Search Results (1,049)

The highly stochastic nature of rainfall presents significant challenges for the accurate prediction of its time series. To enhance the prediction performance of non-stationary rainfall time series, this study proposes a hybrid deep learning forecasting framework—VMD-IWMA-BiLSTM—that integrates Variational Mode Decomposition (VMD), Improved Whale Migration Algorithm (IWMA), and Bidirectional Long Short-Term Memory network (BiLSTM). Firstly, VMD is employed to decompose the original rainfall series into multiple modes, extracting Intrinsic Mode Functions (IMFs) with more stable frequency characteristics. Secondly, IWMA is utilized to globally optimize multiple hyperparameters of the BiLSTM model, enhancing its ability to capture complex nonlinear relationships and long-term dependencies. Finally, experimental validation is conducted using daily rainfall data from 2020 to 2024 at the Xinzheng National Meteorological Observatory. The results demonstrate that the proposed framework outperforms traditional models such as LSTM, ARIMA, SVM, and LSSVM in terms of prediction accuracy. This research provides new insights and effective technical pathways for improving rainfall time series prediction accuracy and addressing the challenges posed by high randomness. Full article

This study evaluates the impact of digital systems and IT audit frameworks on tax performance and integrity within tax administrations. Using international data from organizations like the World Bank, OECD (Organisation for Economic Co-operation and Development), and IMF (International Monetary Fund), the research examines the relationship between tax revenue as a percentage of GDP, digital infrastructure, corruption perception, e-government development, and cybersecurity readiness. Quantitative analysis, including correlation, regression, and clustering methods, reveals a strong positive relationship between digital maturity, e-governance, and tax performance. Countries with advanced digital governance systems and robust IT audit frameworks, such as COBIT, tend to show higher tax revenues and lower corruption levels. The study finds that e-government development and anti-corruption measures explain over 40% of the variance in tax performance. Cluster analysis distinguishes between digitally advanced, high-compliance countries and those lagging in IT adoption. The findings suggest that digital transformation strengthens fiscal integrity by automating compliance and reducing human contact, which in turn mitigates bribery risks and enhances fraud detection. The study highlights the need for adopting international best practices to guide the digitalization of tax administrations, improving efficiency, transparency, and trust in public finance. Full article

Magnetotelluric (MT) sounding is a crucial technique in mineral exploration. However, MT data are highly susceptible to various types of noise. Traditional data processing methods, which rely on the assumption of signal stationarity, often result in severe distortion when suppressing non-stationary noise. In this study, we propose a novel, adaptive, and less parameter-dependent signal decomposition method for MT signal denoising, based on time–frequency domain analysis and the application of modal decomposition. The method uses Variational Mode Decomposition (VMD) to adaptively decompose the MT signal into several intrinsic mode functions (IMFs), obtaining the instantaneous time–frequency energy distribution of the signal. Subsequently, robust statistical methods are introduced to extract the independent components of each IMF, thereby identifying signal and noise components within the decomposition results. Synthetic data experiments show that our method accurately separates high-amplitude non-stationary interference. Furthermore, it maintains stable decomposition results under various parameter settings, exhibiting strong robustness and low parameter dependency. When applied to field MT data, the method effectively filters out non-stationary noise, leading to significant improvements in both apparent resistivity and phase curves, indicating its practical value in mineral exploration. Full article

Rolling bearings serve as the most widely utilized general components in drive systems for rotating machinery, and they are susceptible to regular malfunctions. To address the performance degradation encountered by current convolutional neural network-based rolling-bearing-fault diagnosis methods due to significant noise interference and variable working conditions in industrial settings, we propose a rolling-bearing-fault diagnosis method based on dual multi-scale mechanism applicable to noisy-variable operating conditions. The suggested approach begins with the implementation of Variational Mode Decomposition (VMD) on the initial vibration signal. This is succeeded by a denoising process that utilizes the goodness-of-fit test based on the Anderson–Darling (AD) distance for enhanced accuracy. This approach targets the intrinsic mode functions (IMFs), which capture information across multiple scales, to obtain the most precise denoised signal possible. Subsequently, we introduce the Dynamic Weighted Multi-Scale Feature Convolutional Neural Network (DWMFCNN) model, which integrates two structures: multi-scale feature extraction and dynamic weighting of these features. Ultimately, the signal that has been denoised is utilized as input for the DWMFCNN model to recognize different kinds of rolling-bearing faults. Results from the experiments show that the suggested approach shows an improved denoising performance and a greater adaptability to changing working conditions. Full article

