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In order to further clarify the growth-promoting effect of the non-core Metarhizium sp. Marquandomyces marquandii on plants, M. marquandii SGSF043, which was obtained via pre-screening in the laboratory, was selected as a test strain and the seed soaking method was adopted. The effects of a fermentation broth obtained from this strain on the seed germination, seedling growth, and rhizosphere soil enzyme activity of maize were studied. The results were as follows: In seed germination tests, M. marquandii SGSF043 fermentation liquid had a certain inhibitory effect on corn seed germination, and the germination rate was only 15%. When the fermentation solution was diluted 10 times, the germination rate reached 97%. After the germination test, the growth of maize plumules was promoted in the groups treated with 10-times and 1000-times dilutions. In the field community experiment, based on the comprehensive evaluation of seedling biomass indicators, the solution diluted 100 times had the best growth-promoting effect. The aboveground fresh weight was increased by 127.13% compared with the control group. The results show that M. marquandii SGSF043 has the potential to promote the growth of maize and improve the soil environment, which provides a theoretical basis for the research on and the application of M. marquandii in farmland.

With the sudden advancement of glucose biosensors for monitoring blood glucose levels for the prevention and diagnosis of diabetes, non-enzymatic glucose sensors have aroused great interest owing to their sensitivity, stability, and economy. Recently, researchers have dedicated themselves to developing non-enzymatic electrochemical glucose sensors for the rapid, convenient, and sensitive determination of glucose. However, it is desirable to explore economic and effective nanomaterials with a high non-enzymatic catalysis performance toward glucose to modify electrodes. Metal oxides (MOs) and metal sulfides (MSs) have attracted extensive interest among scholars owing to their excellent catalytic activity, good biocompatibility, low cost, simple synthesis process, and controllable morphology and structure. Nonetheless, the exploitation of MOs and MSs in non-enzymatic electrochemical glucose sensors still suffers from relatively low conductivity and biocompatibility. Therefore, it is of significance to integrate MOs and MSs with metal/carbon/conducive polymers to modify electrodes for compensating the aforementioned deficiency. This review introduces the recent developments in non-enzymatic electrochemical glucose sensors based on MOs and MSs, focusing on their preparation methods and how their structural composition influences sensing performance. Finally, this review discusses the prospects and challenges of non-enzymatic electrochemical glucose sensors.

Effective control of the health operating condition of multi-support, ultra-long shaft system water-lubricated stern bearings is crucial for supporting the intelligent maintenance and health management of ships. This study investigates the failure modes of water-lubricated stern bearings and focuses on the critical failure modes of abnormal wear and high-temperature meltdown to analyze the mechanisms and influencing factors of these failures. It discusses the conditions for healthy operation of water-lubricated stern bearings, as well as methods for controlling lubrication and temperature rise. Based on this, controllable parameters for the healthy operation of water-lubricated stern bearings were selected, an experimental rig was constructed, and experiments were conducted using SF-2A material water-lubricated bearings. The experimental results indicate that by controlling parameters such as shaft rotational speed, inlet lubrication water temperature, clear-water lubrication, sediment-laden-water lubrication, bearing specific pressure, and the surface morphology of the bearing liner, the velocity characteristics, lubrication characteristics, and temperature rise characteristics of the bearings can be effectively altered. The sensitivity of the lubrication and temperature rise characteristics of SF-2A material water-lubricated stern bearings to controllable parameters varies under different environmental conditions. The study finds that precise control of these parameters can improve the operating condition and reliability of water-lubricated bearings.

Dynamic facades allow for effective climate adaptability, representing a new trend in future building envelope design. Present research on dynamic facades often focuses solely on certain aspects of the built environment or relies entirely on simulation outcomes. Meanwhile, the real-time changing nature of dynamic facades poses challenges in accurately simulating these schemes. Therefore, it remains essential to quantify the energy consumption performances of different types of dynamic facades and their influence on the indoor environment comfort in response to ventilation, light, and thermal environment to improve energy savings. This study uses an energy management system to simulate the ability of five dynamic facades—an intelligent ventilated facade, a dynamic exterior shading, a dynamic interior shading, a buffer layer, and phase-change material (PCM) facades—to provide adequate comfort and reduce energy consumption in four climate zones in China. The simulation model of a nearly net-zero energy Solar Decathlon house “Nature Between” was validated with experimental data. Among the five dynamic facades, the energy-saving efficiency of intelligent ventilation was highest, followed by exterior shading. Compared with houses without dynamic facades, the use of the dynamic facades reduced energy consumption (and annual glare time) by 19.87% (90.65%), 22.37% (74.84%), 15.19% (72.09%), and 9.23% (75.53%) in Xiamen, Shanghai, Beijing, and Harbin, respectively. Findings regarding the dynamic facade-driven energy savings and favorable indoor environment comfort provide new and actionable insights into the design and application of dynamic facades in four climate regions in China.

Pan-sharpening aims to generate high-resolution (HR) multispectral (MS) images by fusing HR panchromatic (PAN) and low-resolution (LR) MS images covering the same area. However, due to the lack of real HR MS reference images, how to accurately evaluate the quality of a fused image without reference is challenging. On the one hand, most methods evaluate the quality of the fused image using the full-reference indices based on the simulated experimental data on the popular Wald’s protocol; however, this remains controversial to the full-resolution data fusion. On the other hand, existing limited no reference methods, most of which depend on manually crafted features, cannot fully capture the sensitive spatial/spectral distortions of the fused image. Therefore, this paper proposes a perceptual quality assessment method based on deep feature similarity measure. The proposed network includes spatial/spectral feature extraction and similarity measure (FESM) branch and overall evaluation network. The Siamese FESM branch extracts the spatial and spectral deep features and calculates the similarity of the corresponding pair of deep features to obtain the spatial and spectral feature parameters, and then, the overall evaluation network realizes the overall quality assessment. Moreover, we propose to quantify both the overall precision of all the training samples and the variations among different fusion methods in a batch, thereby enhancing the network’s accuracy and robustness. The proposed method was trained and tested on a large subjective evaluation dataset comprising 13,620 fused images. The experimental results suggested the effectiveness and the competitive performance.

A green chemical shear-thickening polishing (GC-STP) method was studied to improve the surface precision and processing efficiency of monocrystalline silicon. A novel green shear-thickening polishing slurry composed of silica nanoparticles, alumina abrasive, sorbitol, plant ash, polyethylene glycol, and deionized water was formulated. The monocrystalline silicon was roughly ground using a diamond polishing slurry and then the GC-STP process. The material removal rate (MRR) during GC-STP was 4.568 μmh−1. The material removal mechanism during the processing of monocrystalline silicon via GC-STP was studied using elemental energy spectroscopy and FTIR spectroscopy. After 4 h of the GC-STP process, the surface roughness (Ra) of the monocrystalline silicon wafer was reduced to 0.278 nm, and an excellent monocrystalline silicon surface quality was obtained. This study shows that GC-STP is a green, efficient, and low-damage polishing method for monocrystalline silicon.

