Centrality, system size and energy dependences of charged-particle pseudo-rapidity distribution

Utilizing the three-fireball picture within the quark combination model, we study systematically the charged particle pseudorapidity distributions in both Au+Au and Cu+Cu collision systems as a function of collision centrality and energy, $\sqrt{s_{NN}}=$ 19.6, 62.4, 130 and 200 GeV, in full pseudorapidity range. We find that: (i)the contribution from leading particles to $dN_{ch}/d\eta$ distributions increases with the decrease of the collision centrality and energy respectively; (ii)the number of the leading particles is almost independent of the collision energy, but it does depend on the nucleon participants $N_{part}$; (iii)if Cu+Cu and Au+Au collisions at the same collision energy are selected to have the same $N_{part}$, the resulting of charged particle $dN/d\eta$ distributions are nearly identical, both in the mid-rapidity particle density and the width of the distribution. This is true for both 62.4 GeV and 200 GeV data. (iv)the limiting fragmentation phenomenon is reproduced. (iiv) we predict the total multiplicity and pseudorapidity distribution for the charged particles in Pb+Pb collisions at $\sqrt{s_{NN}}= 5.5$ TeV. Finally, we give a qualitative analysis of the $N_{ch}/$ and $dN_{ch}/d\eta/|_{\eta\approx0}$ as function of $\sqrt{s_{NN}}$ and $N_{part}$ from RHIC to LHC.Comment: 12 pages, 8 figure

High-order volterra model predictive control and its application to a nonlinear polymerisation process

Model Predictive Control (MPC) has recently found wide acceptance in the process industry, but the existing design and implementation methods are restricted to linear process models. A chemical process involves, however, severe nonlinearity which cannot be ignored in practice. This paper aims to solve this nonlinear control problem by extending MPC to nonlinear models. It develops an analytical framework for nonlinear model predictive control (NMPC), and also offers a third-order Volterra series based nonparametric nonlinear modelling technique for NMPC design which relieves practising engineers from the need for first deriving a physical-principles based model. An on-line realisation technique for implementing the NMPC is also developed. The NMPC is then applied to a Mitsubishi Chemicals polymerisation reaction process. The results show that this nonlinear MPC technique is feasible and very effective. It considerably outperforms linear and low-order Volterra model based methods. The advantages of the approach developed lie not only in control performance superior to existing NMPC methods, but also in relieving practising engineers from the need for deriving an analytical model and then converting it to a Volterra model through which the model can only be obtained up to the second order

Enhanced Efficiency and Stability of Planar Perovskite Solar Cells Using a Dual Electron Transport Layer of Gold Nanoparticles Embedded in Anatase TiO2 Films

Incorporating plasmonic nanostructures is a promising strategy to enhance both the optical and electrical characteristics of photovoltaic devices via more efficient harvesting of incident light. Herein, we report a facile fabrication scheme at low temperature for producing gold nanoparticles embedded in anatase TiO2 films, which can simultaneously improve the efficiency and stability of n-i-p planar heterojunction perovskite solar cells (PSCs). The PSCs based on rigid and flexible substrates with 0.2 wt % Au-TiO2/TiO2 dual electron transport layers (ETLs) achieved power conversion efficiencies up to 20.31 and 15.36%, superior to that of devices with TiO2 as a single ETL. Moreover, 0.2 wt % Au-TiO2/TiO2 devices demonstrated significant stability in light soaking, which is attributed to improved light absorption, low charge recombination loss, and enhanced carrier transport, and extraction with the plasmonic Au-TiO2/TiO2 dual ETL. The present work improves the practicability of high-performance and flexible PSCs by engineering the photogenerated carrier dynamics at the interface

Deep centers in a free-standing GaN layer

Schottky barrierdiodes, on both Ga and N faces of a ∼300-μm-thick free-standing GaN layer, grown by hydride vapor phase epitaxy(HVPE) on Al2O3 followed by laser separation, were studied by capacitance–voltage and deep level transient spectroscopy(DLTS) measurements. From a 1/C2 vs V analysis, the barrier heights of Ni/Au Schottky contacts were determined to be different for the two polar faces: 1.27 eV for the Ga face, and 0.75 eV for the N face. In addition to the four common DLTS traps observed previously in other epitaxial GaN including HVPE-grown GaN a new trap B′ with activation energyET=0.53 eV was found in the Ga-face sample. Also, trap E1 (ET=0.18 eV), believed to be related to the N vacancy, was found in the N-face sample, and trap C (ET=0.35 eV) was in the Ga-face sample. Trap C may have arisen from reactive-ion-etching damage

