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Recently there have been appearing new applications of genetic algorithms to information retrieval, most of them specifically to relevance feedback. The evolution of the possible solutions are guided by fitness functions that are designed as measures of the goodness of the solutions. These functions are naturally the key to achieving a reasonable improvement, and which function is chosen most distinguishes one experiment from another. In previous work, we found that, among the functions implemented in the literature, the ones that yield the best results are those that take into account not only when documents are retrieved, but also the order in which they are retrieved. Here, we therefore evaluate the efficacy of a genetic algorithm with various order-based fitness functions for relevance feedback (some of them of our own design), and compare the results with the Ide dec-hi method, one of the best traditional methods.

There have been recent applications of genetic algorithms to information retrieval, mostly with respect to relevance feedback. Nevertheless, they are yet to be evaluated in a way that allows them to be compared with each other and with other relevance feedback techniques. We here implement the different genetic algorithms that have been applied in the literature together with some of our own variations, and evaluate them using the residual collection method described by Salton in 1990 for the evaluation of relevance feedback techniques. We compare the results with those of the Ide dec-hi method, which is one of the traditional methods that yields the best results.

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The present work is the continuation of an earlier study which reviewed the literature on relevance feedback genetic techniques that follow the vector space model (the model that is most commonly used in this type of application), and implemented them so that they could be compared with each other as well as with one of the best traditional methods of relevance feedback--the Ide dec-hi method. We here carry out the comparisons on more test collections (Cranfield, CISI, Medline, and NPL), using the residual collection method for their evaluation as is recommended in this type of technique. We also add some fitness functions of our own design.

En este trabajo se analizan las desinformaciones difundidas sobre el coronavirus en España y Latinoamérica en el periodo comprendido entre el 23/01/2020 y el 03/05/2020; se estudian cuantitativamente los siguientes datos: volumen de desinformación por país, la línea de evolución temporal, tipo de desinformación, canal de difusión, las fuentes, y redes de circulación de bulos entre distintos países. En el caso de España, se examina también la correlación en la producción de desinformaciones con la evolución de la pandemia, y las tendencias de búsquedas en internet sobre el coronavirus. Los resultados muestran claramente que la evolución de la pandemia influye en la propagación de los bulos, disparándose estos en momentos críticos como la declaración de pandemia por parte de la OMS y, en el caso de España, en los momentos de mayor tasa de crecimiento de la curva, al tiempo que las búsquedas sobre el tema alcanzan su máxima popularidad.

The conceptual structure of the field of Animal Science (AS) research is examined by means of a longitudinal science mapping analysis. The whole of the AS research field is analysed, revealing its conceptual evolution. To this end, an automatic approach to detecting and visualizing hidden themes or topics and their evolution across a consecutive span of years was applied to AS publications of the JCR category 'Agriculture, Dairy & Animal Science' during the period 1945-2011. This automatic approach was based on a coword analysis and combines performance analysis and science mapping. To observe the conceptual evolution of AS, six consecutive periods were defined: 1945-1969, 1970-1979, 1980-1989, 1990-1999, 2000-2005 and 2006-2011. Research in AS was identified as having focused on ten main thematic areas: ANIMAL-FEEDING, SMALL-RUMINANTS, ANIMAL-REPRODUCTION, DAIRY-PRODUCTION, MEAT-QUALITY, SWINE-PRODUCTION, GENETICS-AND-ANIMAL-BREEDING, POULTRY, ANIMAL-WELFARE and GROWTH-FACTORS-AND-FATTY-ACIDS. The results show how genomic studies gain in weight and integrate with other thematic areas. The whole of AS research has become oriented towards an overall framework in which animal welfare, sustainable management and human health play a major role. All this would affect the future structure and management of livestock farming.

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