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To address the issues of corrosion weakening of solid-waste-based backfill material caused by mine water, a novel hydrophobic solid waste backfill (HSBF) material was developed using polydimethylsiloxane (PDMS) and a silane coupling agent (SCA) as hydrophobic modification additives, and NaOH (SH) and sodium silicate (SS) as alkali activators. Fly ash and slag were chosen as the primary raw solid waste materials. The rheological properties of the hydrophobic-treated backfill slurries were measured, and the resulting physicochemical properties were compared with the unmodified reference group. This study reveals that the fresh HSBF slurry follows a Modified Bingham (M-B) model with shear-thinning characteristics. The addition of PDMS causes an increase in the water contact angle of the hardened HSBF material with F8S2 to up to 134.9°, indicating high hydrophobicity. Morphological observations indicated that PDMS mainly attaches to the inorganic particles' surface through the bridging action of SCA for the hydrophobic modification of the backfill material. The overall strength of the HSBF materials was further ensured via fly ash–slag ratio optimization, and was found to be enhanced up to 98% by increasing slag content from 20% to 50%. This is mainly attributed to the hydration of slag, forming C-S(A)-H gel, which contributes to the increased strength. The novel HSBF material enables the elimination of cement in mine backfilling applications, demonstrating good economic benefits. Its excellent mechanical and hydrophobic properties can not only prevent overburden displacement in goaf areas, but can also mitigate water resource loss from overlying strata and simultaneously reduce the safety risks associated with long-term mine water deterioration. [ABSTRACT FROM AUTHOR]

With the widespread integration of renewable energy sources into the power system and the diversification and complexity of the energy consumption patterns, which makes it more difficult to maintain a source-load balance of the power system. In the power systems, the results of the power load forecasting can be used for generator scheduling and load distribution. Therefore, the accurate power load forecasting is critical to operation of the power system. In this paper, a load forecasting model based on a dynamic adaptive and adversarial graph convolutional network (DAAGCN) is proposed, which combines a dynamic adaptive graph generation network (DAGG) and a generative adversarial network (GAN). Firstly, DAGG utilizes integrated time-varying embedding and node embedding to generate dynamic adaptive graphs for inferring spatial–temporal dependencies between different temporal loads. Second, the underlying spatial–temporal prediction model is adversarial trained based on the idea of a zero-sum game. The module parameters are optimized using the L1 loss and the adversarial training loss together as the training objectives of the model. Finally, short-term forecasting of electricity loads was performed on the datasets using DAAGCN, and the results were compared with several other forecasting modules. [ABSTRACT FROM AUTHOR]