Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

FDserver: A web service for protein folding research

*Summary:* To facilitate the study of protein folding, we have developed a web service for protein folding rate and folding type prediction as well as for the calculation of a variety of topological parameters of protein structure, which is freely available to the community.
*Availability:* http://sdbi.sdut.edu.cn/FDserve

Vortices in Bose-Einstein Condensate Dark Matter

If dark matter in the galactic halo is composed of bosons that form a Bose-Einstein condensate then it is likely that the rotation of the halo will lead to the nucleation of vortices. After a review of the Gross-Pitaevskii equation, the Thomas-Fermi approximation and vortices in general, we consider vortices in detail. We find strong bounds for the boson mass, interaction strength, the shape and quantity of vortices in the halo, the critical rotational velocity for the nucleation of vortices and, in the Thomas-Fermi regime, an exact solution for the mass density of a single, axisymmetric vortex.Comment: 10 pages, 3 figures; minor corrections, references adde