Mobile robots represent one of the most relevant areas of study within robotics due to their potential for designing and developing new nonlinear control structures that can be implemented in simulations and applications in specific environments. In this work, a fuzzy steering controller with a symmetric distribution of fuzzy numbers is proposed and designed for implementation in the kinematic model of a non-holonomic mobile robot. The symmetry in the distribution of triangular fuzzy numbers contributes to a balanced response to disturbances and minimizes systematic errors in direction estimation. Additionally, it improves the system’s adaptability to various reference paths, ensuring accurate tracking and optimized performance in robot navigation. Furthermore, this fuzzy logic-based controller emulates the behavior of a classic PID controller by offering a robust and flexible alternative to traditional methods. A virtual environment was also developed using the UNITY platform to evaluate the performance of the fuzzy controller. The results were evaluated by considering the average tracking error, maximum error, steady-state error, settling time, and total distance traveled, emphasizing the trajectory error. The circular trajectory showed high accuracy with an average error of 0.0089 m, while the cross trajectory presented 0.01814 m, reflecting slight deviations in the turns. The point-to-point trajectory registered a more significant error of 0.9531 m due to abrupt transitions, although with effective corrections in a steady state. The simulation results validate the robustness of the proposed fuzzy controller, providing quantitative insights into its precision and efficiency in a virtual environment, and demonstrating the effectiveness of the proposal.

The problems of learning mathematics are a particularly important field in the educational relationship. In the degree of any engineering and specifically in Chemical Engineering (IQ) requires a series of mathematical skills for learning and problem solving. By virtue of having made a revision on the indices of failure in the educational experiences of the IQ program, it was detected that the highest values were precisely the basic mathematics, this led to propose the solution proposing a change in the teaching with the objective of improve learning and with it, reduce failure rates from the use of a didactic model. The didactic model is based on 3 stages that include bibliographic, documentary and statistical information, for the intervention part, framed within the quantitative field research modality of descriptive type, with characteristics of participant observation. When implemented, it allowed to observe some significant changes in reference to the habits of study and attitude that the students present before the mathematics and on the styles adopted by the professors for this chair, with which finally it was evaluated concluding that it effectively improves the indexes of approval and meaningful learning.

The National Survey on Drug, Alcohol and Tobacco, 2016-2017, notes that 15.6 million Mexicans are active smokers and, by 2030, expect the death of 8 million cancers of the larynx or lung. Therefore, the World Health Organization (WHO) recommends detecting precancerous lesions of the larynx. This is possible, as they are characterized by a biomarker pattern manifested by the alteration of the biomechanical interpretation of the vocal cords, regardless of the sex and age of the smoker. The goal of this article is to evaluate three machine learning methods: neural networks, Gaussian networks, and decision tree to determine the method that best solves the problem of detecting patterns of precancerous vocal cord injury biomarkers. It uses the WEKA tool and a knowledge bank, endorsed by NOM-012-SSA3-2012, with 250 patterns, and provided by the Luis Guillermo Ibarra National Institute of Rehabilitation, Ibarra. The performance of the methods was compared by ROC curves and confusion matrices, under the criteria established by ISO-5725. The decision tree the method that best responds to the detection of patterns of biomarkers of precancerous lesions of the vocal cords.

Cognitive stress detection based on electrocardiogram (ECG) is challenged by the high dimensionality of multichannel analysis, redundancy between heart rate variability (HRV) and morphological descriptors, and variability in classifier performance. We developed and evaluated a cognitive stress classification framework based on a standardized ECG acquisition protocol, the integration of HRV and morphological descriptors extracted from 12 leads, and an adaptive feature selection strategy using a binary genetic algorithm with explicit penalization of dimensionality. Seventy healthy students aged 18–25 years participated, and cognitive stress was induced using a task based on PMA-R Factor R. The initial dataset included 27 descriptors per lead, and the proposed dimensionality reduction method was compared with two reference schemes: no dimensionality reduction and conventional principal component analysis (PCA) with a 99% cumulative explained variance threshold. Performance was assessed over 30 data splits using five classifiers: logistic regression, linear support vector machine (SVM), radial basis function SVM (SVM-RBF), k-nearest neighbors (KNN), and decision tree. The best trade-off between parsimony and predictive performance was achieved with $\lambda =0.05$, yielding a compact subset of 11 features on average and a mean AUC of 0.830. In the final comparison, the adaptive strategy achieved the best overall performance with SVM-RBF (AUC $=0.830\pm 0.047$; specificity $=0.814\pm 0.115$), outperforming both the full feature set and PCA. These findings indicate that penalized genetic selection validated across multiple classifiers is an effective strategy for identifying compact, discriminative, and robust feature subsets for ECG-based cognitive stress classification.