The main aim of this study is to analyze the fuel saving possibilities of optimized gear shifting strategies in a vehicle with a flex-fuel engine powered by both ethanol and gasoline when submitted to a real-world driving cycle. A computational model is implemented and, for each fuel option, an optimization problem is solved aiming at minimization of fuel consumption and traveling time. The standard and optimized gear shifting strategies obtained are experimentally evaluated where the new gear shifting shows a significant fuel consumption reduction without a significant increase in traveling time.

This research introduces a cost-effective converter for single-phase machines, aiming to enhance efficiency and reduce energy consumption in retrofit applications. Single-phase motors commonly found in household appliances often suffer from low efficiency, resulting in wasted energy. To tackle this problem, a dedicated converter was proposed to replace the existing capacitors and improve the motor performance. This study presents a proof of concept for retrofit applications, discussing the converter design methodology and prototype evaluation. Additionally, a cost analysis comparing single-phase and three-phase motors is included. It aims to demonstrate the long-term cost savings and improved energy efficiency of the proposed converter. The findings highlight the converter’s potential as a promising solution for enhancing energy efficiency and decreasing costs in single-phase motor applications.

The misalignment of steel strips in relation to the roller table centerline still is an impairment for the rolling mill production lines. Nowadays, the strip position correction remains largely in the purview of human analysis, in which the strip steering is traditionally a semi-manual operation. Automating the alignment process could reduce the maintenance costs, damage to the plant, and prevent material losses. The first step into the automatization is to determine the strip position and its referred error. This study presents a method that employs semantic segmentation based on convolution neural networks to estimate steel strips positioning error from images of the process. Additionally, the system mitigates the influences of mechanical vibration on the images. The system performance was assessed by standard semantic segmentation evaluation metrics and in comparison with the dataset ground truth. The results showed that 97% of the estimated positioning errors are within a 2-pixel margin. The method demonstrated to be a robust real-time solution as the networks were trained from a set of low-resolution images acquired in a complex environment.

Fault modeling in induction motors is suitable for analyzing operational characteristics, enabling simulation of transient and steady-state phenomena, and facilitating the development of control systems that can extend their operational life. Therefore, this work presents a hybrid model of a three-phase induction machine simultaneously subjected to an inter-turn short-circuit fault in one of the stator phases and to mechanical eccentricity. The mathematical formulation is developed in the dq reference frame, encompassing the electromagnetic and mechanical equations, with parameters derived from finite element analysis. The developed equations were simulated inMATLAB, considering direct-on-line starting and step-load application, and the torque was validated using the finite element software FEMM. Even for small eccentricities (20% static, 10% dynamic) and a few shorted turns (2.08%), the three-phase currents became unbalanced in both amplitude and phase. The fault current clearly indicates potential for damage and rapid propagation of the inter-turn fault. A pulsating torque component at nearly twice the supply frequency was also observed, attributed to the negative-sequence current.

Background: Hand movements are crucial in daily activities, sparking extensive interest and research in biomechanical models. While existing models offer valuable insights, their complexity and processing costs may limit their suitability for all applications, sometimes impeding research efficiency. Objectives: This study aimed to develop a biomechanical model of the human hand for analyzing the physiology of lateral pinch movement. Unlike conventional methodologies, this approach focuses on delivering a computationally efficient model while incorporating the trapeziometacarpal joint into the analysis. Methods: The model, which operates in a multibody environment, simulates lateral pinching movement by applying external time-varying torques to digit joints, emulating musculature, tendons, and ligaments. Torque estimation was achieved through the Euler-Lagrange approach. The model generates animated representations of the movement, aiding pathology identification and outputting dynamic variables. The model’s was validated through data acquired from asymptomatic subjects via an OptiTrack system. Results: The average disparity between the expected and obtained joint angular displacements was $$\varvec{6.06~\%}$$6.06 % and $$\varvec{1.90~\%}$$1.90 % during validation and verification stages, suggesting high fidelity in the model performance. Correlation analysis revealed strong positive linear relationships and robust correlations between the obtained and expected configuration data. Model-generated pinch postures closely resembled expected physiological patterns, with results falling within the range for asymptomatic individuals documented in the scientific literature. Conclusion: The system efficiently analyzes dynamic variables at a low computational cost, offering animated representations for pathology identification. The model’s potential for rehabilitation solutions and adaptability, coupled with its accuracy and versatility, make it an asset for advancing hand biomechanics research.