To address the challenges posed by weak early fault signal features, strong noise interference, low diagnostic accuracy, poor reliability when using single information sources, and the limited availability of high-quality samples in practical applications for permanent magnet synchronous motor (PMSM) bearings, this paper proposes an early bearing fault diagnosis method based on Hippopotamus Optimization Variational Mode Decomposition (HO-VMD) and weighted evidence fusion of current–vibration signals. The HO algorithm is employed to optimize the parameters of VMD for adaptive modal decomposition of current and vibration signals, resulting in the generation of intrinsic mode functions (IMFs). These IMFs are then selected and reconstructed based on their kurtosis to suppress noise and harmonic interference. Subsequently, the reconstructed signals are demodulated using the Teager–Kaiser Energy Operator (TKEO), and both time-domain and energy spectrum features are extracted. The reliability of these features is utilized to adaptively weight the basic probability assignment (BPA) functions. Finally, a weighted modified Dempster–Shafer evidence theory (WMDST) is applied to fuse multi-source feature information, enabling an accurate assessment of the PMSM bearing health status. The experimental results demonstrate that the proposed method significantly enhances the signal-to-noise ratio (SNR) and enables precise diagnosis of early bearing faults even in scenarios with limited sample sizes. Full article

This paper considers the impact of Central Bank Digital Currencies (CBDCs) on the world’s financial systems with a special emphasis on G20 economies. Using quarterly macro-financial data for the period of 2000 to 2024, collected from the IMF, BIS, World Bank, and Atlantic Council, a Global Vector Autoregression (GVAR) model is applied to 20 G20 countries. The results reveal significant heterogeneity across economies: CBDC shocks intensify emerging market financial instability (e.g., India, Brazil), while more digitally advanced countries (e.g., UK, Japan) experience stabilization. Retail CBDCs increase disintermediation risks in more fragile banking systems, while wholesale CBDCs improve cross-border liquidity. This article contributes to the literature by providing the first GVAR-based estimation of CBDC spillovers globally. Full article

This study takes the daily temperature series of Taipei City as an example and proposes a data projection method based on Empirical Mode Decomposition (EMD), which effectively resolves the challenge of modeling non-stationary sequences. According to the daily mean temperature records from 1971 to 2023, Taipei has experienced an average warming rate of 0.02 °C per year. After applying EMD, the data were decomposed into 12 intrinsic mode functions (IMFs) and one residual trend. Among them, IMF5, with a period of 352 days (approximately one year), contributes 78% of the total energy, representing the dominant climatic cycle component. In this study, daily temperatures were categorized into five thermal levels: Cold (<12 °C), Cool (12–18 °C), Moderate (18–27 °C), Warm (27–32 °C), and Hot (>32 °C). In addition, using a 5-year moving process based on the annual EMD results, the IMFs and residuals were recombined to generate 390,625 synthetic sequences per year. Results show that the monthly mean temperatures of each year’s simulations closely match the observations, capturing the non-stationary characteristics of temperature variations. The overall classification accuracy of simulated versus observed daily temperature categories ranges from 60% to 71%, with an average of 65.1%. In summary, the EMD combined with the 5-year moving process developed in this study demonstrates a helpful data projection approach with effective reconstruction of periodic structures and stable simulation accuracy. It offers practical value for reconstructing urban climate variability, conducting risk assessments, and analyzing long-term warming trends. Moreover, it serves as a vital tool for modeling non-stationary climate data and supporting future projections. Full article

Accurate carbon price forecasting is essential for market stability, risk management, and policy-making. To address the nonlinear, non-stationary, and multiscale nature of carbon prices, this paper proposes a forecasting framework integrating secondary decomposition, two-stage feature selection, and dynamic ensemble learning. Firstly, the original price series is decomposed into intrinsic mode functions (IMFs), using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The IMFs are then grouped into low- and high-frequency components based on multiscale entropy (MSE) and K-Means clustering. To further alleviate mode mixing in the high-frequency components, an improved variational mode decomposition (VMD) optimized by particle swarm optimization (PSO) is applied for secondary decomposition. Secondly, a two-stage feature-selection method is employed, in which the partial autocorrelation function (PACF) is used to select relevant lagged features, while the maximal information coefficient (MIC) is applied to identify key variables from both historical and external data. Finally, this paper introduces a dynamic integration module based on sliding windows and sequential least squares programming (SLSQP), which can not only adaptively adjust the weights of four base learners but can also effectively leverage the complementary advantages of each model and track the dynamic trends of carbon prices. The empirical results of the carbon markets in Hubei and Guangdong indicate that the proposed method outperforms the benchmark model in terms of prediction accuracy and robustness, and the method has been tested by Diebold Mariano (DM). The main contributions are the improved feature-extraction process and the innovative use of a sliding window-based SLSQP method for dynamic ensemble weight optimization. Full article

