No abstract

Historically, fires were frequent events in many grassland habitats and important factors shaping their biodiversity. Nowadays, although mostly prevented, they still happen occasionally. Using the occasion of an extensive fire that occurred at the floodplain meadow complex in the Vistula river valley, southern Poland, in March 2012, we investigated its impact on one of the largest European metapopulations of endangered Maculinea teleius and Maculinea nausithous butterflies. Maculinea are regarded as flagships of grassland conservation and useful indicators of insect species richness. Over 50 local populations of both butterflies, intensively monitored with catch‐per‐time‐unit method, proved to act as independent demographic units, and no spatial autocorrelation in their year‐to‐year trends was detected. The changes in butterfly abundances between summer season of 2011 and 2012 indicated absolutely no impact of fire. Although about one fifth of area was burnt, the entire metapopulations remained unaffected. The changes of 14 local populations inhabiting burnt meadows were not significantly different from those at meadows spared by the fire. Moreover, population changes in the former group were independent of the proportion of burnt area. The impact of fire was obviously minimized by its early spring timing, but in this part of Europe, grassland fires prevail in this period because of a combination of ecological and climatic factors. Together with the lack of a negative effect of flood documented in an earlier study, our findings indicate strong resilience to natural catastrophes in the investigated butterfly species. Copyright © 2014 John Wiley & Sons, Ltd.

This paper presents a complexity-based methodology for the design of aero engine components. Upon a rigorous definition of complex system, a metric for the complexity is introduced as a function of system’s topology and entropy. As a consequence, complexity becomes a measurable and manageable property of systems. Furthermore, a novel definition of robustness is provided, based on the shape of the probability density functions (PDF) of the performances. Complexity and robustness are related together by a simple, qualitative law. Based on these premises, two algorithms are introduced, namely the Stochastic Design Improvement (SDI) and the Complex Systems Analyzer (CSA). The former searches the design space seeking for solutions which meet the design requirements. The latter extracts the fundamental features of the design, previously perturbed by means of Monte Carlo Simulation (MCS). The SDI is proposed as a competitor of the practice of optimization. Though both can be used separately, the combination of SDI and CSA provides a powerful novel method for design. The capabilities of the algorithms are illustrated on three test-cases, namely an LPT Casing, a Turbo-prop bearing retainer and an LPT disk. It is important to point out that response surfaces or other surrogates have never been used.

The paper discusses automotive crash simulation in a stochastic context, whereby the uncertainties in vehicle properties, boundary and initial conditions are taken in to account by means of Monte Carlo simulation techniques. It is argued that, since crash is a non-repeatable phenomenon, qualification for crashw orthinessbased on a single test is not meaningful neither from a physical nor from a conceptual standpoint, and should be replaced by stochastic simulation in which the mentioned uncertainties are taken into account. In addition, a broad spectrum of impact angles and velocities should be considered in each scenario (frontal, rear, side, etc.). The advocated approach, due to the fact that it addresses a sample of the population of the to-be-manufactured cars, instead of a single idealized nominal car, possesses built-in robustness and therefore enables the entire problem to be viewed with a high level of confidence. Finally, it is shown that based on today’s deterministic CAE techniques, validation of numerical models via a single test is impossible.

The QCT (Quantitative Complexity Theory) algorithm has been applied to the analysis of the folding process of a protein composed of 435 atoms, monitoring its complexity at each step thereof. The folding has been simulated using Molecular Dynamics Simulation. The analysis has revealed that, while in the native state, the protein's configuration minimizes its energy, its complexity reaches a maximum. This result is interesting in that, according to the QCT, complexity is information that is encoded in structure. It is conjectured that the native state of a protein is a minimum energy-maximum information state. Moreover, QCT allows us to determine the footprint of each constituent amino acid in the dynamics, information content and robustness of a protein's structure. The application of QCT on proteins generates data and information about structure, complexity, special arrangements, etc., of proteins. The knowledge about the biological functions of such proteins derived from the above -which is crucial e.g., for designing new drugs -will have to be generated in collaboration with specialists from pharmaceutical R&D.