The growth trend of publications in the field of Proton Exchange Membrane Fuel Cell (PEMFC) was analyzed using bibliometric techniques to the identification of the areas with significant development and the orientations that have guided the research on energy cells. This study extracted the data from Scopus and Web of Science (WoS) databases to compare the bibliometric indicators of the published productions. In spite of bibliometric analysis advantages to knowing about the trends in a study area, this research requires methods to support the investigation process through the selection of a relevant bibliographic portfolio. This study applied the Methodi Ordinatio that provides a new approach to achieve it. A proposed list of the articles ranked by InOrdinatio is presented to compose the final portfolio. The obtained results in the research sub-theme of the Mass Transport in Gas Diffusion Layer (GDL) confirm the complexity in the study area by presenting erratic patterns of exponential growth. United States, China, and Japan are the leading countries on PEMFC publications. These countries have in common a strong spending by the business sector for R&D, and their gross domestic product is greater than 2%.

We take the concept of typicality from the field of cognitive psychology, and we apply the meaning to the interpretation of numerical data sets and color images through fuzzy clustering algorithms, particularly the GKPFCM, looking to get better information from the processed data. The Gustafson Kessel Possibilistic Fuzzy c-means (GKPFCM) is a hybrid algorithm that is based on a relative typicality (membership degree, Fuzzy c-means) and an absolute typicality (typicality value, Possibilistic c-means). Thus, using both typicalities makes it possible to learn and analyze data as well as to relate the results with the theory of prototypes. In order to demonstrate these results we use a synthetic data set and a digitized image of a glass, in a first example, and images from the Berkley database, in a second example. The results clearly demonstrate the advantages of the information obtained about numerical data sets, taking into account the different meaning of typicalities and the availability of both values with the clustering algorithm used. This approach allows the identification of small homogeneous regions, which are difficult to find.

Computational Fluid Dynamics (CFD) software is well known for its application feasibility as well as reliable results in modeling electrochemical, thermal, and fluid transport processes. CFD has been used to investigate the phenomena involved in the operation of fuel cells, providing a large amount of data that must be analyzed to improve cell efficiency. This paper aims to demonstrate that programming can be used in the post-processing phase, using scripts in Python language to automate data analysis, based on the results of the simulation of oxygen transport in Polymer Electrolyte Membrane Fuel Cell (PEMFC). The OpenFOAM open-source CFD tool solved the fluid governing equations through the SIMPLE algorithm of three proposed Gas Diffusion Layer (GDL) case studies. In this work, an algorithm is presented to extract, compute and visualize the post-process results, supporting the GDL selection.

The Gas Diffusion Layer (GDL) provides a transport route for the reactant species from the flow channels to the catalytic layer in a fuel cell. The components design requires that the species present a uniform diffusion towards the electrodes and that the formed products are shifted as faster as possible, to avoid the increase of the ohmic resistance due to the blocking of the active sites, or it constitutes a bottleneck that the performance decrease fast. This work presents a solution proposal for the continuity equation in incomprenssible fluids in a 3D model of a gas diffuser layer with titanium wired mesh characteristics of a fuel cell using "OpenFOAM" open source software. The aim of the study is to show that under different scales or size of the gas diffusion layer there are significant changes in the velocities of propagation of the reactant species towards the active area of the fuel cell.

The accurate detection of soil organic carbon (SOC) is essential due to its role in nutrient availability for plants and its contribution to sustainable agricultural practices that benefit the environment. However, detecting SOC in computed tomography (CT) images poses challenges, as this component is small, and its gray intensity resembles that of pores. This study developed a novel methodology that characterizes two soil samples accurately by detecting spaces such as pores/macropores, gravel, solids, and especially candidate SOC regions (cSOCRs). The approach includes 3D representation for analyzing the distribution, density, and connectivity of macropores/pores and candidate SOC regions. We proposed a dual segmentation method: first, identifying pores/macropores and non-pores (gravel, solids, and cSOCRs) using standard algorithms; second, sub-segmenting to detect solid and gravel regions, highlighting atypical sub-regions. One of these atypical regions in the gravel space corresponds to cSOCRs. Quality was assessed through a homogeneity measure, assuring consistency in the detected regions. The results confirm the effectiveness of dual segmentation in accurately detecting and characterizing cSOCRs in soil samples. The detected cSOCR content ranged from 2% to 9% in Sample I and from 5% to 10% in Sample II, demonstrating the method’s ability to capture variations in organic carbon distribution. This approach stands out as a promising and reliable alternative for soil characterization studies. The findings contribute to precision agriculture by enabling informed decision-making, reducing unnecessary tillage in areas with a high cSOCR content, and optimizing the application of fertilizers and compost in regions with low cSOCR levels.

This literature review explores the use of genetic algorithms in digital marketing, emphasizing their evolution and practical applications. Inspired by natural selection, these algorithms have become essential for optimizing campaign targeting, customer segmentation, and decision-making. The study analyzes 36 selected papers published between 2008 and 2024, highlighting the integration of genetic algorithms with artificial intelligence and big data. Findings show how this technology enhances user experience and improves marketing effectiveness in data-driven environments. Current trends demonstrate their growing relevance for real-time personalization and strategy optimization, making them valuable tools in the digital marketing landscape.