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Dating from the lunar magma ocean solidification period, the Procellarum KREEP Terrane (PKT) occupies 16% of the surface but has a much higher thorium abundance compared to the rest of the Moon and is thus interpreted to carry 40% of the radioactive elements by volume in the form of an anomalously thick KREEP-rich layer. Subsequent research has focused on the processes responsible for PKT concentration and localization (e.g., degree-1 convection, farside impact basin effects, etc.), and the effect of PKT high-radioactivity localization on lunar thermal evolution (e.g., topography relaxation, mantle heating, late-stage mare basalt generation, etc.). Here we use a stratigraphic approach and new crustal thickness data to probe the nature of the PKT with depth. We find that most PKT characteristics can be explained by sequential impact cratering events that excavated and redistributed to the surface/near-surface a much thinner Th-rich KREEP layer at depth, implying that no anomalous conditions of PKT thickness, radioactive abundances, geodynamics, thermal effects or magma generation are likely to be required as in the previous studies.

Accurately predicting the quality of depth-image-based-rendering (DIBR) synthesized images is of great significance in promoting DIBR techniques. Recently, many DIBR-synthesized image quality assessment (IQA) algorithms have been proposed to quantify the distortion that existed in texture images. However, these methods ignore the damage of DIBR algorithms on the depth structure of DIBR-synthesized images and thus fail to accurately evaluate the visual quality of DIBR-synthesized images. To this end, this paper presents a DIBR-synthesized image quality assessment metric with Texture and Depth Information, dubbed as TDI. TDI predicts the quality of DIBR-synthesized images by jointly measuring the synthesized image's colorfulness, texture structure, and depth structure. The design principle of our TDI includes two points: (1) DIBR technologies bring color deviation to DIBR-synthesized images, and so measuring colorfulness can effectively predict the quality of DIBR-synthesized images. (2) In the hole-filling process, DIBR technologies introduce the local geometric distortion, which destroys the texture structure of DIBR-synthesized images and affects the relationship between the foreground and background of DIBR-synthesized images. Thus, we can accurately evaluate DIBR-synthesized image quality through a joint representation of texture and depth structures. Experiments show that our TDI outperforms the competing state-of-the-art algorithms in predicting the visual quality of DIBR-synthesized images.

—The capabilities of a combination of passive seismic methods to study the geological structure of the upper part of the Earth’s crust compared to active methods are analyzed using case examples. The passive methods include microseismic sounding, Nakamura’s horizontal-to-vertical spectral ratio method (HVSR), seismic interferometry, and, for anthropogenic sites, ambient vibration testing using industrial signals. Three examples are considered: a zone of a platform tectonic earthquake, a kimberlite pipe, and a hydroelectric dam with foundation site. The results of the passive and active seismic methods agree well. Passive methods give more diffuse horizontal boundaries but clearly identify near-vertical heterogeneities. Combining passive methods is effective for reconnaissance studies and in the remote regions that are difficult to access by active observation techniques. Combination of passive methods enables simultaneous processing of seismic records obtained through different passive methods, with a minimum of two sensors required.

Previous functional magnetic resonance (fMRI) studies in humans and monkeys have demonstrated that the anterior intraparietal sulcus (IPS) is sensitive to the depth structure defined by binocular disparity. However, in the macaque monkey, a single large activation was measured in the anterior lateral bank of the IPS, whereas in human subjects two separate regions were sensitive to depth structure from disparity. We performed fMRI and single-cell experiments in the same animals, in a large number of recording sites in the lateral bank of the IPS. The fMRI interaction effect between the factors curvature (curved or flat) and disparity (stereo or control) correctly predicted the location of higher-order disparity selective neurons that encoded the depth structure of objects. However the large region in the IPS activated by depth structure consisted of two patches of higher-order disparity-selective neurons, one in the anterior IPS and one located more posteriorly, surrounded by regions lacking such selectivity. Thus the IPS region activated by curved surfaces consists of at least two patches of higher-order disparity selective neurons, which may reconcile previous fMRI studies in monkeys and humans.

AbstractThe possibility of using river seismic data for oil and gas exploration to study the deep structure of the Earth’s crust is shown. This method uses water seismic source points and bank-mounted autonomous seismic recorders installed for continuous seismic recording. About 2700 km of seismic profiles were completed by the CDP-2D method along the rivers of Eastern Siberia (Lena, Nizhnyaya Tunguska, and Vitim). The structure of the upper part of the Earth’s crust (up to several kilometers) has been studied, while its deep structure remains unknown. It is shown that the materials of seismic river surveys carried out along a 60-km section of the profile in the lower reaches of the Lena River, using the method developed by the Geophysical Survey, Russian Academy of Sciences, contain data allowing us to construct cross sections throughout the thickness of the Earth’s crust up to the Moho boundary. Low-amplitude fluctuations of reflected waves from deep boundaries are distinguished due to the wide dynamic range of the devices used and the multiple summation, which is significantly higher than in the case of the traditional seismic surveys. The high multiplicity is achieved by reducing the distance between blast points, increasing the sounding bases and the binning area.

The possibility of jointly inverting the receiver function waveforms and the seismic noise horizontal-to-vertical spectral ratio to study the Earth’s structure is substantiated. Both data types are widely used for constructing a velocity model beneath a single seismic station. The main difference between the methods is associated with the different frequency content of input data which is 0.02–0.2 Hz in receiver functions and 0.5–20 Hz in seismic noise. It is shown that notwithstanding these differences, the joint inversion approach more effectively reconstructs the model of the medium in case when a station is underlain by a complexly structured sedimentary cover. In the practical implementation, the parameters of both seismic methods are described in a flat-layer representation of the medium. Besides, both methods are most sensitive to the depth distribution of S-wave velocities. In this work, we use records from the Monakovo seismic station, Nizhny Novgorod region, Russia, to construct a model of the medium consistent with both data types. It is shown that the allowance for the H/V spectral curve in the receiver function interpretation provides additional constraints on the small-scale structure of the upper part of the velocity section thus stabilizing the reconstruction procedure.

