Search Results (1,168)

To address the periodic impacts and amplitude-modulated high-frequency resonance phenomena caused by bearing faults in rotating machinery, this paper proposes a detection method. The core innovation lies in: firstly, constructing a composite fault feature index (CFFI) that integrates normalized kurtosis and fuzzy entropy, which synchronously quantifies the fault impact intensity and periodic structure, and serves as an optimization objective; secondly, definining a spectral energy retention rate (SERR) that includes both the full spectrum and characteristic frequency bands to evaluate the denoising effect and fault feature retention, respectively. Based on this, the method adaptively determines the Variational Mode Decomposition (VMD) parameters through the Triangular Topology Aggregation Optimizer (TTAO), and uses Dynamic Weighted Center Clustering (DWCC) to screen key IMFs containing fault-envelope information. On the IMS bearing dataset, the SERR of the reconstructed signal is 0.21356, which is higher than the actual collected signal value of 0.22465, with a relative error of 4.9%, indicating a higher reconstruction accuracy. These quantitative results indicate that CFFI-guided optimization enhances impulsive and periodic fault components while maintaining stable feature-band retention. This approach is suitable for real-world equipment monitoring and exhibits strong engineering applicability. Full article

To reduce random errors effectively and improve measurement precision in MEMS gyroscopes, we establish a dual-layer noise suppression method named EMD-SSA-VMD. The algorithm is grounded in empirical mode decomposition (EMD) and variational mode decomposition (VMD), incorporating the sparrow search algorithm (SSA) and entropy theory. The process starts by breaking down the signal into a series of intrinsic mode functions (IMFs) and a residual via EMD. By calculating the power spectral entropy (PSE) of IMFs, we can sort the signal components into three categories: noise signals, mixed signals, and effective signals. The mixed signals then undergo VMD processing, where SSA optimizes the key decomposition parameters. The sample entropy (SE) of the IMFs from VMD is computed to distinguish between actual signal components and noise. Finally, we combine all valuable signals to reconstruct the denoising signal. MATLAB(R2024b) simulation results show that this algorithm improves both the Signal-to-Noise Ratio (SNR) and the Root Mean Square Error (RMSE), demonstrating a more efficient removal of noise. Experiments on actual gyroscope data confirm these improvements, yielding higher SNR and a waveform that closely matches the original signal. This proves the algorithm’s practical value in engineering applications. Full article

Notably, one of the key points to address low accuracy and delayed responsiveness of dam deformation prediction models lies in the timely detection of the outliers caused by environmental disturbances, sensor failures, or operational anomalies of dam monitoring sequences. Therefore, our work offers an unambiguous method for overcoming this challenge. In this paper, a robust prediction framework that integrates Complete Ensemble Empirical Mode Decomposition (CEEMD) and Isolation Forest (iForest) for effective outlier detection, followed by a Multi-Head Attention Bidirectional Gated Recurrent Unit (MHA-BiGRU) model for dam deformation prediction, is presented. The original deformation time series is first decomposed using CEEMD into a set of intrinsic mode functions (IMFs). This decomposition separates the series into trend-related components and noise components. Subsequently, the iForest algorithm is applied in outlier detection for noise components. Then, the BiGRU model is enhanced with an MHA mechanism to give more weight to the features that affect the sequences of monitoring dam deformation. By enabling the proposed model to focus on the key factors affecting dam deformation, the accuracy of the prediction results has been enhanced. Finally, a case study introducing monitoring data from a practical project in China demonstrates the performance of the proposed method. The proposed MHA-BiGRU model demonstrates superior performance across all tested scenarios. Notably, the coefficient of determination is consistently maintained above 0.98, peaking at 0.9880. In terms of error control, the model exhibits a maximum mean absolute error of 0.1789, thereby substantiating its exceptional prediction accuracy and robustness. In comparison with classical time series forecasting models, including LSTM, GRU and BiGRU, the proposed approach demonstrates enhanced robustness and delivers greater prediction accuracy. The findings provide a promising reference framework for dam structural characteristics prediction in similar projects. Full article

