Cellular Automata Models of Traffic Behavior in Presence of Speed Breaking Structures

In this article, we study traffic flow in the presence of speed breaking structures. The speed breakers are typically used to reduce the local speed of vehicles near certain institutions such as schools and hospitals. Through a cellular automata model we study the impact of such structures on global traffic characteristics. The simulation results indicate that the presence of speed breakers could reduce the global flow under moderate global densities. However, under low and high global density traffic regime the presence of speed breakers does not have an impact on the global flow. Further the speed limit enforced by the speed breaker creates a phase distinction. For a given global density and slowdown probability, as the speed limit enforced by the speed breaker increases, the traffic moves from the reduced flow phase to maximum flow phase. This underlines the importance of proper design of these structures to avoid undesired flow restrictions.

Multi-state-choice automata model for expedient learning in random environment

A new automata model Mr,k, with a conceptually significant innovation in the form of multi-state alternatives at each instance, is proposed in this study. Computer simulations of the Mr,k, model in the context of feature selection in an unsupervised environment has demonstrated the superiority of the model over similar models without this multi-state-choice innovation.

LANDSCAPE DYNAMICS MODELING THROUGH INTEGRATED MARKOV, FUZZY-AHP AND CELLULAR AUTOMATA

Multi temporal land use information were derived using two decades remote sensing data and simulated for 2012 and 2020 with Cellular Automata (CA) considering scenarios, change probabilities (through Markov chain) and Multi Criteria Evaluation (MCE). Agents and constraints were considered for modeling the urbanization process. Agents were nornmlized through fiizzyfication and priority weights were assigned through Analytical Hierarchical Process (AHP) pairwise comparison for each factor (in MCE) to derive behavior-oriented rules of transition for each land use class. Simulation shows a good agreement with the classified data. Fuzzy and AHP helped in analyzing the effects of agents of growth clearly and CA-Markov proved as a powerful tool in modelling and helped in capturing and visualizing the spatiotemporal patterns of urbanization. This provided rapid land evaluation framework with the essential insights of the urban trajectory for effective sustainable city planning.

An efficient design of serial and parallel memory using Quantum dot cellular automata

Quantum cellular automata (QCA) is a new technology in the nanometer scale and has been considered as one of the alternative to CMOS technology. In this paper, we describe the design and layout of a serial memory and parallel memory, showing the layout of individual memory cells. Assuming that we can fabricate cells which are separated by 10nm, memory capacities of over 1.6 Gbit/cm2 can be achieved. Simulations on the proposed memories were carried out using QCADesigner, a layout and simulation tool for QCA. During the design, we have tried to reduce the number of cells as well as to reduce the area which is found to be 86.16sq mm and 0.12 nm2 area with the QCA based memory cell. We have also achieved an increase in efficiency by 40%.These circuits are the building block of nano processors and provide us to understand the nano devices of the future.

Analysis of DNA sequence transformations on grids

Study of the evolution of species or organisms is essential for various biological applications. Evolution is typically studied at the molecular level by analyzing the mutations of DNA sequences of organisms. Techniques have been developed for building phylogenetic or evolutionary trees for a set of sequences. Though phylogenetic trees capture the overall evolutionary relationships among the sequences, they do not reveal fine-level details of the evolution. In this work, we attempt to resolve various fine-level sequence transformation details associated with a phylogenetic tree using cellular automata. In particular, our work tries to determine the cellular automata rules for neighbor-dependent mutations of segments of DNA sequences. We also determine the number of time steps needed for evolution of a progeny from an ancestor and the unknown segments of the intermediate sequences in the phylogenetic tree. Due to the existence of vast number of cellular automata rules, we have developed a grid system that performs parallel guided explorations of the rules on grid resources. We demonstrate our techniques by conducting experiments on a grid comprising machines in three countries and obtaining potentially useful statistics regarding evolutions in three HIV sequences. In particular, our work is able to verify the phenomenon of neighbor-dependent mutations and find that certain combinations of neighbor-dependent mutations, defined by a cellular automata rule, occur with greater than 90% probability. We also find the average number of time steps for mutations for some branches of phylogenetic tree over a large number of possible transformations with standard deviations less than 2.

