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Identifying ships is essential for maritime situational awareness. Automatic identification system (AIS) data and remote sensing (RS) images provide information on ship movement and properties from different perspectives. This study develops an efficient spatiotemporal association approach that combines AIS data and RS images for point–track association. Ship detection and feature extraction from the RS images are performed using deep learning. The detected image characteristics and neighboring AIS data are compared using a multi-dimensional feature similarity model that considers similarities in space, time, course, and attributes. An efficient spatial–temporal association analysis of ships in RS images and AIS data is achieved using the interval type-2 fuzzy system (IT2FS) method. Finally, optical images with different resolutions and AIS records near the waters of Yokosuka Port and Kure are collected to test the proposed model. The results show that compared with the multi-factor fuzzy comprehensive decision-making method, the proposed method can achieve the best performance (F1 scores of 0.7302 and 0.9189, respectively, on GF1 and GF2 images) while maintaining a specific efficiency. This work can realize ship positioning and monitoring based on multi-source data and enhance maritime situational awareness.

High-voltage initial anode-free lithium metal batteries (AFLMBs) promise the maximized energy densities of rechargeable lithium batteries. However, the reversibility of the high-voltage cathode and lithium metal anode is unsatisfactory in sustaining their long lifespan. In this research, a concentrated electrolyte comprising dual salts of LiTFSI and LiDFOB dissolved in mixing solvents of dimethyl carbonate (DMC) and fluoroethylene carbonate (FEC) with a LiNO3 additive was formulated to address this challenge. FEC and LiNO3 regulate the anion-rich solvation structure and help form a LiF, Li3N-rich solid electrolyte interphase (SEI) with a high lithium plating/stripping Coulombic efficiency of 98.3%. LiDFOB preferentially decomposes to effectively suppress the side reaction at the high-voltage operation of the Li-rich Li1.2Mn0.54Ni0.13Co0.13O2 cathode. Moreover, the large irreversible capacity during the initial charge/discharge cycle of the cathode provides supplementary lithium sources for cycle life extension. Owing to these merits, the as-fabricated AFLMBs can operate stably for 80 cycles even at an ultrahigh voltage of 4.6 V. This study sheds new insights on the formulation of advanced electrolytes for highly reversible high-voltage cathodes and lithium metal anodes and could facilitate the practical application of AFLMBs.

In the cigarette manufacturing industry, machine vision and artificial intelligence algorithms have been employed to improve production efficiency by detecting product defects. However, achieving both high accuracy and real-time defect detection for cigarettes with complex patterns remains a challenge. To address these issues, this study proposes a model based on RESNET18, combined with a feature enhancement algorithm, to improve detection accuracy. Additionally, a method is designed to deploy the model on a field-programmable gate array (FPGA) with high parallel processing capabilities to achieve high-speed detection. Experimental results demonstrate that the proposed detection model achieves a detection accuracy of 95.88% on a cigarette filter defect dataset with an end-to-end detection speed of only 9.38 ms.

Link prediction is crucial in forecasting potential distribution channels within the dynamic and heterogeneous Xinjiang jujube sales market. This study utilizes knowledge graphs to represent entities and constructs a complex network model for market analysis. Graph neural networks (GNNs) have shown excellent performance in handling graph-structured data, but they do not necessarily significantly outperform in link prediction tasks due to an overreliance on node features and a neglect of structural information. Additionally, the Xinjiang jujube dataset exhibits unique complexity, including multiple types, attributes, and relationships, distinguishing it from typical GNN datasets such as DBLP and protein-protein interaction datasets. To address these challenges, we introduce the Heterogeneous Multi-Head Attention Graph Neural Network model (HMAGNN). Our methodology involves mapping isomeric nodes to common feature space and labeling nodes using an enhanced Weisfeiler–Lehman (WL) algorithm. We then leverage HMAGNN to learn both structural and attribute features individually. Throughout our experimentation, we identify the critical influence of local subgraph structure and size on link prediction outcomes. In response, we introduce virtual nodes during the subgraph extraction process and conduct validation experiments to underscore the significance of these factors. Compared to alternative models, HMAGNN excels in capturing structural features through our labeling approach and dynamically adapts to identify the most pertinent link information using a multi-head attention mechanism. Extensive experiments on benchmark datasets consistently demonstrate that HMAGNN outperforms existing models, establishing it as a state-of-the-art solution for link prediction in the context of jujube sales market analysis.

Building clustering is a key problem that needs to be solved in the realization of the automatic synthesis of large-scale maps. The selection of different feature and spatial distance calculation methods has a great impact on the clustering results, and the need to manually select appropriate feature and distance metrics leads to the problem of not being able to fully consider the complexity and diversity of buildings. In this paper, we propose a multi-scale clustering method for buildings that takes visual perception into account using the Gestalt principle to simulate how humans classify buildings through visual perception. Moreover, by analyzing the spatial features and texture attributes of buildings, a visual distance is designed to be used as a condition for building classification to assess the similarity between buildings, solving the complexity of manually selecting feature vectors and spatial distances and realizing the adaptive selection of features. Through experimental validation at different scales (macro, meso and micro), the present method is able to achieve the accurate clustering of buildings, and a frequency threshold of 91% is found, which is able to determine the optimal clustering results. The experimental results show that the proposed method can not only fully consider the complexity and diversity of buildings but also effectively support the understanding and analysis of urban spatial structure and provide a scientific decision-making basis for urban planning and management.

Typical forms of seismic damage to laminated-rubber-bearing girder bridges in the transverse direction are falling beams, girder displacement, and bearing damage. However, the damage to piers and foundations is generally lighter. This is mainly due to slippage of the bearings. Therefore, we propose a new type of arc-shaped shear key to improve the lateral seismic performance. A 1/12-scale highway continuous-girder bridge isolated by different shear keys was tested utilizing a 4 m × 4 m shaking table with six DOFs. The seismic responses of the bridge were analyzed in terms of phenomenon, displacement, strain, and acceleration. The main girder and pier exhibited different seismic responses because the bridge had different stops. A numerical simulation based on FEM showed that the established finite element model can well reproduce the displacement time history of the main girder and the cap girder. By analyzing the finite element model, the relative displacement of the bearing under different seismic waves was obtained. A comparison between the measured and FEM responses showed that the arc-shaped shear key can well limit the displacement of the main girder and the bearing. In addition, it does not significantly amplify the seismic response of the substructure. The arc-shaped shear key dissipates more energy while limiting the displacement of the main girder, and the comprehensive seismic performance is better than that of the rubber pad shear key.

Converting solar energy into fuels/chemicals through photochemical approaches holds significant promise for addressing global energy demands. Currently, semiconductor photocatalysis combined with redox techniques has been intensively researched in pollutant degradation and secondary energy generation owing to its dual advantages of oxidizability and reducibility; however, challenges remain, particularly with improving conversion efficiency. Since graphene’s initial introduction in 2004, three-dimensional (3D) graphene-based photocatalysts have garnered considerable attention due to their exceptional properties, such as their large specific surface area, abundant pore structure, diverse surface chemistry, adjustable band gap, and high electrical conductivity. Herein, this review provides an in-depth analysis of the commonly used photocatalysts based on 3D graphene, outlining their construction strategies and recent applications in photocatalytic degradation of organic pollutants, H2 evolution, and CO2 reduction. Additionally, the paper explores the multifaceted roles that 3D graphene plays in enhancing photocatalytic performance. By offering a comprehensive overview, we hope to highlight the potential of 3D graphene as an environmentally beneficial material and to inspire the development of more efficient, versatile graphene-based aerogel photocatalysts for future applications.

