Search Results (34)

Reservoir-bank landslides may become unstable after reservoir impoundment and generate impulse waves, posing serious threats to hydropower hub structures, reservoir navigation, and downstream safety. The H1 landslide, with a volume of approximately 5.35 million m³, is located on the left bank of the near-dam reservoir area of Yangqu Hydropower Station. Reservoir operation and water-level fluctuations may affect the stability of the H1 landslide; if instability occurs, rapid water entry could generate impulse waves that threaten the hydraulic structures. To evaluate the impulse-wave hazards associated with the H1 landslide, a three-dimensional physical model was constructed at a geometric scale of 1:200. Representative experiments were conducted under landslide instability volumes of 100 × 10⁴, 200 × 10⁴, and 500 × 10⁴, reservoir water levels of 2710.0 m and 2715.0 m, and the corresponding prototype-oriented water-entry conditions. The generation, propagation, opposite-bank run-up, and wave responses in front of the hydraulic structures were investigated. The results show that both the initial wave height near the water-entry point and the wave height in front of the hydraulic structures increase with increasing landslide volume. At the water-entry point, the maximum wave height increases from 3.10–3.27 m for the 100 × 10⁴ m³ landslide to 4.50–4.64 m for the 200 × 10⁴ m³ landslide and further to 7.18–7.21 m for the 500 × 10⁴ m³ landslide. In front of the hydraulic structures, the maximum wave height at the spillway reaches 5.61 m for the 200 × 10⁴ m³ landslide under the ecological restricted water level of 2710 m. After superposition with the wind-wave run-up, the corresponding total wave elevation is 2718.8 m, which remains below both the dam crest elevation of 2721.0 m and the wave-wall crest elevation of 2722.2 m. However, under the normal pool level of 2715 m, the total wave elevations at the spillway and power station intake after superposition with wind-wave run-up exceed the wave-wall crest elevation, indicating a potential overtopping risk. The research results provide a scientific basis for reservoir impoundment scheduling, landslide-generated impulse wave risk prevention, and the safety protection of key hydraulic structures at Yangqu Hydropower Station. Full article

The expansion of deep-buried tunnels into complex geological settings has heightened the risk of encountering high-pressure gas strata. This study addresses a critical knowledge gap regarding safe rock-pillar thickness when tunneling through high-pressure CO₂-bearing formations, motivated by a 2.3 MPa CO₂ blowout event encountered during the geological investigation of a deep railway tunnel in western China. Numerical simulations were conducted using Phase2/RS2 (2D plane-strain models for the tunnel floor) and FLAC3D (3D models for the tunnel face) to evaluate plastic zone evolution and displacement responses under prescribed equivalent static CO₂ pressure conditions and rock-mass degradation scenarios. The simulations represent a mechanical assessment under a prescribed pressure condition rather than a fully coupled gas-flow–mechanical analysis. Under the equivalent static CO₂ pressure assumption, the calculated plastic zone depth increased from 10.2 m in the no-pressure case to 17.4 m under the 2.3 MPa pressure condition, while the maximum floor displacement increased from 0.4 cm to 11.0 cm. These results represent a conservative mechanical response under the adopted pore-pressure efficiency assumption and should not be interpreted as a direct simulation of gas compressibility, capillary effects, pressure diffusion, or gas–water two-phase behavior. Under the adopted parametric degradation scenarios, rock-mass strength reduction further increases the calculated plastic zone depth and displacement. In the strong degradation case, the plastic zone depth reaches 32.6 m and the maximum displacement reaches 19.0 cm. These values should be interpreted as sensitivity-analysis results for the assumed degraded rock-mass conditions, rather than as general predictions for all fractured or weathered rock masses. For face stability, the critical coalescence distance between excavation-disturbed and high-pressure-affected zones was identified as 15 m for intact rock, advancing to 20 m and 30 m under 10% and 20% strength reductions, respectively. Based on these findings, preliminary conservative reference values are proposed for risk identification when tunneling toward high-pressure CO₂-bearing fractured zones. The calculated floor plastic zone depth of 17.4–32.6 m and the face coalescence distance of 15–30 m should be interpreted as mechanical warning indicators under the adopted equivalent static pressure assumption. These values have not yet been validated by construction-stage monitoring data and should therefore be updated using gas-pressure measurements, deformation monitoring, support response, drainage performance, and field back-analysis during tunnel construction. Full article

