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Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision agriculture. However, the unique agronomic practice (i.e., varied stubble height treatment) in rice ratooning could lead to inconsistent rice phenology, which had a significant impact on yield prediction of ratoon rice. Multi-temporal unmanned aerial vehicle (UAV)-based remote sensing can likely monitor ratoon rice productivity and reflect maximum yield potential across growing seasons for improving the yield prediction compared with previous methods. Thus, in this study, we explored the performance of combination of agronomic practice information (API) and single-phase, multi-spectral features [vegetation indices (VIs) and texture (Tex) features] in predicting ratoon rice yield, and developed a new UAV-based method to retrieve yield formation process by using multi-temporal features which were effective in improving yield forecasting accuracy of ratoon rice. The results showed that the integrated use of VIs, Tex and API (VIs & Tex + API) improved the accuracy of yield prediction than single-phase UAV imagery-based feature, with the panicle initiation stage being the best period for yield prediction (R2 as 0.732, RMSE as 0.406, RRMSE as 0.101). More importantly, compared with previous multi-temporal UAV-based methods, our proposed multi- temporal method (multi-temporal model VIs & Tex: R2 as 0.795, RMSE as 0.298, RRMSE as 0.072) can increase R2 by 0.020–0.111 and decrease RMSE by 0.020–0.080 in crop yield forecasting. This study provides an effective method for accurate pre-harvest yield prediction of ratoon rice in precision agriculture, which is of great significance to take timely means for ensuring ratoon rice production and food security.

Shale oil and gas resources are abundant in the Chang 7 shale of the Yanchang Formation in Ordos Basin. To determine the characteristics and influencing factors of hydrocarbon generation evolution of the Chang 7 shale, a series of thermal simulation experiments were conducted on low‐maturity shale and kerogen samples. The results indicate that the maximum yield of shale oil are 294.5 and 304.3 mg/g TOC for kerogen sample at heating rates of 20 and 2°C/h, and the corresponding experimental temperatures are 360.2°C and 408.0°C, respectively. The utilization of lower heating rates is favourable for shale oil generation and it is recommended to employ a lower heating rate during in situ heating processes to maximize the economic benefits. The formation of crude oil cracking gas begins when simulating temperature exceeds 528.0°C (Easy Ro 2.6%) at a heating rate of 20°C/h and 480.0°C (Easy Ro 2.5%) at a heating rate of 2°C/h, as indicated by the carbon isotopic composition of gaseous hydrocarbons. The maximum oil production rate of the rock powder sample is 159.8 mg/g TOC, which is lower than that of the kerogen sample. It suggests that certain minerals in the Chang 7 shale may impede hydrocarbon generation. After the addition of pyrite, the highest yield of shale oil is 213.96 mg/g TOC, 33.9% higher than the yield of the original rock powder sample, reflecting the positive catalytic effect of pyrite on hydrocarbon generation of Chang 7 shale. Under geologic conditions, pyrite catalytic hydrocarbon generation may act primarily on the migration of organic matter by macromolecules, which considerably increases the probability of direct contact between pyrite and organic matter. Therefore, the organic‐rich shale with high pyrite content in Chang 7 member is the preferred target for in situ conversion of shale oil and gas in the Ordos Basin. [ABSTRACT FROM AUTHOR]

Exploration and development effects of Lucaogou Formation laminated shale are not ideal in the Santanghu Basin, and the reasons are not clear. Therefore, in this study, laminated and massive shales were systematically sampled, and experiments of cryogenic adsorption of nitrogen and multi‐dimensional nuclear magnetic resonance comparison were carried out. The results show that Lucaogou Formation shale is rich in volcanic ash and low content of clay, which can be divided into tuff, dolomite, tuffaceous dolomite, dolomitic tuff, and other lithologic types. The laminar structure is developed in tuffaceous dolomite and dolomitic tuff. The pores of Lucaogou Formation shale with a diameter of more than 10 nm can be used as an effective storage space for movable oil. The oil saturation and mobile oil ratio of laminated shale are lower than that of massive shale. Movable oil is controlled by carbonate content, and the higher the content, the higher the proportion of movable oil. Millimetre laminae are rich in organic matter and have poor interlaminar pore connectivity, which leads to strong adsorption capacity for oil. Therefore, the migration of oil between laminae is blocked, and oil mainly distributes or migrates in the bedding plane between laminae in laminated shale, which cannot be used as favourable reservoirs. [ABSTRACT FROM AUTHOR]

