942 resultados para Geology--Mexico--Maps
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John N. Jackson was born in Nottingham, England in 1926. He developed a passion for landforms and geography from his father, a high school math and science teacher who had studied geology. During the Second World War, he served in the British Navy. He received his BA from the University of Birmingham, and a PhD from the University of Manchester. After spending a year as a visiting professor at the University of British Columbia, he was hired in 1965 as the founding head of the Geography Department at Brock University. He taught at Brock for more than 25 years, immersing himself in the geography and history of the Niagara area. He became particularly interested in the history of the Welland Canals. He authored 20 books on various topics, including land use in Niagara, the history of St. Catharines, the Welland Canal, and railways in the Niagara Peninsula. He died in 2010, at the age of 84.
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Hebes Chasma is an 8 km deep, 126 by 314 km, isolated basin that is partially filled with interior layered deposits (ILD), massive deposits of water altered strata. By analyzing the ILD’s structure, stratigraphy and mineralogy, as well as the perimeter faults exposed in the plateau adjacent to the chasma, the evolution and depositional history of Hebes Chasma is interpreted. Three distinct ILD units were found and are informally referred to as the Lower, Upper and Late ILDs. These units have differing layer thicknesses, layer attitudes, mineralogies and erosional landforms. Based on observations of the plateau, wall morphology and slump blocks within the chasma’s interior, chasma evolution appears to be controlled by cross-faults that progressively detached sections of the wall. A scenario involving the loss of subsurface volume and ash fall events is proposed as the dominant setting throughout Hebes’ geologic history.
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The goal of most clustering algorithms is to find the optimal number of clusters (i.e. fewest number of clusters). However, analysis of molecular conformations of biological macromolecules obtained from computer simulations may benefit from a larger array of clusters. The Self-Organizing Map (SOM) clustering method has the advantage of generating large numbers of clusters, but often gives ambiguous results. In this work, SOMs have been shown to be reproducible when the same conformational dataset is independently clustered multiple times (~100), with the help of the Cramérs V-index (C_v). The ability of C_v to determine which SOMs are reproduced is generalizable across different SOM source codes. The conformational ensembles produced from MD (molecular dynamics) and REMD (replica exchange molecular dynamics) simulations of the penta peptide Met-enkephalin (MET) and the 34 amino acid protein human Parathyroid Hormone (hPTH) were used to evaluate SOM reproducibility. The training length for the SOM has a huge impact on the reproducibility. Analysis of MET conformational data definitively determined that toroidal SOMs cluster data better than bordered maps due to the fact that toroidal maps do not have an edge effect. For the source code from MATLAB, it was determined that the learning rate function should be LINEAR with an initial learning rate factor of 0.05 and the SOM should be trained by a sequential algorithm. The trained SOMs can be used as a supervised classification for another dataset. The toroidal 10×10 hexagonal SOMs produced from the MATLAB program for hPTH conformational data produced three sets of reproducible clusters (27%, 15%, and 13% of 100 independent runs) which find similar partitionings to those of smaller 6×6 SOMs. The χ^2 values produced as part of the C_v calculation were used to locate clusters with identical conformational memberships on independently trained SOMs, even those with different dimensions. The χ^2 values could relate the different SOM partitionings to each other.
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Tesis (Maestría en Ciencias para la Planificación de los Asentamientos Humanos) U.A.N.L.
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