Phylogenetic Predictions on Grids

A phylogenetic or evolutionary tree is constructed from a set of species or DNA sequences and depicts the relatedness between the sequences. Predictions of future sequences in a phylogenetic tree are important for a variety of applications including drug discovery, pharmaceutical research and disease control. In this work, we predict future DNA sequences in a phylogenetic tree using cellular automata. Cellular automata are used for modeling neighbor-dependent mutations from an ancestor to a progeny in a branch of the phylogenetic tree. Since the number of possible ways of transformations from an ancestor to a progeny is huge, we use computational grids and middleware techniques to explore the large number of cellular automata rules used for the mutations. We use the popular and recurring neighbor-based transitions or mutations to predict the progeny sequences in the phylogenetic tree. We performed predictions for three types of sequences, namely, triose phosphate isomerase, pyruvate kinase, and polyketide synthase sequences, by obtaining cellular automata rules on a grid consisting of 29 machines in 4 clusters located in 4 countries, and compared the predictions of the sequences using our method with predictions by random methods. We found that in all cases, our method gave about 40% better predictions than the random methods.

Simultaneous effect of lead and cadmium on granulosa cells: A cellular model for ovarian toxicity

Lead (Pb) and cadmium (Cd) are known reproductive toxicants, which accumulate in granulosa cells of the ovary. Female Charles foster rats were treated with sodium acetate (control), lead acetate and cadmium acetate either alone or in combination at a dose 0.05 mg/kg body weight intra-peritoneally for 15 days daily. Animals were killed at proestrous stage and granulosa cells were isolated from the ovaries. Binding of I-125-luteinizing hormone (I-125-LH), I-125-follicle stimulating hormone (I-125-FSH) and 17 beta-hydroxysteroid dehydrogenase activity were measured. As these receptors are localized on the surface of the cell membrane, we also estimated the membrane parameters of these cells. Our results demonstrated that both lead and cadmium caused a significant reduction in gonadotropin binding, which altered steroidogenic enzyme activity of granulosa cells. These changes exhibited a positive correlation with membrane changes of the granulosa cells.

A hierarchical system of learning automata that can learn die globally optimal path

Systems of learning automata have been studied by various researchers to evolve useful strategies for decision making under uncertainity. Considered in this paper are a class of hierarchical systems of learning automata where the system gets responses from its environment at each level of the hierarchy. A classification of such sequential learning tasks based on the complexity of the learning problem is presented. It is shown that none of the existing algorithms can perform in the most general type of hierarchical problem. An algorithm for learning the globally optimal path in this general setting is presented, and its convergence is established. This algorithm needs information transfer from the lower levels to the higher levels. Using the methodology of estimator algorithms, this model can be generalized to accommodate other kinds of hierarchical learning tasks.

Relaxation Labeling with Learning Automata

Relaxation labeling processes are a class of mechanisms that solve the problem of assigning labels to objects in a manner that is consistent with respect to some domain-specific constraints. We reformulate this using the model of a team of learning automata interacting with an environment or a high-level critic that gives noisy responses as to the consistency of a tentative labeling selected by the automata. This results in an iterative linear algorithm that is itself probabilistic. Using an explicit definition of consistency we give a complete analysis of this probabilistic relaxation process using weak convergence results for stochastic algorithms. Our model can accommodate a range of uncertainties in the compatibility functions. We prove a local convergence result and show that the point of convergence depends both on the initial labeling and the constraints. The algorithm is implementable in a highly parallel fashion.

Arabidopsis thaliana J-class heat shock proteins: cellular stress sensors

Plants are sessile organisms that have evolved a variety of mechanisms to maintain their cellular homeostasis under stressful environmental conditions. Survival of plants under abiotic stress conditions requires specialized group of heat shock protein machinery, belonging to Hsp70:J-protein family. These heat shock proteins are most ubiquitous types of chaperone machineries involved in diverse cellular processes including protein folding, translocation across cell membranes, and protein degradation. They play a crucial role in maintaining the protein homeostasis by reestablishing functional native conformations under environmental stress conditions, thus providing protection to the cell. J-proteins are co-chaperones of Hsp70 machine, which play a critical role by stimulating Hsp70s ATPase activity, thereby stabilizing its interaction with client proteins. Using genome-wide analysis of Arabidopsis thaliana, here we have outlined identification and systematic classification of J-protein co-chaperones which are key regulators of Hsp70s function. In comparison with Saccharomyces cerevisiae model system, a comprehensive domain structural organization, cellular localization, and functional diversity of A. thaliana J-proteins have also been summarized. Electronic supplementary material The online version of this article (doi:10.1007/s10142-009-0132-0) contains supplementary material, which is available to authorized users.

