Reliability assessment of water distribution systems with statistical entropy and other surrogate measures

There is ever increasing commercial and regulatory pressure to minimise the cost of water distribution networks even as the demand for them keeps on growing. But cost minimizing is only one of the demands placed on network design. Satisfactory networks are required to operate above a minimum level even if they experience failure of components. Reliable hydraulic performance can be achieved if sufficient redundancy is built in the network. This has given rise to various water distribution system optimization methods including genetic algorithms and other evolutionary computing methods. Evolutionary computing approaches frequently assess the suitability of enormous numbers of potential solutions for which the calculation of accurate reliability measures could be computationally prohibitive. Therefore, surrogate reliability measures are frequently used to ease the computational burden. The aim of this paper is to assess the correlation of surrogate reliability measures in relation to more accurate measures. The surrogate measures studied are statistical entropy, network resilience, resilience index and modified resilience index. The networks were simulated with the prototype software PRAAWDS that produces more realistic results for pressure-deficient water distribution systems. Statistical entropy outperformed resilience index in this study. The results also demonstrate there is a strong correlation between entropy and failure tolerance

Revival of the Thermal Sneutrino Dark Matter

The left-handed sneutrino in the Minimal Supersymmetric Standard Model (MSSM) has been ruled out as a viable thermal dark matter candidate, due to conflicting constraints from direct detection experiments and from the measurement of the dark matter relic density. The intrinsic fine-tuning problem of the MSSM, however, motivates an extension with a new U(1)' gauge symmetry. We show that in the U(1)'-extended MSSM the right-handed sneutrino becomes a good thermal dark matter candidate. We identify two generic parameter space regions where the combined constraints from relic density determinations, direct detection and collider searches are all satisfied.Comment: 5 pages, 3 figures. Version to appear in Phys. Rev.

Plasticisation effects of high-pressure carbon dioxide on polymers

This thesis examines the effects derived from the ability of high pressure carbon dioxide to soften polymers. This has potential applications in the shape forming of polymers at lower temperatures, dye impregnation and the foaming of polymers. This study was conducted in two parts: (i) mechanical measurement of polymer softening under CO2 at high pressure; and (ii) foaming behaviour of polymers containing dissolved CO2 during depressurisation. In the first study the softening of polymers as a function of applied CO2 pressure and temperature was measured using a novel mechanical 3-point bend test rig. In initial experiments the temperature was slowly ramped upwards and the nominal glass transition temperature was recorded as where the central deflection suddenly begins to increase. Significant reductions in the bending onset temperatures were observed on the application of CO2 for polycarbonate, poly(methyl-methacrylate), glycol modified poly(ethylene-terepthalate) and polystyrene, of typically 50–100°C over the range of pressures applied (24 to 120 bar). [Continues.

S-PRAC: Fast Partial Packet Recovery with Network Coding in Very Noisy Wireless Channels

Well-known error detection and correction solutions in wireless communications are slow or incur high transmission overhead. Recently, notable solutions like PRAC and DAPRAC, implementing partial packet recovery with network coding, could address these problems. However, they perform slowly when there are many errors. We propose S-PRAC, a fast scheme for partial packet recovery, particularly designed for very noisy wireless channels. S-PRAC improves on DAPRAC. It divides each packet into segments consisting of a fixed number of small RLNC encoded symbols and then attaches a CRC code to each segment and one to each coded packet. Extensive simulations show that S-PRAC can detect and correct errors quickly. It also outperforms DAPRAC significantly when the number of errors is high

A deep level set method for image segmentation

This paper proposes a novel image segmentation approachthat integrates fully convolutional networks (FCNs) with a level setmodel. Compared with a FCN, the integrated method can incorporatesmoothing and prior information to achieve an accurate segmentation.Furthermore, different than using the level set model as a post-processingtool, we integrate it into the training phase to fine-tune the FCN. Thisallows the use of unlabeled data during training in a semi-supervisedsetting. Using two types of medical imaging data (liver CT and left ven-tricle MRI data), we show that the integrated method achieves goodperformance even when little training data is available, outperformingthe FCN or the level set model alone

Rapid generation of angular momentum in bounded magnetized plasma

Direct numerical simulations of two-dimensional decaying MHD turbulence in bounded domains show the rapid generation of angular momentum in nonaxisymmetric geometries. It is found that magnetic fluctuations enhance this mechanism. On a larger time scale, the generation of a magnetic angular momentum, or angular field, is observed. For axisymmetric geometries, the generation of angular momentum is absent; nevertheless, a weak magnetic field can be observed. The derived evolution equations for both the angular momentum and angular field yield possible explanations for the observed behavior

Wild Poliovirus Type 1 in Oman: A re-emerging threat that requires urgent, targeted and strategic preparedness

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