Ground microtremor test in shaking table experimental investigation on the steel corrugated utility tunnel

Shaking table test is an important method to study the seismic performance of structures. The accuracy of the test model which is designed based on the similarity ratio theory has a crucial impact on the reliability of the shaking table test results. In this paper, a steel corrugated utility tunnel model was made. Before it was fixed to the shaking table, the natural frequency of the model structure was measured by ground microtremor test, and the natural frequency of the prototype structure was obtained by numerical simulation. The ratio between test value and simulate value was calculated and compared with a pre-set similarity ratio to verify the accuracy of the model. The results demonstrate that the natural frequency of the model structure could be effectively obtained by ground microtremor test. The method of comparing frequency can comprehensively evaluate whether there gets some problem in designing and manufacturing the model structure. This paper can provide some references for the preliminary model design and preparation of the shaking table tests

A Persistent Entropy Automaton for the Dow Jones Stock Market

Complex systems are ubiquitous. Their components, agents, live in an environment perceiving its changes and reacting with appropriate actions; they also interact with each other causing changes in the environment itself. Modelling an environment that shows this feedback loop with agents is still a big issue because the model must take into account the emerging behaviour of the whole system. In this paper, following the S[B] paradigm, we exploit topological data analysis and the information power of persistent entropy for deriving a persistent entropy automaton to model a global emerging behaviour of the Dow Jones stock market index. We devise early warning states of the automaton that signal a possible evolution of the system towards a financial crisis

Weighted-persistent-homology-based machine learning for RNA flexibility analysis

With the great significance of biomolecular flexibility in biomolecular dynamics and functional analysis, various experimental and theoretical models are developed. Experimentally, Debye-Waller factor, also known as B-factor, measures atomic mean-square displacement and is usually considered as an important measurement for flexibility. Theoretically, elastic network models, Gaussian network model, flexibility-rigidity model, and other computational models have been proposed for flexibility analysis by shedding light on the biomolecular inner topological structures. Recently, a topology-based machine learning model has been proposed. By using the features from persistent homology, this model achieves a remarkable high Pearson correlation coefficient (PCC) in protein B-factor prediction. Motivated by its success, we propose weighted-persistent-homology (WPH)-based machine learning (WPHML) models for RNA flexibility analysis. Our WPH is a newly-proposed model, which incorporate physical, chemical and biological information into topological measurements using a weight function. In particular, we use local persistent homology (LPH) to focus on the topological information of local regions. Our WPHML model is validated on a well-established RNA dataset, and numerical experiments show that our model can achieve a PCC of up to 0.5822. The comparison with the previous sequence-information-based learning models shows that a consistent improvement in performance by at least 10% is achieved in our current model