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 > Scientific Production
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
Abierto
Inglés
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.
metadata
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 |
---|---|
Uncontrolled Keywords: | SMOTE; artificial intelligence; projects; fuzzy logic |
Subjects: | Subjects > Engineering |
Divisions: | Europe University of Atlantic > Research > Scientific Production 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 |
Actions (login required)
![]() |
View Item |
en
close
Technological firms invest in R&D looking for innovative solutions but assuming high costs and great (technological) uncertainty regarding final results and returns. Additionally, they face other problems related to R&D management. This empirical study tries to determine which of the factors favour or constrain the decision of these firms to engage in R&D. The analysis uses financial data of 14,619 ICT listed companies of 22 countries from 2003 to 2018. Additionally, macroeconomic data specific for the countries and the sector were used. For the analysis of dynamic panel data, a System-GMM method is used. Among the findings, we highlight that cash flow, contrary to the known theoretical models and empirical evidences, negatively impacts on R&D investment. Debt is neither the right source for R&D funding, as the effect is also negative. This suggests that ICT companies are forced to manage their R&D activities differently, relying more on other funding sources, taking advantage of growth opportunities and benefiting from a favourable macroeconomic environment in terms of growth and increased business sector spending on R&D. These results are similar in both sub-sectors and in all countries, both bank- and market based. The exception is firms with few growth opportunities and little debt.
Inna Alexeeva-Alexeev mail inna.alexeeva@uneatlantico.es, Cristina Mazas Pérez-Oleag mail cristina.mazas@uneatlantico.es,
Alexeeva-Alexeev
en
close
The aim of this study was to investigate the effects of enzymatic treatments (pectinase, pectin lyase, and cellulase) on the in vitro digestion and fermentation characteristics of whole mulberry fruit juice. The analysis focused on changes in carbohydrate properties within the black mulberry fruit matrix during simulated digestion and fermentation. Human fecal microbiota were collected and introduced to the fruit matrix to monitor the fate of both soluble and insoluble polysaccharides during fermentation. The results revealed that enzymatic treatments enhanced the solubilization of carbohydrates from mulberry fruits, with pectinase showing the most significant effect. Throughout the process of in vitro digestion, there was a gradual increase in the percentage of solubilized carbohydrates from the mulberry juice substrate. The digested suspensions underwent dialysis to remove degradation fragments, and a lower quantity of carbohydrate in the enzyme-treated groups compared to the control. Polysaccharide populations with varying molecular weights (Mw) were obtained from the soluble fractions of mulberry residues for subsequent fermentation. An increase in Mw of soluble polysaccharides was detected by HPSEC during fermentation in certain cases. The gut microbiota demonstrated the ability to convert specific insoluble fractions into soluble components, which were subsequently subjected to microbial utilization. Enzymatic treatments during mulberry juice preparation can potentially positively impact health by influencing gut microbiota and short-chain fatty acid (SCFA) modulations. Enzymes could serve as valuable tools for producing functional fruit and vegetable juices, with the need to specify processing conditions for specific raw materials remaining a subject of further investigation.
Peihuan Luo mail , Jian Ai mail , Yuxin Wang mail , Songen Wang mail , Henk A. Schols mail , Hauke Smidt mail , Maurizio Battino mail maurizio.battino@uneatlantico.es, Weibin Bai mail , Lingmin Tian mail ,
Luo
en
close
Manuka honey, which is rich in pinocembrin, quercetin, naringenin, salicylic, p-coumaric, ferulic, syringic and 3,4-dihydroxybenzoic acids, has been shown to have pleiotropic effects against colon cancer cells. In this study, potential chemosensitizing effects of Manuka honey against 5-Fluorouracil were investigated in colonspheres enriched with cancer stem cells (CSCs), which are responsible for chemoresistance. Results showed that 5-Fluorouracil increased when it was combined with Manuka honey by downregulating the gene expression of both ATP-binding cassette sub-family G member 2, an efflux pump and thymidylate synthase, the main target of 5-Fluorouracil which regulates the ex novo DNA synthesis. Manuka honey was associated with decreased self-renewal ability by CSCs, regulating expression of several genes in Wnt/β-catenin, Hedgehog and Notch pathways. This preliminary study opens new areas of research into the effects of natural compounds in combination with pharmaceuticals and, potentially, increase efficacy or reduce adverse effects.
