Search Results (1,176)

Humans face environmental deterioration. Scholars have identified carbon dioxide as one of the culprits, and they emphasize carbon offset. Researchers are investigating carbon offset investments. Some researchers have encouragingly deployed multivariate variational mode decomposition methods, but they have not fully optimized them. Some researchers have opportunely assessed capital asset pricing models, but they have not fully justified them. We devise multiple-objective portfolio selection models, fully optimize them, and dominate carbon offset indexes. We extend the classical methodology of advancing from portfolio selection to capital asset pricing models into the methodology of advancing from multiple-objective portfolio selection to multiple-objective capital asset pricing models. Specifically, we explore multiple-objective capital asset pricing models by numerically verifying many tangent lines (instead of the traditionally singular tangent line) and suggesting a tangent plane (instead of tangent lines). For multiple-objective zero-covariance capital asset pricing models, we numerically compute a set of zero-covariance portfolios (instead of the traditionally singular zero-covariance portfolio) and suggest picking an advantageous zero-covariance portfolio. We consider the second-level indicators of carbon offset and generalize three-objective portfolio selection to k-objective portfolio selection. As for contributions, first, this paper’s methodology is to logically advance from multiple-objective portfolio selection to multiple-objective capital asset pricing models, whereas the literature typically covers multiple-objective portfolio selection alone and barely covers multiple-objective capital asset pricing models. Second, this paper numerically demonstrates some difficulties and proposes hypothetical solutions in the process of obtaining multiple-objective capital asset pricing models. Full article

Accurate time-series simulation of carbon emissions for both the whole society and the electricity industry is pivotal for realizing China’s “Dual Carbon” goals. This research constructs a hybrid simulation architecture integrating factor decomposition with deep learning to quantify emission trajectories for both the whole society and the electricity industry in Anhui Province. First, the extended Kaya identity and Logarithmic Mean Divisia Index (LMDI) are employed to analyze socioeconomic drivers. The decomposition analysis indicates that per capita income is the primary driver of carbon emissions, whereas energy intensity exerts the strongest inhibitory effect. Subsequently, Variational Mode Decomposition (VMD) is applied to the nonstationary emission series to produce multi-scale sub-signals, which are then fed into a predictive model comprising a Bayesian-optimized (BO) Transformer coupled with Long Short-Term Memory (LSTM) networks. The study establishes three distinct evolution scenarios: Moderate Sustainability (MS), Business as Usual (BAU), and Strong Economic Growth (SEG). Simulation results indicate that under MS, carbon emissions from the whole society and the electricity industry peak in 2029 at 435.2 Mt and 2030 at 281.2 Mt, respectively. Conversely, the SEG scenario delays the peak of the whole society to 2034, while the electricity industry fails to peak before 2035. These findings reveal significant risks of temporal asynchrony between the whole society and the electricity industry peaks, providing robust methodological support for regional decarbonization planning. Full article

Wheel–rail coupled excitation introduces strong low-frequency modulation, random impact interference, and broadband background noise into the vibration system of locomotive gearboxes, causing early weak bearing fault features to become submerged and making traditional deconvolution methods insufficient for effective enhancement. To address this challenge, this study proposes an adaptive parameter optimization method for MCKD based on the weighted envelope spectrum factor (WESF). WESF integrates the Hoyer index, kurtosis, and envelope spectrum energy to jointly characterize sparsity, impulsiveness, and periodicity of signal components. By using WESF as the fitness function, the sparrow search algorithm (SSA) is employed to simultaneously optimize the key MCKD parameters L, T, and M, enabling optimal enhancement of weak periodic impacts. To further mitigate modal aliasing caused by wheel–rail excitation, the original signal is first adaptively decomposed using successive variational mode decomposition (SVMD), and modes with WESF values above the average are selected for signal reconstruction. The reconstructed signal is subsequently enhanced via SSA–MCKD, and fault characteristic frequencies are extracted using envelope spectrum analysis. Experimental validation using gearbox bearing data collected under 40, 50, and 60 Hz operating conditions shows that the proposed method achieves fault feature coefficient (FFC) values of 12.8%, 7.5%, and 7.2%, respectively—representing an average improvement of approximately 156% compared with traditional methods (average FFC of 3.6%). These results demonstrate that the proposed SVMD–WESF–SSA–MCKD approach can significantly enhance weak periodic impact features under strong background noise and wheel–rail excitation, exhibiting strong practical applicability for engineering implementation. Full article

