Abstract Machine learning-based predictive modeling is to develop machine learning-based
or data-driven models to predict failures before they occur and estimate the remaining useful …

We propose a mechanism for the low frequency electromagnetic emissions and other
electromagnetic and electric phenomena which have been associated with earthquakes …

The mechanism of critical phenomena or critical transitions has been recently studied from
various aspects, in particular considering slow parameter change and small noise. In this …

One of core technologies for prognostics is to predict failures before they occur and estimate
time to failure (TTF) by using built-in predictive models. The predictive model could be either …

Using the mechanics of creep in material sciences as a metaphor, this paper presents a
general framework to understand the evolution of financial, economic and social systems …

We introduce a method to estimate the failure time of a class of weighted k-out-of-n systems
using the idea of rational expectations, which to the best of our knowledge is a new …

The word “socio-finance” used in the title of this book is meant as a description that catches
the underlying nature of price formation in financial markets. Since the term as such does not …

Catastrophes of all kinds can be roughly defined as short-duration, large-amplitude events
following and followed by long periods of “ripening.” Major earthquakes surely belong to the …

We investigate the origin of Paris' law, which states that the velocity of a crack at subcritical
load grows like a power law, da/dt∼(Δ K) m, where Δ K is the stress-intensity-factor …

Despite the availability of huge amounts of data and a variety of powerful data analysis
methods, prognostic models are still often failing to provide accurate and precise time to …