In south China, suffering from the rainiest climate, porous asphalt mixtures have been receiving increasing attention. However, with the increase in the application of pavement and the growth of service life, the importance of the recycling application of old reclaimed porous asphalt pavement (RPAP) has gradually become prominent. Based on this, this paper established RPAP content ranging from 0% to 30% in increments of 5% and designed experimental groups with and without regenerating agent to investigate the effects of RAP content and regenerating agent addition on the high-temperature stability, low- and normal-temperature crack resistance, moisture susceptibility, drainage capacity, and mechanical properties of PAC-13 reclaimed porous asphalt mixtures. Subsequently, the practical performance of PAC-13 RPAP was verified through a pavement test. The results indicate that, as the RPAP content increases, the high-temperature stability and mechanical properties of the recycled mixture improve. Specifically, as the planer content is increased to 30%, the dynamic stability of the regenerated porous asphalt increases by 61.1%, and the dynamic modulus at 25 Hz also shows an increase of 25.3%. However, the crack resistance, moisture susceptibility, and drainage capacity at both low temperatures and room temperature exhibited accelerated weakening. When the RPAP content increases to 30%, the reduction in failure strain of regenerated PAC-13 reaches 41.8%, and the reduction in submergence stability reaches 21%. Simultaneously, the water permeability coefficient, void ratio, and interconnected void ratio all demonstrate significant reductions of 23.5%, 6.5%, and 10.0%, respectively, indicating a diminished drainage capacity in the recycled porous pavement mixture. Then again, with the addition of the regenerant, the high-temperature stability of the regenerated porous mixture is reduced by 10.8%, and the mechanical properties are reduced by 6.48%, while the crack resistance at low temperature and room temperature, moisture susceptibility, and drainage ability are enhanced. The verification results of the test section demonstrate the feasibility of utilizing reclaimed asphalt pavement (RAP) material in the porous asphalt mixture. Additionally, it is recommended to select RAP material with a particle size of 4.75 mm or larger while ensuring that the proportion of RAP does not exceed 20%. The research findings of this paper are anticipated to offer guidance for the preparation of PAC-13 reclaimed porous asphalt mixtures while facilitating the recycling and large-scale utilization of old porous pavement materials.

Against the backdrop of electricity market liberalization and deregulation, accurate electricity price forecasting is critical for optimizing power dispatch and promoting the low-carbon transition of energy structures. However, electricity prices exhibit inherent complexities such as seasonality, high volatility, and non-stationarity, which undermine the efficacy of traditional forecasting methodologies. To address these challenges, this study proposes a four-stage Decomposition-Reconstruction-Optimization-Integration (DROI) framework, coupled with an econometric breakpoint test, to evaluate forecasting performance across distinct time segments of Spanish electricity price data. The framework employs CEEMDAN for signal decomposition, decomposing complex price sequences into intrinsic mode functions to retain essential features while mitigating noise, followed by frequency-based data reconstruction; integrates the Improved Sparrow Search Algorithm (ISSA) to optimize initial model parameters, minimizing errors induced by subjective factors; and leverages Convolutional Neural Networks (CNN) for frequency-domain feature extraction, enhanced by an attention mechanism to weight channels and prioritize critical attributes, paired with Long Short-Term Memory (LSTMs) for temporal sequence forecasting. Experimental results validate the method’s robustness in both interval forecasting (IPCP = 100% and IPNAW is the smallest, Experiment 1.3) and point forecasting tasks (MAPE = 1.3758%, Experiment 1.1), outperforming naive approaches in processing stationary sequence clusters and demonstrating substantial economic utility to inform sustainable power system management.

Accurate gold price forecasting is crucial for economic stability and investment decision-making. In order to improve the accuracy of gold price prediction and quantify the uncertainty of gold price fluctuation, this paper proposes a hybrid model (CNN-QRLSTM) that integrates convolutional neural network (CNN) and quantile regression long- and short-term memory network (QRLSTM) and innovatively introduces news text data to quantify the media sentiment. We combine EEMD with the Hurst index to remove white noise from the original signal, and the processed data is used as the input layer of the prediction model. Furthermore, to demonstrate the impact of news sentiment on gold prices, this paper employs entropy measurement methods based on information theory to quantify the uncertainty and information content embedded within processed gold price sequences and derived sentiment indicators. The mutual information (MI) algorithm, based on information entropy, captures the nonlinear correlations between financial keywords and market sentiment. It constructs a financial sentiment lexicon (covering keywords such as economic policies and geopolitical conflicts), combines semantic rules with context-weighted strategies, calculates sentiment scores for news texts, and generates daily aggregated media sentiment indicators. This entropy-based perception method not only enhances the interpretability of emotion-driven fluctuations but also provides a theoretical foundation for reducing prediction uncertainty through multi-source data fusion. The experiment uses 2022–2025 daily London gold spot price data, Shanghai Gold Exchange gold price data, and the same period of Gold Investment Network gold market news to carry out the study. The empirical study shows that the synergy of multi-source data fusion and the quantile regression mechanism can improve the accuracy of gold price prediction and the new paradigm of risk interpretation while providing theoretical support for the formulation of quantitative investment strategies.

Macroscopic fatigue tests, mesoscopic finite element simulations, and microscopic molecular dynamics simulations were composed to study the damage and failure of drainage asphalt mixtures in multiscale. The applicability of the fatigue models fit by strain, stress, and the linear fitting slope of the indirect tensile modulus curves were compared. The mesoscopic damage and failure distribution and evolution characteristics were studied, considering the single or coupling effect of traffic loading, hydrodynamic pressure, mortar aging, and interfacial attenuation. The microscopic molecular mechanism of the interface adhesion failure between the aggregate and mortar under water-containing conditions was analyzed. Results show that the fatigue model based on the linear fitting slopes of the indirect tensile modulus curves has significant applicability for drainage asphalt mixtures with different void rates and gradations. The damage and failure have an obvious leap development when traffic loading increases from 0.7 MPa to 0.8 MPa. The hydrodynamic pressure significantly increases the stress of the mortar around the voids and close to the aggregate, promoting damage development and crack extension, especially when it is greater than 0.3 MPa. With the aging deepening of the mortar, the increase rate of the damage degree gradually decreases from the top to the bottom of the mixture. With the development of interfacial attenuation, the damage and failure of interfaces continue increasing, while that of the mortar increases first and then decreases, which is related to the loading concentration in the interface and the stress decrease in the mortar. Under the coupling effects, whether the cracks mainly generate in the mortar or interface depends on their damage degrees, thus causing the stripping of the aggregate wrapped or not wrapped by the mortar, respectively. The van del Waals force is the main molecular effect of interface adhesion, and both acidic and alkaline aggregate components significantly tend to form hydrogen bonds with water rather than asphalt, thus attenuating the interface adhesion.