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Intelligent vehicle trajectory tracking exhibits problems such as low adaptability, low tracking accuracy, and poor robustness in complex driving environments with uncertain road conditions. Therefore, an improved method of adaptive model predictive control (AMPC) for trajectory tracking was designed in this study to increase the corresponding tracking accuracy and driving stability of intelligent vehicles under uncertain and complex working conditions. First, based on the unscented Kalman filter, longitudinal speed, yaw speed, and lateral acceleration were considered as the observed variables of the measurement equation to estimate the lateral force of the front and rear tires accurately in real time. Subsequently, an adaptive correction estimation strategy for tire cornering stiffness was designed, an AMPC method was established, and a dynamic prediction time-domain adaptive model was constructed for optimization according to vehicle speed and road adhesion conditions. The improved AMPC method for trajectory tracking was then realized. Finally, the control effectiveness and trajectory tracking accuracy of the proposed AMPC technique were verified via co-simulation using CarSim and MATLAB/Simulink. From the results, a low lateral position error and heading angle error in trajectory tracking were obtained under different vehicle driving conditions and road adhesion conditions, producing high trajectory-tracking control accuracy. Thus, this work provides an important reference for improving the adaptability, robustness, and optimization of intelligent vehicle tracking control systems. [ABSTRACT FROM AUTHOR]

Goethite and ferrihydrite are the two major iron hydroxides, essential mineral constituents in the terrestrial surface system. Aluminum (Al) is the most common substituent in iron hydroxides, and it may significantly change the bulk and surficial physicochemical properties of iron hydroxides. Consequently, a practical and convenient approach is needed to efficiently identify the Al substitution degrees of iron hydroxides in natural occurrences. This study presents a comprehensive investigation of the VSWIR characteristics of laboratory-synthesized Al-substituted goethite and ferrihydrite, to establish diagnostic VSWIR parameters for the identification and quantification of Al substitution levels in iron hydroxides. The findings revealed that Al substitution can affect the band positions (P) of goethite and ferrihydrite at ~650 nm, ~900 nm, and ~1400 nm. The relationships between the Al substitution of ferrihydrite and VSWIR parameters can be expressed as P900 = −0.43 × Al(%) + 931 and P1400 = −0.07 × Al(%) + 1428, while that of goethite can be expressed as P650 = 0.42 × Al(%) + 657 and P900 = 2.29 × Al(%) + 936. The peak fitting results showed that the absorption intensity at 480–550 nm linearly decreases with increased Al substitution. The obtained VSWIR spectra of Al-substituted goethite and ferrihydrite provide a critical supplement to the spectral library for (Al) iron hydroxides, and these VSWIR parameters can be utilized for the semi-quantitative determination of Al substitution in natural iron hydroxides [ABSTRACT FROM AUTHOR]

Electrokinetic mining (EKM) is a novel method for rare earth element (REE) mining that can achieve green and efficient recovery of REEs. However, as yet, there is no accurate model for describing the electrokinetic transport of REEs in weathering crusts, and this hinders the wider application of EKM. The conventional model fails to capture the microscale transport physics occurring in the nanochannels that exist ubiquitously in weathering crusts. Consequently, the existing models cannot distinguish the mobilities of different REEs. Here, we report a new model for a more faithful description of the electrokinetic transport of REEs in weathering crusts that considers the ionic size, which has previously been neglected. We reveal that the electrokinetic transport of heavy REEs (HREEs) is faster than that of light REEs (LREEs) in weathering crusts, which is contrary to the predictions of conventional models. Our model was validated experimentally by measurements of the electrokinetic transport of two LREEs (La and Sm) and an HREE (Er) in weathering crusts. The speed of electrokinetic transport follows the order Er > Sm > La. Our findings suggest that the ionic size is a non-negligible factor affecting the electrokinetic transport of REEs in weathering crusts containing nanochannels. This work offers a constitutive model to describe the electrokinetic transport of REEs in weathering crusts, which promotes both theoretical developments and practical applications of EKM. [ABSTRACT FROM AUTHOR]

