Nonlinear regression for the characterization of peristaltic pumps, an alternative in the control of biological fluids
This paper presents a modeling approach to characterize two peristaltic pumps using nonlinear regression. Accuracy tests were performed by varying the height level of the pump with respect to the vessel, and it was found to have no effect on the flow rate. Filling data was recorded for 300 ml consid...
- Autores:
-
Ramírez Carvajal, Luis Eduardo
Puerto, Karla
López Barrera, German Luciano
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2024
- Institución:
- Universidad Francisco de Paula Santander
- Repositorio:
- Repositorio Digital UFPS
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.ufps.edu.co:ufps/9150
- Acceso en línea:
- https://repositorio.ufps.edu.co/handle/ufps/9150
- Palabra clave:
- Nonlinear regression
peristaltic pumps
Characterization pumps
Engineering
Regresión no lineal
bombas peristálticas
Caracterización de bombas
Ingeniería
- Rights
- openAccess
- License
- https://creativecommons.org/licenses/by-nc-sa/4.0/
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Nonlinear regression for the characterization of peristaltic pumps, an alternative in the control of biological fluids |
| dc.title.spa.fl_str_mv |
Regresión no lineal para la caracterización de bombas peristálticas, una alternativa en el control de fluidos biológicos |
| title |
Nonlinear regression for the characterization of peristaltic pumps, an alternative in the control of biological fluids |
| spellingShingle |
Nonlinear regression for the characterization of peristaltic pumps, an alternative in the control of biological fluids Nonlinear regression peristaltic pumps Characterization pumps Engineering Regresión no lineal bombas peristálticas Caracterización de bombas Ingeniería |
| title_short |
Nonlinear regression for the characterization of peristaltic pumps, an alternative in the control of biological fluids |
| title_full |
Nonlinear regression for the characterization of peristaltic pumps, an alternative in the control of biological fluids |
| title_fullStr |
Nonlinear regression for the characterization of peristaltic pumps, an alternative in the control of biological fluids |
| title_full_unstemmed |
Nonlinear regression for the characterization of peristaltic pumps, an alternative in the control of biological fluids |
| title_sort |
Nonlinear regression for the characterization of peristaltic pumps, an alternative in the control of biological fluids |
| dc.creator.fl_str_mv |
Ramírez Carvajal, Luis Eduardo Puerto, Karla López Barrera, German Luciano |
| dc.contributor.author.none.fl_str_mv |
Ramírez Carvajal, Luis Eduardo Puerto, Karla López Barrera, German Luciano |
| dc.subject.proposal.eng.fl_str_mv |
Nonlinear regression peristaltic pumps Characterization pumps Engineering |
| topic |
Nonlinear regression peristaltic pumps Characterization pumps Engineering Regresión no lineal bombas peristálticas Caracterización de bombas Ingeniería |
| dc.subject.proposal.spa.fl_str_mv |
Regresión no lineal bombas peristálticas Caracterización de bombas Ingeniería |
| description |
This paper presents a modeling approach to characterize two peristaltic pumps using nonlinear regression. Accuracy tests were performed by varying the height level of the pump with respect to the vessel, and it was found to have no effect on the flow rate. Filling data was recorded for 300 ml considering the voltage applied to the pumps and the filling time. A least squares curve fitting was performed with the recorded data. The exponential model was determined to be the most accurate for the two pumps, and using a simple rule of three, the equation for each desired volume was found. Finally, filling tests were performed to compare the model data with the real data. The coefficient of determination of the model for the first pump was 0.9875 and for the second pump was 0.9956. It can be concluded that the models are accurate, which is also confirmed in the comparative filling tests, where an assertiveness of more than 99% was demonstrated in all the tests performed. It is proposed to extend the studies on the use of this mathematical method as an alternative in the non-invasive control of fluids whose instruments must be handled in a sterile manner. |
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2024 |
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2024-02-24 |
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2025-02-27T16:10:23Z |
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2025-02-27T16:10:23Z |
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Artículo de revista |
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https://repositorio.ufps.edu.co/handle/ufps/9150 |
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10.25100/iyc.v26i1.12943 |
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https://repositorio.ufps.edu.co/handle/ufps/9150 |
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10.25100/iyc.v26i1.12943 |
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eng |
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eng |
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Vol.26 No.1 (2024) |
