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Background: China has a unique cotton planting pattern. Cotton is densely planted in alternating wide and narrow rows to increase yield in Xinjiang, China, causing the difficulty in the accurate estimation of cotton yield using remote sensing in such field with branches occluded and overlapped. Results: In this study, unmanned aerial vehicle (UAV) imaging and deep convolutional neural networks (DCNN) were used to estimate densely planted cotton yield. Images of cotton fields were acquired by the UAV at an altitude of 5 m. Cotton bolls were manually harvested and weighed afterwards. Then, a modified DCNN model (CD-SegNet) was constructed for pixel-level segmentation of cotton boll images by reorganizing the encoder-decoder and adding dilated convolutions. Besides, linear regression analysis was employed to build up the relationship between cotton boll pixels ratio and cotton yield. Finally, the estimated yield for four cotton fields were verified by weighing harvested cotton. The results showed that CD-SegNet outperformed the other tested models, including SegNet, support vector machine (SVM), and random forest (RF). The average error in yield estimates of the cotton fields was as low as 6.2%. Conclusions: Overall, the estimation of densely planted cotton yields based on low-altitude UAV imaging is feasible. This study provides a methodological reference for cotton yield estimation in China. [ABSTRACT FROM AUTHOR]

The development of the sedimentary basin in the South Yellow Sea that lies between the Chinese mainland and the Korean Peninsula well documents the tectonic evolution of eastern China. However, the Moho morphology and its relationship with the basin in this area remain poorly understood. Here we used high‐resolution 2D seismic lines and well data to map the geometry, structure, and distribution of the sedimentary basin in the South Yellow Sea. Our results suggest that the South Yellow Sea Basin can be divided into two distinct depressions and three uplifts. The depressions consist of eight depositional sags that are largely controlled by ENE‐, EW‐, and WNW‐striking normal faults. The basin depth reaches 9,500 m in the depressions, but is only <2000 m in the uplift zones. Calculated using a gravity stripping method, the Moho depth varies between 27.4 and 31.5 km. The basin is isostatically compensated and the Moho morphology approximately mirrors that of the basin basement. The Moho lows correspond to the uplift zones and Moho highs correspond to the depression zones, with exception of a sag in the Northern Depression. This exception is caused by the presence of a geological body of high velocity and high density beneath the sag. This body is likely to represent high‐pressure metamorphic rock that initially formed during the collision between the Sino‐Korean and Yangtze cratons and are currently overlain by the sedimentary basin. Based on the stretching factors and strain rates of the South Yellow Sea Basin, we propose that development of the basin was primarily driven by the subduction and retreat of the Pacific Plate since the Late Cretaceous, combined by far‐field effects from the convergence between the Indian and Eurasian plates during the Cenozoic. [ABSTRACT FROM AUTHOR]

Returning biochar to farmland has become one of the nationally promoted technologies for soil remediation and improvement in China. Rapid detection of heavy metals in biochar derived from varied materials can provide a guarantee for contaminated soil, avoiding secondary pollution. This work aims first to apply laser-induced breakdown spectroscopy (LIBS) for the quantitative detection of Cr in biochar. Learning from the principles of traditional matrix effect correction methods, calibration samples were divided into 1–3 classifications by an unsupervised hierarchical clustering method based on the main elemental LIBS data in biochar. The prediction samples were then divided into diverse classifications of calibration samples by a supervised K-nearest neighbor (KNN) algorithm. By comparing the effects of multiple partial least squares regression (PLSR) models, the results show that larger numbered classifications have a lower averaged relative standard deviations of cross-validation (ARSDCV) value, signifying a better calibration performance. Therefore, the 3 classification regression model was employed in this study, which had a better prediction performance with a lower averaged relative standard deviations of prediction (ARSDP) value of 8.13%, in comparison with our previous research and related literature results. The LIBS technology combined with matrix effect classification regression model can weaken the influence of the complex matrix effect of biochar and achieve accurate quantification of contaminated metal Cr in biochar. [ABSTRACT FROM AUTHOR]

