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The Unified Theory of Energy. I developed The Unified Theory of Energy in effort to "unify the fields" where language and logic fall short in advancing physics as currently presented by academia and its online communities. Unfortunately, forum flaming and quick dismissals persist, even 30 years after the advent of the internet. I hoped time ...
1 day ago · The current standard model of particle physics is based on quantum field theory. QFT treats particles as excited states (also called quantum levels) of their underlying quantum fields, which are more fundamental than the particles.
5 days ago · The Æ Unified Theory will allow me to detail all this precisely, thus understanding that my first theory called Æ Ion Quantum Theory has served as the origin to understand the architecture of...
5 days ago · To study this in more depth, this paper’s authors chose five major working theories of consciousness. These cover three of the four suggested types of models laid out by two influential ...
2 days ago · Single-chain nanoparticles (SCNPs) are a fascinating class of soft nano-objects with promising properties and relevance to protein condensates, polymer nanocomposites, nanomedicine, bioimaging, catalysis, and drug delivery. We combine molecular dynamics simulations and equilibrium and time-dependent statistical mechanical theory to construct a unified understanding of how the internal ...
4 days ago · The Eliksni engineer Niik waits just beyond the black and yellow dashed line to greet the vessel on its arrival, joined by Ana Bray and her mechanical hound, Archie. Niik kneels to run a hand down Archie's back as the Thresher finishes its docking maneuver and throws open its cargo hatch, revealing the battered and blackened hull of a Scale, a ...
1 day ago · In this study, we introduce a unified neural network architecture, the Deep Equilibrium Density Functional Theory Hamiltonian (DEQH) model, which incorporates Deep Equilibrium Models (DEQs) for predicting Density Functional Theory (DFT) Hamiltonians. The DEQH model inherently captures the self-consistency nature of Hamiltonian, a critical aspect often overlooked by traditional machine learning ...
