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Applied Sciences

Applied Sciences is an international, peer-reviewed, open access journal on all aspects of applied natural sciences published semimonthly online by MDPI.

Quartile Ranking JCR - Q2 (Engineering, Multidisciplinary)

All Articles (83,772)

The accurate prediction of crack initiation and propagation is essential for assessing the structural integrity of mechanically joined components and other complex assemblies. To overcome the limitations of existing finite element tools, a modular Python framework has been developed to automate three-dimensional crack growth simulations. The program combines geometric reconstruction, adaptive remeshing, and the numerical evaluation of fracture mechanics parameters within a single, fully automated workflow. The framework builds on open-source components and remains solver-independent, enabling straightforward integration with commercial or research finite element codes. A dedicated sequence of modules performs all required steps, from mesh separation and crack insertion to local submodeling, stress and displacement mapping, and iterative crack-front update, without manual interaction. The methodology was verified using a mini-compact tension (Mini-CT) specimen as a benchmark case. The numerical results demonstrate the accurate reproduction of stress intensity factors and energy release rates while achieving high computational efficiency through localized refinement. The developed approach provides a robust basis for crack growth simulations of geometrically complex or residual stress-affected structures. Its high degree of automation and flexibility makes it particularly suited for analyzing cracks in clinched and riveted joints, supporting the predictive design and durability assessment of joined lightweight structures.

30 December 2025

Schematic representation of program workflow and associated file-processing steps.
  • Systematic Review
  • Open Access

Efficacy of Systemic and Local Premedication on Anesthetic Success of Teeth with Irreversible Pulpitis: An Umbrella Review

  • Márcia Valente de Brito Dantas,
  • Luiz Renato Paranhos and
  • Ricardo Sérgio Fernandes da Silva-Filho
  • + 4 authors

This umbrella review summarized evidence from systematic reviews of randomized clinical trials regarding the efficacy of systemic and local premedication on anesthetic success in nonsurgical root canal treatment of teeth with symptomatic irreversible pulpitis. Searches were conducted in PubMed, Scopus, Web of Science, Cochrane Library, EMBASE, LILACS/BBO, and gray literature sources up to February 2025. Methodological quality was assessed using AMSTAR-2. The risk of bias was evaluated using the ROBIS tool. The Corrected Covered Area (CCA) was calculated to quantify the primary study overlap. Data regarding risk ratios and anesthetic success rates were synthesized qualitatively. Sixteen systematic reviews were included. The narrative synthesis suggests that oral NSAIDs (particularly ibuprofen > 400 mg and ketorolac 10–20 mg) and corticosteroids (dexamethasone) are associated with increased anesthetic success compared to placebo, with no significant difference between systemic and local administration. However, the reliability of these findings is impacted by the quality of the primary evidence: according to the appraisal, 13 reviews presented a high overall risk of bias/low methodological quality, while only three were classified as having low risk of bias. Furthermore, the CCA was 19.5%, indicating a high degree of redundancy among reviews. Consequently, while premedication appears effective, these conclusions must be interpreted with caution due to the substantial overlap and predominantly high risk of bias in the available literature.

30 December 2025

The rise of large language models (LLMs) has marked a significant leap in natural language processing (NLP) capabilities, enabling machines to understand, generate, and interact with human language at a sophisticated level [...]

30 December 2025

  • Correction
  • Open Access

In the published publication [...]

30 December 2025

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Appl. Sci. - ISSN 2076-3417