Traditional whole-brain dynamical modeling techniques are typically constrained by static single features, neglecting dynamic fluctuations in brain networks and lacking qualitative analysis of corresponding indicators, which limits modeling accuracy and comprehensibility. In order to address this issue, a multi-objective expectation maximization algorithm based on bifurcation analysis was proposed. This approach integrates a dynamic mean-field model with brain structural-functional features extracted from multi-mode imaging data for modeling purposes. Bifurcation theory was employed to qualitatively analyze multiple constraint indicators of the model, including functional connectivity, dynamic functional connectivity, and metastability for model inversion. Initial parameter values were determined through bifurcation analysis, and parameter combinations were iteratively refined using an expectation maximization algorithm. Quantitative analysis validates the accuracy and stability of this method.

Over the past half-century, global warming and humidification have led to an accelerated rate of glacier melting in China, highlighting the increasing importance of monitoring glacier distribution. However, current automated glacier extraction methods have significant limitations, such as boundary fragmentation, omission of glaciers in shaded mountain areas, and misclassification in cloud-covered regions. To address these issues, this study selected Menyuan County in Qinghai Province as the experimental area. Sentinel-2 imagery and DEM data were utilized, applying object-oriented automatic classification technology in combination with the C5.0 decision tree model to develop a multi-feature glacier extraction rule set and a neighborhood feature rule set. An improved two-stage object-oriented glacier extraction method was subsequently proposed. The findings revealed that glaciers exhibited distinct response patterns across various features, including spectral mean, spectral standard deviation, NDSI (normalized difference snow index), DEM (digital elevation model), adjacency, and slope orientation. A two-stage glacier extraction method effectively enabled automatic glacier extraction. It also significantly enhanced the recognition accuracy in cloud-covered and shaded mountain regions, achieving an overall glacier recognition accuracy of 98.50%.

Crack detection is crucial to maintaining the structural safety of buildings. In recent years, convolutional neural networks based on deep learning have provided new solutions for crack detection. However, this comes at the cost of huge computing resources, so there are problems of poor real-time performance and low detection efficiency in practical applications. To address this problem, a lightweight MSFC (multi-scale dynamic fusion convolution module) based on the U-Net architecture was proposed to improve the efficiency of crack segmentation. To verify the effectiveness of the proposed method, a dataset Crack2045 containing 2 045 crack images was constructed and experiments were conducted on this dataset. The experimental results show that compared with the original U-Net model, the model using the MSFC module reduces 78.51% of the parameters and 63.75% of the computational complexity while maintaining the same accuracy. At the same time, the MSFC module has a certain degree of generalization and can be seamlessly integrated into different semantic segmentation models. This study not only provides an efficient deep learning method for crack detection, but also provides new possibilities for model deployment in resource-constrained environments.

To analyze the crack development characteristics of different reinforcement concrete beams during bending failure, and to explore the acoustic emission characteristics and bending performance degradation of the beams, a four-point bending test was conducted in combination with acoustic emission technology to establish the relationship between the bending failure process and acoustic emission signals of three different concrete beams. The experimental results show that the RA(rise time/maximum amplitude)-AF(average frequency) signals during the damage evolution of concrete beams with different reinforcements have obvious differences, the proportion of shear crack signal RA of reinforced beams, less-reinforced beams, and super-reinforced beams is much higher than the proportion of diagonal crack signal AF. The common characteristics of the amplitude distribution of bending damage at various stages of differently reinforced beams are mainly reflected in the amplitude peaks in the frequency bands 4~6 kHz, 13.5~16 kHz, and 53~57 kHz. When the beam reaches a certain load, the amplitude peaks in the high-frequency band will suddenly rise, indicating the yielding of the internal reinforcement of the beam and the entry of the structure into the failure stage. This can be used as an important basis for monitoring the extent of internal damage to the structure through time-frequency transformation of acoustic emission signals.

To evaluate the micro-structural degradation characteristics and strength attenuation law of multi source solid waste solidified lake sediment under freeze-thaw cycle conditions, the macroscopic engineering characteristics of the solidified sediment material under freeze-thaw cycle conditions, such as unconfined compressive strength, volume deformation and permeability coefficient were observed through multiple repeated freeze-thaw cycle tests on samples of granulated blast furnace slag, desulfurization gypsum, and construction waste co solidified sediment. By combining micro testing methods such as X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, energy spectrum analysis, then the mineral composition, functional groups, surface morphology and elemental composition of solidified sediment materials during freeze-thaw cycles were systematically analyzed, revealing the micro mechanism of structural degradation of solidified sediment under freeze-thaw cycles. The results show that with the increase of freeze-thaw cycles, fibrous ettringite and columnar gypsum and other cementitious products fracture and overlapped with each other to form a network, and the internal pores increased. This may be the reason for the decrease in strength and increase in permeability coefficient of the solidified sediment. The obtained mechanical characteristics and degradation patterns of solidified sediment during freeze-thaw cycles can provide basic data for the application and promotion of this material in regions with significant freeze-thaw cycle characteristics such as northwest and northeast of China.

