International publishers of academic, scientific and professional journals since 1979.

In this paper, the dynamic case of a system made up of two blocks connected by a string over a smooth pulley is revisited. One mass lies on a horizontal surface without friction, and the other mass has a vertical displacement. The motion equation is obtained and its solution is determined using the Mathematica package. Also an experimental montage for this system is made and experimental data for the vertical position y in the function of time t are obtained using a data acquisition system and Tracker video analysis. The relation y vs t can be represented by a polynomial of degree six. An average relative error of 3.61(10.14)% is obtained between the theoretical results acquired with Mathematica and the data taken from Tracker (data acquisition system).

This paper introduces the modelling, simulation of anaerobic digestion process for biogas production from organic waste. Insight on the relevant model parameters is obtained by performing a sensitivity analysis, followed by the estimation of the selected candidates using a sequential quadratic programming algorithm. Calibration and validation of the model are based on experimental data obtained from a pilot-scaled plant, showing that the proposed model is able to correctly predict the Methane production dynamics from few key measurements.

Edge detection is a technique in digital image processing that detects the contours of objects based on changes in brightness. Edges can be used to determine the size, orientation, and properties of the object of interest within an image. There are different techniques employed for edge detection, one of them being phase congruency, a recently developed but still relatively unknown technique due to its mathematical and computational complexity compared to more popular methods. Additionally, it requires the adjustment of a greater number of parameters than traditional techniques. Recently, a unique formulation was proposed for the mathematical description of phase congruency, leading to a better understanding of the technique. This formulation consists of three factors, including a quantification function, which, depending on its characteristics, allows for improved edge detection. However, a detailed study of the characteristics had not been conducted. Therefore, this article proposes the development of a generalized function for quantifying phase congruency, based on the family of functions that, according to a previous study, yielded the best results in edge detection.

In this corrigendum we correct some typing errors. The corrections do not have any impact on the conclusions of the paper.