Search Results (3)

With increasing urbanization and population growth, the volume of municipal solid waste (MSW) continues to rise. Efficient and environmentally responsible waste processing has become a core issue in sustainable development. Incineration plays a key role in reducing landfill usage and recovering energy from waste, contributing to circular economy initiatives. However, fluctuations in furnace temperature significantly affect combustion efficiency and emissions, undermining the environmental benefits of incineration. To address these challenges under dynamic operational conditions, this paper proposes a hybrid model combining an Improved Snow Ablation Optimizer (ISAO), Dual-Branch Convolutional Neural Network (DBCNN), and Bidirectional Long Short-Term Memory (BiLSTM). The model extracts dynamic features from control and condition variables and incorporates time series characteristics for accurate temperature prediction, thereby enhancing the overall efficiency of the incineration process. ISAO integrates Lévy flight, differential mutation, and elitism strategies to optimize parameters, contributing to better energy recovery and reduced emissions. Experimental results on real MSWI data demonstrate that the proposed method achieves high prediction accuracy and adaptability under varying operating conditions, showcasing its robustness and application potential in promoting sustainable waste management practices. By improving combustion efficiency and minimizing environmental impact, this model aligns with global sustainability goals, supporting a more efficient, eco-friendly waste-to-energy process. Full article

Total Electron Content (TEC) is a fundamental parameter characterizing the electron density distribution in the ionosphere. Traditional global TEC modeling approaches predominantly rely on mathematical methods (such as spherical harmonic function fitting), often resulting in models suffering from excessive smoothing and low accuracy. While the 1° high-resolution global TEC model released by MIT offers improved temporal-spatial resolution, it exhibits regions of data gaps. Existing ionospheric image completion methods frequently employ Generative Adversarial Networks (GANs), which suffer from drawbacks such as complex model structures and lengthy training times. We propose a novel high-resolution global ionospheric TEC modeling method based on a Dual-Branch Convolutional Neural Network (DB-CNN) designed for the completion and restoration of incomplete 1°-resolution ionospheric TEC images. The novel model utilizes a dual-branch input structure: the background field, generated using the International Reference Ionosphere (IRI) model TEC maps, and the observation field, consisting of global incomplete TEC maps coupled with their corresponding mask maps. An asymmetric dual-branch parallel encoder, feature fusion, and residual decoder framework enables precise reconstruction of missing regions, ultimately generating a complete global ionospheric TEC map. Experimental results demonstrate that the model achieves Root Mean Square Errors (RMSE) of 0.30 TECU and 1.65 TECU in the observed and unobserved regions, respectively, in simulated data experiments. For measured experiments, the RMSE values are 1.39 TECU and 1.93 TECU in the observed and unobserved regions. Validation results utilizing Jason-3 altimeter-measured VTEC demonstrate that the model achieves stable reconstruction performance across all four seasons and various time periods. In key-day comparisons, its STD and RMSE consistently outperform those of the CODE global ionospheric model (GIM). Furthermore, a long-term evaluation from 2021 to 2024 reveals that, compared to the CODE model, the DB-CNN achieves average reductions of 38.2% in STD and 23.5% in RMSE. This study provides a novel dual-branch input convolutional neural network-based method for constructing 1°-resolution global ionospheric products, offering significant application value for enhancing GNSS positioning accuracy and space weather monitoring capabilities. Full article

Soil vanadium contamination poses a significant threat to ecosystems. Hyperspectral remote sensing plays a critical role in extracting spectral features of heavy metal contamination, mapping its spatial distribution, and monitoring its trends over time. This study targets a vanadium-contaminated area in Panzhihua City, Sichuan Province. Soil sampling and spectral measurements occurred in the laboratory. Hyperspectral (Gaofen-5, GF-5) and multispectral (Gaofen-2, GF-2; Sentinel-2) images were acquired and preprocessed, and feature bands were extracted by combining laboratory spectral data. A dual-branch convolutional neural network (DB-CNN) fused hyperspectral and multispectral images and confirmed the fusion’s effectiveness. Six prevalent machine learning models were adopted, and a unified learning framework leveraged a Random Forest (RF) as a second-layer model to enhance the predictive performance of these base models. Both the base models and the ensemble learning model were evaluated based on predictive accuracy. The fusion process enhanced the predictive performance of the base models, improving R² values for vanadium (V) and pentavalent vanadium (V5+) from 0.54 and 0.3 to 0.58 and 0.39, respectively, at a 4 m resolution. Further optimization using RF as a second-layer model to refine Extreme Trees (ETs) significantly increased R² values to 0.83 and 0.75 for V and V5+, respectively, at this scale. The 934 nm and 464 nm wavelengths were identified as the most critical spectral bands for predicting soil vanadium contamination. This integrated approach robustly delineates the spatial distribution characteristics of V and V5+ in soils, facilitating precise monitoring and ecological risk assessments of vanadium contamination through a comparative analysis of predictive accuracy across diverse models. Full article