Accurately estimating village-level winter wheat yield in coastal saline–alkali farmland is challenging because this region has strong spatial differences and multiple environmental stresses. In this study, Huanghua City, Hebei Province, was selected as a typical coastal saline–alkali area. Sentinel-2 images, climate factors, and topographic variables, including elevation, topographic wetness index, distance to the coastline, and distance to water systems, were combined to build a phenology-guided feature set for winter wheat yield prediction in coastal areas. The results showed that Phenology-Guided Feature Integration XGBoost achieved an R² of 0.6382 and an RMSE of 450.15 kg/ha, which was slightly better than Gradient Boosting (R² = 0.6256) and Random Forest (R² = 0.6098), and clearly better than SVR (R² = 0.4792), Ridge regression (R² = 0.4582), and a single Decision Tree (R² = 0.3088). Then, a three-stage branch was designed to identify the main drivers of SI, NDVI, and winter wheat yield at different stages, helping explain how environmental constraints and vegetation responses jointly affect final yield. The Three-Stage Fusion XGBoost Model achieved an R² of 0.6439, an RMSE of 446.24 kg/ha, and an MAE of 363.38 kg/ha, showing a slight improvement in prediction accuracy. SHAP analysis showed that SI, distance-related factors, elevation, TWI, and NDVI were important drivers of winter wheat yield variation. Spatial prediction results showed higher winter wheat yield in inland areas (5145 kg/ha) and lower yield in coastal areas (4198 kg/ha). This framework supports village-scale winter wheat yield prediction in coastal saline–alkali farmland and improves model interpretability. Full article

Advancing hydropower development is crucial for supporting China’s “Dual Carbon” strategy and ensuring energy security. A key safety challenge in this endeavor is the stability of arch dam abutments under the combined action of high in situ stress and reservoir water loads. This study addresses this issue by proposing an integrated methodology that links detailed geological characterization, in situ stress quantification, and mechanical stability analysis. Using the Guxue high-arch dam as a case study, we first established a three-dimensional geological model to identify controlling discontinuities and delineate potential sliding blocks. A finite difference model was then developed to simulate the in situ geo-stress field and operational water pressures. Through stress tensor transformation, the stress state on potential slip surfaces was accurately determined, and safety factors were calculated based on the Mohr–Coulomb strength criterion. The results show that the critical left and right abutment rock blocks exhibit safety factors of 1.30 and 1.24, respectively, meeting design specifications while indicating a relatively lower safety margin on the right bank. The proposed approach, grounded in precise stress analysis, provides a reliable framework for assessing abutment stability under complex loading conditions, offering practical support for the safety evaluation and targeted reinforcement of high-arch dam projects in similar geological settings. Full article

This study addresses the challenge of multi-order resonance in base structures within the low-frequency range (10~300 Hz), a common issue in shipbuilding and aerospace applications. Traditional vibration control methods, including those based on the Acoustic Black Hole (ABH) effect, often undermine base structural integrity, offer limited effective bandwidth, or pose practical implementation challenges. To overcome these limitations, this paper proposes a Gradient Variable-Thickness Composite (GVTC) damping plate for passive vibration control, integrating the ABH effect with anti-resonance theory. The key innovation is an engineering-oriented integrated design characterized by external mounting, multi-level stacking, and efficient shell-element modeling rather than a fundamental modification to the ABH principle itself. The composite plate comprises a uniform-thickness region, a gradient-thickness region, and a damping layer, with thickness variation defined by a power-law function. By tuning geometric and material parameters, the plate’s natural frequencies are matched to the base panel’s resonant peaks. Employing shell elements over solid elements significantly reduces computational cost while maintaining high accuracy (relative error of the first three natural frequencies < 0.6%). Finite element simulations and experimental tests have demonstrated significant vibration suppression: peak reductions of 10 dB, 12.1 dB, 9.7 dB, and 22.9 dB at 94 Hz, 188 Hz, 244 Hz, and 294 Hz, respectively, under simulation conditions, and 7.5 dB, 11.1 dB, 8.5 dB, and 7 dB at 82 Hz, 172 Hz, 234 Hz, and 286 Hz, respectively, in experiments. The additional mass of the damping plate accounts for only 1.53% of the base panel mass. This work provides a practical and efficient solution for low-frequency vibration control and facilitates the engineering application of ABH technology in high-end equipment. Full article

