Shandong University

About: Shandong University is a education organization based out in Jinan, Shandong, China. It is known for research contribution in the topics: Laser & Cancer. The organization has 99070 authors who have published 99160 publications receiving 1625094 citations. The organization is also known as: Shāndōng Dàxué.

Long range rapidity correlations and jet production in high energy nuclear collisions

Abstract: The STAR Collaboration at the Relativistic Heavy Ion Collider presents a systematic study of high-transverse-momentum charged-di-hadron correlations at small azimuthal pair separation Delta phi in d+Au and central Au+Au collisions at s(NN)=200 GeV. Significant correlated yield for pairs with large longitudinal separation Delta eta is observed in central Au+Au collisions, in contrast to d+Au collisions. The associated yield distribution in Delta eta x Delta phi can be decomposed into a narrow jet-like peak at small angular separation which has a similar shape to that found in d+Au collisions, and a component that is narrow in Delta phi and depends only weakly on Delta eta, the "ridge." Using two systematically independent determinations of the background normalization and shape, finite ridge yield is found to persist for trigger p(t)>6 GeV/c, indicating that it is correlated with jet production. The transverse-momentum spectrum of hadrons comprising the ridge is found to be similar to that of bulk particle production in the measured range (2 < p(t)< 4 GeV/c).

Strain energy and electronic structures of silicon carbide nanotubes: Density functional calculations

Abstract: We perform density functional calculations for the geometrics, strain energy, and electronic structures of silicon carbide nanotubes ($\mathrm{SiCNT}$'s). We find that the strain energy in $\mathrm{SiCNT}$'s is as higher as $0.686\phantom{\rule{0.3em}{0ex}}\mathrm{eV}∕\mathrm{atom}$ relative to $3\mathrm{C}\text{\ensuremath{-}}\mathrm{SiC}$ for (5,5) $\mathrm{SiCNT}$ and decreases with increasing tube diameter. All the $\mathrm{SiCNT}$'s are semiconductors, the band gap of which increases with increasing tube diameter. In contrast to $3\mathrm{C}\text{\ensuremath{-}}\mathrm{SiC}$, zigzag $\mathrm{SiCNT}$ has a direct band gap at the $\ensuremath{\Gamma}$ point, whereas armchair and chiral tubes have an indirect band gap. The highest occupied valance band and the lowest unoccupied conduction band highly localize to $\mathrm{C}$ and $\mathrm{Si}$ atoms, respectively. Hydrogen-decorated $\mathrm{SiCNT}$'s display the characters of $p$- or $n$-type semiconductors depending on the adsorbing site.

Synergetic Effect of Ti3+ and Oxygen Doping on Enhancing Photoelectrochemical and Photocatalytic Properties of TiO2/g-C3N4 Heterojunctions.

Abstract: To improve the utilization of visible light and reduce photogenerated electron/hole recombination, Ti3+ self-doped TiO2/oxygen-doped graphitic carbon nitride (Ti3+-TiO2/O-g-C3N4) heterojunctions were prepared via hydrothermal treatment of a mixture of g-C3N4 and titanium oxohydride sol obtained from the reaction of TiH2 with H2O2. In this way, exfoliated O-g-C3N4 and Ti3+-TiO2 nanoparticles were obtained. Simultaneously, strong bonding was formed between Ti3+-TiO2 nanoparticles and exfoliated O-g-C3N4 during the hydrothermal process. Charge transfer and recombination processes were characterized by transient photocurrent responses, electrochemical impedance test, and photoluminescence spectroscopy. The photocatalytic performances were investigated through rhodamine B degradation test under an irradiation source based on 30 W cold visible-light-emitting diode. The highest visible-light photoelectrochemical and photocatalytic activities were observed from the heterojunction with 1:2 mass ratio of Ti3+-TiO2 ...

Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms

Abstract: A completely automated algorithm to detect poor-quality electrocardiograms (ECGs) is described. The algorithm is based on both novel and previously published signal quality metrics, originally designed for intensive care monitoring. The algorithms have been adapted for use on short (5-10 s) single- and multi-lead ECGs. The metrics quantify spectral energy distribution, higher order moments and inter-channel and inter-algorithm agreement. Seven metrics were calculated for each channel (84 features in all) and presented to either a multi-layer perceptron artificial neural network or a support vector machine (SVM) for training on a multiple-annotator labelled and adjudicated training dataset. A single-lead version of the algorithm was also developed in a similar manner. Data were drawn from the PhysioNet Challenge 2011 dataset where binary labels were available, on 1500 12-lead ECGs indicating whether the entire recording was acceptable or unacceptable for clinical interpretation. We re-annotated all the leads in both the training set (1000 labelled ECGs) and test dataset (500 12-lead ECGs where labels were not publicly available) using two independent annotators, and a third for adjudication of differences. We found that low-quality data accounted for only 16% of the ECG leads. To balance the classes (between high and low quality), we created extra noisy data samples by adding noise from PhysioNet's noise stress test database to some of the clean 12-lead ECGs. No data were shared between training and test sets. A classification accuracy of 98% on the training data and 97% on the test data were achieved. Upon inspection, incorrectly classified data were found to be borderline cases which could be classified either way. If these cases were more consistently labelled, we expect our approach to achieve an accuracy closer to 100%.