13,362 research outputs found

    Baseline measurements of ethene in 2002: Implications for increased ethanol use and biomass burning on air quality and ecosystems

    Get PDF
    While it is well known that combustion of ethanol as a biofuel will lead to enhanced emissions of methane, ethene (ethylene), acetaldehyde, formaldehyde, and oxides of nitrogen (primarily NO) when compared to gasoline alone, especially during cold starts or if catalytic converters are not operating properly, the impacts of increases in atmospheric ethene levels from combustion of fuels with higher ethanol content has not received much attention. Ethene is a well known and potent plant growth hormone and exposure to agricultural crops and natural vegetation results in yield reductions especially when combined with higher levels of PAN and ozone also expected from the increased use of ethanol/gasoline blends. We report here some baseline measurements of ethene obtained in 2002 in the southwestern and south central United States. These data indicate that current ethene background levels are less than 1 ppb. Anticipated increases in fuel ethanol content of E30 or greater is expected to lead to higher atmospheric levels of ethene on regional scales due to its atmospheric lifetime of 1.5-3 days. These background measurements are discussed in light of the potential enhancement of ethene levels expected from the anticipated increases in ethanol use as a renewable biofuel. © 2012 Elsevier Ltd

    Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images

    Get PDF
    Breast cancer is one of the most common types of cancer and leading cancer-related death causes for women. In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology Images, we compare one handcrafted feature extractor and five transfer learning feature extractors based on deep learning. We find out that the deep learning networks pretrained on ImageNet have better performance than the popular handcrafted features used for breast cancer histology images. The best feature extractor achieves an average accuracy of 79.30%. To improve the classification performance, a random forest dissimilarity based integration method is used to combine different feature groups together. When the five deep learning feature groups are combined, the average accuracy is improved to 82.90% (best accuracy 85.00%). When handcrafted features are combined with the five deep learning feature groups, the average accuracy is improved to 87.10% (best accuracy 93.00%)

    Testing for Network and Spatial Autocorrelation

    Full text link
    Testing for dependence has been a well-established component of spatial statistical analyses for decades. In particular, several popular test statistics have desirable properties for testing for the presence of spatial autocorrelation in continuous variables. In this paper we propose two contributions to the literature on tests for autocorrelation. First, we propose a new test for autocorrelation in categorical variables. While some methods currently exist for assessing spatial autocorrelation in categorical variables, the most popular method is unwieldy, somewhat ad hoc, and fails to provide grounds for a single omnibus test. Second, we discuss the importance of testing for autocorrelation in data sampled from the nodes of a network, motivated by social network applications. We demonstrate that our proposed statistic for categorical variables can both be used in the spatial and network setting

    Clades and clans: a comparison study of two evolutionary models

    Get PDF
    The Yule-Harding-Kingman (YHK) model and the proportional to distinguishable arrangements (PDA) model are two binary tree generating models that are widely used in evolutionary biology. Understanding the distributions of clade sizes under these two models provides valuable insights into macro-evolutionary processes, and is important in hypothesis testing and Bayesian analyses in phylogenetics. Here we show that these distributions are log-convex, which implies that very large clades or very small clades are more likely to occur under these two models. Moreover, we prove that there exists a critical value κ(n)\kappa(n) for each n4n\geqslant 4 such that for a given clade with size kk, the probability that this clade is contained in a random tree with nn leaves generated under the YHK model is higher than that under the PDA model if 1<k<κ(n)1<k<\kappa(n), and lower if κ(n)<k<n\kappa(n)<k<n. Finally, we extend our results to binary unrooted trees, and obtain similar results for the distributions of clan sizes.Comment: 21page

    Munchausen by internet: current research and future directions.

    Get PDF
    The Internet has revolutionized the health world, enabling self-diagnosis and online support to take place irrespective of time or location. Alongside the positive aspects for an individual's health from making use of the Internet, debate has intensified on how the increasing use of Web technology might have a negative impact on patients, caregivers, and practitioners. One such negative health-related behavior is Munchausen by Internet

    Development of a generic activities model of command and control

    Get PDF
    This paper reports on five different models of command and control. Four different models are reviewed: a process model, a contextual control model, a decision ladder model and a functional model. Further to this, command and control activities are analysed in three distinct domains: armed forces, emergency services and civilian services. From this analysis, taxonomies of command and control activities are developed that give rise to an activities model of command and control. This model will be used to guide further research into technological support of command and control activities

    The Berkeley Contact Lens Extended Wear Study. Part II : Clinical results.

    Get PDF
    ObjectiveTo describe the principal clinical outcomes associated with 12 months use of rigid gas-permeable (RGP) extended wear contact lenses and address two primary study questions: (1) does extended wear (EW) of high oxygen transmissibility (Dk/t) RGP lenses reduce the incidence of ocular complications, and (2) does the wearing of high-Dk/t lenses reduce the rate of failure to maintain 6-night RGPEW over 12 months?DesignA randomized, concurrently controlled clinical trial.InterventionSubjects who adapted to EW with high Dk (oxygen permeability) RGP lenses were randomized to either high Dk or medium-Dk RGP lenses for 12 months of 6-night EW.Main outcome measuresContact lens-associated keratopathies (CLAK), changes in refractive error and corneal curvature, and survival in EW.ResultsTwo hundred one subjects were randomized to medium or high-Dk lenses for 12 months of EW. Sixty-two percent of the subjects in each group completed 12 months of EW; however, the probability of failure was significantly greater for the medium-Dk group. Although the risk of complications was similar for the two groups, the number of CLAK events that led to termination were 16 versus 5 for the medium-Dk and high-Dk groups, respectively. This suggests that the type of adverse response or the inability to reverse an adverse event was different for the group being exposed to the lower oxygen dose.ConclusionsThe level of oxygen available to the cornea has a significant impact on maintaining successful RGP extended contact lens wear, but not on the initial onset of CLAK. The number of clinical events leading to termination was substantially higher for the medium Dk group, which suggests that corneal hypoxia is an important factor in the development of CLAK. Although overnight contact lens wear should be recommended with caution and carefully monitored for early detection of ocular complications, it appears that high-Dk RGP lenses can be a safe and effective treatment for correction of refractive error for most individuals who can adapt to EW
    corecore