In agricultural production, Deoxynivalenol (DON) generally exists and contaminates wheat, corn, and other grains, causing intestinal damage and immunotoxicity. Lycopene (LYC), an antioxidant, anti-inflammatory carotenoid, is mainly found in red fruits such as tomatoes and has been investigated for its great medicinal advantages. This study aimed to investigate the protective effect of LYC against DON-induced enterotoxicity. Our findings demonstrated that incubation of IPEC-J2 cells with 0.5 μM DON for 24 h caused intestinal barrier impairment and oxidative stress induction, which subsequently led to increased secretion of pro-inflammatory factors (TNF-α, IL-1β, IL-18, and IL-6) and decreased secretion of the counterregulatory factor (IL-10). Furthermore, DON ultimately induced NLRP3 inflammasome activation through the stimulation of the MAPK/NF-κB pathway. It is worth mentioning that the above changes were reversed after adding 30 μg/mL of LYC to DON-exposed IPEC-J2 cells. In addition, further experiments confirmed that ERK activator (4-Methylbenzylidene camphor, 4-MBC) eliminated the positive effect of LYC on alleviating enterotoxicity induced by DON in IPEC-J2 cells. In addition, further experiments confirmed that 4-MBC eliminated the positive effect of LYC on alleviating enterotoxicity induced by DON. In general, our study certified that ERK is a therapeutic target for LYC protecting DON-induced intestinal barrier dysfunction and NLRP3 inflammasome activation. Full article

Objectives: The construction of subunit vaccines based on antigens that can induce strong cellular immunity is a widely accepted strategy to develop new tuberculosis vaccines. This study screens immunogens with potential for subunit vaccine development from seven candidate antigens and then verifies their vaccine efficacy. Design: C57BL/6 mice were immunized subcutaneously with purified PPE19, PPE50, FadD21, Rv1505c, Rv1506c, Rv2035, and Rv0976c proteins formulated with Freund’s adjuvant to evaluate both the antigen-specific Th1 cellular immune responses and IgG level. After the vaccination of mice with recombined pcDNA3.1 expressing Rv0976c, intravenous or aerosol infection with M. tb were further challenged to assess protective efficacy. Results: Purified PPE19, PPE50, FadD21, and Rv0976c proteins generated strong antigen-specific Th1 cellular immune responses in mice. Compared to Ag85A, Rv0976c also stimulated higher IgG antibody level in mice. In particular, Rv0976c stimulated high and specific IgG antibody levels in serum from TB patients. The vaccination of mice with DNA vaccines expressing Rv0976c, followed by intravenous challenge with Bacillus Calmette–Guerin (BCG) Pasteur or M. tb, resulted in significant levels of protection that are comparable to or better than that afforded by the two leading antigens, Ag85A and PPE18. Conclusions: These results indicated that Rv0976c was a better protective antigen. Future studies to combine Rv0976c with other antigens and evaluate its effectiveness as a booster of BCG or as a therapeutic vaccine are warranted. Full article

This study focuses on the effect of window openings on the seismic performance of the stone walls of Tibetan and Qiang dwellings. A typical stone wall of a Tibetan and Qiang dwelling constructed using irregular stone and yellow mud masonry in Li County, Sichuan Province, was chosen as a prototype, and two stone walls with different structural window openings were designed for proposed static tests and microscopic electron microscope scanning (SEM), which obtained the damage patterns and microscopic damage mechanisms of the walls and analyzed them in comparison with the test results of the stone walls without window openings. At the same time, a finite element model was established based on the test parameters to study the effects of opening size, shape, and aspect ratio on the seismic performance of the stone walls of Tibetan and Qiang dwellings. The findings indicate that “X”-shaped cracks at the corners of the window openings and extending to the surrounding areas are the primary damage characteristics. The unique microstructure of yellow clay and schist leads to the faster appearance of wall cracks. The peak load, stiffness, and energy dissipation of the windowed walls were less than those of the windowless walls. It was found through simulation that the seismic performance of the wall decreases with the increase in the opening size; as the wall’s openings take on different shapes, the ultimate bearing capacity steadily declines with the order of circular, square, triangular, trapezoidal, and rectangular; and under a range of aspect ratios, the wall’s seismic performance is best when the opening’s aspect ratio is 1:1. The research results of this paper are of reference value for the research, design, and construction of stone walls and other non-engineered masonry works of Tibetan and Qiang dwellings. Full article

