Search

Sequestration of CO2 in the unmineable coal seams is not only one of the most ideal options for reducing greenhouse gas effects, but also the only way for the coal industry to reduce CO2 emissions and achieve low carbonization sustainable development. However, the key unresolved issues regarding the CO2 geo-sequestration in coal seams is: “how long does CO2 injected into a coal seam remain in the seam?”. In this regard, on the basis of clarifying the hysteresis law of CO2 desorption in coals, this paper reveals the mechanism of supercritical CO2 desorption hysteresis, establishes a quantitative model for the geological storage of CO2, and explores the use of desorption hysteresis to achieve a long-term safe storage of CO2 in coal seams. The study results shows that the degree of desorption hysteresis of supercritical CO2 in coal is greater than that of subcritical CO2, and a stable hysteresis characteristic similar to a “parallel line” in the supercritical phase is formed between the adsorption and desorption isotherm. The fundamental reason for the desorption hysteresis is that the micro and nano sized pores in coal form curved surfaces due to their hydrophilicity, which generate strong capillary pressure following the Laplace’s equation, absorb liquid water, truncate and fix the supercritical CO2 fluid, and ultimately form CO2 residual trapping. For example, the cylindrical inorganic pores with a diameter of 40–10 nm in coal can generate a capillary pressure of 7.30–29.12 MPa, which is sufficient to block supercritical CO2. Taking the desorption isotherm of Jiulishan coal as an example, using the quantitative model for the geological storage of CO2 established in this study, it has been estimated that the total trapping capacity of the No.21 coal seam at depths of 900–1 500 m is stable at 35–37 m3/t. Among them, the adsorption trapping capacity accounts for about 80%, residual trapping capacity accounts for about 15%, and structural trapping capacity only accounts for 5%. Desorption hysteresis suggests that some measures should be taken to increase the proportion of CO2 residual trapping in coal seams as much as possible, the reason is that the residual CO2 sealed by capillary blockage is safer and has no risk of leakage compared to the free and adsorbed CO2 sealed by surrounding rock. The physical parameters such as ash content, moisture content, pore size, and morphology of coal seams are the main factors affecting the residual trapping efficiency.

Abstract Background Cassava is one of three major potato crops and the sixth most important food crop globally. Improving yield remains a primary aim in cassava breeding. Notably, plant height significantly impacts the yield and quality of crops; however, the mechanisms underlying cassava plant height development are yet to be elucidated. Results In this study, we investigated the mechanisms responsible for cassava plant height development using phenotypic, anatomical, and transcriptomic analyses. Phenotypic and anatomical analysis revealed that compared to the high-stem cassava cultivar, the dwarf-stem cassava cultivar exhibited a significant reduction in plant height and a notable increase in internode tissue xylem area. Meanwhile, physiological analysis demonstrated that the lignin content of dwarf cassava was significantly higher than that of high cassava. Notably, transcriptome analysis of internode tissues identified several differentially expressed genes involved in cell wall synthesis and expansion, plant hormone signal transduction, phenylpropanoid biosynthesis, and flavonoid biosynthesis between the two cassava cultivars. Conclusions Our findings suggest that internode tissue cell division, secondary wall lignification, and hormone-related gene expression play important roles in cassava plant height development. Ultimately, this study provides new insights into the mechanisms of plant height morphogenesis in cassava and identifies candidate regulatory genes associated with plant height that can serve as valuable genetic resources for future crop dwarfing breeding.

Geological storage of CO2 as an idea carbon reduction technology is expected to become an important means of mitigating the greenhouse effect. Therefore, quantitatively assessing the potential of geological storage of CO2 in deep coal seams and investigating the interaction between supercritical CO2 and deep coal rocks have become a hot research topic. Taking Jiulishan coal sample from Jiaozuo mining area in Henan, China as the experimental research object, we analyse the mechanism of supercritical CO2 adsorption and sequestration in deep coal seams, carry out CO2 isothermal adsorption experiments of the coal samples at 35 ℃ and 45 ℃, explain and correct the error of negative adsorption isotherms under high pressure, and obtain the actual adsorption amount of CO2 of the coal sample at different temperatures. Here, we propose a new method for calculating CO2 geological storage capacity, which can not only correct the storage capacity miscalculation caused by Gibbs adsorption, but also can accurately evaluate CO2 theory and effective storage capacity in different burial depths of coal seams. results show that: ① In theory, adsorption saturation means that all adsorption sites have been occupied, the volume and density of the adsorption phase have stabilized, and the adsorption amount should no longer changes. However, all adsorption isotherms measured in the laboratory show that the adsorption amount decreases with the increase of pressure under high-pressure saturation stage, which does not conform to the Langmuir adsorption principle. Therefore, the adsorption isotherm measured in the laboratory must be corrected before it can be applied to the assessment of CO2 storage capacity in deep coal seams; ② The CO2 storage capacity in coal mainly consists of the adsorption and free CO2 amount. The adsorption CO2 amount needs to be calculated using the adsorption phase density and Gibbs adsorption amount, while the free CO2 amount needs to know the pore volume occupied by the free phase in coal. It can only be calculated based on the total pore volume in coal minus the adsorption phase volume. Therefore, the adsorption phase is the decisive factor for accurately evaluating the adsorption and free CO2 storage capacity; ③ Using a modified CO2 geological storage quantification model and taking the 800-2000 m deep coal seam in the Xiuwu research area of Jiaozuo mining area as an example, it is calculated that The theoretical storage capacity of CO2 per unit mass of coal is 1.52−2.16 mmol/g, The total effective storage capacity is 11.19×109 m3, which is equivalent to 21.97 Mt. This case not only corrected the mass balance miscalculation of Gibbs adsorption data, but also considered the impact of adsorption phase occupying pore space on free storage capacity, and thus, it has important implication for improving the accuracy of predicting CO2 geological storage capacity in deep coal seams.

IntroductionHaving multiple pharmacological effects is a characteristic of Traditional Chinese Medicine (TCM). Currently, there is a lack of suitable methods to explore and discover modern diseases suitable for TCM treatment using this characteristic. Unsupervised machine learning technology is an efficient strategy to predict the pharmacological activity of drugs. This study takes Yuxuebi Tablet (YXB) as the research object. Using the unsupervised machine learning technology of drug cell functional fingerprint similarity research, the potential pharmacological effects of YXB were discovered and verified.MethodsLC-MS combined with the in vitro intestinal absorption method was used to identify components of YXB that could be absorbed by the intestinal tract of rats. Unsupervised learning hierarchical clustering was used to calculate the degree of similarity of cellular functional fingerprints between these components and 121 marketed Western drugs whose indications are diseases and symptoms that YXB is commonly used to treat. Then, based on the Library of Integrated Network-based Cellular Signatures database, pathway analysis was performed for selected Western drugs with high similarity in cellular functional fingerprints with the components of YXB to discover the potential pharmacological effects of YXB, which were validated by animal experiments.ResultsWe identified 40 intestinally absorbed components of YXB. Through predictive studies, we found that they have pharmacological effects very similar to non-steroidal anti-inflammatory drugs (NSAIDs) and corticosteroids. In addition, we found that they have very similar pharmacological effects to anti-neuropathic pain medications (such as gabapentin, duloxetine, and pethidine) and may inhibit the NF-κB signaling pathway and biological processes related to pain perception. Therefore, YXB may have an antinociceptive effect on neuropathic pain. Finally, we demonstrated that YXB significantly reduced neuropathic pain in a rat model of sciatic nerve chronic constriction injury (CCI). Transcriptome analysis further revealed that YXB regulates the expression of multiple genes involved in nerve injury repair, signal transduction, ion channels, and inflammatory response, with key regulatory targets including Sgk1, Sst, Isl1, and Shh.ConclusionThis study successfully identified and confirmed the previously unknown pharmacological activity of YXB against neuropathic pain through unsupervised learning prediction and experimental verification.

