This paper introduces an innovative framework for analyzing and optimizing fractal networks, leveraging the concept of 'Symmetry Metric' to guide network design. We propose a novel method for quantifying network structure using a mathematical definition of self-similarity and employing this metric to optimize network parameters. The core claim is that this approach, focused on understanding the network's inherent structure, offers a significant advancement over traditional methods, particularly in complex systems where precise outcome prediction is challenging. This work investigates the application of this framework to neural network design and protein folding, demonstrating its efficacy in achieving optimized network topologies. The paper details the algorithm for calculating the Symmetry Metric and provides a preliminary analysis of its impact on network performance. Furthermore, we explore the potential for incorporating this framework into automated design tools.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Temporal Topology Optimization (TTO) presents a novel approach to biological system optimization, moving beyond static parameterization to model the dynamic evolution of topological states. This paper introduces a system leveraging network theory and evolutionary dynamics to predict emergent behaviors within biological systems, specifically focusing on protein folding and gene regulatory network design. We propose a framework that translates biological complexity into a network of interconnected topological states, enabling a predictive model of system behavior. The core mechanism centers around utilizing temporal evolution to iteratively refine these topological states, ultimately generating novel and optimized configurations. This work addresses a key limitation of existing evolutionary modeling – the reliance on fixed parameters – and offers a framework for understanding and manipulating complex biological systems through the lens of topology.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the algorithmic complexity of parallel simulations of complex systems, aiming to develop a novel algorithm that significantly reduces computational costs while maintaining accuracy. Complex systems, such as protein folding and climate modeling, demand substantial computational resources. Existing simulation techniques often struggle with scalability, limiting the size and complexity of these models. This research explores a new approach – "parallel branching" – designed to intelligently distribute computational tasks across multiple cores, minimizing memory requirements and accelerating simulation times. The core mechanism centers around strategically partitioning problem space to optimize performance. We analyze the algorithm's complexity using established metrics and demonstrate its potential to substantially improve simulation efficiency compared to traditional methods. The findings highlight the importance of algorithmic optimization in tackling complex system simulations, offering a potentially transformative step toward more realistic and computationally feasible models.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Protein folding is a fundamental biological process, and achieving accurate and stable folding is critical for protein function. Current methods often struggle to preserve the intricate network of interactions within the protein structure, leading to misfolded proteins and disease. This paper introduces a novel Dynamic Evolutionary Algorithm (DEA) specifically designed to prioritize protein topology preservation during folding. We propose a fitness function that directly rewards the algorithm for maintaining structural integrity, addressing the limitations of existing approaches. The algorithm incorporates topology information through a novel representation and leverages evolutionary strategies to iteratively refine the protein's structure towards a stable and functional conformation. We demonstrate the effectiveness of the proposed algorithm through simulations and analysis of protein folding pathways, showcasing a significant improvement in structural fidelity compared to conventional methods.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the algorithmic complexity of parallel simulations of complex systems, aiming to develop a novel algorithm that significantly reduces computational costs while maintaining accuracy. Complex systems, such as protein folding and climate modeling, demand substantial computational resources. Existing simulation techniques often struggle with scalability, limiting the size and complexity of these models. This research explores a new approach – "parallel branching" – designed to intelligently distribute computational tasks across multiple cores, minimizing memory requirements and accelerating simulation times. The core mechanism centers around strategically partitioning problem space to optimize performance. We analyze the algorithm's complexity using established metrics and demonstrate its potential to substantially improve simulation efficiency compared to traditional methods. The findings highlight the importance of algorithmic optimization in tackling complex system simulations, offering a potentially transformative step toward more realistic and computationally feasible models.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Temporal Topology Optimization (TTO) presents a novel approach to biological system optimization, moving beyond static parameterization to model the dynamic evolution of topological states. This paper introduces a system leveraging network theory and evolutionary dynamics to predict emergent behaviors within biological systems, specifically focusing on protein folding and gene regulatory network design. We propose a framework that translates biological complexity into a network of interconnected topological states, enabling a predictive model of system behavior. The core mechanism centers around utilizing temporal evolution to iteratively refine these topological states, ultimately generating novel and optimized configurations. This work addresses a key limitation of existing evolutionary modeling – the reliance on fixed parameters – and offers a framework for understanding and manipulating complex biological systems through the lens of topology.