Accurate forecasting of equity returns remains fundamentally constrained by weak short-horizon predictability, pronounced noise, and structural non-stationarity. While deep learning models have been widely applied to financial time series, most studies prioritize point prediction and provide limited guidance on reliable uncertainty quantification, particularly in emerging markets. This study developed an uncertainty-aware forecasting framework for the South African equity market by integrating variational mode decomposition (VMD), gated recurrent units (GRUs), and temporal conformal prediction (TCP) to construct distribution-free prediction intervals with finite-sample coverage guarantees. Using daily returns from the FTSE/JSE All Share Index, we first confirmed that baseline recurrent models applied directly to raw returns exhibited negligible out-of-sample explanatory power, consistent with weak-form market efficiency. Incorporating VMD enhanced representation learning and improved point forecast accuracy by isolating latent frequency components. However, model-based predictive variance alone proved insufficient for reliable calibration. Embedding the models within a rolling conformal prediction framework restored near-nominal coverage across multiple confidence levels while allowing interval widths to adapt dynamically to changing volatility regimes. Robustness analyses, including walk-forward validation, stress-regime evaluation, and block permutation negative control experiments, indicated that the observed performance was not driven by temporal leakage or alignment artifacts. The results further highlight a trade-off between interval sharpness and tail-risk protection, particularly during extreme market events. Overall, the findings support a shift from return-level prediction toward calibrated uncertainty estimation as a more stable and economically meaningful objective in non-stationary financial environments. Full article

Seismic attributes are widely used to enhance the interpretation of structural, stratigraphic, and lithologic features in subsurface data. Their effectiveness, however, can be limited by noise, resolution constraints, and processing artifacts. This study suggests new seismic attributes computed using 2D-Variational Mode Decomposition (2D-VMD), which are specifically Mode-Weighted Spectral Discontinuity (MWSD) (in Module and Phase modes), VMD-Directionality Coherence (VDC), Instantaneous Frequency Concentration (IFC-VMD), and Instantaneous Bandwidth Dispersion (IBD-VMD). The proposed 2D-VMD-based attributes are compared with seven key conventional seismic attributes: dip, azimuth, chaos, coherence (semblance), curvature (mean curvature), instantaneous frequency, and instantaneous bandwidth (Hilbert transform). Through applications on simulated and real seismic data, each method is compared in terms of its ability to enhance attribute stability, resolution, and interpretability while mitigating limitations such as noise sensitivity and loss of detail. Results indicate that MWSD (Module) is optimal for amplitude stability, MWSD (Phase) for phase-sensitive applications, VDC for high-resolution structural delineation, IFC-VMD for complex geological settings, and IBD-VMD for abrupt feature changes. The findings demonstrate that these new 2D-VMD-based techniques provide significant advantages over traditional approaches and that combining complementary methods can further improve seismic interpretation outcomes. Full article

The stable and accurate output of the inertial measurement unit array (IMU) of a micro-electro-mechanical system (MEMS) is the key to ensuring the data fusion of the MEMS IMU array. However, due to the large number of MEMS IMUs contained in the MEMS IMU array, it is susceptible to interference and has difficulty avoiding failures. The output of the MEMS IMU contains noise, outliers, and other related errors, which can seriously lead to low fault detection and isolation accuracy in the MEMS IMU. In this study, a new method of fault detection and isolation based on multivariate variational mode decomposition (MVMD), a whale optimization algorithm (WOA), and a generalized likelihood test (GLT) is proposed, which is called WOA-MVMD-GLT. Firstly, a multi-index fitness function WOA is proposed to optimize the parameters of MVMD. Secondly, MVMD is used to extract the features of the MEMS IMU’s signals. Finally, a GLT is used to construct a fault detection function and a fault isolation function to detect and isolate the faults of gyroscopes and accelerometers. The experimental results show that the method proposed in this paper can significantly reduce the false alarm rate and false isolation rate. Full article

