Nowadays, the use of renewable, green/eco-friendly technologies is attracting the attention of researchers, with a view to overcoming recent challenges that must be faced to guarantee the availability of Electric Vehicles (EVs). Therefore, this work proposes a methodology based on Genetic Algorithms (GA) and multivariate regression for estimating and modeling the State of Charge (SOC) in Electric Vehicles. Indeed, the proposal considers the continuous monitoring of six load-related variables that have an influence on the SOC (State of Charge), specifically, the vehicle acceleration, vehicle speed, battery bank temperature, motor RPM, motor current, and motor temperature. Thus, these measurements are evaluated in a structure comprised of a Genetic Algorithm and a multivariate regression model in order to find those relevant signals that better model the State of Charge, as well as the Root Mean Square Error (RMSE). The proposed approach is validated under a real set of data acquired from a self-assembly Electric Vehicle, and the obtained results show a maximum accuracy of approximately 95.5%; thus, this proposed method can be applied as a reliable diagnostic tool in the automotive industry.

In this paper, a new form-finding analysis methodology for a class 2 tensegrity robot is proposed. The methodology consists of two steps: first, the analysis of the possible geometric configurations of the robot is carried out through the results of the kinematic position analysis; and, second, from the static analysis, the equilibrium positions of the robot are found, which represents its workspace. Both kinematics and static analysis are resolved in a closed-form using basic tools of linear algebra instead of the strategies used in literature. Four numerical experiments are presented using the finite element analysis software ANSYS^©. Additionally, a comparison between the results of the form-finding analysis methodology proposed and the ANSYS^© results is presented.

This study introduces a non-destructive computer vision method for estimating postharvest quality parameters of oranges, including maturity index, soluble solid content (expressed in degrees Brix), and firmness. A novel image-based descriptor, termed Citrus Color Index—Gray Level Co-occurrence Matrix Texture Features (CCI–GLCM-TF), was developed by integrating the Citrus Color Index (CCI) with texture features derived from the Gray Level Co-occurrence Matrix (GLCM). By combining contrast, correlation, energy, and homogeneity across multiscale regions of interest and applying geometric calibration to correct image acquisition distortions, the descriptor effectively captures both chromatic and structural information from RGB images. These features served as input to an Adaptive Neuro-Fuzzy Inference System (ANFIS), selected for its ability to model nonlinear relationships and gradual transitions in citrus ripening. The proposed ANFIS models achieved R-squared values greater than or equal to 0.81 and root mean square error values less than or equal to 1.1 across all quality parameters, confirming their predictive robustness. Notably, representative models (ANFIS 2, 4, 6, and 8) demonstrated superior performance, supporting the extension of this approach to full-surface exploration of citrus fruits. The results outperform methods relying solely on color features, underscoring the importance of combining spectral and textural descriptors. This work highlights the potential of the CCI–GLCM-TF descriptor, in conjunction with ANFIS, for accurate, real-time, and non-invasive assessment of citrus quality, with practical implications for automated classification, postharvest process optimization, and cost reduction in the citrus industry.

Shadows in remote sensing images often introduce challenges in accurate segmentation due to their variability in shape, size, and texture. To address these issues, this study proposes the Supervised Dynamic Kernel U-Net (SDKU-Net), a novel architecture designed to enhance shadow detection in complex remote sensing scenarios. SDKU-Net integrates dynamic kernel adjustment, a combined loss function incorporating Focal and Tversky Loss, and optimizer switching to effectively tackle class imbalance and improve segmentation quality. Using the AISD dataset, the proposed method achieved state-of-the-art performance with an Intersection over Union (IoU) of 0.8552, an F1-Score of 0.9219, an Overall Accuracy (OA) of 96.50%, and a Balanced Error Rate (BER) of 5.08%. Comparative analyses demonstrate SDKU-Net’s superior performance against established methods such as U-Net, U-Net++, MSASDNet, and CADDN. Additionally, the model’s efficient training process, requiring only 75 epochs, highlights its potential for resource-constrained applications. These results underscore the robustness and adaptability of SDKU-Net, paving the way for advancements in shadow detection and segmentation across diverse fields.

This paper presents a novel class 2 tensegrity robot which has contact between its rigid elements with a universal joint. Also, an strategy to obtain the forward and inverse position kinematic analysis using the parameters Denavit–Hartenberg in the distal convention is presented, obtaining the closed–form solution for the inverse position analysis and it was validated through simulation where a point of the robot followed the desired trajectory. Finally, the results were implemented in the experimental prototype of the novel class 2 tensegrity robot.

The use of computer-aided engineering (CAE) tools has become essential in modern design processes, significantly streamlining mechanical design tasks. The integration of optimization algorithms further enhances these processes by facilitating studies on mechanical behavior and accelerating iterative operations. A key focus lies in understanding and mitigating the detrimental effects of cavitation on injector surfaces, as it can reduce the injector lifespan and induce material degradation. By combining advanced numerical finite element tools with algorithmic optimization, these adverse effects can be effectively mitigated. The incorporation of computational tools enables efficient numerical analyses and rapid, automated modifications of injector designs, significantly enhancing the ability to explore and refine geometries. The primary goal remains the minimization of cavitation phenomena and the improvement in injector performance, while the collaborative use of specialized software environments ensures a more robust and streamlined design process. Specifically, using the simulated annealing algorithm (SA) helps identify the optimal configuration that minimizes cavitation-induced effects. The proposed approach provides a robust set of tools for engineers and researchers to enhance injector performance and effectively address cavitation-related challenges. The results derived from this integrated framework illustrate the effectiveness of the optimization methodology in facilitating the development of more efficient and reliable injector systems.