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10 resultados, página 1 de 1

Modular ontology to support manufacturing SMEs toward industry 4.0

ZAIDA ANTONIETA MORA ALVAREZ OSCAR HERNANDEZ URIBE RAMON ALBERTO LUQUE MORALES LEONOR ADRIANA CARDENAS ROBLEDO (2023, [Artículo])

Industry 4.0 (I4.0) implementation is a hot topic among manufacturing organizations to reach smart factory status and integrate a fully connected ecosystem. Achieving such a transition presents notable challenges for Small and Medium Enterprises (SMEs) since they often face resource and skilled personnel limitations. This study developed a domain ontology to represent various stages of maturity toward I4.0 implementation. Ontology provides a tool for SMEs to self-assess in situations of machines, processes, and factories for the dimensions of control, integration, and intelligence. This study focused on the identification of classes and relationships according to I4.0 implementation situations in the context of a manufacturing setting, the reuse of ontologies related to the domain of observations to model situations, and the creation and validation of the ontology through the information obtained from the questionnaires applied to SMEs. Finally, the ontology delivers a tool to understand SMEs' current state concerning I4.0 implementation and plan based on informed decisions about the maturity state and the technology required to advance to the next stage in their manufacturing processes.

This study was partially supported by the grants CONAHCYT-CIATEQ CVU 899567 and 162867 and CONAHCYT SNI.

We express our gratitude to Teresa Novales Hernandez for the library support.

Domain ontology Industry 4.0 SMEs Smart factory SPARQL Semantic web INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS OTRAS ESPECIALIDADES TECNOLÓGICAS OTRAS OTRAS

Systematic Literature Review on Smart Specialization: Future Prospects and Opportunities

Beatriz Rosas Michael Demmler (2023, [Artículo])

"Smart specialisation (SS) has been the new cohesion policy in the European Union during the last two periods. The present study aims to analyse the most relevant existing state-of-the-art literature on smart specialisation through a systematic and bibliometric review. Using the Web of Science bibliographic database, we analysed the content of 207 articles under the TCCM methodology and constructed a network of citations in order to summarize theories, characteristics, context and methods presented in existing studies on the topic. Our results show the theoretical and methodological gaps of the past, such as Entrepreneurial Discovery Process and SS indicators. These remain to the present day. The context analysis showed that the scope of smart specialisation extended beyond the frontiers of the European Union, given how it has been adopted by other countries as well. These results suggest the importance of developing a more robust theoretical, conceptual and methodological framework. Consequently, the guides need to be more accurate and should be continuously updated. Our results are valuable for the EDP actors and have policymaking implications".

Especialización inteligente Estrategias de innovación regional Revisión de literatura sistemática Métodos de especialización inteligente Smart specialization Smart specialization methods CIENCIAS SOCIALES CIENCIAS SOCIALES

Climate-smart agricultural practices influence the fungal communities and soil properties under major agri-food systems

madhu choudhary ML JAT Parbodh Chander Sharma (2022, [Artículo])

Fungal communities in agricultural soils are assumed to be affected by climate, weather, and anthropogenic activities, and magnitude of their effect depends on the agricultural activities. Therefore, a study was conducted to investigate the impact of the portfolio of management practices on fungal communities and soil physical–chemical properties. The study comprised different climate-smart agriculture (CSA)-based management scenarios (Sc) established on the principles of conservation agriculture (CA), namely, ScI is conventional tillage-based rice–wheat rotation, ScII is partial CA-based rice–wheat–mungbean, ScIII is partial CSA-based rice–wheat–mungbean, ScIV is partial CSA-based maize–wheat–mungbean, and ScV and ScVI are CSA-based scenarios and similar to ScIII and ScIV, respectively, except for fertigation method. All the scenarios were flood irrigated except the ScV and ScVI where water and nitrogen were given through subsurface drip irrigation. Soils of these scenarios were collected from 0 to 15 cm depth and analyzed by Illumina paired-end sequencing of Internal Transcribed Spacer regions (ITS1 and ITS2) for the study of fungal community composition. Analysis of 5 million processed sequences showed a higher Shannon diversity index of 1.47 times and a Simpson index of 1.12 times in maize-based CSA scenarios (ScIV and ScVI) compared with rice-based CSA scenarios (ScIII and ScV). Seven phyla were present in all the scenarios, where Ascomycota was the most abundant phyla and it was followed by Basidiomycota and Zygomycota. Ascomycota was found more abundant in rice-based CSA scenarios as compared to maize-based CSA scenarios. Soil organic carbon and nitrogen were found to be 1.62 and 1.25 times higher in CSA scenarios compared with other scenarios. Bulk density was found highest in farmers' practice (Sc1); however, mean weight diameter and water-stable aggregates were found lowest in ScI. Soil physical, chemical, and biological properties were found better under CSA-based practices, which also increased the wheat grain yield by 12.5% and system yield by 18.8%. These results indicate that bundling/layering of smart agricultural practices over farmers' practices has tremendous effects on soil properties, and hence play an important role in sustaining soil quality/health.