To address the challenge of poor separation performance exhibited by conventional magnetic separation equipment when processing coarse-grained, low-grade magnetite ore, this paper proposes a novel ore recognition method that integrates empirical mode decomposition (EMD) with a convolutional neural network (CNN). First, the original signal undergoes standardization to suppress sensor baseline drift. Then, it is decomposed by using EMD to obtain a series of intrinsic mode functions (IMFs). Subsequently, based on scaling exponents and kurtosis values, IMFs containing significant feature information are selected and fused, resulting in a reconstructed signal with substantially reduced noise. To preserve effective features, the absolute values of the reconstructed signal are taken, followed by normalization and dimensional transformation to convert it into a two-dimensional matrix format, thereby constructing training, validation, and test sets. Finally, a CNN is designed and optimized to automatically extract discriminative features from the preprocessed samples, enabling accurate classification of magnetite ore grades. Experimental results demonstrate that the proposed comprehensive identification method achieves effective and stable classification performance across different ore grades. Specifically, the implementation of standardization and EMD-based denoising has been demonstrated to enhance the accuracy of CNNs in recognizing diverse ores. Full article

Intramuscular fat (IMF) content determines marbling levels and influences the sensory and edible qualities of livestock meat. Its deposition is influenced by the animal’s age and gut microbial community. This study assessed age-related differences in IMF deposition and shifts in gut microbiota between yearlings (1-year-old) and mature (4-year-old) grazing Tan sheep. Then correlations among these factors were examined to investigate the potential role of gut bacteria in IMF deposition. The results demonstrated that mature sheep exhibited higher IMF content in shoulder and rump muscles (p < 0.05), elevated serum lipid levels (p < 0.001), and increased lipolytic enzyme abundances in the liver and pancreas (p < 0.05), compared with yearlings. In contrast, the concentrations of acetate and propionate in ruminal and colonic contents were lower in mature sheep (p < 0.05), despite a higher abundance of lipolytic and synthetic enzymes in colonic content (p < 0.05). Gut microbial diversity differed between age groups, particularly in the rumen and colon, with clear shifts in specific bacterial taxa. Correlation analyses revealed that the abundance of Copromorpha and RUG420 in the colon were positively correlated with IMF content in shoulder and rump muscles, and serum lipid levels (including free fatty acids, FFA; low-density lipoprotein, LDL; high-density lipoprotein, HDL; and very-low-density lipoprotein, VLDL), but negatively correlated with propionate content (|r| > 0.45, FDR < 0.05). Conversely, the abundance of Cryptobacteroides in the colon was negatively correlated with IMF content in shoulder muscle (r < −0.6, FDR < 0.05), and with the levels of triglyceride (TG), LDL, HDL, and VLDL, while showing positive correlations with acetate and propionate contents (r > 0.45, FDR < 0.05). These findings highlight the potential role of specific colon bacteria (Copromorpha, RUG420, and UBA5905) in IMF deposition, identifying them as candidate bacteria for further investigation regarding their effects on meat quality. Full article

Marbling, or intramuscular fat (IMF), is a primary determinant of high-quality beef, defining key sensory attributes and nutritional value. Yunling (YL) cattle, an indigenous breed from Yunnan, China, are renowned for their superior marbling, yet the underlying molecular mechanisms remain unclear. This study employed an integrated transcriptomic, lipidomic, and amino acid metabolomic approach to systematically compare the multi-omics profiles of the longissimus dorsi muscle among YL, Angus (AGS), and Simmental (XMTE) cattle. Transcriptome analysis identified 2053 and 2156 differentially expressed genes (DEGs) in XMTE vs. YL and AGS vs. YL, respectively. These DEGs were primarily enriched in the PI3K-Akt and MAPK signaling pathways, as well as oxidative phosphorylation. Lipidomic analysis revealed a distinct lipid profile in YL cattle, identifying 27 characteristic lipid molecules (e.g., SM(d20:0/24:1), DG(16:0/18:1(11Z)/0:0)) compared to XMTE and 17 differential lipids compared to AGS. The amino acid metabolome showed that Beta-Alanine and L-Aspartic acid levels in YL were 42.6% and 54.8% lower than in XMTE, respectively (p < 0.01), and levels of several functional amino acids were significantly reduced compared to AGS. Weighted Gene Co-expression Network Analysis (WGCNA) constructed a gene-metabolite network, identifying key modules strongly correlated with lipid and amino acid metabolism (|r| > 0.6). Within these modules, energy metabolism-related genes such as NDUFB1, COX7C, and IDH3B, along with signal transduction genes including ITGB3, PDGFRA, and FN1, were found to synergistically regulate marbling formation in YL cattle. This study systematically elucidates the molecular mechanisms underlying both marbling formation and the nutritional characteristics of meat in Yunling cattle. This provides a theoretical foundation for genetic improvement and offers potential molecular targets to enhance both marbling and overall meat quality in other indigenous cattle breeds worldwide. Full article

