At present, there is a proliferation of virtualization technologies (VTs), which are part of the basic and underlying infrastructure of popular cloud computing. Those interested in VTs are faced with a non-unified volume of information and various approaches to modes of operation, classification structures, and the performance implications of these technologies. This makes it difficult to decide which type of VT is appropriate for a particular context. Therefore, this paper reviews the state of the art on VT taxonomic models. Methodologically, a literature review is carried out to identify VT classification models, recognizing their features and weaknesses. With this in mind, a new taxonomy of virtualization technologies is proposed, which responds to the weaknesses identified in the analyzed schemes. The new VT taxonomy combines the Abstraction Level and Virtual Machine Type approaches, providing the reader with a means to visualize VTs. In doing so, the reader can locate the level of abstraction at which each VT is developed, in addition to the type of machine projected, whether it is a complete system or an execution environment for processes. The proposed taxonomy can be used in the academic environment to facilitate teaching processes or in the business environment to facilitate decision-making when implementing VTs.

Computational geometry is a discipline focused on solving problems in the geometric domain. In this context, the algorithm for computing the convex polygon called Convex Hull (CH) is important, because it is the basis for many other algorithms. The objective of the research was to implement algorithms that compute the CH incorporating modifications to reduce the execution time. The research started with a bibliographic review of computational geometry and the algorithms highlighted in the calculation of CH. Subsequently, the QuickHull, Gift Wrapping, and Graham Scan algorithms were implemented in JAVA in their original versions; some versions with modifications were also implemented. Upon completion of implementation, tests were run to verify the execution times. Finally, the QuickHull algorithm was found to be the fastest among the implementations performed in this research. It is also noted a reduction in execution times in the modified implementations in relation to the original ones of the Gift Wrapping and Graham Scan algorithms.

Objetivo: En el trabajo se construyó un SMS (Systematic Mapping Study) para la identificación y clasificación de problemas y causas en la realización de pruebas en la MSA (Micro-Service Architecture). Metodología: Este SMS adoptó prácticas establecidas en ingeniería de software basada en evidencias. El SMS consideró artículos publicados desde 2016 en bases de datos como ACM, IEEE Xplore, Science Direct, WoS, Springer y Scopus. Además, la estrategia de búsqueda incluyó cuatro iteraciones de bola de nieve. Resultados: El SMS comenzó con 842 estudios y terminó con 32 estudios seleccionados, identificando nueve problemas y ocho causas relacionadas con las pruebas de software en MSA. Finalmente, los estudios se clasificaron por problemas y causas. Conclusiones: El SMS permitió inferir que la Alta Complejidad en pruebas MSA es el problema más representativo. Igualmente, las causas más destacadas son el Alto Número de Servicios, la Falta de Investigación y la proliferación de cada versión de servicio (Service Versioning).

Agriculture is one of the fundamental pillars of all societies worldwide, and the proper management of information allows timely decisions to be made about the advancement of companies. National and departmental government entities support emergent agricultural endeavors that provide a good opportunity to increase levels of production and commercialization of products [1], such as the cultivation of Hass avocado (Persea americana Mill). Among the challenges associated with this type of cultivation is the need to find potential areas for planting and suitable productivity and to contribute to technological developments in the agricultural sector, benefiting Hass avocado growers from the department of Risaralda. Therefore, in this study, a model that allows the determination of current and potential areas of cultivation for Hass avocado (Persea americana Mill) in this department based on edaphoclimatic variables and fruit quality is proposed. This model takes advantage of current trends in precision agriculture, including techniques derived from machine learning and supervised learning algorithms, among which is random forest

This article aims to propose a unifying conceptualization for the leadership domain utilizing ontology engineering to address conceptual ambiguity and information overlap. The approach involves identifying and defining structural, common, and underlying elements to organize leadership information. The methodology is based on a literature review guided by principles of ontology engineering. It focuses on collecting, analyzing, disambiguating, and synthesizing information to represent general elements in the leadership domain as categories, subcategories, and instances, including their relationships. The result is a unifying conceptualization comprising defined categories, subcategories, general instances, and relationships. This structure creates a taxonomic framework to organize the leadership’s domain information, classifying approaches, theories, models, styles, variables, characteristics, and organizational results. Expert validation confirms their suitability to represent the leadership domain in a general manner. Researchers can use it to understand and compare information, identify conceptual similarities and differences, and support knowledge dissemination within the leadership domain, reducing ambiguity and overlap.