Crystallization abounds in nature and industrial practice. A plethora of indispensable products ranging from agrochemicals and pharmaceuticals to battery materials are produced in crystalline form in industrial practice. Yet, our control over the crystallization process across scales, from molecular to macroscopic, is far from complete. This bottleneck not only hinders our ability to engineer the properties of crystalline products essential for maintaining our quality of life but also hampers progress toward a sustainable circular economy in resource recovery. In recent years, approaches leveraging light fields have emerged as promising alternatives to manipulate crystallization. In this review article, we classify laser-induced crystallization approaches where light-material interactions are utilized to influence crystallization phenomena according to proposed underlying mechanisms and experimental setups. We discuss nonphotochemical laser-induced nucleation, high-intensity laser-induced nucleation, laser trapping-induced crystallization, and indirect methods in detail. Throughout the review, we highlight connections among these separately evolving subfields to encourage the interdisciplinary exchange of ideas.
-A new methodology for the parameterization of the technical analysis of the financial market indicator coinedMoving Average Convergence-Divergence (MACD) is presented in this paper. The architecture of the MACD involves the use of exponential moving averages that in turn use different time windows, tracking securities prices trends and signalising the right moment to purchase and sale shares. By using genetic algorithms, it was possible to establish an optimal value for the time window which could yield higher profits, when compared to the time window used in literature. The use of fuzzy logic indicates the best moment for purchasing and sale of shares, raising the security of each transaction, thus resulting in the increase of the success rate. The methodology proposed was validated by taking into account the Petrobras shares (PETR4) in the period between February 2005 and August 2008, achieving a profit higher than that in the usual parameterisation.Keywords -Genetic Algorithm, Fuzzy Logic, Financial Time Series. Resumo -Uma nova metodologia de parametrização do indicador de análise técnica do mercado financeiro chamadoMoving Average Convergence-Divergence (MACD) é apresentada neste artigo. A composição do MACD envolve o uso de médias móveis exponenciais que, por sua vez, utilizam janelas temporais diferentes, acompanhando a tendência dos preços dos valores mobiliários e indicando o melhor momento de compra e venda. Com o uso da técnica de algoritmos genéticos foi possível a escolha de janelas temporais que gerassem melhores lucros, quando comparados às janelas temporais utilizadas em diversas literaturas. A utilização de lógica fuzzy possibilitou a classificação das ordens de compra elevando a segurança de cada operação, traduzida no aumento da taxa de acerto. Para o trabalho aqui proposto entende-se com taxa de acerto a percentagem de operações de compra e venda que geraram rentabilidade positiva. A metodologia proposta foi validada utilizando as ações da Petrobras PETR4 no período entre novembro de 2006 e agosto de 2008, alcançando um lucro superior a parametrização usual.Palavras-chave -Algoritmo Genético, Lógica Fuzzy, Séries Temporais Financeiras. IntroduçãoA predição de dados em séries temporais é de extrema relevância para diversas áreas do conhecimento científico e vem sendo estudada desde o início do século XX quando alguns modelos matemáticos foram empregados. Até 1920, a predição de dados em séries temporais era basicamente realizada através da extrapolação dos dados por meio de um ajuste global no domínio do tempo. Entretanto, somente a partir de 1926 é que iniciou-se o que poderíamos chamar de moderna predição de séries, quando a técnica auto-regressiva foi publicada por Yule [1].Na área financeira, a análise de séries temporais tem grande importância no estudo dos fenômenos sócio-econômicos [2] nos quais não se é possível determinar a dinâmica de evolução dos processos envolvidos em sua composição.Muitos estudos têm sido desenvolvidos nesta área. Chen e colaboradores propuseram um mode...
Crystallization is abound in nature and industrial practice. A plethora of indispensable products ranging from agrochemicals and pharmaceuticals to battery materials, are produced in crystalline form in industrial practice. Yet, our control over the crystallization process across scales, from molecular to macroscopic, is far from complete. This bottleneck not only hinders our ability to engineer the properties of crystalline products essential for maintaining our quality of life but also hampers progress toward a sustainable circular economy in resource recovery. In recent years, approaches leveraging light fields have emerged as promising alternatives to manipulate crystallization. In this review article, we classify laser-induced crystallization approaches where light-material interactions are utilized to influence crystallization phenomena according to proposed underlying mechanisms and experimental setups. We discuss non-photochemical laser-induced nucleation, high-intensity laser-induced nucleation, laser trapping-induced crystallization, and indirect methods in detail. Throughout the review, we highlight connections amongst these separately evolving sub-fields to encourage interdisciplinary exchange of ideas.
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