Agriculture Management Fungal Community Diversity Indices Climate-Smart Agricultural Practices CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURE TILLAGE CLIMATE-SMART AGRICULTURE SOIL ORGANIC CARBON

Farmers’ perspectives as determinants for adoption of conservation agriculture practices in Indo-Gangetic Plains of India

Ajay Kumar Mishra ML JAT (2022, [Artículo])

Understanding the farmer's perspective has traditionally been critical to influencing the adoption and out-scaling of CA-based climate-resilient practices. The objective of this study was to investigate the biophysical, socio-economic, and technical constraints in the adoption of CA by farmers in the Western- and Eastern-IGP, i.e., Karnal, Haryana, and Samastipur, Bihar, respectively. A pre-tested structured questionnaire was administered to 50 households practicing CA in Western- and Eastern-IGP. Smallholder farmers (<2 ha of landholding) in Karnal are 10% and Samastipur 66%. About 46% and 8% of households test soil periodically in Karnal and Samastipur, respectively. Results of PCA suggest economic profitability and soil health as core components from the farmer's motivational perspective in Karnal and Samastipur, respectively. Promotion and scaling up of CA technologies should be targeted per site-specific requirements, emphasizing biophysical resource availability, socio-economic constraints, and future impacts of such technology.

Smallholder Farmers Agents of Change Technology Diffusion Climate-Smart Practices CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SMALLHOLDERS SOCIAL STRUCTURE IRRIGATION MANAGEMENT TECHNOLOGY CLIMATE-SMART AGRICULTURE CONSERVATION AGRICULTURE

Contratos inteligentes para la gestión de datos de sensado móvil y vestible para aplicaciones en salud

Smart contracts for mobile and wearable sensing data management for health applications

José Ricardo Cedeño García (2023, [Tesis de maestría])

El aumento en la producción de datos derivado de la adopción de tecnologías móviles y de IoT está revolucionando la salud, pero también plantea importantes retos éticos y de privacidad. Los recientes avances en el aprendizaje automático han resaltado la importancia de recopilar y etiquetar datos correctamente, en especial para fines críticos, como el desarrollo de aplicaciones para cuidados médicos. La recopilación de datos médicos para tareas de aprendizaje automático presenta limitaciones en cuanto a la cantidad, variedad y calidad de las fuentes disponibles. Una forma de abordar este dilema es el uso de Blockchain para la recopilación y el uso de datos de pacientes. El anonimato de una red centralizada permite proteger la identidad del paciente. La estructura formada por nodos permite que la información esté siempre disponible y no dependa de un servidor principal. La inmutabilidad de los registros en la cadena garantiza la trazabilidad inequívoca del flujo de los datos del paciente. Por último, los mecanismos de consenso y recompensa de la red podrían motivar a nuevos usuarios a participar del sensado activo. Presentamos TRHEAD, una arquitectura de referencia basada en la Blockchain para recopilar datos sanitarios, firmar consentimientos, anotar datos y obtener crédito por los mismos, permitiendo a los usuarios rastrear el uso de sus datos, a los científicos rastrear su procedencia y proteger al mismo tiempo la privacidad de los pacientes. Exponemos dos implementaciones de nuestra arquitectura aplicadas a distintas campañas de sensado para comprobar su viabilidad, así como los resultados de su aplicación en estos escenarios y las conclusiones que desprendieron de su análisis. Dado que uno de los objetivos principales de TRHEAD es la recopilación de datos mediante sensado activo para el entrenamiento legal/consciente de modelos de aprendizaje automático, se realizó el entrenamiento de un modelo con los datos obtenidos de la campaña de sensado correspondiente a imágenes de rostros humanos, con el fin de detectar estados de ánimo. Finalmente se discute el papel de TRHEAD en el aseguramiento del trato justo y consciente de la información de los pacientes y el camino por recorrer en el perfeccionamiento de la arquitectura.

The increase in data production resulting from the adoption of mobile and IoT technologies is revolutionizing healthcare, but it also poses significant ethical and privacy challenges. Recent advances in machine learning have highlighted the importance of collecting and labeling data correctly, especially for critical purposes such as deploying healthcare software. Collecting medical data for machine learning tasks presents limitations in terms of the quantity, variety, and quality of available sources. One way to address this dilemma is the use of Blockchain for the collection and use of patient data. The anonymity of a centralized network allows the patient’s identity to be protected. The structure formed by nodes allows information to be always available and not dependent on a main server. The immutability of the records in the chain guarantees the unequivocal traceability of the flow of patient data. Finally, the network’s consensus and reward mechanisms could motivate new users to participate in active sensing. We present TRHEAD, a Blockchain-based reference architecture for collecting healthcare data, signing consents, annotating data and getting credit for it, allowing users to track the use of their data, scientists to track its provenance while protecting patients privacy. We present two implementations of our architecture applied to different sensing campaigns to test their feasibility, as well as the results of their application in these scenarios and the conclusions drawn from those results. Since one of the main objectives of TRHEAD is the collection of data through active sensing for the legal/conscious training of machine learning models, a model was trained with the data obtained from the sensing campaign corresponding to images of human faces, in order to detect moods. Finally, the role of TRHEAD in ensuring the fair and conscientious treatment of patient information and the road ahead in refining the architecture is discussed.

Contratos Inteligentes, Blockchain, Privacidad, Aprendizaje de Máquina Etico, Recopilación Consciente de Datos, Consentimiento, Arquitectura de Referencia Smart Contracts, Blockchain, Privacy, Ethical Machine Learning, Conscious Data Collection, Consent, Reference Architecture INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE LOS ORDENADORES INTELIGENCIA ARTIFICIAL INTELIGENCIA ARTIFICIAL