In order to analyze the influence of uncertain factors on power system, PCA (polynomial chaos approximation) method, which is both fast and accurate, is widely used in probabilistic power flow calculation. The polynomial chaotic approximation method requires that the probability density function of the random input variable is known, and the random input variable must satisfy the independent condition. A probabilistic power flow method based on DDPCA (data driven polynomial chaos approximation) was proposed for the known random input variables which are historical data. First, DDPCA selects the optimal orthogonal polynomial according to the historical data, and then determines the Gaussian sample considering the nonlinear correlation of random input variables, and then computes the weights with Monte Carlo integral. Then, a small amount of power flow was calculated based on Gaussian samples, and the approximation coefficient was solved according to the power flow results and weights, and then the statistical characteristics of the random output variables were obtained. The proposed method was compared with the point estimation method, and the effectiveness of the proposed method was verified by the results of three examples.

The total organic carbon content in shale reservoirs is a crucial parameter for assessing hydrocarbon generation potential and shale gas enrichment. Accurate prediction of TOC(total organic carbon) is essential for oil and gas exploration and development. Conventional linear regression methods are limited in their predictive accuracy due to the complex nonlinear relationships among regional and well logging data. To address this issue, a prediction model based on Adaboost-WOA-BP was proposed for predicting TOC content. This model integrates WOA(whale optimization algorithm) optimized Backpropagation neural networks as weak learners within the Adaboost framework to construct a strong learner. Use of optimal natural gamma, density, acoustic time difference, and other sensitive logging parameters associated with TOC content calculation as inputs for the prediction model. Compared to conventional linear regression, BP neural networks and WOA-BP neural networks, the Adaboost-WOA-BP model demonstrates higher predictive accuracy, achieving a 95% match between predicted and measured TOC values.

During the geothermal development of dry hot rock, the high temperature rock mass is subjected to repeated cold and thermal cycles. It leads to the rupture of thermal reservoirs and the change of physical and mechanical properties. In order to further explore the mechanism of the influence of temperature and cooling-heating cycle on rock characteristics, the granite specimens subjected to different high temperature nodal heat treatment were treated with natural cooling, fresh water cooling and seawater cooling respectively. The physical and mechanical indexes and microstructure were studied. The damage constitutive equations of granite under uniaxial compression with three cooling cycles were established. The results show as follows. With the increase of temperature and cycle times, the mass loss rate is in the order of freshwater cooling > natural cooling > seawater cooling, but at 600 ℃, serious particle breakup and shedding cause the mass loss of seawater cooling rock sample to exceed that of natural cooling. The elastic modulus, compressive strength and tensile strength are decreasing. The damage of water cooling to high temperature rock is greater than that of natural cooling. The damage effect of high temperature is more obvious than that of cycle times. The micro-cracks of seawater cooling rock sample are more developed. The damage variables consider the effects of temperature and cycle times, and add the damage coefficient to consider the damage effects of freshwater cooling and seawater cooling. The uniaxial compressive stress-strain curves combined with damage analysis under load are compared with the experimental results in a high degree of fitting, which reflects the rationality of the model.

Aiming at the technical problems of "untimely perception, poor transmission and difficult equipment deployment" in the monitoring and early warning of mountain disasters in the complex environment of the Qinghai-Tibetan Plateau, a UAV-throwing monitoring device, LoRa networking and edge computing gateway, as well as other embedded hardware and software equipment applicable to deformation and micro-motion monitoring of high-level and high-risk mountain disasters were developed, and focused on the research of the system low-power adaptive data acquisition algorithm and RF frequency adaptive technology, were developed the self-organised network routing algorithm based on LoRa and Beidou RDSS, as well as the multimodal communication intelligent switching technology, so as to solve the problems of timeliness of data perception in complex environments and the problems of low-power consumption and environmental adaptability. The results show that the developed system had a good on-site pilot application effect, which meeting the requirements for long-term monitoring of mountain disasters in alpine mountainous areas, and the average packet loss rate of data transmission in extreme environments is 2.328 8 percent, providing new technologies and methods for disaster prevention and mitigation in the construction and operation of major projects in alpine and complex mountainous areas.

Yuehai is the largest wetland in Yinchuan of poor water quality during spring, with a significant contribution from agricultural non-point source pollution. In order to gain a deeper understanding of the distribution characteristics of nitrogen and phosphorus in the Yuehai Lake and its eutrophication status, water samples from 28 representative sites in Yuehai Lake and its outflow river were collected during the spring irrigation period of 2021.The distribution characteristics and regularities of TN (total nitrogen) and TP (total phosphorus), as well as different forms of nitrogen and phosphorus in Yuehai Lake were analyzed. The differences in nitrogen and phosphorus concentration between Yuehai Lake and its outflowing river were revealed. The results showed that during the spring irrigation period, the TN concentrations at 78% of the sampling points in Yuehai Lake was lower than the Class III surface water standard, and the TN concentration at 57% of the sampling points belonged to the heavily eutrophic type. During the spring irrigation period, the TP concentration at all sampling points was lower than Class III surface water standard, and the TP concentration at 70% of the sampling points belonged to the heavily eutrophic type. There were differences in the sources and transformation processes of different nitrogen fractions in the water of the Yuehai Lake. pH and TDS (total dissolved solids) affect the concentration of nitrogen and phosphorus in the water, and DO(dissolved oxygen) does not have a significant effect on the nitrogen and phosphorus forms in the water of the Yuehai Lake. The distribution characteristics of nitrogen and phosphorus in the outflow river of the Yuehai and the Yuehai water were different, and the differences in nitrogen concentration and non-orthophosphate phosphorus concentration between the Yuehai Lake and its outflow river were not significant in the direction of river and lake water flow (north-south direction). The nitrogen and phosphorus pollutants in the Yuehai converged into the Yellow River along with the rivers out of the Yuehai Lake, and the nitrogen and non-orthophosphate phosphorus in the Yuehai Lake might impact on the water of the Ningxia section of the Yellow River and increase the risk of pollution of the Yellow River, and the control of the nitrogen and non-orthophosphate phosphorus in the Yuehai Lake should be strengthened. This work provides a reference for continuously and accurately improving the water quality in the middle and upper reaches of the Yellow River Basin.

