustering is very much dependent on the initial seed values. We use a genetic algorithm to find a near-optimal partitioning of the given data set by selecting proper initial seed values in the K-means algorithm. Results obtained are very encouraging and in most of the cases, on data sets having well separated clusters, the proposed scheme reached a global minimum.