An implementation of structural change detection procedure based on experimental and numerical model correlation

A correlation of the experimental and the numerical model is implemented in order to detect structural change. The implementation of the method involves two steps: in the first the respective location is detected, and in the second the structural change is quantified. The method can detect changes in mass, stiffness or both. The numerical simulations demonstrate that the method is accurate and reliable. The method is implemented analytically and experimentally in parallel with laboratory test-cases. Improvements of the procedure are suggested and implemented in order to study the applicability to real-world cases. This study shows that model update significantly contributes to the successful structural change detection. The influence of the model reduction and model update on the detection accuracy is studied.     

Multi-catenary isogeometric discretization with the sliding constraint

The purpose of this paper is to present the formulation of isogeometric dynamic analysis with frictionless sliding constraint between two catenaries.The sliding...     

Essential oil composition of Prasium majus from Croatia

The essential oils from the aerial parts of Prasium majus L., collected during two years in Croatia, were analysed by GC and GC/MS. Fifty-two compounds were identified, representing 90.3-91.8% of the total oils. The major constituents in both samples were fatty acids (particularly hexadecanoic acid and (Z)-octadec-9-enoic acid), lower aliphatic alcohols, aldehydes and acids (major ones oct-1-en-3-ol and (E,E)-hepta-2,4-dienal) and phenylpropane derivatives (e.g. eugenol). Beta-Caryophyllene was the most abundant terpene and (E)-beta-ionone was the major norisoprenoid.     

Impact of uncertainty in expected return estimation on stock price volatility

We investigate the origin of volatility in financial markets by defining an analytical model for time evolution of stock share prices. The defined model is similar to the GARCH class of models, but can additionally exhibit bimodal behaviour in the supply–demand structure of the market. Moreover, it differs from existing Ising-type models. It turns out that the constructed model is a solution of a thermodynamic limit of a Gibbs probability measure when the number of traders and the number of stock shares approaches infinity. The energy functional of the Gibbs probability measure is derived from the Nash equilibrium of the underlying game.