Search

The subgrade structure of high-speed railways is an important foundation for the safe and smooth operation of high-speed trains, and the scientific design of the subgrade structure provides a fundamental guarantee of its durability and technical economy. As, in the development of high-speed railways in China, higher speeds are being pursued, more requirements have been put forward for the dynamic stability of subgrade structures. To address this issue, this article focuses on the control requirements for the long-term stability of subgrade deformation, and various design methods for high-speed railway subgrade structures are presented. Considering the energy dissipation and dynamic stability characteristics of subgrade filling materials, the dynamic performance of coarse-grained soil filling materials in the bottom layer and graded crushed stones in the surface layer are revealed. The methods for determining the values of dynamic parameters such as the dynamic modulus and damping ratio are provided. Based on the dynamic shakedown theory, the stress–strain hysteresis characteristics of fillers and the variation law of dissipated energy are revealed. The correlation between unit volume dissipated energy and shakedown state under cyclic loading conditions is identified. A criterion for determining the critical shakedown state of high-speed railway subgrade structures based on equivalent unit volume dissipated energy is proposed, and a method for determining the design threshold of dynamic stress and dynamic strain is also proposed. The results show that the shakedown design critical values of equivalent unit volume dissipated energy in the bottom and surface layers of the foundation were between 0.0103~0.0133 kJ/m3 and 0.0121~0.0149 kJ/m3, respectively. The critical dynamic strain range was 0.8 × 10−3~1.3 × 10−3. On this basis, a high-speed railway subgrade design method based on energy dissipation and dynamic shakedown characteristics was developed. The results can provide theoretical support for the design of high-speed railway subgrade structures with different filling material alternatives and control standards.

A non-proportional reduction in strength parameters is widely used in slope stability assessment, but the current asynchronous reduction in strength parameters only considers the cohesion c and internal friction angle φ, which is suitable for slope stability assessment under static loads. Under seismic loads, however, tension at the rear edge of the slope often accompanies the appearance of ground cracks. In order to consider the relationship between tensile strength, cohesion, and the internal friction angle reduction coefficient, starting with the linear softening attenuation law of soil material strength parameters, a functional relationship between cohesion and internal friction angle is obtained. Then, considering that the failure of microelements in the tensile and shear zones conforms to the tension and shear of joint failure, the relationship between tensile strength, cohesion, and the internal friction angle reduction coefficient is derived. By establishing a homogeneous slope model and comparing and analyzing the progressive instability failure modes of slopes under static and seismic conditions, the stability and potential slip surface differences of slopes under two different working conditions are explored. The research results indicate that slope instability is a gradual, cumulative failure process under both static and dynamic conditions. The instability mode of the slope under static conditions is shear failure. In contrast, under dynamic loads, the instability failure of the slope is manifested as shear failure upward at the foot of the slope and tensile failure downward at the top of the slope. The stability coefficient of slopes under earthquake conditions is reduced by 17.3% compared to that under static conditions. Under earthquake conditions, the potential sliding surface under an asynchronous reduction in strength parameters is shallower than that under static conditions and deeper than that without an asynchronous reduction in strength parameters. Overall, the research results provide a reference for slope stability analysis and support design optimization under earthquake loads.

With the gradual densification of China’s railway network, more and more railways are inevitably crossing active fault zones. Creep dislocation and stick–slip dislocation of the fault zones can lead to uneven deformation and cracks in the railway subgrade, threatening the sustainability of the railway. Therefore, a model test and numerical simulation were used to analyze the deformation mechanism and sustainability of the subgrade and foundation induced by a strike–slip fault dislocation in a flat site under different angles between the subgrade and fault strikes. The test and simulation results show that (1) under the influence of strike–slip fault dislocation, the horizontal direction of the subgrade appeared to have an S–shaped displacement. The top surface of the subgrade appeared to have a vertical settlement. Further, tension–shear cracks and compression crush zones appeared on the subgrade’s surface. (2) When the angle between the fault and subgrade strikes increased, the subgrade’s uneven deformations induced by fault dislocation first decreased and then increased. The angle between the main tension–shear crack on the top surface of the subgrade and the fault strike showed a trend of increasing and then decreasing. In addition, the compressive stress along the subgrade strike decreased, while the tensile stress along the subgrade strike gradually increased. (3) The tension–shear cracks in the subgrade and foundation were spiral and converged to the basement strike–slip fault along the depth direction. This study provides a reference for the safe operation and sustainability of railways near faults.

