PIM moulding of post consumer mixed plastics

Post consumer plastics from household are highly mixed and contaminated and are thus particularly difficult to recycle. Although advances in sorting and cleaning technologies for waste plastics have enable the relatively pure and clean streams such as bottles to be recycled, there is a increasing need for processing technologies that can utilise the low grade and mixed plastic residues from the plastics recovery facilities (PRF). In this work, potentials of utilisation of such feedstock in Powder Impression Moulding (PIM), a process capable of fabricating lightweight sandwich structures, are investigated in terms effects of loading and size of flakes from PE-rich mixed plastics in the formulations of the core on flexural properties of the sandwich panels. It was demonstrated that sandwich panels can be made by incorporating about 75 wt% of coarse flakes of a low-grade mixed plastics material directly obtained from a PRF

Influences of impurities in recycled plastics on properties of PIM sandwich panels

Powder impression moulding (PIM) is a novel technology for manufacturing lightweight sandwich panels from plastics in powder form. The process is featured by its high tolerant to impurities or contaminants in the feedstock and thus requires much less materials segregation and cleaning operations when use recycled plastics. This paper investigate the influences of polymer impurities and soil contamination on structure and properties of PIM sandwich panels using compositions that simulate a PE-rich recycled plastic feedstock. It is demonstrated that the PIM process can accommodate considerable impurities (rPET residues or soil contamination) in a core dominated by LDPE/HDPE blendes. The variation of flexural properties can be predicted or controlled through monitoring of impurities. There exist significant scopes for reduction of the degree of sorting and cleaning in recycling systems by using lower grades of recyclates and for reduction of the associated costs and energy consumption

VISHNU hybrid model for viscous QCD matter at RHIC and LHC energies

In this proceeding, we briefly describe the viscous hydrodynamics + hadron cascade hybrid model VISHNU for relativistic heavy ion collisions and report the current status on extracting the QGP viscosity from elliptic flow data.Comment: 4 pages, 1 figure, the proceedings of 7th International Workshop on Critical Point and Onset of Deconfinement, Wuhan, China, Nov. 7-11, 201

DNA mismatch binding and antiproliferative activity of rhodium metalloinsertors

Deficiencies in mismatch repair (MMR) are associated with carcinogenesis. Rhodium metalloinsertors bind to DNA base mismatches with high specificity and inhibit cellular proliferation preferentially in MMR-deficient cells versus MMR-proficient cells. A family of chrysenequinone diimine complexes of rhodium with varying ancillary ligands that serve as DNA metalloinsertors has been synthesized, and both DNA mismatch binding affinities and antiproliferative activities against the human colorectal carcinoma cell lines HCT116N and HCT116O, an isogenic model system for MMR deficiency, have been determined. DNA photocleavage experiments reveal that all complexes bind to the mismatch sites with high specificities; DNA binding affinities to oligonucleotides containing single base CA and CC mismatches, obtained through photocleavage titration or competition, vary from 10^4 to 10^8 M^−1 for the series of complexes. Significantly, binding affinities are found to be inversely related to ancillary ligand size and directly related to differential inhibition of the HCT116 cell lines. The observed trend in binding affinity is consistent with the metalloinsertion mode where the complex binds from the minor groove with ejection of mismatched base pairs. The correlation between binding affinity and targeting of the MMR-deficient cell line suggests that rhodium metalloinsertors exert their selective biological effects on MMR-deficient cells through mismatch binding in vivo

Recognition of Microseismic and Blasting Signals in Mines Based on Convolutional Neural Network and Stockwell Transform

The microseismic monitoring signals which need to be determined in mines include those caused by both rock bursts and by blasting. The blasting signals must be separated from the microseismic signals in order to extract the information needed for the correct location of the source and for determining the blast mechanism. The use of a convolutional neural network (CNN) is a viable approach to extract these blast characteristic parameters automatically and to achieve the accuracy needed in the signal recognition. The Stockwell Transform (or S-Transform) has excellent two-dimensional time-frequency characteristics and thus to obtain the microseismic signal and blasting vibration signal separately, the microseismic signal has been converted in this work into a two-dimensional image format by use of the S-Transform, following which it is recognized by using the CNN. The sample data given in this paper are used for model training, where the training sample is an image containing three RGB color channels. The training time can be decreased by means of reducing the picture size and thus reducing the number of training steps used. The optimal combination of parameters can then be obtained after continuously updating the training parameters. When the image size is 180 × 140 pixels, it has been shown that the test accuracy can reach 96.15% and that it is feasible to classify separately the blasting signal and the microseismic signal based on using the S-Transform and the CNN model architecture, where the training parameters were designed by synthesizing LeNet-5 and AlexNet