Danila Cianciosi mail , Yasmany Armas Diaz mail , José M. Alvarez-Suarez mail , Xiumin Chen mail , Di Zhang mail , Nohora Milena Martínez López mail nohora.martinez@uneatlantico.es, Mercedes Briones Urbano mail mercedes.briones@uneatlantico.es, José L. Quiles mail jose.quiles@uneatlantico.es, Adolfo Amici mail , Maurizio Battino mail maurizio.battino@uneatlantico.es, Francesca Giampieri mail francesca.giampieri@uneatlantico.es,
Cianciosi
en
close
The Internet of Things (IoT) has recently surpassed wired communication. WiMAX is a wireless transmission technology that allows for faster internet access. Wireless network innovations, like some other communication networks, are not safe and secure. Security and authorization models are intended to prevent unauthorized use of network services. Numerous authorization and encrypted communication mechanisms have been introduced for WiMAX privacy, but the communication systems are still insecure and vulnerable to attacks such as zero-day attacks, rouge base station attacks, Man in the Middle (MITM) attacks, and Denial of Service (DoS) attacks. Wireless technologies have come a long way in the last few decades. Because most wireless transmission systems rely on radio signals, the system channel is essentially vulnerable to interception. As a result, data security is always critical in the presentation of a system. Because WiMAX is a wireless communication technology, it is particularly vulnerable to interception, so security is a top priority. Individuals must be protected from security breaches that occur across network interfaces, networking devices, and everything in between. Robust security management is required to protect WiMAX from attacks and vulnerabilities, despite the fact that emerging Artificial Intelligence (AI) technologies necessitate different security governance than existing technologies. We proposed an Optimized Privacy Information Exchange Schema for Explainable AI Empowered WiMAX-based IoT that addresses vulnerabilities and threats during the identification and authorization phases to improve the functionality and performance characteristics of the traditional system. The Scyther tool was used to validate the proposed privacy scheme, which is safer and more secure than existing systems.
Premkumar Chithaluru mail , Aman Singh mail aman.singh@uneatlantico.es, Jagjit Singh Dhatterwal mail , Ali Hassan Sodhro mail , Marwan Ali Albahar mail , Anca Jurcut mail , Ahmed Alkhayyat mail ,
Chithaluru
en
close
Morenas-Aguilar, MD, Ruiz-Alias, SA, Blanco, AM, Lago-Fuentes, C, García-Pinillos, F, and Pérez-Castilla, A. Does the menstrual cycle impact the maximal neuromuscular capacities of women? An analysis before and after a graded treadmill test to exhaustion. J Strength Cond Res 37(11): 2185–2191, 2023. This study explored the effect of the menstrual cycle (MC) on the maximal neuromuscular capacities of the lower-body muscles obtained before and after a graded exercise test conducted on a treadmill to exhaustion. Sixteen physically active women were tested at −11 ± 3, −5 ± 3, and 5 ± 3 days from the luteinizing peak for the early follicular, late follicular, and midluteal phases. In each session, the individualized load-velocity (L-V) relationship variables (load-axis intercept [L0], velocity-axis intercept [v0], and area under the L-V relationship line [Aline]) were obtained before and after a graded exercise test conducted on a treadmill to exhaustion using the 2-point method (3 countermovement jumps with a 0.5-kg barbell and 2 back squats against a load linked to a mean velocity of 0.55 m·second−1). At the beginning of each session, no significant differences were reported for L0 (p = 0.726; ES ≤ 0.18), v0 (p = 0.202; ES ≤ 0.37), and Aline (p = 0.429; ES ≤ 0.30) between the phases. The MC phase × time interaction did not reach statistical significance for any L-V relationship variable (p ≥ 0.073). A significant main effect of “time” was observed for L0 (p < 0.001; ES = −0.77) and Aline (p = 0.002; ES = −0.59) but not for v0 (p = 0.487; ES = 0.12). These data suggest that the lower-body maximal neuromuscular capacities obtained before and after a graded treadmill test are not significantly affected by MC, although there is a high variability in the individual response.
María Dolores Morenas-Aguilar mail , Santiago A. Ruiz-Alias mail , Aitor Marcos Blanco mail , Carlos Lago-Fuentes mail carlos.lago@uneatlantico.es, Felipe García-Pinillos mail , Alejandro Pérez-Castilla mail ,
Morenas-Aguilar