Implementation of information and communication technologies to increase sustainable productivity in freshwater finfish aquaculture – A review
The practice of continental aquaculture has grown globally, mainly due to its ability to provide protein to vulnerable populations and stimulate local economies in many regions of the world. In recent years, information and communication technologies (ICT) have been implemented to monitor, control,...
- Autores:
-
Bernal-Higuita, Faisal
Acosta-Coll, Melisa
Ballester Merelo, Francisco Jose
De-La-Hoz-Franco, Emiro
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2023
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/10483
- Acceso en línea:
- https://hdl.handle.net/11323/10483
https://repositorio.cuc.edu.co/
- Palabra clave:
- Information and communication technologies
Sustainable productivity
Aquaculture
- Rights
- embargoedAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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Implementation of information and communication technologies to increase sustainable productivity in freshwater finfish aquaculture – A review |
| title |
Implementation of information and communication technologies to increase sustainable productivity in freshwater finfish aquaculture – A review |
| spellingShingle |
Implementation of information and communication technologies to increase sustainable productivity in freshwater finfish aquaculture – A review Information and communication technologies Sustainable productivity Aquaculture |
| title_short |
Implementation of information and communication technologies to increase sustainable productivity in freshwater finfish aquaculture – A review |
| title_full |
Implementation of information and communication technologies to increase sustainable productivity in freshwater finfish aquaculture – A review |
| title_fullStr |
Implementation of information and communication technologies to increase sustainable productivity in freshwater finfish aquaculture – A review |
| title_full_unstemmed |
Implementation of information and communication technologies to increase sustainable productivity in freshwater finfish aquaculture – A review |
| title_sort |
Implementation of information and communication technologies to increase sustainable productivity in freshwater finfish aquaculture – A review |
| dc.creator.fl_str_mv |
Bernal-Higuita, Faisal Acosta-Coll, Melisa Ballester Merelo, Francisco Jose De-La-Hoz-Franco, Emiro |
| dc.contributor.author.none.fl_str_mv |
Bernal-Higuita, Faisal Acosta-Coll, Melisa Ballester Merelo, Francisco Jose De-La-Hoz-Franco, Emiro |
| dc.subject.proposal.eng.fl_str_mv |
Information and communication technologies Sustainable productivity Aquaculture |
| topic |
Information and communication technologies Sustainable productivity Aquaculture |
| description |
The practice of continental aquaculture has grown globally, mainly due to its ability to provide protein to vulnerable populations and stimulate local economies in many regions of the world. In recent years, information and communication technologies (ICT) have been implemented to monitor, control, correct and even predict the behavior of critical parameters in fish farms to obtain sustainable productivity. A systematic literature review (SLR) was applied to identify the main variables contributing to the increase in sustainable productivity in freshwater aquaculture and the most used ICT tools to monitor or control these variables. It was found that aquaculture uses IoT and AI mainly on five fronts: water quality, fish feeding, water recirculation, fish transport and traceability, and fish welfare. The use of ICT tools in aquaculture has evolved from the use of simple sensors to the construction of predictive models using deep learning. However, it is worth highlighting the few articles that evaluate the impact of ICT tools on the productivity metrics of a freshwater fish farm under real conditions, and the information presented in the reviewed literature is difficult to compare due to the lack of uniformity in aquaculture projects where ICT tools are applied. |
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2023 |
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2023-09-13T14:21:03Z |
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2023-09-13T14:21:03Z 2025 |
| dc.date.issued.none.fl_str_mv |