The enrichment of organic matter in high-quality marine shale is generally controlled by factors such as the redox conditions of sedimentary environments, productivity levels, terrigenous input, and ancient productivity. However, the controlling effect of the sedimentary environment on organic matter enrichment in intracratonic sag is still unclear. This study takes samples from the Qiongzhusi formation shale in southern Sichuan Basin as the research object, focusing on trace elements as well as rare earth elements in different stratigraphic intervals. The provenance of the Qiongzhusi formation shale is mainly terrigenous, with sediment sources mainly consisting of sedimentary rocks and granites. The primary sedimentary environment transitions from a continental margin setting, influenced by rift-related tectonic activity and sediment influx from adjacent landmasses, to an open oceanic environment characterized by mid-ocean ridge processes and oceanic plate subduction zones. During sedimentation, saline water was present, with predominant sedimentary environments ranging from shallow water to deep water continental shelves. The shale in the study area is characterized by a higher content of silicates and a lower content of carbonate minerals. Its siliceous sources are mainly influenced by biogenic and terrigenous debris, indicating higher ancient primary productivity and representing a favorable target for shale gas exploration.

The Kongquehe Sag, located in the western Lop Nur, has abundant pore subsurface brine. In order to study the source and origin, we tested and analyzed the hydrochemical composition and stable isotopes of the subsurface brine. The findings reveal that the brine exhibits a moderate to low degree of mineralization, with values ranging from 50.50 g/L to 91.14 g/L. The stable isotope compositions of unconfined and confined waters are different, with the mean values of δD being −8.00‰ and −51.75‰ and the mean values of δ18O being 10.08‰ and −6.01‰. These values are indicative of an intense evaporative environment prevalent in the Kongquehe Sag area. Furthermore, the 87Sr/86Sr ratios vary between 0.710642 and 0.710837, and δ34S values range from 9.2 to 10.7. These data suggest the long-term evolution of sulfur substances, predominantly through dissolution and sedimentation processes, with minimal influence from redox reactions. The data garnered from this research not only offer a novel perspective of the insights gained into the hydrochemical characteristics and the stable isotope signatures of the brines in the Kongquehe Sag area but also enriches the theoretical framework concerning the source and origin of subsurface brines, potentially informing future exploration strategies.

For the first time, a novel catalyst named Dy2EuSbO7 was successfully synthesized via the high-temperature solid-state sintering method (HTSSM). Dy2EuSbO7/ZnBiDyO4 heterojunction photocatalyst (DZHP) was fabricated through the HTSSM for degrading chlorpyrifos (CPS) in the pesticide wastewater under visible light irradiation (VSLID). Under VSLID, DZHP could effectively degrade CPS in pesticide wastewater. The experimental outcomes suggested that the kinetic curve with the Dy2EuSbO7/ZnBiDyO4 heterojunction (DZH) as a photocatalyst for the reduction of CPS under VSLID conformed to the first-order kinetics (FOKT). After VSLID of 156 min, the photocatalytic degradation (PTD) removal rate of CPS using DZH as photocatalyst was 1.12 times, 1.21 times, or 2.96 times that using Dy2EuSbO7 as a photocatalyst, ZnBiDyO4 as a photocatalyst, or nitrogen-doped titanium dioxide as a photocatalyst. After VSLID of 156 min for four cycle degradation tests (FCDTS) with DZH as a photocatalyst, the removal rate of CPS reached 98.78%, 97.66%, 96.59%, and 95.69%, respectively. Above results indicated that the DZHP possessed high stability. Experiments with the addition of trapping agents showed that hydroxyl radicals (•OH) owned the strongest oxidative removal ability for degrading CPS compared with superoxide anions (•O2−) or holes (h+). The oxidation capacity of three oxidation radicals for eliminating CPS was ranked in the ascending order as follows: h+ < •OH < •O2−. Lastly, the possible degradation pathway and degradation mechanism of CPS were discussed in detail. A visible light responsive heterojunction catalyst with high catalytic activity and a photocatalytic reaction system which were capable of efficiently removing toxic organic pollutants from pesticide wastewater were obtained.

Forest disturbance mapping plays an important role in furthering our understanding of forest dynamics. The Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) algorithm is widely used in forest disturbance mapping. However, it neglects the quality of the temporal trajectory and its change trends for forest disturbance mapping. Therefore, the aim of this paper is to improve LandTrendr (iLandTrendr) for forest disturbance mapping by optimizing its temporal trajectories and the post-processing of detection results. Specifically, the temporal trajectory of complex forest disturbance types was optimized using the Savitzky–Golay (SG) filter with constraints. That is, the smooth value generated from the SG filter for the disturbance year was replaced by the satellite observations when the nonlinear abrupt signals were included in the multi-temporal data. The forest disturbance detected by LandTrendr was further modified using the consistency of spectral variation trends. A case study using iLandTrendr to detect forest disturbance in Yunnan Province was conducted. Compared to the LandTrendr method, which has an overall accuracy (OA) of 35.88%, iLandTrendr generated forest disturbance mapping with an OA of 89.32%, which was significantly higher. The total mapped area of disturbance was 1,985,820.9 km2, accounting for 49.69% of the total area. The disturbances were predominately caused by natural factors, such as wildfires, pests and diseases, and forest degradation, accounting for 85.31% of the total disturbed area. iLandTrendr can quickly and accurately detect the occurrence year of complex forest disturbance types and can be extended for the forest disturbance mapping of a large area.

In an arid climate with minimal rainfall, plant growth is constrained by water scarcity and soil salinity. Ecological Water Conveyance (EWC) can mitigate degradation risks faced by riparian plant communities in these regions. However, its effects on long-term dynamics of root zone soil water content, salt levels, and root water uptake remain unclear. This study examined how groundwater affects salt and water dynamics, in addition to root water uptake, under different scenarios involving Tamarix ramosissima Ledeb. The research was conducted in the lower reaches of the Tarim River in northwestern China. The Hydrus-1D model was used, following the EWC strategy. The results show that the distribution of T. ramosissima roots was significantly influenced by soil water and salt distributions, with 56.8% of roots concentrated in the 60–100 cm soil layer. Under water stress conditions, root water uptake reached 91.0% of the potential maximum when considering water stress alone, and 41.0% when accounting for both water and salt stresses. Root water uptake was highly sensitive to changes in Depth-to-Water Table (DWT), notably decreasing with lower or higher DWT at 40% of the reference level. EWC effectively enhances root water uptake by using water to leach salts from the root zone soil, with optimal results observed at 500–600 mm. This study advocates for sustainable EWC practices to support vegetation and combat desertification in the lower reaches of arid inland rivers.