Mining the nutritional function of Nannochloropsis gaditana is crucial for its development and utilization. This study evaluated the protective effect of purified Nannochloropsis gaditana polysaccharides (NPS) on alcohol-induced dyslipidemia. The study used a liquid alcohol diet and an NPS intervention of 200 mg/kg.bw and 400 mg/kg.bw for 5 weeks. Compared with the model group (MC), the NPS group had a lower triglyceride (TG) and total cholesterol (TC). The relative abundance of probiotics Akkermansia, Bifidobacterium and Ligilactobacillus were significantly higher in NPS groups than those in the model group. Compared with the MC, NPS group had higher serum phosphatidylcholine/phosphatidylethanolamine (PC/PE) ratio and lower ceramide. Moreover, the expression of lipidolysis-related genes such as PGC1α and CPT-1A in the NPS group were significantly higher compared to MC. In summary, NPS attenuated ethanol-induced dyslipidemia probably by regualating gut bateria and serum metabolites, which could serve as a potential prebiotic.

Hyperspectral image (HSI) is pivotal in ground object classification, owing to its rich spatial and spectral information. Recently, convolutional neural networks and graph neural networks have become hotspots in HSI classification. Although various methods have been developed, the problem of detail loss may still exist when extracting complex features within homogenous regions. To solve this issue, in this article, we proposed a double-hop graph attention multiview fusion network. This model is adept at pinpointing precise attention features by integrating a double-hop graph with the graph attention network, thereby enhancing the aggregation of multilevel node information and surmounting the limitations of a restricted receptive field. Furthermore, the spectral-coordinate attention module (SCAM) is presented to seize more nuanced spectral and spatial attention features. SCAM harnesses the coordinate attention mechanism for in-depth pixel-level global spectral–spatial view. Coupled with the multiscale Gabor texture view, we forge a multiview fusion network that meticulously highlights edge details across varying scales and captures beneficial features. Our experimental validation across four renowned benchmark HSI datasets showcases our model's superiority, outstripping comparative methods in classification accuracy with limited labeled samples.

Semantic change detection (SCD) aiming to localize changes and identify their types is a complex pixel-level classification task. In SCD, detecting changes of narrow and long objects (NLOs) common in various scenarios is more challenging than detecting these of typical small objects, primarily because NLOs' narrowness makes them carry local details, while their length gives them a global view. The fusion of semantic with spatial information and the interaction of semantic with spatiotemporal information are crucial to accurately detecting changed NLOs. Insufficient utilization of these pieces of information carried by multilayer semantic feature and change feature would limit the representation of the characteristics of changed NLOs, thereby leading to missed detection and false detection. We propose a new model that integrates semantic, spatial, and temporal information, named SSTNet, for SCD. This model utilizes a designed multilayer feature fusion module to fuse features from different layers in the encoder, leveraging global semantic information and local spatial information. In addition, the semantic change feature interaction module facilitates the interaction between semantic feature and change feature, enhancing the correlation between semantic information and spatial information. A series of experiments conducted on two public datasets demonstrate that SSTNet improves the detection accuracy of changed NLOs and outperforms state-of-the-art methods.

With the development of Earth observation technology, it becomes easier and easier to acquire multimodal image data at the same time. To improve the performance of a multimodal remote-sensing detection algorithm, a new fusion feature optimization detection network is proposed. The method is designed to solve the problem of performance degradation caused by the unreliability of single-modal data in multimodal remote-sensing data. The key to obtain high-quality fusion features from multimodal data with interference is to suppress single-modal redundant features and fully integrate multimodal features. The proposed method mainly includes two improvements. First, a novel joint expression optimization module is designed to enhance the target features and suppress the redundant and interference features that affect the fusion effect. In addition, we propose a novel specific information enhancement module to further enhance the discriminative feature information of targets within each modal image. Experiments on the DroneVehicle dataset show that our proposed method is state of the art on this dataset.

Deep learning methodologies have demonstrated considerable effectiveness in hyperspectral anomaly detection (HAD). However, the practicality of deep learning-based HAD in real-world applications is impeded by challenges arising from limited labeled data, large-scale hyperspectral images (HSIs), and constrained computational resources. In light of these challenges, this article introduces a convolutional neural network (CNN)-based HAD model through the incorporation of Tucker decomposition, named TD-CNND. Drawing inspiration from transfer learning, the proposed model initially constructs pixel sample pairs from known labeled HSIs in the source domain, feeding them into the designed CNN to train the network learning spectral feature differences to obtain a CNN containing knowledge from the source domain. Subsequently, to prevent the need for network retraining caused by structural changes and to reduce model parameters for improving detecting timeliness, a general network compression scheme based on Tucker decomposition is applied to the CNN, where the convolutional layers of the above CNN undergo Tucker tensor decomposition to compress the network and alleviate parameter redundancy. Finally, spectral features realignment is used to recover the detection accuracy loss caused by Tucker tensor decomposition. In addition, a dual-windows structure is implemented during the detection phase, incorporating spatial information to the aforementioned spectral-level learning model, facilitating spectral-spatial collaborative HAD. Experimental evaluations using three real hyperspectral datasets and artificially expanded datasets demonstrate that, in comparison with state-of-the-art methods, the proposed TD-CNND method exhibits effectiveness and superiority in terms of both time cost and detection accuracy, where the notable advantages in terms of time cost become more pronounced with an increasing number of pixels.

Hyperspectral target detection plays a pivotal role in various civil and military applications. Although recent advancements in deep learning have largely embraced supervised learning approaches, they often hindered by the limited availability of labeled data. Unsupervised learning, therefore, emerges as a promising alternative, yet its potential has not been fully realized in current methodologies. This article proposes an innovative unsupervised learning framework employing a momentum contrastive learning-based transformer network specifically tailored for hyperspectral target detection. The proposed approach innovatively combines transformer-based encoder and momentum encoder networks to enhance feature extraction capabilities, adeptly capturing both local spectral details and long-range spectral dependencies through the novel overlapping spectral patch embedding and a cross-token feedforward layer. This dual-encoder design significantly improves the model's ability to discern relevant spectral features amidst complex backgrounds. Through unsupervised momentum contrastive learning, a dynamically updated queue of negative sample features is utilized so that the model can demonstrate superior spectral discriminability. This is further bolstered by a unique background suppression mechanism leveraging nonlinear transformations of cosine similarity detection results, with two nonlinearly pull-up operations, significantly enhancing target detection sensitivity, where the nonlinearly operations are the exponential function with its normalization and the power function with its normalization, respectively. Comparative analysis against seven state-of-the-art hyperspectral target detection methods across four real hyperspectral images demonstrates the effectiveness of the proposed method for hyperspectral target detection, with an increase in detection accuracy and a competitive computational efficiency. An extensive ablation study further validates the critical components of the proposed framework, confirming its comprehensive capability and applicability in hyperspectral target detection scenarios.

Rapid and reliable gully erosion (GE) extraction from high-resolution remote sensing (HRRS) images is crucial for the development of land protection measures. For this task, semantic segmentation methods are widely considered the state-of-the-art solutions. Nevertheless, providing sufficient and clean training labels for segmentation models demands substantial expenses and time investment. In this context, a label correction learning (LCL) framework is proposed to effectively extract GE from HRRS images by leveraging more readily available noisy labels. The core objective of this framework is to suppress the adverse impact of noise within noisy labels on the model performance. To achieve this, we introduce three key components in the framework, including an adaptive correction loss function, a multitree refinement module, and a noise correction module. These components collaborate to rectify noisy labels during training, thereby providing the model with a training set containing less noise. To validate the effectiveness of the LCL framework, three severely eroded regions in Northeast China are selected as study areas and corresponding noisy datasets are generated. Extensive experiments on these datasets demonstrate that our framework can significantly mitigate the negative influence of label noise and ultimately achieve superior GE extraction performance. Moreover, by employing the proposed framework, we generate GE coverage maps for the study areas and obtain measurements of gully area and length that are very close to the true statistics. Such a framework that can effectively learn from noisy labels holds promise as a practical and cost-efficient means to provide reliable data references for land resource protection.

