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.

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.

Aiming at the U-turn scenario of autonomous vehicles in two-way single lanes, a safety decision-making method was proposed by fuzzy reasoning, and a U-turn mathematical model was established based on the spatial distribution relationship of vehicles, seven key control points were determined, the search strategy of particle swarm optimization was improved, and an efficient and comfortable U-turn trajectory planning method was proposed. The safety decision-making method firstly establishes a membership relationship between the relative distance between the vehicle and the vehicle on the target lane and the minimum safety distance during steering when making a U-turn, and selects the time with higher safety to make a U-turn. The trajectory planning method combines the spatial distribution characteristics of vehicles, improves the constraints of particle swarm optimization, and proposes a new search strategy, which can quickly converge to the optimal extreme value and plan the optimal path of U-turn. The results show that the proposed decision-making and trajectory planning methods can complete the U-turn safely and efficiently.

The degradation of mechanical properties of PE pipe materials caused by hot oxygen aging will inevitably reduce the service life of the pipe, which is a serious hidden danger to social and economic development and people’s life and property safety. A nonlinear ultrasonic evaluation method for thermo-oxygen aging pipes was proposed. The nonlinear ultrasonic detection signals of PE pipes were extracted by building a nonlinear ultrasonic detection system for water immersion, and a correlation model between the characteristic parameters of nonlinear ultrasonic detection and elongation at break and fracture stress was established. The nonlinear ultrasonic detection mechanism was revealed by observing the micro-structure changes of PE pipes after aging. The experimental results show that the elongation at break and profit at break of PE pipe materials decrease with the increase of the nonlinear coefficient. The surface micro-cracks, holes and folds formed after the aging of the pipe are the main reasons for the mechanical degradation of the pipe and the increase of the nonlinear ultrasonic detection coefficient. The nonlinear coefficient can be used to evaluate the mechanical degradation of the pipe caused by thermal oxygen aging.

To explore the impact of the community built environment on the walking time of elderly people, and considering gender differences among the elderly group, a CatBoost model was constructed and the SHAP (Shapley additive explanations) explanation method was integrated. The relative importance and nonlinear relationships of the community built environment features with the walking time of elderly people of different genders were comparatively analyzed. The study findings indicate that the community built environment variables have a more significant influence on the walking time of the elderly compared to personal socioeconomic attributes. However, the impact varies between genders. Compared to elderly males, elderly females pay more attention to built environment variables closely related to social needs, such as NDVI and population density. In contrast, the walking time of elderly males is more closely associated with personal socioeconomic attributes, often linked to transportation convenience and travel efficiency.

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.

The technological demand for distributed wind energy and wind-solar complementary energy utilization on building roofs was addressed. A new type of spatial support frame for wind-solar complementary systems was proposed. The power generation efficiency of small vertical axis wind turbines was enhanced by using flow deflectors with combined wind collection and flow stabilization functions. The wind collection effect and power generation efficiency of the framework were analyzed through theoretical methods. Numerical simulations were conducted to study the impact of flow deflector distancing and width on internal airflow velocity and turbulent kinetic energy at different wind attack angles. Optimization parameters were identified. Wind tunnel experiments were performed to investigate the power generation performance of small wind turbines with the spatial support framework. The results show that the framework significantly increases the airflow velocity entering the wind collection device and reduces turbulent kinetic energy in the internal space. When the flow deflectors have a distance of 0.53 m and a width of 0.12 m, the wind speed increases by 1.21 times and the generator power increases by about 1.77 times.

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.

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%.