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Fine structural interpretation is one of the effective methods to find favorable structural traps and targets. Aiming at the fault interpretation of Jurassic Badaowan Fm in Madong area of Xiayan fault zone is not fine and structural characteristics are not implemented, the structural characteristics of Jurassic Badaowan Fm are implemented and favorable traps are found through the study of seismic, well logging and geological comprehensive methods. The results show that the structure of Jurassic Badaowan Fm in Madong area is high in the north and low in the south. The first member of Badaowan Fm is a delta front subaqueous distributary channel deposit, which is a hydrocarbon enrichment zone. Oil and gas are adjusted along the relay of deep and shallow faults, and the great fault zone and the inherited uplifting zone are favorable accumulation areas of Jurassic Badaowan Fm in Madong area. In this paper, the relationship between deep and shallow fault system and hydrocarbon distribution of Badaowan Fm in Madong area is systematically reviewed for the first time. Combined with the palaeohigh distribution, three favorable hydrocarbon accumulation zones are identified, which provides favorable targets for exploring the hydrocarbon accumulation potential of Badaowan Fm.

Accurate moisture prediction aids in taking early preventive measures to minimize damage caused by severe storms, droughts, and floods. The study collected 24 types of statistical data from the Xilingol Grassland, Inner Mongolia, spanning from 2011 to 2021. By integrating Elman, backpropagation (BP), and soil moisture prediction model (Elman‐BP) neural networks, soil moisture to 2 m was predicted. The soil moisture mechanism model analysis revealed that soil moisture is related to water supply rather than directly correlated with time. Based on these findings, a two‐stage soil moisture prediction model was established. In the first stage, the Elman neural network model predicted future precipitation data based on observed values. In the second stage, soil moisture was predicted using a BP neural network model based on predicted precipitation data. Comparing the mean squared errors of training and prediction data across the three models revealed that the Elman‐BP neural network had superior predictive accuracy. The maximum relative errors for the trained Elman neural network, BP neural network, and Elman‐BP model were 31.35%, 20.98%, and 3.94%. Overall, results showed a link between soil moisture and water supply and that soil moisture decreased with increasing temperatures. Soil moisture at 10‐ and 40‐cm depths was increased with increasing rainfall. Furthermore, a delay exists between the 40‐ and 100‐cm soil depths. Moreover, soil moisture stability increased with depth. Core Ideas: Accurate soil moisture prediction aids disaster preparedness, reducing damage from severe weather.By comparing three models and making optimal model selection, the maximum error decreased from 31.15% to 94%.Enhance data presentation through vivid visualization to effectively display soil moisture prediction outcomes.The BP‐Elman neural network integrates soil moisture–rainfall–surface evaporation, boosting prediction accuracy.Long‐term data: 11‐year Xilingol Grassland data evaluates models. [ABSTRACT FROM AUTHOR]

See also the Commentary on this article by Zhang et al., 242: 327–330. [ABSTRACT FROM AUTHOR]

Microphysiological systems (MPSs), also known as organ-on-a-chip models, aim to recapitulate the functional components of human tissues or organs in vitro. Over the last decade, with the advances in biomaterials, 3D bioprinting, and microfluidics, numerous MPSs have emerged with applications to study diseased and healthy tissue models. Various organs have been modeled using MPS technology, such as the heart, liver, lung, and blood–brain barrier. An important aspect of in vitro modeling is the accurate phenotypical and functional characterization of the modeled organ. However, most conventional characterization methods are invasive and destructive and do not allow continuous monitoring of the cells in culture. On the other hand, microfluidic biosensors enable in-line, real-time sensing of target molecules with an excellent limit of detection and in a non-invasive manner, thereby effectively overcoming the limitation of the traditional techniques. Consequently, microfluidic biosensors have been increasingly integrated into MPSs and used for in-line target detection. This review discusses the state-of-the-art microfluidic biosensors by providing specific examples, detailing their main advantages in monitoring MPSs, and highlighting current developments in this field. Finally, we describe the remaining challenges and potential future developments to advance the current state-of-the-art in integrated microfluidic biosensors. [ABSTRACT FROM AUTHOR]

Accurate estimation and quantification of the corneal nerve fiber tortuosity in corneal confocal microscopy (CCM) is of great importance for disease understanding and clinical decision-making. However, the grading of corneal nerve tortuosity remains a great challenge due to the lack of agreements on the definition and quantification of tortuosity. In this paper, we propose a fully automated deep learning method that performs image-level tortuosity grading of corneal nerves, which is based on CCM images and segmented corneal nerves to further improve the grading accuracy with interpretability principles. The proposed method consists of two stages: 1) A pre-trained feature extraction backbone over ImageNet is fine-tuned with a proposed novel bilinear attention (BA) module for the prediction of the regions of interest (ROIs) and coarse grading of the image. The BA module enhances the ability of the network to model long-range dependencies and global contexts of nerve fibers by capturing second-order statistics of high-level features. 2) An auxiliary tortuosity grading network (AuxNet) is proposed to obtain an auxiliary grading over the identified ROIs, enabling the coarse and additional gradings to be finally fused together for more accurate final results. The experimental results show that our method surpasses existing methods in tortuosity grading, and achieves an overall accuracy of 85.64% in four-level classification. We also validate it over a clinical dataset, and the statistical analysis demonstrates a significant difference of tortuosity levels between healthy control and diabetes group. We have released a dataset with 1500 CCM images and their manual annotations of four tortuosity levels for public access. The code is available at: https://github.com/iMED-Lab/TortuosityGrading. [ABSTRACT FROM AUTHOR]

With the continuous development of the global economy, the degree of internationalization and openness has gradually deepened. Higher English internationalization education also needs to keep pace with the times and keep pace with international development. At this time, it is necessary to build a multi-source information fusion algorithmic higher English international education model and evaluation index system to better adapt to the trend of higher English international education in the future. In the current higher English teaching process, it is necessary to change the traditional teaching concepts, change the previous teaching ideas, continuously expand the horizons, build an international and diversified English teaching training program, and actively absorb excellent education concepts from foreign excellent teaching models, which has better promoted the development of English teaching. [ABSTRACT FROM AUTHOR]

Trigeminal neuralgia caused by paroxysmal and severe pain in the distribution of the trigeminal nerve is a rare chronic pain disorder. It is generally accepted that compression of the trigeminal root entry zone by vascular structures is the major cause of primary trigeminal neuralgia, and vascular decompression is the prior choice in neurosurgical treatment. Therefore, accurate preoperative modeling/segmentation/visualization of trigeminal nerve and its surrounding cerebrovascular is important to surgical planning. In this paper, we propose an automated method to segment trigeminal nerve and its surrounding cerebrovascular in the root entry zone, and to further reconstruct and visual these anatomical structures in three-dimensional (3D) Magnetic Resonance Angiography (MRA). The proposed method contains a two-stage neural network. Firstly, a preliminary confidence map of different anatomical structures is produced by a coarse segmentation stage. Secondly, a refinement segmentation stage is proposed to refine and optimize the coarse segmentation map. To model the spatial and morphological relationship between trigeminal nerve and cerebrovascular structures, the proposed network detects the trigeminal nerve, cerebrovasculature, and brainstem simultaneously. The method has been evaluated on a dataset including 50 MRA volumes, and the experimental results show the state-of-the-art performance of the proposed method with an average Dice similarity coefficient, Hausdorff distance, and average surface distance error of 0.8645, 0.2414, and 0.4296 on multi-tissue segmentation, respectively. [ABSTRACT FROM AUTHOR]