Entropy analysis of DNA code dynamics in human chromosomes

Deoxyribonucleic acid, or DNA, is the most fundamental aspect of life but present day scientific knowledge has merely scratched the surface of the problem posed by its decoding. While experimental methods provide insightful clues, the adoption of analysis tools supported by the formalism of mathematics will lead to a systematic and solid build-up of knowledge. This paper studies human DNA from the perspective of system dynamics. By associating entropy and the Fourier transform, several global properties of the code are revealed. The fractional order characteristics emerge as a natural consequence of the information content. These properties constitute a small piece of scientific knowledge that will support further efforts towards the final aim of establishing a comprehensive theory of the phenomena involved in life.

Shannon, rényie and tsallis entropy analysis of DNA using phase plane

This paper analyzes DNA information using entropy and phase plane concepts. First, the DNA code is converted into a numerical format by means of histograms that capture DNA sequence length ranging from one up to ten bases. This strategy measures dynamical evolutions from 4 up to 410 signal states. The resulting histograms are analyzed using three distinct entropy formulations namely the Shannon, Rényie and Tsallis definitions. Charts of entropy versus sequence length are applied to a set of twenty four species, characterizing 486 chromosomes. The information is synthesized and visualized by adapting phase plane concepts leading to a categorical representation of chromosomes and species.

Reactive power compensation by EPSO technique

This paper presents a methodology to address reactive power compensation using Evolutionary Particle Swarm Optimization (EPSO) technique programmed in the MATLAB environment. The main objective is to find the best operation point minimizing power losses with reactive power compensation, subjected to all operational constraints, namely full AC power flow equations, active and reactive power generation constraints. The methodology has been tested with the IEEE 14 bus test system demonstrating the ability and effectiveness of the proposed approach to handle the reactive power compensation problem.

Optimal dispatch with reactive power compensation by Genetic Algorithm

This paper presents a Unit Commitment model with reactive power compensation that has been solved by Genetic Algorithm (GA) optimization techniques. The GA has been developed a computational tools programmed/coded in MATLAB. The main objective is to find the best generations scheduling whose active power losses are minimal and the reactive power to be compensated, subjected to the power system technical constraints. Those are: full AC power flow equations, active and reactive power generation constraints. All constraints that have been represented in the objective function are weighted with a penalty factors. The IEEE 14-bus system has been used as test case to demonstrate the effectiveness of the proposed algorithm. Results and conclusions are dully drawn.

Entropy diversity in multi-objective particle swarm optimization

Multi-objective particle swarm optimization (MOPSO) is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominated Pareto front for a given problem. Several approaches have been proposed to study the convergence and performance of the algorithm, particularly by accessing the final results. In the present paper, a different approach is proposed, by using Shannon entropy to analyzethe MOPSO dynamics along the algorithm execution. The results indicate that Shannon entropy can be used as an indicator of diversity and convergence for MOPSO problems.

Power law and entropy analysis of catastrophic phenomena

Catastrophic events, such as wars and terrorist attacks, tornadoes and hurricanes, earthquakes, tsunamis, floods and landslides, are always accompanied by a large number of casualties. The size distribution of these casualties has separately been shown to follow approximate power law (PL) distributions. In this paper, we analyze the statistical distributions of the number of victims of catastrophic phenomena, in particular, terrorism, and find double PL behavior. This means that the data sets are better approximated by two PLs instead of a single one. We plot the PL parameters, corresponding to several events, and observe an interesting pattern in the charts, where the lines that connect each pair of points defining the double PLs are almost parallel to each other. A complementary data analysis is performed by means of the computation of the entropy. The results reveal relationships hidden in the data that may trigger a future comprehensive explanation of this type of phenomena.

Shannon entropy analysis of the genome code

This paper studies the chromosome information of twenty five species, namely, mammals, fishes, birds, insects, nematodes, fungus, and one plant. A quantifying scheme inspired in the state space representation of dynamical systems is formulated. Based on this algorithm, the information of each chromosome is converted into a bidimensional distribution. The plots are then analyzed and characterized by means of Shannon entropy. The large volume of information is integrated by averaging the lengths and entropy quantities of each species. The results can be easily visualized revealing quantitative global genomic information.

