Probing the location of displayed cytochrome b562 on amyloid by scanning tunnelling microscopy.

Amyloid fibres displaying cytochrome b562 were probed using scanning tunnelling microscopy (STM) in vacuo. The cytochromes are electron transfer proteins containing a haem cofactor and could, in principle, mediate electron transfer between the tip and the gold substrate. If the core fibres were insulating and electron transfer within the 3D haem network was detected, then the electron transport properties of the fibre could be controlled by genetic engineering. Three kinds of STM images were obtained. At a low bias (<1.5 V) the fibres appeared as regions of low conductivity with no evidence of cytochrome mediated electron transfer. At a high bias, stable peaks in tunnelling current were observed for all three fibre species containing haem and one species of fibre that did not contain haem. In images of this kind, some of the current peaks were collinear and spaced around 10 nm apart over ranges longer than 100 nm, but background monomers complicate interpretation. Images of the third kind were rare (1 in 150 fibres); in these, fully conducting structures with the approximate dimensions of fibres were observed, suggesting the possibility of an intermittent conduction mechanism, for which a precedent exists in DNA. To test the conductivity, some fibres were immobilized with sputtered gold, and no evidence of conduction between the grains of gold was seen. In control experiments, a variation of monomeric cytochrome b562 was not detected by STM, which was attributed to low adhesion, whereas a monomeric multi-haem protein, GSU1996, was readily imaged. We conclude that the fibre superstructure may be intermittently conducting, that the cytochromes have been seen within the fibres and that they are too far apart for detectable current flow between sites to occur. We predict that GSU1996, being 10 nm long, is more likely to mediate successful electron transfer along the fibre as well as being more readily detectable when displayed from amyloid.

Muitiobjective pressurized water reactor reload core design by nondominated genetic algorithm search

The design of pressurized water reactor reload cores is not only a formidable optimization problem but also, in many instances, a multiobjective problem. A genetic algorithm (GA) designed to perform true multiobjective optimization on such problems is described. Genetic algorithms simulate natural evolution. They differ from most optimization techniques by searching from one group of solutions to another, rather than from one solution to another. New solutions are generated by breeding from existing solutions. By selecting better (in a multiobjective sense) solutions as parents more often, the population can be evolved to reveal the trade-off surface between the competing objectives. An example illustrating the effectiveness of this novel method is presented and analyzed. It is found that in solving a reload design problem the algorithm evaluates a similar number of loading patterns to other state-of-the-art methods, but in the process reveals much more information about the nature of the problem being solved. The actual computational cost incurred depends: on the core simulator used; the GA itself is code independent.

Neuro-Fuzzy genetic system for selection of construction project managers

Choosing a project manager for a construction project—particularly, large projects—is a critical project decision. The selection process involves different criteria and should be in accordance with company policies and project specifications. Traditionally, potential candidates are interviewed and the most qualified are selected in compliance with company priorities and project conditions. Precise computing models that could take various candidates’ information into consideration and then pinpoint the most qualified person with a high degree of accuracy would be beneficial. On the basis of the opinions of experienced construction company managers, this paper, through presenting a fuzzy system, identifies the important criteria in selecting a project manager. The proposed fuzzy system is based on IF-THEN rules; a genetic algorithm improves the overall accuracy as well as the functions used by the fuzzy system to make initial estimates of the cluster centers for fuzzy c-means clustering. Moreover, a back-propagation neutral network method was used to train the system. The optimal measures of the inference parameters were identified by calculating the system’s output error and propagating this error within the system. After specifying the system parameters, the membership function parameters—which by means of clustering and projection were approximated—were tuned with the genetic algorithm. Results from this system in selecting project managers show its high capability in making high-quality personnel predictions