Assessing carbon emission reduction potential is vital for achieving carbon peak and neutrality in the maritime sector. In this study, we proposed a universal framework for assessing the effectiveness of different measures on carbon emission reduction from ships, including port and ship electrification (PSE), ship speed optimization (SSO), and clean fuel substitution (CFS). Firstly, the projection method of future ship traffic flows and activity levels relies on a neural network, and the ARIMA model was proposed. Then, the potential of various emission reduction measures was detailed and analyzed under different intensity scenarios. The proposed model was applied to Wuhan port, the results indicate that CFS is the most effective for long-term decarbonization, potentially achieving a carbon peak by 2025 under an aggressive scenario. For the short to medium term, PSE is favored due to technical maturity. SSO primarily delays emissions growth, making it a suitable auxiliary measure. These findings guide emission reduction strategies for ports, fostering green and sustainable shipping development.

Silicon (Si) as the anode material for lithium-ion batteries (LIBs) has attracted much attention due to its high theoretical specific capacity (4200 mAh/g). However, the specific capacity and cycle stability of the LIBs are reduced due to the pulverization caused by the expansion of Si coated on Cu (copper) foil during cycles. In order to solve this problem, researchers have used an ultra-thin Si deposition layer as the electrode, which improves cyclic stability and obtains high initial coulomb efficiency of LIBs. However, suitable substrate selection is crucial to fabricate an ultrathin Si deposition layer electrode with excellent performance, and a substrate with a three-dimensional porous structure is desirable to ensure the deposition of an ultrathin Si layer on the whole surface of the substrate. In this paper, the Si thin layer has been deposited on a binder-free hybrid film of carbon nanotubes (CNTs) and carbon nanocoils (CNCs) by magnetron sputtering. Compared with densely packed CNT film and flat Cu foil, the loose and porous film provides a large surface area and space for Si deposition, and Si can be deposited not only on the surface but also in the interior part of the film. The film provides a large number of channels for the diffusion and transmission of Li+, resulting in the rapid diffusion rate of Li+, which improves the effective lithium storage utilization of Si. Furthermore, the CNC itself is super elastic, and film provides an elastic skeleton for the Si deposition layer, which eases its volume expansion during charge and discharge processes. Electrochemical tests have showed that the Si/CNT–CNC film electrode has excellent performance as anode for LIBs. After 200 cycles, the Si/CNT–CNC film electrode still had possessed a specific capacity of 2500 mAh/g, a capacity retention of 92.8% and a coulomb efficiency of 99%. This paper provides an effective way to fabricate high performance Si-nanocarbon composite electrodes for LIBs.

Why are men less likely to use electronic coupons than women? Previous studies have explained the gender difference in coupon usage by exploring roles within the household and personality traits of the sexes. However, this research offers a novel explanation for this phenomenon, that men’s reluctance to use e-coupons may derive from the prevalent stereotype that e-coupon users are feminine. Because of the feminine stereotype associated with e-coupon usage, acquiring and using e-coupons are inconsistent with men’s gender identity. Five studies combining real data analysis, an online survey and experiments are used to support the previous notion. Using e-coupon acquisition data from a platform, study 1 tests whether females are more likely to acquire and use e-coupons than males in the real world. Study 2 experimentally tests the coupon–feminine stereotype. Study 3 explores the mediation effect of gender identity threat. Study 4 and study 5 consider two boundary conditions under which male consumers may be motivated to use e-coupons: when male consumers’ masculine identity is affirmed, and when the association between e-coupons and femininity is weakened. The results explain the gender difference in e-coupon usage from the novel lens of the coupon–feminine stereotype, offering a new and important perspective to explore the effect of gender identity on coupon use. Practical implications such as breaking the coupon–feminine stereotype, adopting a masculine design and communicating an affirmation of gender identity are further discussed.

The knowledge within wheat production chain data has multiple levels and complex semantic relationships, making it difficult to extract knowledge from them. Therefore, this paper proposes a fine-grained knowledge extraction method for the wheat production chain based on ontology. For the first time, the conceptual layers of ploughing, planting, managing, and harvesting were defined around the main agricultural activities of the wheat production chain. Based on this, the entities, relationships, and attributes in the conceptual layers were defined at a fine-grained level, and a spatial–temporal association pattern layer with four conceptual layers, twenty-eight entities, and forty-two relationships was constructed. Then, based on the characteristics of the self-constructed dataset, the Word2vec-BiLSTM-CRF model was designed for extracting the knowledge within it, i.e., the entity–relationship–attribute model and the Word2vec-BiLSTM-CRF model in this paper were compared with the four SOTA models. The results show that the accuracy and F1 value improved by 8.44% and 8.89%, respectively, compared with the BiLSTM-CRF model. Furthermore, the entities of the pest and disease dataset were divided into two different granularities for the comparison experiment; the results show that for entities with “disease names” and “pest names”, the recognition accuracy at the fine-grained level is improved by 32.71% and 31.58%, respectively, compared to the coarse-grained level, and the recognition performance of various fine-grained entities has been improved.

In the infrared remote sensing imaging system, the output of the detector is uneven, resulting in noticeable stripe noise in the image, which significantly reduces the image quality. Therefore, in response to the problem of establishing a variational model for the image globally and fixing the fractional order of the regularization term in the existing stripe noise removal algorithms based on model optimization, a stripe noise removal algorithm for infrared remote sensing images based on an adaptive weighted variable order model is proposed. In this algorithm, the vertical and approximate components containing stripe noise in the image are first separated through multi-level and multi-scale wavelet transform, and only these two components are processed; secondly, the global sparsity and the gradient sparsity of stripe noise and the variable order gradient sparsity of the information component are constrained by L1 norm, so as to establish the stripe noise removal model. The order of the fractional derivative is adaptively assigned to each pixel of the information component through local variance; then, an adaptive weight operator is introduced, which can assign different weights to the pixels in the variable order derivative of the information component on the basis of the image gradient information. Finally, through the ADMM algorithm, we can obtain the optimal solution of this model. The processing results of simulated and actual data indicate that the proposed algorithm performs well in all indexes and has obvious advantages in removing stripe noise and preserving image details.

High-performance computing (HPC) plays a crucial role in scientific computing, and the efficient utilization of HPC to accomplish computational tasks remains a focal point of research. This study addresses the issue of parameter tuning for Earth system models by proposing a comprehensive solution based on the concept of scientific workflows. This solution encompasses detailed methods from sensitivity analysis to parameter tuning and incorporates various approaches to enhance result accuracy. We validated the reliability of our methods using five cases in the Single Column Atmosphere Model (SCAM). Specifically, we investigated the influence of fluctuations of 11 typical parameters on 10 output variables. The experimental results show that the magnitude of the impact on the results varies significantly when different parameters are perturbed. These findings will help researchers develop more reasonable parameterization schemes for different regions and seasons.

Climate change is exacerbating the vulnerability of temperate forests to severe disturbances, potentially increasing tree mortality rates. Despite the significance of this issue, there has been a lack of comprehensive research on tree survival across extensive forest areas under the background of global climate change. To fill this gap, we conducted a detailed analysis of tree survival within a canopy nitrogen and water addition experimental platform in central China, utilizing data from two censuses and evaluating contributing factors. Our findings revealed 283 dead trees within the plots, predominantly of very small diameters (1–10 cm). The distribution of these dead trees varied among subplots, influenced by both biotic and abiotic factors. Notably, three dominant tree species were responsible for 64.8% of the deadwood biomass. The study determined that both the breast diameter and the quantity of dead trees, affected by surrounding trees and environmental conditions, played a critical role in deadwood biomass accumulation. This research offers an in-depth examination of deadwood biomass patterns in a temperate forest, highlighting the need to consider both experiment treatments and abiotic elements like topography in studies of forest ecosystem carbon. The insights gained from this study enhance our understanding of warm temperate forests’ role in the global carbon cycle and offer valuable guidance for forest conservation and management strategies.

