131,109 research outputs found
Leveraging the cultural value of ancient (tomatoes) varieties: toward a biodiversity marketing approach to enhance Agriculture as a driver for Sustainability
No full textDue to economic interests in producing varieties resistant to diseases, insects, and climate change, ancient tomato varieties in the Mediterranean area have been neglected causing biodiversity loss. By reversing the common view of agriculture and sustainability, this work shifts attention from investigating how to make agriculture more sustainable to how to make agriculture a driver for promoting sustainability.
The hypothesis is that marketing can successfully support strategies that leverage the cultural value of biodiversity. Accordingly, this study aims to explore the current interest in marketing studies for biodiversity to identify possible paths to follow for driving attention to ancient tomato varieties as a strategy for promoting sustainability
SENSOR NETWORKS LOCALISATION AND TRACKING VIA CONSENSUS UPDATE
No full textIn this paper,we propose a distributed Kalman Filter based algorithm,known in literature as Consensus Filter,applied on a sensor network. Distributing the estimation of the state of the nodes, which can be still or dynamic,is a crucial target respect to computation and energetic capabilities of typical commercial nodes. After introducing the general and analytic concepts of this filter, we show simulations results and real data based on experiments
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
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