This paper presents a type-2 fuzzy inference tree designed for a differential wheeled mobile robot that navigates in indoor environments. The proposal consists of a controller designed for obstacle avoidance, a controller for path recovery and goal reaching, and a third controller for the real-time selection of behaviors. The system takes as inputs the information provided for a 2D laser range scanner, i.e., the distance of nearby objects to the robot, as well as the robot position in space, calculated from mechanical odometry. The real performance is evaluated through metrics such as clearance, path smoothness, path length, travel time and success rate. The experimental results allow us to demonstrate an appropriate performance of our proposal for the navigation task, with a higher efficiency than the reference methods taken from the state of the art.

This study provides a comprehensive structural, chemical, and optical characterization of CZTS thin films deposited on flexible Kapton substrates via the Successive Ionic Layer Adsorption and Reaction (SILAR) method. The investigation explored the effects of varying deposition cycles (40, 60, 70, and 80) and annealing treatments on the films. An X-ray diffraction (XRD) analysis demonstrated enhanced crystallinity and phase purity, particularly in films deposited with 70 cycles. These films exhibited a notable reduction in secondary phases in the as-deposited state, with further improvements observed after annealing at 400 °C and 450 °C in a sulfur atmosphere. A pole figure analysis indicates a decrease in texture disorder with annealing, suggesting improved crystalline orientation at higher temperatures. Field emission scanning electron microscopy (FE-SEM) showed enhancements in surface morphology, with increased grain size and uniformity post-annealing. Chemical uniformity was confirmed through Secondary Ion Mass Spectrometry (SIMS), Energy-Dispersive Spectroscopy (EDS), and X-ray Photoelectron Spectroscopy (XPS). XPS revealed the presence of CZTS phases alongside oxidized phases. Annealing effectively reduced secondary phases, such as ZnO, SnO₂, CuO, and SO₂, enhancing the CZTS phase. An optical analysis demonstrated that annealing at 200 °C in an air atmosphere reduced the band gap from 1.53 eV to 1.38 eV. In contrast, annealing at 400 °C and 450 °C in a sulfur atmosphere increased the band gap to 1.59 eV and 1.63 eV, respectively. The films exhibited p-type conductivity, as inferred from a valence band structure analysis. Density Functional Theory (DFT) calculations provided insights into the observed band gap variations, further substantiating the findings.

La agricultura de precisión es una práctica agrícola que utiliza tecnologías avanzadas, como sensores remotos, sistemas de riego inteligente y nanotecnología, para optimizar la gestión de los recursos naturales y aumentar la productividad agrícola. Esta disciplina surge como respuesta a los desafíos contemporáneos de la agricultura, como el aumento de la demanda de alimentos, la escasez de recursos y los impactos ambientales. Mediante la recopilación, análisis y aplicación de grandes volúmenes de datos en tiempo real, la agricultura de precisión permite a los agricultores tomar decisiones informadas y adaptarse rápidamente a las condiciones cambiantes del entorno. Si bien la agricultura de precisión ofrece soluciones innovadoras, su pleno potencial aún no ha sido alcanzado. Es necesario continuar investigando y desarrollando nuevas tecnologías, así como mejorar la accesibilidad y la adopción por parte de los agricultores.

In this study, a high-performing scheme is introduced to delimit benign and malignant masses in breast ultrasound images. The proposal is built upon by the Nonlocal Means filter for image quality improvement, an Intuitionistic Fuzzy C-Means local clustering algorithm for superpixel generation with high adherence to the edges, and the DBSCAN algorithm for the global clustering of those superpixels in order to delimit masses’ regions. The empirical study was performed using two datasets, both with benign and malignant breast tumors. The quantitative results with respect to the BUSI dataset were $JSC\ge 0.907$, $DM\ge 0.913$, $HD\ge 7.025$, and $MCR\le 6.431$ for benign masses and $JSC\ge 0.897$, $DM\ge 0.900$, $HD\ge 8.666$, and $MCR\le 8.016$ for malignant ones, while the MID dataset resulted in $JSC\ge 0.890$, $DM\ge 0.905$, $HD\ge 8.370$, and $MCR\le 7.241$ along with $JSC\ge 0.881$, $DM\ge 0.898$, $HD\ge 8.865$, and $MCR\le 7.808$ for benign and malignant masses, respectively. These numerical results revealed that our proposal outperformed all the evaluated comparative state-of-the-art methods in mass delimitation. This is confirmed by the visual results since the segmented regions had a better edge delimitation.

In the self-driving vehicles domain, steering control is a process that transforms information obtained from sensors into commands that steer the vehicle on the road and avoid obstacles. Although a greater number of sensors improves perception and increases control precision, it also increases the computational cost and the number of processes. To reduce the cost and allow data fusion and vehicle control as a single process, this research proposes a data fusion approach by formulating a neurofuzzy aggregation deep learning layer; this approach integrates aggregation using fuzzy measures $\mu$ as fuzzy synaptic weights, hidden state using the Choquet fuzzy integral, and a fuzzy backpropagation algorithm, creating a data processing from different sources. In addition, implementing a previous approach, a self-driving neural model is proposed based on the aggregation of a steering control model and another for obstacle detection. This was tested in an ROS simulation environment and in a scale prototype. Experimentation showed that the proposed approach generates an average autonomy of $95\%$ and improves driving smoothness by $9\%$ compared to other state-of-the-art methods.