Enhanced toluene removal using granular activated carbon and a yeast strain candida tropicalis in bubble-column bioreactors

The yeast strain Candida tropicalis was used for the biodegradation of gaseous toluene. Toluene was effectively treated by a liquid culture of C. tropicalis in abubble-column bioreactor, and the tolueneremoval efficiency increased with decreasing gas flow rate. However, toluene mass transfer from the gas-to-liquid phase was a major limitation for the uptake of toluene by C. tropicalis. The tolueneremoval efficiency was enhanced when granularactivatedcarbon (GAC) was added as a fluidized material. The GAC fluidized bioreactor demonstrated tolueneremoval efficiencies ranging from 50 to 82% when the inlet toluene loading was varied between 13.1 and 26.9 g/m3/h. The yield value of C. tropicalis ranged from 0.11 to 0.21 g-biomass/g-toluene, which was substantially lower than yield values for bacteria reported in the literature. The maximum elimination capacity determined in the GAC fluidized bioreactor was 172 g/m3/h at atoluene loading of 291 g/m3/h. Transient loading experiments revealed that approximately 50% of the toluene introduced was initially adsorbed onto the GAC during an increased loading period, and then slowly desorbed and became available to the yeast culture. Hence, the fluidized GAC mediated in improving the gas-to-liquid mass transfer of toluene, resulting in a high tolueneremoval capacity. Consequently, the GAC bubble-column bioreactor using the culture of C. tropicalis can be successfully applied for the removal of gaseous toluene

A Quantum Computer Architecture using Nonlocal Interactions

Several authors have described the basic requirements essential to build a scalable quantum computer. Because many physical implementation schemes for quantum computing rely on nearest neighbor interactions, there is a hidden quantum communication overhead to connect distant nodes of the computer. In this paper we propose a physical solution to this problem which, together with the key building blocks, provides a pathway to a scalable quantum architecture using nonlocal interactions. Our solution involves the concept of a quantum bus that acts as a refreshable entanglement resource to connect distant memory nodes providing an architectural concept for quantum computers analogous to the von Neumann architecture for classical computers.Comment: 4 pages, 2 figures, Slight modifications to satisfy referee, 2 new references, modified acknowledgement. This draft to appear in PRA Rapid Communication

Drastic Reduction of Shot Noise in Semiconductor Superlattices

We have found experimentally that the shot noise of the tunneling current $I$ through an undoped semiconductor superlattice is reduced with respect to the Poissonian noise value $2eI$, and that the noise approaches 1/3 of that value in superlattices whose quantum wells are strongly coupled. On the other hand, when the coupling is weak or when a strong electric field is applied to the superlattice the noise becomes Poissonian. Although our results are qualitatively consistent with existing theories for one-dimensional mulitple barriers, the theories cannot account for the dependence of the noise on superlattice parameters that we have observed.Comment: 4 Pages, 3Figure

Shear capacity of reinforced concrete beams using neural network

NoOptimum multi-layered feed-forward neural network (NN) models using a resilient back-propagation algorithm and early stopping technique are built to predict the shear capacity of reinforced concrete deep and slender beams. The input layer neurons represent geometrical and material properties of reinforced concrete beams and the output layer produces the beam shear capacity. Training, validation and testing of the developed neural network have been achieved using 50%, 25%, and 25%, respectively, of a comprehensive database compiled from 631 deep and 549 slender beam specimens. The predictions obtained from the developed neural network models are in much better agreement with test results than those determined from shear provisions of different codes, such as KBCS, ACI 318-05, and EC2. The mean and standard deviation of the ratio between predicted using the neural network models and measured shear capacities are 1.02 and 0.18, respectively, for deep beams, and 1.04 and 0.17, respectively, for slender beams. In addition, the influence of different parameters on the shear capacity of reinforced concrete beams predicted by the developed neural network shows consistent agreement with those experimentally observed

Systematic study of nuclear matrix elements in neutrinoless double-beta decay with a beyond mean-field covariant density functional theory

We report a systematic study of nuclear matrix elements (NMEs) in neutrinoless double-beta decays with a state-of-the-art beyond mean-field covariant density functional theory. The dynamic effects of particle-number and angular-momentum conservations as well as quadrupole shape fluctuations are taken into account with projections and generator coordinate method for both initial and final nuclei. The full relativistic transition operator is adopted to calculate the NMEs. The present systematic studies show that in most of the cases there is a much better agreement with the previous non-relativistic calculation based on the Gogny force than in the case of the nucleus $^{150}$Nd found in Song et al. [Phys. Rev. C 90, 054309 (2014)]. In particular, we find that the total NMEs can be well approximated by the pure axial-vector coupling term with a considerable reduction of the computational effort.Comment: 9 pages with 7 figures and 3 table