Abstract The 2023 M7.8 Kahramanmaraş/Pazarcik earthquake was larger and more destructive than what had been expected. Here we analyzed nearfield seismic records and developed a dynamic rupture model that reconciles different currently conflicting inversion results and reveals spatially non-uniform propagation speeds in this earthquake, with predominantly supershear speeds observed along the Narli fault and at the southwest (SW) end of the East Anatolian Fault (EAF). The model highlights the critical role of geometric complexity and heterogeneous frictional conditions in facilitating continued propagation and influencing rupture speed. We also constrained the conditions that allowed for the rupture to jump from the Narli fault to EAF and to generate the delayed backpropagating rupture towards the SW. Our findings have important implications for understanding earthquake hazards and guiding future response efforts and demonstrate the value of physics based dynamic modeling fused with near-field data in enhancing our understanding of earthquake mechanisms and improving risk assessment.

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.

This paper explores a pursuit–evasion game (PEG) based on quadrotors by combining fuzzy Q-learning (FQL) and model-predictive control (MPC) algorithms. Initially, the FQL algorithm is employed to perceive, make decisions, and predict the trajectory of the evader. Based on the position and velocity information of both players in the game, the pursuer quadrotor determines its action strategy using the FQL algorithm. Subsequently, a state feedback controller is designed using the MPC algorithm, with reference inputs derived from the FQL algorithm. Within each MPC cycle, the FQL algorithm dynamically provides reference inputs to the MPC, thereby enhancing its robust control and dynamic optimization for the quadrotor. Finally, simulation results verify the effectiveness of the proposed algorithm.

This paper addresses the problem of multi-source survey data sharing in power system engineering by proposing two improved methods: a survey data sharing method combined with differential privacy and a permission change method based on attribute encryption. The survey data sharing method integrated with differential privacy achieves effective cross-professional and cross-departmental data sharing while ensuring data security by introducing multi-discriminator architecture and dynamic noise adjustment. To reduce the computational and communication overhead when user permissions change during survey data sharing, the attribute encryption-based permission change method supports dynamic changes in user permissions. The effectiveness of the proposed methods has been validated through targeted experiments in different scenarios. The work in this thesis provides a new solution for dynamic sharing of survey data in power network engineering. It contributes to the digital transformation of power network projects.

Lard is highly appreciated for its flavor. However, it has not been elucidated how to consume lard while at the same time eliminating its adverse effects on postpartum cognitive function. Female mice were divided into three groups (n = 10): soybean oil (SO), lard oil (LO), and a mixture of soybean oil and lard at a ratio of 1:1 (LS). No significant difference was observed between the SO and LS groups in behavioral testing of the maternal mice, but the LO group was significantly worse compared with these two groups. Moreover, the SO and LS supplementation increased docosahexaenoic acid (DHA) and total n-3 polyunsaturated fatty acid (PUFA) levels in the brain and short-chain fatty acid (SCFA)-producing bacteria in feces, thereby mitigating neuroinflammation and lowering the p-ERK(1/2)/ERK(1/2), p-CREB/CREB, and BDNF levels in the brain compared to the LO group. Collectively, the LS group inhibited postpartum cognitive impairment by regulating the brain fatty acid composition, neuroinflammation, gut microbiota, and the SCFA/ERK(1/2)/CREB/BDNF signaling pathway compared to lard.

It has been suggested that dietary intake of lipids and fatty acids may influence cognitive function, however, the effect of lard intake during pregnancy and postpartum periods on cognitive function of mother remains to be elucidated. We investigated the effect and mechanism of consuming soybean oil (SO), the mixed oil of lard and soybean oil at the ratio of 1:1 (LS) and lard oil (LO) during the pregnancy and postpartum periods on cognitive function of the maternal mice. All pregnant C57BL/6JNifdc mice were fed with soybean oil diet during day 0–10 (the day when vaginal plugs appeared in female mice was recorded as day 0), and then randomly assigned to SO, LS and LO groups (n = 10) from day 11 to day 44. The time in center zone and the number of times to enter in center zone were significantly higher in the SO group than in the LO group detected by the open-field test. The levels of neuroglial cells, NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome complex and pyroptosis related proteins in brain of the LO group were significantly higher than those in the SO group. RNA-sequencing results showed that the calcium signaling pathway related genes in brain, including Adcy8, Ntsr1, Trhr, Oxtr, Htr5b and Camk2d levels significantly higher in the LO group than in the SO group. Lipidomic analysis indicated that PG 18:2_18:2, PG 20:5_22:6, and CL 12:0_16:0_22:3_22:5 of glycerophospholipid metabolism in brain significantly connected with Htr5b of calcium signaling pathway. In conclusion, the intake of lard during the pregnancy and postpartum periods is detrimental to the cognitive function of maternal mice, which probably due to changes in the composition of fatty acid in the brain, thereby activating neuroinflammation via calcium signaling pathway in brain.

Millimeter-Wave Radar is one promising sensor to achieve robust perception against challenging observing conditions. In this paper, we propose a Radar Inertial Odometry (RIO) pipeline utilizing a long-range 4D millimeter-wave radar for autonomous vehicle navigation. Initially, we develop a perception frontend based on radar point cloud filtering and registration to estimate the relative transformations between frames reliably. Then an optimization-based backbone is formulated, which fuses IMU data, relative poses, and point cloud velocities from radar Doppler measurements. The proposed method is extensively tested in challenging on-road environments and in-the-air environments. The results indicate that the proposed RIO can provide a reliable localization function for mobile platforms, such as automotive vehicles and Unmanned Aerial Vehicles (UAVs), in various operation conditions.

Synthetic aperture radar (SAR) plays a crucial role in maritime surveillance due to its capability for all-weather, all-day operation. However, SAR ship recognition faces challenges, primarily due to the imbalance and inadequacy of ship samples in publicly available datasets, along with the presence of numerous outliers. To address these issues, this paper proposes a SAR ship classification method based on text-generated images to tackle dataset imbalance. Firstly, an image generation module is introduced to augment SAR ship data. This method generates images from textual descriptions to overcome the problem of insufficient samples and the imbalance between ship categories. Secondly, given the limited information content in the black background of SAR ship images, the Tokens-to-Token Vision Transformer (T2T-ViT) is employed as the backbone network. This approach effectively combines local information on the basis of global modeling, facilitating the extraction of features from SAR images. Finally, a Squeeze-and-Excitation (SE) model is incorporated into the backbone network to enhance the network’s focus on essential features, thereby improving the model’s generalization ability. To assess the model’s effectiveness, extensive experiments were conducted on the OpenSARShip2.0 and FUSAR-Ship datasets. The performance evaluation results indicate that the proposed method achieves higher classification accuracy in the context of imbalanced datasets compared to eight existing methods.