To optimize rice yield and enhance quality through targeted field management at each growth stage, rapid and accurate identification of rice growth stages is crucial. This study presents the Mobilenetv3-YOLOv8 rice growth-stage recognition model, designed for high efficiency and accuracy using Unmanned Aerial Vehicle (UAV) imagery. A UAV captured images of rice fields across five distinct growth stages from two altitudes (3 m and 20 m) across two independent field experiments. These images were processed to create training, validation, and test datasets for model development. Mobilenetv3 was introduced to replace the standard YOLOv8 backbone, providing robust small-scale feature extraction through multi-scale feature fusion. Additionally, the Coordinate Attention (CA) mechanism was integrated into YOLOv8’s backbone, outperforming the Convolutional Block Attention Module (CBAM) by enhancing position-sensitive information capture and focusing on crucial pixel areas. Compared to the original YOLOv8, the enhanced Mobilenetv3-YOLOv8 model improved rice growth-stage identification accuracy and reduced the computational load. With an input image size of 400 × 400 pixels and the CA implemented in the second and third backbone layers, the model achieved its best performance, reaching 84.00% mAP and 84.08% recall. The optimized model achieved parameters and Giga Floating Point Operations (GFLOPs) of 6.60M and 0.9, respectively, with precision values for tillering, jointing, booting, heading, and filling stages of 94.88%, 93.36%, 67.85%, 78.31%, and 85.46%, respectively. The experimental results revealed that the optimal Mobilenetv3-YOLOv8 shows excellent performance and has potential for deployment in edge computing devices and practical applications for in-field rice growth-stage recognition in the future. Full article

A concurrent heuristic search iterative process (CHSIP) is used for estimating groundwater pollution sources and aquifer parameters in this work. Frequent calls to carry out a numerical simulation of groundwater pollution have generated a huge calculated load during the CHSIP. Therefore, a valid means to mitigate this is building a substitute to emulate the numerical simulation at a low calculated load. However, there is a complicated nonlinear correlativity between the import and export of the numerical simulation on account of the large quantity of variables. This leads to a poor approach accuracy of the substitute compared to the simulation when using shallow learning methods. Therefore, we first built a stacked autoencoder substitute, using the deep learning method, to boost the approach accuracy of the substitute compared to the numerical simulation. In total, 400 training samples and 100 testing samples for the substitute were collected by employing the Latin hypercube sampling method and running the numerical simulator. The CHSIP was then employed for estimating the groundwater pollution sources and aquifer parameters, and the estimated outcome was obtained when the CHSIP was terminated. The data analysis, including interval estimation and point estimation, was implemented on the MATLAB platform. A relevant hypothetical case is set to verify our approaches, which shows that the CHSIP is helpful for estimating the groundwater pollution source and aquifer parameters and that the stacked autoencoder method can effectively boost the approach precision of the substitute for the simulator. Full article

Raw potato fries are a type of potato by-product (PBP), and they have great potential as a partial replacement of grain in animal feeds to improve the environmental sustainability of food production. This study aimed to investigate the effects of replacing corn with different levels of PBP (0%, 12.84%, 25.65%, and 38.44%) in the total mixed ration (TMR) of Angus bull. Sixty 16-month-old Angus bulls (548.5 ± 15.0 kg, mean ± SD) were randomly assigned to four treatments. The results indicated that with the increase in the substitution amount of PBP, the body weight decreased significantly. The dry matter apparent digestibility and starch apparent digestibility linearly decreased as PBP replacement increased. The feed ingredient composition in the TMR varied, leading to a corresponding change in the rumen microbiota, especially in cellulolytic bacteria and amylolytic bacteria. The abundance of Succiniclasticum in the 12.84% PBP and 38.44% PBP diets was significantly higher than that in the 0% PBP and 25.65% PBP diets. The abundance of Ruminococcus linearly increased. In conclusion, using PBP to replace corn for beef cattle had no negative impact on rumen fermentation, and the decrease in apparent digestibility explained the change in growth performance. Its application in practical production is highly cost-effective and a strategy to reduce food waste. Full article

Groundwater LNAPL (Light Non-Aqueous Phase Liquid) contamination source identification (GLCSI) is essential for effective remediation and risk assessment. Addressing the GLCSI problem often involves numerous repetitive forward simulations, which are computationally expensive and time-consuming. Establishing a surrogate model for the simulation model is an effective way to overcome this challenge. However, how to obtain high-quality samples for training the surrogate model and which method should be used to develop the surrogate model with higher accuracy remain important questions to explore. To this end, this paper innovatively adopted the quasi-Monte Carlo (QMC) method to sample from the prior space of unknown variables. Then, this paper established a variety of individual machine learning surrogate models, respectively, and screened three with higher training accuracy among them as the base-learning models (BLMs). The Stacking ensemble framework was utilized to integrate the three BLMs to establish the ensemble surrogate model for the groundwater LNAPL multiphase flow numerical simulation model. Finally, a hypothetical case of groundwater LNAPL contamination was designed. After evaluating the accuracy of the Stacking ensemble surrogate model, the differential evolution Markov chain (DE-MC) algorithm was applied to jointly identify information on groundwater LNAPL contamination source and key hydrogeological parameters. The results of this study demonstrated the following: (1) Employing the QMC method to sample from the prior space resulted in more uniformly distributed and representative samples, which improved the quality of the training data. (2) The developed Stacking ensemble surrogate model had a higher accuracy than any individual surrogate model, with an average R² of 0.995, and reduced the computational burden by 99.56% compared to the inversion process based on the simulation model. (3) The application of the DE-MC algorithm effectively solved the GLCSI problem, and the mean relative error of the identification results of unknown variables was less than 5%. Full article

