Rural ecological protection and restoration projects are actively underway worldwide, yet in‐depth research on the evolution of rural ecosystems and their underlying mechanisms remains limited. This study investigates the distribution characteristics of rural ecosystems in Ganzhou District by analyzing their number, spatial type, and density. Geodetectors are employed to examine the spatial heterogeneity and key driving factors of these ecosystems. In addition, we assess how the integrated ecosystem service index responds to land use changes, revealing that the proportion of ecological land—contributing up to 50%—is the most significant factor, with grassland showing a strong positive effect (average coefficient 7.99) and construction land exhibiting a negative correlation with the CES index. These findings offer scientific guidance for enhancing rural ecological protection through improved legislation, ecological compensation, and legal aid.

The internal defects and concrete strength detection of concealed mass concrete structures (dams, fan foundations, tunnel arches, etc.) has been a difficult problem in the industry, and there is a lack of effective nondestructive testing technology, conventional single-sided nondestructive testing technology (ground-penetrating radar, ultrasonic array, impact echo method, etc.) in reinforced concrete structures can not be more than 3m in depth, and the practical application is limited. For this reason, we have developed a new face wave CT inspection technique based on elastic wave face wave, combining the excellent wavelength method and multiple filtering method to solve the problem of difficult extraction of frequency dispersion curves of the face wave in concrete, and through finite element simulation and example verification, it is confirmed that the method can detect the defects and strength of the concrete structure on a single side, and the effective detection depth is more than 4m, which has a strong practical application value.

With the advancement of information technology, universities accumulate vast amounts of data, but effectively extracting and utilizing this information remains a challenge. Existing studies on university management data often rely on shallow analysis with basic models and tools, offering limited efficiency improvements. This paper explores an optimized higher education management data analysis algorithm, leveraging artificial intelligence and multimedia technology to enhance efficiency. A comparative study with traditional methods shows that the proposed algorithm improves university management data analysis efficiency by 11.4%.

Three remarkable determinant identities of skew–symmetric matrices are reviewed in a more transparent manner.

In graph theory, the center function identifies a set of vertices in a connected graph G that minimizes the maximum distance from any other vertex. We examine the behavior of the center function on connected graphs through a set of axioms. While universal axioms apply to all connected graphs, they cannot fully characterize certain graphs. To address this limitation, non-universal axioms for specific graph classes were introduced. This study is focused on establishing an axiomatic characterization of the center function on fan graphs by utilizing a combination of universal and non-universal axioms.

To counter threats to low-orbit communication satellites from hacker attacks and spectrum interference, this study develops an adversarial sample detection model using a variational self-encoder and a fast region-based convolutional network for spectrum interference detection. The proposed model achieves 97.68% accuracy and an F1 score of 96.86% in intrusion traffic detection, with AUC values above 95% for various network attacks. For single-tone interference, it attains 98.65% accuracy, 96.21% recall, and 93.14% precision, converging within 200 iterations with an average recognition accuracy of 95.47%. These results confirm the model’s ability to detect adversarial threats and interference, enhancing satellite communication security.

The wires and ground wires on transmission towers cannot be straight lines, but present different sizes of arcs, which directly affect the safety and transmission quality of the line. In response to this, a research proposes an online monitoring system for transmission towers based on computer video algorithms. The system collects environment and mechanism parameters of transmission lines by installing sensors on transmission towers, monitors them through computer video algorithms, and combines grey wolf algorithm and deep learning models to predict sag, thereby achieving crisis warning of the power grid around transmission towers. The outcomes denoted that during the field testing process, the warning accuracy of the system reaches over 98.57%, and the response time is only 0.5 seconds. The false negative rate and false positive rates are 2% and 0.5%, respectively. Based on the above content, it can be concluded that the proposed online monitoring system for transmission towers can effectively achieve line anomaly warning and maintain stable line operation.

The study analyzes the stylistic evolution of contemporary Chinese literary works using the MONK project. Text mining tools in the project are used to analyze the thematic classification, emotional tendency and stylistic type changes of the works. Among them, LDA model and GBDT algorithm are used to identify the thematic classification of Chinese modern and contemporary literary works, SO-PMI algorithm is used to identify the emotional tendency in the works, and the vector space model can classify the style of the works. Based on the above methods, the theme and emotional changes of modern and contemporary Chinese literary works can be categorized into 3 stages: the awakening of Enlightenmentism at the beginning of the 20th century, the diversified presentation during the revolutionary period, and the diversified development after the reform and opening up. The styles of modern and contemporary Chinese literary works can be divided into epic style, lyrical style, rural theme style and intellectual theme style.

Forecasting the volatility of the stock market price is indispensable for managing the risks associated with market dynamics and provides valuable insights for financial decision in trading strategies. This study aims to enhance the accuracy of volatility prediction for stock market price using hybrid models combining econometric and deep learning approaches. Specifically, it introduces a novel GARCH-CNN-LSTM hybrid model for more precise volatility forecasting of stock market price. The GARCH model is efficient at capturing volatility clusters and kurtosis features, while the CNN excels in extracting spatial patterns from time series data, and LSTM effectively preserves essential information over extended periods. GARCH(1,1) model is selected based on AIC, maximum log-likelihood, and parameter significance. Subsequently, CNN and LSTM models are chosen for their complementary capabilities in volatility prediction. We evaluated the forecasting performance of the hybrid models from out-sample test data, employing Mean Square Error, Root Mean Square Error and Mean Absolute Error. The result indicates that the new model outperforms the existing models with an improvement of 8% to 13% accuracy. Furthermore, we conduct the Diebold-Mariano test to confirm significant differences in performance.

The study constructs a solar cell simulation model and tracks the maximum power output from the solar cell using the MPPT algorithm. Simulation simulation experiments are conducted to analyze the effects of changes in environmental factors such as season, weather, light, temperature, wind speed, etc. on the current and power output of solar cells. The total output power and the peak output power of the solar cell are the largest in summer, which are 7407.69kW and 114.93kW, respectively, and the total output power and the peak output power of the solar cell are the smallest in fall, which are 1748.96kW and 31.58kW, respectively. The peak power output of the solar cell is the largest in sunny days, which is 107.56kW, and the smallest in rainy days, which is 37.06kW. The total solar cell power output is maximum (7896.93kW) on clear to cloudy days and minimum (1955.27kW) on rainy days. The solar cell output current and maximum power values decreased with decreasing light intensity. The ambient temperature has little effect on the short circuit current, the output current increases slightly with increasing temperature, the open circuit voltage decreases drastically with increasing temperature and the maximum output power decreases with increasing temperature. The maximum output power of the solar cell increases with increasing wind speed.