The Effect of the Nature of Surfaces on Resistance as Tested on Struts

The chief concern was to measure the variations of resistance brought about by the nature of the surface of the struts. The struts were spanned with aviation linen, and then covered with one coat of varnish. The top surface was not perfectly smooth after this treatment, being slightly rough owing to the threads and raised fibers of the fabric. The results of the measurements of the surfaces are shown by the dotted lines of the curves plotted in several figures. The resistance is given in terms of the characteristic value. Next, the surface was altered by the removal of any roughness on it by means of filing with sandpaper. The measurements of surfaces thus treated gave values represented by extended lines. The increase of resistance with increasing characteristic value, more or less marked in the first series of measurements, was no longer observable. Resistance always decreases with the increase of characteristic value, excepting in the case of strut 7, which shows a slight tendency to rise again. The reasons for this phenomenon have not yet been fully explained

Dynamical Effects from Asteroid Belts for Planetary Systems

The orbital evolution and stability of planetary systems with interaction from the belts is studied using the standard phase-plane analysis. In addition to the fixed point which corresponds to the Keplerian orbit, there are other fixed points around the inner and outer edges of the belt. Our results show that for the planets, the probability to move stably around the inner edge is larger than the one to move around the outer edge. It is also interesting that there is a limit cycle of semi-attractor for a particular case. Applying our results to the Solar System, we find that our results could provide a natural mechanism to do the orbit rearrangement for the larger Kuiper Belt Objects and thus successfully explain the absence of these objects beyond 50 AU.Comment: accepted by International Journal of Bifurcation and Chaos in Aug. 2003, AAS Latex, 27 pages with 6 color figure

It is Not Black and White: A Comparison of Skin Tone by Playing Position in the Premier League and English Football

Within the present manuscript we explore the role of skin tone on playing position within English football’s top four professional leagues. Player data (N = 4,515) was collected across five seasons (2010-2015). Results indicate that in general, darker skin toned players are more likely to operate within peripheral rather than central positions. Using both one and two-way ANOVAs, results suggest significant differences between skin tone and individual playing positions. Between league differences were, however, non-significant. Although darker skin toned players are still more likely to occupy peripheral positions, the situation is more nuanced than first thought. Instead of segregating players by central versus peripheral roles, it appears that darker skin toned players occupy positions associated with athleticism and strength. In contrast, lighter skin toned players appear to fulfill positions requiring organizational skills and creativity

Optimisation of neural network with simultaneous feature selection and network prunning using evolutionary algorithm

Most advances on the Evolutionary Algorithm optimisation of Neural Network are on recurrent neural network using the NEAT optimisation method. For feed forward network, most of the optimisation are merely on the Weights and the bias selection which is generally known as conventional Neuroevolution. In this research work, a simultaneous feature reduction, network pruning and weight/biases selection is presented using fitness function design which penalizes selection of large feature sets. The fitness function also considers feature and the neuron reduction in the hidden layer. The results were demonstrated using two sets of data sets which are the cancer datasets and Thyroid datasets. Results showed backpropagation gradient descent error weights/biased optimisations performed slightly better at classification of the two datasets with lower misclassification rate and error. However, features and hidden neurons were reduced with the simultaneous feature/neurons switching using Genetic Algorithm. The number of features were reduced from 21 to 4 (Thyroid dataset) and 9 to 3 (cancer dataset) with only 1 hidden neuron in the processing layer for both network structures for the respective datasets. This research work will present the chromosome representation and the fitness function design