Fresh raspberries are highly perishable, but lactic acid bacteria fermentation offers a favourable method for developing healthy products. This study investigated the effects of Lacticaseibacillus paracasei fermentation on the nutrients and phytochemicals of red raspberry nectar using widely targeted metabolomics, as well as its antioxidant activity. The fermentation notably disrupted the raspberry tissue structure, reshaped its non-volatile composition, and increased its DPPH and hydroxyl free radical scavenging abilities. A total of 261 compounds showed significant differences, with 198 upregulated and 63 downregulated. Among these, certain flavonoid glucosides (e.g., pelargonid-in-3-O-rutinoside, delphinidin-3-O-rutinoside-7-O-glucoside, and kaempferol-3-O-glucoside) were significantly downregulated, while some bioactive phenolic acids (e.g., 3-(4-Hydroxyphenyl)-propionic acid and DL-3-phenyllactic acid), alkaloids (e.g., deoxymutaaspergillic acid and indole-3-lactic acid), amino acids (e.g., L-phenylalanine and L-glutamine), and B vitamins (e.g., VB6, VB7, and VB3) were substantially upregulated. Furthermore, the Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation and enrichment analysis revealed that metabolic pathways and the biosynthesis of secondary metabolites contributed significantly to the new profile of fermented red raspberry nectar. These findings provide valuable insights for developing fermented raspberry products using Lacticaseibacillus paracasei, which can help minimise fresh raspberry loss and enhance their valorisation.

With the rapid development of artificial intelligence, deep learning has been widely applied to complex tasks such as computer vision and natural language processing, demonstrating its outstanding performance. This study aims to exploit the high precision and efficiency of deep learning to develop a system for the identification of pollen. To this end, we constructed a dataset across 36 distinct genera. In terms of model selection, we employed a pre-trained ResNet34 network and fine-tuned its architecture to suit our specific task. For the optimization algorithm, we opted for the Adam optimizer and utilized the cross-entropy loss function. Additionally, we implemented ELU activation function, data augmentation, learning rate decay, and early stopping strategies to enhance the training efficiency and generalization capability of the model. After training for 203 epochs, our model achieved an accuracy of 97.01% on the test set and 99.89% on the training set. Further evaluation metrics, such as an F1 score of 95.9%, indicate that the model exhibits good balance and robustness across all categories. To facilitate the use of the model, we develop a user-friendly web interface. Users can upload images of pollen grains through the URL link provided in this article) and immediately receive predicted results of their genus names. Altogether, this study has successfully trained and validated a high-precision pollen grain identification model, providing a powerful tool for the identification of pollen.

Insecticides are widely used to boost crop yields, but their effects on non-target insects like Vespa magnifica are still poorly understood. Despite its ecological and economic significance, Vespa magnifica has been largely neglected in risk assessments. This study employed physiological, biochemical, and transcriptomic analyses to investigate the impact of sublethal concentrations of thiamethoxam, avermectin, chlorfenapyr, and β-cypermethrin on Vespa magnifica. Although larval survival rates remained unchanged, both pupation and fledge rates were significantly reduced. Enzymatic assays indicated an upregulation of superoxide dismutase and catalase activity alongside a suppression of peroxidase under insecticide stress. Transcriptomic analysis revealed increased adenosine triphosphate-related processes and mitochondrial electron transport activity, suggesting elevated energy expenditure to counter insecticide exposure, potentially impairing essential functions like flight, hunting, and immune response. The enrichment of pathways such as glycolysis, hypoxia-inducible factor signaling, and cholinergic synaptic metabolism under insecticide stress highlights the complexity of the molecular response with notable effects on learning, memory, and detoxification processes. These findings underscore the broader ecological risks of insecticide exposure to non-target insects and highlight the need for further research into the long-term effects of newer insecticides along with the development of strategies to safeguard beneficial insect populations.

Wind turbine rolling bearings are crucial components for ensuring the reliability and stability of wind power systems. Their failure can lead to significant economic losses and equipment downtime. Therefore, the accurate diagnosis of bearing faults is of great importance. Although existing deep learning fault diagnosis methods have achieved certain results, they still face limitations such as inadequate feature extraction capabilities, insufficient generalization to complex working conditions, and ineffective multi-scale feature capture. To address these issues, this paper proposes an advanced fault diagnosis method named the two-stream feature fusion convolutional neural network (TSFFResNet-Net). Firstly, the proposed method combines the advantages of one-dimensional convolutional neural networks (1D-ResNet) and two-dimensional convolutional neural networks (2D-ResNet). It transforms one-dimensional vibration signals into two-dimensional images through the empirical wavelet transform (EWT) method. Then, parallel convolutional kernels in 1D-ResNet and 2D-ResNet are used to extract multi-scale features, respectively. Next, the Convolutional Block Attention Module (CBAM) is introduced to enhance the network’s ability to capture key features by focusing on important features in specific channels or spatial areas. After feature fusion, CBAM is introduced again to further enhance the effect of feature fusion, ensuring that the features extracted by different network branches can be effectively integrated, ultimately providing more accurate input features for the classification task of the fully connected layer. The experimental results demonstrate that the proposed method outperforms other traditional methods and advanced convolutional neural network models on different datasets. Compared with convolutional neural network models such as LeNet-5, AlexNet, and ResNet, the proposed method achieves a significantly higher accuracy on the test set, with a stable accuracy of over 99%. Compared with other models, it shows better generalization and stability, effectively improving the overall performance of rolling bearing vibration signal fault diagnosis. The method provides an effective solution for the intelligent fault diagnosis of wind turbine rolling bearings.

The straight part of the corrugated steel plate (CSP) pipe-arch structure in soil may cause local buckling instability due to insufficient load-bearing capacity. Recently, composite CSP pipe-arch has been widely utilized to enhance structural stability, and their stability needs to be thoroughly investigated. This paper studies the local buckling stability problem of the straight part of composite CSP pipe-arch in soil by simplifying the soil support and introducing the inter-layer bonding effect. Based on elastic stability theory, a theoretical mechanical model of composite CSP pipe-arch was proposed. The Rayleigh–Ritz method and the semi-combined composite structure stiffness approximation were used to derive the critical buckling conditions for the straight part of the composite CSP pipe-arch. Through numerical calculation and influencing factors analysis, it is concluded that the critical buckling load of the straight part of the composite CSP pipe-arch structure is affected by the elastic modulus, thickness, Poisson’s ratio, rotational restraint stiffness and side length of the straight part of the material. In particular, it is found that as the inter-layer bonding coefficient increases, the critical buckling load is improved, while the critical buckling wave number is mainly influenced by the width of the straight part, elastic modulus, and inter-layer bonding coefficient. Additionally, we discussed the coupling effect of several key parameters on the stability of the structure. The results of this study offer theoretical foundations and guidance for the application of composite CSP pipe-arch in soil engineering, such as culverts, tunnels, and pipeline transportation.

Low-frequency noise absorbers often require large structural dimensions, constraining their development in practical applications. In order to improve space utilization, an acoustic metamaterial with a spatial double helix, called a spatial double helix resonator (SDHR), is proposed in this paper. An analytical model of the spatial double-helix resonator is established and verified by numerical simulations and impedance tube experiments. By comparing the acoustic absorption coefficients of the spatial double-helix resonator, it is shown that the results of the analytical model, the numerical model, and the experiments are in good agreement, proving the accuracy of the theoretical model. The effects of different structural parameters on the peak sound absorption coefficient and resonance frequency are quantitatively revealed. The impedance variation law of the model is obtained, and the resistance and reactance distributions at the resonance frequency are analyzed. In the optimization model, the Back Propagation (BP) network is used to construct the mapping between the structural parameters and the resonance frequency and sound absorption coefficient, and this is used as the constraints of the equation, which is combined with Wild Horse Optimization (WHO) to establish the BP-WHO optimization model to minimize the volume of the spatial double helix resonator. The results show that, for a given noise frequency, the optimized structural parameters enhance the space utilization without affecting the performance of the space double helix resonator.

