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Plant community on a volcano mountaintop reveals unique high-altitude vegetation in southeastern Brazil

Journal of Mountain Science 2024年第9期21卷 3018-3030页

作者： Igor Musauer KESSOUS Ruy JoséValka ALVES Nílber Gonçalves da SILVA amilcar walter saporetti junior Biodiversity Laboratory Federal Institute of EducationScience and Technology of Southern Minas Gerais(IFSULDEMINAS)Av.Dirce Pereira Rosa300-Jardim Esperança37713-100Poços de CaldasMGBrazil Department of Botany National MuseumFederal University of Rio de JaneiroQuinta da Boa Vista s.n.São Cristóvão20949-040Rio de JaneiroRJBrazil Gothenburg Global Biodiversity Centre Carl Skottsbergs Gata 22BSE 41319GothenburgSweden

Mountains exhibit a high degree of endemism and diversity,however,quantifying their biodiversity can be *** to islands,species isolation in mountainous regions results in comparable patterns of evolution and extinction,rendering their biodiversity unique and highly susceptible to anthropogenic *** topographic relief in mountains plays a crucial role in creating habitat complexity,which in turn contribute to high plant ***,we investigated plant diversity in the volcano mountaintop vegetation on the Poços de Caldas Plateau,a region situated in the ecotone between the Atlantic Forest and Cerrado,characterized by natural radiation and significant anthropogenic *** employed an automated approach through the filtering of georeferenced and non-georeferenced data to obtain a list of plant species in the ***,we statistically investigated the similarity among different high-altitude vegetations belonging to the campos de altitude from the Atlantic Forest and campos rupestres from the *** plateau exhibits high plant diversity,including 1,659 specific and infraspecific taxa,especially belonging to Asteraceae and *** analyses suggest that geographical distance is a strong predictor of dissimilarity and that the Poços de Caldas Plateau is more floristically related to the campos rupestres,despite being associated with campos de *** region possesses a unique set of biodiversity,indicating that it may be a distinct ***,we hypothesize that Pleistocene events likely influenced the conformation of the current floristic composition in the region through species interchange between the Cerrado and Atlantic *** study also highlights the few taxa assessed for conservation status and anthropogenic threats that this habitat is facing.

关键词： Atlantic Forest Biogeography Cerrado Community ecology Grassland

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Performance of evolutionary optimized machine learning for modeling total organic carbon in core samples of shale gas fields

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Petroleum 2024年第1期10卷 150-164页

作者： Leonardo Goliatt C.M.saporetti L.C.Oliveira E.Pereira Department of Computational and Applied Mechanics Federal University of Juiz de ForaJuiz de ForaBrazil Department of Computational Modeling Polytechnic InstituteRio de Janeiro State UniversityNova FriburgoBrazil Petrobras Rio de JaneiroBrazil Department of Stratigraphy and Paleontology Rio de Janeiro State UniversityRio de JaneiroBrazil

Rock samples'TOC content is the best indicator of the organic matter in source *** origin rock samples’analysis is used to calculate it manually by *** method requires time and resources because it relies on samples from many well intervals in source ***,research has been done to aid this *** learning algorithms can estimate total organic carbon instead of well logs and stratigraphic *** light of these efforts,the current work present a study on automating the total organic carbon estimation using machine learning approaches improved by an evolutionary methodology to give the model flexibility and *** algorithms,differential evolution,particle swarm optimization,grey wolf optimization,artificial bee colony,and evolution strategies were used to improve machine learning models to predict *** six metaheuristics were integrated into four machine learning methods:extreme learning machine,elastic net linear model,linear support vector regression,and multivariate adaptive regression *** samples from the YuDong-Nan shale gas field,located in the Sichuan basin,were used to evaluate the hybrid *** findings show that combining machine learning models with an evolutionary algorithms in a hybrid fashion produce flexible models that accurately predict *** results show that,independent of the metaheuristic used to guide the model selection,optimized extreme learning machines attained the best performance scores according to six *** hybrid models can be used in exploratory geological research,particularly for unconventional oil and gas resources.

关键词： Total organic carbon Hybrid models Automated machine learning Evolutionary algorithms Geology

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