scispace - formally typeset
F

Francisco Manzano-Agugliaro

Researcher at University of Almería

Publications -  217
Citations -  8535

Francisco Manzano-Agugliaro is an academic researcher from University of Almería. The author has contributed to research in topics: Renewable energy & Energy consumption. The author has an hindex of 41, co-authored 188 publications receiving 6158 citations.

Papers
More filters
Journal ArticleDOI

Energy Recovery from Waste Tires Using Pyrolysis: Palestine as Case of Study

TL;DR: The first industrial-scale pyrolysis plant for solid tire wastes has been installed in Jenin, northern of the West Bank in Palestine, to dispose of the enormous solid tire waste in the north of West Bank as mentioned in this paper.
Journal ArticleDOI

Photogrammetry as a New Scientific Tool in Archaeology: Worldwide Research Trends

TL;DR: The main lines of research in photogrammetry applied to archaeology are close-range photogrammetric, aerial-photogrammetry, cultural heritage, excavation, cameras, GPS, laser scan, and virtual reconstruction including 3D printing as discussed by the authors.
Journal ArticleDOI

DNA Damage Repair System in Plants: A Worldwide Research Update

TL;DR: Bibliometric results highlight the current interest in DDR research in plants among DDR studies and can open new perspectives in the research field of DNA damage repair.
Journal ArticleDOI

Global Research on Plant Nematodes

TL;DR: It is detected that the interest in nematodes affecting plants has not stopped growing in the last decades, and that tomato, soybean, and potato crops are the ones that generate the most interest, as well as nematode of the genus Meloidogyne and Globodera.
Journal ArticleDOI

Genetic algorithm for S-transform optimisation in the analysis and classification of electrical signal perturbations

TL;DR: The design of a genetic algorithm is described that optimises the S-transform for analysis and classification of the perturbations in electrical signals and provides the best parameter values for optimising the Gaussian window, maximising the precision obtained with regard to classification and analysis.