(1) Background: Electroencephalography (EEG) is widely used to monitor the depth of anesthesia; however, conventional Fourier-based analyses are limited in their ability to characterize non-stationary anesthetic-induced EEG dynamics. In this study, we investigated the utility of singular spectrum analysis (SSA) combined with the Hilbert transform for extracting physiologically meaningful EEG features under sevoflurane general anesthesia. (2) Methods: Frontal EEG data from ten patients undergoing sevoflurane anesthesia were analyzed from the maintenance phase through emergence. Using SSA, short EEG segments were decomposed into six intrinsic mode functions (IMFs) without pre-specified basis functions or frequency bands. Hilbert spectral analysis was applied to each IMF to obtain instantaneous frequency and amplitude characteristics. (3) Results: The SSA-based decomposition clearly captured phase-dependent EEG changes, including α spindle activity during maintenance and increasing high-frequency components preceding emergence. Multiple linear regression models incorporating IMF center frequencies and total power demonstrated strong correlations with the bispectral index (BIS), achieving high predictive accuracy (R² = 0.88, MAE < 4). Compared with conventional spectral approaches, SSA provided superior temporal resolution and stable feature extraction for non-stationary EEG signals. (4) Conclusions: These findings indicate that SSA combined with Hilbert analysis is a robust framework for quantitative EEG analysis during general anesthesia and may enhance real-time, individualized assessments of anesthetic depth. Full article

This article examines the behavior of seismic noise fields over the Japanese islands recorded by the F-net seismic network for 1997–2025. This paper uses nonlinear noise statistics: the entropy of the wavelet coefficient distribution, the Donoho–Johnston (DJ) wavelet index, and the multifractal singularity spectrum support width. These parameters were chosen because their changes reflect the complication or simplification of the noise structure. Changes in the structure of seismic noise properties are analyzed in comparison with a sequence of strong earthquakes. Using a model of the intensity of interacting point processes, the effect of the leading of local noise property extrema relative to the seismic event times is estimated. Using the Hilbert–Huang decomposition, the synchronization of the amplitudes of the envelopes of noise property time series for different IMF levels is estimated. A sequence of weighted probability density maps of extreme values of noise properties is analyzed in comparison with the mega-earthquake of 11 March 2011 and the preparation of another possible strong seismic event. Full article

In multirotor unmanned aerial vehicle (UAV) GNSS/INS integrated navigation systems, a single filter such as the extended Kalman filter (EKF) or the error-state extended Kalman filter (ESKF) is commonly adopted. However, both methods have inherent performance limitations. The EKF suffers from significant linearization errors in highly nonlinear flight scenarios, leading to degraded estimation accuracy. Although ESKF achieves higher precision during steady flight, its model assumptions may no longer strictly hold during aggressive maneuvers, causing performance degradation in complex flight missions. To address the limitations of using a single filter, this study proposes a dynamic filter selection strategy under the interaction multi-filter (IMF) framework. The approach builds on the interactive multiple model (IMM) method and establishes a cooperative mechanism between EKF and ESKF. By computing the filter likelihoods at each time step and updating the probability switching matrix, the framework adaptively selects the optimal filter based on the current flight conditions. Simulation results demonstrate that the proposed IMF-based strategy effectively avoids the performance bottlenecks of individual filters. In highly nonlinear environments, it reduces linearization errors and suppresses divergence trends; compared with traditional ESKF, the proposed algorithm 3D RMSE is reduced by 57.2%, compared with the adaptive robust EKF (AREKF), the proposed approach reduces positioning errors by up to 21.3%. The results confirm that IMF-based adaptive switching between EKF and ESKF yields a robust, high-precision solution for UAV navigation in complex operational scenarios. Full article

