The comparative analysis of the energies of conjugated triple base triplets and their applications

We built 64 sets of 3D models of DNA triplex base triplets (TBT) and minimized their energies. The TBTs were divided into 32 pairs of conjugated ones on the basis of their sequence characteristic, and the energies of each pair of them were compared and analyzed, the results showed: (i) The duplex DNA of which any strand contains at least a couple of A or T, has a preference for selecting the oligodeoxyribonucleic acid (ODN) strand containing abundant T to form TBT. (ii) The duplex DNA of which any strand contains at least a couple of G or C has a preference for selecting ODN containing abundant G to form symmetric antiparallel TBT, but selecting ODN containing abundant C to form asymmetric parallel TBT. (iii) The duplex DNA of which any strand contains only one of A, T, G or C has a preference for selecting ODN containing abundant pyrimidines (T or C) to form antiparallel TBT. Additionally, two examples of TBTs applications, in designing ODN to form triplex with duplex were presented. The energy calculation result revealed that 15-TCG is the best ligand of the HIV PPT duplex. The comparative analysis of energies of the conjugated TBTs provides directive significance for designing ODN strand that is easy to form triplex in theory. (C) 2002 Elsevier Science B.V. All rights reserved.

Precracked reinforced concrete T-Beams repaired in shear with bonded carbon Fiber-Reinforced Polymer sheets

This study investigates the structural behavior of precracked reinforced concrete (RC) T-beams strengthened in shear with externally bonded carbon fiber-reinforced polymer (CFRP) sheets. It reports on seven tests on unstrengthened and strengthened RC T-beams, identifying the influence of load history, beam depth, and percentage of longitudinal steel reinforcement on the structural behavior. The experimental results indicate that the contributions of the external CFRP sheets to the shear force capacity can be significant and depend on most of the investigated variables. This study also investigates the accuracy of the prediction of the fiber-reinforced polymer (FRP) contribution in ACI 440.2R-08, UK Concrete Society TR55, and fib Bulletin 14 design guidelines for shear strengthening. A comparison of predicted values with experimental results indicates that the guidelines can overestimate the shear contribution of the externally bonded FRP system. © 2012, American Concrete Institute.

Deposition of carbon nanotubes on CMOS

We demonstrate the growth of multi wall and single wall carbon nanotubes (CNT) onto substrates containing commercial 1-m CMOS integrated circuits. The low substrate temperature growth (450°C) was achieved by using hot filament (1000 °C) to preheat the source gases (C 2H 2 and NH 3) and in situ mass spe-ctroscopy was used to identify the gas species present. Field effect transistors based on Single Walled Carbon Nanotube (SWNT) grown under such conditions were fabricated and examined. CNT growth was performed directly on the passivation layer of the CMOS integrated circuits. Individual n- and p-type CMOS transistors were compared before and after CNT growth. The transistors survive and operate after the CNT growth process, although small degradations are observed in the output current (for p-transistors) and leakage current (for both p- and n-type transistors). © 2010 IEEE.

Making a shallow network deep: Conversion of a boosting classifier into a decision tree by boolean optimisation

This paper presents a novel way to speed up the evaluation time of a boosting classifier. We make a shallow (flat) network deep (hierarchical) by growing a tree from decision regions of a given boosting classifier. The tree provides many short paths for speeding up while preserving the reasonably smooth decision regions of the boosting classifier for good generalisation. For converting a boosting classifier into a decision tree, we formulate a Boolean optimization problem, which has been previously studied for circuit design but limited to a small number of binary variables. In this work, a novel optimisation method is proposed for, firstly, several tens of variables i.e. weak-learners of a boosting classifier, and then any larger number of weak-learners by using a two-stage cascade. Experiments on the synthetic and face image data sets show that the obtained tree achieves a significant speed up both over a standard boosting classifier and the Fast-exit-a previously described method for speeding-up boosting classification, at the same accuracy. The proposed method as a general meta-algorithm is also useful for a boosting cascade, where it speeds up individual stage classifiers by different gains. The proposed method is further demonstrated for fast-moving object tracking and segmentation problems. © 2011 Springer Science+Business Media, LLC.