Brain networks based on functional magnetic resonance imaging (fMRI) provide a crucial perspective for diagnosing brain diseases. Representation learning has recently attracted tremendous attention due to its strong representation capability, which can be naturally applied to brain disease analysis. However, traditional representation learning only considers direct and local node interactions in original brain networks, posing challenges in constructing higher-order brain networks to represent indirect and extensive node interactions. To address this problem, we propose the Continuous Dictionary of Nodes model and Bilinear-Diffusion (CDON-BD) network for brain disease analysis. The CDON model is innovatively used to learn the original brain network, with its encoder weights directly regarded as latent features. To fully integrate latent features, we further utilize Bilinear Pooling to construct higher-order brain networks. The Diffusion Module is designed to capture extensive node interactions in higher-order brain networks. Compared to state-of-the-art methods, CDON-BD demonstrates competitive classification performance on two real datasets. Moreover, the higher-order representations learned by our method reveal brain regions relevant to the diseases, contributing to a better understanding of the pathology of brain diseases.

Effective agricultural management in maize production operations starts with the early quantification of seedlings. Accurately determining plant presence allows growers to optimize planting density, allocate resources, and detect potential growth issues early on. This study provides a comprehensive analysis of the performance of various object detection models in maize production, with a focus on the effects of planting density, growth stages, and flight altitudes. The findings of this study demonstrate that one-stage models, particularly YOLOv8n and YOLOv5n, demonstrated superior performance with AP50 scores of 0.976 and 0.951, respectively, outperforming two-stage models in terms of resource efficiency and seedling quantification accuracy. YOLOv8n, along with Deformable DETR, Faster R-CNN, and YOLOv3-tiny, were identified for further examination based on their performance metrics and architectural features. The study also highlights the significant impact of plant density and growth stage on detection accuracy. Increased planting density and advanced growth stages (particularly V6) were associated with decreased model accuracy due to increased leaf overlap and image complexity. The V2–V3 growth stages were identified as the optimal periods for detection. Additionally, flight altitude negatively affected image resolution and detection accuracy, with higher altitudes leading to poorer performance. In field applications, YOLOv8n proved highly effective, maintaining robust performance across different agricultural settings and consistently achieving rRMSEs below 1.64% in high-yield fields. The model also demonstrated high reliability, with Recall, Precision, and F1 scores exceeding 99.00%, affirming its suitability for practical agricultural use. These findings suggest that UAV-based image collection systems employing models like YOLOv8n can significantly enhance the accuracy and efficiency of seedling detection in maize production. The research elucidates the critical factors that impact the accuracy of deep learning detection models in the context of corn seedling detection and selects a model suited for this specific task in practical agricultural production. These findings offer valuable insights into the application of object detection technology and lay a foundation for the future development of precision agriculture, particularly in optimizing deep learning models for varying environmental conditions that affect corn seedling detection.

Applications of unmanned aerial vehicle (UAV) spectral systems in precision agriculture require raw image data to be converted to reflectance to produce time-consistent, atmosphere-independent images. Complex light environments, such as those caused by varying weather conditions, affect the accuracy of reflectance conversion. An experiment was conducted here to compare the accuracy of several target radiance correction methods, namely pre-calibration reference panel (pre-CRP), downwelling light sensor (DLS), and a novel method, real-time reflectance calibration reference panel (real-time CRP), in monitoring crop reflectance under variable weather conditions. Real-time CRP used simultaneous acquisition of target and CRP images and immediate correction of each image. These methods were validated with manually collected maize indictors. The results showed that real-time CRP had more robust stability and accuracy than DLS and pre-CRP under various conditions. Validation with maize data showed that the correlation between aboveground biomass and vegetation indices had the least variation under different light conditions (correlation all around 0.74), whereas leaf area index (correlation from 0.89 in sunny conditions to 0.82 in cloudy days) and canopy chlorophyll content (correlation from 0.74 in sunny conditions to 0.67 in cloudy days) had higher variation. The values of vegetation indices TVI and EVI varied little, and the model slopes of NDVI, OSAVI, MSR, RVI, NDRE, and CI with manually measured maize indicators were essentially constant under different weather conditions. These results serve as a reference for the application of UAV remote sensing technology in precision agriculture and accurate acquisition of crop phenotype data.