The Shanxi Graben is a transitional zone between the Ordos Block and North China Plain with complex structures and frequent earthquakes. Six earthquakes with M ≥ 7.0 have been recorded in the area, including the 1303 Hongtong M 8 and 1695 Linfen M 7.8 earthquakes in the Linfen Basin. Research on these two large earthquakes, closely related in time and space, is lacking. Our objective was to use deep seismic reflection profiles and 3D velocity structure data from previous research, along with seismological observation results, to interpret the geological structure near the source of the two earthquakes. A 3D geometric model of the seismogenic fault was constructed, and the relationships among the deep and shallow structures, deep seismogenic environment, and two large earthquakes were explored. Differences in seismogenic environment between the southern and northern Linfen Basin were identified. The distribution of small earthquakes in the southern Linfen Basin was scattered, and the overall distribution was at depths Mw 7.7, so the magnitude of the 1303 Hongtong earthquake might be overestimated. Numerical simulation results of Coulomb stress showed that the 1303 Hongtong earthquake had a stress-loading effect on the 1695 Linfen earthquake. The change in Coulomb rupture stress was 1.008–2.543 bar, which is higher than the generally considered earthquake trigger threshold (0.1 bar). We created a new 3D source model of large earthquakes in the Linfen Basin, Shanxi Province, providing a reference and typical cases for risk assessment of large earthquakes in different regions of the Shanxi Graben.

This paper investigates the structural performance of novel steel-plate concrete containment structures, focusing on third-generation nuclear power plants. To address the challenges of increased complexities and costs associated with double-layer containment designs, this study explores the potential of steel-plate concrete structures to enhance safety, economic efficiency, and construction simplicity. The steel-plate concrete structure, characterized by its core concrete and dual steel plates, shows superior compressive strength, bending resistance, and elastoplasticity. Extensive numerical analyses, including finite element modeling and thermal-stress coupling, were conducted under various load conditions. Under structural integrity test conditions, the maximum radial displacement observed was 24.59 mm. Under design basis conditions, the maximum radial displacement was 47.61 mm; under severe accident conditions, it was 53.83 mm. The ultimate bearing capacity was 0.91 MPa, 2.17 times the design pressure. This study concludes that the steel-plate concrete containment structure maintains a high safety margin under all tested conditions, with stress and strain well within acceptable limits. It can effectively serve as a robust barrier against radioactive leakage and malicious impacts, providing a viable alternative to conventional containment designs.

Three-dimensional plasma nanostructures with high light–thermal conversion efficiency show the prospect of industrialization in various fields and have become a research hotspot in areas of light–heat utilization, solar energy capture, and so on. In this paper, a simple chemical synthesis method is proposed to prepare gold nanoparticles, and the electrophoretic deposition method is used to assemble large-area three-dimensional gold nanostructures (3D-GNSs). The light–thermal water evaporation monitoring and surface-enhanced Raman scattering (SERS) measurements of 3D-GNSs were performed via theoretical simulation and experiments. We reveal the physical processes of local electric field optical enhancement and the light–thermal conversion of 3D-GNSs. The results show that with the help of the efficient optical trapping and super-hydrophilic surface properties of 3D-GNSs, they have a significant effect in accelerating water evaporation, which was increased by nearly eight times. At the same time, the three-dimensional SERS substrates based on gold nanosphere particles (GNSPs) and gold nanostar particles (GNSTs) had limited sensitivities of 10−10 M and 10−12 M to R6G molecules, respectively. Therefore, 3D-GNSs show strong competitiveness in the fields of solar-energy-induced water purification and the Raman trace detection of organic molecules.

The Chenghai Fault in the Chuan–Dian block terminates at the northwestern segment of the Red River Fault, and is a significant seismogenic structure. The kinematic evolution of this fault should be closely related to the regional tectonic deformation. However, it is difficult to obtain information on structural deformation of the Chenghai Fault due to the large amount of precipitation and well-developed vegetation. The Chenghai normal faulting may drive drainage reorganization in this region, which provides a new perspective for reconstructing and evaluating the tectonic history. High-resolution digital elevation models (DEM) obtained by remote sensing greatly facilitate the study of drainage evolution and active tectonics. We use two methods (χ-plot and Gilbert metrics) to measure the drainage divide stability based on the ALOS DEM (12.5 m resolution) and further reproduce the drainage evolution process in response to the asymmetric uplift by numerical modeling. The results show that the Chenghai–Jinsha drainage divide, hosted by the footwall block of the Chenghai Fault, is migrating westward (away from the Chenghai Fault) and will continue moving ~2.2–3.5 km to reach a steady state. Its migration is controlled by the Chenghai normal faulting. The Chenghai–Jinsha drainage divide formed close to the Chenghai Fault’s surface trace and continues to migrate westward in response to the asymmetric uplift. It only took a few million years for the Chenghai–Jinsha drainage divide to migrate to its current location based on the numerical modeling. The restoration of the drainage reorganization implies that the Chenghai Fault initiated in the Pliocene, which probably results from kinematic reversal along the Red River Fault.

Green process innovation is an important strategy in the high-quality development of enterprises. Digital technology is becoming a key factor in helping businesses address environmental issues and contributes to their green process innovation and sustainable growth. Nevertheless, there is a lack of studies on how particular digital technology categories affect corporate green process innovation. Artificial intelligence (AI) is an important part of digitalization as it can provide new technical means and guidance for enterprise’s innovation of green processes. This study aims to fills this research gap by revealing the logical relationship between digital technology and the green development of enterprises. Using China’s A-share-listed companies as the research object from 2013 to 2022, this study employed a two-way fixed-effects model and investigated the impact of artificial intelligence (AI) on corporate green process innovation and the moderating effect of multidimensional intellectual capital. The results revealed that AI positively impacts corporate green process innovation. Human capital, structural capital, employed capital, and relational capital strengthen this positive effect. Robustness tests validated these conclusions. This study expands the literature on digital technology and corporate green innovation and provides a reference for enterprises to implement green practices using digital technology.

In traditional slope stability analysis, it is assumed that some “average” or appropriately “conservative” properties operate over the entire region of interest. This kind of deterministic conservative analysis often results in higher costs, and thus, a stochastic analysis considering uncertainty and spatial variability was developed to reduce costs. In the past few decades, machine learning has been greatly developed and extensively used in stochastic slope stability analysis, particularly used as surrogate models to improve computational efficiency. To better summarize the current application of machine learning and future research, this paper reviews 159 studies of supervised learning published in the past 20 years. The achievements of machine learning methods are summarized from two aspects—safety factor prediction and slope stability classification. Four potential research challenges and suggestions are also given.

Based on the structural and geological characteristics of the F1 weathering trough of a submarine tunnel and its spatial relationship with the cavern, a simplified calculation model of the weathering trough water inrush was established, and the formation, development process and influencing factors of the water inrush channel in the water-resistant rock layer were carried out by a numerical simulation of particle flow. It shows that the integrity and stability of the critical water-resistant rock mass is the key to preventing water inrush, and the identification and positioning of the water inrush channel is the basis for the grouting reinforcement design of the weathering groove of the submarine tunnel. Based on above research results, the F1 weathering trough was blocked and reinforced by the composite grouting method, and the engineering reinforcement effect was good.

