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Natural Language Processing (NLP) is recognized as essential in Artificial Intelligence for the interaction between computers and humans. In this article, the use of NLP and visualization techniques in the creation of a model for insights acquisition in the design of new academic programs was explored. The quantitative methodology use techniques such as tokenization and TextRank, and with the support of the Scattertext Plot, discriminate categories on doctoral programs in information technologies. This methodology, validated by experts and widely used, highlighted particularities, general knowledge, and emerging trends in the compared programs. The results identify four essential quadrants for decision-making in program design, and shows current and future needs in program naming. In conclusion, the study underscores the importance of NLP in contemporary academic design and provides a robust tool for program designers. [ABSTRACT FROM AUTHOR]

This study presents a visualized approach for tracking joint surface morphology. Three-dimensional laser scanning (3DLS) and 3D printing (3DP) techniques are adopted to record progressive failure during rock joint shearing. The 3DP resin is used to create transparent specimens to reproduce the surface morphology of a natural joint precisely. The freezing method is employed to enhance the mechanical properties of the 3DP specimens to reproduce the properties of hard rock more accurately. A video camera containing a charge-coupled device (CCD) camera is utilized to record the evolution of damaged area of joint surface during the direct shear test. The optimal shooting distance and shooting angle are recommended to be 800 mm and 40°, respectively. The images captured by the CCD camera are corrected to quantitatively describe the damaged area on the joint surface. Verification indicates that this method can accurately describe the total sheared areas at different shear stages. These findings may contribute to elucidating the shear behavior of rock joints.

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