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It has been shown that the variability of the marginal capital product ratio is what determines the growth rate of economies according to Harrod's model; therefore, it is important to predict its behavior. This paper develops a methodology based on probabilistic random walk to predict the annual marginal capital product ratio of Colombia for the year 2017 based on information from the Penn World Table database from 1993 to 2016. It was found that this variable has underlying mathematical orders, which allows its prediction with an accuracy of 97.69%. [ABSTRACT FROM AUTHOR]

Background: Pathological interpretation of cellular form in cervical cytology is very important for preven- tion of cervical cancer. The methods most frequently used for assessment of this test have reproducibility and inter-observer variability problems. Objective: To make fractal and Euclidean measure- ments to mathematically diagnose normal and pre- malignant cells of cervical squamous epithelium. Methodology: 21 cells with normal, ASCUS or LSIL diagnosis according to the Bethesda system were assessed. Fractal and Euclidean geometric measures of three mathematical objects were calculated: cyto- plasm, nucleus and whole cell. Mathematical propor- tions between these measurements were calculated in order to compare them with conventional classification methods. Results: It was found that the nuclear border measures calculated with the 2-pixel grill and the surface measures could mathematically and objectively differentiate normal cells from the pre-malignant ones (ASCUS and LSIL). Conclusions: An objective and reproducible diagnos- tic method was developed; it allows to identify the evolution towards malignant cellular states based on simultaneous fractal and Euclidean measures, estab- lishing the severity level of ASCUS and LSIL cells.