This study analyzed the differences in ozone (O3) sensitivity in four different urban areas in China from February 2019 to January 2020 based on data on various near-surface pollutants from passive multi-axis differential optical absorption spectroscopy (MAX-DOAS) sites and nearby China National Environmental Monitoring Center (CNEMC) sites. Across the four cities, the nitrogen dioxide (NO2) and formaldehyde (HCHO) concentrations varied seasonally. Xianghe consistently displayed the lowest NO2 levels, suggesting reduced emissions compared to other cities. Guangzhou, a city with a robust economy and a high level of vehicle ownership, exhibited higher concentrations in spring. Summer brought elevated HCHO levels in Guangzhou, Xianghe, and Shenyang due to intensified photochemical processes. Autumn and winter showed higher HCHO concentrations in Guangzhou and Xianghe compared to Lanzhou and Shenyang. Overall, Guangzhou recorded the highest annual averages, due to its developed economy, while Xianghe's lower NO2 levels were offset by the elevated HCHO due to higher O3 values. The analysis delved into primary and secondary HCHO sources across seasons and used carbon monoxide (CO) and O3 data. Xianghe showcased the dominance of secondary sources in summer and autumn, while Lanzhou was characterized by primary dominance throughout the year. Shenyang mirrored Xianghe's evolution due to industrial emissions. In Guangzhou, due to the high levels of vehicular traffic and sunlight conditions, secondary sources predominantly influenced HCHO concentrations. These findings highlight the interplay between primary and secondary emissions in diverse urban settings. This study explored O3 sensitivity variations across seasons. Xianghe exhibited a balanced distribution among volatile organic compound (VOC)-limited conditions, nitrogen oxide (NOx)-limited conditions, and transitional influences. Lanzhou was mainly affected by VOC-limited conditions in winter and NOx-limited conditions in other seasons. Shenyang's sensitivity varied with the seasons and was primarily influenced by transitions between VOCs and NOx in autumn and NOx-limited conditions otherwise. Guangzhou experienced varied influences. During periods of high O3 pollution, all regions were affected by NOx-limited conditions, indicating the necessity of NOx monitoring in these areas, especially during summer in all regions and during autumn in Xianghe and Guangzhou. [ABSTRACT FROM AUTHOR]

The existing deep-learning-based hyperspectral anomaly detection methods detect anomalies by reconstructing a clean background. However, these methods model the background of the hyperspectral image (HSI) through global features, neglecting local features. In complex background scenarios, these methods struggle to obtain accurate background priors for training constraints, thereby limiting the anomaly detection performance. To enhance the capability of the network in extracting local features and improve anomaly detection performance, a hyperspectral anomaly detection method based on differential network is proposed. First, we posit that anomalous pixels are challenging to be reconstructed through the features of surrounding pixels. A differential convolution method is introduced to extract local punctured neighborhood features in the HSI. The differential convolution contains two types of kernels with different receptive fields. These kernels are adopted to obtain the outer window features and inner window features. Second, to improve the feature extraction capability of the network, a local detail attention and a local Transformer attention are proposed. These attention modules enhance the inner window features. Third, the obtained inner window features are subtracted from the outer window features to derive differential features, which encapsulate local punctured neighborhood characteristics. The obtained differential features are employed to reconstruct the background of the HSI. Finally, the anomaly detection results are extracted from the difference between the input HSI and the reconstructed background of the HSI. In the proposed method, for each receptive field kernel, the optimization objective is to reconstruct the input HSI rather than the background HSI. This way circumvents problems where the background constraint biases might affect detection performance. The proposed method offers researchers a new and effective approach for applying deep learning in a local area to the field of hyperspectral anomaly detection. The experiments are conducted with multiple metrics on five real-world datasets. The proposed method outperforms eight state-of-the-art methods in both subjective and objective evaluations. [ABSTRACT FROM AUTHOR]

This paper explores the effects of climate change on the wind energy input (WEI) to the surface waves (SWs) in the northern Indian Ocean (NIO), a region with great potential for green renewable energy from waves and wind. We used the newly developed Coupled Model Intercomparison Project Phase 6 (CMIP6) model data to predict the spatiotemporal variations of the WEI to the SW. We found that, under the global warming scenario, the WEI to the SWs decreased significantly in most of the NIO, and it will drop by 18% to 27% in the central and southern regions by the end of the 21st century under the SSP5–8.5 scenario. However, the WEI to the SWs increased in the Red Sea, Persian Gulf, northwestern Arabian Sea, and northern Bay of Bengal, with the largest increase in the Persian Gulf region (up to 27%). We also examined the interannual and interdecadal variability characteristics of the WEI to the SW after the accumulation of the whole study region and found that it showed a long-term increasing trend only under the SSP1–2.6 scenario, while it showed a significant decreasing trend under the SSP2–4.5 and SSP5–8.5 scenarios. Furthermore, we show that the WEI to the SWs in the Indian Ocean mainly occurs in summer, followed by winter. [ABSTRACT FROM AUTHOR]