The Chishui River Basin has been recognized as an important ecological security barrier in the upstream of the Yangtze River Basin. Research on the ecosystem service value of the Chishui River Basin under different future development scenarios is of great significance to carry out the environmental protection policies and the environmental protection measures. Therefore, Chishui River Basin has been selected as the research area and the FLUS (future land use simulation) model and InVEST (integrated valuation of ecosystem services and trade off) model were functioned to predict the ecosystem services, including water yield, soil conservation, and water purification, under three scenarios, such as natural development, environmental protection, and economic development in 2040. The high ecosystem services functions in all three scenarios are identified as the key protected areas. The results show as follows. There are significant changes in agriculture area and urban area under different scenarios, especially, the agriculture area under the economic development scenario has increased 510.55 km² and 1 475.76 km² compared to the natural development and environmental protection scenarios, respectively. In the environmental protection scenario, the areas with high water yield (>700 mm) and high soil conservation function (>2 000 t/hm²) account for approximately 36.81% and 47.15% of the Chishui River basin area, respectively. The proportion of key protected areas for ecosystem services functions in the Zunyi City account for approximately 62.65%. The results of this study aim to provide certain support for identifying key protected areas in the Chishui River Basin and promoting the implementation of spatial refinement protection and management.

In order to solve the problem of insufficient feature extraction of human dynamic skeleton features in abnormal behavior recognition, an unsupervised abnormal behavior recognition method based on enhanced spatiotemporal graph normalization flow was proposed. Transformer and convolution block attention module were employed to enhance the feature expression capability of the model and the performance of the abnormal behavior recognition algorithm in the global and spatiotemporal domains. Firstly, the Transformer module was incorporated into the affine layer of the normalized flow to augment the efficacy of dynamic skeleton feature information at the global level. Subsequently, the convolution attention was introduced into the convolution module of space and time graphs respectively to effectively enhance the spatial and temporal representation of dynamic skeleton features. Finally, simulation verification was conducted on the ShanghaiTech and UBnormal datasets, and the recognition accuracy attains 86.4% and 70.2% respectively, thereby demonstrating the effectiveness of the method.

The extremely short-term prediction of seaplane motion can provide the rocking motion posture over the next few seconds, which is considered essential for ensuring safety during take-off and landing phases under adverse wind and wave conditions. Although some research has been conducted on extremely short-term prediction methods for seaplane motion, limited attention has been given to analyzing differences in the applicability of various methods. In this context, the NACA TN 2929 aircraft was taken as an example, and the three degree of freedom motion simulation data under typical working conditions were calculated based on potential flow theory. To compare the forecasting performance under different forecasting conditions, three typical extremely short-term prediction of seaplane motion models, namely AR (auto-regressive), LSTM (long short term memory), and TCN (temporal convolutional network), were constructed. The results show that compared to the AR model, the LSTM and TCN neural network models exhibit superior forecasting accuracy for longer prediction durations, effectively enabling accurate predictions of the heave, roll, and pitch motions of the seaplane at the ten-second level, providing a valuable theoretical reference for the selection of seaplane motion prediction 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.

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.

In view of the problem of limited pressure relief and anti-impact ability of large diameter boreholes in high stress and strong disturbance coal seams. The methods of theoretical analysis, numerical simulation and field test were adopted to study the mechanism of pressure relief and anti-impact of large diameter boreholes, and the control measures of pressure relief and anti-impact of large diameter boreholes were proposed to improve the pressure relief and anti-impact performance of high stress and strong disturbance coal seams. The results show that the pressure relief range of large diameter boreholes is directly proportional to the drilling radius, surrounding rock stress, and disturbance stress increment, and inversely proportional to the constraint force of the fracture zone and plastic zone. Increasing the pressure relief range of large diameter boreholes can increase the attenuation of dynamic stress wave energy, transfer static load energy to the deep surrounding rock, and reduce the risk of coal seam rockburst. As the disturbance coefficient increases, the range of pressure relief for large diameter boreholes and the gathering elastic energy around the boreholes show an increasing trend. The vibration acceleration of borehole increases exponentially with the increase of disturbance coefficient and shock energy. When the disturbance coefficient is greater than 2, the impact dynamic load is superimposed, and the large diameter borehole is blocked to achieve the ultimate anti-impact ability. The use of dense drilling method and repeated drilling method can increase the pressure relief range of large diameter drilling, reduce the risk of coal seam rockburst. After field test, the deformation of surrounding rock of roadway is reduced by 31.5%~67.0%, the stress value of coal body is lower than the critical warning value, and the stability of surrounding rock of roadway is significantly improved.

Power plants utilizing substandard coal for electricity generation often face reduced denitrification efficiency due to high fly ash concentrations in the flue gas, which complicates achieving ultra-low emissions. The flue gas duct from the economizer outlet to the riser duct entrance in the W-shaped flame boiler at Guizhou Chayuan Power Plant was investigated. Numerical simulations were conducted to analyze flue gas flow and fly ash particle trajectories. The results show that the economizer ash hopper is minimally effective, with a collection rate of only 9.99%. Simulation results reveale fly ash deposition on the wall at the SCR riser flue bend at 1.12 kg/s, representing 11.33% of total fly ash. Based on these findings, two design schemes for positioning a denitrification ash hopper below the riser flue are proposed to enhance fly ash collection efficiency. Optimal results are obtained with a 45° angle between the inclined surface and horizontal wall, increasing fly ash collection by 8.38% with a 2.25 Pa pressure drop increase.

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%.

The hybrid DC transmission system has problems such as inconsistent boundary components, inconsistent fault response characteristics, difficult resolution of high resistance fault effects, and low accuracy in identifying near end faults, which reduces the reliability of protection schemes. Therefore, the phase characteristics of the regional refractive index of the hybrid DC transmission system were analyzed for the first time, and a single ended protection scheme suitable for hybrid boundaries was proposed based on this. Firstly, establish a hybrid DC transmission system model and analyze the traveling wave transmission characteristics of different fault types. Subsequently, the fault areas of the hybrid DC transmission system were divided, and the refractive index expressions and phase frequency characteristics of the areas were derived separately. Finally, a single ended protection scheme based on a specific frequency refractive index is proposed and its performance is tested. The test results show that the proposed protection scheme not only has the speed of traditional protection schemes, but also has better resistance to high impedance faults, noise interference, and other abilities.

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.