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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.

As UAV technology has been leaping forward, small consumer-grade UAVs equipped with optical sensors are capable of easily acquiring high-resolution images, which show bright prospects in a wide variety of terrains and different fields. First, the crater rim landscape of the Dinosaur Valley ring formation located on the central Yunnan Plateau served as the object of the surface change detection experiment, and two repetitive UAV ground observations of the study area were performed at the same altitude of 180 m with DJI Phantom 4 RTK in the rainy season (P1) and the dry season (P2). Subsequently, the UAV-SfM digital three-dimensional (3D) modeling method was adopted to build digital models of the study area at two points in time, which comprised the Digital Surface Model (DSM), Digital Orthomosaic Model (DOM), and Dense Image Matching (DIM) point cloud. Lastly, a quantitative analysis of the surface changes at the pit edge was performed using the point-surface-body surface morphological characterization method based on the digital model. As indicated by the results, (1) the elevation detection of the corresponding check points of the two DSM periods yielded a maximum positive difference of 0.2650 m and a maximum negative value of −0.2279 m in the first period, as well as a maximum positive difference of 0.2470 m and a maximum negative value of −0.2589 m in the second period. (2) In the change detection of the two DOM periods, the vegetation was 9.99% higher in the wet season than in the dry season in terms of coverage, whereas the bare soil was 10.54% more covered than the wet season. (3) In general, the M3C2-PM distances of the P1 point cloud and the P2 point cloud were concentrated in the interval (−0.2,0.2), whereas the percentage of the interval (−0.1,0) accounted for 26.69% of all intervals. The numerical model of UAV-SfM was employed for comprehensive change detection analysis. As revealed by the result of the point elevation difference in the constant area, the technique can conform to the requirements of earth observation with certain accuracy. The change area suggested that the test area can be affected by natural conditions to a certain extent, such that the multi-source data can be integrated to conduct more comprehensive detection analysis.

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.

Driver identification is very important to realizing customized service for drivers and road traffic safety for electric vehicles and has become a research hotspot in the field of modern automobile development and intelligent transportation. This paper presents a comprehensive review of driver identification methods. The basic process of driver identification task is proposed as four steps, the advantages and disadvantages of different data sources for driver identification are analyzed, driver identification models are divided into three categories, and the characteristics and research progress of driver identification models are summarized, which can provide a reference for further research on driver identification. It is concluded that on-board sensor data in the natural driving state is objective and accurate and could be the main data source for driver identification. Emerging technologies such as big data, artificial intelligence, and the internet of things have contributed to building a deep learning hybrid model with high accuracy and robustness and representing an important gradual development trend of driver identification methods. Developing a driver identification method with high accuracy, real-time performance, and robustness is an important development goal in the future.

To prevent the possible accident of a large passenger plane due to rapid decompression, transient load analysis is of vital importance in the assessment of structure strength and also an important clause of airworthiness standard. A 0-D isentropic model and a 1-D model based on the characteristic line are developed to simulate the rapid decompression process of the cockpit-cabin model due to a cracked windshield. The accuracy of these models is presented by comparing them with experiments and 3-D CFD simulations. Then, the 1-D model is applied to study the influence of cabin and cockpit volume, windshield and decompression panel area, compartments, and environment pressure on the decompression load. The non-dimensional decompression time and the non-dimensional decompression load are developed to evaluate the decompression characteristics, and the correlation equations are established. The relative deviation between the results of the correlation equation fit and the results of the one-dimensional simulation is less than 3%. This work provides a new engineering method for structure strength design and decompression load analysis with high accuracy and low resource consumption.

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.

In order to deepen the understanding of rotating effects on internal cooling, the flow and heat transfer characteristics of 2-pass rotating rectangular smooth/ribbed channels are investigated by Reynolds-Averaged Navier-Stokes (RANS) simulation. Three rotating numbers (Ro = 0.10, 0.25, and 0.40) are simulated, and the maximum buoyancy parameter (Bo) reaches 5.0. The results show that the rotating buoyancy has significant effects on the flow and heat transfer under high Bo conditions. When Bo > 1.0, rotating buoyancy inducts flow separation near the leading edge (LE) in the first passage, while the air flow in the second passage shows a double-peak profile. With increased Bo, the heat transfer in the first passage is greatly increased, and the maximum growth rate occurs at Bo = 0.6~1.0. However, the heat transfer in the second passage has no obvious changes due to a strong turn effect. In the ribbed channel, rotating effects are much weaker than those in the smooth channel. This research helps to improve the understanding of the internal cooling heat transfer mechanism in land-based gas turbines under typical operating conditions.

Methanol is hygroscopic in a gaseous state and is a promising alternative fuel for internal combustion engines. It is understood that adding water can improve the antiknock performance for spark ignition engines, but this will also affect the flame speed and stability. In this work, laminar flame characteristics of methanol/water/air mixtures were experimentally investigated at a temperature range of 380–450 K, a pressure range of 1–4 bar, and water fractions (vaporous water molar fraction in the water–methanol fuel gas) of 0–40%. The results show that laminar burning velocity increases with temperature but decreases with pressure. The burning velocity decreases linearly with water fraction at a stoichiometric ratio. For rich mixtures and high pressures, the laminar flames tend to be more sensitive to stretch and, thus, more prone to being unstable. Increasing the water fraction can slightly increase the Markstein length. Increasing the initial pressure enhances the general flame instability, while increasing the initial temperature suppresses the general flame instability. Increasing the water fraction can lead to a decreasing thermal expansion ratio and an elevated flame thickness, both of which can lead to a suppression of hydrodynamic instability. An increase in the water fraction decreases the Lewis number, resulting in preferential diffusion instability. There is no direct relationship between the onset of cellularity and general flame instability.

Daidzein, the main bioactive soy isoflavone in Nature, has been found to possess many biological functions. It has been investigated in particular as a phytoestrogen owing to the similarity of its structure with that of the human hormone estrogen. Due to the lack of comprehensive studies on daidzein metabolism, further research is still required to clarify its in vivo metabolic fate and intermediate processes. In this study, an efficient strategy was established using UHPLC-LTQ-Orbitrap mass spectrometry to profile the metabolism of daidzein in rats. Meanwhile, multiple data-mining methods including high-resolution extracted ion chromatogram (HREIC), multiple mass defect filtering (MMDF), neutral loss fragment (NLF), and diagnostic product ion (DPI) were utilized to investigate daidzein metabolites from the HR-ESI-MS1 to ESI-MSn stage in both positive and negative ion modes. Consequently, 59 metabolites, including prototype compounds, were positively or tentatively elucidated based on reference standards, accurate mass measurements, mass fragmentation behaviors, chromatographic retention times, and corresponding calculated ClogP values. As a result, dehydration, hydrogenation, methylation, dimethylation, glucuronidation, glucosylation, sulfonation, ring-cleavage, and their composite reactions were ascertained to interpret its in vivo biotransformation. Overall, our results not only revealed the potential pharmacodynamics forms of daidzein, but also aid in establishing a practical strategy for rapid screening and identifying metabolites of natural compounds.