An informed search strategy based on random statistical analysis was developed for synchronous recognition of groundwater pollution source information and aquifer parameters. An informed search iterative course (ISIC) was accordingly designed, and each iteration included the determination of attempt point and state transition. In this paper, two improvement techniques were first adopted for choosing attempt points and judging state transition in ISIC to improve search efficiency and precision. The first improvement was that the variable radius free search method was applied to choosing the attempt point, and the size of the search radius was constantly adjusted in ISIC, taking the search ergodicity and efficiency into account. The second improvement technique was a Tsallis formula used for state transition judgment, and the controlled factor in the Tsallis formula was regulated continuously so that the search could consider ergodicity and efficiency simultaneously. Furthermore, frequent calls to the groundwater pollution numerical simulator to calculate the likelihood have inflicted a huge computational burden during ISIC. An effective way is to construct a substitute for emulating the simulator with a low calculating load. However, the mapping relation between the import and export of the numerical simulator was complex and had many variables. The precision of the substitute based on shallow learning is low sometimes. Therefore, we adopted the deep learning method and built an improved deep confidence network (DCN) substitute to emulate the highly nonlinear simulator. Finally, the synchronous recognition results for groundwater pollution source information and aquifer parameters were gained when ISIC ceased. The above-mentioned methods were verified in a case involving groundwater pollution. The consequence indicated that the ISIC with an improved DCN substitute can synchronously recognize groundwater pollution source information and aquifer parameters with a high degree of precision and efficiency. Full article

In the context of intensive urban development, urban complexes have emerged as crucial public spaces that address the needs of urban populations. However, current research on urban complexes is predominantly qualitative and lacks a rigorous scientific and quantitative analysis. Therefore, this study employs the analytic hierarchy process (AHP) to construct a standardized system encompassing five dimensions: spatial function, spatial perception, architectural style, surrounding environment, and energy-saving technology. The objective is to determine the weights of the indices that influence people’s use of urban complexes under the goal of “humanization”. Additionally, the study quantitatively analyzes key indices using spatial syntax and other analytical methods. Subsequently, we employ multi-criteria decision making (MCDM) analysis to examine three real-world cases in China, aiming to validate further the importance of the AHP + MCDM approach, which incorporates the TOPSIS method based on grey correlation. This methodology considers both the subjective factors of crowd evaluations of urban complex usage and the interrelationships among indicators, ensuring that the statistical calculations of the indicators remain objective and scientifically robust. The results indicate that (1) the degree of facility improvement has the greatest impact on the crowd’s use of urban complexes; (2) there is a discrepancy between the results of the TOPSIS method and the MCDM evaluation model, with the MCDM evaluation method aligning more closely with real-world scenarios; and (3) the Shanghai MOSCHINO received the highest evaluation score, while the Nanjing Central Emporium received the lowest. Finally, we discuss the experimental results and propose targeted strategies for optimizing the design of urban complexes to achieve the goal of “humanization”. Full article

Groundwater contamination source recognition is an important prerequisite for subsequent remediation efforts. To overcome the limitations of single inversion methods, this study proposed a two-stage inversion framework by integrating two primary inversion approaches—simulation-optimization and simulation-data assimilation—thereby enhancing inversion accuracy. In the first stage, the ensemble smoother with multiple data assimilation method (a type of simulation-data assimilation) conducted a global broad search to provide better initial values and ranges for the second stage. In the subsequent stage, a collective decision optimization algorithm (a type of simulation-optimization) was used for a refined deep search, further enhancing the final inversion accuracy. Additionally, a deep learning method, the multilayer perceptron, was utilized to establish a surrogate of the simulation model, reducing computational costs. These theories and methods were applied and validated in a hypothetical scenario for the synchronous identification of the contamination source and boundary conditions. The results demonstrated that the proposed two-stage inversion framework significantly improved search accuracy compared to single inversion methods, with a mean relative error and mean absolute error of just 4.95% and 0.1756, respectively. Moreover, the multilayer perceptron surrogate model offered greater approximation accuracy to the simulation model than the traditional shallow learning surrogate model. Specifically, the coefficient of determination, mean relative error, mean absolute error, and root mean square error were 0.9860, 9.72%, 0.1727, and 0.47, respectively, highlighting its significant advantages. The findings of this study can provide more reliable technical support for practical case applications and improve subsequent remediation efficiency. Full article