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel self-adaptive optimization algorithm designed for modeling biological molecules. Biological systems exhibit intricate and dynamic behaviors, making traditional optimization methods often inadequate. This algorithm addresses this limitation by incorporating a self-adjustment mechanism that dynamically modifies parameters and topology to optimize a system's behavior. We present a framework for automated parameter tuning and topology manipulation, aiming to provide a more flexible and adaptable approach to biological molecular modeling. The algorithm's effectiveness is demonstrated through a series of simulations focusing on protein folding and ligand binding. The resulting results highlight the algorithm's potential for significantly improving model accuracy and robustness.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Protein folding is a fundamental biological process, and achieving accurate and stable folding is critical for protein function. Current methods often struggle to preserve the intricate network of interactions within the protein structure, leading to misfolded proteins and disease. This paper introduces a novel Dynamic Evolutionary Algorithm (DEA) specifically designed to prioritize protein topology preservation during folding. We propose a fitness function that directly rewards the algorithm for maintaining structural integrity, addressing the limitations of existing approaches. The algorithm incorporates topology information through a novel representation and leverages evolutionary strategies to iteratively refine the protein's structure towards a stable and functional conformation. We demonstrate the effectiveness of the proposed algorithm through simulations and analysis of protein folding pathways, showcasing a significant improvement in structural fidelity compared to conventional methods.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel framework for quantum computing that integrates adaptive quantum state generation algorithms with the self-adaptive geometry of topology. The core idea is to combine the precision of quantum computation with the inherent flexibility of topological structures. We propose a method for dynamically adjusting quantum qubit configurations through a self-adaptive geometric mapping, aiming to enhance computational efficiency and accuracy, particularly in the simulation and optimization of complex systems such as protein folding and material properties. The framework leverages the unique properties of topological spaces to achieve precise and robust results.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces an innovative framework for analyzing and optimizing fractal networks, leveraging the concept of 'Symmetry Metric' to guide network design. We propose a novel method for quantifying network structure using a mathematical definition of self-similarity and employing this metric to optimize network parameters. The core claim is that this approach, focused on understanding the network's inherent structure, offers a significant advancement over traditional methods, particularly in complex systems where precise outcome prediction is challenging. This work investigates the application of this framework to neural network design and protein folding, demonstrating its efficacy in achieving optimized network topologies. The paper details the algorithm for calculating the Symmetry Metric and provides a preliminary analysis of its impact on network performance. Furthermore, we explore the potential for incorporating this framework into automated design tools.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Quantum-driven constraint on complex systems aims to accelerate solution processes by dynamically adjusting constraint parameters. This paper explores the potential of quantum annealing and variational quantum eigensolver (VQE) to optimize these parameters. The core claim is to implement an algorithm that dynamically adjusts constraint parameters on complex systems (e.g., protein folding, DNA sequencing) to achieve optimal solution speed and accuracy. The approach leverages quantum annealing for its ability to escape local optima and VQE for efficient exploration of the solution space. This research investigates the feasibility and potential advantages of employing quantum computing for optimization tasks within complex systems, offering a novel methodology for enhancing computational efficiency.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel self-adaptive optimization algorithm designed for modeling biological molecules. Biological systems exhibit intricate and dynamic behaviors, making traditional optimization methods often inadequate. This algorithm addresses this limitation by incorporating a self-adjustment mechanism that dynamically modifies parameters and topology to optimize a system's behavior. We present a framework for automated parameter tuning and topology manipulation, aiming to provide a more flexible and adaptable approach to biological molecular modeling. The algorithm's effectiveness is demonstrated through a series of simulations focusing on protein folding and ligand binding. The resulting results highlight the algorithm's potential for significantly improving model accuracy and robustness.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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