509 research outputs found
A study of blow-ups in the Keller-Segel model of chemotaxis
We study the Keller-Segel model of chemotaxis and develop a composite
particle-grid numerical method with adaptive time stepping which allows us to
accurately resolve singular solutions. The numerical findings (in two
dimensions) are then compared with analytical predictions regarding formation
and interaction of singularities obtained via analysis of the stochastic
differential equations associated with the Keller-Segel model
The Complexity of Graph-Based Reductions for Reachability in Markov Decision Processes
We study the never-worse relation (NWR) for Markov decision processes with an
infinite-horizon reachability objective. A state q is never worse than a state
p if the maximal probability of reaching the target set of states from p is at
most the same value from q, regard- less of the probabilities labelling the
transitions. Extremal-probability states, end components, and essential states
are all special cases of the equivalence relation induced by the NWR. Using the
NWR, states in the same equivalence class can be collapsed. Then, actions
leading to sub- optimal states can be removed. We show the natural decision
problem associated to computing the NWR is coNP-complete. Finally, we ex- tend
a previously known incomplete polynomial-time iterative algorithm to
under-approximate the NWR
Biomaterial-induced sarcomagenesis is not associated with microsatellite instability
Sarcomagenesis, in contrast to carcinogenesis, is poorly understood. Microsatellite instability has been implicated in the development of many cancers, in particular those associated with chronic inflammatory conditions. In an experimental animal model, rats developed not only a peri-implantational chronic inflammatory reaction, but also malignant mesenchymal tumors in response to different biomaterials. Therefore, it was the aim of our study to test if the development of biomaterial-induced sarcomas is characterized by a mutator phenotype. A multiplex-PCR approach was designed to screen biomaterial-induced sarcomas for the presence of microsatellite instability. Seven different microsatellite loci were tested in ten tumors for microsatellite instability using a fluorochrome-labelled multiplex-PCR and subsequent fragment analysis. All tumors provided a microsatellite-stable phenotype at all loci tested. Our data suggest that microsatellite instability is rarely or not at all a feature of malignant transformation of biomaterial-induced soft tissue tumors. Thus, there is no evidence that a mutator phenotype is a hallmark of biomaterial-induced sarcomagenesi
Assessment of the effectiveness of a risk-reduction measure on pluvial flooding and economic loss in Eindhoven, the Netherlands
Open Access journalCopyright © 2013 The Authors. Published by Elsevier Ltd.12th International Conference on Computing and Control for the Water Industry, CCWI2013Cities are increasingly assessing and reducing pluvial flood risk. Quantitative assessment of the effectiveness of risk-reduction measures is required. We use hydraulic simulation with GIS-based financial analysis to assess the pluvial flood risk for Eindhoven, The Netherlands. Analysis is carried out for four scenarios: two rainfall events, with and without separation of the combined sewer-stormwater network. Flooding statistics show how the risk-reduction measure impacts local flooding. Financial analysis demonstrates the saving resulting from the risk-reduction measure. Expected annual damage is reduced by c.€130,500. City authorities are better equipped in making cost-benefit decisions regarding implementation of pluvial flood risk-reduction measures.EC FP7 project PREPARED: Enabling Chang
Assessing Financial Loss due to Pluvial Flooding and the Efficacy of Risk-Reduction Measures in the Residential Property Sector
The final publication is available at Springer via http://dx.doi.org/10.1007/s11269-014-0833-6A novel quantitative risk assessment for residential properties at risk of pluvial flooding in Eindhoven, The Netherlands, is presented. A hydraulic model belonging to Eindhoven was forced with low return period rainfall events (2, 5 and 10-year design rainfalls). Three scenarios were analysed for each event: a baseline and two risk-reduction scenarios. GIS analysis identified areas where risk-reduction measures had the greatest impact. Financial loss calculations were carried out using fixed-threshold and probabilistic approaches. Under fixed-threshold assessment, per-event Expected Annual Damage (EAD) reached €38.2 m, with reductions of up to €454,000 resulting from risk-reduction measures. Present costs of flooding reach €1.43bn when calculated over a 50-year period. All net-present value figures for the risk-reduction measures are negative. Probabilistic assessment yielded EAD values up to more than double those of the fixed-threshold analysis which suggested positive net-present value. To the best of our knowledge, the probabilistic method based on the distribution of doorstep heights has never before been introduced for pluvial flood risk assessment. Although this work suggests poor net-present value of risk-reduction measures, indirect impacts of flooding, damage to infrastructure and the potential impacts of climate change were omitted. This work represents a useful first step in helping Eindhoven prepare for future pluvial flooding. The analysis is based on software and tools already available at the municipality, eliminating the need for software upgrading or training. The approach is generally applicable to similar cities.European Commission Seventh Framework Program (EC FP7
The Influence of Specimen Thickness on the High Temperature Corrosion Behavior of CMSX-4 during Thermal-Cycling Exposure
