A benchmark database of ten years of prospective next-day earthquake forecasts in California from the Collaboratory for the Study of Earthquake Predictability. [PDF]
Serafini F +6 more
europepmc +1 more source
The goal of this work is to look at how a nonlinear model describes hematopoiesis and its complexities utilizing commonly used techniques with historical and material links. Based on time delay, the Mackey–Glass model is explored in two instances. To offer a range, the relevance of the parameter impacting stability (bifurcation) is recorded.
Shuai Zhang +5 more
wiley +1 more source
Machine learning predicts meter-scale laboratory earthquakes. [PDF]
Norisugi R, Kaneko Y, Rouet-Leduc B.
europepmc +1 more source
ABSTRACT Saturated high plasticity clays show complex nonlinear, rate‐dependent, and hysteresis behaviors under non‐monotonic stress paths, requiring advanced mathematical constitutive equations for accurate description. Taking into account the inherent advantages of kinematic hardening mechanisms in simulating complex stress histories, this paper ...
Wei Cheng, Zhen‐Yu Yin
wiley +1 more source
Tidal and hydrological seismicity modulations reveal pore fluid diffusion during earthquake nucleation. [PDF]
Zhao Z +5 more
europepmc +1 more source
Molecular Insights Into the Water Freezing on Kaolinite Surfaces: The Key Role of Surface Properties
ABSTRACT Unfrozen water content is a key factor governing the physical properties of frozen soils, and variations in mineral surface properties play a critical role in determining this content. However, the fundamental physical mechanisms of how different mineral surfaces affect soil water freezing remain poorly understood.
Yijie Wang +2 more
wiley +1 more source
Deep learning unlocks global prediction of earthquake-triggered landslides. [PDF]
Catani F.
europepmc +1 more source
Elastoplasticity Informed Kolmogorov–Arnold Networks Using Chebyshev Polynomials
ABSTRACT Multilayer perceptron (MLP) networks are predominantly used to develop data‐driven constitutive models for granular materials. They offer a compelling alternative to traditional physics‐based constitutive models in predicting non‐linear responses of these materials, for example, elastoplasticity, under various loading conditions. To attain the
Farinaz Mostajeran, Salah A. Faroughi
wiley +1 more source
Digital Technologies and Machine Learning in Environmental Hazard Monitoring: A Synthesis of Evidence for Floods, Air Pollution, Earthquakes, and Fires. [PDF]
Wilk-Jakubowski JL +4 more
europepmc +1 more source
ABSTRACT Integrating interdisciplinary strategies with artificial intelligence (AI), particularly machine learning (ML), is an effective way of addressing urgent engineering challenges. Therefore, a thorough evaluation of existing methodologies is essential, taking into account their respective strengths, limitations and opportunities.
Lina‐María Guayacán‐Carrillo +2 more
wiley +1 more source

