Taiyuan Iron and Steel Group (China)

Accurate building load forecasting is essential for smart grid and energy management, yet nonlinearity, non-stationarity, and multi-scale characteristics of load data challenge traditional methods. To address these issues, we propose a hybrid deep learning framework, CEEMDAN-MultiScale-CNN-BiLSTM-MultiAttention. First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the load sequence into intrinsic mode functions (IMFs), mitigating mode mixing and complexity. Then, a MultiScale Convolutional Neural Network extracts multi-scale local features from each IMF. A Bidirectional Long Short-Term Memory network captures bidirectional temporal dependencies, and a Multi-Attention mechanism dynamically emphasizes critical time steps and feature channels, enhancing interpretability and prediction. The framework is validated on the Building Data Genome Project 2 dataset, achieving a Mean Absolute Percentage Error (MAPE) of 2.6464% and a coefficient of determination R2 of 0.8999, outperforming mainstream methods across multiple metrics. The main contributions are: (1) a hybrid framework integrating CEEMDAN, multi-scale feature extraction, and attention mechanisms to handle nonlinearity and non-stationarity; (2) a MultiScale-CNN to capture multi-scale temporal features and adapt to multi-frequency components; (3) a Multi-Attention mechanism to dynamically focus on key time steps and channels, improving accuracy and robustness. This work provides an effective solution for building load forecasting in complex energy systems.

Precise assessment of the vulnerability characteristics of human–land systems is es-sential for ensuring ecological security and sustainable development in regions affected by large-scale engineering projects. Using the Three Gorges Reservoir Area as a case study, this research develops a comprehensive evaluation index system based on a coupled framework of “Geo-environmental Background—Ecosystem Structure—Anthropogenic Perturbation.” By integrating deep neural networks (DNN), convolutional neural networks (CNN), and the analytic hierarchy process (AHP) with multi-source data, we perform a thorough assessment of eco-geological vulnerability. The results reveal the following key findings: (1) In eco-geological vulnerability assessment, deep learning methods (DNN and CNN) significantly outperform traditional AHP, with CNN showing superior precision and specificity due to its ability to extract local spatial features effectively, while DNN exhibits stronger overall robustness. (2) The spatial distribution of eco-geological vulnerability in the reservoir area is notably heterogeneous, with high and Extreme vulnerability zones concentrated along the main riverbanks, major tributary estuaries, and urban peripheries. These zones are strongly coupled with steep terrain, erodible lithology, high geological hazard risks, and intensive human activity. (3) Although the overall vulnerability remains relatively stable, local sensitivity is increasing. Ecological restoration projects in mountainous regions have effectively mitigated vulnerability in the hinterlands, while rapid urbanization has exacerbated vulnerability in emerging urban areas. The study concludes that the spatial pattern of vulnerability is primarily influenced by the geological–ecological background, with human disturbance—especially land use intensity—acting as the primary driver of vulnerability dynamics and local hotspots of high vulnerability. Based on these findings, we recommend a differentiated management approach tailored to eco-geological units: for high and extreme vulnerability zones along river and urban corridors, efforts should focus on spatial constraints and systemic resto-ration; for low and negligible vulnerability zones in mountainous areas, strategies should aim to enhance ecosystem quality and stability, thus fostering a coordinated regional ecological security framework.

This study systematically investigates the effects of drawing processes and subsequent high‐temperature annealing on the mechanical and anticorrosion performance of 2507 duplex stainless steel through microstructural characterization and relevant testing. The results indicate that yield strength and ultimate tensile strength significantly increase with drawing strain, while uniform elongation decreases. The combined effects of grain refinement and reduced high‐angle grain boundary density improve corrosion resistance. After annealing at 1050–1100 °C for 1 h, the absence of precipitation phases contributes to acquiring a good combination of mechanical properties and pitting corrosion resistance via enhancing stability of the passivation film. This research provides theoretical guidance for optimizing the synergistic strengthening process of drawing and heat treatment in duplex stainless steel to meet increasingly demanding service conditions. Furthermore, it presents this synergistic strengthening approach and discusses the relevant mechanisms.

Abstract Nb microalloying is of significance to the grain refinement and mechanical properties of DP steels. In the present study, two DP steels without/with addition of Nb were produced by the TMCP process and the effect of Nb on the microstructure and mechanical properties was comparatively investigated by characterization methods of optical microscopy (OM), electron back-scattered diffraction (EBSD), transmission electron microscopy (TEM), and X-ray diffraction (XRD). The results show that the addition of 0.02 wt% Nb in the DP steels, the volume fraction of martensite increases from 43 to 85.2 vol%, and the equiaxed grain and elongated grains are obtained with grain size decreasing from 1.88 to 1.66 μm during the hot rolling process. Furthermore, during the tempering, the martensite lath of the DP steel with Nb grows slower (from 127.4 to 139.3 nm). The DP steel with addition of Nb exhibits a better combination of strength (∼1,281 MPa tensile strength) and ductility (∼16.0 % total elongation), attributed to grain boundary strengthening, dislocation strengthening and solid solution strengthening.