The real-time, full-field simulation of the tube hydroforming process is crucial for deformation monitoring and the timely prediction of defects. However, this is rather difficult for finite-element simulation due to its time-consuming nature. To overcome this drawback, in this paper, a surrogate model framework was proposed by integrating the finite-element method (FEM) and machine learning (ML), in which the basic methodology involved interrupting the computational workflow of the FEM and reassembling it with ML. Specifically, the displacement field, as the primary unknown quantity to be solved using the FEM, was mapped onto the displacement boundary conditions of the tube component with ML. To this end, the titanium tube material as well as the hydroforming process was investigated, and a fairly accurate FEM model was developed based on the CPB06 yield criterion coupled with a simplified Kim–Tuan hardening model. Numerous FEM simulations were performed by varying the loading conditions to generate the training database for ML. Then, a random forest algorithm was applied and trained to develop the surrogate model, in which the grid search method was employed to obtain the optimal combination of the hyperparameters. Sequentially, the principal strain, the effective strain/stress, as well as the wall thickness was derived according to continuum mechanics theories. Although further improvements were required in certain aspects, the developed FEM-ML surrogate model delivered extraordinary accuracy and instantaneity in reproducing multi-physical fields, especially the displacement field and wall-thickness distribution, manifesting its feasibility in the real-time, full-field simulation and monitoring of deformation states.

In underground spaces, corrugated steel plate (CSP) pipe-arches may experience local buckling instability, which can subsequently lead to the failure of the entire structure. Recently, sandwich CSP pipe-arches have been used to enhance the stability of embedded engineering outcomes, and their buckling behaviors require in-depth research. In this paper, we establish a theoretical model by simplifying soil support and using Hoff sandwich plate theory to focus on the local buckling stability of the straight segment in embedded sandwich CSP pipe-arches using the Rayleigh–Ritz method. Through stability analysis, the instability criteria for embedded sandwich CSP pipe-arches are analytically determined. Numerical calculations reveal that the critical buckling load of a sandwich CSP pipe-arch is affected by several factors, including the elastic modulus, thickness, Poisson’s ratio, rotational constraint stiffness, and the length of the straight segment. Specifically, increasing the thickness of the sandwich CSP pipe-arch can substantially enhance the critical buckling load. Meanwhile, the wavenumber is affected by the elastic modulus and the length of the straight segment. The analytical results are in agreement with those obtained from finite element analysis. These findings provide a theoretical basis and guidance for the application of sandwich CSP pipe-arches in fields such as subway stations, tunnel construction, underground passages, and underground parking facilities.

This study aims to propose a hydration kinetic model for the cement–eggshell powder binary system and predict the performance development of composite concrete through this model. The specific content and results of the model are as follows. First, based on the cumulative hydration heat of the cement and eggshell powder binary system in the first seven days, the parameters of the cement hydration model and the eggshell powder nucleation parameter are calibrated. These parameters remain constant regardless of the mix ratio. Secondly, the hydration heat of the cement–eggshell powder binary system over 28 days is calculated using the hydration model. The results show that at 28 days, for specimens with 0%, 7.5%, and 15% eggshell powder substitution, the cement hydration degrees are 0.832, 0.882, and 0.923, respectively. The hydration heat per gram of cement is 402.69, 426.88, and 446.73 J/g cement, respectively, while the hydration heat per gram of binder is 402.69, 394.86, and 379.72 J/g binder, respectively. Additionally, the hydration model is used to calculate the chemically bound water and calcium hydroxide content of the cement–eggshell powder binary system. At 28 days, for samples with 0%, 7.5%, and 15% eggshell powder, the chemically bound water content is 0.191, 0.188, and 0.180 g/g binder, respectively, and the calcium hydroxide content is 0.183, 0.179, and 0.173 g/g binder, respectively. Finally, a power function is used to regress the calculated hydration heat with experimentally measured compressive strength and surface electrical resistivity. The correlation coefficients for compressive strength and surface electrical resistivity are 0.8474 and 0.9714, respectively. This is because the strength weak point effect of eggshell powder has minimal impact on hydration heat and surface electrical resistivity experiments but significantly affects the strength experiment.

A prestressed concrete containment is the enclosure structure of a nuclear reactor that serves as the last physical barrier of a nuclear power plant (NPP) safety defense system. It plays a key role in the prestress time-limited aging analysis (TLAA) required for operating license extensions for NPPs. Considering prestress containment systems without long-term monitoring tendons in a nuclear power plant, the technical route for prestress TLAAs involves analyzing operating license extension regulations and in-service monitoring technical requirements for prestressed tendons performance. Using tendons based on theoretical calculations of prestress loss in a nuclear power plant and the minimum required value of prestress determined by numerical simulation, the theoretical predictive value can be compared with the minimum required value for prestressed steel tendons. This comparison can be used to evaluate the 20-year life extension of the prestress system and provide a reference for aging and life management of NPPs.

The zoning excavation method is fully employed to control the deformation of foundation pits constructed in urban soft soil areas. However, the similarities and differences in forces and deformations between foundation pits excavated by the zonal method and those excavated by the conventional method still need to be further explored. In this study, the deformation was monitored and analyzed by taking the zonal excavation of a foundation pit of the ‘New World’ project in Hangzhou City as the research object. The measured results showed that the pre-built diaphragm wall for the first excavated foundation pit restricted the deformation of the first excavated diaphragm wall. The presence of extensive construction and unloading activities also changed the deformation pattern of the soil. Further, finite element simulations were carried out. The simulation results revealed that excavating the foundation pit first caused displacements in the pre-built diaphragm wall. The displacements transmitted by non-adjacent pits through the pre-built diaphragm wall were small and were concentrated at the junction of the two sub-pits. Adjacent foundation pits caused large displacements of the pre-built diaphragm wall with similar displacement patterns. The results of the study can provide effective guidance for foundation pit excavation in soft soil areas in the future.

Cancer continues to pose a significant threat to global health, with its status as a leading cause of death remaining unchallenged. Within the realm of cancer research, the tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) stands out as a critical player, having been identified in the 1990s as the tenth member of the TNF family. This review examines the pivotal role of TRAIL in cancer biology, focusing on its ability to induce apoptosis in malignant cells through both endogenous and exogenous pathways. We provide an in-depth analysis of TRAIL’s intracellular signaling and intercellular communication, underscoring its potential as a selective anticancer agent. Additionally, the review explores TRAIL’s capacity to reshape the tumor microenvironment, thereby influencing cancer progression and response to therapy. With an eye towards future developments, we discuss the prospects of harnessing TRAIL’s capabilities for the creation of tailored, precision-based cancer treatments, aiming to enhance efficacy and improve patient survival rates.

Ferroptosis is a novel and iron-dependent form of programmed cell death, which has been implicated in the pathogenesis of various human cancers. EBV is a well-recognized oncogenic virus that controls multiple signaling pathways within the host cell, including ferroptosis signaling. Recent studies show that inducing ferroptosis could be an efficient therapeutic strategy for EBV-associated tumors. This review will firstly describe the mechanism of ferroptosis, then summarize EBV infection and EBV-associated tumors, as well as the crosstalk between EBV infection and the ferroptosis signaling pathway, and finally discuss the role and potential application of ferroptosis-related reagents in EBV-associated tumors.

