A polypropylene (PP) film is usually used as a dielectric material in capacitors as well as cables. However, PP films may degrade because of the combined effect of temperature and electric field. In an earlier study, plain PP films and PP films loaded with nano-metric natural clay were studied under sinusoidal (AC) electric fields at power frequency and temperatures above the ambient. To better understand the electrical characteristics of PP film under various conditions, the objective of this study is to determine the time-to-breakdown of the plain PP and PP filled with 2% (wt) natural nano-clay when subjected to time-invariant (DC) electric fields at elevated temperatures. In order to achieve this objective, the effects of uniform as well as non-uniform electric fields were compared at the same temperature for the PP film. In this study, experimental results indicated that the time-to-breakdown of all PP films, plain or filled with nano-clay, decreases with the increase in electric field intensity, non-uniformity of the electric field, and temperature. It was also found that the time-to-breakdown of PP film filled with 2% (wt) natural nano-clay under DC electric field is longer and less sensitive to temperature. Furthermore, when compared with the results under the uniform electric field, PP film filled with 2% (wt) nano-metric natural clay indicates shorter time-to-failure under non-uniform DC electric fields. Finally, the morphology of the samples was observed by digital camera, optical micrography, and SEM, to better understand the mechanism of the breakdown.

Prediction of the purchase willingness of passengers has great benefits for airlines to promote auxiliary services, however, the datasets stored in passenger travel information systems are often high-dimensional and incomplete. This study develops a prediction method of airline additional service consumption willingness based on high-dimensional and incomplete datasets with a triple-layer hybrid PSO-XGBoost model, which consists of an incomplete data processing layer, a high-dimensional data processing layer, and a predicting layer. The raw dataset is converted into a complete and low-dimensional dataset through the first two layers and inputted into the predicting layer to train and optimize the XGBoost model together with the PSO algorithm and 10-fold cross-validation. The experimental results show that the proposed method outperforms other traditional machine learning models, presenting the highest prediction score with 0.9879 in terms of AUC. The findings help predict airline additional services consumption intentions of passengers and are beneficial to efficient and low-cost precise marketing for airlines.

The discharge and flashover phenomenon of post insulators in rainy weather has not been given sufficient consideration; however, with the construction of ultra-high voltage power grids, the performance of the external insulation and the ability to withstand special climate conditions need to be guaranteed. Therefore, it is meaningful to conduct studies on the discharge characteristics of contaminated post insulators under rainfall conditions. Moreover, the conventional perception tends to confuse the flashover of polluted insulators in the rain with the pollution flashover that occurs in the fog; however, in fact, the discharge of contaminated insulators that occurs during rainfall has characteristics that can be distinguished from the pollution flashover. In this study, firstly, the current status of research on the external insulation characteristics of post insulators was analyzed through an examination of the available literature. Secondly, the concept of a ‘pollution rain flashover’ of insulators was established and clarified, to distinguish it from the traditional meaning of ‘pollution flashover’ or ‘contamination flashover’. Thirdly, research results on the pollution rain flashover of post insulators used in power stations in recent years were summarized, which included the characteristics and mechanism of the discharge, parameters and factors influencing the flashover voltage, and their influence laws. Particularly, the gap discharge between insulator sheds triggered by raindrops, which is the most significant feature of the pollution rain flashover, and the profile optimization of sheds, which is an effective way to improve the performance, were emphasized in this work. Fourthly, the prevention methods were studied, which mainly include the application of rainproof sheds and the shed optimization for pollution rain flashover of post insulators. Finally, a brief prospect is given for future research.

Content graphs are essential for representing domain knowledge and play a significant role in digital education. To overcome the challenges associated with manual knowledge extraction and content structuring, including low efficiency, high costs, and a high risk of errors, and to improve teaching and learning quality, this paper presents an automated approach for constructing course content graphs for digital teaching platforms by utilizing a large language model. Specifically, the pre-trained GLM-4 model is utilized for semantic parsing and entity extraction, while a group query attention mechanism is applied to infer relationships between knowledge points, automatically transforming raw course content into structured tabular datasets. The process involves two steps: entity extraction and relationship recognition, which convert the original dataset into discrete knowledge points and output them in a structured format. The structured data is then visualized using the Neo4j graph database, enabling automated content graph construction. Experimental results show that this method significantly enhances the accuracy and efficiency of content graph construction, achieving an F1 score above 0.85 for entity extraction and relationship recognition accuracies of 0.82 and 0.80 before and after processing, respectively. Leveraging the model’s strong performance, a content graph with 393,600 triples—consisting of 9 entity types and 2 relationship types—has been constructed, covering the domain of software engineering courses. These results offer an effective and scalable solution to advance the intelligent development of digital teaching platforms.

The study focuses on the Xilingol grassland in Inner Mongolia, collecting climate, surface, and human-related data for various banners and counties in Xilingol League. Initially, the correlation coefficient method was used to select the nine indicator factors most closely related to the degree of land desertification. Then, the Analytic Hierarchy Process (AHP) was utilized to rank the weights of these factors, constructing a system for evaluating the degree of land desertification, and an evaluation model was established using the comprehensive index method. This model introduced the land desertification evaluation index, dividing the degree of desertification into four levels, with highly and moderately desertified areas mainly distributed in the western part of Xilingol League, while moderately and mildly desertified areas mainly distributed in the eastern and southern regions. The study indicated that climatic factors are the primary factors affecting land desertification, followed by Humanistic and surface factors. Additionally, measures for preventing and controlling land desertification in different regions of the Xilingol Grassland were proposed, offering a scientific basis for formulating desertification prevention and control policies in the Xilingol League.

In tennis matches, “momentum” describes the situation where players are inspired by positive factors during the game. This paper focuses on the quantification of momentum, the impact of momentum on match trends, and the optimisation of players’ match strategies based on momentum. In this research, the Markov chain is used to quantify the momentum, and the “momentum score” of players is obtained. The Eta and Spearman correlation coefficients are used to study the impact mechanism of momentum on match trends. The results show that, within a 95% confidence interval, momentum has a strong correlation with the results of the current game of players, but a weak correlation with the result of each current point. In addition, in this paper, we construct a match strategy optimisation model for players based on momentum scores. On the one hand, the entropy weight method and TOPSIS are combined to evaluate players’ performances. On the other hand, a BP neural network prediction model is established based on a multiple linear regression model with 13 indicators in 10 categories to predict match trends. According to the evaluation and prediction results, a series of strategy optimisation suggestions are put forward for players to cope with matches.