In UAV autonomous exploration, large frontier clusters are commonly associated with high information gain and are visited first. In contrast, small and isolated frontier clusters with fewer frontiers are associated with smaller information gain and are thus explored with low priority. However, these small and isolated frontier clusters are often in close proximity to UAVs and surrounded by explored areas, which could result in back-and-forth flights that lower exploration efficiency. This paper proposes LAEA, a LiDAR-assisted and depth camera-dominated UAV exploration algorithm that aims to improve UAV autonomous exploration efficiency. A hybrid map is obtained that characterizes rich environmental profile information in real time, enabling us to detect small and isolated frontier clusters that can lead to repeated visits to explored areas. An environmental information gain optimization strategy is incorporated such that frontier clusters with larger unexplored areas behind them, as well as small and isolated frontier clusters close to the UAV, are assigned higher weights to prioritize their visit order. An optimized flight trajectory is generated to cover unexplored frontier clusters in the immediate vicinity of the UAV while flying to the next target. A comprehensive comparison between the proposed algorithm and state-of-the-art algorithms was conducted via a simulation study, which showed that our algorithm exhibits superior exploration efficiency in various environments. Experiments were also carried out to verify the feasibility of the proposed approach in real-world scenarios.

Cross-view geolocation is a valuable yet challenging task. In practical applications, the images targeted by cross-view geolocation technology encompass multi-domain remote sensing images, including those from different platforms (e.g., drone cameras and satellites), different perspectives (e.g., nadir and oblique), and different temporal conditions (e.g., various seasons and weather conditions). Based on the characteristics of these images, we have designed an effective framework, Image Reconstruction and Multi-Unit Mutual Learning Net (IML-Net), for accomplishing cross-view geolocation tasks. By incorporating a deconvolutional network into the architecture to reconstruct images, we can better bridge the differences in remote sensing image features across different domains. This enables the mapping of target images from different platforms and perspectives into a shared latent space representation, obtaining more discriminative feature descriptors. The process enhances the robustness of feature extraction for locating targets across a wide range of perspectives. To improve the network’s performance, we introduce attention regions learned from different units as augmented data during the training process. For the current cross-view geolocation datasets, the use of large-scale datasets is limited due to high costs and privacy concerns, leading to the prevalent use of simulated data. However, real data allow the network to learn more generalizable features. To make the model more robust and stable, we collected two groups of multi-domain datasets from the Zurich and Harbin regions, incorporating real data into the cross-view geolocation task to construct the ZHcity750 Dataset. Our framework is evaluated on the cross-domain ZHcity750 Dataset, which shows competitive results compared to state-of-the-art methods.

Ammonia (NH3) is widely used in a wide range of fields because of its high energy density, and NH3 is simple to liquefy and transport. Nitrate is also a source of pollution of the environment and drinking water sources. Therefore, there is a pressing demand for the design and production of high-efficiency catalysts for the nitrate reduction reaction (NO3RR). Herein, two nickel-added polyoxometalates (NiAPs), namely, [Ni(en)2][Ni6(μ3-OH)3(en)3(H2O)6(B-α-SiW9O34)]2·6H2O (Ni6en) and [Ni(enMe)2(H2O)2][Ni6(μ3-OH)3(H2O)6(enMe)3(B-α-SiW9O34)]2·8H2O (Ni6enMe) (en = ethylenediamine, enMe = 1,2-diaminopropane), were effectively synthesized under hydrothermal conditions that contained several electrons and were used as electrocatalytic nitrate reduction reaction (e-NO3RR) catalysts. The structures of the compounds were characterized by using various instruments such as powder X-ray diffraction (PXRD) spectroscopy, infrared (IR) spectroscopy, thermogravimetric analysis (TGA), Brunauer–Emmett–Teller (BET) method, scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS). e-NO3RR tests were performed using electrochemical workstation. Results show that Ni6en and Ni6enMe have high-efficient electrochemical catalytic nitrogen reduction to NH3. The highest NH3 yield rate for Ni6en was 3.66 mg∙h−1∙mgcat.−1 with Faradaic efficiency (FE) of 89.32%, whereas that for Ni6enMe was 3.46 mg∙h−1∙mgcat.−1 with FE of 86.75% at a low voltage (−0.5 V vs. reversible hydrogen electrode (RHE)). This finding creates a novel path for manufacturing highly effective NO3RR electrocatalysts using metal-added polyoxometalate as the catalyst in ambient settings. Furthermore, the findings of this research provide practical advice for creating effective electrocatalytic catalysts.

Abstract Monocular 3D vehicle localization is an important task for vehicle behaviour analysis, traffic flow parameter estimation and autonomous driving in Intelligent Transportation System (ITS) and Cooperative Vehicle Infrastructure System (CVIS), which is usually achieved by monocular 3D vehicle detection. However, monocular cameras cannot obtain depth information directly due to the inherent imaging mechanism, resulting in more challenging monocular 3D tasks. Currently, most of the monocular 3D vehicle detection methods still rely on 2D detectors and additional geometric constraint modules to recover 3D vehicle information, which reduces the efficiency. At the same time, most of the research is based on datasets of onboard scenes, instead of roadside perspective, which is limited in large-scale 3D perception. Therefore, we focus on 3D vehicle detection without 2D detectors in roadside scenes. We propose a 3D vehicle localization network CenterLoc3D for roadside monocular cameras, which directly predicts centroid and eight vertexes in image space, and the dimension of 3D bounding boxes without 2D detectors. To improve the precision of 3D vehicle localization, we propose a multi-scale weighted-fusion module and a loss with spatial constraints embedded in CenterLoc3D. Firstly, the transformation matrix between 2D image space and 3D world space is solved by camera calibration. Secondly, vehicle type, centroid, eight vertexes, and the dimension of 3D vehicle bounding boxes are obtained by CenterLoc3D. Finally, centroid in 3D world space can be obtained by camera calibration and CenterLoc3D for 3D vehicle localization. To the best of our knowledge, this is the first application of 3D vehicle localization for roadside monocular cameras. Hence, we also propose a benchmark for this application including a dataset (SVLD-3D), an annotation tool (LabelImg-3D), and evaluation metrics. Through experimental validation, the proposed method achieves high accuracy with $$A{P_{3D}}$$ A P 3 D of 51.30%, average 3D localization precision of 98%, average 3D dimension precision of 85% and real-time performance with FPS of 41.18.

Abstract Background Growing evidence shows that C-Type Lectin Domain Containing 7A (Clec7a) may be involved into neuroinflammatory injury of various neurological diseases. However, its roles in neuropathic pain remain unclear. Methods A chronic constriction injury (CCI) rat model was constructed, and gene expression profilings in spinal cord tissues of CCI-insulted rats were detected by both microarray and RNA-seq studies. A series of bioinformatics analyses identified C/EBPβ-Clec7a to be a candidate axis involved into neuropathic pain. Then, its roles in mechanical allodynia, and pathological and molecular changes during CCI progression were determined by various gain-of-function and loss-of-function experiments in vivo and in vitro. Results Significant upregulation of Clec7a at both mRNA and protein levels were verified in spinal cord tissues of CCI-insulted rats. Clec7a knockdown markedly attenuated CCI-induced mechanical allodynia, obstructed Syk, ERK and JNK phosphorylation, inhibited NLRP3 inflammasome and caspase-1 activation, GSDMD cleavage, and consequently reduced the release of pro-inflammatory cytokines (all P

Over the past few decades, researchers have shown sustained and robust investment in exploring methods for hyperspectral image classification (HSIC). The utilization of hyperspectral imagery (HSI) for crop classification in agricultural areas has been widely demonstrated for its feasibility, flexibility, and cost-effectiveness. However, numerous coexisting issues in agricultural scenarios, such as limited annotated samples, uneven distribution of crops, and mixed cropping, could not be explored insightfully in the mainstream datasets. The limitations within these impractical datasets have severely restricted the widespread application of HSIC methods in agricultural scenarios. A benchmark dataset named Heilongjiang (HLJ) for HSIC is introduced in this paper, which is designed for large-scale crop classification. For practical applications, the HLJ dataset covers a wide range of genuine agricultural regions in Heilongjiang Province; it provides rich spectral diversity enriched through two images from diverse time periods and vast geographical areas with intercropped multiple crops. Simultaneously, considering the urgent demand of deep learning models, the two images in the HLJ dataset have 319,685 and 318,942 annotated samples, along with 151 and 149 spectral bands, respectively. To validate the suitability of the HLJ dataset as a baseline dataset for HSIC, we employed eight classical classification models in fundamental experiments on the HLJ dataset. Most of the methods achieved an overall accuracy of more than 80% with 10% of the labeled samples used for training. Furthermore, the advantages of the HLJ dataset and the impact of real-world factors on experimental results are comprehensively elucidated. The comprehensive baseline experimental evaluation and analysis affirm the research potential of the HLJ dataset as a large-scale crop classification dataset.

