GitHub

Hansen Solubility Parameters in Python.

HSPiPy is a Python library designed for calculating and visualizing Hansen Solubility Parameters (HSP). It provides machine-learning–friendly estimators, convenient data import, and plotting tools for ...
InfoWorld

Django tutorial: Get started with Django 6

Get up and running with routes, views, and templates in Python’s most popular web framework, including new features found ...
IEEE

Comprehensive Study on Zeroing Neural Network With High-Order Evolutionary Formula, Nonlinear Functions, and Variable Parameter for Time-Changing Matrix Cholesky Decomposition

Abstract: In this article, a low-order zeroing neural network (LZNN), a high-order ZNN (HZNN), and a variable-parameter ZNN (VZNN) are designed and applied to the time-changing Cholesky decomposition ...
SCOTUSblog

The long and short of Supreme Court oral arguments

Empirical SCOTUS is a recurring series by Adam Feldman that looks at Supreme Court data, primarily in the form of opinions and oral arguments, to provide insights into the justices’ decision making ...
GitHub

Hansen Solubility Parameters (HSP) core functions.

from hspcore import get_hsp #hsp grid is a list of solvents as [[Solvent,D,P,H,Score],...] hsp_grid = [ ['Acetonitrile', 15.3, 18.0, 6.1, 1], ['1-Bromonaphthalene ...
IEEE

Design of Reconfigurable Intelligent Surfaces by Using S-Parameter Multiport Network Theory—Optimization and Full-Wave Validation

Abstract: Multiport network theory has been proved to be a suitable abstraction model for analyzing and optimizing reconfigurable intelligent surfaces (RISs) in an electromagnetically consistent ...