Accurate wind power forecasting is critical for enhancing the operational efficiency and stability of electrical power grids. Conventional single-variable signal decomposition forecasting methods ignore the coupling relationship between wind power and multiple meteorological data, thus limiting prediction accuracy. This study proposes an accurate and fast short-term wind power prediction approach based on series-core fusion technology considering multiple meteorological data. In the data preprocessing stage, the multivariate variational mode decomposition (MVMD) algorithm decomposes wind power and meteorological variables into the same predefined number of frequency-aligned intrinsic mode functions (IMFs), thereby enhancing feature representation and improving forecasting accuracy via a more comprehensive and detailed dataset representation. During the training stage, the series-core fused time series (SOFTS) model establishes the connection among wind power channel and other meteorological variable channels for each IMF, achieving fast convergence through its streamlined and parallel structure. In the forecasting stage, the final wind power prediction is generated by the reconstruction of all IMFs. Furthermore, we conducted a comprehensive performance evaluation by comparing the proposed MVMD-SOFTS model with eight alternative models, including the CNN model, the TCN model, the LSTM model, the GRU model, the Transformer model, the SOFTS model, the CEEMDAN-SOFTS model, and the VMD-SOFTS model. The results indicate that MVMD-SOFTS outperformed all other models, demonstrating its effectiveness in capturing the multifaceted relationships in wind power forecasting. Full article

Conjugated linoleic acid (CLA) is produced by bacterial biohydrogenation in the rumen of cattle, fulfills various biological functions, and is known for anti-obesity, anti-inflammation, anti-cancer, and other beneficial effects. It has numerous isomers, of which cis-9,trans-11 CLA accounts for 80% of total CLA, followed by trans-10,cis-12 CLA (t10,c12 CLA), with distinct molecular structures, oxidation efficiencies, activities, and functions. Different effects were observed, when isomers were individually supplemented in livestock nutrition. Currently, CLA is supplemented into the diets of dairy cows to improve the energy balance, and avoid negative effects of energy loss during the transition period. Furthermore, t10,c12 CLA was shown to reduce subcutaneous fat and to improve intramuscular fat (IMF) content in the carcasses of ruminants and pigs. Increasing the IMF content without increasing other fat depots and without compromising feed efficiency is an important goal in beef production. However, inconsistent and conflicting results were reported partly based on different study designs. This review aims to summarize studies on CLA supplementation in cattle, focusing on t10,c12 CLA and the effects of the dose, time, and method of supplementation on energy balance, milk yield and body composition, as well as on individual cells in vitro. This may improve our understanding of energy-saving and repartitioning effects of CLA in cattle. Full article

Effective and intelligent fault diagnosis is essential for ensuring the operational safety and reliability of gearbox systems. In practical engineering environments, however, weak fault-related features are often obscured by strong background noise, pronounced nonstationarity, and time-varying operating conditions, which significantly degrade the performance of conventional feature extraction techniques. To address these challenges, this paper proposes an adaptive feature extraction approach that integrates the complementary advantages of variational mode decomposition (VMD), Teager energy operator (TEO), and multi-scale permutation entropy (MPE) to enhance the characterization of weak and transient fault signatures. Vibration signals associated with different fault conditions are first adaptively decomposed into a series of intrinsic mode functions (IMFs) using VMD, enabling the effective separation of fault-sensitive components and enrichment of fault-related information. Subsequently, an enhanced multi-scale permutation entropy (EMPE) method is developed to emphasize transient impulsive characteristics and capture fault-induced complexity variations across multiple temporal scales. By jointly exploiting instantaneous energy modulation and multi-scale dynamical complexity analysis, the proposed approach exhibits improved sensitivity to weak fault signatures and enhanced robustness against variable operating conditions. The effectiveness and generalization capabilities of the proposed framework are validated using three experimental datasets involving gearboxes and rolling bearings under diverse operating conditions. Comparative results demonstrate that the proposed method outperforms conventional entropy-based approaches in terms of fault feature separability and diagnostic performance, highlighting its potential for practical condition monitoring and fault diagnosis of rotating machinery. Full article