Entropy analysis of integer and fractional dynamical systems

This paper investigates the adoption of entropy for analyzing the dynamics of a multiple independent particles system. Several entropy definitions and types of particle dynamics with integer and fractional behavior are studied. The results reveal the adequacy of the entropy concept in the analysis of complex dynamical systems.

Maximização da valorização energética em aterros sanitários recorrendo a equipamentos baseados no Ciclo Orgânico de Rankine (ORC)

Atualmente, os aterros sanitários representam uma solução para a gestão e tratamento dos resíduos sólidos urbanos. Da deposição, ocorrem duas formas de emissões ao longo do tempo, a produção de biogás e de lixiviados, que resultam sobretudo da decomposição da matéria orgânica. Um dos principais constituintes do biogás é o metano, o qual tem elevado poder calorífico. O presente trabalho aborda, a maximização da valorização energética em aterros sanitários, recorrendo a equipamentos baseados no Ciclo Orgânico de Rankine (ORC) para a produção de eletricidade. É apresentado como caso de estudo a central de valorização energética da Suldouro, em Sermonde, que produz eletricidade a partir do biogás resultante da decomposição da matéria orgânica depositada em aterro. O biogás é utilizado como combustível para os motogeradores utilizados para o seu aproveitamento energético, sendo que apenas cerca de 40% do potencial energético contido no biogás é transformado em eletricidade, registando-se perdas sobretudo nas emissões dos gases de exaustão e na água de arrefecimento dos motores. Para avaliação do potencial da recuperação energética dos gases de escape é avaliado o desempenho termodinâmico do ciclo ORC. Para tal foi desenvolvida uma ferramenta em MATLAB utilizando como modelo a configuração do ORC com recuperador de calor. O cálculo das propriedades termodinâmicas dos fluidos foi obtido através da criação de uma sub-rotina que chama o programa CoolProp. Este programa restitui propriedades como a entalpia, entropia, pressões e temperaturas em cada ponto do ciclo, permitindo assim ao utilizador otimizar o tempo na obtenção de resultados. A avaliação económica é fundamental na tomada de decisões por parte do investidor e dos financiadores do projeto. É então apresentada a análise económica e efetuada uma análise de sensibilidade, onde foram efetuadas variações nos vetores mais importantes de forma a poder avaliar-se o impacto em termos da sua rentabilidade. A ferramenta desenvolvida permite obter de forma prática, os três indicadores económicos extremamente influentes no que se refere à tomada de decisão. A utilização dos sistemas ORC e os seus benefícios não se esgotam na maximização dos aproveitamentos da valorização energética em aterros sanitários. Também a recuperação de calor para a produção de energia elétrica pode ter um impacto importante em muitos setores intensivos de energia, contribuindo significativamente para a redução do consumo e aumentando a eficiência de todo o processo de produção.

Integer and Fractional Order Entropy Analysis of Earthquake Data-series

This paper studies the statistical distributions of worldwide earthquakes from year 1963 up to year 2012. A Cartesian grid, dividing Earth into geographic regions, is considered. Entropy and the Jensen–Shannon divergence are used to analyze and compare real-world data. Hierarchical clustering and multi-dimensional scaling techniques are adopted for data visualization. Entropy-based indices have the advantage of leading to a single parameter expressing the relationships between the seismic data. Classical and generalized (fractional) entropy and Jensen–Shannon divergence are tested. The generalized measures lead to a clear identification of patterns embedded in the data and contribute to better understand earthquake distributions.

Entropy Analysis of Industrial Accident Data Series

Complex industrial plants exhibit multiple interactions among smaller parts and with human operators. Failure in one part can propagate across subsystem boundaries causing a serious disaster. This paper analyzes the industrial accident data series in the perspective of dynamical systems. First, we process real world data and show that the statistics of the number of fatalities reveal features that are well described by power law (PL) distributions. For early years, the data reveal double PL behavior, while, for more recent time periods, a single PL fits better into the experimental data. Second, we analyze the entropy of the data series statistics over time. Third, we use the Kullback–Leibler divergence to compare the empirical data and multidimensional scaling (MDS) techniques for data analysis and visualization. Entropy-based analysis is adopted to assess complexity, having the advantage of yielding a single parameter to express relationships between the data. The classical and the generalized (fractional) entropy and Kullback–Leibler divergence are used. The generalized measures allow a clear identification of patterns embedded in the data.