The alkali–silica reaction significantly impacts the durability of reinforced concrete structures. This paper aims to investigate the structural expansion properties of reinforced concrete beams under different partial alkali–silica reactions. Alkali–silica reaction tests were conducted on four reinforced concrete beams, focusing on immersion depth and NaOH solution position as key parameters. Subsequently, the spatial and temporal distribution characteristics of the beam expansion rate were analyzed. Results indicate notable variations in the expansion’s initiation, rate, and final magnitude at different measurement points on the concrete beam, depending on the soaking positions and depths used. The expansion rate was higher in areas directly immersed in the NaOH solution, decreasing near the reinforcement regions. However, strain distribution, along with beam height, satisfied plane-section assumption in the reinforced beam section. Finally, a regional expansion index was established to quantitatively assess the non-uniform damage by alkali aggregates in beams, and the uniaxial restraint mechanism in reinforced concrete structures was also described.

The high-efficiency preparation of large-area microstructures of optical materials and precision graphic etching technology is one of the most important application directions in the atomic and near-atomic-scale manufacturing industry. Traditional focused ion beam (FIB) and reactive ion etching (RIE) methods have limitations in precision and efficiency, hindering their application in automated mass production. The pulsed ion beam (PIB) method addresses these issues by enhancing ion beam deflection to achieve high-resolution material removal on a macro scale, which can reach the equivalent removal resolution of 6.4 × 10−4 nm. Experiments were conducted on a quartz sample (10 × 10 × 1 mm) with a specific pattern mask using the custom PIB processing device. The surface morphology, etching depth, and roughness were measured post-process. The results demonstrated that precise control over cumulative sputtering time yielded well-defined patterns with expected average etching depths and surface roughness. This confirms the PIB technique’s potential for precise atomic depth image transfer and its suitability for industrial automation, offering a significant advancement in microfabrication technology.

Radish is an important root vegetable that is widely grown in Asia. The root-knot nematode (RKN) Meloidogyne incognita seriously affects the growth and development of radish root and causes poor appearance quality. However, the molecular mechanism of radish response to RKNs remains poorly understood. In this study, a total of 220 lncRNAs, 1144 mRNAs, 20 miRNAs and 153 proteins were differential expressed between the RKN-infected and WT samples. Correlation analysis of all DEPs compared with all DGEs showed that 8 mRNAs-DEPs showed a changed abundance. The results showed that 18 DEmiRNAs have 167 target DEGs in 220 miRNA-target modules and 29 DElncRNAs were predicted as putative targets of 16 DEmiRNAs in 37 miRNA-target modules. In all, 6 DGEs in the ABA pathway and 2 DGEs in the JA pathway were identified under RKN infection, respectively. The four regulatory networks of lncRNA-miRNA-mRNA were constructed in response to RKN infection. qRT-PCR analysis found that the expression pattern of 6 DElncRNAs, 6 DEmRNAs, 6 DEmiRNAs and 6 DEPs were consistent with sequencing results. These results provide a theoretical basis for studying the molecular mechanism of radish in response to M. incognita and breeding resistant varieties to this nematode.

Microbial community composition, function, and viability are important for biofilm-based sewage treatment technologies. Most studies of microbial communities mainly rely on the total deoxyribonucleic acid (DNA) extracted from the biofilm. However, nucleotide materials released from dead microorganisms may interfere with the analysis of viable microorganisms and their metabolic potential. In this study, we developed a protocol to assess viability as well as viable community composition and function in biofilm in a sewage treatment system using propidium monoazide (PMA) coupled with real-time quantitative polymerase chain reaction (qPCR) and metagenomic technology. The optimal removal of PMA from non-viable cells was achieved by a PMA concentration of 4 μM, incubation in darkness for 5 min, and exposure for 5 min. Simultaneously, the detection limit can reach a viable bacteria proportion of 1%, within the detection concentration range of 102–108 CFU/mL (colony forming unit/mL), showing its effectiveness in removing interference from dead cells. Under the optimal conditions, the result of PMA–metagenomic sequencing revealed that 6.72% to 8.18% of non-viable microorganisms were influenced and the composition and relative abundance of the dominant genera were changed. Overall, this study established a fast, sensitive, and highly specific biofilm viability detection method, which could provide technical support for accurately deciphering the structural composition and function of viable microbial communities in sewage treatment biofilms.

In 2022, China actively carried out economic transformation and sought high-quality development. To date, enhancing enterprise value is still one of the top priorities for enterprises. Enterprises should take various measures to continuously enhance their value in order to strive for their survival and development. The fulfillment of social responsibilities not only brings benefits to all stakeholders, but also establishes a good corporate image in front of the public and can increase enterprise value. At the same time, in the context of “carbon peaking and carbon neutrality”, carbon information disclosure has an important impact on enterprises and their stakeholders. Taking the data of listed companies within China’s Shanghai and Shenzhen A-share highly polluting industries from 2018 to 2022 as samples, this paper studies the relationship between the level of social responsibility fulfillment, carbon information disclosure, and enterprise value, and makes an empirical analysis. This research finds that social responsibility has a significant positive impact on enterprise value; carbon information disclosure has a significant positive impact on enterprise value; and carbon information disclosure plays a significant positive regulating role in the relationship between social responsibility and enterprise value. Finally, according to the research results, this paper puts forward relevant suggestions from two perspectives: enterprise and government.

This review explores the use of Ultra-High Molecular Weight Polyethylene (UHMWPE) fiber cloth as an innovative solution for the repair and reinforcement of concrete structures. UHMWPE is a polymer formed from a very large number of repeated ethylene (C2H4) units with higher molecular weight and long-chain crystallization than normal high-density polyethylene. With its superior tensile strength, elongation, and energy absorption capabilities, UHMWPE emerges as a promising alternative to traditional reinforcement materials like glass and carbon fibers. The paper reviews existing literature on fiber-reinforced polymer (FRP) applications in concrete repair in general, highlighting the unique benefits and potential of UHMWPE fiber cloth compared to other commonly used methods of strengthening concrete structures, such as enlarging concrete sections, near-surface embedded reinforcement, and externally bonded steel plate or other FRPs. Despite the scarcity of experimental data on UHMWPE for concrete repair, this review underscores its feasibility and calls for further research to fully harness its capabilities in civil engineering applications.

The spindle unit is the core component of a machine, and its stiffness is a key performance characteristic. The stiffness consistency is a key index to measure the stiffness of spindle units; however, there is a lack of unified evaluation methods for the spindle unit stiffness consistency. This paper proposes a comprehensive method for the evaluation of the spindle unit stiffness consistency based on fluctuations and comprehensive weights. First, the consistency of the spindle unit stiffness evaluation indices is expressed based on the fluctuations. Secondly, the subjective and objective weights of the spindle unit stiffness evaluation indices are given based on the AHP and CRITIC weight assignment methods. To obtain the comprehensive weights of the evaluation indices, the subjective and objective weights are synthesized based on the linear weighting method. Then, the consistency level of the spindle unit stiffness is obtained by the weighted summation of the comprehensive weights and fluctuations of the evaluation indices. Finally, the stiffness consistency of a group of spindle units is evaluated based on this method, and the evaluation result shows a 10.63% fluctuation, which indicates that its stiffness consistency level is class B, in line with the factory production requirements.

