Tree of Science (ToS) is a web-based tool which uses the network structure of paper citation to identify relevant literature. ToS shows the information in the form of a tree, where the articles located in the roots are the classics, in the trunk are the structural publications, and leaves are the most current papers. It has been found that some results in the leaves can be separated from the tree. Therefore, an algorithm (SAP) is proposed, in order to improve results in the leaves. Two improvements are presented: articles located in the leaves are from the last five years, and they are connected to root and trunk articles through their citations. This improvement facilitates construction of current literature for researchers.

Big Data se ha convertido en una tendencia a nivel mundial y aunque aún no cuenta con un concepto científico o académico consensuado, se augura cada día mayor crecimiento del mercado que lo envuelve y de las áreas de investigación asociadas. En este artículo se reporta una exploración de literatura sobre Big Data, que comprende un estado del arte de las técnicas y tecnologías asociadas a Big Data, las cuales abarcan captura, procesamiento, análisis y visualización de datos. Se exploran también las características, fortalezas, debilidades y oportunidades de algunas aplicaciones y modelos que incluyen Big Data, principalmente para el soporte al modelado de datos, análisis y minería de datos. Asimismo, se introducen algunas de las tendencias futuras para el desarrollo de Big Data por medio de la definición de aspectos básicos, alcance e importancia de cada una. La metodología empleada para la exploración incluye la aplicación de dos estrategias, una primera corresponde a un análisis cienciométrico; y la segunda, una categorización de documentos por medio de una herramienta web de apoyo a los procesos de revisión literaria. Como resultados se obtiene una síntesis y conclusiones en torno a la temática y se plantean posibles escenarios para trabajos investigativos en el campo de dominio.

La difusión de productos a través de redes sociales es un campo de aplicación del mercadeo, donde la decisión de compra de un consumidor es influenciada por factores internos y externos como su red de conocidos y familiares. El propósito de esta investigación es identificar las principales perspectivas y plantear futuras investigaciones, apoyados en la revisión selectiva del estado del arte. Para la orientación de la búsqueda y la selección de artículos se utilizó la teoría de grafos, aprovechando las posibilidades de reconocer las conexiones entre los diferentes trabajos, arrojando para su análisis 18 artículos clásicos y 23 artículos actuales. A partir de esto se obtuvo, como resultado de la investigación, cuatro (4) estrategias de mercadeo diferentes: enfocadas a los influenciadores, a los no influenciadores, grupos pequeños y estrategias tradicionales de mercadeo.Palabras clave: difusión de productos, redes sociales, teoría de grafos. ABSTRACT  The diffusion of products through social networking is an application field of marketing, where the buying decision of a consumer is influenced by internal and external factors as their network of friends and relatives. The purpose of this research is to identify the main perspectives and propose future research, supported in state of the art selective review. As input for the orientation of search and articles selection, graph theory was used, leveraging the odds of recognizing the links among different works, providing for analysis 18 classic articles and 23 current articles. The result of the investigation showed four different marketing strategies: focused on influencers, non-influencers, small groups and traditional marketing strategies.Keywords: diffusion of products, social networks, graph theory.

Sediment transport in irrigation canals is an important issue in the design and operation of irrigation systems. Frequently observed problems in irrigation systems are, for example, clogging of turnouts and reduction of the conveyance capacity of canals by siltation, and instability of side slopes and of structures due to erosion. Each year large investments are required to maintain or to rehabilitate these systems and to keep them in an acceptable condition for irrigation purposes.Irrigation canals are generally designed based on the assumption of uniform and steady flow of water and sediments. However, the flow is predominantly non-uniform, due to time-dependent discharges and constant water levels at regulation and division points. A strong relationship exists between the sediment transport and flow conditions.The aim of this article is to present some new developments in the behaviour of sediment and associated sediment transport in irrigation canals under changing flow conditions, as well as the deposition and/or entrainment rate in time and place for various flow conditions and sediment inputs. This article will discuss an approach to compute sediment transport in irrigation canals under non-uniform flow conditions. The sediment transport has been analysed for the flow conditions that prevail in irrigation canals. The Ackers-White and Brownlie equations are recommended to compute the sediment transport under equilibrium conditions for prismatic canal cross-sections.

The implementation of tools such as Genetic Algorithms has not been exploited for asset price prediction despite their power, robustness, and potential application in the stock market. This paper aims to fill the gap existing in the literature on the use of Genetic Algorithms for predicting asset pricing of investment strategies into stock markets and investigate its advantages over its peers Buy & Hold and traditional technical analysis. The Genetic Algorithms strategy applied to the MACD was carried out in two different validation periods and sought to optimize the parameters that generate the buy-sell signals. The performance between the machine learning-based approach, technical analysis with the MACD and B&H was compared. The results suggest that it is possible to find optimal values of the technical indicator parameters that result in a higher return on investment through Genetic Algorithms, beating the traditional technical analysis and B&H by around 4%. This study offers a new insight for practitioners, traders, and finance researchers to take advantage of Genetic Algorithms for trading rules application in forecasting financial asset returns under a more efficient and robust methodology based on historical data analysis.