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  • Format: ePub

This white paper introduces a new theoretical framework that complements Einstein's mass-energy equivalence by proposing a fundamental equivalence between force and power. It redefines static force as an energetic process and invites readers to explore a deeper symmetry among physical quantities. Keying Guan is an independent researcher and retired professor with a lifelong passion for fundamental physics. Based in the United States, he has spent recent years exploring the energetic interpretation of static forces and proposing the force-power equivalence principle as a counterpart to…mehr

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Produktbeschreibung
This white paper introduces a new theoretical framework that complements Einstein's mass-energy equivalence by proposing a fundamental equivalence between force and power. It redefines static force as an energetic process and invites readers to explore a deeper symmetry among physical quantities. Keying Guan is an independent researcher and retired professor with a lifelong passion for fundamental physics. Based in the United States, he has spent recent years exploring the energetic interpretation of static forces and proposing the force-power equivalence principle as a counterpart to Einstein's mass-energy equivalence. His work reflects a deep commitment to theoretical clarity, logical rigor, and the advancement of physical understanding in the age of artificial intelligence. This white paper was developed in collaboration with ChatGPT, reflecting a new era of human-AI co-creation in scientific research.

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Autorenporträt
Keying Guan is a retired professor and independent researcher with a lifelong dedication to foundational physics. After a distinguished academic career in China, he relocated to the United States in 2008 and continued his independent exploration of static force and energy. His blog essays on ScienceNet.cn laid the groundwork for a new principle--the force-power equivalence--as a conceptual complement to Einstein's mass-energy equivalence. His work emphasizes clarity, dimensional rigor, and the reevaluation of classical assumptions. This white paper, co-developed with ChatGPT, exemplifies the transformative potential of human-AI collaboration in modern physics.