【Objective】Populus euphratica is widely grown in Xinjiang, Northwestern China. The growth and development of its understory herbaceous plants are influenced by various factors. This paper investigates how groundwater depth, soil moisture, and the stand structure affect the biodiversity of these herbaceous plants.【Method】The analysis was based on data on diversity of the understory plants, groundwater depth, and soil physical properties collected from three representative Populus euphratica forests in the Ejina Oasis.【Result】① The richness and Shannon-Wiener diversity index of the herbaceous plants were negatively correlated with groundwater depth, stem diameter at the breast height (DBH), and soil nitrogen content, while they were positively correlated with stand density and soil moisture. ② Groundwater depth, stand structure, and soil moisture collectively explained 37.60% and 48.60% of the variation in species richness and the Shannon-Wiener index, respectively, indicating that these factors interact to shape the diversity of the understory herbaceous plants. ③ Soil moisture was the dominant driver, explaining 24.05% and 23.89% of the variation in species richness and the Shannon-Wiener index, respectively, and playing a key role in shaping the plant diversity. ④ Groundwater depth explained 11.14% and 12.36% of the variation in species richness and the Shannon-Wiener index, respectively, influencing the diversity both directly and indirectly through its regulation of stand structure and soil moisture. ⑤ DBH directly impacted species diversity, while stand density affected the diversity indirectly by altering soil moisture.【Conclusion】Groundwater depth and stand structure regulate the diversity of understory herbaceous plants primarily through their effects on soil moisture. These findings highlight the integrated roles of hydrology, stand structure, and soil physical properties in shaping the biodiversity of the herbaceous vegetation in the Populus euphratica forests in arid regions.

Computer-aided diagnosis has emerged as a rapidly evolving field, garnering increased attention in recent years. At the forefront of this field is the segmentation of lesions in medical images, which is a critical preliminary stage in subsequent treatment procedures. Among the most challenging tasks in medical image analysis is the accurate and automated segmentation of brain tumors in various modalities of brain tumor MRI. In this article, we present a novel end-to-end network architecture called MMGan, which combines the advantages of residual learning and generative adversarial neural networks inspired by classical generative adversarial networks. The segmenter in the MMGan network, which has a U-Net architecture, is constructed using a deep residual network instead of the conventional convolutional neural network. The dataset used for this study is the BRATS dataset from the Brain Tumor Segmentation Challenge at the Medical Image Computing and Computer Assisted Intervention Society. Our proposed method has been extensively tested, and the results indicate that this MMGan framework is more efficient and stable for segmentation tasks. On BRATS 2019, the segmentation algorithm improved accuracy and sensitivity in whole tumor, tumor core, and enhanced tumor segmentation. Particularly noteworthy is the higher dice score of 0.86 achieved by our proposed method in tumor core segmentation, surpassing those of stateof-the-art models. This study improves the accuracy and sensitivity of the tumor segmentation task, which we believe is significant for medical image analysis. And it should be further improved by replacing different loss functions such as cross-entropy loss function and other methods.

The dissociation of a figure from its background is an essential feat of visual perception, as it allows us to detect, recognize, and interact with shapes and objects in our environment. In order to understand how the human brain gives rise to the perception of figures, we here review experiments that explore the links between activity in visual cortex and performance of perceptual tasks related to figure perception. We organize our review according to a proposed model that attempts to contextualize figure processing within the more general framework of object processing in the brain. Overall, the current literature provides us with individual linking hypotheses as to cortical regions that are necessary for particular tasks related to figure perception. Attempts to reach a more complete understanding of how the brain instantiates figure and object perception, however, will have to consider the temporal interaction between the many regions involved, the details of which may vary widely across different tasks.

The accurate identification of corn leaf diseases is crucial for preventing disease spread and improving corn yield. Plant leaf images are often affected by factors such as complex backgrounds, climate, light, and sample data imbalance. To address these issues, we propose a lightweight convolutional neural network, ICS-ResNet, based on ResNet50. This network incorporates improved spatial and channel attention modules as well as a deep separable residual structure to enhance recognition accuracy. (1) The residual connections in the ResNet network prevent gradient loss during deep network training. (2) The improved channel attention (ICA) and spatial attention (ISA) modules fully utilize semantic information from different feature layers to accurately localize key features of the network. (3) To reduce the number of parameters and lower computational costs, we replace traditional convolutional computation with a depth-separable residual structure. (4) We also employ cosine annealing to dynamically adjust the learning rate, enhancing the network’s training stability, improving model convergence, and preventing local optima. Experiments on the corn dataset in Plant Village compare the proposed ICS-ResNet with eight popular networks: CSPNet, InceptionNet_v3, EfficientNet, ShuffleNet, MobileNet, ResNet50, ResNet101 and ResNet152. The results show that the ICS-ResNet achieves an accuracy of 98.87%, which is 5.03%, 3.18%, 1.13%, 1.81%, 1.13%, 0.68%, 0.44% and 0.60% higher than the other networks, respectively. Furthermore, the number of parameters and computations are reduced by 69.21% and 54.88%, respectively, compared to the original ResNet50 network, significantly improving the efficiency of corn leaf disease classification. The study provides strong technical support for sustainable agriculture and the promotion of agricultural science and technology innovation.