Laizhou Bay Sag is a typical continental rift lake basin, which is affected by strike–slip activity, salt rock activity, volcanic activity, and paleoclimate change in the Tanlu fault zone, where three major lithologies—volcanic rock, clastic rock, and evaporite—mainly develop. In order to determine the sequence correspondence between the different lithologies in the study area based on drilling, logging data, and seismic data, it is determined that the fourth member of the Shahejie Formation in the southern Laizhou Bay mainly develops three types of sequences: clastic rock type sequences, salt lake type sequences, and volcanic rock/clastic rock type sequences. Two second-order sequence boundaries (SBs3L, SBs4L) and one third-order sequence boundary (SBs4U) were identified, and the principle of three divisions of system tracts (LST, TST, HST) and sedimentary processes in different types of sequences were determined by analyzing the stacking patterns of different sequences. Compared with the previous research results, this study provides a more detailed analysis of the sequence boundary definition of different blocks and different lithologic strata, and it improves the previous general sequence stratigraphic division method. Based on the study of distinct types of sequences, a unified stratigraphic framework is established in the whole zone, and the stratum thickness distribution of the fourth member of Shahejie Formation is smaller in the southwest and larger in the northeast. The study area is generally filled with pyroclastic rocks–evaporite–clastic rocks, changing from fan delta–saline lake filling to braided river delta–freshwater lake–fan delta filling from bottom to top. A sequence stratigraphic model for the comparison of steep slope–gentle slope differences is established to provide guidance for future oil and gas field exploration and development.

Abstract Long‐term monitoring of fetal heart is vital for early clinical diagnosis and timely treatment of fetus. However, the collected signals from the abdomen of the pregnant woman are always corrupted by many interfering sources, most significantly affected by the maternal electrocardiograph (ECG). Similar with blind source separation (BSS), fetal ECG extraction can be considered as the separation of fetal ECG from the recordings. This letter proposes a novel multi‐layer polynomial network to extract the fetal ECG waveform, where the non‐linear mixing process is approximated with multiple polynomials. The network starts with the first‐order polynomial layer, and further requirements to decrease the error between the output and target signal is achieved by increasing the number of layers. The proposed approach turns the inverse transformation of traditional BSS into a model learning problem, which is then solved by a simple convex optimization algorithm. Compared with the state‐of‐the‐art BSS methods, experimental results show that the proposed method has better performance.

Background & purposeObesity and metabolic disorders were associated with increased risk of MM, a disease characterized by high risk of relapsing and require frequent hospitalizations. In this study, we conducted a retrospective cohort study to explore the association of metabolic obesity phenotypes with the readmission risk of MM.Patients & methodsWe analyzed 34,852 patients diagnosed with MM from the Nationwide Readmissions Database (NRD), a nationally representative database from US. Hospitalization diagnosis of patients were obtained using ICD-10 diagnosis codes. According to obesity and metabolic status, the population was divided into four phenotypes: metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO). The patients with different phenotypes were observed for hospital readmission at days 30-day, 60-day, 90-day and 180-day. Multivariate cox regression model was used to estimate the relationship between obesity metabolic phenotypes and readmissions risk.ResultsThere were 5,400 (15.5%), 7,255 (22.4%), 8,025 (27.0%) and 7,839 (35.6%) unplanned readmissions within 30-day, 60-day, 90-day and 180-day follow-up, respectively. For 90-day and 180-day follow-up, compared with patients with the MHNO phenotype, those with metabolic unhealthy phenotypes MUNO (90-day: P = 0.004; 180-day: P = < 0.001) and MUO (90-day: P = 0.049; 180-day: P = 0.004) showed higher risk of readmission, while patients with only obesity phenotypes MHO (90-day: P = 0.170; 180-day: P = 0.090) experienced no higher risk. However, similar associations were not observed for 30-day and 60-day. Further analysis in 90-day follow-up revealed that, readmission risk elevated with the increase of the combined factor numbers, with aHR of 1.068 (CI: 1.002-1.137, P = 0.043, with one metabolic risk factor), 1.109 (CI: 1.038-1.184, P = 0.002, with two metabolic risk factors) and 1.125 (95% CI: 1.04-1.216, P = 0.003, with three metabolic risk factors), respectively.ConclusionMetabolic disorders, rather than obesity, were independently associated with higher readmission risk in patients with MM, whereas the risk elevated with the increase of the number of combined metabolic factors. However, the effect of metabolic disorders on MM readmission seems to be time-dependent. For MM patient combined with metabolic disorders, more attention should be paid to advance directives to reduce readmission rate and hospitalization burden.

Background: Skeletal muscles are organs with high energy requirements, especially during vigorous exercise. Adequate mitochondrial function is essential to meet the high energy needs of skeletal muscle cells. Recent studies have reported that red ginseng can significantly improve chronic fatigue; however, the specific mechanism of action is still not clear.Methods: A chronic fatigue syndrome mouse model was developed using C57BL/6J mice through long-term compound stimulation of stress factors. Following this, the animals were orally administered 200, 400, or 600 mg/kg red ginseng extracts for 28 days. Skeletal muscle lactate acid, serum lactate dehydrogenase, urea concentrations, ATP level, mitochondrial membrane potential, activities of Na+-K+-ATPase and cytochrome c oxidase were determined using assay kits or an automatic biochemical analyser detection system. Skeletal muscle mitochondria morphology was observed using electron microscopy and the expression of p-AMPK, PGC-1α, ACO2 and complex I in skeletal muscle protein was determined by western blotting.Results: Oral administration of 400 or 600 mg/kg red ginseng extract in mice with chronic fatigue reduced lactic acid, lactate dehydrogenase and urea, rescued the density and morphology of skeletal muscle mitochondria, increased the activities of Na+-K+-ATPase and cytochrome c oxidase, and activated the AMPK/PGC-1α cascade pathway, resulting in improved skeletal muscle mitochondrial function by restoring ATP level, mitochondrial membrane potential, complex I and mitochondrial biogenesis.Conclusion: The anti-fatigue effects of red ginseng are partly related to its potent mitochondrial improving activity, including decreasing mitochondrial swelling and mitochondrial membrane permeability, increasing mitochondrial biogenesis, thus ameliorating mitochondrial dysfunction.

