Because of their small size and large specific surface area, nanoparticles (NPs) have special properties that are different from bulk materials. In particular, Au/Ag NPs have been intensively studied for a long time, especially for biomedical applications. Thereafter, they played a significant role in the fields of biology, medical testing, optical imaging, energy and catalysis, MRI contrast agents, tumor diagnosis and treatment, environmental protection, and so on. When synthesizing Au/Ag NPs, the laser ablation and biosynthesis methods are very promising green processes. Therefore, this review focuses on the progress in the laser ablation and biological synthesis processes for Au/Ag NP generation, especially in their fabrication fundamentals and potential applications. First, the fundamentals of the laser ablation method are critically reviewed, including the laser ablation mechanism for Au/Ag NPs and the controlling of their size and shape during fabrication using laser ablation. Second, the fundamentals of the biological method are comprehensively discussed, involving the synthesis principle and the process of controlling the size and shape and preparing Au/Ag NPs using biological methods. Third, the applications in biology, tumor diagnosis and treatment, and other fields are reviewed to demonstrate the potential value of Au/Ag NPs. Finally, a discussion surrounding three aspects (similarity, individuality, and complementarity) of the two green synthesis processes is presented, and the necessary outlook, including the current limitations and challenges, is suggested, which provides a reference for the low-cost and sustainable production of Au/Ag NPs in the future.

Transparent nano-ceramics have an important high-transmittance, material-integrating structure and function and a variety of potential applications, such as use in infrared windows, optical isolators, composite armors, intelligent terminal screens, and key materials of solid-state lasers. Transparent ceramics were originally developed to replace single crystals because of their low fabricating cost, controllable shape, and variable composition. Therefore, this study reviews and summarizes the development trends in transparent nano-ceramics and their potential applications. First, we review the research progress and application of laser nano-ceramic materials, focusing on the influence of controllable doping of rare earth ions on thermal conductivity and the realization of large-scale fabrication technology. Second, the latest research progress on magneto-optical transparent nano-ceramics, mainly including terbium gallium garnet (Tb₃Ga₅O₁₂, TGG) ceramics and terbium aluminum garnet (Tb₃Al₅O₁₂, TAG) ceramics, are summarized, and their performance is compared. Third, the research progress of transparent armor nano-ceramic materials, represented by MgAl₂O₃ and Aluminum oxynitride (AlON), are reviewed. Lastly, the progress in electro-optical transparent nano-ceramics and scintillation transparent nano-ceramics is reported, and the influence of the material-fabrication process on electro-optic effect or luminous intensity is compared. Moreover, the effect of particle diameter on fabrication, the relationship between nano powder and performance, and different sintering methods are discussed. In summary, this study provides a meaningful reference for low-cost and sustainable production in the future.

Accurately and efficiently predicting the fuel consumption of vehicles is the key to improving their fuel economy. This paper provides a comprehensive review of data-driven fuel consumption prediction models. Firstly, by classifying and summarizing relevant data that affect fuel consumption, it was pointed out that commonly used data currently involve three aspects: vehicle performance, driving behavior, and driving environment. Then, from the model structure, the predictive energy and the characteristics of the traditional machine learning model (support vector machine, random forest), the neural network model (artificial neural network and deep neural network), and this paper point out that: (1) the prediction model of fuel consumption based on neural networks has a higher data processing ability, higher training speed, and stable prediction ability; (2) by combining the advantages of different models to build a hybrid model for fuel consumption prediction, the prediction accuracy of fuel consumption can be greatly improved; (3) when comparing the relevant indicts, both the neural network method and the hybrid model consistently exhibit a coefficient of determination above 0.90 and a root mean square error below 0.40. Finally, the summary and prospect analysis are given based on various models’ predictive performance and application status.

Ultrasonic-assisted wire–arc additive manufacturing (WAAM) can refine microstructures, enhancing performance and improving stress concentration and anisotropy. It has important application prospects in aerospace, weaponry, energy, transportation, and other frontier fields. However, the process parameters of ultrasonic treatment as an auxiliary technology in the WAAM process still have an important impact on product performance indicators, such as the amplitude of the ultrasonic tool, the distance between the points of action of the product, and the scanning speed. The number of ultrasonic impacts influences the performance indexes. Therefore, these parameters must be optimized. This paper describes the advantages and the defects of WAAM components, as well as the principle and development status of ultrasonic treatment technology. Subsequently, this paper also briefly describes how ultrasonic-assisted technology can refine the crystal and improve the mechanical properties of WAAM components. Finally, we review the influence of process parameters (such as ultrasonic amplitude, application direction, and impact times) on the product materials. In this paper, a comprehensive optimization method for ultrasonic parameters is proposed to improve the mechanical properties of WAAM components.

An improved YOLOv8 detection method is proposed for detecting distracted driving behavior and driver’s emotion. Unlike the commonly used YOLOv8 method, an attention mechanism named MHSA and a CNN module are synthesized to ensure improved performance in terms of accuracy and convergence, where MHSA is used to detect distracted driving behavior and CNN is used to detect driver’s emotion. The FER2013 dataset and collected dataset are used to train the improved YOLOv8. The training results show that the proposed YOLOv8 demonstrates improved performance compared with the commonly used YOLO based methods. Finally, the validity of the proposed YOLOv8 method is illustrated through implementations in Jetson Nano platform, where the TensorRT and DeepStream methods in the Jetson Nano device are used to optimize the volume and operational speed of the proposed YOLOv8 method, respectively. Test results show that the proposed YOLOv8 method can yield better real-time and accuracy properties.