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This study addresses the challenge of reducing the transmission of low-frequency road excitation vibrations to the cab of mining dump trucks to enhance ride comfort. Given the harsh working conditions of these vehicles, a novel methodology combining experimental data collection and advanced signal processing techniques was developed. The research established a comprehensive vibration testing program aligned with earth-moving machinery standards, collecting vibration acceleration data under both idling and full-load operation at 35 km/h. To improve data accuracy, Singular Value Decomposition (SVD) was employed to denoise the experimental vibration data, effectively mitigating environmental interference. Subsequent Fourier transform analysis revealed the vibration energy transfer patterns of the vehicle suspension system in the frequency domain. The results indicated a significant vibration isolation rate of 89 % for the frame suspension system, contrasting with only 7 % for the cab suspension system. Notably, the cab seat suspension system was found to amplify low-frequency road excitations. Compared to previous methods, this study innovatively integrates SVD and Fourier transform techniques to provide a more accurate and detailed understanding of vibration transmission. The key result of achieving an 89 % vibration isolation rate for the frame suspension system demonstrates the effectiveness of the proposed methodology. This study offers practical optimization directions for improving suspension system performance and ride comfort in mining dump trucks, outperforming traditional approaches by providing a more comprehensive analysis and actionable insights for vibration isolation. The findings also serve as valuable references for addressing similar engineering challenges in heavy machinery.

The differential spiral bevel gear of Baja off-road vehicle is designed and verified. Based on the competition rules and vehicle transmission parameters, the key geometric parameters of the gear pair are determined, and the three-dimensional model and finite element analysis software are established by UG for static contact analysis and modal analysis. The results show that the maximum contact stress of the tooth surface is 411.4 MPa, and the natural frequency of the gear pair is much higher than the excitation frequency of the system, which can effectively avoid the resonance risk. Through analysis and verification, it meets its application conditions.

The stock market, a cornerstone of the global financial system, is characterized by its dynamic and volatile nature, which makes accurate price-trend prediction challenging. However, traditional statistical models often fail to capture this complexity. Recent advancements in Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), have transformed stock market forecasting by using diverse datasets and algorithms. This review examines recent studies on AI methodologies for stock market price trend prediction models by analyzing architectures, datasets, performance metrics, and limitations, with a focus on hybrid models, sentiment analysis, and dataset diversity. Hybrid approaches, including the Multi-Model Generative Adversarial Network Hybrid Prediction Algorithm (MMGAN-HPA), K-means long short-term memory (LSTM), and LSTM autoregressive output (LSTM-ARO), improve predictive accuracy by combining statistical methods with deep learning. Sentiment analysis models such as Stock Senti WordNet (SSWN) and Hybrid Quantum Neural Network (HQNN) integrate social media sentiment to capture market dynamics. Real-time frameworks that use stream processing show promise for high-frequency trading applications. This review addresses key challenges including data noise, nonstationarity, overfitting risks, and black-box model interpretability. Solutions include GAN-based synthetic data generation, transformer-based architectures such as SpectralGPT, and optimization techniques for computational efficiency. This review provides a taxonomy of AI-based approaches, while identifying gaps for future research. These findings highlight the potential of AI in financial forecasting while emphasizing the need for interdisciplinary collaboration to address its limitations in data quality, methodology, interpretability, and ethics.

