Neutrosophic method to measure the need for the implementation of the proposed reform of the Ordinance regulating the care of pets and domestic animals

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Andrea Katherine Bucaram Caicedo
Giovana Fernanda Vinueza Arroyo
Diego Gustavo Jácome Salinas
Jeanpierre Iván Rengifo Aispur

Abstract

This research focuses on the dynamics of urban fauna in Ambato, Ecuador, exploring beyond the conventional association with dogs and cats to encompass a diversity of species in urban environments. The management of this fauna, under the responsibility of municipal governments, is governed by ministerial guidelines, emphasizing the importance of harmonious coexistence between the population and animals. Despite existing regulations, such as the Comprehensive Organic Criminal Code and the Organic Environmental Code, the 2008 Municipal Ordinance for the care of companion animals in Ambato has not been updated in 15 years. This lack of updating presents challenges, from the lack of accurate records to situations of abuse and slaughter. The objective of the research was to develop a Neutrosophic Method to measure the need for the implementation of the reform project to the Ordinance that regulates the care of companion animals or pets and domestic animals. Inspired by the experience of Quito, the proposal aims to regulate and control urban fauna, promoting animal welfare and a balanced coexistence. The research highlights the importance of these actions in the context of urban growth, offering a key initiative to protect the rights of all living beings in Ambato and improve public health.

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Neutrosophic method to measure the need for the implementation of the proposed reform of the Ordinance regulating the care of pets and domestic animals. (2024). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 35, 300-309. http://fs.unm.edu/NCML2/index.php/112/article/view/641
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How to Cite

Neutrosophic method to measure the need for the implementation of the proposed reform of the Ordinance regulating the care of pets and domestic animals. (2024). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 35, 300-309. http://fs.unm.edu/NCML2/index.php/112/article/view/641

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