VUCA is an acronym for volatility, uncertainty, complexity, and ambiguity, used to describe an environment that defies confident predictions. An example of this environment is the Covid-19 pandemic, which has created uncertainty worldwide because it is an unknown and highly contagious disease that neither society nor institutions were prepared to face. This article aims to describe the scientific production of VUCA to understand its main research focus. This research analyzes 105 documents from the Web of Science (WoS) database using Bibliometrics and Content Analysis. The bibliometric analysis reported several production indexes: annual, personal, national, institutional, and journal productivity. The content analysis analyzed 95 article abstracts in nineteen clusters selected by comparing two clustering methods, Latent Dirichlet Allocation and K-Means, using the coherence and silhouette indices, respectively. VUCA is an emerging topic with increased scientific production in the last four years. However, there are no major producers to date. The most frequent topics are management, leadership, and change, where several works emphasize the role of the leader in dealing with change. The literature has focused on understanding the skills needed to cope with a VUCA environment and how to teach them. In addition, the use of two methods based on machine learning techniques to estimate the number of clusters of scientific papers is highlighted as an alternative to splitting articles into topics when the dataset is small. VUCA es un acrónimo de volatilidad, incertidumbre, complejidad y ambigüedad que se utiliza de diversas formas para describir un entorno que desafía las predicciones seguras. Un ejemplo de este entorno es la pandemia por Covid-19, la cual ha instalado un contexto de incertidumbre a nivel mundial, por tratarse de una enfermedad desconocida y altamente contagiosa, para la cual, ni la sociedad ni las instituciones se encontraban preparadas para afrontar. El propósito de este artículo es describir la producción científica de VUCA para entender su principal enfoque de investigación. Esta investigación analiza 105 documentos de la base de datos Web of Science (WoS) mediante Bibliometría y Análisis de Contenido. El análisis bibliométrico reportó varios índices de producción: anual, personal, nacional, institucionalidad y productividad de las revistas. El análisis de contenido analizó 95 resúmenes de artículos en diecinueve clusters seleccionados por comparación de dos métodos de clustering Latent Dirichlet Allocation y K-Means, utilizando los índices de coherencia y silueta respectivamente. Se encontró que VUCA es un tema emergente con una mayor producción científica en los últimos cuatro años. Sin embargo, no hay grandes productores a la fecha. Los temas más frecuentes son la gestión, el liderazgo y el cambio, donde varias investigaciones hacen hincapié en el papel del líder a la hora de afrontar los cambios. La literatura se ha centrado en la comprensión de las habilidades necesarias para hacer frente a un entorno VUCA y en cómo enseñarlas. Además, se destaca el uso de dos métodos basados en técnicas de aprendizaje automático para estimar la cantidad de grupos de trabajos científicos, como una alternativa para dividir artículos en tópicos cuando el set de datos es pequeño.

Technologies for live presentations should consider users' capabilities to manage large amounts of data in real-time, particularly, exchanges of short texts (e.g., phrases). This study examines the effects on time and quality of text clustering algorithms applied to short, medium, and long size texts, and examines whether short text clustering shows a reasonable performance for live presentations. We run several simulations in which we varied the number of phrases (from 5 to 200) contained in each text type (long, medium, and short) and the number of generated clusters (from 2 to 10). The algorithms used were snowball steamers, TF-IDF, and K-means for clustering; and the text types were Reuters, 20 NewsGroup and an experimental data set, for the long, medium, and short size texts, respectively. The first result showed that text size had a large effect on the algorithms execution time, with the shortest average time for the short texts and longer average time for the longest texts. The second result showed that the number of phrases in each text type significantly predicts execution time but not the number of clusters generated by K-means. Inertia and purity measures were used to test the quality of the clusters generated. Text size, number of phrases and number of clusters predict inertia; showing the lowest inertia for the short texts. Purity measures were like previously reported results for all text types. Thus, clustering algorithms for short texts can confidently be used in real-time presentations.

Words are not isolated entities within a language. In this paper, we measure the number of choices transmitted in natural language by means of the von Neumann entropy of language networks. This quantity, introduced in Quantum Information accounts, provides a detailed characterization of network complexities. The simulations are based on a large parallel corpus of 362 languages across 55 linguistic families (focusing on the sub-sample of 85 languages from the Americas). With this, we constructed language networks as a simple way to describe word connectivity patterns for each language. We studied several aspects of the von Neumann entropy of language networks. First, we discovered large groups of languages with low average degree and high von Neumann entropy. The results suggested also that large von Neumann entropy is associated with word entropy (as a proxy for morphological complexity), and is inversely related to degree regularity. This means that there are pressures at play that keep a balance between word morphological complexity and patterns of connections between words. We suggested also a strong influence of functional words on low von Neumann entropy languages. Our approach is thus a simple network-based contribution to establish cross-linguistic language comparisons from textual data.

We research the potential of Support Vector Machines (SVMs) for forecasting in chaotic series of copper's price; based on different combination of structure, and possibilities of discovering knowledge in big data. It was built models of SVMs to forecast the copper's price of the London Metal Exchange (LME).

This work present a solution for locating people inside of mines with a system designed with the service oriented architecture and RFID technology to help the security of workers. We propose a pervasive computing system designed with a UML process extension, to discover the rights web services to transmit and process data collected from RFID sensors. The data captured from nodes allow calculating the location of workers and machines by reference tags in conjunction with techniques of trilateration. The results were successful, since it achieves a design of pervasive system with web services and a database model to be used by localization algorithm that shown an improvement in its accuracy with an increasing the number of reference tags.