Danlu tongdu tablets (DLTD) is a listed Chinese patent medicine collected in the Pharmacopoeia of the People’s Republic of China (version 2020). This prescription has been applied in clinics in China for lumbar spinal stenosis and lumbosacral disc herniations. The wide application of Danlu tongdu in therapy has raised some clinical adverse reactions, such as significant elevation of alanine transaminase (ALT) and aspartate transaminase (AST) in individual patients after use. The present study aimed to investigate the safety of Danlu tongdu and analyze its adverse effects on the liver. The maximum feasible dose (MFD) was used to carry out the acute toxicity tests. Mortality, adverse effects, body weight and food consumption were recorded for up to 14 days post treatment. In the 6-month chronic toxicity test, sprague-dawley rats were randomly divided into four groups according body weight, the experimental groups were administrated to rats at the concentrations of 1.67, 3.34 and 6.67 g/kg/day, whereas the control group was received the ultrapure water (vehicle) only, 10 ml/kg, once a day. The animal’s body weight, food consumption was monitored weekly. In addition, their hematological and biochemical parameters, body and organ weights and histopathology, were all measured at specific observation time points. Additionally, we further explored the adverse effects mechanism of Danlu tongdu on the liver through transcriptome analysis. No deaths or substance-relative toxicity were observed in the acute toxicity study or the 6-month chronic toxicity study with doses of 1.67 g/kg and 3.34 g/kg, respectively. We found that mild hypertrophy and hyperplasia of hepatic interlobular bile ducts were detected in some rats with doses of 6.67 g/kg after repeated oral administration of Danlu tongdu for 13 and 26 weeks, but the above changes in liver were reversible. The results of transcriptome sequencing showed that Danlu tongdu had a significant effect on cytochrome P450 enzymes in rat liver, especially cytochrome P450 1 (CYP1) subtype. Therefore, the toxic target organ of Danlu tongdu is the liver and the mechanism of mild liver injury is closely related to the up-regulation of cytochrome P450 1A1 (CYP1A1) and cytochrome P450 1A2 (CYP1A2) expression.

Today, as PHP application technology is becoming increasingly mature, the functions of modern multi-layer web applications are becoming more and more complete, and the complexity is also gradually increasing. While providing developers with various business functions and interfaces, multi-tier Web applications also successfully cover the details of application development. This type of web application adopts a unified entrance, many object-oriented codes are used, and features such as encapsulation, inheritance, and polymorphism bring challenges to vulnerability mining from the perspective of static analysis. A large amount of object-oriented code makes it impossible for a simple function name-matching method to build a complete call graph (CG), resulting in the inability to perform a comprehensive interprocedural analysis. At the same time, the encapsulation feature of the class makes the data hidden in the object attribute, and the vulnerability path cannot be obtained through the general data-flow analysis. In response to the above issues, we propose a vulnerability detection method that supports vulnerability detection for multi-layer web applications based on MVC (Model-View-Control) architecture. First, we improve the construction of the call graph and Code Property Graph (CPG). Then, based on the enhanced Code Property Graph, we propose a technique to support vulnerability detection for multi-layer web applications. Based on this approach, we implemented a prototype of VulPathsFinder, a security analysis tool extended from the PHP security analyzer Joern-PHP. Then, we select ten MVC based and ten non-MVC-based applications to form a test dataset and validate the effectiveness of VulPathsFinder based on this dataset. Experimental results show that, compared with currently available tools, VulPathsFinder can handle framework applications more effectively, build a complete code property map, and detect vulnerabilities in framework applications that existing tools cannot detect.

In this work, a novel object-level building-matching method using cross-dimensional data, including 2D images and 3D point clouds, is proposed. The core of this method is a newly proposed plug-and-play Joint Descriptor Extraction Module (JDEM) that is used to extract descriptors containing buildings’ three-dimensional shape information from object-level remote sensing data of different dimensions for matching. The descriptor is named Signed Distance Descriptor (SDD). Due to differences in the inherent properties of different dimensional data, it is challenging to match buildings’ 2D images and 3D point clouds on the object level. In addition, features extracted from the same building in images taken at different angles are usually not exactly identical, which will also affect the accuracy of cross-dimensional matching. Therefore, the question of how to extract accurate, effective, and robust joint descriptors is key to cross-dimensional matching. Our JDEM maps different dimensions of data to the same 3D descriptor SDD space through the 3D geometric invariance of buildings. In addition, Multi-View Adaptive Loss (MAL), proposed in this paper, aims to improve the adaptability of the image encoder module to images with different angles and enhance the robustness of the joint descriptors. Moreover, a cross-dimensional object-level data set was created to verify the effectiveness of our method. The data set contains multi-angle optical images, point clouds, and the corresponding 3D models of more than 400 buildings. A large number of experimental results show that our object-level cross-dimensional matching method achieves state-of-the-art outcomes.

Over the past decade, traditional Chinese medicine (TCM) has widely embraced systems biology and its various data integration approaches to promote its modernization. Thus, integrative pharmacology-based traditional Chinese medicine (TCMIP) was proposed as a paradigm shift in TCM. This review focuses on the presentation of this novel concept and the main research contents, methodologies and applications of TCMIP. First, TCMIP is an interdisciplinary science that can establish qualitative and quantitative pharmacokinetics–pharmacodynamics (PK–PD) correlations through the integration of knowledge from multiple disciplines and techniques and from different PK–PD processes in vivo. Then, the main research contents of TCMIP are introduced as follows: chemical and ADME/PK profiles of TCM formulas; confirming the three forms of active substances and the three action modes; establishing the qualitative PK–PD correlation; and building the quantitative PK–PD correlations, etc. After that, we summarize the existing data resources, computational models and experimental methods of TCMIP and highlight the urgent establishment of mathematical modeling and experimental methods. Finally, we further discuss the applications of TCMIP for the improvement of TCM quality control, clarification of the molecular mechanisms underlying the actions of TCMs and discovery of potential new drugs, especially TCM-related combination drug discovery.

Summary: Less than 35% of advanced patients with high-grade serous ovarian cancer (HGSOC) survive for 5 years after diagnosis. Here, we developed radiomics-based models to predict HGSOC clinical outcomes using preoperative contrast-enhanced computed tomography (CECT) images. 891 radiomics features were extracted between primary, metastatic, or lymphatic lesions from preoperative venous phase CECT images of 217 patients with HGSOC. A heuristic method, Frequency Appearance in Multiple Univariate preScreening (FAMUS), was proposed to identify stable and task-relevant radiomic features. Using FAMUS, we constructed predictive models of overall survival and disease-free survival in patients with HGSOC based on these stable radiomic features. According to their CT images, patients with HGSOC can be accurately stratified into high-risk or low-risk groups for cancer-related death within 2-6 years or for likely recurrence within 1-5 years. These radiomic models provide convincing and reliable non-invasive markers for individualized prognostic evaluation and clinical decision-making for patients with HGSOC.

Soil dry depth is a key parameter that determines soil fertility and nutrient availability, ultimately affecting crop yield and quality. However, accurately measuring the dry depth of soil has been a challenge. In this work, we propose using reflective ultrasonic waves to measure dry depth in soil. Four soil types, including clay, sandy loam, silty loam, and sandy were prepared and the feasibility of the method was demonstrated through theoretical analysis. An experimental measurement system was established to verify the consistency between ultrasonic measurements and manually measured values. Two statistics were used in Ordinary Least Squares (OLS) regression to evaluate the model fit: R-square (R2) and Root mean square error (RMSE). The results indicate that the proposed method provides a higher accuracy in estimating the dry depth of sandy loam and silty loam (R2 values of 0.9899 and 0.992 for sandy loam, RMSE values of 1.57% and 1.5% for silty loam) than those of the clay and sandy samples (R2 values of 0.9896 and 0.9874 for clay, RMSE values of 1.66% and 1.77% for sandy). The maximum measurement errors for all the soil type predictions are below 6%; the overall accuracy was acceptable. Our findings suggest that ultrasonic measurement is an efficient and cost-effective approach for measuring soil dry depth, which could enable the precise control of irrigation water usage and the conservation of valuable water resources.