CMSX-4 is a single-crystalline Ni-base superalloy designed to be used at very high temperatures and high mechanical loadings. Its excellent corrosion resistance is due to external alumina-scale formation, which however can become less protective under thermal-cycling conditions. The metallic substrate in combination with its superficial oxide scale has to be considered as a composite suffering high stresses. Factors like different coefficients of thermal expansion between oxide and substrate during temperature changes or growing stresses affect the integrity of the oxide scale. This must also be strongly influenced by the thickness of the oxide scale and the substrate as well as the ability to relief such stresses, e.g., by creep deformation. In order to quantify these effects, thin-walled specimens of different thickness (t = 100500 lm) were prepared. Discontinuous measurements of their mass changes were carried out under thermal-cycling conditions at a hot dwell temperature of 1100 C up to 300 thermal cycles. Thin-walled specimens revealed a much lower oxide-spallation rate compared to thick-walled specimens, while thinwalled specimens might show a premature depletion of scale-forming elements. In order to determine which of these competetive factor is more detrimental in terms of a component’s lifetime, the degradation by internal precipitation was studied using scanning electron microscopy (SEM) in combination with energy-dispersive X-ray spectroscopy (EDS). Additionally, a recently developed statistical spallation model was applied to experimental data [D. Poquillon and D. Monceau, Oxidation of Metals, 59, 409–431 (2003)]. The model describes the overall mass change by oxide scale spallation during thermal cycling exposure and is a useful simulation tool for oxide scale spallation processes accounting for variations in the specimen geometry. The evolution of the net-mass change vs. the number of thermal cycles seems to be strongly dependent on the sample thickness
CLaSPS: a new methodology for Knowledge extraction from complex astronomical dataset
In this paper we present the Clustering-Labels-Score Patterns Spotter
(CLaSPS), a new methodology for the determination of correlations among
astronomical observables in complex datasets, based on the application of
distinct unsupervised clustering techniques. The novelty in CLaSPS is the
criterion used for the selection of the optimal clusterings, based on a
quantitative measure of the degree of correlation between the cluster
memberships and the distribution of a set of observables, the labels, not
employed for the clustering. In this paper we discuss the applications of
CLaSPS to two simple astronomical datasets, both composed of extragalactic
sources with photometric observations at different wavelengths from large area
surveys. The first dataset, CSC+, is composed of optical quasars
spectroscopically selected in the SDSS data, observed in the X-rays by Chandra
and with multi-wavelength observations in the near-infrared, optical and
ultraviolet spectral intervals. One of the results of the application of CLaSPS
to the CSC+ is the re-identification of a well-known correlation between the
alphaOX parameter and the near ultraviolet color, in a subset of CSC+ sources
with relatively small values of the near-ultraviolet colors. The other dataset
consists of a sample of blazars for which photometric observations in the
optical, mid and near infrared are available, complemented for a subset of the
sources, by Fermi gamma-ray data. The main results of the application of CLaSPS
to such datasets have been the discovery of a strong correlation between the
multi-wavelength color distribution of blazars and their optical spectral
classification in BL Lacs and Flat Spectrum Radio Quasars and a peculiar
pattern followed by blazars in the WISE mid-infrared colors space. This pattern
and its physical interpretation have been discussed in details in other papers
by one of the authors.Comment: 18 pages, 9 figures, accepted for publication in Ap
The anti-glucocorticoid receptor antibody clone 5E4: raising awareness of unspecific antibody binding
Unspecific antibody binding takes a significant toll on researchers in the form of both the economic burden and the disappointed hopes of promising new therapeutic targets. Despite recent initiatives promoting antibody validation, a uniform approach addressing this issue has not yet been developed. Here, we demonstrate that the anti-glucocorticoid receptor (GR) antibody clone 5E4 predominantly targets two different proteins of approximately the same size, namely AMP deaminase 2 (AMPD2) and transcription intermediary factor 1-beta (TRIM28). This paper is intended to generate awareness of unspecific binding of well-established reagents and advocate the use of more rigorous verification methods to improve antibody quality in the future
Kernel Spectral Clustering and applications
In this chapter we review the main literature related to kernel spectral
clustering (KSC), an approach to clustering cast within a kernel-based
optimization setting. KSC represents a least-squares support vector machine
based formulation of spectral clustering described by a weighted kernel PCA
objective. Just as in the classifier case, the binary clustering model is
expressed by a hyperplane in a high dimensional space induced by a kernel. In
addition, the multi-way clustering can be obtained by combining a set of binary
decision functions via an Error Correcting Output Codes (ECOC) encoding scheme.
Because of its model-based nature, the KSC method encompasses three main steps:
training, validation, testing. In the validation stage model selection is
performed to obtain tuning parameters, like the number of clusters present in
the data. This is a major advantage compared to classical spectral clustering
where the determination of the clustering parameters is unclear and relies on
heuristics. Once a KSC model is trained on a small subset of the entire data,
it is able to generalize well to unseen test points. Beyond the basic
formulation, sparse KSC algorithms based on the Incomplete Cholesky
Decomposition (ICD) and , , Group Lasso regularization are
reviewed. In that respect, we show how it is possible to handle large scale
data. Also, two possible ways to perform hierarchical clustering and a soft
clustering method are presented. Finally, real-world applications such as image
segmentation, power load time-series clustering, document clustering and big
data learning are considered.Comment: chapter contribution to the book "Unsupervised Learning Algorithms
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