Estimating Frequencies of Minority Nevirapine-Resistant Strains in Chronically HIV-1-Infected Individuals Naive to Nevirapine by Using Stochastic Simulations and a Mathematical Model

Nevirapine forms the mainstay of our efforts to curtail the pediatric AIDS epidemic through prevention of mother-to-child transmission of HIV-1. A key limitation, however, is the rapid selection of HIV-1 strains resistant to nevirapine following the administration of a single dose. This rapid selection of resistance suggests that nevirapine-resistant strains preexist in HIV-1 patients and may adversely affect outcomes of treatment. The frequencies of nevirapine-resistant strains in vivo, however, remain poorly estimated, possibly because they exist as a minority below current assay detection limits. Here, we employ stochastic simulations and a mathematical model to estimate the frequencies of strains carrying different combinations of the common nevirapine resistance mutations K103N, V106A, Y181C, Y188C, and G190A in chronically infected HIV-1 patients naive to nevirapine. We estimate the relative fitness of mutant strains from an independent analysis of previous competitive growth assays. We predict that single mutants are likely to preexist in patients at frequencies (similar to 0.01% to 0.001%) near or below current assay detection limits (>0.01%), emphasizing the need for more-sensitive assays. The existence of double mutants is subject to large stochastic variations. Triple and higher mutants are predicted not to exist. Our estimates are robust to variations in the recombination rate, cellular superinfection frequency, and the effective population size. Thus, with 10(7) to 10(8) infected cells in HIV-1 patients, even when undetected, nevirapine-resistant genomes may exist in substantial numbers and compromise efforts to prevent mother-to-child transmission of HIV-1, accelerate the failure of subsequent antiretroviral treatments, and facilitate the transmission of drug resistance.

Stability of synchronization in a multi-cellular system

Networks of biochemical reactions regulated by positive-and negative-feedback processes underlie functional dynamics in single cells. Synchronization of dynamics in the constituent cells is a hallmark of collective behavior in multi-cellular biological systems. Stability of the synchronized state is required for robust functioning of the multi-cell system in the face of noise and perturbation. Yet, the ability to respond to signals and change functional dynamics are also important features during development, disease, and evolution in living systems. In this paper, using a coupled multi-cell system model, we investigate the role of system size, coupling strength and its topology on the synchronization of the collective dynamics and its stability. Even though different coupling topologies lead to synchronization of collective dynamics, diffusive coupling through the end product of the pathway does not confer stability to the synchronized state. The results are discussed with a view to their prevalence in biological systems. Copyright (C) EPLA, 2010

Imaging in vivo Neuronal Transport in Genetic Model Organisms Using Microfluidic Devices

Microfluidic devices have been developed for imaging behavior and various cellular processes in Caenorhabditis elegans, but not subcellular processes requiring high spatial resolution. In neurons, essential processes such as axonal, dendritic, intraflagellar and other long-distance transport can be studied by acquiring fast time-lapse images of green fluorescent protein (GFP)-tagged moving cargo. We have achieved two important goals in such in vivo studies namely, imaging several transport processes in unanesthetized intact animals and imaging very early developmental stages. We describe a microfluidic device for immobilizing C. elegans and Drosophila larvae that allows imaging without anesthetics or dissection. We observed that for certain neuronal cargoes in C. elegans, anesthetics have significant and sometimes unexpected effects on the flux. Further, imaging the transport of certain cargo in early developmental stages was possible only in the microfluidic device. Using our device we observed an increase in anterograde synaptic vesicle transport during development corresponding with synaptic growth. We also imaged Q neuroblast divisions and mitochondrial transport during early developmental stages of C. elegans and Drosophila, respectively. Our simple microfluidic device offers a useful means to image high-resolution subcellular processes in C. elegans and Drosophila and can be readily adapted to other transparent or translucent organisms.

Adaptive optimization of continuous bioreactor using neural network model

An adaptive optimization algorithm using backpropogation neural network model for dynamic identification is developed. The algorithm is applied to maximize the cellular productivity of a continuous culture of baker's yeast. The robustness of the algorithm is demonstrated in determining and maintaining the optimal dilution rate of the continuous bioreactor in presence of disturbances in environmental conditions and microbial culture characteristics. The simulation results show that a significant reduction in time required to reach optimal operating levels can be achieved using neural network model compared with the traditional dynamic linear input-output model. The extension of the algorithm for multivariable adaptive optimization of continuous bioreactor is briefly discussed.

Learning the global maximum with parameterized learning automata

A feedforward network composed of units of teams of parameterized learning automata is considered as a model of a reinforcement teaming system. The internal state vector of each learning automaton is updated using an algorithm consisting of a gradient following term and a random perturbation term. It is shown that the algorithm weakly converges to a solution of the Langevin equation implying that the algorithm globally maximizes an appropriate function. The algorithm is decentralized, and the units do not have any information exchange during updating. Simulation results on common payoff games and pattern recognition problems show that reasonable rates of convergence can be obtained.