Seismic Activity Seen Through Evolution of the Hurst Exponent Model in 3d

The aim of this work is to quantitatively explore the texture evolution of amphibole aggregation and residual melt with pressure and temperature. The amphibole aggregation growth from a basaltic melt and the residual melt at high pressure (0.6–2.6[Formula: see text]GPa) and high temperature (860–970[Formula: see text]C) exhibit statistical self-similarity which made us consider studying such characteristic by fractal analysis. The bi-phase box counting method was applied for fractal analysis of each product to identify the fractal phase and the fractal dimension was estimated. In the experimental products, the residual melt is identified as the fractal and amphibole as the Euclidean except for one experiment. The results show that the residual melt can be quantified by the fractal dimension [Formula: see text] within the range of 1.782–1.848. The temperature has a significant effect on the morphology of amphibole and the fractal dimension of the residual melt. The higher the crystallization temperature is, the more regular the amphibole grains are. At lower temperature (from 860[Formula: see text]C to 915[Formula: see text]C), the fractal dimension of the residual melt decreased with the increasing crystallization temperature, but at higher temperature (970[Formula: see text]C), the fractal phase changed to amphibole and the fractal dimension of amphibole is 1.816. The pressure may be the dominant factor that controls the morphology of the mineral aggregation and the residual melt. The fractal dimension of melt decreased linearly with the increasing pressure and if the linear relationship between the fractal dimension and pressure can be further verified in the future, it can be used as a potential geological barometer.

Fractal Analysis of Amphibole Aggregation Growth From a Basaltic Melt and Residual Melt at High Pressure and High Temperature

Zhang

Tang

et al. 2018

Inherent Features of Fractal Sets and Key Attributes of Fractal Models

Balankin

Ortíz

2022

The main goal of this work is to develop a robust framework for an exhaustive description of essential properties of a fractal object. For this purpose, the inherent features of fractal sets are scrutinized. The topological, metrological, morphological, and topographical attributes of fractal systems are delineated. The criteria of the fractal connectedness are established. The characteristics of the fractal connectivity and ramification are ascertained. The index of the fractal loopiness is introduced. The quantifications of the fractal heterogeneity, lacunarity, and anisotropy are briefly sketched out. A set of key attributes which enable a proper characterization of fractal system are suggested.

BLOCKCHAIN-BASED IoFLT FEDERATED LEARNING IN A FUZZY/GAN ENVIRONMENT FOR A SMART TRADING BOT

AGUILERA¹,

Ortíz

ESTEVA³

et al. 2022

A DAPP is performed with collaborative training, where “Federated Learning” uses each device client to work as a singular artificial intelligence model using machine learning. The purpose is to reduce the latency by using computing resources from all client devices and increase privacy since personal data does not leave the client’s devices. Applying machine learning massively in decentralized trading bots using blockchain seems to be a great solution. This learning solution can be improved using a fuzzy generative adversarial network environment to help the training. In this case, the expert system has a Python bot to interact with the Binance API to place buy/sell orders for the BTC–USD pair.