Van der Waals (vdW) heterostructures are mainly fabricated by a classic dry transfer procedure, but the interface quality is often subject to the vdW gap, residual strains, and defect species. The realization of interface fusion and repair holds significant implications for the modulation of multiple photoelectric conversion processes. In this work, we propose a thermally mismatched strategy to trigger broad-band and high-speed photodetection performance based on a type-I heterostructure composed of black phosphorus (BP) and FePS3 (FPS) nanoflakes. The BP acts as photothermal source to promote interface fusion when large optical power is adopted. The regulation of optical power enables the device from pyroelectric (PE) and/or alternating current photovoltaic (AC–PV) mode to a mixed photovoltaic (PV)/photothermoelectric (PTE)/PE mode. The fused heterostructure device presents an extended detection range (405~980 nm) for the FPS. The maximum responsivity and detectivity are 329.86 mA/W and 6.95 × 1010 Jones, respectively, and the corresponding external quantum efficiency (EQE) approaches ~100%. Thanks to these thermally-related photoelectric conversion mechanism, the response and decay time constants of device are as fast as 290 μs and 265 μs, respectively, superior to current all FPS-based photodetectors. The robust environmental durability also renders itself as a high-speed and broad-band imaging sensor.

As field effect transistors are reduced to nanometer dimensions, experimental and theoretical research has shown a gradual change in noise generation mechanisms. There are few studies on noise theory for small nanoscale transistors, and Monte Carlo (MC) simulations mainly focus on 2D devices with larger nanoscale dimensions. In this study, we employed MC simulation techniques to establish a 3D device simulation process. By setting device parameters and writing simulation programs, we simulated the raw data of channel current noise for a silicon-based metal–oxide–semiconductor field-effect transistor (MOSFET) with a 10 nm channel length and calculated the drain output current based on these data, thereby achieving static testing of the simulated device. Additionally, this study obtained a 3D potential distribution map of the device channel surface area. Based on the original data from the simulation analysis, this study further calculated the power spectral density of the channel current noise and analyzed how the channel current noise varies with gate voltage, source–drain voltage, temperature, and substrate doping density. The results indicate that under low-temperature conditions, the channel current noise of the 10 nm MOSFET is primarily composed of suppressed shot noise, with the proportion of thermal noise in the total noise slightly increasing as temperature rises. Under normal operating conditions, the channel current noise characteristics of the 10 nm MOSFET device are jointly characterized by suppressed shot noise, thermal noise, and cross-correlated noise. Among these noise components, shot noise is the main source of noise, and its suppression degree decreases as the bias voltage is reduced. These findings are consistent with experimental observations and theoretical analyses found in the existing literature.

In the rapid development of urban construction, underground pipelines play a crucial role. However, the current underground pipelines have poor association with relevant management departments, and there are deficiencies in data completeness, accuracy, and information content. Managing and sharing information resources is relatively difficult, transforming the constructed 3D underground pipeline geographic information systems into an ‘Information silo’. This results in redundant construction and resource wastage of underground utilities. The complex distribution characteristics of underground utilities make rapid batch modeling and post-model maintenance challenging. Therefore, researching a 3D spatial data fusion model for urban underground utilities becomes particularly important. Given the above problem, this paper proposes a spatial data fusion model for underground pipeline scene modeling. It elaborates on the geometric, semantic, and temporal characteristics of underground pipelines, encapsulating these features. With underground pipeline objects as the core and pipeline characteristics as the foundation, a spatial data fusion model integrating multiple characteristics of underground pipelines has been constructed. Through software development, the data model designed in this paper facilitates rapid construction of underground pipeline scenes. This further enhances the consistency and integrity of underground pipeline data, enabling shared resources and comprehensive supervision of facility operations on a daily basis.

Low-light images are prevalent in intelligent monitoring and many other applications, with low brightness hindering further processing. Although low-light image enhancement can reduce the influence of such problems, current methods often involve a complex network structure or many iterations, which are not conducive to their efficiency. This paper proposes a Zero-Reference Camera Response Network using a camera response model to achieve efficient enhancement for arbitrary low-light images. A double-layer parameter-generating network with a streamlined structure is established to extract the exposure ratio K from the radiation map, which is obtained by inverting the input through a camera response function. Then, K is used as the parameter of a brightness transformation function for one transformation on the low-light image to realize enhancement. In addition, a contrast-preserving brightness loss and an edge-preserving smoothness loss are designed without the requirement for references from the dataset. Both can further retain some key information in the inputs to improve precision. The enhancement is simplified and can reach more than twice the speed of similar methods. Extensive experiments on several LLIE datasets and the DARK FACE face detection dataset fully demonstrate our method’s advantages, both subjectively and objectively.

Radiation-induced salivary gland injury (RISGI) is a common complication of radiotherapy in patients with head and neck cancer. Intragland expression of the Sonic Hedgehog (Shh) gene may partially rescue irradiation (IR)-induced hyposalivation by preserving salivary stem/progenitor cells and parasympathetic innervation, maintaining resident macrophages, and maintaining microvascular density. Previous studies have revealed that Ad-Rat Shh transduction through the salivary glands of miniature pigs can ameliorate oxidative stress-induced microvascular dysfunction after radiotherapy. Changes in the parotid salivary flow rate were analyzed, and the parotid tissue was collected at 5 and 20 weeks after IR. Changes in the Hedgehog pathway and vascular function-related markers (vascular endothelial growth factor (VEGF) and CD31) and oxidative stress-related markers were detected via immunohistochemistry, immunofluorescence, and Western blotting. A stable Shh-overexpressing cell line was generated from human umbilical vein endothelial cells (HUVECs) and exposed to 10 Gy X-ray irradiation, after which endothelial cell proliferation, senescence, apoptosis, and vascular function were evaluated. We found that intragland expression of the Shh gene efficiently alleviated IR-induced parotid gland injury in a miniature pig model. Our results indicate that the antioxidative stress and microvascular-protective effects of the Hh pathway are regulated by nuclear factor-erythroid 2-related factor 2 (Nrf2).

Peritonitis is a common and life-threatening inflammatory disease. Myeloid cells are elevated in the peripheral blood and contribute to peritonitis, but their circulating dynamics are not clear. In vivo flow cytometry (IVFC) is a noninvasive technique for monitoring the dynamics of circulating cells in live animals. It has been extensively used to detect circulating tumor cells, but rarely for monitoring immune cells. Here, we describe a method adapting an intravital microscope for IVFC so that we can monitor LysM-EGFP-labeled circulating myeloid cells in a tumor necrosis factor (TNF) α-induced peritonitis mouse model. Using this IVFC method, we quantified the blood flow velocity and cell concentration in circulation. We observed a significant increase in LysM-EGFP+ cells in circulation after TNFα intraperitoneal (i.p.) injection, which reached a plateau in ~20 min. Conventional cytometry analysis showed that most LysM-EGFP+ cells were neutrophils. Increasing blood neutrophils were accompanied by neutrophil recruitment to the peritoneal cavity and neutrophil emigration from the bone marrow. We then monitored neutrophil CD64 expression in vivo and found a significant increase in TNFα-induced peritonitis. We also found that CD18 blockade doubled the circulating neutrophil number in TNFα-induced peritonitis, suggesting that CD18 is critical for neutrophil recruitment in peritonitis. Overall, we demonstrate that IVFC techniques are useful for studying the circulating dynamics of immune cells during inflammatory diseases.