Background/Objectives: Daily caloric restriction (DCR) is a common dietary weight loss strategy, but leads to metabolic and behavioral adaptations, including maladaptive eating behaviors and dysregulated appetite. Intermittent fasting (IMF) may mitigate these effects by offering diet flexibility during energy restriction. This secondary analysis compared changes in eating behaviors and appetite-related hormones between 4:3 intermittent fasting (4:3 IMF) and DCR and examined their association with weight loss over 12 months. Methods: Adults with overweight or obesity were randomized to 4:3 IMF or DCR for 12 months. Both randomized groups received a matched targeted weekly dietary energy deficit (34%), comprehensive group-based behavioral support, and a prescription to increase moderate-intensity aerobic activity to 300 min/week. Eating behaviors were assessed using validated questionnaires at baseline and months 3, 6, and 12. Fasting levels of leptin, ghrelin, peptide YY, brain-derived neurotrophic factor, and adiponectin were measured at baseline and months 6 and 12. Linear mixed models and Pearson correlations were used to evaluate outcomes. Results: Included in this analysis were 165 adults (mean ± SD; age 42 ± 9 years, BMI 34.2 ± 4.3 kg/m², 74% female) randomized to 4:3 IMF (n = 84) or DCR (n = 81). At 12 months, binge eating and uncontrolled eating scores decreased in 4:3 IMF but increased in DCR (p < 0.01 for between-group differences). Among 4:3 IMF, greater weight loss was associated with decreased uncontrolled eating (r = −0.27, p = 0.03), emotional eating (r = −0.37, p < 0.01), and increased cognitive restraint (r = 0.35, p < 0.01) at 12 months. There were no between-group differences in changes in fasting appetite-related hormones at any time point. Conclusions: Compared to DCR, 4:3 IMF exhibited improved binge eating and uncontrolled eating behaviors at 12 months. This may, in part, explain the greater weight loss achieved by 4:3 IMF versus DCR. Future studies should examine mechanisms underlying eating behavior changes with 4:3 IMF and their long-term sustainability. Full article

Surface ozone is a pollutant linked to higher risks of cardiopulmonary diseases with long-term exposure. Timely forecasting of ozone levels helps authorities implement preventive measures to protect public health and safety. However, few studies have been able to reliably provide long-term hourly ozone forecasts due to the complexity of ozone’s diurnal variations. To address this issue, this study constructs a hybrid prediction model integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), bi-directional long short-term memory neural network (BiLSTM), and the persistence model to forecast the hourly ozone concentrations for the next continuous 36 h. The model is trained and tested at the Wanshouxigong site in Beijing. The ICEEMDAN method decomposes the ozone time series data to extract trends and obtain intrinsic mode functions (IMFs) and a residual (Res). Fourier period analysis is employed to elucidate the periodicity of the IMFs, which serves as the basis for selecting the prediction model (BiLSTM or persistence model) for different IMFs. Extensive experiments have shown that a hybrid model of ICEEMDAN, BiLSTM, and persistence model is able to achieve a good performance, with a prediction accuracy of R² = 0.86 and RMSE = 18.70 µg/m³ for the 36th hour, outperforming other models. Full article

Mechanical failures frequently occur in On-Load Tap Changers (OLTCs) during operation, potentially compromising the reliability and stability of power systems. The goal of this study is to develop an intelligent and accurate diagnostic approach for OLTC mechanical fault identification, particularly under the challenge of non-stationary vibration signals. To achieve this, a novel hybrid method is proposed that integrates the Gazelle Optimization Algorithm (GOA), Feature Mode Decomposition (FMD), and a Transformer-based classification model. Specifically, GOA is employed to automatically optimize key FMD parameters, including the number of filters (K), filter length (L), and number of decomposition modes (N), enabling high-resolution signal decomposition. From the resulting intrinsic mode functions (IMFs), statistical time domain features—peak factor, impulse factor, waveform factor, and clearance factor—are extracted to form feature vectors. After feature extraction, the resulting vectors are utilized by a Transformer to classify fault types. Benchmark comparisons with other decomposition and learning approaches highlight the enhanced performance of the proposed framework. The model achieves a 95.83% classification accuracy on the test set and an average of 96.7% under five-fold cross-validation, demonstrating excellent accuracy and generalization. What distinguishes this research is its incorporation of a GOA–FMD and a Transformer-based attention mechanism for pattern recognition into a unified and efficient diagnostic framework. With its high effectiveness and adaptability, the proposed framework shows great promise for real-world applications in the smart fault monitoring of power systems. Full article

