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Plantes protagonistes de transtorns ambientals al territori valenci&agrave

The Lichen Genus Usnea On Quercus Suber In Iberian Cork-Oak Forests

Fifteen species of Usnea are recorded from Iberian cork-oak forests: U. ceratina, U. comma, U. dasaea, U. esperantiana, U. flammea, U. fulvoreagens, U. glabrata, U. hirta, U. mutabilis, U. rubicunda, U. subcornuta, U. subfloridana U. subscabrosa, U. substerilis and U. wasmuthii. A key for these species is provided. Details of morphology, chemistry, distribution, ecology and taxonomy are discussed. Usnea dasaea is reported as new to the Iberian Peninsula. New chemotypes of U. fulvoreagens (with squamatic acid) and U. wasmuthii (with psoromic acid) have been identified. Distribution maps of U. dasaeaand U. subcornuta in Europe are presented. A new combination, Usnea subfloridana subsp. praetervisa (Asahina) P. Clerc, is propose

Una nova edició de l'Origen destinada al gran públic

Una nova edició de l'Origen destinada al gran públi

Inferring reporting biases in hedge fund databases from hedge fund equity holdings

This paper formally analyzes the biases related to self-reporting in hedge fund databases by matching the quarterly equity holdings of a complete list of 13F-filing hedge fund companies to the union of five major commercial databases of self-reporting hedge funds between 1980 and 2008. We find that funds initiate selfreporting after positive abnormal returns which do not persist into the reporting period. Termination of selfreporting is followed by both return deterioration and outflows from the funds. The propensity to self-report is consistent with the trade-offs between the benefits (e.g., access to prospective investors) and costs (e.g., partial loss of trading secrecy and flexibility in selective marketing). Finally, returns of self-reporting funds are higher than that of non-reporting funds using characteristic-based benchmarks. However, the difference is not significant using alternative choices of performance measures

Combining the Performance Strengths of the Logistic Regression and Neural Network Models: A Medical Outcomes Approach

The assessment of medical outcomes is important in the effort to contain costs, streamline patient management, and codify medical practices. As such, it is necessary to develop predictive models that will make accurate predictions of these outcomes. The neural network methodology has often been shown to perform as well, if not better, than the logistic regression methodology in terms of sample predictive performance. However, the logistic regression method is capable of providing an explanation regarding the relationship(s) between variables. This explanation is often crucial to understanding the clinical underpinnings of the disease process. Given the respective strengths of the methodologies in question, the combined use of a statistical (i.e., logistic regression) and machine learning (i.e., neural network) technology in the classification of medical outcomes is warranted under appropriate conditions. The study discusses these conditions and describes an approach for combining the strengths of the models