Evaluation of the Sustainable Development Goals in the Diagnosis and Prediction of the Sustainability of Projects Aimed at Local Communities in Latin America and the Caribbean
Article
Subjects > Engineering
Europe University of Atlantic > Research > Articles and books
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
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The purpose of this article is to help to bridge the gap between sustainability and its application to project management by developing a methodology based on artificial intelligence to diagnose, classify, and forecast the level of sustainability of a sample of 186 projects aimed at local communities in Latin American and Caribbean countries. First, the compliance evaluation with the Sustainable Development Goals (SDGs) within the framework of the 2030 Agenda served to diagnose and determine, through fuzzy sets, a global sustainability index for the sample, resulting in a value of 0.638, in accordance with the overall average for the region. Probabilistic predictions were then made on the sustainability of the projects using a series of supervised learning classifiers (SVM, Random Forest, AdaBoost, KNN, etc.), with the SMOTE resampling technique, which provided a significant improvement toward the results of the different metrics of the base models. In this context, the Support Vector Machine (SVM) + SMOTE was the best classification algorithm, with accuracy of 0.92. Lastly, the extrapolation of this methodology is to be expected toward other realities and local circumstances, contributing to the fulfillment of the SDGs and the development of individual and collective capacities through the management and direction of projects.
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García Villena, Eduardo and Pascual Barrera, Alina Eugenia and Álvarez, Roberto Marcelo and Dzul López, Luis Alonso and Tutusaus, Kilian and Vidal Mazón, Juan Luis and Miró Vera, Yini Airet and Brie, Santiago and López Flores, Miguel A.
mail
eduardo.garcia@uneatlantico.es, alina.pascual@unini.edu.mx, roberto.alvarez@uneatlantico.es, luis.dzul@uneatlantico.es, kilian.tutusaus@uneatlantico.es, juanluis.vidal@uneatlantico.es, yini.miro@uneatlantico.es, santiago.brie@uneatlantico.es, miguelangel.lopez@uneatlantico.es
(2022)
Evaluation of the Sustainable Development Goals in the Diagnosis and Prediction of the Sustainability of Projects Aimed at Local Communities in Latin America and the Caribbean.
Applied Sciences, 12 (21).
p. 11188.
ISSN 2076-3417
|
Text
applsci-12-11188.pdf Available under License Creative Commons Attribution. Download (3MB) | Preview |
Abstract
The purpose of this article is to help to bridge the gap between sustainability and its application to project management by developing a methodology based on artificial intelligence to diagnose, classify, and forecast the level of sustainability of a sample of 186 projects aimed at local communities in Latin American and Caribbean countries. First, the compliance evaluation with the Sustainable Development Goals (SDGs) within the framework of the 2030 Agenda served to diagnose and determine, through fuzzy sets, a global sustainability index for the sample, resulting in a value of 0.638, in accordance with the overall average for the region. Probabilistic predictions were then made on the sustainability of the projects using a series of supervised learning classifiers (SVM, Random Forest, AdaBoost, KNN, etc.), with the SMOTE resampling technique, which provided a significant improvement toward the results of the different metrics of the base models. In this context, the Support Vector Machine (SVM) + SMOTE was the best classification algorithm, with accuracy of 0.92. Lastly, the extrapolation of this methodology is to be expected toward other realities and local circumstances, contributing to the fulfillment of the SDGs and the development of individual and collective capacities through the management and direction of projects.
Item Type: | Article |
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Uncontrolled Keywords: | SMOTE; artificial intelligence; projects; fuzzy logic |
Subjects: | Subjects > Engineering |
Divisions: | Europe University of Atlantic > Research > Articles and books Fundación Universitaria Internacional de Colombia > Research > Scientific Production Ibero-american International University > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production |
Date Deposited: | 11 Nov 2022 23:30 |
Last Modified: | 12 Jul 2023 23:31 |
URI: | https://repositorio.uneatlantico.es/id/eprint/4474 |
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Uterine leiomyomas are the most common benign, monoclonal, gynaecological tumors in a woman’s uterus, while leiomyosarcoma is a rare but aggressive condition caused by the malignant transformation of the myometrium. To overcome the common obstacles related to the methods usually used to study these pathologies, we aimed to devise three-dimensional models of myometrium, uterine leiomyoma and leiomyosarcoma cell lines, using two different types of biocompatible scaffolds. Specifically, we exploited the agarose gel matrix in common 6-well plates and the alginate matrix using Bioprinting INKREDIBLE + (CELLINK), a pneumatic extruded base equipped with a system with double printheads, and a UV printer LED curing system. Both methods allowed the development of 3D spheroids of all three cell types, that were also suitable for morphological investigations. We showed that all cell types embedded in both agarose and alginate formed spheroids in their growth medium. The spheroids successfully proliferated and self-organized into complex structures, developing a sustainable system that emulated the condition of the tissues through the accumulation of extracellular matrix. These models could be useful for a better understanding of pathophysiology, etiopathogenesis, and testing new methods or molecules from a preventive and therapeutic point of view.
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