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Spherical nanoindentation was performed at room temperature to characterize the time-dependent plasticity behavior of the single-phase CoCrFeMnNi high-entropy alloy (HEA). It was observed that incipient plasticity occurred after a period of waiting time in the apparent elastic regime. Through the experimental data, an activation energy ∼0.74 eV and an activation volume ∼1.54b3 (b = Burgers vector) were extracted for the delayed plasticity. Our results indicate that heterogeneous dislocation nucleation governs the delayed plasticity in the single-phase CoCrFeMnNi, and also provide quantitative insights into viscoplastic behavior and creep mechanisms in HEAs.

The plasticity property of biological neural networks allows them to perform learning and optimize their behavior by changing their configuration. Inspired by biology, plasticity can be modeled in artificial neural networks by using Hebbian learning rules, i.e. rules that update synapses based on the neuron activations and reinforcement signals. However, the distal reward problem arises when the reinforcement signals are not available immediately after each network output to associate the neuron activations that contributed to receiving the reinforcement signal. In this work, we extend Hebbian plasticity rules to allow learning in distal reward cases. We propose the use of neuron activation traces (NATs) to provide additional data storage in each synapse to keep track of the activation of the neurons. Delayed reinforcement signals are provided after each episode relative to the networks' performance during the previous episode. We employ genetic algorithms to evolve delayed synaptic plasticity (DSP) rules and perform synaptic updates based on NATs and delayed reinforcement signals. We compare DSP with an analogous hill climbing algorithm that does not incorporate domain knowledge introduced with the NATs, and show that the synaptic updates performed by the DSP rules demonstrate more effective training performance relative to the HC algorithm., GECCO2019

During the last decade, a large rockslide along the NW slope of the Mont de la Saxe (Courmayeur, AO—Italy) showed a significant increase in the rate of activity in conjunction with snow melting periods and more recently with important amount of rainfall. The ca 8 mm3 rockslide affects a heavily tectonised and intensely fractured mass of black schists, for a surface area of about 150,000 m2. The rockslide failure surface is typically located at depths of 60–80 m b.g.l. Because of the highly valuable exposed elements regional authorities commissioned a comprehensive ground investigation and monitoring campaign, as well as theoretical and numerical analyses, in order to assess slope stability conditions, the possible evolution and the suitable stabilization and mitigation countermeasures. Here we present the recent rockslide evolution till spring 2014 when a major seasonal reactivation of a 400,000 m3 mass occurred. Finally, with the aim to develop a possible tool for displacement prediction, the results of a series of 1D visco-plastic simulations are presented, discussing the possible role of each of the main controlling factors (e.g. groundwater recharge and piezometric level oscillation, rock degradation). .