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8 |
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1 (2024) |
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1 |
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26 |
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Ramírez-Carvajal, L., Puerto-López, K., López-Barrera, G.L. Nonlinear regression for the characterization of peristaltic pumps, an alternative in the control of biological fluids. Ingeniería y Competitividad, 2024, 26(1) e-21113256. |
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Cali- Colombia |
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Ramírez Carvajal, Luis EduardoPuerto, KarlaLópez Barrera, German Luciano2025-02-27T16:10:23Z2025-02-27T16:10:23Z2024-02-24https://repositorio.ufps.edu.co/handle/ufps/915010.25100/iyc.v26i1.12943This paper presents a modeling approach to characterize two peristaltic pumps using nonlinear regression. Accuracy tests were performed by varying the height level of the pump with respect to the vessel, and it was found to have no effect on the flow rate. Filling data was recorded for 300 ml considering the voltage applied to the pumps and the filling time. A least squares curve fitting was performed with the recorded data. The exponential model was determined to be the most accurate for the two pumps, and using a simple rule of three, the equation for each desired volume was found. Finally, filling tests were performed to compare the model data with the real data. The coefficient of determination of the model for the first pump was 0.9875 and for the second pump was 0.9956. It can be concluded that the models are accurate, which is also confirmed in the comparative filling tests, where an assertiveness of more than 99% was demonstrated in all the tests performed. It is proposed to extend the studies on the use of this mathematical method as an alternative in the non-invasive control of fluids whose instruments must be handled in a sterile manner.Este artículo presenta un modelamiento para la caracterización de dos bombas peristálticas utilizando regresión no lineal. Se realizaron pruebas de precisión variando el nivel de altura de la bomba respecto al recipiente y se concluye que esto no influye en la rapidez del flujo. Se tomaron datos de llenado para 300ml teniendo en cuenta el voltaje aplicado a las bombas y el tiempo de llenado. Con los datos registrados se realizó ajuste de curvas por mínimos cuadrados determinando el modelo exponencial como el más acertado para las dos bombas y aplicando regla de tres simple se halló la ecuación para cualquier volumen solicitado. Finalmente se realizaron pruebas de llenado para comparar los datos del modelo con datos reales. El coeficiente de determinación del modelo de la primera bomba fue de 0,9875 y para la segunda fue de 0,9956 concluyendo que los modelos son acertados, lo cual se reitera en las pruebas de llenado comparativas donde se evidencia un asertividad superior a 99% en todas las pruebas realizadas. Se sugiere ampliar las investigaciones en el uso de este método matemático como alternativa en el control no invasivo de fluidos cuyos instrumentos requieran un tratamiento estéril.8 Páginasapplication/pdfengIngeniería y CompetitividadCali- ColombiaThis work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike4.0 International License.https://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)http://purl.org/coar/access_right/c_abf2http://www.scielo.org.co/scielo.php?pid=S0123-30332024000100014&script=sci_abstractNonlinear regression for the characterization of peristaltic pumps, an alternative in the control of biological fluidsRegresión no lineal para la caracterización de bombas peristálticas, una alternativa en el control de fluidos biológicosArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Vol.26 No.1 (2024)81 (2024)126Ramírez-Carvajal, L., Puerto-López, K., López-Barrera, G.L. Nonlinear regression for the characterization of peristaltic pumps, an alternative in the control of biological fluids. Ingeniería y Competitividad, 2024, 26(1) e-21113256.Nonlinear regressionperistaltic pumpsCharacterization pumpsEngineeringRegresión no linealbombas peristálticasCaracterización de bombasIngenieríaLuna, Hector & Valderrama-Rincon, Juan & Martínez, Andrés & Rincón J. Bomba peristáltica con cabezal tipo rodamiento y portamanguera para desgaste reducido. Rev SayWa [Internet]. 2020. Available from: https://www.researchgate. net/publication/332553328_Bomba_peristaltica_con_cabezal_tipo_rodamiento_y_ portamanguera_para_desgaste_reducidoC. López García. Diseño y fabricación de bomba peristáltica basada en motores paso a paso [Internet]. Universidad de Sevilla; 2015. Available from: https://idus. us.es/handle/11441/36281A. F. Díaz Viloria. Diseño y caracterización de un sistema de control preciso de dosificación y frecuencia de riego para el ensayo de estrategias de fertirriego en un banco de pruebas hidropónico [Internet]. Universidad de los Andes; 2018. Available from: https://repositorio.uniandes.edu.co/handle/1992/39535Luis de Moraes D. Desenvolvimento de um Sistema de Controle e Medição de Vazão para Bombas Peristálticas. UniversidadeFederaldeOuroPreto - UFOP; 2016.Díaz-Montes E, Martínez-Hernández J, Cerón-Montes G, Vargas-León E. Transferencia de calor en el contenedor de alimentación de un secador por aspersión. Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI. 2022 ;10(19):84–93. Available from: https://repository.uaeh.edu.mx/revistas/index.php/ icbi/article/view/8896Rojas-Meza E, Recalde-Dicado K. Implementación de un módulo IoT hidropónico NFT semiautomático con alimentador de nutrientes por control difuso. Universidad Politécnica Salesiana [Internet]. 2023 Available from: https://dspace.ups.edu.ec/ handle/123456789/24134Rivera-García J. Modelado y caracterización hidrodinámica de celdas de flujo redox con electrodos tridimensionales para su aplicación en sistemas de almacenamiento de energía. Centro de Investigación y Desarrollo Tecnológico en Electroquímica, S. C. [Internet] 2023. Available from: https://cideteq.repositorioinstitucional.mx/jspui/ handle/1021/514Herrera-Baños E. Modelación matemática y simulación de un sistema enfriador-calentador, mediante el uso de un enfriador termoeléctrico (TEC). ESPOL. FIEC [Internet]. 2023 Available from: https://www.dspace.espol.edu.ec/ handle/123456789/57631S. Chapra y R. Canale. Métodos numéricos para ingenieros. Interamericana MG-H, editor. México, D. F; 2007.David J, Vel´ V, Henao V. Pronóstico de la serie de Mackey-Glass usando modelos de regresión no lineal. DYNA. 2004;71(142):85–95.de-los-Cobos-Silva S, Gutiérrez-Andrade MA, Rincón-García EA, Lara-Velázquez P, Aguilar-Cornejo M. Colonia de abejas artificiales y optimización por enjambre de partículas para la estimación de parámetros de regresión no lineal artificial. Rev Matemática Teoría y Apl [Internet]. 2014. 21(1):107–26. Available from: http://www. scielo.sa.cr/scielo.php?script=sci_arttext&pid=S1409-24332014000100007&lng=e n&nrm=iso&tlng=esGaleano-Vasco L, Cerón-Muñoz M. Modelación del crecimiento de pollitas Lohmann LSL con redes neuronales y modelos de regresión no lineal. Rev MVZ Córdoba [Internet]. 2013.18(3):3861–7. Available from: https://revistamvz.unicordoba.edu. co/article/view/158Vera-Dávila AG, Delgado-Ariza JC, Sepúlveda-Mora SB, Vera-Dávila AG, DelgadoAriza JC, Sepúlveda-Mora SB. Validación del modelo matemático de un panel solar empleando la herramienta Simulink de Matlab. Rev Investig Desarro e Innovación [Internet]. 2018. 8(2):343–56. Available from: http://www.scielo.org.co/scielo. php?script=sci_arttext&pid=S2027-83062018000100343&lng=en&nrm=iso&tlng =esEduardo Ramírez Carvajal L, Puerto López KC, Luciano G, Barrera L. Análisis de curvas de covid-19 en Colombia utilizando ajuste por mínimos cuadrados. Ingeniare [Internet]. 2020. (29):41–55. Available from: https://revistas.unilibre.edu.co/index. php/ingeniare/article/view/7434Alejandro J, Bastidas O, Priscila A, Pita P, Nathalia L, Vargas O, et al. Importancia de los modelos de regresión no lineales en la interpretación de datos de la COVID-19 en Colombia. Rev Habanera Ciencias Médicas [Internet]. 2020. 19. Available from: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S1729-519X2020000400014 &lng=es&nrm=iso&tlng=esGómez-Rojas J, Pérez JR, Hernández MT. Análisis y estimación gráfica del comportamiento del COVID-19 en Colombia, Santa Marta y Cartagena enfocado a la letalidad. Respuestas [Internet]. 2021, 26(1):118–31. Available from: https:// revistas.ufps.edu.co/index.php/respuestas/article/view/2854/4204Huancachoque-Mamani L, Pérez-Paredes MGS, Nolasco-Pérez IM, HuancachoqueMamani L, Pérez-Paredes MGS, Nolasco-Pérez IM. Análisis predictivo de casos confirmados de Covid-19 en el Perú basado en el Modelo de Regresión no lineal de Gompertz usando datos de casos fatales. Tecnia [Internet]. 2021, 31(2):48–53. Available from: http://www.scielo.org.pe/scielo.php?script=sci_arttext&pid=S2309- 04132021000200048&lng=es&nrm=iso&tlng=es. Carrillo E, Montero M, Jiménez A, Portelles J, Otero J. Diseño y pruebas realizadas en sensores piezoelectricos TTFM para la medición de flujo sanguíneo en implantes coronarios. Revista Cubana de Física. 2020; 40 (1)PublicationORIGINALNonlinear regression for the characterization of.pdfNonlinear regression for the characterization of.pdfArtículo de Investigaciónapplication/pdf828296https://repositorio.ufps.edu.co/bitstreams/e1ba5a81-e012-4a6c-8b8d-854b2c443726/download1f31304c33a25f00bdc52dcdec56631dMD51trueAnonymousREADLICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.ufps.edu.co/bitstreams/10728f24-6fdc-4720-811d-2a655b2f79f1/download2f9959eaf5b71fae44bbf9ec84150c7aMD52falseAnonymousREADTEXTNonlinear regression for the characterization of.pdf.txtNonlinear regression for the characterization of.pdf.txtExtracted texttext/plain18159https://repositorio.ufps.edu.co/bitstreams/2128a3a6-0fd0-4176-9ca5-adff13450876/download8536927fb8f511f40bdc4c15e275aa80MD53falseAnonymousREADTHUMBNAILNonlinear regression for the characterization of.pdf.jpgNonlinear regression for the characterization of.pdf.jpgGenerated 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 incorporada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
 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