Due to the widespread applications of change detection technology in urban change analysis, environmental monitoring, agricultural surveillance, disaster detection, and other domains, the task of change detection has become one of the primary applications of Earth orbit satellite remote sensing data. However, the analysis of dual-temporal change detection (CD) remains a challenge in high-resolution optical remote sensing images due to the complexities in remote sensing images, such as intricate textures, seasonal variations in imaging time, climatic differences, and significant differences in the sizes of various objects. In this paper, we propose a novel U-shaped architecture for change detection. In the encoding stage, a multi-branch feature extraction module is employed by combining CNN and transformer networks to enhance the network’s perception capability for objects of varying sizes. Furthermore, a multi-branch aggregation module is utilized to aggregate features from different branches, providing the network with global attention while preserving detailed information. For dual-temporal features, we introduce a spatiotemporal discrepancy perception module to model the context of dual-temporal images. Particularly noteworthy is the construction of channel attention and token attention modules based on the transformer attention mechanism to facilitate information interaction between multi-level features, thereby enhancing the network’s contextual awareness. The effectiveness of the proposed network is validated on three public datasets, demonstrating its superior performance over other state-of-the-art methods through qualitative and quantitative experiments.

In this paper, a novel three-degree-of-freedom piezoelectric-driven micro-positioning platform based on a lever combination compound bridge-type displacement amplification mechanism is proposed. The micro-positioning platform proposed in this paper aims to solve the current problem of the large size and small travel of the three-degree-of-freedom piezoelectric-driven micro-positioning platform. In this paper, a lever combination compound bridge-type displacement amplification mechanism combined with a new biaxial flexible hinge is proposed, the structural dimensions of the lever mechanism and the compound bridge mechanism are optimized, and the amplification multiplier is determined. The maximum output simulation analysis of the micro-positioning platform is carried out by using ANSYS, and the experimental test system is built for verification. The validation results show that the maximum errors between simulation and experiment in the z-direction, rotation direction around x, and rotation direction around y are 64 μm, 0.016°, and 0.038°, respectively, and the corresponding maximum relative errors are 5.6%, 2.4%, and 6.6%, respectively, which proves the feasibility of the theoretical design.

The classification of fetuses as Small for Gestational Age (SGA) and Large for Gestational Age (LGA) is a critical aspect of neonatal health assessment. SGA and LGA, terms used to describe fetal weights that fall below or above the expected weights for Appropriate for Gestational Age (AGA) fetuses, indicate intrauterine growth restriction and excessive fetal growth, respectively. Early prediction and assessment of latent risk factors associated with these classifications can facilitate timely medical interventions, thereby optimizing the health outcomes for both the infant and the mother. This study aims to leverage first-trimester data to achieve these objectives. This study analyzed data from 7943 pregnant women, including 424 SGA, 928 LGA, and 6591 AGA cases, collected from 2015 to 2021 at the Third Affiliated Hospital of Sun Yat-sen University in Guangzhou, China. We propose a novel algorithm, named the Weighted Inheritance Voting Ensemble Learning Algorithm (WIVELA), to predict the classification of fetuses into SGA, LGA, and AGA categories based on biochemical parameters, maternal factors, and morbidity during pregnancy. Additionally, we proposed algorithms for relevance determination based on the classifier to ascertain the importance of features associated with SGA and LGA. The proposed classification solution demonstrated a notable average accuracy rate of 92.12% on 10-fold cross-validation over 100 loops, outperforming five state-of-the-art machine learning algorithms. Furthermore, we identified significant latent maternal risk factors directly associated with SGA and LGA conditions, such as weight change during the first trimester, prepregnancy weight, height, age, and obstetric factors like fetal growth restriction and birthing LGA baby. This study also underscored the importance of biomarker features at the end of the first trimester, including HDL, TG, OGTT-1h, OGTT-0h, OGTT-2h, TC, FPG, and LDL, which reflect the status of SGA or LGA fetuses. This study presents innovative solutions for classifying and identifying relevant attributes, offering valuable tools for medical teams in the clinical monitoring of fetuses predisposed to SGA and LGA conditions during the initial stage of pregnancy. These proposed solutions facilitate early intervention in nutritional care and prenatal healthcare, thereby contributing to enhanced strategies for managing the health and well-being of both the fetus and the expectant mother.

Tunnel Boring Machines (TBMs) have become prevalent in tunnel construction due to their high efficiency and reliability. The proliferation of data obtained from site investigations and data acquisition systems provides an opportunity for the application of machine learning (ML) techniques. ML algorithms have been successfully applied in TBM tunnelling because they are particularly effective in capturing complex, non-linear relationships. This study focuses on commonly used ML techniques for TBM tunnelling, with a particular emphasis on data processing, algorithms, optimisation techniques, and evaluation metrics. The primary concerns in TBM applications are discussed, including predicting TBM performance, predicting surface settlement, and time series forecasting. This study reviews the current progress, identifies the challenges, and suggests future developments in the field of intelligent TBM tunnelling construction. This aims to contribute to the ongoing efforts in research and industry toward improving the safety, sustainability, and cost-effectiveness of underground excavation projects.

Effective features play an important role in synthetic aperture radar (SAR) image interpretation. However, since SAR images contain a variety of terrain types, it is not easy to extract effective features of different terrains from SAR images. Deep learning methods require a large amount of labeled data, but the difficulty of SAR image annotation limits the performance of deep learning models. SAR images have inevitable geometric distortion and coherence speckle noise, which makes it difficult to extract effective features from SAR images. If effective semantic context features cannot be learned for SAR images, the extracted features struggle to distinguish different terrain categories. Some existing terrain classification methods are very limited and can only be applied to some specified SAR images. To solve these problems, a jigsaw puzzle self-supervised learning (JPSSL) framework is proposed. The framework comprises a jigsaw puzzle pretext task and a terrain classification downstream task. In the pretext task, the information in the SAR image is learned by completing the SAR image jigsaw puzzle to extract effective features. The terrain classification downstream task is trained using only a small number of labeled data. Finally, fully connected conditional random field processing is performed to eliminate noise points and obtain a high-quality terrain classification result. Experimental results on three large-scene high-resolution SAR images confirm the effectiveness and generalization of our method. Compared with the supervised methods, the features learned in JPSSL are highly discriminative, and the JPSSL achieves good classification accuracy when using only a small amount of labeled data.

In laser beam processing, the angle or offset between the auxiliary gas and the laser beam axis have been proved to be two new process optimization parameters for improving cutting speed and quality. However, a traditional electromechanical actuator cannot achieve high-speed and high-precision motion control with a compact structure. This paper proposes a magnetic levitation actuator which could realize the 5-DOF motion control of a lens using six groups of differential electromagnets. At first, the nonlinear characteristic of a magnetic driving force was analyzed by establishing an analytical model and finite element calculation. Then, the dynamic model of the magnetic levitation actuator was established using the Taylor series. And the mathematical relationship between the detected distance and five-degree-of-freedom was determined. Next, the centralized control system based on PID control was designed. Finally, a driving test was carried out to verify the five-degrees-of-freedom motion of the proposed electromagnetic levitation actuator. The results show it can achieve a stable levitation and precision positioning with a desired command motion. It also proves that the proposed magnetic levitation actuator has the potential application in an off-axis laser cutting machine tool.