Introduction Protein kinase A (PKA) and protein kinase G (PKG) are the main downstream effectors of second messengers cAMP and cGMP, which play important roles in physiological and pathological processes. Recently, there are two findings: one is PKA catalytic subunits α (PKACα) and PKG II can be secreted, the other is that the two secretory protein kinases are associated with the progression of tumors. Previous data also demonstrate that the two kinases, as signal cascades, involved in inflammation-associated disorders. However, it remains unclear whether the secreted PKACα or PKG II could serve as diagnostic biomarkers for inflammation-associated disorders. Methods The serum from suffered coronary disease, diabetes, rheumatoid arthritis, and schizophrenia were collected. The serum PKACα and PKG II were detected by ELISA. All the patients were consent informed. Results Our results showed that the serum PKACα and PKG II had obvious changes in coronary disease, rheumatoid arthritis, schizophrenia patients. However, the trends was opposite, especially in rheumatoid arthritis. Conclusions Serum PKACα and PKG II could serve as potentially diagnostic biomarkers for some inflammation-associated disorders, such as coronary disease, rheumatoid arthritis, and schizophrenia.

The purpose is to strengthen the life education of contemporary college students and give better play to the vital role of life education in preventing college students’ mental diseases. Specifically, it discusses the role of dance therapy (DT) in College Students’ Life Education (CSLE). Firstly, based on educational psychology (EP), this manuscript analyzes the relevant theoretical concepts of EP and life education and discusses the importance of life education to contemporary college students. Secondly, following a Questionnaire Survey (QS) and using deep learning (DL) Convolutional Neural Network (CNN) and Facial Emotion Recognition (FER), this manuscript reviews and examines the CSLE’s current situation and the DT effect. Research findings are summarized combined with the QS results and scores of 20 subjects before and after five activities in 3 months. (I) After DT intervention, the positive dimensions of college students’ life values have improved, especially self-development and dedication, and their quality of life is refined. Thus, DT group counseling proves the positive role of DT in CSLE. (II) After DT intervention, 96.5% of the members think DT is effective. Therefore, EP-based DT is more effective and scientific in CSLE. The research findings provide a DT-based teaching concept for CSLE, explore the feasibility and effectiveness of life education, and enrich the DT scheme of CSLE. The research provides a practical reference for further applying DT in college students’ psychological education.

This study aims to help enterprises enhance their innovation capabilities in the environment of knowledge economy globalization and stand out in the fierce industry competition. Firstly, data on existing higher education theories and innovation theories are analyzed. Secondly, two companies in the sample data are selected for detailed analysis. Finally, research conclusion and corresponding talent management strategies are presented. The results show that the cumulative contribution value of employees is 87.496%. The cumulative contribution value of human capital is 70.322%. The contribution value of cumulative innovation performance is 61.658%. The cumulative contribution value of R&D investment is 45.306%. The coefficient for the overall sample size is 0.509. The employee quality coefficient is 0.452. The correlation coefficient for educational attainment is 0.598. The high-tech service industry has the highest correlation coefficient at 0.504. The auto industry has the highest coefficient at 0.669. The experimental research has drawn the following conclusion: (1) Talents positively affect enterprise innovation performance; (2) Research and Development (R&D) investment has a positive correlation with enterprise innovation performance; (3) R&D investment has a positive correlation with talents. Through experimental research, the education level of employees is measured by academic qualifications, but the essence of academic qualification measurement is the level of knowledge and skills that employees have. In summary, the study can extend the strategic analysis of the cultivation of innovative talents and play a valuable auxiliary role in cultivating innovative talents.

Anomaly target detection methods for hyperspectral images (HSI) often have the problems of potential anomalies and noise contamination when representing background. Therefore, a spectral-spatial hyperspectral anomaly detection method is proposed in this article, which is based on fractional Fourier transform (FrFT) and saliency weighted collaborative representation. First, hyperspectral pixels are projected to the fractional Fourier domain by the FrFT, which can enhance the capability of the detector to suppress the noise and make anomalies to be more distinctive. Then, a saliency weighted matrix is designed as the regularization matrix referring to context-aware saliency theory and combined with the FrFT-based collaborative representation detector. The saliency-weighted regularization matrix assigns different pixels with different weights by using both spectral and spatial information, which can reduce the influence of the potential anomalous pixels embedded in the background when applying collaborative representation theory. Finally, to further improve the performance of the proposed method, a spectral-spatial detection procedure is employed to calculate final anomaly scores by using both spectral information and spatial information. The proposed method is compared with nine state-of-the-art hyperspectral anomaly detection methods on six HSI datasets, including two synthetic HSI datasets and four real-world HSI datasets. Extensive experimental results illustrate that the proposed method's detection performance outperforms other nine well-known compared methods in terms of area under the receiver operating characteristic (ROC) curve values, visual detection characteristics, ROC curve, and separability.

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.

The breeding of triploid banana cultivars with improved traits, such as yield and disease resistance, remains a major challenge for breeders. One reason is that the molecular study and functional gene analysis in bananas fall behind due to the difficulties of its genetic manipulation. The plant protoplast-based transient transformation has been documented and widely used as a versatile and convenient system for functional gene analysis in many plant species. However, an efficient high-quality protoplast isolation and transformation system is still lacking for bananas. Here, we established an efficient protoplast isolation and transformation method for bananas by selecting proper source materials, optimizing conditions for enzymatic hydrolysis and PEG-mediated transfection. We found the best source materials for banana protoplasts’ isolation are young suckers, which give a yield of protoplasts ranging from 2.5 × 106 to 10.1 × 107 g−1 fresh weight after 5 to 6 h of enzymolysis. The yield is sufficient for most assays that have been established in protoplasts-based systems, such as protein subcellular localization and protein interaction assays. Moreover, using the established transient gene expression system in banana protoplasts, we validated the subcellular localization of Arabidopsis VESICLE SORTING RECEPTOR 1 (VSR1) and the protein self-interaction of Arabidopsis CNGC20 on the cell membrane. The results indicated this system works well and could be routinely used for the functional characterization of banana genes.

Multispectral image change detection is an important application in the field of remote sensing. Multispectral images usually contain many complex scenes, such as ground objects with diverse scales and proportions, so the change detection task expects the feature extractor is superior in adaptive multi-scale feature learning. To address the above-mentioned problems, a multispectral image change detection method based on multi-scale adaptive kernel network and multimodal conditional random field (MSAK-Net-MCRF) is proposed. The multi-scale adaptive kernel network (MSAK-Net) extends the encoding path of the U-Net, and designs a weight-sharing bilateral encoding path, which simultaneously extracts independent features of bi-temporal multispectral images without introducing additional parameters. A selective convolution kernel block (SCKB) that can adaptively assign weights is designed and embedded in the encoding path of MSAK-Net to extract multi-scale features in images. MSAK-Net retains the skip connections in the U-Net, and embeds an upsampling module (UM) based on the attention mechanism in the decoding path, which can give the feature map a better expression of change information in both the channel dimension and the spatial dimension. Finally, the multimodal conditional random field (MCRF) is used to smooth the detection results of the MSAK-Net. Experimental results on two public multispectral datasets indicate the effectiveness and robustness of the proposed method when compared with other state-of-the-art methods.

Gully erosion is a major threat to ecosystems, potentially leading to desertification, land degradation, and crop loss. Developing viable gully erosion prevention and remediation strategies requires regular monitoring of the gullies. Nevertheless, it is highly challenging to automatically access the monitoring results of the gullies from the latest monitoring data by training historical data acquired by different sensors at different times. To this end, this paper presents a novel semi-supervised semantic segmentation with boundary-guided pseudo-label generation strategy and adaptive loss function method. This method takes full advantage of the historical data with labels and the latest monitoring data without labels to obtain the latest monitoring results of the gullies. The boundary-guided pseudo-label generation strategy (BPGS), guided by the inherent boundary maps of real geographic objects, fuses multiple evidence data to generate reliable pseudo-labels. Additionally, we propose an adaptive loss function based on centroid similarity (CSIM) to further alleviate the impact of pseudo-label noise. To verify the proposed method, two datasets for gully erosion monitoring are constructed according to the satellite data acquired in northeastern China. Extensive experiments demonstrate that the proposed method is more appropriate for automatic gully erosion monitoring than four state-of-the-art methods, including supervised methods and semi-supervised methods.