2023 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Faisal Bernal-Higuita, Melisa Acosta-Coll, Francisco Ballester-Merelo, Emiro De-la-Hoz-Franco, Implementation of information and communication technologies to increase sustainable productivity in freshwater finfish aquaculture – A review, Journal of Cleaner Production, Volume 408, 2023, 137124, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2023.137124. |
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0959-6526 |
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https://hdl.handle.net/11323/10483 |
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10.1016/j.jclepro.2023.137124 |
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1879-1786 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
Faisal Bernal-Higuita, Melisa Acosta-Coll, Francisco Ballester-Merelo, Emiro De-la-Hoz-Franco, Implementation of information and communication technologies to increase sustainable productivity in freshwater finfish aquaculture – A review, Journal of Cleaner Production, Volume 408, 2023, 137124, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2023.137124. 0959-6526 10.1016/j.jclepro.2023.137124 1879-1786 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/10483 https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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Journal of Cleaner Production |
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A.L. Ahmad et al. Environmental impacts and imperative technologies towards sustainable treatment of aquaculture wastewater: a review J. Water Process Eng. (2022) M. Ankrah Twumasi et al. Increasing Ghanaian fish farms' productivity: does the use of the internet matter? Mar. Pol. (2021) D.E. Antwi et al. Productivity and constraints analysis of commercial tilapia farms in Ghana Kasetsart J. Soc. Sci. (2017) B. Belton et al. The emerging quiet revolution in Myanmar's aquaculture value chain Aquaculture (2018) Á. Cobo et al. A decision support system for fish farming using particle swarm optimization Comput. Electron. Agric. (2019) M. Dolatabadi et al. Modeling of simultaneous adsorption of dye and metal ion by sawdust from aqueous solution using of ANN and ANFIS Chemometr. Intell. Lab. Syst. (2018) A.A. Ewees et al. Optimized support vector machines for unveiling mortality incidence in Tilapia fish Ain Shams Eng. J. (2021) A. Fakhri et al. Chapter 20 - Smart Material-based Micro/nanostructures for the Detection and Removal of Water Impurities M. Føre et al. Precision fish farming: a new framework to improve production in aquaculture Biosyst. Eng. (2018) G. Fu et al. The role of deep learning in urban water management: a critical review Water Res. (2022) G. Gao et al. An intelligent IoT-based control and traceability system to forecast and maintain water quality in freshwater fish farms Comput. Electron. Agric. (2019) G. Gao et al. An intelligent IoT-based control and traceability system to forecast and maintain water quality in freshwater fish farms Comput. Electron. Agric. (2019) J. Gladju et al. Applications of data mining and machine learning framework in aquaculture and fisheries: a review Smart Agric. Technol. (2022) E. Gutiérrez et al. Efficiency data analysis in EU aquaculture production Aquaculture (2020) L. Hang et al. A secure fish farm platform based on blockchain for agriculture data integrity Comput. Electron. Agric. (2020) J. Huan et al. Prediction of dissolved oxygen in aquaculture based on gradient boosting decision tree and long short-term memory network: a study of Chang Zhou fishery demonstration base, China Comput. Electron. Agric. (2020) J. Huan et al. Design of water quality monitoring system for aquaculture ponds based on NB-IoT Aquacult. Eng. (2020) S.J. Im et al. Prediction of forward osmosis membrane engineering factors using artificial intelligence approach J. Environ. Manag. (2022) M. James et al. AIS data to inform small scale fisheries management and marine spatial planning Mar. Pol. (2018) D. Karimanzira et al. Enhancing aquaponics management with IoT-based Predictive Analytics for efficient information utilization Inf. Process. Agric. (2019) S.K. Khanal et al. Artificial intelligence and machine learning for smart bioprocesses Bioresour. Technol. (2023) B. Kitchenham et al. Systematic literature reviews in software engineering - a systematic literature review Inf. Software