Post-processing based on HF etching has become a highly preferred technique in the fabrication of fused silica optical elements in various high-power laser systems. Previous studies have thoroughly examined and confirmed the elimination of fragments and contamination. However, limited attention has been paid to nano-sized chemical structural defects and secondary precursors that arise during the etching process. Therefore, in this paper, a set of fused silica samples are prepared and undergo the etching process under different parameters. Subsequently, an atomic force microscope, scanning electron microscope and fluorescence spectrometer are applied to analyze sample surfaces, and then an LIDT test based on the R-on-1 method is applied. The findings revealed that appropriate etching configurations will lead to certain LIDT improvement (from initial 7.22 J/cm2 to 10.76 J/cm2), and HF-based etching effectively suppresses chemical structural defects, while additional processes are recommended for the elimination of micron- to nano-sized secondary deposition contamination.

The significant differences in data domains between SAR images and the expensive and time-consuming process of data labeling pose significant challenges to terrain classification. Current terrain classification methodologies face challenges in addressing domain disparities and detecting uncommon terrain effectively. Based on Style Transformation and Domain Metrics (STDMs), we propose an unsupervised domain adaptive framework named STDM-UDA for terrain classification in this paper, which consists of two steps: image style transfer and domain adaptive segmentation. As a first step, image style transfer is performed within the image space to mitigate the differences in low-level features between SAR image domains. Subsequently, leveraging this process, the segmentation network extracts image features, employing domain metrics and adversarial training to enhance alignment between domain gaps in the semantic feature space. Finally, experiments conducted on several pairs of SAR images, each exhibiting varying degrees of differences in key imaging parameters such as source, resolution, band, and polarization, demonstrate the robustness of the proposed method. It achieves remarkably competitive classification accuracy, particularly for unlabeled, high-resolution broad scenes, effectively overcoming the domain gaps introduced by the diverse imaging parameters under studies.

Well-designed urban public spaces often attract residents and play a critical role in improving people’s wellbeing. Many studies have examined the importance of one or a few environmental features in urban public spaces, such as the size of the space, greenery coverage, seating arrangements, recreational facilities, etc. However, there is a lack of systematic understanding regarding (1) which environmental features have a significant impact on the usage of urban public spaces and (2) how these features influence people’s environmental preferences. To answer these questions, this investigation adopts a two-fold analytical structure: (1) first, an expert inquiry was conducted to evaluate the environmental features, and the analytic hierarchy process (AHP) was applied to determine the weight of each influencing factor; then, (2) on-site measurements were conducted across 104 spaces, accompanied by structured interviews with users of the spaces, based on which a decision tree analysis was employed to elucidate the decision-making processes of residents regarding their outdoor activities. The main findings of this investigation are as follows: (1) the site size, internal pedestrian flow, sky view factor, green-vision rate, and seat–circumference ratio are primary indicators affecting outdoor space usage, which are used in the objective evaluation index; (2) advantage value intervals for the sky view factor, green-vision rate, and seat–circumference ratio variables were calculated, and these three factors were found to significantly outweigh site size and internal pedestrian flow in terms of their effect on spatial preference. The interaction between the green-vision rate and seat–circumference ratio can affect the environmental preferences of residents: spaces with more seats exhibit lower requirements for greenery, while spaces with fewer seats should prioritise trees and greenery. Based on this study, an index based on influencing factors is proposed, enabling a better understanding of the environmental features affecting the usage of space. This study also provides valuable insights for future neighbourhood design through investigating the environmental preferences of residents, as well as the importance of various spatial features and their associated advantage value intervals.

Under irrigation with saline wastewater, SO42−, Cl−, and Ca2+ aggregated in the topsoil, and Mg2+ was significantly higher in the deeper soil than in the topsoil and 40 cm soil layers. The abundance of Zoopagomycota, Ascomycota, Mortierellomycota, Basidiomycota, Chytridiomycota, Rozellomycota, Blastocladiomycota, Monoblepharomycota, Mucoromycota and Olpidiomycota in the surface soil was influenced by Mg2+, whereas Ca2+ affected the abundance of Zoopagomycota and Chytridiomycota. In the 40 cm soil layer, Mg2+ and Cl− promoted Actinobacteria, Proteobacteria, Nitrospirae, Firmicutes, Entotheonellaeota, Myxococcota, Gemmatimonadota and Methylomirabilota, whereas they inhibited Planctomycetota, Acidobacteria, Chloroflexi, Patescibacteria and Bacteroidota. In the 80 cm soil layer, SO42− and Cl− promoted Rozellomycota, Mortierellomycota, Chytridiomycota, Ascomycota, and Mucoromycota, but had a negative effect on Glomeromycota, Blastocladiomycota, Olpidiomycota and Monoblepharomycota. The increase in salinity significantly reduced the abundance of the Actinomycetes phylum and the Amoebozoa phylum. Both saprophytic and symbiotic fungi decreased with increasing salinity.

In the blood sample management pipeline environment, we have innovatively improved the traditional A-star algorithm to enhance the efficiency of mobile robots. This study employs a grid environmental modeling approach to accurately simulate medical testing laboratories. On the grid map, we utilize an 8-neighbor search method for path planning to accommodate the complex structure within the laboratory. By introducing an improved evaluation function and a bidirectional search strategy, we have successfully reduced the number of search nodes and significantly improved path search efficiency. Additionally, we eliminate redundant nodes in the path, smooth the path using cubic uniform B-spline curves, remove unnecessary inflection points, and further optimize the motion trajectory of the robot. The experimental results of the path planning simulation under different scenarios and specifications show that the improved A-star algorithm has higher search efficiency and traverses fewer nodes compared to the traditional A-star algorithm and the bidirectional A-star algorithm. Overall, the simulation experiments verify the feasibility of the improved A-star algorithm, which can better meet the needs of actual mobile robots in real medical testing laboratories.

The combined application of manure and chemical fertilizers has been recognized as a critical factor driving significant changes in crop yield and nutrient use efficiency, holding the potential to optimize agricultural management to achieve high yields. In this 40-year study, we investigated the effect of manure amendment on soybean and maize yields, water and nitrogen use efficiencies (WUE and NUE), and water and mineral N storage at 0–100 cm soil depths from 2017 to 2018 to explore the optimization of fertilization management strategies for soybean and maize production in Northeast China. To elucidate the impact of chemical fertilizers and manure, twelve treatments—control (CK); single N fertilizer at a low rate (N1) and that at a high rate (N2); N1, phosphorus (P), and potassium (K) fertilizer (N1PK); manure alone at 13.5 and 27 t ha−1 (M1 and M2); and those combined with N, P, or K fertilizer (M1N1, M1N2, and M1N1PK and M2N1, M2N2, and M2N1PK)—were selected and studied. The results showed that long-term amendment with manure significantly increased crop biomass and yield in the soybean–maize–maize rotation system. Combining with manure increased the WUE, the partial factor productivity of N fertilizer (PFPN), and N physiological efficiency (PEN) in both the soybean and maize seasons; conserved soil water (mainly at 40–60 cm); and increased soil N retention (in the upper 60 cm layer), which reduced the risk of N leaching, with a better effect being observed after the application of 13.5 t ha−1 manure. These results provide insight into the potential of using fertilization management strategies that include amendment with 13.5 t ha−1 manure in combination with N, P, and K fertilizer in the maize season and only chemical fertilizer in the soybean season, as these results indicate that such strategies can achieve high yields and be used to implement agricultural sustainable development in brown soil regions in Northeast China.

