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Computational study of asphaltene aggregation: Impact of low concentration species
No full textAsphaltenes are a collection of complex hydrocarbon molecules commonly found in crude oil, known for their significant effect on the refining, production, and transportation processes due to their tendency to aggregate. The aggregation process is driven by a variety of factors, which are subject of much debate and research. One such factor is the presence of radicals in asphaltene mixtures. Radical molecules, denoted by their unpaired electrons, show varying effects on asphaltene aggregation. These effects are studied through computational modeling and simulation of methyl radicals and dimers containing the radical and non-radical of asphaltenes to look at such interactions. Quantum chemistry calculations utilizing Density Functional Theory were performed to find binding energies. The results indicate that methyl radicals disrupt the aggregation process by modifying molecular interactions and structural stability. The results of the dimers indicate short-term intramolecular forces, which is also seen at a higher capacity in asphaltene complexes. Interaction energies for various model asphaltene systems are presented and future work is discussed. By understanding the mechanisms of interactions between asphaltenes and radical molecules, researchers can develop better strategies to solve asphaltene-related problems and to understand the long-range dispersion forces present. Continued investigation into the behavior of asphaltenes in the presence of radical molecules is essential for innovation in this sector, ensuring that advancements in understanding asphaltene behavior contribute to maintaining oil as a viable energy source for the future
Game theory in split learning
No full textSplit Learning is a distributed deep learning paradigm in which multiple clients collaboratively train a shared model through a central server while maintaining data privacy. In Split Learning, each client processes its own data and transmits only intermediate activations or gradients, ensuring that raw data remains local. In this work, we conceptualize client participation in Split Learning as a game theoretic optimization problem. Each client independently selects an optimal participation probability, striving to maximize its utility by balancing the benefits of contributing to overall model performance against the computational and communication costs incurred. Key factors influencing this decision making include individual resource constraints, the potential incentives for improved model accuracy, and the broader impact of participation on the learning process. The server’s role is to secure a sufficient level of participation, which is critical for achieving stable model convergence and effective training dynamics. To capture the interactive relationship between clients and the server, we formulate the problem as a Nash equilibrium, where every client’s strategy is optimal given the strategies adopted by others. An iterative best-response algorithm is proposed to compute the equilibrium participation levels, enabling clients to update their strategies dynamically based on both observed interactions and anticipated responses from other participants. This framework provides a structured approach to understanding how strategic behaviors and resource limitations among clients can influence the training stability and efficiency of distributed learning
Does Earth Feel?
No full texthttps://digitalcommons.montclair.edu/iapc_nature_intervention_gallery/1002/thumbnail.jp
Lorax
No full texthttps://digitalcommons.montclair.edu/iapc_nature_intervention_gallery/1007/thumbnail.jp
Cat Way
No full texthttps://digitalcommons.montclair.edu/iapc_nature_different_gallery/1002/thumbnail.jp
Ugly Place
No full texthttps://digitalcommons.montclair.edu/iapc_nature_different_gallery/1005/thumbnail.jp
Buzzing with Questions
No full texthttps://digitalcommons.montclair.edu/iapc_nature_questions_gallery/1001/thumbnail.jp
My Octopus Teacher
No full texthttps://digitalcommons.montclair.edu/iapc_adults_gallery/1002/thumbnail.jp
Wolves of Yellowstone
No full texthttps://digitalcommons.montclair.edu/iapc_middlegrades_gallery/1007/thumbnail.jp
