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Understanding the heterogeneity of reservoirs is crucial for enhancing the efficiency of hydrocarbon exploration and development. The primary porosity of samples from modern braided river sands and outcrops of braided river sandstone was calculated using a model previously proposed by the authors. The characteristic parameters (Vx) for calculating primary porosity are closely related to the architectural–elemental configurations (AEC), and the AEC of braided river sand bodies (BRSD) has apparent effects on the distribution of the primary porosity heterogeneities. Analysis of our results has established a simple primary porosity heterogeneity model of BRSD. The center of braided river channel and mid-channel bars have excellent strong primary petrophysical properties with high primary porosity exceeding 38%. The contact areas between the braided river channel and channel bars exhibit relatively low primary porosities of less than 33%. The area between the center and edge of the braided bars and channels displays medium primary porosities. The nonlinear correlation in the Q–Q plot of the primary porosity and present porosity of samples from BRSD in the Ahe Formation is mainly caused by chemical diagenesis. The present porosity heterogeneity of BRSD in the Ahe Formation is less influenced by compaction and cementation, it predominantly arises from the differential of dissolution. Q–Q plots attempt to correlate the geological information from an individual sample with the heterogeneity of present porosity in BRSD. In addition, by utilizing Q–Q plots of the primary and current petrophysical properties of the sand body, the relative extent of heterogeneity modification caused by different diagenetic processes can be assessed. This assessment is crucial for modeling macroscopic models of physical properties during geological history periods.

The research of metamaterial shows great potential in the field of solar energy harvesting. In the past decade, the design of broadband solar metamaterial absorber (SMA) has attracted a surge of interest. The conventional design typically requires brute‐force optimizations with a huge sampling space of structure parameters. Very recently, deep learning (DL) has provided a promising way in metamaterial design, but its application on SMA development is barely reported due to the complicated features of broadband spectrum. Here, this work develops the DL model based on metamaterial spectrum transformer (MST) for the powerful design of high‐performance SMAs. The MST divides the optical spectrum of metamaterial into N patches, which overcomes the severe problem of overfitting in traditional DL and boosts the learning capability significantly. A flexible design tool based on free customer definition is developed to facilitate the real‐time on‐demand design of metamaterials with various optical functions. The scheme is applied to the design and fabrication of SMAs with graded‐refractive‐index nanostructures. They demonstrate the high average absorptance of 94% in a broad solar spectrum and exhibit exceptional advantages over many state‐of‐the‐art counterparts. The outdoor testing implies the high‐efficiency energy collection of about 1061 kW h m−2 from solar radiation annually. This work paves a way for the rapid smart design of SMA, and will also provide a real‐time developing tool for many other metamaterials and metadevices. This work develops the deep learning model based on metamaterial spectrum transformer (MST) for the powerful design of high‐performance solar metamaterial absorbers. The MST divides the optical spectrum of metamaterial into N patches, which overcomes the severe problem of overfitting in traditional deep learning model and boosts the learning capability significantly.

Ship detection based on synthetic aperture radar (SAR) images has made a breakthrough in recent years. However, small ships, which may be regarded as speckle noise, pose enormous challenges to the accurate detection of SAR images. In order to enhance the detection performance of small ships in SAR images, a novel detection method named a spatial information integration network (SII-Net) is proposed in this paper. First, a channel-location attention mechanism (CLAM) module which extracts position information along with two spatial directions is proposed to enhance the detection ability of the backbone network. Second, a high-level features enhancement module (HLEM) is customized to reduce the loss of small target location information in high-level features via using multiple pooling layers. Third, in the feature fusion stage, a refined branch is presented to distinguish the location information between the target and the surrounding region by highlighting the feature representation of the target. The public datasets LS-SSDD-v1.0, SSDD and SAR-Ship-Dataset are used to conduct ship detection tests. Extensive experiments show that the SII-Net outperforms state-of-the-art small target detectors and achieves the highest detection accuracy, especially when the target size is less than 30 pixels by 30 pixels.

Artesunate (ART) is a classic antimalarial drug with high efficiency, low toxicity and tolerance. It has been shown to be safe and has good anti-tumor effect. Existing clinical studies have shown that the anti-tumor mechanisms of ART mainly include inducing apoptosis and autophagy of tumor cells, affecting tumor microenvironment, regulating immune response, overcoming drug resistance, as well as inhibiting tumor cell proliferation, migration, invasion, and angiogenesis. ART has been proven to fight against lung cancer, hepatocarcinoma, lymphoma, multiple myeloma, leukemia, colorectal cancer, ovarian cancer, cervical cancer, malignant melanoma, oral squamous cell carcinoma, bladder cancer, prostate cancer and other neoplasms. In this review, we highlight the effects of ART on various tumors with an emphasis on its anti-tumor mechanism, which is helpful to propose the potential research directions of ART and expand its clinical application.

The mammalian cerebrum performs high-level sensory perception, motor control and cognitive functions through highly specialized cortical and subcortical structures 1 . Recent surveys of mouse and human brains with single-cell transcriptomics 2 – 6 and high-throughput imaging technologies 7 , 8 have uncovered hundreds of neural cell types distributed in different brain regions, but the transcriptional regulatory programs that are responsible for the unique identity and function of each cell type remain unknown. Here we probe the accessible chromatin in more than 800,000 individual nuclei from 45 regions that span the adult mouse isocortex, olfactory bulb, hippocampus and cerebral nuclei, and use the resulting data to map the state of 491,818 candidate cis -regulatory DNA elements in 160 distinct cell types. We find high specificity of spatial distribution for not only excitatory neurons, but also most classes of inhibitory neurons and a subset of glial cell types. We characterize the gene regulatory sequences associated with the regional specificity within these cell types. We further link a considerable fraction of the cis -regulatory elements to putative target genes expressed in diverse cerebral cell types and predict transcriptional regulators that are involved in a broad spectrum of molecular and cellular pathways in different neuronal and glial cell populations. Our results provide a foundation for comprehensive analysis of gene regulatory programs of the mammalian brain and assist in the interpretation of noncoding risk variants associated with various neurological diseases and traits in humans. A comprehensive analysis of gene regulatory elements in 160 distinct cell types from the mouse cerebrum.