Groundwater is one of the important freshwater resources on Earth and is closely related to human activities. As a good biological vector, a more diverse repertory of antibiotic resistance genes in the water environment would have a profound impact on human medical health. Therefore, this study conducted a metagenomic sequencing analysis of water samples from groundwater monitoring points in the middle and lower reaches of the Yangtze River to characterize microbial community composition and antibiotic resistance in the groundwater environment. Our results show that different microbial communities and community composition were the driving factors in the groundwater environment, and a diversity of antibiotic resistance genes in the groundwater environment was detected. The main source of antibiotic resistance gene host was determined by correlation tests and analyses. In this study, metagenomics was used for the first time to comprehensively analyze microbial communities in groundwater systems in the middle and lower reaches of the Yangtze River basin. The data obtained from this study serve as an invaluable resource and represent the basic metagenomic characteristics of groundwater microbial communities in the middle and lower reaches of the Yangtze River basin. These findings will be useful tools and provide a basis for future research on water microbial community and quality, greatly expanding the depth and breadth of our understanding of groundwater.

Blowers are widely used in tasks such as ventilation, exhaust, drying, cooling, heat dissipation, or conveying medium, and they usually consume a lot of energy. There is an inevitable gap between the rotating impeller and static volute casing due to manufacturing tolerance and thermal deformation. The circulating flow in the gap has an important effect on the performance of the blower. In this study, computational fluid dynamics (CFD) was used to investigate the performance of the blower under different flow conditions and gaps, and the accuracy of the numerical simulation was verified by performance experiments. The results show that the flow separation under low flow conditions in the impeller channel can be suppressed by the circulating flow. However, the efficiency of the blower is decreased because a part of the power is used to maintain the circulating flow. Under design conditions, efficiency is reduced by 5.3~8.2%, depending on the gap sizes. Due to the increased flow rate in the impeller channel caused by the gap circulating flow, the net flow rate of the impeller under design conditions is about 12% higher than the inlet flow rate of the blower. Therefore, it leads to an increase of about 12% in impeller efficiency calculated by the net flow rate compared with the inlet flow rate. Finally, the flow field distribution on the impeller channel under different gap conditions was compared, and the effects of the gap on the blower performance were analyzed from the perspective of flow field structure.

Road extraction from high-resolution remote sensing images (HRSIs) is one of the tasks in image analysis. Deep convolutional neural networks have become the primary method for road extraction due to their powerful feature representation capability. However, roads are often obscured by vegetation, buildings, and shadows in HRSIs, resulting in incomplete and discontinuous road extraction results. To address this issue, we propose a lightweight post-processing network called RIRNet in this study, which include an information inference module and a road direction inference task branch. The information inference module can infer spatial information relationships between different rows or columns of feature images from different directions, effectively inferring and repairing road fractures. The road direction inference task branch performs the road direction prediction task, which can constrain and promote the road extraction task, thereby indirectly enhancing the inference ability of the post-processing model and realizing the optimization of the initial road extraction results. Experimental results demonstrate that the RIRNet model can achieve an excellent post-processing effect, which is manifested in the effective repair of broken road segments, as well as the handling of errors such as omission, misclassification, and noise, proving the effectiveness and generalization of the model in post-processing optimization.

Most space shuttle fuel tanks use a center column to hold the Propellant Management Device (PMD). This paper analyzes the gas–liquid interface state in the tanks with a central column during microgravity experiments conducted in the Chinese Space Station. It launches an extended study to investigate the gas–liquid interface state under different gravity conditions. Using the perturbation method and boundary layer theory, we numerically calculated the morphology of the gas–liquid interface under varying gravity conditions based on the Young–Laplace equation. The results were then compared to those obtained from existing commercial software and were found to be consistent. Based on this, the study develops two types of calculation procedures. The first procedure generates the corresponding shape of the liquid surface by inputting the height of the liquid surface endpoints and the gravity level. The second procedure is based on the targeting method and generates the corresponding liquid surface by inputting the volume of the liquid in the storage tank and the gravity level. The procedures were used to analyze the variation of gas–liquid interface properties under different gravity conditions. This study offers theoretical support for liquid management in aerospace engineering fuel tanks.

The human epidermal growth factor receptor 2 (HER2) is a transmembrane tyrosine kinase receptor and tumor-associated antigen abnormally expressed in various types of cancer, including breast, ovarian, and gastric cancer. HER2 overexpression is highly correlated with increased tumor aggressiveness, poorer prognosis, and shorter overall survival. Consequently, multiple HER2-targeted therapies have been developed and approved; however, only a subset of patients benefit from these treatments, and relapses are common. More potent and durable HER2-targeted therapies are desperately needed for patients with HER2-positive cancers. In this study, we developed a lipid nanoparticle (LNP)-based therapy formulated with mRNA encoding a novel HER2-CD3-Fc bispecific antibody (bsAb) for HER2-positive cancers. The LNPs efficiently transfected various types of cells, such as HEK293S, SKOV-3, and A1847, leading to robust and sustained secretion of the HER2-CD3-Fc bsAb with high binding affinity to both HER2 and CD3. The bsAb induced potent T-cell-directed cytotoxicity, along with secretion of IFN-λ, TNF-α, and granzyme B, against various types of HER2-positive tumor cells in vitro, including A549, NCI-H460, SKOV-3, A1847, SKBR3, and MDA-MB-231. The bsAb-mediated antitumor effect is highly specific and strictly dependent on its binding to HER2, as evidenced by the gained resistance of A549 and A1847 her2 knockout cells and the acquired sensitivity of mouse 4T1 cells overexpressing the human HER2 extracellular domain (ECD) or epitope-containing subdomain IV to the bsAb-induced T cell cytotoxicity. The bsAb also relies on its binding to CD3 for T-cell recruitment, as ablation of CD3 binding abolished the bsAb’s ability to elicit antitumor activity. Importantly, intratumoral injection of the HER2-CD3-Fc mRNA-LNPs triggers a strong antitumor response and completely blocks HER2-positive tumor growth in a mouse xenograft model of human ovarian cancer. These results indicate that the novel HER2-CD3-Fc mRNA-LNP-based therapy has the potential to effectively treat HER2-positive cancer.

With the increasingly severe climate change situation and the trend of green energy transformation, the development and utilization of hydrogen energy has attracted extensive attention from government, industry, and academia in the past few decades. Renewable energy electrolysis stands out as one of the most promising hydrogen production routes, enabling the storage of intermittent renewable energy power generation and supplying green fuel to various sectors. This article reviews the evolution and development of green hydrogen policies in the United States, the European Union, Japan, and China, and then summarizes the key technological progress of renewable energy electrolysis while introducing the progress of hydrogen production from wind and photovoltaic power generation. Furthermore, the environmental, social, and economic benefits of different hydrogen production routes are analyzed and compared. Finally, it provides a prospective analysis of the potential impact of renewable energy electrolysis on the global energy landscape and outlines key areas for future research and development.

