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Artificial intelligence (AI) is becoming increasingly popular and useful for modeling landslide movement processes due to its advantages of providing excellent generalization ability and accurately describing complex and nonlinear behavior. However, the identification of key variables is a crucial step in ensuring robustness and accuracy in AI modeling, but thus far, little attention has been given to this topic. In the present study, mutual information (MI)-based measures are proposed for input variable selection (IVS) and incorporated into optimized support vector regression (SVR) for the displacement prediction of seepage-driven landslides. The performance of optimized SVR models with ten MI-based IVS strategies is compared. A typical seepage-driven landslide was chosen for comparison. The experimental results indicate that IVS-based optimized SVR can significantly improve predictions. When the variable-reduced inputs were input into the optimized artificial bee colony (ABC)-SVR model, the mean values of normalized root mean square error (NRMSE) and Kling-Gupta efficiency (KGE) decreased and increased by as much as 71.6 and 95.2%, respectively, relative to those for the base model with all candidates. Furthermore, the joint mutual information (JMI) and double input symmetrical relevance (DISR) criteria are recommended for IVS for seepage-driven landslides because they achieve the best tradeoff between accuracy and stability.

Coastal wetland soil organic carbon (CW-SOC) is crucial for both “blue carbon” and carbon sequestration. It is of great significance to understand the content of soil organic carbon (SOC) in soil resource management. A total of 133 soil samples were evaluated using an indoor spectral curve and were categorized into silty soil and sandy soil. The prediction model of CW-SOC was established using optimized support vector machine regression (OSVR) and optimized random forest regression (ORFR). The Leave-One-Out Cross-Validation (LOO-CV) method was used to verify the model, and the performance of the two prediction models, as well as the models’ stability and uncertainty, was examined. The results show that (1) The SOC content of different coastal wetlands is significantly different, and the SOC content of silty soils is about 1.8 times that of sandy soils. Moreover, the characteristic wavelengths associated with SOC in silty soils are mainly concentrated in the spectral range of 500–1000 nm and 1900–2400 nm, while the spectral range of sandy soils is concentrated in the spectral range of 600–1400 nm and 1700–2400 nm. (2) The organic carbon prediction model of silty soil based on the OSVR method under the first-order differential of reflectance (R′) is the best, with the Adjusted-R2 value as high as 0.78, the RPD value is much greater than 2.0 and 5.07, and the RMSE value as low as 0.07. (3) The performance of the OSVR model is about 15~30% higher than that of the support vector machine regression (SVR) model, and the performance of the ORFR model is about 3~5% higher than that of the random forest regression (RFR) model. OSVR and ORFR are better methods of accurately predicting the CW-SOC content and provide data support for the carbon cycle, soil conservation, plant growth, and environmental protection of coastal wetlands.

Slope entropy (SlopEn) has been widely applied in fault diagnosis and has exhibited excellent performance, while SlopEn suffers from the problem of threshold selection. Aiming to further enhance the identifying capability of SlopEn in fault diagnosis, on the basis of SlopEn, the concept of hierarchy is introduced, and a new complexity feature, namely hierarchical slope entropy (HSlopEn), is proposed. Meanwhile, to address the problems of the threshold selection of HSlopEn and a support vector machine (SVM), the white shark optimizer (WSO) is applied to optimize both HSlopEn and an SVM, and WSO-HSlopEn and WSO-SVM are proposed, respectively. Then, a dual-optimization fault diagnosis method for rolling bearings based on WSO-HSlopEn and WSO-SVM is put forward. We conducted measured experiments on single- and multi-feature scenarios, and the experimental results demonstrated that whether single-feature or multi-feature, the WSO-HSlopEn and WSO-SVM fault diagnosis method has the highest recognition rate compared to other hierarchical entropies; moreover, under multi-features, the recognition rates are all higher than 97.5%, and the more features we select, the better the recognition effect. When five nodes are selected, the highest recognition rate reaches 100%.