Technol. (2009) R. Kropp et al. A novel advanced oxidation process (AOP) that rapidly removes geosmin and 2-methylisoborneol (MIB) from water and significantly reduces depuration times in Atlantic salmon Salmo salar RAS aquaculture Aquacult. Eng. (2022) Daoliang Li et al. System and Platform for Water Quality Monitoring (2019) L. Li et al. A model for food nutrient dynamics of semi-intensive pond fish culture Aquacult. Eng. (2003) Daoliang Li et al. Automatic recognition methods of fish feeding behavior in aquaculture: a review Aquaculture (2020) W.J. Lim et al. Applications of responsive hydrogel to enhance the water recovery via membrane distillation and forward osmosis: a review J. Water Process Eng. (2022) X. Liu et al. A web-based combined nutritional model to precisely predict growth, feed requirement and waste output of gibel carp (Carassius auratus gibelio) in aquaculture operations Aquaculture (2018) W. Long et al. Preparation, photocatalytic and antibacterial studies on novel doped ferrite nanoparticles: characterization and mechanism evaluation Colloids Surf. A Physicochem. Eng. Asp. (2022) Y. Mao et al. A strategy of silver Ferrite/Bismuth ferrite nano-hybrids synthesis for synergetic white-light photocatalysis, antibacterial systems and peroxidase-like activity J. Photochem. Photobiol. Chem. (2022) E.B. Moustafa et al. A new optimized artificial neural network model to predict thermal efficiency and water yield of tubular solar still Case Stud. Therm. Eng. (2022) N.A.G. Moyo et al. A review of the factors affecting tilapia aquaculture production in Southern Africa Aquaculture (2021) M. Pule et al. Wireless sensor networks: a survey on monitoring water quality J. Appl. Res. Technol. (2017) J. Qian et al. Food traceability system from governmental, corporate, and consumer perspectives in the European Union and China: a comparative review Trends Food Sci. Technol. (2020) A. Rohani et al. Application of artificial intelligence for separation of live and dead rainbow trout fish eggs Artif. Intell. Agric. (2019) S. Santorio et al. Microalgae-bacterial biomass outperforms PN-anammox biomass for oxygen saving in continuous-flow granular reactors facing extremely low-strength freshwater aquaculture streams Chemosphere (2022) F. Akhter et al. Recent advancement of the sensors for monitoring the water quality parameters in smart fisheries farming Computers (2021) G. Alam et al. Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: recent advances and prospects Chem. Eng. J. (2022) A. Angani et al. Vertical recycling aquatic system for internet-of-things-based smart fish farm Sensor. Mater. (2019) F. Antonucci et al. Precision aquaculture: a short review on engineering innovations Aquacult. Int. (2020) E. Anzola et al. M. Benghanem et al. Monitoring of solar still desalination system using the internet of things technique Energies (2021) G. Beniwal et al. A systematic literature review on IoT gateways J. King Saud Univ. Comput.Inf. Sci. (2021) A. Bhatnagar et al. Water quality guidelines for the management of pond fish culture Int. J. Environ. Sci. (2013) A. Bhawiyuga et al. A LPWAN based wireless sensor node for aquaculture water quality monitoring system A. Bhawiyuga et al. LoRa-MQTT gateway device for supporting sensor-to-cloud data transmission in smart aquaculture IoT application M.M. Billah et al. Quality maintenance of fish farm: development of real-time water quality monitoring system R. Bitter et al. LabView: Advanced Programming Techniques (2007) R.A. Bórquez Lopez et al. Camaronicultura por medio de un hardware de acceso Biotec (2016) S.B. Chandanapalli Design and deployment of aqua monitoring system using wireless sensor networks and IAR-Kick J. Aquacult. Res. Dev. (2014) |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© 2023 Published by Elsevier Ltd.