Rapid development of artificial intelligence techniques significantly promotes important medical imaging applications such as disease diagnosis, medical image analysis and cross-modality synthesis. It is valuable to synthesize Computed Tomography (CT) scans of human brains from Magnetic Resonance Imaging (MRI) scans because CT images contain particular information for diseases such as malignant tumors, cerebrovascular malformations and aneurysms. Although there has been many studies trying to apply machine learning methods (e.g., fully convolutional neural networks and generative adversarial networks) to predict CT images based on MRI, there are still opportunities to increase the quality of synthesized CT images using the latest machine learning algorithms. In this study, we aim to synthesize high-credibility human brain CT images from MRI images using machine learning approaches. We build a dataset of both CT and MRI images of 41 patients from The Cancer Imaging Archive. A data pre-processing pipeline is designed to guarantee that the processed images are suitable as inputs to machine learning algorithms. The pipeline includes (1) pairing MRI and CT scans according to a certain time interval between CT and MRI exams of the same patient, (2) registering all MRI images to a standard MRI template, (3) registering all CT images to paired MRI images, (4) intensity normalization and (5) extracting 2D slices from 3D CT and MRI volumes. Three deep neural networks with encoder–decoder structure are selected and implemented including U-Net, U-Net++ and pix2pix to synthesize human brain CT images from MRI images. Experimental results illustrate that U-Net++ shows better performance than U-Net and pix2pix in synthesizing CT images from MRI by achieving a Mean Squared Error of 0.025, a Mean Absolute Error of 0.082 and a Peak Signal-to-Noise Ratio of 67.90, respectively. The trained machine learning models from this work can be applied in future human brain CT image prediction where CT images are absent and MRI images are available for specific patients, thereby supporting disease diagnosis based on CT images. [ABSTRACT FROM AUTHOR]

One of the advantages of cement and the cement concrete industry in sustainability is the ability to utilize large amounts of industrial solid wastes such as fly ash and ground granulated blast furnace slag. Tailings are solid wastes of the ore beneficiation process in the extractive industry and are available in huge amounts in some countries. This paper reviews the potential utilization of tailings as a replacement for fine aggregates, as supplementary cementitious materials (SCMs) in mortar or concrete, and in the production of cement clinker. It was shown in previous research that while tailings had been used as a replacement for both fine aggregate and cement, the workability of mortar or concrete reduced. Also, at a constant water to cement ratio, the compressive strength of concrete increased with the tailings as fine aggregate. However, the compressive strength of concrete decreased as the replacement content of the tailings as SCMs increased, even whentailings were ground into smaller particles. Not much research has been dedicated to the durability of concrete with tailings, but it is beneficial for heavy metals in tailings to stabilize/solidify in concrete. The clinker can be produced by using the tailings, even if the tailings have a low SiO2 content. As a result, the utilization of tailings in cement and concrete will be good for the environment both in the solid waste processing and virgin materials using in the construction industry.

The rapid development of computer vision techniques has brought new opportunities for manufacturing industries, accelerating the intelligence of manufacturing systems in terms of product quality assurance, automatic assembly, and industrial robot control. In the electronics manufacturing industry, intensive variability in component shapes and colors, background brightness, and visual contrast between components and background results in difficulties in printed circuit board image classification. In this paper, we apply computer vision techniques to detect diverse electronic components from their background images, which is a challenging problem in electronics manufacturing industries because there are multiple types of components mounted on the same printed circuit board. Specifically, a 13-layer convolutional neural network (ECON) is proposed to detect electronic components either of a single category or of diverse categories. The proposed network consists of five Convolution-MaxPooling blocks, followed by a flattened layer and two fully connected layers. An electronic component image dataset from a real manufacturing company is applied to compare the performance between ECON, Xception, VGG16, and VGG19. In this dataset, there are 11 categories of components as well as their background images. Results show that ECON has higher accuracy in both single-category and diverse component classification than the other networks. [ABSTRACT FROM AUTHOR]

Background PET can be used for amyloid-tau-neurodegeneration (ATN) classification in Alzheimer disease, but incurs considerable cost and exposure to ionizing radiation. MRI currently has limited use in characterizing ATN status. Deep learning techniques can detect complex patterns in MRI data and have potential for noninvasive characterization of ATN status. Purpose To use deep learning to predict PET-determined ATN biomarker status using MRI and readily available diagnostic data. Materials and Methods MRI and PET data were retrospectively collected from the Alzheimer's Disease Imaging Initiative. PET scans were paired with MRI scans acquired within 30 days, from August 2005 to September 2020. Pairs were randomly split into subsets as follows: 70% for training, 10% for validation, and 20% for final testing. A bimodal Gaussian mixture model was used to threshold PET scans into positive and negative labels. MRI data were fed into a convolutional neural network to generate imaging features. These features were combined in a logistic regression model with patient demographics, APOE gene status, cognitive scores, hippocampal volumes, and clinical diagnoses to classify each ATN biomarker component as positive or negative. Area under the receiver operating characteristic curve (AUC) analysis was used for model evaluation. Feature importance was derived from model coefficients and gradients. Results There were 2099 amyloid (mean patient age, 75 years ± 10 [SD]; 1110 male), 557 tau (mean patient age, 75 years ± 7; 280 male), and 2768 FDG PET (mean patient age, 75 years ± 7; 1645 male) and MRI pairs. Model AUCs for the test set were as follows: amyloid, 0.79 (95% CI: 0.74, 0.83); tau, 0.73 (95% CI: 0.58, 0.86); and neurodegeneration, 0.86 (95% CI: 0.83, 0.89). Within the networks, high gradients were present in key temporal, parietal, frontal, and occipital cortical regions. Model coefficients for cognitive scores, hippocampal volumes, and APOE status were highest. Conclusion A deep learning algorithm predicted each component of PET-determined ATN status with acceptable to excellent efficacy using MRI and other available diagnostic data. © RSNA, 2023 Supplemental material is available for this article.