In order to investigate the water-holding mechanism and infiltration law of modified glutinous rice-based reconstructed soil layer under rainfall, soil column infiltration tests were firstly conducted to analyze the influence of modified glutinous rice-based material dosage variations on macroscopic vertical infiltration patterns of reconstructed soil. NMR (nuclear magnetic resonance) and scanning electron microscopy technologies were employed to investigate microporous structure and water-holding characteristics under different material dosages. Based on the findings, reconstructed soil with optimal material dosage (12.5%) was selected for rainfall slope modeling tests, through which moisture transport patterns and post-precipitation water redistribution characteristics in reconstructed soil layers were investigated.The results show as follows. With the increase of the dosage of modified glutinous rice-based materials, the number of effective pores (mesopores) increases and then decreases, the number of small pores gradually increases and the number of large pores gradually decreases, and the soil water-holding capacity is optimal when the dosage is 12.5%. increases and the number of large pores gradually decreases, and the soil water-holding capacity is optimal when the dosage is 12.5%. Modified glutinous rice-based materials wrap around, adsorb to soil particles, and combine with gravel to form agglomerates, thereby changing the pore structure of the soil, enhancing the soil water retention capacity, and improving the effectiveness of soil water. Under the condition of 25 mm/h rainfall intensity, an increase in slope gradient led to the decrease of infiltration depth of each cross-section, and the infiltration site shifted significantly (from the top to the foot of the slope). The depth of slope infiltration during the entire rainfall period decreased significantly with the increase of slope gradient, and water in the slope was redistributed at the end of rainfall. The average infiltration depth of the slope at 35°, 55°, and 75° was 10 cm, 8 cm, and 5 cm, respectively. This study is significant for improving the technical system of ecological slope restoration and guiding conservation and management efforts.

In order to study the development law of pile top cumulative displacement of ring wing single pile foundation under horizontal cyclic load, a three-dimensional numerical model of the interaction between ring wing single pile and saturated clay was established through secondary development using ABAQUS, and also simulated the process of soil stiffness attenuation. Numerical results indicate that installing gravity type ring wings at the mud surface position of traditional single pile foundations can enhance the overall horizontal resistance of the ring wing single pile foundation, thereby reducing the cumulative displacement of the ring wing single pile under cyclic loads. The displacement of the pile top will decrease with the increase of the height and diameter of the gravity type ring wing, but increasing the diameter of the gravity type ring wing has a more significant effect on reducing the horizontal displacement of the pile top. Increasing the depth of the pile into the soil can significantly reduce the cumulative displacement at the top of a single circular wing pile under cyclic loading.

Strong pulsation is one of the most important causes of damage to hydraulic structures such as stilling basins, so it is crucial to clarify the characteristics of pulsation in hydraulic structures to ensure the safe operation of the project. In this study, a fine numerical model of the hydraulic model of a stilling basin was established based on DES (detached eddy simulation) model and VOF (volume of fluid) method, and the simulation results were in good agreement with the experimental results. Based on the numerical simulation results, the distribution of pressure fluctuation, pulsating velocity, vorticity, and turbulent kinetic energy in the stilling basin were analyzed. The results show that the pressure fluctuation in the stilling basin shows a bimodal distribution along the flow direction, and the pulsation accounts for more than 10% in the flow impact area. The spanwise and streamwise velocity pulsation dominate in the stilling basin, and the vertical pulsation is weaker, while the spanwise pulsation at the centerline of the bottom floor in the collision area decreases rapidly. The streamwise and vertical vorticity on the centerline of bottom floor and surface outlet are characterized by “small time-average value and large pulsation value”, and the transverse-axis vortex caused by the drop sill has a large influence on the distribution of the spanwise vortices. The results of turbulent kinetic energy spectrum analysis show that the pulsations in the stilling basin mainly consist of a large number of low-frequency pulsations below 1 Hz, indicating that the turbulent fluctuations in the stilling basin are mainly controlled by large-scale and low-frequency vortices. The analysis helps to deepen the knowledge of the flow characteristics of flood discharge and energy dissipation structures such as stilling basins, and provides a certain reference for design and safety assessment.

In order to solve the contradiction between ecological maintenance and agricultural production water demand, the minimum water demand for ecological restoration in arid areas was determined. The lower reaches of the Tarim River were selected as the typical study area. Suitable areas for vegetation growth were determined through the PNV (potential natural vegetation) simulation method, while the suitable growth ranges and distribution areas of trees, shrubs, and grasslands were analyzed. The minimum ecological water demand for the lower reaches of the Tarim River was calculated by the multi-year evapotranspiration data from the AET dataset. The results show as follows. The PNV results obtaine in the lower reaches of the Tarim River are dominated by shrubs, forests and grasslands are highly dependent on water resources, mainly distributed around river channels. The forest and grassland in the study area show high growth potential, while the shrub distribution areas far from the river show a degradation trend. According to PNV simulation results, the ecological water demand in the lower reaches of Tarim River is about 11 279.23×10⁴ m³, of which shrubs account for the largest proportion, while woodland and grassland account for 7.4% and 5.36% respectively due to their small areas. The research results provide a new method for ecological restoration and determination of water transport capacity in arid areas, which can clarify the scope of ecological restoration and vegetation types in the basin, and contribute to the management and optimal allocation of regional water resources.

The internal damage of the steel rail is serious, but the non-destructive testing B-display detection image has a lot of noise and noise, and the spatiotemporal distribution characteristics of different damages are not obvious, making it difficult to effectively identify. In response to this situation, a rail screw hole crack B-image recognition algorithm based on improved YOLOv8 was studied to improve the accuracy of intelligent identification of rail damage. Firstly, to reduce the missed detection of small damage targets, RepHGNetv2 network was used to optimize the YOLOv8 backbone network and improve the detection recall rate. Then, in order to improve the adaptability of the model to different types of damage detection, the detection head of YOLOv8 was replaced with Effientnet to improve the detection accuracy of the model. Finally, the LSKA attention mechanism was introduced into the SPPF module to enhance the model’s anti-interference ability against noise signals and improve its accuracy. The actual line detection results have verified that the detection accuracy of the above model reaches 95.1%, the recall rate reaches 93.8%, and the average accuracy reaches 97.6%, which is improved compared to other commonly used algorithms.