The issue of soil erosion in the Loess Plateau was addressed, with a comprehensive analysis of the application and evolution of vegetation restoration technologies for loess slopes. The text highlights that vegetation restoration serves as an effective method to mitigate soil erosion and rejuvenate ecological functions. Vegetation restoration not only enhances soil stability but also boosts the ecological quality of slopes and contributes to the sustainable development of ecosystems. Considering the geographical characteristics of the Loess Plateau and the factors contributing to soil erosion, a review of the research history and current progress of vegetation restoration techniques in China was conducted. Detailed discussions on methods such as spray seeding and slope coverage restoration, each characterized by distinct benefits and drawbacks, were included. These techniques were selectively implemented based on specific site conditions and environmental characteristics, and were continuously refined to address any arising challenges during their application. Furthermore, the document outlined future directions for advancing vegetation restoration technology, stressing that a thoughtful integration of plant species selection, soil matrix enhancement, and technological innovation is essential for improving the ecological restoration outcomes on slopes. The adoption of modern technological tools like remote sensing and artificial intelligence for monitoring and managing geological hazards was recommended to enhance the effectiveness and accuracy of ecological restoration efforts. Finally, an interdisciplinary approach was advocated to spur innovative developments in slope management and ecological restoration technologies, with the goal of achieving ecological sustainability in the Loess Plateau.

Aiming at the problems that the large-scale pre-training language model faces when dealing with news headlines, such as huge parameters, inefficient use of contextual semantic features and circular convolution neural network’s neglect of the importance of initial input elements, a news headline classification method that combines ERNIE(enhanced representation through knowledge integration) of mixture-of-expert model and recurrent convolution neural network with attention mechanism were proposed. Firstly, the text was encoded with the help of MoE’s improved ERNIE technology, and then the text was classified with attention RCNN (recurrent convolutional neural networks)on the basis of preserving the word order and characteristics of the text. In order to improve the classification ability, RCNN was improved by calculating the input fusion context weight. In the process of calculating the weights of experts in MoE, Gumbel-Softmax was selected as a new gating function to improve the traditional Softmax function, so as to better control the smoothness. According to the experimental results, it is found that compared with the traditional classification methods, the classification method proposed in this study shows significant advantages and greatly reduces the number of parameters. On this basis, the F₁ value is increased by 0.51% compared with the traditional model. After the ablation experiment, the feasibility of this classification method in the classification task has been confirmed.

In order to study the effect of basalt fiber on the durability of recycled concrete under the erosion of salt solution, the durability of recycled concrete specimens with different basalt fiber contents after salt-dry-wet cycle coupling erosion was studied. A comprehensive durability index D value was established to evaluate the durability of recycled concrete based on the entropy weight method. The effects of dry-wet cycle period of salt solution and basalt fiber content on D value were analyzed. A GM (1,1) mean model was constructed to reveal the time-varying law of the D-value of recycled concrete, and the predicted life of recycled concrete under different conditions was obtained. The results show that the D value can reflect the influence of different salt solution dry-wet cycle cycles and basalt fiber content on the durability of recycled concrete. As the salt solution's dry-wet cycle increased, the D value of the specimen gradually decreased, indicating a severe change. However, adding basalt fiber to the recycled concrete can effectively enhance its D value and durability. When the content of basalt fiber is 1.0%, the durability of recycled concrete is the best. The GM(1,1) model can more accurately predict the time-varying pattern of D values of recycled concrete under coupled salt-dry-wet cycle erosion when the amount of data is small.

With the rapid development of China's civil aviation, the air traffic flow in terminal areas is experiencing a consistent and significant increase. The accurate forecast of short-term air traffic flow is of great significance for the efficient implementation of air traffic flow management. To enhance the accuracy of short-term air traffic flow forecast, a model combining EMD (empirical mode decomposition) and LSTM (long short-term memory) based on data differential processing was proposed. Firstly, the model performed empirical mode decomposition on short-term air traffic flow sequences. Secondly, to improve prediction accuracy, data difference was utilized to stabilize the time series. Finally, the processed sequences were input into the LSTM network model for prediction, and the final short-term traffic prediction value was obtained through data reconstruction. Experimental verification was conducted using the data from Zhengzhou Xinzheng International Airport. The results demonstrate that the model achieves a significant improvement in prediction accuracy, as indicated by the typical indexes RSME, MAE, and R², which are 0.29, 0.08, and 96.40%, respectively. This approach outperforms other methods and provides valuable reference for short-term air traffic flow prediction.

In order to evaluate the quality of professional athletes in martial arts, the camera array based measurement and multi-view geometry were combined to develop a refined recognition method of human movements under the constraints of human parametric model, and a quantitative evaluation method system of martial arts movements was established based on the obtained joint position and angle information, and the technical movements of athletes of different levels in the five-step boxing event were measured and evaluated. The results show that the method developed in this paper can effectively realize the identification and quality evaluation of athletes’ movements in Wushu events, and the research results can also be extended to other competitive sports and public health, so as to provide support for scientific training and sports rehabilitation.