1,721,389 research outputs found
Materiali per la flora pisana (Toscana) - III - La flora delle colline pisane nell'opera di Giovanni Mariti, erudito toscano del XVIII secolo.
No full textMateriali per la flora pisana (Toscana) - I - Le specie censite nei territori di Pontedera,Ponsacco, Capannoli, Peccioli e Palaia, in Valdera
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Different connective tissue disorders lead to similar outcomes of aortic disease: Are we missing something?
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Materiali per la flora pisana (Toscana) - II - Le specie censite nei territori di Fauglia, Crespina, Lari, Lorenzana e Casciana Terme.
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Strategy Optimization by Means of Evolutionary Algorithms With Multiple Closing Criteria for Energy Trading
Full text linkThe energy markets are experiencing an enhanced volatility and unpredictability due to the growing integration of renewable energy sources in the grid and to the unstable geopolitical situation that is developing worldwide. Energy traders are therefore raising concerns on how to achieve solutions that not only ensure stability in terms of energy needs, both on the supply and demand side, but also enable profits within these markets. To cope with the complexity of this emerging scenario, tools that support traders in their decisions, such as algorithmic trading strategies, are attracting always more and more attention. In particular, evolutionary algorithms have emerged as an effective tool for developing robust and innovative trading strategies. Indeed, their flexibility and adaptability allow for the inclusion of various performance metrics. This article employs a recently issued evolutionary algorithm, called social network optimization, to identify the optimal closing criteria of already opened positions in an energy commodity market. More specifically, the proposed trading strategy is based on five self-defined parameters, which determine a profitable solution over nearly six years of available data. In particular, the overall average positive return achieved and the maximum monthly yield of 1.9% highlight the adaptability and robustness of the developed algorithmic trading strategy. Therefore, the results suggest the potentialities of developing and upgrading novel trading strategies by exploiting evolutionary computation techniques in the actual complex energy markets
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