Dielectronic Recombination of Fe XV forming Fe XIV: Laboratory Measurements and Theoretical Calculations

We have measured resonance strengths and energies for dielectronic recombination (DR) of Mg-like Fe XV forming Al-like Fe XIV via N=3 -> N' = 3 core excitations in the electron-ion collision energy range 0-45 eV. All measurements were carried out using the heavy-ion Test Storage Ring at the Max Planck Institute for Nuclear Physics in Heidelberg, Germany. We have also carried out new multiconfiguration Breit-Pauli (MCBP) calculations using the AUTOSTRUCTURE code. For electron-ion collision energies < 25 eV we find poor agreement between our experimental and theoretical resonance energies and strengths. From 25 to 42 eV we find good agreement between the two for resonance energies. But in this energy range the theoretical resonance strengths are ~ 31% larger than the experimental results. This is larger than our estimated total experimental uncertainty in this energy range of +/- 26% (at a 90% confidence level). Above 42 eV the difference in the shape between the calculated and measured 3s3p(^1P_1)nl DR series limit we attribute partly to the nl dependence of the detection probabilities of high Rydberg states in the experiment. We have used our measurements, supplemented by our AUTOSTRUCTURE calculations, to produce a Maxwellian-averaged 3 -> 3 DR rate coefficient for Fe XV forming Fe XIV. The resulting rate coefficient is estimated to be accurate to better than +/- 29% (at a 90% confidence level) for k_BT_e > 1 eV. At temperatures of k_BT_e ~ 2.5-15 eV, where Fe XV is predicted to form in photoionized plasmas, significant discrepancies are found between our experimentally-derived rate coefficient and previously published theoretical results. Our new MCBP plasma rate coefficient is 19-28% smaller than our experimental results over this temperature range

Dielectronic Recombination In Active Galactic Nuclei

XMM-Newton and Chandra observations of active galactic nuclei (AGN) show rich spectra of X-ray absorption lines. These observations have detected a broad unresolved transition array (UTA) between ˜ 15-17 Å. This is attributed to inner-shell photoexcitation of M-shell iron ions. Modeling these UTA features is currently limited by uncertainties in the low-temperature dielectronic recombination (DR) data for M-shell iron. In order to resolve this issue, and to provide reliable iron M-shell DR data for plasma modeling, we are carrying out a series of laboratory measurements using the heavy-ion Test Storage Ring (TSR) at the Max-Plank-Institute for Nuclear Physics in Heidelberg, Germany. Currently, laboratory measurements of low temperature DR can only be performed at storage rings. We use the DR data obtained at TSR, to calculate rate coefficients for plasma modeling and to benchmark theoretical DR calculations. Here we report our recent experimental results for DR of Fe XIV forming Fe XIII

Combined effect of steel fibres and steel rebars on impact resistance of high performance concrete

The investigation on the impact properties of normal concrete (NC)and reinforced concrete (RC) specimens, steel fibre reinforced concrete (SFRC) specimens and RC+SFRC specimens with different steel fibres dosages were carried out with the drop-weight impact test recommended by ACI Committee 544. The results indicate that the number of blows to final failure greatly increased by addition of steel fibres. Moreover, the combination of steel fibres and steel rebars demonstrates a significant positive composite effect on the impact resistance, which results on the improvement in impact toughness of concrete specimens. In view of the variation of impact test results, the two-parameter Weibull distribution was adopted to analyze the experimental data. It is proved that the probabilistic distribution of the blows to first crack and to final failure of six types of samples are approximately two-parameter Weibull distribution.Funded by the National Natural Science Foundation of China (NSFC) (Grant No.50578026)Financial support provided by the Research Center of Mathematics of the University of Minho through the FCT Pluriannual Funding Progra

Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

In this paper, a psychologically-inspired binary cascade classification schema is proposed for speech emotion recognition. Performance is enhanced because commonly confused pairs of emotions are distinguishable from one another. Extracted features are related to statistics of pitch, formants, and energy contours, as well as spectrum, cepstrum, perceptual and temporal features, autocorrelation, MPEG-7 descriptors, Fujisakis model parameters, voice quality, jitter, and shimmer. Selected features are fed as input to K nearest neighborhood classifier and to support vector machines. Two kernels are tested for the latter: Linear and Gaussian radial basis function. The recently proposed speaker-independent experimental protocol is tested on the Berlin emotional speech database for each gender separately. The best emotion recognition accuracy, achieved by support vector machines with linear kernel, equals 87.7%, outperforming state-of-the-art approaches. Statistical analysis is first carried out with respect to the classifiers error rates and then to evaluate the information expressed by the classifiers confusion matrices. © Springer Science+Business Media, LLC 2011

Isolation and Culture of Larval Cells from C. elegans

Cell culture is an essential tool to study cell function. In C. elegans the ability to isolate and culture cells has been limited to embryonically derived cells. However, cells or blastomeres isolated from mixed stage embryos terminally differentiate within 24 hours of culture, thus precluding post-embryonic stage cell culture. We have developed an efficient and technically simple method for large-scale isolation and primary culture of larval-stage cells. We have optimized the treatment to maximize cell number and minimize cell death for each of the four larval stages. We obtained up to 7.8×104 cells per microliter of packed larvae, and up to 97% of adherent cells isolated by this method were viable for at least 16 hours. Cultured larval cells showed stage-specific increases in both cell size and multinuclearity and expressed lineage- and cell type-specific reporters. The majority (81%) of larval cells isolated by our method were muscle cells that exhibited stage-specific phenotypes. L1 muscle cells developed 1 to 2 wide cytoplasmic processes, while L4 muscle cells developed 4 to 14 processes of various thicknesses. L4 muscle cells developed bands of myosin heavy chain A thick filaments at the cell center and spontaneously contracted ex vivo. Neurons constituted less than 10% of the isolated cells and the majority of neurons developed one or more long, microtubule-rich protrusions that terminated in actin-rich growth cones. In addition to cells such as muscle and neuron that are high abundance in vivo, we were also able to isolate M-lineage cells that constitute less than 0.2% of cells in vivo. Our novel method of cell isolation extends C. elegans cell culture to larval developmental stages, and allows use of the wealth of cell culture tools, such as cell sorting, electrophysiology, co-culture, and high-resolution imaging of subcellular dynamics, in investigation of post-embryonic development and physiology

A graph-search framework for associating gene identifiers with documents

BACKGROUND: One step in the model organism database curation process is to find, for each article, the identifier of every gene discussed in the article. We consider a relaxation of this problem suitable for semi-automated systems, in which each article is associated with a ranked list of possible gene identifiers, and experimentally compare methods for solving this geneId ranking problem. In addition to baseline approaches based on combining named entity recognition (NER) systems with a "soft dictionary" of gene synonyms, we evaluate a graph-based method which combines the outputs of multiple NER systems, as well as other sources of information, and a learning method for reranking the output of the graph-based method. RESULTS: We show that named entity recognition (NER) systems with similar F-measure performance can have significantly different performance when used with a soft dictionary for geneId-ranking. The graph-based approach can outperform any of its component NER systems, even without learning, and learning can further improve the performance of the graph-based ranking approach. CONCLUSION: The utility of a named entity recognition (NER) system for geneId-finding may not be accurately predicted by its entity-level F1 performance, the most common performance measure. GeneId-ranking systems are best implemented by combining several NER systems. With appropriate combination methods, usefully accurate geneId-ranking systems can be constructed based on easily-available resources, without resorting to problem-specific, engineered components