In recent times, the significance of advancing road transportation technologies has notably increased. This is mainly driven by the escalating need for road transportation systems that are not only safe but also environmentally sustainable. Moreover, enhancing fuel efficiency in road vehicles (i.e., automobiles) holds the potential to contribute significantly to the reduction of a country’s economic vulnerability (i.e., improved energy security), by reducing the reliance on energy imports. While research reports and scientific journals provide valuable information on automobile fuel consumption, it is essential to continually update our understanding of the factors that influence it. This document aims to review and describe the various factors, both direct and indirect, that play a role in an automotive’s overall energy efficiency and fuel consumption.

Improvement of wear resistance and mechanical performance of rails used in heavy-haul railway are essential to reduce railroad maintenance costs. A novel heat treatment based on quenching and partitioning (Q&P) processing was proposed to improve the wear resistance of a hypereutectoid pearlitic rail. 50% of austenite was transformed into martensite under an interrupted quenching from full austenitization temperature to 140 °C. A multiphase microstructure resulted from the quenching and partitioning process, consisting of tempered martensite, bainite, retained austenite, and pearlite colonies. The partitioning step was performed in the range of 350–650 °C. Microstructure characteristics were investigated using scanning electron microscopy, microhardness measurements, X-ray and electron backscattered diffraction. Uniaxial tensile and pin-on-disc tests were also performed to evaluate the mechanical properties and wear resistance. The best combination of wear resistance and mechanical performance was obtained in samples partitioned at 450 and 550 °C, which may be applied in the railway industries.

In the context of mathematics education, the integration of artificial intelligence (AI) in teaching calculus is revolutionizing instructional methodologies and enhancing learning experiences both inside and outside the classroom. This study explores the use of specific AI tools, including ChatGPT, MathGPT, Gemini, and Wolfram Alpha, to deepen students’ understanding of key mathematical concepts such as derivatives and rates of change through continuous interaction with a virtual tutor. By employing well-designed prompts, these tools facilitated problem-solving exercises that were verified and refined by AI, fostering both precision in calculations and conceptual clarity. Observations from the classroom implementation reveal that students not only improved their accuracy in performing derivative calculations but also developed a clear understanding of the distinctions between average and instantaneous rates of change. The AI tools created a dynamic, adaptive learning environment, providing immediate feedback and simulations that significantly boosted student engagement and motivation. These findings underscore the potential of AI to transform mathematics education by making learning more personalized and accessible, ultimately enhancing educational outcomes and preparing students for future academic and professional challenges. Furthermore, this study introduces an innovative approach to refining AI prompts and interactions, highlighting the importance of iterative improvement to enhance the quality of AI feedback. This approach is crucial for developing better problem-solving skills and ensuring a comprehensive understanding of mathematical concepts.

A mathematical model to calculate the build-up of residual stresses during quenching of carbon (AISI 1045) and low-alloy (AISI 4140 and 4340) cylindrical steel bars is proposed. The model is implemented as a combination of the commercial software AC3®, to simulate the microstructure evolution, and Abaqus®, to model the heat transfer and the elastic, plastic, thermal, and phase transformation strains/stresses by the finite element method. All steel properties required in the model are calculated as an average of the properties of individual microconstituents (austenite, pearlite, bainite, or martensite) weighted by their local volume fractions, enabling the model application to any type of carbon or low-alloy steel. To thoroughly verify the simulation results, experimental measurements were carried out in cylindrical bars quenched in stirred water and these measurements were compared with model results. The heat transfer coefficient between the bar and the water was calculated by an inverse solution technique, resulting in the constant value of 7200 W m–2 K–1 for the whole quenching period. For the low-alloy steels, measured and calculated volume fractions of martensite in the bar cross sections are in very good agreement, but for the carbon steel, large discrepancies are observed in the fractions of most constituents. Tangential and axial residual stresses were measured on the lateral surface of the quenched bars using the X-ray diffraction method. These stresses, which are compressive, agree well with those calculated by the present model, showing discrepancies generally lower than 10%.CNP