Suchergebnisse

Las Universidades europeas, para ser más funcionales respecto al importante papel que la sociedad les exige, tendrán que ver mejorada de forma notable su financiación. Paralelamente, deberán diseñar y dotarse de una mejora organizativa que garantice una gestión más eficaz de sus recursos; para, con todo ello, mejorar su atractivo en la formación de las personas y en la aplicación del conocimiento al mundo empresarial e industrial, es decir, incrementar su capacidad de generar conocimiento y transmitirlo y ponerlo en valor en el tejido social.Estas tres necesidades de futuro (financiación como base, gestión en la ejecución y atracción como objetivo) deben ser afrontadas por el gobierno de las universidades mediante sendas ideas directrices: fomentar la calidad, transformar la gobernanza e incrementar la financiación, tanto pública como privada.AbstractThe higher education finance must be increased and improved in order to enhance the European universities performance about the social role they are impelled. At the same time, they have to design and create an organizational reform in terms of management efficacy, as a part of their attractiveness in educative services and knowledge appliableness in industrial and corporative sectors. They must increase their capability to create knowledge and to transmit it and transformer it into a social value.Finance, management and attractiveness as future needs, they must be presented at the universities government by means of some clear lines: quality assurance, governance improvement and increasing public and private finance.

This article belongs to the Section Metal Casting, Forming and Heat Treatment. ; Machine Learning classification models have been trained and validated from a dataset (73 features and 13,616 instances) including experimental information of a clean cold forming steel fabricated by electric arc furnace and hot rolling. A classification model was developed to identify inclusion contents above the median. The following algorithms were implemented: Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forests, AdaBoost, Gradient Boosting, Support Vector Classifier and Artificial Neural Networks. Random Forest displayed the best results overall and was selected for the subsequent analyses. The Permutation Importance method was used to identify the variables that influence the inclusion cleanliness and the impact of these variables was determined by means of Partial Dependence Plots. The influence of the final diameter of the coil has been interpreted considering the changes induced by the process of hot rolling in the distribution of inclusions. Several variables related to the secondary metallurgy and tundish operations have been identified and interpreted in metallurgical terms. In addition, the inspection area during the microscopic examination of the samples also appears to influence the inclusion content. Recommendations have been established for the sampling process and for the manufacturing conditions to optimize the inclusionary cleanliness of the steel. ; This Project was carried out with the financial grant of the programs I + C = +C 2017 and INNOVA 2018. The financial contribution from SODERCAN, the European Union (program FEDER) and the Government of Cantabria are gratefully acknowledged. ; Peer reviewed