To explore the effects of different water-saving treatments on rice plant growth and select suitable water-saving irrigation strategies for aerobic rice varieties, we conducted relevant field experiments from June to October 2023 at Jiangsu Runguo Agricultural Development Co., Ltd., China, which is located in a north subtropical monsoon climate where the soil is alkaline sandy loam. Four water treatments were set up, including the control of local conventional irrigation (CK, without water stress), mild water-saving treatment (W1, 20% more water saved than CK), moderate water-saving treatment (W2, 30% more water saved than CK), and severe water-saving treatment (W3, 40% more water saved than CK). The experiment results showed that rice plant heights were inhibited and leaf chlorophyll contents increased under all water-saving treatments compared to CK. Among them, the MDA content in paddy leaves under the W1 treatment decreased, while the activities of SOD and POD were enhanced and the membrane lipid peroxidation capacity of rice was also enhanced. Meanwhile, the results showed that the rice yield and quality under the W1 treatment significantly improved. Based on those experiments, a comprehensive evaluation of rice plant height, chlorophyll content, grain yield, yield components, and rice quality was conducted using the TOPSIS entropy weight method. It was preliminarily concluded that the suitable irrigation scheme for south and central Jiangsu was 20% water-saving irrigation compared with CK. In summary, under the premise of maintaining the economic yield of rice cultivation, an appropriate water irrigation plan helped save water resources and promote rice growth.

This study presents a pioneering synthesis of a direct Z-scheme Y2TmSbO7/GdYBiNbO7 heterojunction photocatalyst (YGHP) using an ultrasound-assisted hydrothermal synthesis technique. Additionally, novel photocatalytic nanomaterials, namely Y2TmSbO7 and GdYBiNbO7, were fabricated via the hydrothermal fabrication technique. A comprehensive range of characterization techniques, including X-ray diffractometry, Fourier-transform infrared spectroscopy, Raman spectroscopy, UV-visible spectrophotometry, X-ray photoelectron spectroscopy, transmission electron microscopy, X-ray energy-dispersive spectroscopy, fluorescence spectroscopy, photocurrent testing, electrochemical impedance spectroscopy, ultraviolet photoelectron spectroscopy, and electron paramagnetic resonance, was employed to thoroughly investigate the morphological features, composition, chemical, optical, and photoelectric properties of the fabricated samples. The photocatalytic performance of YGHP was assessed in the degradation of the pesticide acetochlor (AC) and the mineralization of total organic carbon (TOC) under visible light exposure, demonstrating eximious removal efficiencies. Specifically, AC and TOC exhibited removal rates of 99.75% and 97.90%, respectively. Comparative analysis revealed that YGHP showcased significantly higher removal efficiencies for AC compared to the Y2TmSbO7, GdYBiNbO7, or N-doped TiO2 photocatalyst, with removal rates being 1.12 times, 1.21 times, or 3.07 times higher, respectively. Similarly, YGHP demonstrated substantially higher removal efficiencies for TOC than the aforementioned photocatalysts, with removal rates 1.15 times, 1.28 times, or 3.51 times higher, respectively. These improvements could be attributed to the Z-scheme charge transfer configuration, which preserved the preferable redox capacities of Y2TmSbO7 and GdYBiNbO7. Furthermore, the stability and durability of YGHP were confirmed, affirming its potential for practical applications. Trapping experiments and electron spin resonance analyses identified active species generated by YGHP, namely •OH, •O2−, and h+, allowing for comprehensive analysis of the degradation mechanisms and pathways of AC. Overall, this investigation advances the development of efficient Z-scheme heterostructural materials and provides valuable insights into formulating sustainable remediation strategies for combatting AC contamination.

Due to the high content of impurities such as proteins in tamarind seed polysaccharide (TSP), they must be separated and purified before it can be used. TSP can disperse in cold water, but a solution can only be obtained by heating the mixture. Therefore, it is important to understand the dispersion and dissolution process of TSP at different temperatures to expand the application of TSP. In this study, pasting behavior and rheological properties as a function of temperature were characterized in comparison with potato starch (PS), and their relationship with TSP molecular features and microstructure was revealed. Pasting behavior showed that TSP had higher peak viscosity and stronger thermal stability than PS. Rheological properties exhibited that G′ and G′′ of TSP gradually increased with the increase in temperature, without exhibiting typical starch gelatinization behavior. The crystalline or amorphous structure of TSP and starch was disrupted under different temperature treatment conditions. The SEM results show that TSP particles directly transformed into fragments with the temperature increase, while PS granules first expanded and then broken down into fragments. Therefore, TSP and PS underwent different dispersion mechanisms during the dissolution process: As the temperature gradually increased, TSP possibly underwent a straightforward dispersion and was then dissolved in aqueous solution, while PS granules initially expanded, followed by disintegration and dispersion.

Metal-oxide-based gas sensors are extensively utilized across various domains due to their cost-effectiveness, facile fabrication, and compatibility with microelectronic technologies. The copper (Cu)-based multifunctional polymer-enhanced sensor (CuMPES) represents a notably tailored design for non-invasive environmental monitoring, particularly for detecting diverse gases with a low concentration. In this investigation, the Cu-CuO/PEDOT nanocomposite was synthesized via a straightforward chemical oxidation and vapor-phase polymerization. Comprehensive characterizations employing X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), X-ray diffraction (XRD), and micro Raman elucidated the composition, morphology, and crystal structure of this nanocomposite. Gas-sensing assessments of this CuMPES based on Cu-CuO/PEDOT revealed that the response current of the microneedle-type CuMPES surpassed that of the pure Cu microsensor by nearly threefold. The electrical conductivity and surface reactivity are enhanced by poly (3,4-ethylenedioxythiophene) (PEDOT) polymerized on the CuO-coated surface, resulting in an enhanced sensor performance with an ultra-fast response/recovery of 0.3/0.5 s.

Activated carbon (AC) serves as extensively researched adsorbents, with numerous established methods for their preparation. This study originated from the hypothesis that compressing a hydrocarbon substance to create a densely compacted pellet, known as pelletizing, would enhance the development of porous features of the resulting AC. The anticipated enhancement is attributed to the rise in spatial proximity amidst HEC polymer chains within the bulk of the pellet, which facilitates aromatization both in extent and functionality. 2-Hydroxyethyl cellulose (HEC) pellets were prepared by adjusting the duration of load holding, aiming to increase the packing density of HEC polymer chains via creeping. The BET analysis of the resulting AC samples demonstrates the efficacy of compression on HEC pellets in enhancing their porous properties. The FE-SEM study revealed diverse AC surface morphologies that are associated with a set of specific pelletizing conditions. The 13C NMR spectroscopy for carbon skeletons, FT-IR spectroscopy for organic functionality, and XPS spectroscopy for surface composition collectively report the leverage of compression treatment before pyrolyzing HEC pellets. Furthermore, the assessment of hydrogen sulfide adsorption by the resulting AC samples revealed distinctive breakthrough curves, providing validation for the proposed compression effect.

