Non-Linear Gravitational Clustering Using the Zel'dovich Approximation

As an alternative to computationally expensive N-body simulations for gravitional clustering, the Zel'dovich approximation (ZA) was studied in 3D, 2D and 1D. Plots of the density contrast were compared against linear theory and the exact solution. The ZA was found to perform very well in the linear regime, better than linear theory, and to give a good approximation well into the non-linear regime. This revised version was published online in July 2006 with corrections to the Cover Date.     

Record of Late Pleistocene Glaciation and Deglaciation in the Southern Cascade Range. I. Petrological Evidence from Lacustrine Sediment in Upper Klamath Lake, Southern Oregon

Petrological and textural properties of lacustrine sediments from Upper Klamath Lake, Oregon, reflect changing input volumes of glacial flour and thus reveal a detailed glacial history for the southern Cascade Range between about 37 and 15 ka. Magnetic properties vary as a result of mixing different amounts of the highly magnetic, glacially generated detritus with less magnetic, more weathered detritus derived from unglaciated parts of the large catchment. Evidence that the magnetic properties record glacial flour input is based mainly on the strong correlation between bulk sediment particle size and parameters that measure the magnetite content and magnetic mineral freshness. High magnetization corresponds to relatively fine particle size and lower magnetization to coarser particle size. This relation is not found in the Buck Lake core in a nearby, unglaciated catchment. Angular silt-sized volcanic rock fragments containing unaltered magnetite dominate the magnetic fraction in the late Pleistocene sediments but are absent in younger, low magnetization sediments. The finer grained, highly magnetic sediments contain high proportions of planktic diatoms indicative of cold, oligotrophic limnic conditions. Sediment with lower magnetite content contains populations of diatoms indicative of warmer, eutrophic limnic conditions. During the latter part of oxygen isotope stage 3 (about 37–25 ka), the magnetic properties record millennial-scale variations in glacial-flour content. The input of glacial flour was uniformly high during the Last Glacial Maximum, between about 21 and 16 ka. At about 16 ka, magnetite input, both absolute and relative to hematite, decreased abruptly, reflecting a rapid decline in glacially derived detritus. The decrease in magnetite transport into the lake preceded declines in pollen from both grass and sagebrush. A more gradual decrease in heavy mineral content over this interval records sediment starvation with the growth of marshes at the margins of the lake and dilution of detrital material by biogenic silica and other organic matter.     

EVKG: An interlinked and interoperable electric vehicle knowledge graph for smart transportation system

Over the past decade, the electric vehicle (EV) industry has experienced unprecedented growth and diversification, resulting in a complex ecosystem. To effectively manage this multifaceted field, we present an EV-centric knowledge graph (EVKG) as a comprehensive, cross-domain, extensible, and open geospatial knowledge management system. The EVKG encapsulates essential EV-related knowledge, including EV adoption, EV supply equipment, and electricity transmission network, to support decision-making related to EV technology development, infrastructure planning, and policy-making by providing timely and accurate information and analysis. To enrich and contextualize the EVKG, we integrate the developed EV-relevant ontology modules from existing well-known knowledge graphs and ontologies. This integration enables interoperability with other knowledge graphs in the Linked Data Open Cloud, enhancing the EVKG's value as a knowledge hub for EV decision-making. Using six competency questions, we demonstrate how the EVKG can be used to answer various types of EV-related questions, providing critical insights into the EV ecosystem. Our EVKG provides an efficient and effective approach for managing the complex and diverse EV industry. By consolidating critical EV-related knowledge into a single, easily accessible resource, the EVKG supports decision-makers in making informed choices about EV technology development, infrastructure planning, and policy-making. As a flexible and extensible platform, the EVKG is capable of accommodating a wide range of data sources, enabling it to evolve alongside the rapidly changing EV landscape.     

Accuracy assessment of digital elevation models by means of robust statistical methods

Measures for the accuracy assessment of Digital Elevation Models (DEMs) are discussed and characteristics of DEMs derived from laser scanning and automated photogrammetry are presented. Such DEMs are very dense and relatively accurate in open terrain. Built-up and wooded areas, however, need automated filtering and classification in order to generate terrain (bare earth) data when Digital Terrain Models (DTMs) have to be produced. Automated processing of the raw data is not always successful. Systematic errors and many outliers at both methods (laser scanning and digital photogrammetry) may therefore be present in the data sets. We discuss requirements for the reference data with respect to accuracy and propose robust statistical methods as accuracy measures. Their use is illustrated by application at four practical examples. It is concluded that measures such as median, normalized median absolute deviation, and sample quantiles should be used in the accuracy assessment of such DEMs. Furthermore, the question is discussed how large a sample size is needed in order to obtain sufficiently precise estimates of the new accuracy measures and relevant formulae are presented.