The main problem faced by traditional three-dimensional (3D) holographic displays is the time-consuming and poor flexibility of the hologram generation process. To address this issue, this paper proposes a non-iterative 3D computer-generated hologram (SFS-ORAP-PC-3D) method based on single full-support optimized random phase and phase compensation. Combining the full-support optimized random phase (FS-ORAP) method and the 3D layer-based idea to efficiently and non-iteratively generate the phase-only hologram of a 3D object with arbitrary positions and sizes using single FS-ORAP, thus overcoming the limitations of the original ORAP method in target position and size. Meanwhile, using a Fresnel lens for phase compensation allows for free selection of reconstruction planes. Numerical and optical experiments validate the feasibility of our proposed method.

Switch mode power supplies (SMPSs) are prone to various faults under complex operating environments and variable load conditions. To improve the accuracy and reliability of fault diagnosis, this paper proposes an intelligent diagnosis method based on Dynamic Wavelet Packet Transform (DWPT) and Improved Artificial Bee Colony Optimized Support Vector Machine (APABC-SVM). First, an adaptive wavelet packet decomposition mechanism is used to refine the time–frequency feature extraction of the signal to improve the feature differentiation. Then, a dynamic window statistics method is introduced to construct comprehensive dynamic feature vectors to capture the transient changes in fault signals. Finally, the APABC is used to optimize the SVM classifier parameters to improve the classification performance and avoid the local optimum problem. The experimental results show that the method achieves an average accuracy of 99.091% in the complex fault diagnosis of switching power supplies, which is 21.8 percentage points higher than that of the traditional spectrum analysis method (77.273%). This study provides an efficient solution for the accurate diagnosis of complex fault modes in switching power supplies.

To address the severe deformation and failure of roadway roof and floor encountered when crossing fault zones in coal mines in western China, this study takes the lower gateway of the 11E5-303 working face crossing the SF1 normal fault in Zhaohequan Coal Mine as an engineering case. A comprehensive investigation was conducted using field investigation, laboratory testing, numerical simulation, and engineering applications. The research aims to clarify the deformation mechanisms of the surrounding rock in fault-affected zones and to provide adequate control measures for roadway stability during fault crossing. Studies have shown that the roof and floor strata along the 11E5-303 Working face’s adjacent roadway are primarily composed of siltstone, fine sandstone, and argillaceous siltstone, which are highly susceptible to water-induced softening and swelling, leading to a significant decrease in mechanical strength. This phenomenon is particularly severe near the fault, where substantial roof subsidence and pronounced floor heave are observed. Based on the Mohr–Coulomb failure criterion, the deformation and failure mechanisms of the surrounding rock under the existing support system were analyzed. The study revealed that the roadway surrounding rock within 10 m of the fault zone is subject to intense deformation and damage, with the hanging wall showing a significantly larger failure range than the footwall. Floor heave at the fault zone is also markedly greater than in other sections. These findings identified key support zones and critical reinforcement areas, emphasizing the need for early implementation of high-strength support systems within the fault-affected area to enhance stability. Targeted control technology for surrounding rock stability in fault-crossing roadway was proposed. After optimization, the roof subsidence was reduced by 68% and the floor heaves by 81% compared to the original support system. The optimized support scheme significantly improved the stability of the roadway, demonstrating apparent effectiveness. These results provide valuable guidance for roadway support design and stability control under similar geological conditions.

Traditional PDF document detection technology usually builds a rule or feature library for specific vulnerabilities and therefore is only fit for single detection targets and lacks anti-detection ability. To address these shortcomings, we build a double-layer detection model for malicious PDF documents based on an entropy method with multiple features. First, we address the single detection target problem with the fusion of 222 multiple features, including 130 basic features (such as objects, structure, content stream, metadata, etc.) and 82 dangerous features (such as suspicious and encoding function, etc.), which can effectively resist obfuscation and encryption. Second, we generate the best set of features (a total of 153) by creatively applying an entropy method based on RReliefF and MIC (EMBORAM) to PDF samples with 37 typical document vulnerabilities, which can effectively resist anti-detection methods, such as filling data and imitation attacks. Finally, we build a double-layer processing framework to detect samples efficiently through the AdaBoost-optimized random forest algorithm and the robustness-optimized support vector machine algorithm. Compared to the traditional static detection method, this model performs better for various evaluation criteria. The average time of document detection is 1.3 ms, while the accuracy rate reaches 95.9%.