• Heat-power decoupling by thermal energy storage and electric heat pump are studied. • A dynamic optimization model to maximize wind accommodation is developed. • An operation strategy to accommodate wind power is proposed. • The selection of peak shaving devices on wind power accommodation is studied. • Energy saving of the peak shaving systems with electric heat pump is analyzed. Wind power curtailment becomes a major problem in many countries. The wind accommodation mechanisms and energy saving potentials for the combined heat and power plant with thermal energy storage, electric heat pump and both should be evaluated more systematically and accurately to accommodate more wind power. Heat-power peak shaving capacities for thermal energy storage, electric heat pump and both are analyzed using a graphical method, while the operation strategy is proposed to maximize wind accommodation. A simulation model for wind power accommodation considering the energy balances and constraints of all production units is developed based on EnergyPRO. A regional energy supply system in Jilin Province, China is selected as the case study, where the influences of different peak shaving technologies and their parameters on the wind accommodation and energy saving are studied. The wind curtailment ratio is reduced from 20.31% to 13.04% and 7.51% with thermal energy storage and electric heat pump respectively, and it is further reduced to 4.21% with both. Systems with electric heat pump can save energy from 1.1% to 5.8% with different parameters of the peak shaving devices. It was found that electric heat pump has better accommodation capability than that of thermal energy storage. Wind accommodation can be improved by adding thermal energy storage to electric heat pump, but the effect gradually decreases as the storage size increases. Electric heat pump can increase the system's energy efficiency, but it is not always energy efficient by adding thermal energy storage to electric heat pump. In fact, thermal energy storage should not be too large, otherwise the system's energy efficiency will be reduced. [ABSTRACT FROM AUTHOR]

• The YOLOV7 backbone and YOLO head were improved with the ConvNext and Swin-Transformer modules, resulting in Cotton-YOLO. • Ablation experiments showed ConvNext module speeds up detection; Swin-Transformer module improves detection accuracy. • Foreign fiber detection in seed cotton by Cotton-YOLO: 99.12% accuracy, 96.92% mAP50, and 132.2 FPS detection speed. • Compared to YOLOV7, Cotton-YOLO improved accuracy by 1.13%, mAP50 by 1.17%, and detection speed by 31 FPS. In China, the widespread adoption of machine-picked cotton has greatly improved the efficiency of cotton harvesting. However, this has significantly increased the presence of foreign fibers in seed cotton. Failure to promptly eliminate foreign fibers can have severe consequences for the subsequent quality of cotton processing and textile production. Currently, manual labor is predominantly used for the detection and removal of foreign fibers, which is both inefficient and costly. To address this issue, a high-resolution camera-based seed cotton foreign fibers image acquisition system was developed in this study. In addition, using the YOLOV7 network, a novel algorithm dubbed "Cotton-YOLO" was developed to effectively detect foreign fibers in seed cotton. The Cotton-YOLO algorithm uses ConvNext modules to optimize the YOLOV7′s backbone and incorporates Swin-Transformer and ConvNext modules to optimize the YOLOV7′s head, ensuring exceptional detection performance and speed for foreign fibers. To substantiate the efficacy of the aforementioned methods, this study conducted validation experiments. The study's results demonstrated that Cotton-YOLO detected foreign fibers in seed cotton with a detection accuracy of 99.12 %, a mAP50 of 96.92 %, and a detection speed of 132.2 FPS (7.6 ms/per image) on the test image set. Comparatively, YOLOV7 demonstrated a detection accuracy of 97.99 %, a mAP50 of 95.75 %, and a detection speed of 101.2 FPS (9.9 ms/per image) for foreign fibers in seed cotton on the test image set. The detection accuracy and mAP50 of Cotton-YOLO were found to be comparable to those of YOLOV7. However, the detection speed of Cotton-YOLO was substantially faster than that of YOLOV7. The validation experiments confirmed the effectiveness of the aforementioned methods. Specifically, YOLOV7 exhibited an accuracy of 94.53 % and a mAP50 of 87.44 % for foreign fibers detection, while Cotton-YOLO surpassed YOLOV7 with an accuracy of 96.14 % and a mAP50 of 88.57 %. The results of this study present a novel and efficient method for rapidly detecting foreign fibers in seed cotton. This research provides crucial technical support for the future development of apparatus dedicated to detecting and sorting foreign fibers. [ABSTRACT FROM AUTHOR]