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfBernal-Higuita, Faisal86d5a5ea4dda7389517f787a59776cfdAcosta-Coll, Melisafb8830f36aae2e038701932ca3b89edf600Ballester Merelo, Francisco Josed69f725190b313b0bf847a894c65245b600De-La-Hoz-Franco, Emiro7f8bc6c4d65f444fb00bd3778bc623fc6002023-09-13T14:21:03Z20252023-09-13T14:21:03Z2023Faisal Bernal-Higuita, Melisa Acosta-Coll, Francisco Ballester-Merelo, Emiro De-la-Hoz-Franco, Implementation of information and communication technologies to increase sustainable productivity in freshwater finfish aquaculture – A review, Journal of Cleaner Production, Volume 408, 2023, 137124, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2023.137124.0959-6526https://hdl.handle.net/11323/1048310.1016/j.jclepro.2023.1371241879-1786Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The practice of continental aquaculture has grown globally, mainly due to its ability to provide protein to vulnerable populations and stimulate local economies in many regions of the world. In recent years, information and communication technologies (ICT) have been implemented to monitor, control, correct and even predict the behavior of critical parameters in fish farms to obtain sustainable productivity. A systematic literature review (SLR) was applied to identify the main variables contributing to the increase in sustainable productivity in freshwater aquaculture and the most used ICT tools to monitor or control these variables. It was found that aquaculture uses IoT and AI mainly on five fronts: water quality, fish feeding, water recirculation, fish transport and traceability, and fish welfare. The use of ICT tools in aquaculture has evolved from the use of simple sensors to the construction of predictive models using deep learning. However, it is worth highlighting the few articles that evaluate the impact of ICT tools on the productivity metrics of a freshwater fish farm under real conditions, and the information presented in the reviewed literature is difficult to compare due to the lack of uniformity in aquaculture projects where ICT tools are applied.1 páginaapplication/pdfengElsevier Ltd.United Kingdomhttps://www.sciencedirect.com/science/article/abs/pii/S0959652623012829Implementation of information and communication technologies to increase sustainable productivity in freshwater finfish aquaculture – A reviewArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/drafthttp://purl.org/coar/version/c_b1a7d7d4d402bcceJournal of Cleaner ProductionA.L. Ahmad et al. Environmental impacts and imperative technologies towards sustainable treatment of aquaculture wastewater: a review J. Water Process Eng. (2022)M. Ankrah Twumasi et al. Increasing Ghanaian fish farms' productivity: does the use of the internet matter? Mar. Pol. (2021)D.E. Antwi et al. Productivity and constraints analysis of commercial tilapia farms in Ghana Kasetsart J. Soc. Sci. (2017)B. Belton et al. The emerging quiet revolution in Myanmar's aquaculture value chain Aquaculture (2018)Á. Cobo et al. A decision support system for fish farming using particle swarm optimization Comput. Electron. Agric. (2019)M. Dolatabadi et al. Modeling of simultaneous adsorption of dye and metal ion by sawdust from aqueous solution using of ANN and ANFIS Chemometr. Intell. Lab. Syst. (2018)A.A. Ewees et al. Optimized support vector machines for unveiling mortality incidence in Tilapia fish Ain Shams Eng. J. (2021)A. Fakhri et al. Chapter 20 - Smart Material-based Micro/nanostructures for the Detection and Removal of Water Impurities M. Føre et al. Precision fish farming: a new framework to improve production in aquaculture Biosyst. Eng. (2018)G. Fu et al. The role of deep learning in urban water management: a critical review Water Res. (2022)G. Gao et al. An intelligent IoT-based control and traceability system to forecast and maintain water quality in freshwater fish farms Comput. Electron. Agric. (2019)G. Gao et al. An intelligent IoT-based control and traceability system to forecast and maintain water quality in freshwater fish farms Comput. Electron. Agric. (2019)J. Gladju et al. Applications of data mining and machine learning framework in aquaculture and fisheries: a review Smart Agric. Technol. (2022)E. Gutiérrez et al. Efficiency data analysis in EU aquaculture production Aquaculture (2020)L. Hang et al. A secure fish farm platform based on blockchain for agriculture data integrity Comput. Electron. Agric. (2020)J. Huan et al. Prediction of dissolved oxygen in aquaculture based on gradient boosting decision tree and long short-term memory network: a study of Chang Zhou fishery demonstration base, China Comput. Electron. Agric. (2020)J. Huan et al. Design of water quality monitoring system for aquaculture ponds based on NB-IoT Aquacult. Eng. (2020)S.J. Im et al. Prediction of forward osmosis membrane engineering factors using artificial intelligence approach J. Environ. Manag. (2022)M. James