To address the difficulties in extracting fault features of rolling bearings and the low diagnostic accuracy, a fault diagnosis method for rolling bearings is proposed. This method integrates the Golden Sine Algorithm (GSA) with the Subtraction-Average-Based Optimizer (SABO) to form a Golden Sine Improved SABO Optimization Algorithm (GSABO). The GSABO algorithm is used for parameter optimization of Variational Mode Decomposition (VMD) and Kernel Extreme Learning Machine (KELM) in the fault diagnosis process. Firstly, the chaotic mapping strategy is used to optimize the population initialization of the Subtractive Clustering-Based Adaptive Optimization (SCAO) algorithm, enhancing population diversity. Secondly, the Golden Sine Algorithm (GSA) is integrated to improve the displacement algorithm, enhancing global search capability and effectively avoiding getting trapped in local optima. Then, the GSABO-VMD (Golden Sine Algorithm-Based Optimized Variational Mode Decomposition) is employed to decompose the rolling bearing fault signals, and the envelope entropy minimum criterion is used to select the effective modal components. Finally, time-frequency domain indicators of the selected modal components are computed to form a feature matrix, which is then input into GSABO-KELM (Golden Sine Algorithm-Based Optimized Kernel Extreme Learning Machine) for fault classification and recognition. Experimental analysis shows that compared to the unmodified SABO algorithm, GSABO has significant advantages in terms of escaping local optima, convergence speed, and accuracy. When compared with other traditional algorithms, GSABO-VMD-KELM achieves recognition accuracies of 99.3333 % and 99.0476 % on bearing data from Case Western Reserve University (CWRU) and Xi'an Jiao tong University (XJTU), respectively. This demonstrates the accuracy and superiority of the algorithm and provides valuable insights for engineering applications in rolling bearing fault diagnosis.

The synchronous condenser plays a crucial role in reactive power compensation and voltage support in ultra-high voltage direct current power grids. Vibration is a key bottleneck for the safe and stable operation of the synchronous condenser, especially rotor vibration resulting from electromagnetic torque caused by inter-turn short-circuit. This article takes a 300 MV large synchronous condenser as the research object, and studies the influence of electromagnetic torque caused by inter-turn short-circuit on the vibration of the rotor. Through theoretical analysis and finite element simulation, a theoretical model and a finite element model of rotor bending-torsional coupled vibration were established, and the accuracy of the theoretical model was validated experimentally. The results show that: The first three bending frequencies are 11.93 Hz, 31.27 Hz, and 89.46 Hz; Under both static and dynamic imbalance conditions, the vibration amplitude of the rotor increases with the increase of the shorted turn ratio; The vibration amplitude before and after being stimulated under dynamic unbalance is larger than that under static imbalance. The research results can provide a theoretical basis for the design and safe operation and maintenance of the synchronous condenser.

This case report intends to present a functional appliance designed by the first author, that follow the principles expected for all functional appliances. It is not anchored on teeth. It does not produce mechanical forces and uses tongue and mandible posture change as natural forces. The appliance was used for 24 months, only to sleep. The patient came complaining discomfort with improper occlusion of the teeth and temporomandibular pain. Body balance was evaluated by DIERS and was also altered. It was possible to find out the presence of a unilateral crossbite and alteration on condyle position inside the cavity evaluated by CBCT. After the treatment with CPB (Cranial Posture Balance) appliance associated with osteopathic procedures, the occlusion and the temporomandibular complains were improved.

This study investigates the structural optimization, lightweight design, and fatigue life influencing factors of the excavator working device through comprehensive static and dynamic finite element analysis (FEA). Modal analysis, spectral analysis, and vibration transfer path identification were conducted to assess the vibration response and fatigue failure risks at key joints such as the forearm pinholes. The inclusion of figures, such as vibration paths and stress distribution plots (see Figs. 3-8), supports the findings. The research provides a theoretical basis for assessing the structural reliability and fatigue life of long-arm excavators exceeding 25 meters.

The integration of 3D printing technology with geopolymer materials offers a sustainable alternative to conventional construction methods, significantly reducing CO2 emissions. However, challenges such as rapid setting, limited workability, and weak interlayer bonding limit their broader application. This review summarizes recent progress in 3D printed geopolymer composites, focusing on materials selection, rheological optimization, buildability, and mechanical performance enhancement. Strategies including the use of rheology modifiers, fiber reinforcements, nano-additives, and process optimization have shown promise in improving printability and structural performance. Remaining challenges, such as balancing setting time and printability and enhancing interlayer adhesion, are also discussed. Future research directions are proposed to further advance the development of high-performance, low-carbon geopolymer 3D printing materials for sustainable construction.