Financial data analysis remains a significant challenge due to the inherent stochasticity, non-stationarity, and low signal-to-noise ratio of market data. Conventional methods often struggle to disentangle intrinsic trends from noise and frequently overlook the critical influence of investor sentiment on price dynamics. To address these issues, we propose an adaptive trading model named CBAM-BiLSTM-DDQN, which integrates signal decomposition, multi-source feature fusion, and deep reinforcement learning. First, we construct a comprehensive heterogeneous feature set by combining price signals decomposed via Variational Mode Decomposition (VMD) and investor sentiment indices extracted from financial texts. Subsequently, a Genetic Algorithm (GA) is employed to identify the most significant feature subset, effectively reducing dimensionality and redundancy. Finally, these optimized features are input into a Double Deep Q-Network (DDQN) agent equipped with a Convolutional Block Attention Module (CBAM) and a Bidirectional Long Short-Term Memory (BiLSTM) network to capture complex spatiotemporal dependencies. We evaluated this approach through simulated trading on three major Chinese stock indices—the Shanghai Stock Exchange Composite (SSEC), the Shenzhen Stock Exchange Component (SZSE), and the China Securities 300 (CSI 300). Experimental results demonstrate the superiority of our method over traditional strategies and standard baselines; specifically, the trading agent achieved robust cumulative returns across the SSEC and CSI 300 indices, confirming the model’s exceptional capability in balancing profitability and risk aversion in complex financial environments. Furthermore, additional experiments on individual stocks in the Chinese A-share market reinforce the robustness and generalization ability of our proposed model, validating its practical potential for diverse trading scenarios. Furthermore, additional experiments on individual stocks in the Chinese A-share market reinforce the robustness and generalization ability of our proposed model, validating its practical potential for diverse trading scenarios. Full article

Rolling bearing faults are the primary cause of rotating machinery failure. Under complex operating conditions, the weak fault impact signals are easily overwhelmed by strong noise and exhibit significant non-stationary characteristics, posing severe challenges to accurate diagnosis. To address this, this paper proposes an improved Transformer-based fault diagnosis method driven by the improved black-winged kite algorithm-variational mode decomposition (IBKA-VMD) and hierarchical fractional-order attention entropy (HFrAttE). The method employs the integrated multi-strategy IBKA to adaptively determine the optimal parameters of VMD, utilizes HFrAttE to construct highly discriminative feature sets, and further builds an improved Transformer model integrating bidirectional attention mechanisms and feature decoupling structures for deep feature mining. The classification decision is finalized by the twin extreme learning machine (TELM). Experimental results on the case western reserve university (CWRU) bearing dataset under different noise environments (−2 dB, −5 dB) demonstrate that the proposed method maintains 100% accuracy, recall, and F1-score under −5 dB noise interference, significantly outperforming comparative models. It exhibits excellent anti-noise performance and feature extraction capability, providing an efficient solution for intelligent operation and maintenance of rotating machinery under complex operating conditions. Full article

Groundwater is a critical natural and strategic economic resource, and the accurate prediction of groundwater depth dynamics is essential for the rational development and utilization of water resources. However, under the combined influence of climate variability, human activities, and complex hydrogeological conditions, groundwater level time series exhibit strong nonlinear and non-stationary characteristics, posing great challenges to the accurate prediction of groundwater level dynamics. Most existing prediction models rely on sufficient hydro-meteorological and exploitation data that are difficult to obtain in water-scarce regions, or fail to effectively decouple the multi-scale features of non-stationary groundwater level signals, resulting in limited prediction accuracy and insufficient generalization ability. To address these research gaps, this study takes Zhengzhou, a typical water-deficient city in the Yellow River Basin, as the study area, and proposes a hybrid deep learning framework combining Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) neural network for predicting shallow and intermediate-deep groundwater level changes. Kolmogorov–Arnold Networks (KANs) and Gated Recurrent Units (GRUs) are selected as benchmark models to verify the superior performance of the proposed framework. In this framework, the non-stationary groundwater level signal is adaptively decomposed into Intrinsic Mode Functions (IMFs) with distinct frequency characteristics via VMD. An independent LSTM model is constructed for each IMF to capture its unique temporal variation pattern, and the final groundwater level prediction is obtained by linearly reconstructing the predicted results of all IMFs. The results show that the coefficient of determination (R²) of the VMD-LSTM model exceeds 0.90 for all monitoring datasets, with low Mean Absolute Error (MAE) and Mean Squared Error (MSE). It significantly outperforms the benchmark models in handling nonlinear and non-stationary time series features. Using only historical groundwater level data as input, the proposed framework effectively overcomes the limitation of insufficient driving variables in data-scarce regions and fully explores the multi-scale evolution of groundwater dynamics through the synergistic effect of multi-scale decomposition and deep learning. The method presented in this study provides a novel and reliable technical approach for groundwater level prediction in water-deficient and data-limited areas, and also offers scientific support for the rational management and sustainable utilization of regional groundwater resources. Future research will incorporate driving factors such as meteorology and exploitation to further improve the model’s ability to capture abrupt changes in groundwater level dynamics. Full article