A new catalyst, Sm2NdSbO7, was synthesized for the first time by solid-phase sintering. The study utilized X-ray diffraction, transmission electron microscope energy dispersive X-ray spectroscopy, and X-ray photoelectron spectroscopy to examine the structural characteristics of monocrystal BiDyO3, monocrystal Sm2NdSbO7 and Sm2NdSbO7/BiDyO3 heterojunction photocatalysts (SBHP) prepared by solid-phase sintering. The Sm2NdSbO7 photocatalyst owned a pyrochlorite structure, belonged to the face-centered cubic crystal system, possessed a space group of Fd3m and a bandgap width of 2.750 eV. After 145 min of visible light irradiation (145-VLIRD), the removal rate (RMR) of methyl parathion (MP) or total organic carbon of SBHP was 100% or 97.58%, respectively. After 145-VLIRD, the photocatalytic degradation rates of SBHP to MP were 1.13 times, 1.20 times, and 2.43 times higher than those of the Sm2NdSbO7 photocatalyst, the BiDyO3 photocatalyst, and the nitrogen-doped TiO2 catalyst, respectively. The experimental results showed that SBHP had good photocatalytic activity. After four cycles of cyclic degradation experiments with SBHP, the elimination rates of MP were 98.76%, 97.44%, 96.32%, and 95.72%, respectively. The results showed that SBHP had good stability. Finally, the possible degradation pathways and degradation mechanisms of MP were speculated. In this study, we successfully developed a high-efficiency heterojunction catalyst which responded to visible light and possessed significant photocatalytic activity. The catalyst could be used in photocatalytic reaction system for eliminating the harmful organic pollutants from wastewater.

The efficacy of different echinocandins is assessed by evaluating the in vitro activity of a novel antifungal, rezafungin, against invasive fungal isolates in comparison with anidulafungin and caspofungin. Using the broth microdilution (BMD) method, the susceptibility of 1000 clinical Candida isolates (including 400 C. albicans, 200 C. glabrata, 200 C. parapsilosis, 150 C. tropicalis and 50 C. krusei) and 150 Aspergillus isolates (100 A. fumigatus and 50 A. flavus) from the Eastern China Invasive Fungi Infection Group (ECIFIG) was tested for the antifungals including anidulafungin, rezafungin, caspofungin and fluconazole. The echinocandins showed strong activity against C. albicans that was maintained against fluconazole-resistant isolates. The GM MIC (geometric mean minimum inhibitory concentration) value of rezafungin was found to be comparable to that of anidulafungin or caspofungin against the five tested common Candida species. C. tropicalis exhibited higher resistance rates (about 8.67–40.67% in different antifungals) than the other four Candida species. Through the sequencing of FKS genes, we searched for mutations in echinocandin-resistant C. tropicalis isolates and found that all displayed alterations in FKS1 S654P. The determined MEC (minimal effective concentration) values against A. fumigatus and A. flavus for rezafungin (0.116 μg/mL, 0.110 μg/mL) are comparable to those of caspofungin (0.122 μg/mL, 0.142 μg/mL) but higher than for anidulafungin (0.064 μg/mL, 0.059 μg/mL). Thus, the in vitro activity of rezafungin appears comparable to anidulafungin and caspofungin against most common Candida and Aspergillus species. Rezafungin showed higher susceptibility rates against C. glabrata. Rezafungin indicates its potent activity for potential clinical application.

Currently, an increasing number of people are opting to use transformer models or conformer models for language identification, achieving outstanding results. Among them, transformer models based on self-attention can only capture global information, lacking finer local details. There are also approaches that employ conformer models by concatenating convolutional neural networks and transformers to capture both local and global information. However, this static single-branch architecture is difficult to interpret and modify, and it incurs greater inference difficulty and computational costs compared to dual-branch models. Therefore, in this paper, we propose a novel model called Branch-transformer (B-transformer). In contrast to traditional transformers, it consists of parallel dual-branch structures. One branch utilizes self-attention to capture global information, while the other employs a Convolutional Gated Multi-Layer Perceptron (cgMLP) module to extract local information. We also investigate various fusion methods for integrating global and local information and experimentally validate the effectiveness of our approach on the NIST LRE 2017 dataset.

This study aimed to investigate the efficacy of supercritical CO2 (SC-CO2) extraction in enhancing the extraction rate, purity, and antioxidant activity of Indocalamus latifolius (Keng) McClure (Poaceae) leaf terpenoids (ILLTs). Crude extracts obtained from leaves were subjected to qualitative and quantitative analyses, revealing neophytadiene, phytol, β-sitosterol, β-amyrone, squalene, and friedelin as the primary terpenoid constituents, identified through gas chromatography–mass spectrometry (GC-MS). Compared with steam distillation extraction (SD), simultaneous distillation extraction (SDE), ultra-high pressure-assisted n-hexane extraction (UHPE-Hex), ultra-high pressure-assisted ethanol extraction (UHPE-EtOH), ultrasound-assisted n-hexane extraction (UE-Hex), and ultrasound-assisted ethanol extraction (UE-EtOH), SC-CO2 exhibited a superior ILLT extraction rate, purity, and antioxidant activity. Scanning electron microscopy (SEM) observations of the residues further revealed more severe damage to both the residues and their cell walls after SC-CO2 extraction. Under optimal parameters (4.5 h, 26 MPa, 39 °C, and 20% ethyl alcohol), the ILLT extraction rate with SC-CO2 reached 1.44 ± 0.12 mg/g, which was significantly higher than the rates obtained by the other six methods. The subsequent separation and purification using WelFlash C18-l, BUCHI-C18, and Sephadex LH-20 led to an increase in the purity of the six terpenoid components from 12.91% to 93.34%. Furthermore, the ILLTs demonstrated cytotoxicity against HepG2 cells with an IC50 value of 148.93 ± 9.93 μg/mL. Additionally, with increasing concentrations, the ILLTs exhibited an enhanced cellular antioxidant status, as evidenced by reductions in both reactive oxygen species (ROS) and malondialdehyde (MDA) levels.

The frequent occurrence of landslides poses a serious threat to people’s lives and property. In order to evaluate disaster hazards based on remote sensing images via machine learning means, it is essential to establish an image database with manually labeled boundaries of landslides. However, the existing datasets do not cover diverse types of mountainous landslides. To address this issue, we propose a high-resolution (1 m) diverse mountainous landslide remote sensing dataset (DMLD), including 990 landslide instances across different terrain in southwestern China. To evaluate the performance of the DMLD, seven state-of-the-art deep learning models with different loss functions were implemented on it. The experiment results demonstrate not only that all of these deep learning methods with different characteristics can adapt well to the DMLD, but also that the DMLD has potential adaptability to other geographical regions.

