Early fault detection of degraded gears at different speeds is both essential and challenging. Adaptive blind deconvolution methods have shown considerable promise for extracting fault characteristics from complex vibration signals. Their performance depends on accurate cyclic frequency estimation and optimal filter length selection. However, this estimation often fails due to gear meshing shock interference and early weak fault characteristics. Additionally, determining the filter length relies on additional metrics with inefficient search strategies, thereby limiting the overall reliability and efficiency. To address these issues, an adaptive blind deconvolution via convolutional neural network (ABDCNN) is proposed. First, we employ an envelope harmonic product spectrum guided by gear frequency-domain features to reduce interference from noise and meshing shocks, enabling precise estimation of the target cyclic frequency. Then, an attention mechanism is integrated into the convolutional neural network to jointly optimize filter coefficients and length estimation, thereby improving computational efficiency. Simulations and gear contact fatigue experiments demonstrate that ABDCNN enables more efficient detection of early faults across different speeds while maintaining strong interpretability.

Accurate estimation of remaining useful life (RUL) in aeroengines is essential for improving safety, reducing operational costs, and optimizing maintenance strategies within the aviation sector. This study introduces a novel Bayesian optimization (BO) multiscale dilated convolutional neural networks long short-term memory (LSTM) residual self-attention framework, which synergistically combines multiscale dilated convolutional neural networks to extract intricate spatial features, LSTM networks to model temporal dependencies, residual self-attention to enhance feature stability and relevance, and BO to automate and refine hyperparameter selection. Traditional methods often fall short due to limited feature extraction capabilities and reliance on manual parameter tuning, challenges that this integrated approach effectively addresses. The framework’s performance is rigorously evaluated using the C-MAPSS dataset, a widely recognized benchmark, across its diverse subdatasets, revealing substantial enhancements in predictive accuracy and reduced error metrics. An ablation study systematically assesses the individual contributions of each component, confirming their collective impact on overall performance. Furthermore, a detailed full life cycle time series analysis for a representative engine demonstrates the model’s ability to precisely track degradation patterns over its entire operational duration, offering clear evidence of its predictive reliability. Compared to earlier techniques that depend solely on convolutional or recurrent neural networks, this framework exhibits superior adaptability and precision, with promising potential for extension to other machine learning architectures and a broader range of datasets. The approach mitigates common pitfalls such as overfitting and instability, providing a stable foundation for real-world deployment. This research significantly advances the field of RUL prediction by delivering an automated, scalable, and robust tool tailored for the complex dynamics of aeroengine systems, paving the way for future explorations into adaptive maintenance technologies and enhanced prognostic methodologies.

In this article, we introduce a novel method that uses enhanced sequential multitask learning to predict the remaining useful life (RUL) of an aeroengine accurately and efficiently while evaluating its status. The method innovatively employs extreme gradient boosting to extract critical performance parameters and construct a robust health indicator representing performance degradation. To capture the time-series features of the health indicator, the study modifies the traditional multigate mixture-of-experts (MMoE) model and integrates it with the gated recurrent unit (GRU) network, creating a hybrid MMoE-GRU model. In addition, we propose a dynamic weight balancing method to optimize the tradeoff in the joint loss function for multitask learning. Extensive experiments on the new commercial modular aero-propulsion system simulation (N-CMAPSS) dataset demonstrate that the proposed method significantly outperforms the existing models, achieving lower error rates and higher accuracy in RUL prediction and health status evaluation. The technique has a root-mean-square error (RMSE) of 7.1%, 6.9%, 1.3%, 0.6%, 1.7%, and 1.5% lower than the long short-term memory (LSTM), GRU, sequence-based bottom information gated recurrent unit (SB-GRU), deep gated recurrent unit (DGRU), multi-scale and multi-task convolutional neural network (M2STCNN), and MMoE models. The average accuracy of the proposed method is 96.732%, which is 10.629%, 1.587%, and 2.499% higher than those of the LSTM, DGRU, and MMoE, respectively. The superiority of the proposed method is validated on the N-CMAPSS dataset.

The early stage of brucellosis belongs to the category of latent temperature disease in traditional Chinese medicine, and the symptom is damp-heat epidemic pathogen. But later to joint pain, soreness and weakness of waist and knees, fatigue, joint pain, night sweats, etc., mainly in the joint, belongs to the liver and kidney deficiency, qi and blood deficiency, spleen wet turbidity syndrome, belongs to the category of traditional Chinese medicine rheumatism heat arthralgia syndrome, etiology and pathogenesis due to ' winter does not store essence, spring must disease temperature ' within the virtual cause. Based on the syndrome differentiation of traditional Chinese medicine, Duhuo Jisheng Decoction combined with Simiao Pill was used to treat liver and kidney, nourish qi and blood, and eliminate dampness and turbidity, so as to achieve the purpose of strengthening the body and eliminating evil, restoring healthy qi and eliminating evil qi. To provide reference for the treatment of infectious diseases such as brucellosis.

The genus Culicoides includes biting midges, some of which are vectors for viruses that cause diseases in humans and animals. Knowledge of the roles of Culicoides in viral ecology is inadequate. We collected ~300 000 samples of Culicoides and mosquitoes in 15 representative regions within Yunnan, China. Using mosquitoes as reference vectors, we designed a comparative virome strategy to study the viral composition, diversity, hosts and spatiotemporal distribution of Culicoides. A map of viromes in Culicoides and mosquitoes in Yunan province, China, was constructed. At the same locations, Culicoides and mosquitoes usually share a similar viral diversity. At least 10 important pathogenic viruses were detected from Culicoides. Many novel viruses were discovered, including 21 segmented viruses of Flaviviridae, 180 viruses of Monjiviricetes and 130 viruses of Bunyavirales. The findings demonstrate that Culicoides is an important part of viral ecology and should be studied and monitored for potentially emerging viruses.