Review of The Gates of Paradise. Lorenzo Ghiberti’s Renaissance Masterpiece: A Symposium. The Metropolitan Museum of Art, New York, NY. November 16, 2007 (original) (raw)
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Article 345 TFEU (ex Article 295 EC), Its Meanings and Interpretations
European Law Journal, 2010
Research that has been conducted over the last decades shows that neither the scope of application nor the exact meaning of Article 345 TFEU (ex Article 295 EC) is clear from its wording. This article seeks to clarify its meaning through analysis of the drafting of the Article as well as the use of it by the EU's institutions and by the Member States. Article 345 TFEU, formerly Article 295 EC and, before that, Article 222 EEC, is an Article that limits, but not prevents, the application of the TFEU Treaty as a whole to the way in which rules of a Member State deal with the right of ownership of undertakings. The conclusion can be drawn that Article 345 TFEU only concerns the private or public ownership of undertakings, with which the Community shall not concern itself and which can thus be regulated by the Member States themselves. Most importantly, the Article does not concern the content of the right of ownership, nor the objects of a right of ownership. It does therefore not form an obstacle to the development of a European property law.
Information Sciences, 2010
One of the main challenges that organizations face nowadays, is the efficient use of individual employee intelligence, through machine-facilitated understanding of the collected corporate knowledge, to develop their collective intelligence. Web 2.0 technologies, like wikis, can be used to address the above issue. Nevertheless, their application in corporate environments is limited, mainly due to their inability to ensure knowledge creation and assessment in a timely and reliable manner. In this study we propose CorpWiki, a self-regulating wiki system for effective acquisition of high-quality knowledge content. Inserted articles undergo a quality assessment control by a large number of corporate peer employees. In case the quality is inadequate, CorpWiki uses a novel expert peer matching algorithm (EPM), based on feed-forward neural networks, that searches the human network of the organization to select the most appropriate peer employee who will improve the quality of the article. Performance evaluation results, obtained through simulation modeling, indicate that CorpWiki improves the final quality levels of the inserted articles as well as the time and effort required to reach them. The proposed system, combining machine-learning intelligence with the individual intelligence of peer employees, aims to create new inferences regarding corporate issues, thus promoting the collective organizational intelligence.