Publications by authors named "Juan Julian Merelo"
Creating a story is a challenging task due to the the complex relations between the parts that make it up, which is why many new stories are built on those cohesive elements or patterns, called tropes that have been shown to work in the past. A trope is a recurring storytelling device or pattern, or sometimes a meta-element, used by the authors to express ideas that the audience can recognize or relate to, such as the Hero's Journey. Discovering tropes and how they cluster in popular works and doing it at scale to generate new plots may benefit writers; in this paper, we analyze them and use a principled procedure to identify trope combinations, or communities, that could possible be successful.
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- The COVID-19 pandemic led to global health crises and lockdowns, prompting research on the virus's relationship with environmental factors, though findings have varied.
- DatAC (Data Against COVID-19) is a new web application created to combine COVID-19 data with environmental data in Spain, providing advanced data analysis tools for users.
- Analysis through DatAC revealed significant decreases in air pollution levels during lockdown and indicated that temperature alone is not a key factor in controlling COVID-19 spread in the region.
This paper investigates the performance and scalability of a new update strategy for the particle swarm optimization (PSO) algorithm. The strategy is inspired by the Bak-Sneppen model of co-evolution between interacting species, which is basically a network of fitness values (representing species) that change over time according to a simple rule: the least fit species and its neighbors are iteratively replaced with random values. Following these guidelines, a steady state and dynamic update strategy for PSO algorithms is proposed: only the least fit particle and its neighbors are updated and evaluated in each time-step; the remaining particles maintain the same position and fitness, unless they meet the update criterion.
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- Self-organizing maps (SOMs) are effective tools in bioinformatics for clustering and visualizing high-dimensional genomic data, but require innovative methods to convert nucleotide sequences into numerical vectors to handle complexities like ambiguities and alignment gaps.
- Six different coding variations using Euclidean space were tested on two SOM models with RNA and HIV gene sequences, showing that the weighting of alignment gaps significantly influences clustering accuracy.
- Although the coding methods yielded varying levels of taxonomic accuracy, they aligned well with established phylogenetic analyses, indicating the potential for widespread application in genomic research.