The listsize capacity is computed for the Gaussian channel with a helper that—cognizant of the channel-noise sequence but not of the transmitted message—provides the decoder with a rate-limited description of said sequence. This capacity is shown to equal the sum of the cutoff rate of the Gaussian channel without help and the rate of help. In particular, zero-rate help raises the listsize capacity from zero to the cutoff rate. This is achieved by having the helper provide the decoder with a sufficiently fine quantization of the normalized squared Euclidean norm of the noise sequence.

With the simultaneous acquisition of the infrared and optical remote sensing images of the same target becoming increasingly easy, using multi-modal data for high-performance object detection has become a research focus. In remote sensing multi-modal data, infrared images lack color information, it is hard to detect difficult targets with low contrast, and optical images are easily affected by illuminance. One of the most effective ways to solve this problem is to integrate multi-modal images for high-performance object detection. The challenge of fusion object detection lies in how to fully integrate multi-modal image features with significant modal differences and avoid introducing interference information while taking advantage of complementary advantages. To solve these problems, a new multi-modal fusion object detection method is proposed. In this paper, the method is improved in terms of two aspects: firstly, a new dual-branch asymmetric attention backbone network (DAAB) is designed, which uses a semantic information supplement module (SISM) and a detail information supplement module (DISM) to supplement and enhance infrared and RGB image information, respectively. Secondly, we propose a feature fusion pyramid network (FFPN), which uses a Transformer-like strategy to carry out multi-modal feature fusion and suppress features that are not conducive to fusion during the fusion process. This method is a state-of-the-art process for both FLIR-aligned and DroneVehicle datasets. Experiments show that this method has strong competitiveness and generalization performance.

Hyperspectral video with spatial and spectral information has great potential to improve object tracking performance. However, the limited hyperspectral training samples hinder the development of hyperspectral object tracking. Since hyperspectral data has multiple bands, from which any three bands can be extracted to form pseudocolor images, we propose a Transformer-based multimodality information transfer network (TMTNet), aiming to improve the tracking performance by efficiently transferring the information of multimodality data composed of RGB and hyperspectral in the hyperspectral tracking process. The multimodality information needed to be transferred mainly includes the RGB and hyperspectral multimodality fusion information and the RGB modality information. Specifically, we construct two subnetworks to transfer the multimodality fusion information and the robust RGB visual information, respectively. Among them, the multimodality fusion information transfer subnetwork is designed based on the dual Siamese branch structure. The subnetwork employs the pretrained RGB tracking model as the RGB branch to guide the training of the hyperspectral branch with little training samples. The RGB modality information transfer subnetwork is designed based on a pretrained RGB tracking model with good performance to improve the tracking network’s generalization and accuracy in unknown complex scenes. In addition, we design an information interaction module based on Transformer in the multimodality fusion information transfer subnetwork. The module can fuse multimodality information by capturing the potential interaction between different modalities. We also add a spatial optimization module to TMTNet, which further optimizes the object position predicted by the subject network by fully retaining and utilizing detailed spatial information. Experimental results on the only available hyperspectral tracking benchmark dataset show that the proposed TMTNet tracker outperforms the advanced trackers, demonstrating the effectiveness of this method.

Little has been known whether intensified global population aging has an independent effect on water use (which corresponds to the global water security). We here use panel analysis to quantitatively find out an obvious declining effect of global population aging (measured by proportion of aged population) on water use (measured by total water withdrawal (TWW)) based on the data of 168 countries in 1987–2018 and then analyze the potential mechanisms leading to the effect. We find that the estimated coefficient regarding the aging effect (β) is about −0.0217, indicating that each percent of increase in proportion of aged population caused 2.17 percent decline in TWW. We further demonstrate the obvious aging effect at the country scale using the gridded data from 2000 to 2010. We eventually project that the global aging effect will lead to about 15%–31% of declines in water use under scenarios SSP1 to SSP5 by 2050.

Infrared (IR) target detection is an important technology in the field of remote sensing image application. The methods for IR image target detection are affected by many characteristics, such as poor texture information and low contrast. These characteristics bring great challenges to infrared target detection. To address the above problem, we propose a novel target detection method for IR images target detection in this paper. Our method is improved from two aspects: Firstly, we propose a novel residual thermal infrared network (ResTNet) as the backbone in our method, which is designed to improve the feature extraction ability for low contrast targets by Transformer structure. Secondly, we propose a contrast enhancement loss function (CTEL) that optimizes the weights about the loss value of the low contrast targets’ prediction results to improve the effect of learning low contrast targets and compensate for the gradient of the low-contrast targets in training back propagation. Experiments on FLIR-ADAS dataset and our remote sensing dataset show that our method is far superior to the state-of-the-art ones in detecting low-contrast targets of IR images. The mAP of the proposed method reaches 84% on the FLIR public dataset. This is the best precision in published papers. Compared with the baseline, the performance on low-contrast targets is improved by about 20%. In addition, the proposed method is state-of-the-art on the FLIR dataset and our dataset. The comparative experiments demonstrate that our method has strong robustness and competitiveness.