Rolling bearings are vital components in rotating machinery, and their reliable operation is crucial for maintaining the stability and efficiency of mechanical systems. However, fault detection in rolling bearings is often hindered by noise interference in complex industrial environments. To overcome this challenge, this paper presents a novel fault diagnosis method for rolling bearings, combining Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs), integrated with the envelope analysis and adaptive mean filtering techniques. Initially, envelope analysis and adaptive mean filtering are applied to suppress random noise in the bearing signals, thereby enhancing the visibility of fault features. Subsequently, a deep learning model that combines a CNN and a GRU is developed: the CNN extracts spatial features, while the GRU captures the temporal dependencies between these features. The integration of the CNN and GRU significantly improves the accuracy and robustness of fault diagnosis. The proposed method is validated using the CWRU dataset, with the experimental results achieving an average accuracy of 99.25%. Additionally, the method is compared to four classical fault diagnosis models, demonstrating superior performance in terms of both diagnostic accuracy and generalization ability. The results, supported by various visualization techniques, show that the proposed approach effectively addresses the challenges of fault detection in rolling bearings under complex industrial conditions.

Micro-electro-mechanical systems (MEMS) hemispherical resonant gyroscopes are used in a wide range of applications in defense technology, electronics, aerospace, etc. The surface roughness of the silicon micro-hemisphere concave molds (CMs) inside the MEMS hemispherical resonant gyroscope is the main factor affecting the performance of the gyroscope. Therefore, a new method for reducing the surface roughness of the micro-CM needs to be developed. Micro-ultrasonic machining (MUM) has proven to be an excellent method for machining micro-CMs; shear thickening fluids (STFs) have also been used in the ultra-precision polishing field due to their perfect processing performance. Ultimately, an STF-MUM polishing method that combines STF with MUM is proposed to improve the surface roughness of the micro-CM. In order to achieve the excellent processing performance of the new technology, a Categorical Boosting (CatBoost)-genetic algorithm (GA) optimization model was developed to optimize the processing parameters. The results of optimizing the processing parameters via the CatBoost-GA model were verified by five groups of independent repeated experiments. The maximum absolute error of CatBoost-GA is 7.21%, the average absolute error is 4.69%, and the minimum surface roughness is reduced by 28.72% compared to the minimum value of the experimental results without optimization.

In the context of the rapidly evolving Consumer Consumer Industrial Wireless Sensor Networks (IWSNs), the expansion of the Consumer Industrial Internet of Things (IoT) has intensified the need for energy-efficient routing protocols capable of addressing the energy limitations of sensor nodes. This paper introduces a novel routing protocol, the Chaotic Lévy Random Walk Carnivorous Plant Algorithm for Cluster-based Routing (CLRWCPA-CR), specifically designed to enhance energy efficiency and prolong the operational lifetime of IWSNs. A novel and efficient clustering routing model for IWSNs is proposed, considering multiple key factors during its design, including intra-cluster distance, base station distance, cluster head energy, and cluster member energy. The CLRWCPA-CR protocol integrates chaotic strategies, Lévy flights, random walk techniques, and carnivorous plant strategies to optimize cluster-based routing, thereby accelerating convergence and expanding the solution space. Extensive experimental evaluations, conducted under realistic industrial scenarios, demonstrate that CLRWCPA-CR significantly outperforms traditional protocols such as LEACH-C and heuristic methods like GSHFA-HCP and EEM-CRP. It achieves at least 44.3% throughput improvement and extends at least 68.98% network lifetime. The innovative design of the CLRWCPA-CR protocol not only enhances clustering and routing efficiency, but also demonstrates practical value for real-world IoT deployments by improving scalability, reducing energy costs, and supporting long-term, reliable industrial operations.

Macroeconomic forecasting plays a pivotal role in policy formulation and global risk management. However, dealing with nonlinear high-dimensional data in macroeconomic forecasting has long been proven to be an NP-hard problem that traditional models find extremely challenging to handle. These models often struggle to break free from local optima, thereby failing to provide accurate and reliable forecasts, which is a significant obstacle that urgently needs to be overcome. This study puts forward a hybrid model (EBA-BPNN) that combines an Enhanced Bat Algorithm (EBA) with dynamic inertia weight and adaptive frequency mechanisms to optimize Backpropagation Neural Networks (BPNN). The framework is structured in three stages. Firstly, Dynamic Time Warping (DTW) is employed to align cross-country economic cycles, and then Granger causality analysis along with mutual information is utilized to select 32 core variables. Secondly, during the EBA optimization process, the dynamic inertia weight mechanism is used to strike a balance between exploration and exploitation. Meanwhile, a Cauchy-Gaussian perturbation is introduced to enhance the population diversity, and a variance-driven pulse emission rate is adopted to regulate the local search intensity. Finally, a gradient-assisted search strategy is applied to accelerate convergence and prevent the model from falling into local optima. Experiments conducted on datasets from the World Bank and the OECD demonstrate that the EBA-BPNN model achieves remarkable results. Specifically, it reduces the Mean Absolute Error (MAE) in quarterly GDP forecasting by 29.3%, 17.8%, and 15.6% when compared to BPNN (with an MAE of 2.15), PSO-BPNN (with an MAE of 1.85), and BA-BPNN (with an MAE of 1.92), respectively. Even under extreme scenarios, the increase in MAE is limited to only 12.3%. Overall, this model offers a high-precision approach for dynamic economic modeling, providing valuable support for the formulation of forward-looking fiscal policies and cross-border investment decisions.

A structured optimization approach was applied to ultrasonic surface rolling process (USRP) parameters, aiming to enhance the material surface characteristics of TC4 titanium alloy. To overcome the premature convergence and limited exploration capability of the standard Whale Optimization Algorithm (WOA), three enhancement strategies were introduced, including population initialization based on an optimal point set, a sinusoidal nonlinear convergence factor, and an adaptive inertia-based position update strategy. By optimizing the structural parameters of the RBF neural network with the improved WOA, an IWOA–RBF predictive model for surface performance evaluation was developed and rigorously validated in terms of prediction accuracy. Using the developed IWOA–RBF model, a multi-criteria decision-making framework integrating the CRITIC weighting method and the TOPSIS ranking approach was constructed to evaluate surface quality. This framework was further combined with a multi-objective Grey Wolf Optimization (MOGWO) algorithm to perform Pareto-based optimization and determine the optimal USRP parameter set. Experimental validation showed that the optimized parameters resulted in a significant reduction in surface roughness, while enhancing both surface hardness and residual compressive stress. The results confirm the robustness and effectiveness of the proposed IWOA–RBF and MOGWO optimization framework, providing a reliable strategy for high-precision parameter optimization and coordinated enhancement of surface properties in the TC4 titanium alloy USRP.