With the development of artificial intelligence, the ability to capture the background characteristics of hyperspectral imagery (HSI) has improved, showing promising performance in hyperspectral anomaly detection (HAD) tasks. However, existing methods proposed in recent years still suffer from certain limitations: (1) Constraints are lacking in the deep feature learning process in terms of the issue of the absence of prior background and anomaly information. (2) Hyperspectral anomaly detectors with traditional self-supervised deep learning methods fail to ensure prioritized reconstruction of the background. (3) The architecture of fully connected deep networks in hyperspectral anomaly detectors leads to low utilization of spatial information and the destruction of the original spatial relationship in hyperspectral imagery and disregards the spectral correlation between adjacent pixels. (4) Hypotheses or assumptions for background and anomaly distributions restrict the performance of many hyperspectral anomaly detectors because the distributions of background land covers are usually complex and not assumable in real-world hyperspectral imagery. In consideration of the above problems, in this paper, we propose a novel fully convolutional auto-encoder based on dual clustering and latent feature adversarial consistency (FCAE-DCAC) for HAD, which is carried out with self-supervised learning-based processing. Firstly, density-based spatial clustering of applications with a noise algorithm and connected component analysis are utilized for successive spectral and spatial clustering to obtain more precise prior background and anomaly information, which facilitates the separation between background and anomaly samples during the training of our method. Subsequently, a novel fully convolutional auto-encoder (FCAE) integrated with a spatial–spectral joint attention (SSJA) mechanism is proposed to enhance the utilization of spatial information and augment feature expression. In addition, a latent feature adversarial consistency network with the ability to learn actual background distribution in hyperspectral imagery is proposed to achieve pure background reconstruction. Finally, a triplet loss is introduced to enhance the separability between background and anomaly, and the reconstruction residual serves as the anomaly detection result. We evaluate the proposed method based on seven groups of real-world hyperspectral datasets, and the experimental results confirm the effectiveness and superior performance of the proposed method versus nine state-of-the-art methods.

The construction of large-diameter shield tunnels underwater involves complex variations in water and earth load outside the tunnel segment, as well as intricate mechanical responses. This study analyzes the variation laws of external loads, axial forces, and bending moments acting on the segment ring during the shield assembly and removal from the shield tail. It accomplishes this through the establishment of an on-site monitoring system based on the Internet of Things (IoT) and proposes a Bayesian-genetic algorithm model to estimate the water and earth pressure. The fluctuation section exhibits a peak load twice as high as that in the stable section. These variations are influenced by Jack thrust, shield shell force, and grouting pressure. The peak load observed in the fluctuation section is twice as high as the load observed in the stable section. During the shield tail removal process, the internal forces undergo significant fluctuations due to changes in both load and boundary conditions, and the peak value of the axial force during the fluctuation section is eight times higher than that during the stable section, while the peak value of the bending moment during the fluctuation section is five times higher than that during the stable section. The earth and water pressure calculated using the inversion analysis method, which relies on the measured internal forces, closely matches the actual measured values. The results demonstrate that the accuracy of the water and earth pressure obtained through inversion analysis is twice as high as that obtained using the full coverage pressure method. These results can serve as a valuable reference for similar projects.

With the advancement of engineering techniques, underground shield tunneling projects have also started incorporating emerging technologies to monitor the forces and displacements during the construction and operation phases of shield tunnels. Monitoring devices installed on the tunnel segment components generate a large amount of data. However, due to various factors, data may be missing. Hence, the completion of the incomplete data is imperative to ensure the utmost safety of the engineering project. In this research, a missing data imputation technique utilizing Random Forest (RF) is introduced. The optimal combination of the number of decision trees, maximum depth, and number of features in the RF is determined by minimizing the Mean Squared Error (MSE). Subsequently, complete soil pressure data are artificially manipulated to create incomplete datasets with missing rates of 20%, 40%, and 60%. A comparative analysis of the imputation results using three methods—median, mean, and RF—reveals that this proposed method has the smallest imputation error. As the missing rate increases, the mean squared error of the Random Forest method and the other two methods also increases, with a maximum difference of about 70%. This indicates that the random forest method is suitable for imputing monitoring data.

Although phosphorus (P) fertilizer application is widely used to improve production, irrational P application has a negative impact on the zinc (Zn) nutrition of cereal crops. Previous researchers observed and confirmed that P application decreases grain Zn concentrations and bioavailability in cereal crops. However, it remains unclear whether different P fertilizer types can alleviate the antagonism of P and Zn in the soil and grain and, thus, enhance the Zn nutritional level of cereal crops while maintaining production. Thus, a completely randomized pot experiment was conducted on winter wheat grown in two calcareous soils (lime concretion black soil and fluvo-aquic soil). Five P fertilizer types (single superphosphate, diammonium phosphate, fused calcium–magnesium phosphate, triple superphosphate, and ammonium polyphosphate, abbreviated, respectively, as SSP, DAP, FMP, TSP, and APP) were applied to each soil compared to no P fertilizer (CK). Plant and topsoil samples were collected during the flowering and maturity stages of winter wheat, and biomass, Zn concentrations in each organ, and grain phytic acid concentrations were analyzed. Grain yield was not affected by the application of different P fertilizer types to lime concretion black soil, while it was significantly increased by the application of TSP and APP to fluvo-aquic soil. The application of DAP and APP effectively promoted soil available Zn concentrations in both calcareous soils. In lime concretion black soil, the application of FMP significantly increased Zn remobilization to grains, while the application of DAP increased post-anthesis Zn uptake, thereby increasing grain Zn concentrations and its bioavailability. In fluvo-aquic soil, post-anthesis Zn remobilization and uptake were significantly increased by the application of TSP and APP, finally achieving higher grain Zn concentrations and Zn harvest index and effectively promoting grain Zn bioavailability. In conclusion, the rational application of DAP to wheat grown in lime concretion black soil and of TSP or APP to fluvo-aquic soil can achieve superior grain Zn nutrition quality while concurrently retaining high production and high P use efficiency, reducing micronutrient deficiency and further contributing to green agricultural development and human health.

Visual perception equips unmanned aerial vehicles (UAVs) with increasingly comprehensive and instant environmental perception, rendering it a crucial technology in intelligent UAV obstacle avoidance. However, the rapid movements of UAVs cause significant changes in the field of view, affecting the algorithms’ ability to extract the visual features of collisions accurately. As a result, algorithms suffer from a high rate of false alarms and a delay in warning time. During the study of visual field angle curves of different orders, it was found that the peak times of the curves of higher-order information on the angular size of looming objects are linearly related to the time to collision (TTC) and occur before collisions. This discovery implies that encoding higher-order information on the angular size could resolve the issue of response lag. Furthermore, the fact that the image of a looming object adjusts to meet several looming visual cues compared to the background interference implies that integrating various field-of-view characteristics will likely enhance the model’s resistance to motion interference. Therefore, this paper presents a concise A-LGMD model for detecting looming objects. The model is based on image angular acceleration and addresses problems related to imprecise feature extraction and insufficient time series modeling to enhance the model’s ability to rapidly and precisely detect looming objects during the rapid self-motion of UAVs. The model draws inspiration from the lobula giant movement detector (LGMD), which shows high sensitivity to acceleration information. In the proposed model, higher-order information on the angular size is abstracted by the network and fused with multiple visual field angle characteristics to promote the selective response to looming objects. Experiments carried out on synthetic and real-world datasets reveal that the model can efficiently detect the angular acceleration of an image, filter out insignificant background motion, and provide early warnings. These findings indicate that the model could have significant potential in embedded collision detection systems of micro or small UAVs.