Landslides occur frequently in the Three Gorges in China, posing threats to human life and the normal operation of the Three Gorges Dam. A number of preexisting landslides have been reactivated since the initial impoundment of the Three Gorges Reservoir in June 2003. An effective and accurate method of predicting landslide displacement is necessary to mitigate the effects of these disastrous landslides. This study carries out a landslide displacement prediction for the Shuping landslide using 7 years of monitoring data, wavelet analysis, and a particle swarm-optimized support vector machine (PSO-SVM) model. The landslide’s displacement is strongly influenced by periodic precipitation and reservoir level fluctuations, and the cumulative displacement curve versus time indicates a step-like character. Based on the deformation characteristics of this landslide, the total displacement is divided into its trend and periodic components by means of the wavelet analysis. An S-curve estimation is used to predict the trend displacement via the curve fitting of the historical displacement versus time. Five primary factors are used as the input variables for a PSO-SVM model to predict periodic displacement. These factors include cumulative precipitation over the previous month, cumulative precipitation during a two-month period, maximum continuous decrement in the reservoir level during the current month, and cumulative increments and decrements in the reservoir level during the current month. The mean squared error, squared correlation coefficient, and Akaike’s information criterion of the wavelet-PSO-SVM model at GPS monitoring points ZG85 and ZG87 are 2.45, 0.945, and 20.80 and 10.46, 80.981, and 36.38, respectively. This method can be applied to the prediction of displacement in colluvial landslides in the Three Gorges. This study may provide useful information to engineers and planners involved in landslide prevention and reduction.

The predictive model of aging indicator based on intelligent algorithms has become an auxiliary method for the aging condition of transformer polymer insulation. However, most of the current research on the concentration prediction of aging products focuses on dissolved gases in oil, and the concentration prediction of alcohols in oil is ignored. As new types of aging indicators, alcohols (methanol, ethanol) are becoming prevalent in the aging evaluation of transformer polymer insulation. To address this, this study proposes a prediction model for the concentration of alcohols based on a genetic-algorithm-optimized support vector machine (GA-SVM). Firstly, accelerated thermal aging experiments on oil-paper insulation are conducted, and the concentration of alcohols is measured. Then, the data of the past 4 days of aging are used as the input feature of SVM, and the GA algorithm is utilized to optimize the kernel function parameter and penalty factor of SVM. Moreover, the concentrations of methanol and ethanol are predicted, after which the prediction accuracy of other algorithms and GA-SVM are compared. Finally, an industrial software program for predicting the concentration of methanol and ethanol is established. The results show that the mean square errors (MSE) of methanol and ethanol concentration predictions of the model proposed in this paper are 0.008 and 0.003, respectively. The prediction model proposed in this paper can track changes in methanol and ethanol concentrations well, providing a theoretical basis for the field of alcohol concentration prediction in transformer oil.

The adulteration of pork mixed in mutton is pervasive in the market. However, when the adulterated mutton with multi-part pork is mixed, the discrimination becomes difficult due to differences in composition. Meanwhile, with the diversification of adulteration methods, food additives are also mixed into adulterated mutton to interfere with discrimination. In this study, the discrimination of mutton adulteration with multi-part pork (from back, hind leg, and front leg) under the influence of mutton flavour essence and colourant was explored using NIR-HSI. A novel framework in which three parallel convolutional neural networks (CNNs) serve as feature extractors was designed to obtain the multi-part depth fusion features of the sample. After obtaining fusion features, classification models were established by using back propagation neural network (BPNN), random forest (RF), and support vector machine (SVM). Sparrow search algorithm (SSA), genetic algorithm (GA), and particle swarm optimization (PSO) were employed for parameter optimization of classifiers. The results showed that the performance of models based on fusion features was significantly better than that of models without considering the characteristics of pork parts. The optimized SVM classifier via SSA obtained the best result. The overall accuracy, F1-score, and kappa value of the external validation set were 98.61%, 97.86%, and 96.61%, respectively. Overall, NIR-HSI combined with CNN and optimized SVM could function as a robust and accurate detection method for discriminating adulterated mutton with multi-part pork under food additives effect.