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Articles by Ebrahim Elsayed
USAC: Unified Spectral Alignment & Coherence Framework – A New Mathematical Paradigm for Prime Gaps, Dark Energy, Neural Dynamics, and Spacetime Geometry
This version is not peer-reviewed. You are already at the latest version This work introduces the Unified Spectral Alignment & Coherence Framework (USAC), a new mathematical framework that uncovers deep connections between prime gaps, dark energy, neural dynamics, and spacetime geometry. At the heart of this framework lies a simple yet powerful idea: the zeros of the Riemann zeta function naturally align with one another based on frequency harmony.
ZPIF (Zero Pair Interaction Functional) A Quadratic Spectral Framework for Prime Gaps, Dark Energy, Consciousness, and Spacetime Structure
This version is not peer-reviewed. You are already at the latest version We introduce \textbf{ZPIF (Zero Pair Interaction Functional)} as a quadratic spectral framework that explores potential connections between four fundamental puzzles: prime gaps, dark energy, consciousness, and spacetime structure.
ZPIF (Zero Pair Interaction Functional): The Missing Quadratic Interactions in Riemann’s Explicit Formula—A New Spectral Operator Framework
You are already at the latest version ZPIF (Zero Pair Interaction Functional) is introduced as a quadratic spectral operator framework extending the classical explicit formula of the Riemann zeta function. Unlike the standard linear spectral decomposition, ZPIF incorporates second-order interactions between spectral modes within a Hilbert space formulation.
Artificial intelligence carbon neutrality strategy in sports event management based on STIRPAT-GRU and transfer learning
1 Introduction The development of renewable energy technologies has become a major global concern due to the negative impact of greenhouse gases on the environment and human health. In recent years, there has been growing interest in the application of machine learning and deep learning techniques to optimize renewable energy systems and reduce carbon emissions Elnour et al. (2022a).
Research on system of ultra-flat carrying robot based on improved PSO algorithm
1 Introduction The increasingly mature intelligent driving technology has brought new safety problems, which has resulted in higher requirements for vehicle safety (Stilgoe, 2021). The ultra-flat carrying robot (UCR) is a kind of road test equipment applied to the functional safety of intelligent driving vehicles, which can be equipped with a variety of soft targets to reproduce road hazard scenarios (Steffan et al., 2017; Uchida and Yamazaki, 2018; Bartholomew et al., 2023).
Enhancing energy efficiency with smart grid technology: a fusion of TCN, BiGRU, and attention mechanism
1 Introduction Globally, the close link between energy demand and environmental issues has raised concerns about energy utilization. In order to meet this challenge, countries have launched energy conservation and environmental protection policies to support and encourage the implementation of energy conservation measures.
Optimizing electric vehicle charging schedules and energy management in smart grids using an integrated GA-GRU-RL approach
1 Introduction Smart grid technology is one of the important directions for the future development of the power system. As a representative of new energy vehicles, electric vehicles will become an important carrier of smart grid Mukherjee and Gupta (2014). However, the challenges of power supply and demand balance, efficient energy utilization, and environmental protection are faced by smart grid technology.
Research on CNN-LSTM DC power system fault diagnosis and differential protection strategy based on reinforcement learning
Yun Yang* Feng Tu Shixuan Huang Yuehai Tu Ti Liu State Grid Zhejiang Electric Power Co., Ltd., Construction Company, Hangzhou, China Introduction: With the development of artificial intelligence technology, more and more fields are applying deep learning and reinforcement learning techniques to solve practical problems.
Smart grid energy storage capacity planning and scheduling optimization through PSO-GRU and multihead-attention
1 Introduction Energy issues are major challenges facing society today, and smart grids have become a key solution. One of the key challenges of smart grids is energy storage capacity planning and dispatch optimization, which involves maximizing the utilization of energy storage devices to balance the difference between power supply and demand while ensuring stable operation of the grid (Mohamed et al., 2021).
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