This paper aims to evaluate the impact of cetane improvers on the combustion, performance and emission characteristics of a compression ignition engine fueled with a 20% peppermint bio-oil/diesel blend (P20). It is hypothesized that the low viscosity and boiling point of peppermint oil could improve the atomization characteristics of the fuel. However, the usage of peppermint oil is restricted due to its low cetane index. To improve this, Diethyl Ether (DEE) and Di- tertiary Butyl Peroxide (DTBP) are added to the P20 blend. The tests are performed in a single-cylinder naturally aspirated water-cooled diesel engine and results indicate that NOx emission for P20 + DEE and P20 + DTBP is decreased by 10.4% and 9.8%, respectively, when compared to P20 at full load condition. Among these two cetane improvers, DTBP is more effective in reducing the CO, HC and smoke emission and the performance of the engine was reported to be higher for P20 + DTBP blends.

The integration of advanced image analysis using artificial intelligence (AI) is pivotal for the evolution of autonomous vehicles (AVs). This article provides a thorough review of the most significant datasets and latest state-of-the-art AI solutions employed in image analysis for AVs. Datasets such as Cityscapes, NuScenes, CARLA, and Talk2Car form the benchmarks for training and evaluating different AI models, with unique characteristics catering to various aspects of autonomous driving. Key AI methodologies, including Convolutional Neural Networks (CNNs), Transformer models, Generative Adversarial Networks (GANs), and Vision Language Models (VLMs), are discussed. The article also presents a comparative analysis of various AI techniques in real-world scenarios, focusing on semantic image segmentation, 3D object detection, vehicle control in virtual environments, and vehicle interaction using natural language. Simultaneously, the roles of multisensor datasets and simulation platforms like AirSim, TORCS, and SUMMIT in enriching the training data and testing environments for AVs are highlighted. By synthesizing information on datasets, AI solutions, and comparative performance evaluations, this article serves as a crucial resource for researchers, developers, and industry stakeholders, offering a clear view of the current landscape and future directions in autonomous vehicle image analysis technologies.

Solid biofuels, including straw as production residue, are still the largest energy feedstock in the structure of primary energy production from renewable energy sources. However, the properties of straw as a solid biofuel can vary depending on the species from which it was produced and the harvest period and year. Therefore, this study aimed to assess the thermophysical properties and elemental composition of six types of straw (rye, oat, triticale, wheat, corn, and rapeseed straw) obtained over three consecutive years (2020, 2021, 2022). Rye straw had the lowest moisture (mean: 10.55%), ash (mean: 2.71% DM), nitrogen (mean: 0.54% DM) and chlorine (mean: 0.046% DM) contents and the highest carbon content (mean: 47.93% DM), a higher heating value—HHV (mean: 19.03 GJ Mg⁻¹ DM) and a lower heating value—LHV (mean: 15.71 GJ Mg⁻¹). Triticale straw had similar properties, classifying it into the same cluster as rye straw. Corn straw had a remarkably high moisture content (mean: 48.91%), low LHV and high chlorine content. Rapeseed straw contained high levels of Cl, S, N and ash, and they were 643%, 481%, 104% and 169% higher, respectively, than those in rye straw. The sulfur, chlorine and moisture contents of the six straw types under study were highly variable during the three years of the study. Knowledge of the properties of different types of straw as energy feedstocks facilitates the logistics and organization of the supply of bioenergy installations. However, further research is needed, especially studies assessing the energy intensity and logistical costs of different types of straw used for energy purposes.