4,227 research outputs found

    Manufacturing Assembly Time Estimation Using Structural Complexity Metric Trained Artificial Neural Networks

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    Assembly time estimation is traditionally a time-intensive manual process that requires detailed geometric and process information, which is often subjective and qualitative in nature. As a result, assembly time estimation is rarely applied during early design iterations. In this paper, the authors explore the possibility of automating the assembly time estimation process while reducing the level of design detail required. In this approach, they train artificial neural networks (ANNs) to estimate the assembly times of vehicle subassemblies using either assembly connectivity or liaison graph properties, respectively, as input data. The effectiveness of estimation is evaluated based on the distribution of estimates provided by a population of ANNs trained on the same input data using varying initial conditions. Results indicate that this method can provide time estimates of an assembly process with ±15% error while relying exclusively on the geometric part information rather than process instructions

    Hypothesis driven single nucleotide polymorphism search (HyDn-SNP-S)

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    The advent of complete-genome genotyping across phenotype cohorts has provided a rich source of information for bioinformaticians. However the search for SNPs from this data is generally performed on a study-by-study case without any specific hypothesis of the location for SNPs that are predictive for the phenotype. We have designed a method whereby very large SNP lists (several gigabytes in size), combining several genotyping studies at once, can be sorted and traced back to their ultimate consequence in protein structure. Given a working hypothesis, researchers are able to easily search whole genome genotyping data for SNPs that link genetic locations to phenotypes. This allows a targeted search for cor- relations between phenotypes and potentially relevant systems, rather than utilizing statistical methods only. HyDn-SNP-S returns results that are less data dense, allowing more thorough analysis, including haplotype analysis. We have applied our method to correlate DNA polymerases to cancer phenotypes using four of the available cancer databases in dbGaP. Logistic regression and derived haplotype analysis indicates that ∼80 SNPs, previously overlooked, are statistically significant. Derived haplotypes from this work link POLL to breast cancer and POLG to prostate cancer with an increase in incidence of 3.01- and 9.6-fold, respectively. Molecular dynamics simulations on wild-type and one of the SNP mutants from the haplotype of POLL provide insights at the atomic level on the functional impact of this cancer related SNP. Furthermore, HyDn-SNP-S has been designed to allow application to any system. The program is available upon request from the authors

    The sedimentary imprint of Pleistocene glacio-eustasy: Implications for global correlations of seismic sequences

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    We evaluate lithofacies, chronology, and seismic sequences from the Canterbury Basin, New Zealand passive continental slope (Integrated Ocean Drilling Program [IODP] Expedition 317 Site U1352 and environs) and compare this with slope sequences from the New Jersey passive margin. Our goal is to understand continental slope sedimentation in response to glacio-eustasy and test the concepts of sequence stratigraphy. High-resolution geochemical elemental and lithostratigraphic analyses were calibrated to a chronology constructed from benthic foramininferal oxygen isotopes for the past ~1.8 m.y. We identify lithofacies successions by their unique geochemical and lithologic signature and correlate them with marine isotope stages (MIS) at Milankovitch 100 k.y. (MIS 1–12) and 41 k.y. (MIS 13–63) periods. Eight seismic sequence boundaries (U13–U19) were identified from high-resolution multichannel seismic data, providing a seismic stratigraphic framework. Except for MIS 1–5 and MIS 54–55, there are 2–16 MIS stages and a comparable number of lithofacies contained within each seismic sequence, indicating that it took one to several glacio-eustatic cycles to build each seismic stratigraphic sequence. These findings support prior results obtained by the Ocean Drilling Program (ODP) Leg 174A on the New Jersey continental slope. On both margins, there is a strong correlation between seismic sequences, lithofacies, and MIS, thus linking them to glacio-eustasy. However, the correlation between MIS and seismic sequences is not one-to-one, and Pleistocene seismic sequences on the two margins are not synchronous. Local conditions, including differences in sedimentation rates and creation of accommodation space, strongly influenced sediment preservation at each location, revealing that high-frequency Pleistocene seismic sequences need not correlate globally

    Seismic reflections from depths of less than two meters

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    This is the publisher's version, also available electronically from "http://onlinelibrary.wiley.com".Three distinct seismic reflections were obtained from within the upper 2.1 m of flood-plain alluvium in the Arkansas River valley near Great Bend, Kansas. Reflections were observed at depths of 0.63, 1.46, and 2.10 m and confirmed by finite-difference wave-equation modeling. The wavefield was densely sampled by placing geophones at 5-cm intervals, and near-source nonelastic deformation was minimized by using a very small seismic impulse source. For the reflections to be visible within this shallow range, low seismic P-wave velocities (<300 m/s) and high dominant-frequency content of the data (∼450 Hz) were essential. The practical implementation of high-resolution seismic imaging at these depths has the potential to complement ground-penetrating radar (GPR), chiefly in areas where materials exhibiting high electrical conductivity, such as clays, prevent the effective use of GPR. Potential applications of these results exist in hydrogeology and environmental, Quaternary, and neotectonic geology
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