By extending the context of the Game Theory to the understanding of P vs NP Problem, fundamental principles of observation can be explained to the very limit of existence.

Background: Amongst many suggestions toward fusion power ignition, particle acceleration related model has a advantage of reaching high temperature performance. Objective: Suggesting a plasma enclosure model by applying particle acceleration could be tried. Method: By given equation from unification of energy, application of heat and work transformation gives model to ignition of fusion power by accelerating fusion fuel. Conclusion: By accelerating multiple inputs of fuel for fusion in an initiation of fusion power, higher temperature that meets requirement for nuclear fusion could be established.

By extending metric space to expression in hash of directed graph, spatiotemporal observation could be described in matrix quantized state. (Working paper, more contents would be added soon)

Recent study suggests dynamical topological phase could be derived into additional dimension of time. By adding interpretation of this phenomenon with a topological interaction within simplified model, possibility of approaching many-timeline in real world could be suggested.

This paper aims to suggest method for sustaining vacuum in particle accelerator while in an variety of circumstances.

Backgrounds: Based on SYK-related models, Majorana particle is a fermion that is its own antiparticle. Recent discovery on simulating traversable wormhole by quantum processor based on Majorana particle is a significant progress in quantum simulation. By adding possible extendable approaches, more possibility could be suggested. Objectives: Discovering symmetry conservation attribute from Majorana particle to expand its usage for wormhole. Methods: By applying to the simplified non-unit model, additional considerations for simulating wormhole would be discovered. Results: Additional considerations of expanding holographic wormhole could be discussed in model.

Background: Currently, neural network model needs understanding on what derives conclusion by execution. Neural network model imitates human intelligence but it has a limitation of cannot explaining what it is to construct the knowledge which it derives. Object: By inducing universal framework from existing neural network models, common reasoning that regulates the intelligence that derives from the models would be introduced. Method: Starting from minimizing model’s bias, integrative method of understanding how neural network takes place in numerical ordinals and cardinals would be introduced. After that, explanation of intelligence by neural network would be described. Result: Foundation of artificial neural network intelligence is an optimization with a grid of minimized error from ground truth.