The high prevalence of constipation after fracture surgery brings intolerable discomfort to patients on the one hand, and affects post-surgery nutrient absorption on the other hand, resulting in poor prognosis. Given the acknowledged probiotic properties of Lactobacillus rhamnosus, 100 fracture patients with post-surgery constipation were centrally enrolled and administered orally with L. rhamnosus JYLR-127 to assess the efficacy of probiotic-adjuvant therapy in alleviating post-fracture constipation symptoms. The results showed that L. rhamnosus JYLR-127 improved fecal properties, promoted gastrointestinal recovery, and relieved constipation symptoms, which were mainly achieved by elevating Firmicutes (p < 0.01) and descending Bacteroidetes (p < 0.001), hence remodeling the disrupted intestinal microecology. In addition, blood routine presented a decrease in C-reactive protein levels (p < 0.05) and an increase in platelet counts (p < 0.05) after probiotic supplementation, prompting the feasibility of L. rhamnosus JYLR-127 in anti-inflammation, anti-infection and hemorrhagic tendency prevention after fracture surgery. Our study to apply probiotics in ameliorating constipation after fracture surgery is expected to bless the bothered patients, and provide broader application scenarios for L. rhamnosus preparations.

With the changing global climate, drought stress will pose a considerable challenge to the sustainable development of agriculture in arid regions. The objective of this study was to explore the resistance and water demand of cotton plants to water stress during the flowering and boll setting stage. The experimental plot was in Huaxing Farm of Changji city. The plots were irrigated, respectively, at 100% (as the control), 90%, 85% and 80% of the general irrigation amount in the local area. The relationship between the various measured indexes and final yield under different deficit irrigation (DI) treatments was studied. The results showed that deficit irrigation impacted the growth and development processes of cotton during the flowering and boll setting stage. There was a high negative correlation (R2 > 0.95) between the maximum leaf area index and yield. Similarly, there was a high correlation between malondialdehyde content and yield. Meanwhile, 90% of the local cotton irrigation contributed to water saving and even increasing cotton yield. Furthermore, based on the results, the study made an initial optimization to the local irrigation scheme by utilizing the DSSAT model. It was found that changing the irrigation interval to 12 days during the stage could further enhance cotton yield and conserve resources.

In this paper, a new sensor is proposed to efficiently gather crucial information on corrosion phenomena and their progression within steel components. Fabricated with plastic optical fibers (POF), the sensor can detect corrosion-induced physical changes in the appearance of monitoring points within the steel material. Additionally, the new sensor incorporates an innovative structure that efficiently utilizes bi-directional optical transmission in the POF, simplifying the installation procedure and reducing the total cost of the POF cables by as much as 50% when monitoring multiple points. Furthermore, an extremely compact dummy sensor with the length of 5 mm and a diameter of 2.2 mm for corrosion-depth detection was introduced, and its functionality was validated through experiments. This paper outlines the concept and fundamental structure of the proposed sensor; analyzes the results of various experiments; and discusses its effectiveness, prospects, and economic advantages.

Tree-like structures, characterized by hierarchical relationships and power-law distributions, are prevalent in a multitude of real-world networks, ranging from social networks to citation networks and protein–protein interaction networks. Recently, there has been significant interest in utilizing hyperbolic space to model these structures, owing to its capability to represent them with diminished distortions compared to flat Euclidean space. However, real-world networks often display a blend of flat, tree-like, and circular substructures, resulting in heterophily. To address this diversity of substructures, this study aims to investigate the reconstruction of graph neural networks on the symmetric manifold, which offers a comprehensive geometric space for more effective modeling of tree-like heterophily. To achieve this objective, we propose a graph convolutional neural network operating on the symmetric positive-definite matrix manifold, leveraging Riemannian metrics to facilitate the scheme of information propagation. Extensive experiments conducted on semi-supervised node classification tasks validate the superiority of the proposed approach, demonstrating that it outperforms comparative models based on Euclidean and hyperbolic geometries.

Despite the partial disclosure of driving scenario knowledge graphs, they still fail to meet the comprehensive needs of intelligent connected vehicles for driving knowledge. Current issues include the high complexity of pattern layer construction, insufficient accuracy of information extraction and fusion, and limited performance of knowledge reasoning models. To address these challenges, a hybrid knowledge graph method was adopted in the construction of a driving scenario knowledge graph (DSKG). Firstly, core concepts in the field were systematically sorted and classified, laying the foundation for the construction of a multi-level classified knowledge graph top-level ontology. Subsequently, by deeply exploring and analyzing the Traffic Genome data, 34 entities and 51 relations were extracted and integrated with the ontology layer, achieving the expansion and updating of the knowledge graph. Then, in terms of knowledge reasoning models, an analysis of the training results of the TransE, Complex, Distmult, and Rotate models in the entity linking prediction task of DSKG revealed that the Distmult model performed the best in metrics such as hit rate, making it more suitable for inference in DSKG. Finally, a standardized and widely applicable driving scenario knowledge graph was proposed. The DSKG and related materials have been publicly released for use by industry and academia.

Metal sulfides in waste rocks and tailings are susceptible to serious soil and water contamination due to the generation of acid mine drainage (AMD) during stockpiling. The hydrometallurgical process is one of the most essential heavy metal remediation technologies through harmless disposal and resource utilization of the waste sulfides. However, atmospheric hydrometallurgy of sulfides still faces great challenges due to low leaching efficiency and high cost. In this work, we proposed a cooperative leaching system (Fe2(SO4)3-O3) and investigated the oxidative dissolution process of sphalerite (ZnS). Under the optimal conditions, the extracted zinc reached 97.8%. Reactive oxygen species (ROS) (·OH, 1O2 and ·O2−) were identified in the radical quenching experiments. The dissolution of sphalerite did not show passivation due to the ozone’s capability to oxidize the sulfur in sphalerite to sulfate. In addition, stirring rate, O3 inlet concentration, and Fe2(SO4)3 concentration had a significant effect on the dissolution of sphalerite. Meanwhile, the apparent activation energy was 24.11 kJ/mol based on kinetic fitting, which indicated that the controlling step of the reaction was mainly a diffusion process. This work demonstrated the cooperative effect of sphalerite leaching in the O3-Fe2(SO4)3 system and provided a theoretical reference for efficient and atmospheric dissolution of sphalerite.

An 8-week feeding experiment aimed to investigate the effects of fermented soybean residue in diets on the growth performance, serum antioxidant parameters, intestinal digestive capacity, and microbial composition of the hindgut of Furong crucian carp. The feeds were formulated to contain 0%, 6%, 12%, 18%, and 24% fermented soybean residue (CON, FSR6, FSR12, FSR18, and FSR24, respectively), respectively, to form five diets (crude protein: 33%; crude lipid: 5%). The results showed that incorporating 6% fermented soybean residue into the diet significantly increased the weight gain rate (WGR) and specific growth rate (SGR) and decreased the feed coefficient rate (p < 0.05). Through regression analysis of WGR and SGR, the optimal levels of the dietary fermented soybean residue of Furong crucian carp were determined to be 6.78% and 7.06%. Incorporating fermented soybean residue into the diet decreased the lipid content of the whole body and the levels of glucose, total cholesterol, and triglyceride in the serum of Furong crucian carp. The inclusion of 6% and 12% fermented soybean residue in the diet markedly increased the antioxidant capacity, intestinal amylase activity, and intestinal villous height of Furong crucian carp (p < 0.05). At the 6% level, fermented soybean residue significantly increased the abundance of Romboutsia and Clostridium_sensu_stricto_1 while significantly decreasing the abundance of Vibrio (p < 0.05), indicating that a 6% level of fermented soybean residue was beneficial for intestinal health. In conclusion, incorporating 6–7% fermented soybean residue into the diet of Furong crucian carp was recommended.