Abstract Background The present study aimed to study the relationship between serum 25 hydroxyvitamin D3(25(OH)D3) and insulin-like growth factor-1 (IGF-1) and thyroid nodules. Methods Two hundred eighty-nine cases with thyroid nodules and 109 health subjects (control group) who admitted to the Hebei General Hospital during June 2016 to December 2016 were included in the study. Basic clinical information (age, sex, thyroid function, liver and kidney function, hypertension history, etc.) of patients were collected. Serum 25(OH) D3 and Serum IGF-1 were detected by electrochemiluminescence and radioimmunoassay methods, respectively. The relationship between the above-mentioned factors and thyroid nodules was statistically analyzed. Results Serum 25(OH)D3, IGF-1, fasting blood glucose (FBG), total cholesterol (TC), waist circumference (WC), total triiodothyronine (TT3), total thyroxine (TT4), hypertension history, and drinking history were significantly different between the nodules group and the control group (P

Voice-over IP (VoIP) technology is a kind of digital transmission technology based on IP network. It is one of the important methods to use voice service in VoIP as steganographic carrier to ensure secure transmission. However, the traditional steganographic code has some problems, such as low embedding efficiency and weak concealment, which cannot meet the requirements of VoIP streaming media information hiding for the security of secret information. Therefore, a steganographic algorithm combining F5 and simplified wet paper code (SWPC) algorithm is proposed. The main idea is to embed secret messages in each row of the carrier matrix using the F5 algorithm, and then the SWPC algorithm is used to embed the columns according to the wet and dry characteristics of the wet paper code without affecting the results before row embedding. We use the VoIP streams encoded by the ITU-T G.729a codec as a carrier to verify the proposed scheme. The experimental results demonstrate that the proposed scheme can achieve relatively better IP speech data steganographic transparency and that it can outperform F5-WPC and SWPC approaches.

Chemotherapy is the main postsurgical and adjuvant therapy for glioma, and intrinsic or acquired temozolomide (TMZ) resistance may result in poor prognosis. The miR-181 family was discovered to play an important role in regulating biological functions in glioma, and miR-181b is less expressed in human gliomas as a tumor-suppressive miRNA. The aim of this study was to explore the molecular mechanism of miR-181b-5p and its target gene on modulating TMZ chemosensitivity in glioma cells. The enhanced chemosensitivity effect of miR-181b-5p to TMZ in glioma cells U87MG and U251 was detected by MTT method. Dual luciferase reporter assay, quantitative real-time PCR (qRT-PCR) and Western blotting were performed to demonstrate that miR-181b-5p directly targets Bcl-2 to reduce the expression. Transwell and flow cytometry assays showed that combination of miR-181b-5p and TMZ exerted stronger effects on inhibiting U87MG cells proliferation, migration and invasion as well as promoting apoptosis and S phase arrest than miR-181b-5p and TMZ alone. The same tendency was observed in the upregulation of apoptosis-related protein Bax and downregulation of cycle-related proteins CyclinD1 and CDK4. In vivo experiments indicated that miR-181b-5p could enhance the tumor-suppressive effect of TMZ. In conclusion, our findings indicate that upregulation of miR-181b-5p targets Bcl-2 directly and may function as an important modifier to sensitize glioma cells to TMZ.

The construction method of the covert channel has always been a topic of research and exploration in the field of information security, and the use of IP data packets for covert channel construction is also an important method. Based on the above content, this paper proposes an enlarging-the-capacity packet sorting covert channel. We establish enlarging-the-capacity packet sorting covert channel model and derive the functional relationship between the total number of covert information transmitted and the number of ports. This method can send more secret information when the network status is not ideal. The simulation results show that the covert channel of the extended packet sorting has high performance in different packet loss rates and delays, and is superior to other IP covert channel.

In the context of the new era of high digitization and informatization, the emergence of the internet and artificial intelligence technologies has profoundly changed people’s lifestyles. The traditional cyber attack detection has become increasingly weak in the context of the increasingly complex network environment in the new era, and deep learning technology has begun to play a significant role in the field of network security. There are many kinds of attacks against web applications, which are very harmful, including SQL (Structured Query Language) injection, XSS (Cross-Site Scripting), and command injection. Based on the detection of SQL injection and XSS attacks, this paper combines the detection of command injection attacks, which are also very harmful, and proposes a multi-classification detection method for web injection attacks. We extract features in the URL (Uniform Resource Locator) and request body of HTTP (Hyper Text Transfer Protocol) requests and combine deep learning technology to build a multi-classification model for injection attacks. Firstly, aiming at the problem of imbalanced distribution of training samples and low detection accuracy of command injection attack, a sample generation method is proposed. The experimental results show that the proposed method ensures a higher detection rate of command injection attacks and lower false alarms. Secondly, we propose a more expressive feature fusion model, which effectively combines the features extracted by deep learning with the discrete features extracted manually. The experimental results show that the feature fusion model proposed in this work is more effective compared with a single deep learning model. The accuracy of the model is improved by about 1%.

Object tracking based on RGB images may fail when the color of the tracked object is similar to that of the background. Hyperspectral images with rich spectral features can provide more information for RGB-based trackers. However, there is no fusion tracking algorithm based on hyperspectral and RGB images. In this paper, we propose a reliability-guided aggregation network (RANet) for hyperspectral and RGB tracking, which guides the combination of hyperspectral information and RGB information through modality reliability to improve tracking performance. Specifically, a dual branch based on the Transformer Tracking (TransT) structure is constructed to obtain the information of hyperspectral and RGB modalities. Then, a classification response aggregation module is designed to combine the different modality information by fusing the response predicted through the classification head. Finally, the reliability of different modalities is also considered in the aggregation module to guide the aggregation of the different modality information. Massive experimental results on the public dataset composed of hyperspectral and RGB image sequences show that the performance of the tracker based on our fusion method is better than that of the corresponding single-modality tracker, which fully proves the effectiveness of the fusion method. Among them, the RANet tracker based on the TransT tracker achieves the best performance accuracy of 0.709, indicating the effectiveness and superiority of the RANet tracker.

With countless devices connected to the Internet of Things, trust mechanisms are especially important. IoT devices are more deeply embedded in the privacy of people’s lives, and their security issues cannot be ignored. Smart contracts backed by blockchain technology have the potential to solve these problems. Therefore, the security of smart contracts cannot be ignored. We propose a flexible and systematic hybrid model, which we call the Serial-Parallel Convolutional Bidirectional Gated Recurrent Network Model incorporating Ensemble Classifiers (SPCBIG-EC). The model showed excellent performance benefits in smart contract vulnerability detection. In addition, we propose a serial-parallel convolution (SPCNN) suitable for our hybrid model. It can extract features from the input sequence for multivariate combinations while retaining temporal structure and location information. The Ensemble Classifier is used in the classification phase of the model to enhance its robustness. In addition, we focused on six typical smart contract vulnerabilities and constructed two datasets, CESC and UCESC, for multi-task vulnerability detection in our experiments. Numerous experiments showed that SPCBIG-EC is better than most existing methods. It is worth mentioning that SPCBIG-EC can achieve F1-scores of 96.74%, 91.62%, and 95.00% for reentrancy, timestamp dependency, and infinite loop vulnerability detection.