Accurate estimation of the canopy chlorophyll content (CCC) plays a key role in quantitative remote sensing. Maize (Zea mays L.) is a high-stalk crop with a large leaf area and deep canopy. It has a non-uniform vertical distribution of the leaf chlorophyll content (LCC), which limits remote sensing of CCC. Therefore, it is crucial to understand the vertical heterogeneity of LCC and leaf reflectance spectra to improve the accuracy of CCC monitoring. In this study, CCC, LCC, and leaf spectral reflectance were measured during two consecutive field growing seasons under five nitrogen treatments. The vertical LCC profile showed an asymmetric ‘bell-shaped’ curve structure and was affected by nitrogen application. The leaf reflectance also varied greatly between spatio–temporal conditions, which could indicate the influence of vertical heterogeneity. In the early growth stage, the spectral differences between leaf positions were mainly concentrated in the red-edge (RE) and near-infrared (NIR) regions, whereas differences were concentrated in the visible region during the mid-late filling stage. LCC had a strong linear correlation with vegetation indices (VIs), such as the modified red-edge ratio (mRER, R2 = 0.87), but the VI–chlorophyll models showed significant inversion errors throughout the growth season, especially at the early vegetative growth stage and the late filling stage (rRMSE values ranged from 36% to 87.4%). The vertical distribution of LCC had a strong correlation with the total chlorophyll in canopy, and sensitive leaf positions were identified with a multiple stepwise regression (MSR) model. The LCC of leaf positions L6 in the vegetative stage (R2-adj = 0.9) and L11 + L14 in the reproductive stage (R2-adj = 0.93) could be used to evaluate the canopy chlorophyll status (L12 represents the ear leaf). With a strong relationship between leaf spectral reflectance and LCC, CCC can be estimated directly by leaf spectral reflectance (mRER, rRMSE = 8.97%). Therefore, the spatio–temporal variations of LCC and leaf spectral reflectance were analyzed, and a higher accuracy CCC estimation approach that can avoid the effects of the leaf area was proposed.

One of the primary causes for the failure of glass ionomer cement (GIC) is secondary caries. To enhance the anti-microbial performance of GIC without affecting its mechanical properties, chlorhexidine (CHX) was encapsulated in expanded-pore mesoporous silica nanoparticles (pMSN) to synthesize CHX@pMSN. CHX@pMSN was added at three mass fractions (1%, 5%, and 10% (w/w)) to GIC powder as the experimental groups. Pure GIC was set as the control group. The mechanical and anti-biofilm properties of GIC from each group were tested. The results demonstrated that CHX was successfully encapsulated on/into pMSN, and the encapsulating efficiency of CHX was 44.62% in CHX@pMSN. The anti-biofilm ability was significantly enhanced in all experimental groups (p < 0.001) compared with that in the control group. CHX was continuously released, and anti-biofilm ability was maintained up to 30 days. In addition, the mechanical properties (compressive strength, surface hardness, elastic modulus, water sorption, and solubility) of 1% (w/w) group were maintained compared with those in the control group (p > 0.05). In conclusion, adding 1% (w/w) CHX@pMSN to GIC led to conspicuous anti-biofilm ability and had no adverse effect on the mechanical properties of this restorative material. This study proposes a new strategy for preventing secondary caries by using CHX@pMSN-modified GIC.