et al. AIS data to inform small scale fisheries management and marine spatial planning Mar. Pol. (2018)D. Karimanzira et al. Enhancing aquaponics management with IoT-based Predictive Analytics for efficient information utilization Inf. Process. Agric. (2019)S.K. Khanal et al. Artificial intelligence and machine learning for smart bioprocesses Bioresour. Technol. (2023)B. Kitchenham et al. Systematic literature reviews in software engineering - a systematic literature review Inf. Software Technol. (2009)R. Kropp et al. A novel advanced oxidation process (AOP) that rapidly removes geosmin and 2-methylisoborneol (MIB) from water and significantly reduces depuration times in Atlantic salmon Salmo salar RAS aquaculture Aquacult. Eng. (2022)Daoliang Li et al. System and Platform for Water Quality Monitoring (2019)L. Li et al. A model for food nutrient dynamics of semi-intensive pond fish culture Aquacult. Eng. (2003)Daoliang Li et al. Automatic recognition methods of fish feeding behavior in aquaculture: a review Aquaculture (2020)W.J. Lim et al. Applications of responsive hydrogel to enhance the water recovery via membrane distillation and forward osmosis: a review J. Water Process Eng. (2022)X. Liu et al. A web-based combined nutritional model to precisely predict growth, feed requirement and waste output of gibel carp (Carassius auratus gibelio) in aquaculture operations Aquaculture (2018)W. Long et al. Preparation, photocatalytic and antibacterial studies on novel doped ferrite nanoparticles: characterization and mechanism evaluation Colloids Surf. A Physicochem. Eng. Asp. (2022)Y. Mao et al. A strategy of silver Ferrite/Bismuth ferrite nano-hybrids synthesis for synergetic white-light photocatalysis, antibacterial systems and peroxidase-like activity J. Photochem. Photobiol. Chem. (2022)E.B. Moustafa et al. A new optimized artificial neural network model to predict thermal efficiency and water yield of tubular solar still Case Stud. Therm. Eng. (2022)N.A.G. Moyo et al. A review of the factors affecting tilapia aquaculture production in Southern Africa Aquaculture (2021)M. Pule et al. Wireless sensor networks: a survey on monitoring water quality J. Appl. Res. Technol. (2017)J. Qian et al. Food traceability system from governmental, corporate, and consumer perspectives in the European Union and China: a comparative review Trends Food Sci. Technol. (2020)A. Rohani et al. Application of artificial intelligence for separation of live and dead rainbow trout fish eggs Artif. Intell. Agric. (2019)S. Santorio et al. Microalgae-bacterial biomass outperforms PN-anammox biomass for oxygen saving in continuous-flow granular reactors facing extremely low-strength freshwater aquaculture streams Chemosphere (2022)F. Akhter et al. Recent advancement of the sensors for monitoring the water quality parameters in smart fisheries farming Computers (2021)G. Alam et al. Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: recent advances and prospects Chem. Eng. J. (2022)A. Angani et al. Vertical recycling aquatic system for internet-of-things-based smart fish farm Sensor. Mater. (2019)F. Antonucci et al. Precision aquaculture: a short review on engineering innovations Aquacult. Int. (2020)E. Anzola et al. M. Benghanem et al. Monitoring of solar still desalination system using the internet of things technique Energies (2021)G. Beniwal et al. A systematic literature review on IoT gateways J. King Saud Univ. Comput.Inf. Sci. (2021)A. Bhatnagar et al. Water quality guidelines for the management of pond fish culture Int. J. Environ. Sci. (2013)A. Bhawiyuga et al. A LPWAN based wireless sensor node for aquaculture water quality monitoring systemA. Bhawiyuga et al. LoRa-MQTT gateway device for supporting sensor-to-cloud data transmission in smart aquaculture IoT applicationM.M. Billah et al. Quality maintenance of fish farm: development of real-time water quality monitoring systemR. Bitter et al. LabView: Advanced Programming Techniques (2007)R.A. Bórquez Lopez et al. Camaronicultura por medio de un hardware de acceso Biotec (2016)S.B. Chandanapalli Design and deployment of aqua monitoring system using wireless sensor networks and IAR-Kick J. Aquacult. Res. Dev. 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corporada 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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