Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
This paper investigates the use of dynamic constraint satisfaction within fractal generation algorithms to enhance structure complexity and robustness. Traditional fractal algorithms often rely on fixed constraints, limiting the potential for intricate and diverse forms. We propose an algorithmic approach that utilizes reinforcement learning to automatically adjust these constraints, allowing the fractal structure to evolve organically. The core mechanism involves iteratively refining the constraints of a fractal algorithm based on observed data, fostering a more nuanced and adaptable generation process. This study demonstrates the potential of this dynamic constraint modification to produce fractal structures exhibiting greater diversity and complexity compared to static constraint-based approaches. The research explores the impact of adaptive constraint adjustment on the resulting fractal output, focusing on the formation of self-similar patterns and overall structural richness.
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
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This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach and presents how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework.
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This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
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It is found that roles of MVPs in startups were not fully aware by entrepreneurs, and entrepreneurs should consider a systematic approach to fully explore the value of MVP, as a multiple facet product (MFP).
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A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
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A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