Massive stars less massive than ∼30 $M_{\odot }$ evolve into a red supergiant after the main sequence. Given a standard IMF, this means about 80% of all single massive stars will experience this phase. RSGs are dominated by convection, with a radius that may extend up to thousands of solar radii. Their low temperature and gravity make them prone to losing large amounts of mass, either through pulsationally driven wind or through mass-loss outburst. RSGs are the progenitors of the most common core-collapse supernovae, type II. In the present review, we give an overview of our theoretical understanding about this spectacular phase of massive star evolution. Full article

This study suggests a hybrid approach that combines improved feature selection and intelligent diagnosis to increase the operational safety and intelligent diagnosis capabilities of hydropower units. In order to handle the vibration data, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used initially. A novel comprehensive index is constructed by combining the Pearson correlation coefficient, mutual information (MI), and Kullback–Leibler divergence (KLD) to select intrinsic mode functions (IMFs). Next, feature extraction is performed on the selected IMFs using Refined Composite Multiscale Fluctuation Dispersion Entropy (RCMFDE). Then, time and frequency domain features are screened by calculating dispersion and combined with IMF features to build a hybrid feature vector. The vector is then fed into a CNN-GRU-Attention model for intelligent diagnosis. The improved slime mold algorithm (ISMA) is employed for the first time to optimize the hyperparameters of the CNN-GRU-Attention model. The experimental results show that the classification accuracy reaches 96.79% for raw signals and 93.33% for noisy signals, significantly outperforming traditional methods. This study incorporates entropy-based feature extraction, combines hyperparameter optimization with the classification model, and addresses the limitations of single feature selection methods for non-stationary and nonlinear signals. The proposed approach provides an excellent solution for intelligent optimized diagnosis of hydropower units. Full article

IMF (intramuscular fat, IMF), as a key index for evaluating meat quality traits (shear force and cooking loss, etc.), and its deposition process are jointly regulated by nutritional and genetic factors. In this study, we analyzed the molecular regulation mechanism of IMF deposition in the LD (longissimus dorsal muscle, LD) by dietary energy level in Pamir yaks. Meat quality assessment showed that the meat quality of the High-energy diet group (1.53 MJ/Kg, G) and the Medium-energy diet group (1.38 MJ/Kg, Z) were significantly improved compared with that of the Low-energy diet group (0.75 MJ/Kg, C), in which IMF content in the LD of yaks in G group was significantly higher (p < 0.05) compared with Z and C groups. Further analysis by combined transcriptomics and lipid metabolomics revealed that the differences in IMF deposition mainly originated from the metabolism of lipids, such as TG (triglycerides, TG), PS (phosphatidylserine, PS), and LPC (lysophosphatidylcholine, LPC), and were influenced by SFRP4, FABP4, GADD45A, PDGFRA, RBP4, and DGAT2 genes, further confirming the importance of lipid–gene interactions in IMF deposition. This study reveals the energy-dependent epigenetic regulatory mechanism of IMF deposition in plateau ruminants, which provides molecular targets for optimizing yak nutritional strategies and quality meat production, while having important theoretical and practical value for the sustainable development of livestock husbandry on the Tibetan Plateau. Full article

Advances in Industry 4.0 and the emergence of Industry 5.0 are driving the development of intelligent, sustainable manufacturing systems, where embedded sensing and real-time health diagnostics play a critical role. However, implementing robust predictive maintenance in production environments remains challenging due to the variability in machine operations and the lack of access to internal control data. This paper introduces a lightweight, embedded-compatible framework for health status signature extraction based on empirical mode decomposition (EMD), leveraging only data from a single triaxial accelerometer. The core of the proposed method is a cycle-synchronized segmentation strategy that uses accelerometer-derived velocity profiles and cross-correlation to align signals with machining cycles, eliminating the need for controller or encoder access. This ensures process-aware decomposition that preserves the operational context across diverse and dynamic machining conditions to address the inadequate segmentation of unstable process data that often fails to capture the full scope of the process, resulting in misinterpretation. The performance is evaluated on a challenging real-world manufacturing benchmark where the extracted intrinsic mode functions (IMFs) are analyzed in the frequency domain, including quantitative evaluation. As results show, the proposed method shows its effectiveness in detecting subtle degradations, following a low computational footprint, and its suitability for deployment in embedded predictive maintenance systems on brownfield or controller-limited machinery. Full article