Marcelo A. Moret

Learning computer programming: Implementing a fractal in a Turing Machine

It is common to start a course on computer programming logic by teaching the algorithm concept from the point of view of natural languages, but in a schematic way. In this sense we note that the students have difficulties in understanding and implementation of the problems proposed by the teacher. The main idea of this paper is to show that the logical reasoning of computer programming students can be efficiently developed by using at the same time Turing Machine, cellular automata (Wolfram rule) and fractals theory via Problem-Based Learning (PBL). The results indicate that this approach is useful, but the teacher needs introducing, in an interdisciplinary context, the simple theory of cellular automata and the fractals before the problem implementation.

Self-Affinity Of Vehicle Demand On The Ferry-Boat System

The transportation problems are in evidence due to their importance. This type of problem is usually studied by nonlinear dynamics. In this paper, we study time series of vehicle demand by using the ferry-boat system between Salvador city and Itaparica island, in Bahia, Brazil. We observe an interesting behavior since these time series allow to observe genuine self-affinity effects. The scaling exponent α and the density of crossing points ρ are variables to describe long-range correlations (self-affinity) in time series of the vehicle demand. As the result we show α and ρ variation year by year.

NEWCOMB-BENFORD LAW IN ASTROPHYSICAL SOURCES

We study the behavior of the numbers in 412 light curves of cataclysmic variables, x-ray binary systems, galaxies, pulsars, supernovae remnants and other x-ray sources present in the public data collected by the instrument All Sky Monitor on board of the satellite Rossi x-ray timing explorer. The temporal light curves were analyzed applying Newcomb-Benford Law. The first digit of the x-ray light curves coming from astrophysical systems obeys the Newcomb-Benford Law as an intrinsic behavior. The nonextensive statistical mechanics behavior of astrophysical sources seem to be the cause for these sources to obey the Newcomb-Benford law. Some x-ray binary systems, however, do not follow this behavior. These systems obey either a gaussian or a bimodal distribution.

Multifractal analysis of polyalanines time series

Multifractal properties of the energy time series of short α-helix structures, specifically from a polyalanine family, are investigated through the MF-DFA technique (multifractal detrended fluctuation analysis). Estimates for the generalized Hurst exponent h(q) and its associated multifractal exponents τ(q) are obtained for several series generated by numerical simulations of molecular dynamics in different systems from distinct initial conformations. All simulations were performed using the GROMOS force field, implemented in the program THOR. The main results have shown that all series exhibit multifractal behavior depending on the number of residues and temperature. Moreover, the multifractal spectra reveal important aspects of the time evolution of the system and suggest that the nucleation process of the secondary structures during the visits on the energy hyper-surface is an essential feature of the folding process.

Dihedral-angle Gaussian distribution driving protein folding

The proposal of this paper is to provide a simple angular random-walk model to build up polypeptide structures, which encompass properties of dihedral angles of folded proteins. From this model, structures will be built with lengths ranging from 125 up to 400 amino acids for the different fractions of secondary structure motifs, in which dihedral angles were randomly chosen according to narrow Gaussian probability distributions. In order to measure the fractal dimension of proteins three different cases were analyzed. The first contained α-helix structures only, the second β-strands structures and the third a mix of α-helices and β-sheets. The behavior of proteins with α-helix motifs are more compact than in other situations. The findings herein indicate that this model describes some structural properties of a protein and suggest that randomness is an essential ingredient but proteins are driven by narrow angular Gaussian probability distributions and not by random-walk processes.

Relevance of transportation to correlations among criticality, physical means of propagation, and distribution of dengue fever cases in the state of Bahia

Dengue infection is a public health problem with a complex distribution. The physical means of propagation and the dynamics of diffusion of the disease between municipalities need to be analysed to direct efficient public policies to prevent dengue infection. The present study presents correlations of occurrences of reported cases of dengue infection among municipalities, self-organized criticality (SOC), and transportation between areas, identifying the municipalities that play an important role in the diffusion of dengue across the state of Bahia, Brazil. The significant correlation found between the correlation network and the SOC demonstrates that the pattern of intramunicipal diffusion of dengue is coupled to the pattern of synchronisation between the municipalities. Transportation emerges as influential in the dynamics of diffusion of epidemics by acting on the aforementioned variables.

A Model for Improving the Learning Curves of Artificial Neural Networks

In this article, the performance of a hybrid artificial neural network (i.e. scale-free and small-world) was analyzed and its learning curve compared to three other topologies: random, scale-free and small-world, as well as to the chemotaxis neural network of the nematode Caenorhabditis Elegans. One hundred equivalent networks (same number of vertices and average degree) for each topology were generated and each was trained for one thousand epochs. After comparing the mean learning curves of each network topology with the C. elegans neural network, we found that the networks that exhibited preferential attachment exhibited the best learning curves.

Nonlocal dispersal of dengue in the state of Bahia

Abstract Dengue is an arbovirus that has spread throughout different, especially tropical and subtropical, regions of the world. This disease affects humans through mosquito bites. The occurrence of dengue epidemics has increased alarmingly over the last three decades. To detect a pattern regarding the dispersal process of dengue, this research presents a computational model based on three pillars: complex networks, time-varying graphing (TVG), and time-series synchronisation. To establish the synchronisation networks, the Motif-Synchronisation method with TVG was applied to a time series analysis of georeferenced dengue incidence data from the municipalities of Bahia from 2001 to 2016. After applying the model presented in this work, the dispersal behaviour patterns of these epidemics were found amongst the municipalities within Bahia, when synchronised, in epidemic moments. The results indicate that the incidence of dengue is not directly related to the distance between municipalities; rather, a time relation exists regarding the development of the vector and its capacity to transmit disease. The purpose of this model is to contribute to public disease interventions by providing early information.

A New Direction in the Atmospheric Pollutant Dispersion inside the Planetary Boundary Layer

AbstractIn this study, an analytical solution for the steady-state fractional advection–diffusion equation was obtained to simulate the atmospheric dispersion of pollutants in a vertically inhomogeneous planetary boundary layer. The authors propose a method that uses the modified generalized integral Laplace transform technique to solve the transformed problem with a fractional derivative, resulting in a more general solution. The model results were compared with the fractional Gaussian model and demonstrate that, when considering an experimental dataset under moderately unstable conditions, fractional-derivative models perform better than traditional integer-order models.

Analysis of a Sorter Cascade Applied to Control a Wheelchair

The precise eye state detection is a fundamental stage for various activities that require human-machine interaction (HMI). This chapter presents an analysis of the implementation of a system for navigating a wheelchair with automation (CRA), based on facial expressions, especially eyes closed using a Haar cascade classifier (HCC). Aimed at people with locomotor disability of the upper and lower limbs, the state detection was based on two steps: the capture of the image, which concentrates on the detection actions and image optimization; actions of the chair, which interprets the data capture and sends the action to the chair. The results showed that the model has excellent accuracy in identification with robust performance in recognizing eyes closed, bypassing well occlusion issues and lighting with about 98% accuracy. The application of the model in the simulations opens the implementation and marriage opportunity with the chair sensor universe aiming a safe and efficient navigation to the user.