Current methods for predicting deep excavation deformation suffer from insufficient accuracy and limited generalization capability. Moreover, the applicability of these methods to different types of monitoring data also requires in-depth analysis. To address this, a machine learning-based prediction model, i.e., the VMD-GWO-CNN model, integrating Variational Mode Decomposition (VMD), the Grey Wolf Optimizer (GWO), and the Convolutional Neural Network (CNN), is proposed to predict various types of monitoring data. The GWO algorithm optimizes both the key parameters of VMD and the hyperparameters of the CNN. The optimized CNN model predicts each subsequence decomposed by VMD, and the final prediction is obtained by superimposing these results. Furthermore, the prediction performance of the proposed model is evaluated against the LSTM, CNN, and GWO-CNN models using four metrics (RMSE, MAE, MAPE, R²). The results indicate that all four algorithms possess effective predictive capability for the monitoring data, in which the VMD-GWO-CNN model demonstrates the best performance across all metrics. Specifically, its RMSE for surface settlement prediction is reduced by 59.2%, 34.1%, and 33.0% compared to the LSTM, CNN, and GWO-CNN models, respectively. Moreover, the VMD-GWO-CNN model exhibits strong predictive performance for deformation in slope engineering and subgrade engineering, demonstrating its good applicability across different geotechnical engineering. The findings provide a scientific basis for safe excavation construction and contribute to efficient and rapid execution of foundation pit projects. Full article

Accurate prediction of shipping freight indices, represented by the Baltic Dry Index (BDI), is crucial for operational decision-making and risk management in the shipping industry. Existing models mainly rely on historical time-series data and often overlook the influence of unstructured information such as market sentiment. To address this limitation, this study proposes a dynamic freight rate prediction framework integrating a shipping text sentiment index. First, a shipping news sentiment index is constructed using a RoBERTa-based pre-trained model to quantify the impact of market sentiment on freight rate fluctuations. Second, the BDI series is decomposed and reconstructed through Variational Mode Decomposition (VMD) and Fuzzy C-Means (FCM) clustering to extract multiscale features. Finally, a deep learning based multi-step prediction model is developed by incorporating the sentiment index into the forecasting process. Empirical results show that the proposed model significantly outperforms benchmark models without sentiment information in terms of MAE, RMSE, and R², and demonstrates greater robustness under extreme market conditions. These findings provide a novel methodological framework for improving freight rate forecasting accuracy and offer practical decision support for shipping enterprises. Full article

Accurate forecasting of copper futures prices is crucial for risk management and investment decisions. However, existing approaches primarily rely on historical prices and incorporate behavioral signals without a unified modeling framework. To address this limitation, we propose MBTI-Net (Multi-source Behavior-Triggered Interaction Network), a behavior-aware forecasting framework for heterogeneous copper market data. We first construct a compact behavioral factor from Baidu search indices via a multi-view projection strategy that preserves structural and predictive information. We then develop a complexity-aware reconstruction mechanism that aggregates intrinsic mode functions into multi-frequency components based on fuzzy entropy and energy. To accommodate distributional and volatility differences between behavioral and market variables, we introduce VB-ReVIN (Volatility- and Behavior-aware Reversible Instance Normalization). Building upon these representations, MBTI-Net models dynamic multi-source interactions triggered by behavioral intensity and market conditions, enabling adaptive cross-source information fusion. Experiments on LME and SHFE copper futures datasets demonstrate consistent improvements over state-of-the-art benchmarks, highlighting the importance of explicitly modeling behavior-driven dependencies in financial forecasting. Full article