Deep centers in a free-standing GaN layer

Schottky barrier diodes, on both Ga and N faces of a ∼300-μm-thick free-standing GaN layer, grown by hydride vapor phase epitaxy (HVPE) on Al2O3 followed by laser separation, were studied by capacitance–voltage and deep level transient spectroscopy (DLTS) measurements. From a 1/C2 vs V analysis, the barrier heights of Ni/Au Schottky contacts were determined to be different for the two polar faces: 1.27 eV for the Ga face, and 0.75 eV for the N face. In addition to the four common DLTS traps observed previously in other epitaxial GaN including HVPE-grown GaN a new trap B′ with activation energy ET = 0.53 eV was found in the Ga-face sample. Also, trap E1 (ET = 0.18 eV), believed to be related to the N vacancy, was found in the N-face sample, and trap C (ET = 0.35 eV) was in the Ga-face sample. Trap C may have arisen from reactive-ion-etching damage

High resolution imaging of molecular line emission from high redshift QSOs

We present moderate (1'') and high resolution (0.2'') observations of the CO(2-1) emission at 43 GHz, and radio continuum emission at 1.47 GHz, from the z=4.7 QSO BRI 1202-0725 and the z=4.4 QSO BRI 1335--0417 using the Very Large Array. The moderate resolution observations show that in both cases the CO emission is spatially resolved into two components separated by 1'' for 1335-0417 and 4'' for 1202-0725. The high resolution observations show that each component has sub-structure on scales of 0.2'' to 0.5'', with intrinsic brightness temperatures > 20K. The CO ladder from (2-1) up to (7-6) suggests a high kinetic temperature for the gas (70 K), and a high column density (10^{24} cm^{-2}). In both sources the continuum-to-line ratio: L_{FIR}/L'_{CO(1-0)} = 335. All these characteristics (brightness temperature, excitation temperature, column density, and continuum-to-line ratio) are comparable to conditions found in low redshift, ultra-luminous nuclear starburst galaxies. We find that the CO emitting regions in 1202-0725 and 1335-0417 must be close to face-on in order to avoid having the gas mass exceed the gravitational mass, implying perhaps unreasonably large rotational velocities. While this problem is mitigated by lowering the CO luminosity-to-H_2 mass conversion factor (X), the required X values become comparable to, or lower than, the minimum values dictated by optically thin CO emission. We considered the possibility of magnification by gravitational lensing in order to reduce the molecular gas masses.Comment: aastex 12 postscript figures. to appear in the Astronomical Journa

Heterogeneous network embedding enabling accurate disease association predictions.

BackgroundIt is significant to identificate complex biological mechanisms of various diseases in biomedical research. Recently, the growing generation of tremendous amount of data in genomics, epigenomics, metagenomics, proteomics, metabolomics, nutriomics, etc., has resulted in the rise of systematic biological means of exploring complex diseases. However, the disparity between the production of the multiple data and our capability of analyzing data has been broaden gradually. Furthermore, we observe that networks can represent many of the above-mentioned data, and founded on the vector representations learned by network embedding methods, entities which are in close proximity but at present do not actually possess direct links are very likely to be related, therefore they are promising candidate subjects for biological investigation.ResultsWe incorporate six public biological databases to construct a heterogeneous biological network containing three categories of entities (i.e., genes, diseases, miRNAs) and multiple types of edges (i.e., the known relationships). To tackle the inherent heterogeneity, we develop a heterogeneous network embedding model for mapping the network into a low dimensional vector space in which the relationships between entities are preserved well. And in order to assess the effectiveness of our method, we conduct gene-disease as well as miRNA-disease associations predictions, results of which show the superiority of our novel method over several state-of-the-arts. Furthermore, many associations predicted by our method are verified in the latest real-world dataset.ConclusionsWe propose a novel heterogeneous network embedding method which can adequately take advantage of the abundant contextual information and structures of heterogeneous network. Moreover, we illustrate the performance of the proposed method on directing studies in biology, which can assist in identifying new hypotheses in biological investigation