• Building an integrating sphere-based spectroscopic system to measure the optical properties. • Obtaining the optical properties' parameters of cotton and mulching film. • Selecting the feature bands to detect mulching film using the optical properties of samples. • Analyzing the sources of differences in the optical properties of cotton and mulching film. Cotton is prone to being mixed with mulching film during the harvesting and packing process in China, which can significantly decrease the quality of cotton fiber and impact the quality of subsequent textile products. Mulching film is a translucent material that makes it challenging to detect optically, which presents an urgent challenge for cotton quality testing due to the lack of research on its detection mechanism. In this study, the absorption coefficient (μ a), reduced scattering coefficient (μ s '), and scattering anisotropy factor (g) of cotton lint and mulching film in the spectral region of 400–1120 nm were obtained using an integrating sphere-based spectroscopic measurement system. The accuracy of the system was validated using liquid phantoms made from polystyrene particles and absorbing dyes. The interval random frog (iRF) algorithm was used to select feature bands, and multivariate analysis of variance (MANOVA) was employed to assess the differences between cotton and mulching film in terms of μ a and μ s ' in the selected feature bands. Support vector machine (SVM) and linear discriminant analysis (LDA) were utilized to classify the concatenation of μ a , μ s ' , μ a and μ s ' of cotton and mulching film in full wavelengths and feature bands. The results demonstrated that there were significant differences in μ a , μ s ' and g between cotton and mulching film due to their different chemical compositions, surface morphology, and tensile properties. The selected feature bands were in the blue band (456.1 nm), red bands (605.8 nm, 628.9 nm, 685.6 nm), and near-infrared bands (805.4 nm, 837 nm, 1006.3 nm). The SVM classification results of the concatenation of μ a , μ s ' , μ a and μ s ' for cotton and mulching film were 100 % in both full wavelengths and feature bands. This study makes a significant contribution by reporting the optical properties of cotton and mulching film, which were used to determine the detection sensitive bands. These findings will serve as a theoretical foundation and data set for the development of detection equipment to identify mulching film in the cotton production process. [ABSTRACT FROM AUTHOR]

Early Mesozoic geodynamic and tectonic relations between different blocks in East China remain controversial. The Subei-South Yellow Sea Basin (SB-SYSB) is located at the junction of different blocks and tectonic zones, including the North China Block (NCB), South China Block (SCB), Sulu Orogen and Tan-Lu Fault Zone (TLFZ). Therefore, the basin is a key location for understanding the regional geologic evolution of East China. Here, we present the geometry and kinematics of opposite thrust systems (OTSs) under the SB-SYSB based on seismic profiles, well data, magnetic reversion data and field observations. The OTSs are composed of two sets of thrust systems with opposite senses of motion: the northern top-to-the-southeast thrust system (N-TS) and southern top-to-the-northwest thrust system (S-TS). According to the structural styles, the N-TS and S-TS can be divided into the root, middle and frontal belts. The strikes of thrusts show a shift from NNE to N E - ENE and then to nearly E-W from west to east. The OTSs were suggested to have formed during the end of the middle Triassic to the middle Jurassic, and its deformation intensity decreased from west to east and from the root belt to the frontal belt. Combining these lines of evidence with published geochronological data for the Dabie-Sulu Orogen and other structural elements in East China, we provide a comprehensive synthesis and propose a new tectonic model to explain the early Mesozoic geodynamic and tectonic relations in East China. We emphasize the following points: (1) The Sulu Orogen might extend to the Northern Depression in the offshore SYSB and be bounded by the North Fault of the Central Massif to the south, indicating that the Sulu Orogen is significantly wider than stated in previous studies. (2) The N-TS was driven by the subduction of the YZB and the exhumation of the Sulu HP–UHP metamorphic rocks. In contrast, the S-TS was probably controlled by the orogenic uplift of the Jiangnan Orogen. (3) The dragging of the thrusts in the SB-SYSB and adjacent areas indicates that the sinistral motion of the TLFZ was coeval with the formation of the OTSs and was likely caused by the clockwise rotation of the SCB. (4) Finally, this clockwise rotation probably triggered intense corner compression between the Sulu Orogen, TLFZ and East Marginal Fault of the Yellow Sea, resulting in a genetic relation among the Sulu Orogen, the SCB, the Korean Peninsula and the Xu-Huai Belt. • The OTSs were controlled by subduction of the SCB, the exhumation of the Sulu Orogen and the uplift of the Jiangnan Orogen; • The Sulu Orogen might extend to the Northern Depression in the SYSB and be bounded by the North Fault of the Central Massif; • The TLFZ was coeval with the formation of the OTSs; • We proposed a model to illustrate the relationship among the Sulu Orogen, the SCB, the Korean Peninsula and the Xu-Huai Belt. [ABSTRACT FROM AUTHOR]