Background/Objectives: Predicting individual response to depression therapy prior to treatment initiation remains a critical clinical challenge, as the response rate to both selective serotonin reuptake inhibitors (SSRIs) and repetitive transcranial magnetic stimulation (rTMS) is approximately 50%, leaving treatment selection largely trial-based. This study presents a computer-aided decision (CAD) framework that predicts depression therapy outcomes from pre-treatment electroencephalogram (EEG) signals using advanced time-frequency representations and pretrained convolutional neural networks (CNNs). Methods: EEG signals from 30 SSRI patients and 46 rTMS patients are transformed into time-frequency images using Continuous Wavelet Transform (CWT), Variational Mode Decomposition (VMD), and their pixel-level fusion. Four pretrained CNN architectures, including ResNet-18, MobileNet-V3, EfficientNet-B0, and TinyViT-Hybrid, are fine-tuned and evaluated under both image-independent and subject-independent 6-fold cross-validation (CV). Results: Results reveal a clear therapy-specific pattern: CWT-based representations yield superior discrimination for SSRI outcome prediction, with ResNet-18 achieving 99.43% image-level accuracy, while VMD-based representations are statistically superior for rTMS outcome prediction, with ResNet-18 reaching 98.77%. Pixel-level fusion of CWT and VMD does not consistently improve performance over the best individual representation in either therapy context. Pairwise Wilcoxon signed-rank tests confirm a two-tier architectural hierarchy in which ResNet-18 and TinyViT-Hybrid significantly outperform MobileNet-V3 and EfficientNet-B0 across all conditions, while remaining statistically indistinguishable from each other. At the subject level, the framework achieves 82.50% and 83.53% accuracy for SSRI and rTMS, respectively, under strict subject-independent evaluation. Per-channel analysis reveals occipital dominance for SSRI under CWT and frontotemporal dominance for rTMS under VMD, consistent with known neurophysiological mechanisms. Conclusions: These findings demonstrate that the choice of time-frequency representation is therapy-specific and at least as important as architectural complexity, and that competitive performance can be achieved without recurrent or attention layers by combining well-designed spectral images with a simple pretrained residual network. Full article

The high penetration of photovoltaic and wind power introduces strong non-stationarity and multi-scale fluctuations into power system load profiles, challenging the accuracy of short-term load forecasting (STLF). To address this, we propose a hybrid forecasting framework, IMVMD-XLSTM, which synergistically integrates an optimized multivariate decomposition with an advanced neural network. First, to address the critical issue that MVMD performance is highly sensitive to its parameter settings, which impacts decomposition quality, a multi-strategy Improved Fruit Fly Optimization Algorithm (IFOA) is developed to task-oriented adaptively tune the key parameters of MVMD, forming an Improved MVMD (IMVMD). This optimization aims to ensure decomposition stability and maximize the relevance for the subsequent forecasting task. Second, to fully leverage the characteristics of the frequency-aligned, multi-channel sub-sequences generated by IMVMD, an Extended LSTM (XLSTM) network is designed. Its serially arranged BisLSTM and mLSTM units are specifically tailored to capture the bidirectional long-term dependencies within each stable sub-sequence and the complex high-dimensional interactions across the aligned sub-sequences, respectively. Evaluated on 15 min resolution data from the Austrian grid, the proposed IMVMD-XLSTM framework achieves a day-ahead forecasting Mean Absolute Percentage Error (MAPE) of 2.45% (±1.41%). This study provides a verifiable and effective solution that couples data-adaptive signal processing with a purpose-built neural architecture to enhance forecasting reliability in renewable-rich power systems. Full article

The proliferation of Unmanned Aerial Vehicles (UAVs) poses a significant challenge for ISAR imaging. Conventional multi-target imaging methods, such as sequential CLEAN-based techniques, are often hindered by error propagation and sensitivity to noise, leading to degraded performance or even imaging failure, especially at low SNR. To address these issues, this paper proposes a novel robust imaging framework. The framework is built upon two key innovations: a partitioned block-wise compensation mechanism integrated with PSO for simultaneous and precise motion parameters estimation of multiple targets, which avoids local optima and error accumulation; and the application of Sequential Variational Mode Decomposition (SVMD) to adaptively separate and reconstruct signals, thereby suppressing inter-target aliasing and noise interference overlooked in prior studies. Simulations and measured-data experiments confirm that the proposed method maintains clear focusing and superior image quality even at low SNR, outperforming existing techniques in terms of image entropy, contrast, and resolution. This paper provides a robust and effective solution for high-resolution radar surveillance in complex multi-target scenarios. Full article