Maria Clicia Stelling de Castro

A grid-enabled distributed branch-and-bound algorithm with application on the Steiner problem in graphs

This work introduces a distributed branch-and-bound algorithm to be run on computational Grids. Grids are often organized in a hierarchical fashion: clusters of processors connected via high-speed links, while the clusters themselves are geographically distant and connected through slower links. Our algorithm does not employ the usual master-worker paradigm and it considers the hierarchical structure of Grids in its load balance and fault tolerance procedures. This algorithm was applied over an existing code for the Steiner Problem in graphs. Experiments on real Grid conditions have demonstrated its efficiency and scalability.

Positron trapping in BaTiO3 perovskite

Positron lifetime spectra in BaTiO3 single crystals were measured at temperatures up to 873 K, also at room temperature after quenching from temperatures up to 473 K. The explored temperature range includes the ferroelectric to paraelectric phase transition. The material displays an irreversible behaviour when heated, with anomalies around TC. The temperature dependence of the positron lifetime becomes reversible only after annealing above 550 K. The results suggest modifications of the charge state of non-equilibrium vacancies occurring at moderate temperature and microstructural changes taking place at high temperature. The reversible behaviour of the annealed crystal is consistent with thermally activated positron trap formation.

Runtime System Support for Running Applications with Dynamic and Asynchronous Task Parallelism in Software DSM Systems

State-of-the-art software distributed shared-memory systems (SDSMs) provide a cost-effective solution to run single-program-multiple-data (SPMD) applications on clusters of distributed memory computers. However, SDSMs are unsuitable for running applications with dynamic, highly asynchronous task parallelism (ATP), such as graphics, simulators, and decision support systems. In ATP-based applications, the execution of tasks depends not only on the input data but also on the variable amount of data that each task produces at runtime, which generates high load imbalance and communication traffic that degrades performance of SDSM systems drastically. In this work, we propose a new load balancing (LB) mechanism to enable SDSM systems to support dynamic task scheduling as required by ATP applications. To evaluate the benefits of our LB mechanism, we developed Clik a new multithreaded SDSM system with automatic load balancing. Our preliminary performance results of Clik running on a 16-node Linux SMP cluster for five ATP applications showed that Clik attained significant speedups. For four of our five applications, the speedups varied from 7.2 up to 13.8 on 16 processors

A model for reverberating circuits with controlled feedback

We studied the behavior of a mathematic-computational model for a reverberating neuronal circuit with controlled feedback, verifying the output pattern of the circuit, by means simulations using a program in language C++. Using values obtained from surveying the literature from animal experiments, we observed that the model was able to reproduce the polissynaptic activity of a neuron group of a vigil rat, with looping time of three neurons of the order of magnitude of 102 ms.

A Communication Protocol for Fog Computing Based on Network Coding Applied to Wireless Sensors

A communication protocol for fog computing should be efficient, lightweight and customizable. In this work we focus in a communication protocol for fog nodes composed of wireless sensors, which are spatially distributed autonomous sensors monitoring physical or environmental conditions. Problems with data congestion and limited physical resources are common in these networks. For the optimization of data flow, it is important to apply techniques that reduce the transmitted data. We use the network coding technique to demonstrate through experiments the degree of efficiency of data transmission optimization protocols. The experiments were performed through a wireless sensors programming framework composed of TinyOS operating system, NesC programming language and TOSSIM simulator. In addition, we use the Python programming language to simulate the wireless sensor network topology. The results obtained demonstrate a better performance (50% up to 60%) when the network coding technique is applied to the data communication protocol.

Computer model of a reverberant and parallel circuit coupling

The objective of the present study was to deepen the knowledge about the functioning of the neural circuits by implementing a signal transmission model using the Graph Theory in a small network of neurons composed of an interconnected reverberant and parallel circuit, in order to investigate the processing of the signals in each of them and the effects on the output of the network. For this, a program was developed in C language and simulations were done using neurophysiological data obtained in the literature.

Simulation step size analysis of a whole-cell computational model of bacteria

Understanding how complex phenotypes arise from individual molecules and their interactions is a major challenge in biology and, to meet this challenge, computational approaches are increasingly employed. As an example, a recent paper [1] proposed a whole-cell model Mycoplasma genitalium including all cell components and their interactions. 28 modules representing several cell functions were modeled independently, and then integrated into a single computational model. One assumption considered in the whole-cell model of M.Genitalium is that all 28 modules can be modeled independently given the 1 second step size used in simulations. This is a major assumption, since it simplifies the modeling of several cell functions and makes the modeling of the system as a whole feasible. In this paper we investigate the dependency of experimental results on that assumption. We have simulated the M.Genitalium cell cycle using several simulation time step sizes and compared the results to the ones obtained with the syst...

Computational Modeling of Multidrug-Resistant Bacteria

Understanding how complex phenotypes arise from individual molecules and their interactions is a primary challenge in biology, and computational approaches have been increasingly employed to tackle this task. In this chapter, we describe current efforts by FIOCRUZ and partners to develop integrated computational models of multidrug-resistant bacteria. The bacterium chosen as the main focus of this effort is Pseudomonas aeruginosa, an opportunistic pathogen associated with a broad spectrum of infections in humans. Nowadays, P. aeruginosa is one of the main problems of healthcare-associated infections (HAI) in the world, because of its great capacity of survival in hospital environments and its intrinsic resistance to many antibiotics. Our overall research objective is to use integrated computational models to accurately predict a wide range of observable cellular behaviors of multidrug-resistant P. aeruginosa CCBH4851, which is a strain belonging to the clone ST277, endemic in Brazil. In this chapter, after a brief introduction to P. aeruginosa biology, we discuss the construction of metabolic and gene regulatory networks of P. aeruginosa CCBH 4851 from its genome. We also illustrate how these networks can be integrated into a single model, and we discuss methods for identifying potential therapeutic targets through integrated models.

Mathematical-Computational Modeling in Behavior’s Study of Repetitive Discharge Neuronal Circuits

Mathematical-computational modeling is a tool that has been widely used in the field of Neuroscience. Despite considerable advances of Physiological Sciences, the neuronal mechanisms involved in the abilities of central nervous system remain obscure, but they can be revealed through modeling. Significant amount of experimental data already available has facilitated the development of models that combine experimentation with theory. They allow to evaluate hypotheses and to seek understanding of neuronal circuit functioning capable of explaining neurophysiological deficits. To model the behavior of repetitive discharge of neuronal circuits, we have used differential equations, graph theory, and other mathematical methods. Through computational simulations, using programs developed in C and C ++ language and neurophysiological data obtained in the literature, we can test the model’s behavior in face of numerical variations of their parameters, trying to observe their characteristics.

A novel List-Constrained Randomized VND approach in GPU for the Traveling Thief Problem

Abstract The Traveling Thief Problem (TTP) is a multi-component combinatorial optimization problem that combines two well-known problems in the literature: the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). This paper proposes a novel list-constrained local search process inspired in Variable Neighborhood Descent (VND) for multiple neighborhood structures, combined with a metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). The local search implementation was made in a Graphics Processing Unit (GPU) architecture in order to explore the massive number of computing cores to simultaneously explore neighbor solutions, while the GRASP was implemented exploring the natural parallelism of a multi-core CPU. The computational results were compared to state-of-the-art results in literature and indicate promising research directions for the design of novel search algorithms in high performance architectures.