Showing 1 - 6 results of 6 for search '(( python proof implementation ) OR ( python practical implications ))', query time: 0.19s Refine Results
  1. 1

    Complex Eigenvalues, Orthogonality, and QR Factorization: Analytical Proofs and Numerical Verification by Umar Tabbsum (22058780)

    Published 2025
    “…The work includes both analytical proofs and numerical verification to ensure reproducibility and clarity.…”
  2. 2

    New product development in the industry 4.0 era: a literature review and research agenda by Thiago Augusto Aniceski (22401616)

    Published 2025
    “…Lastly, we propose a conceptual framework connecting NPD stages and their most frequent topics with TOE factors and I4.0 technologies, highlighting strategic purposes, organizational impacts, and managerial considerations as practical implications for industry.</p>…”
  3. 3

    From GIS to HBIM and Back: Multiscale Performance and Condition Assessment for Networks of Public Heritage Buildings and Construction Components by Teresa Fortunato (21076099)

    Published 2025
    “…This case study demonstrates the outcomes and potential applications of the proposed framework, contributing to the debate on its implications for enhancing contemporary heritage evaluation and management practices.…”
  4. 4

    Reproducible visualization strategies for spatially varying coefficient (SVC) models: incorporating uncertainty and assessing replicability by Victor Irekponor (22303109)

    Published 2025
    “…This study introduces <i>svc-viz</i>, an open-source Python tool that codifies best practices into a standardized framework for interpreting and communicating SVC model results. …”
  5. 5

    Core-Based Smart Sampling Framework: A Theoretical and Experimental Study on Randomized Partitioning for SAT Problems by DURGHAM QARALLEH (21904172)

    Published 2025
    “…We provide theoretical guarantees on complexity reduction and probabilistic completeness, apply the method to SAT instances, and evaluate its performance using experimental Python implementations. The results show that smart sampling drastically reduces the effective complexity of SAT problems and offers new insights into the structure of NP-complete problems.…”
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