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A numerical procedure for reinforced concrete columns with a focus on stability analysis
Susana L. Pires,Maria Cecilia A.T. Silva 사단법인 한국계산역학회 2014 Computers and Concrete, An International Journal Vol.14 No.6
The purpose of this paper is to present a numerical procedure to analyse reinforced concrete columns subjected to combined axial loads and bending that rigorously considers nonlinear material and nonlinear geometric characteristics. Column design and stability analysis are simultaneously regarded. A finite element method is used for calculating displacements and the material and geometric nonlinearities are taken into account using an iterative process. A computer program is developed from the proposed numerical procedure, and the efficiency of the program is verified against available experimental data. The model applies to constant rectangular cross sectional columns with symmetric reinforcement distribution.
Pedro R. F. Rende,Joel Machado Pires,Kátia Sakimi Nakadaira,Sara Lopes,João Vale,Fabio Hecht,Fabyan E. L. Beltrão,Gabriel J. R. Machado,Edna T. Kimura,Catarina Eloy,Helton E. Ramos 대한병리학회 2024 Journal of Pathology and Translational Medicine Vol.58 No.3
Background: Among other structures, nuclear grooves are vastly found in papillary thyroid carcinoma (PTC). Considering that the application of artificial intelligence in thyroid cytology has potential for diagnostic routine, our goal was to develop a new supervised convolutional neural network capable of identifying nuclear grooves in Diff-Quik stained whole-slide images (WSI) obtained from thyroid fineneedle aspiration. Methods: We selected 22 Diff-Quik stained cytological slides with cytological diagnosis of PTC and concordant histological diagnosis. Each of the slides was scanned, forming a WSI. Images that contained the region of interest were obtained, followed by pre-formatting, annotation of the nuclear grooves and data augmentation techniques. The final dataset was divided into training and validation groups in a 7:3 ratio. Results: This is the first artificial intelligence model based on object detection applied to nuclear structures in thyroid cytopathology. A total of 7,255 images were obtained from 22 WSI, totaling 7,242 annotated nuclear grooves. The best model was obtained after it was submitted 15 times with the train dataset (14th epoch), with 67% true positives, 49.8% for sensitivity and 43.1% for predictive positive value. Conclusions: The model was able to develop a structure predictor rule, indicating that the application of an artificial intelligence model based on object detection in the identification of nuclear grooves is feasible. Associated with a reduction in interobserver variability and in time per slide, this demonstrates that nuclear evaluation constitutes one of the possibilities for refining the diagnosis through computational models.