Precise short-term forecasting of wind power generation is indispensable for ensuring the security and economic efficiency of power grid operations. Nevertheless, the inherent non-stationarity and stochastic nature of wind power series present significant challenges for prediction accuracy. To address these issues, this paper proposes a dual-optimized hybrid deep learning framework combining Spearman correlation analysis, RIME-VMD, and TCN-BiGRU-SA. First, Spearman correlation analysis is employed to screen meteorological factors, eliminating redundant features and reducing model complexity. Second, an adaptive Variational Mode Decomposition (VMD) strategy, optimized by the RIME algorithm based on Minimum Envelope Entropy, decomposes the non-stationary wind power series into stable intrinsic mode functions (IMFs). Third, a hybrid predictor integrating Temporal Convolutional Network (TCN), Bidirectional Gated Recurrent Unit (BiGRU), and Self-Attention (SA) mechanisms is constructed to capture both local trends and long-term temporal dependencies. Furthermore, the RIME algorithm is utilized again to optimize the hyperparameters of the deep learning predictor to avoid local optima. The proposed framework is validated using full-year datasets from two distinct wind farms in Xinjiang and Gansu, China. Experimental results demonstrate that the proposed model achieves a Root Mean Square Error (RMSE) of 7.5340 MW on the primary dataset, significantly outperforming mainstream baseline models. The multi-dataset verification confirms the model’s superior prediction accuracy, robustness against seasonal variations, and strong generalization capability. Full article

Sheep’s meat production and quality are influenced by genetic and physiological factors that affect muscle development, growth, and fat deposition metabolism. However, the breed-specific transcriptional landscapes driving these traits in Indian sheep breeds, especially in Nellore (meat-type) and Deccani (wool-meat type) breeds are remain unexplored. Therefore, this study aimed to investigate the differences in muscle growth and fat deposition between Nellore and Deccani breeds by integrating transcriptomic profiling, carcass characteristics, and histological analysis of longissimus dorsi muscle and liver tissues. Carcass assessment revealed higher Hot Carcass Weight (HCW), Cold Carcass Weight (CCW), Hot Carcass Yield (HCY) and Cold Carcass Yield (CCY), and larger myofibrillar cross-sectional area (p < 0.05), indicating enhanced musculature, which was observed in Nellore. Deccani showed elevated Intramuscular Fat (IMF) deposition (p < 0.05), indicating improved meat flavour/juiciness. Transcriptomic profiling revealed several Differentially Expressed Genes (DEGs) associated with meat quality and quantity traits. In Nellore, the genes WFIKKN2, FGFRL1, FKBP4, and IRF1 were upregulated, while the gene TAS1R2 was downregulated, leading to enhanced muscle development, superior carcass traits, thermotolerance, and immunity. While Deccani showed higher expression of lipid metabolism genes PLA2G4F, ACSL1, ACOX1, CPT1A, and PLIN1, which are linked to higher IMF content. Functional enrichment analysis revealed 46 significantly enriched GO terms for the DEGs (p < 0.05), including oxidoreductase activity, muscle development, etc. These outcomes demonstrate novel genetic markers and key biological insights into the regulation of muscle development, thermotolerance, immunity, and IMF for future validation in Indian sheep breeds. Full article

We are interested in modelling smaller datasets to generate more accurate, sub-regional or regional climate forecasts. The focus of this paper is to present the findings of a study investigating the application of empirical mode decomposition (EMD) to identify the components of the signal from which we can subsequently derive an iterated function system (IFS). One could develop a series of models, which are not based on big data, but rather allow for a cyclical model to keep the cycle iterating so that the model can be more responsive and adaptive to changes in the climate. The results presented in this paper have identified a new upper bound for the number of intrinsic mode functions (IMFs) obtained after EMD. The goal of the research is to develop a model where climate data could be iterated adaptively between models. Full article