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The multiscale structural engineering strategy presents a powerful method for tailoring the structural attributes of materials at various levels, enabling the flexible control and manipulation of their electromagnetic properties. Nonetheless, orchestrating the multiscale architecture of polymer‐derived carbon aerogels specifically for microwave absorption poses significant challenges. Herein, aramid‐derived hard carbon nanofiber aerogel microspheres (CNFAMs) featuring a hierarchical skin‐core structure are fabricated through a wet‐spinning technique, combined with reprotonation‐mediated self‐assembly and carbonization processes. The presence of large‐scale voids between neighboring microspheres and the microscale porosity within the microspheres themselves improves impedance matching and promotes microwave reflection and scattering. The distinct graphitic domains and defects serve as pivotal elements for conduction and polarization losses, significantly impacting microwave attenuation. By meticulously tailoring the macroscale dimensions, microscale porous architecture, and nanoscale domains, the optimized CNFAMs demonstrate a remarkable absorption bandwidth of 9.62 GHz at an ultralow filling of 0.97 wt%. Additionally, the implementation of application‐oriented microwave absorption through the innovative integration of polysilsesquioxane‐CNFAMs in a host–guest aerogel is explored. This composite system brings together broadband absorption, superhydrophobicity, thermal insulation, resistance to freezing, and robust tolerance to harsh environments. Such a multifaceted approach is designed to tackle the growing challenges associated with complex electromagnetic environments effectively. [ABSTRACT FROM AUTHOR]

Accurate identification of network topology and line parameters is essential for effective management of distribution systems. An innovative joint estimation method for distribution network topology and line parameters is presented, utilizing a power flow graph convolutional network (PFGCN). This approach addresses the limitations of traditional methods that rely on costly voltage phase angle measurements. The node correlation principle is applied to construct a node correlation matrix, and a minimum distance iteration algorithm is proposed to generate candidate topologies, which serve as graph inputs for the parameter estimation model. Based on the topological dependencies and convolutional properties of AC power flow equations, a PFGCN model is designed for line parameter estimation. Parameter refinement is achieved through an alternating iterative process of pseudo-trend calculation and neural network training. Training convergence and loss function values are used as feedback to filter and validate candidate topologies, enabling precise joint estimation of both topologies and parameters. The proposed method's accuracy, transferability, and robustness are demonstrated through experiments on the IEEE-33 and modified IEEE-69 distribution systems. Multiple metrics, including MAPE, IAE, MAE, and R2, highlight the proposed method's advantages over Adaptive Ridge Regression (ARR). In the C33 scenario, the proposed method achieves MAPEs of 4.6% for g and 5.7% for b, outperforming the ARR method with MAPEs of 7.1% and 7.9%, respectively. Similarly, in the IC69 scenario, the proposed method records MAPEs of 3.0% for g and 5.9% for b, surpassing the ARR method's 5.1% and 8.3%. [ABSTRACT FROM AUTHOR]

Phase engineering is considered an effective strategy to regulate the electrocatalytic activity of catalysts for Li–S batteries (LSBs). However, the underlying origin of phase‐dependent catalytic ability remains to be determined, which significantly impedes the design principles of high‐performance catalytic materials for LSBs. Herein, heteroatom‐doped engineering can trigger phase transformation from mixed‐phased cubic and orthorhombic cobalt diselenide into pure orthorhombic structure with a tensile strain and enhanced charge localization. The upshift of the d‐band center and enhanced Bader charge at Se sites synergistically strengthen the interaction with Li and S sites in polysulfide species, thus endowing the transformed P‐MoSe2/MXene with high catalytic activity and uniform lithium deposition for LSBs. Consequently, the P‐CoSe2/MXene Li–S batteries demonstrate a high‐rate capability of 603 mAh g−1 at 4C, and an excellent cyclability of 652 mAh g−1 at 1C over 500 cycles with a degradation rate of 0.076% per cycle. The work provides an in‐depth insight into the fundamental design principles of effective catalysts for LSBs. [ABSTRACT FROM AUTHOR]

Accurate load forecasting, especially in the short term, is crucial for the safe and stable operation of power systems and their market participants. However, as modern power systems become increasingly complex, the challenges of short-term load forecasting are also intensifying. To address this challenge, data-driven deep learning techniques and load aggregation technologies have gradually been introduced into the field of load forecasting. However, data quality issues persist due to various factors such as sensor failures, unstable communication, and susceptibility to network attacks, leading to data gaps. Furthermore, in the domain of aggregated load forecasting, considering the potential interactions among aggregated loads can help market participants engage in cross-market transactions. However, aggregated loads often lack clear geographical locations, making it difficult to predefine graph structures. To address the issue of data quality, this study proposes a model named adversarial graph convolutional imputation network (AGCIN), combined with local and global correlations for imputation. To tackle the problem of the difficulty in predefining graph structures for aggregated loads, this study proposes a learnable adjacency matrix, which generates an adaptive adjacency matrix based on the relationships between different sequences without the need for geographical information. The experimental results demonstrate that the proposed imputation method outperforms other imputation methods in scenarios with random and continuous missing data. Additionally, the prediction accuracy of the proposed method exceeds that of several baseline methods, affirming the effectiveness of our approach in imputation and prediction, ultimately enhancing the accuracy of aggregated load forecasting. [ABSTRACT FROM AUTHOR]

The performance of composite binders with high-volume steel slag (SS), granulated blast furnace slag (GBFS) and desulfurization gypsum (DG) were systematically investigated. The results show that the synergistic effects of high volume (40–60%) of SS with GBFS and less than 15% of DG could perform adequate setting time, higher strength, well volume stability and soundness as well as dense microstructure. The higher compressive strengths exceeding 70 MPa at 28 days and up to 80 MPa at 90 days could be obtained for composite pastes with high-volume SS, which exhibit tight relationships with [CaO]·[SO3]/[Al2O3]·[SiO2] and [CaO]·[Al2O3]/[SO3]2 molar ratios. More (15–19%) AFt and less (about 1%) portlandite produced during the hydration of composite binders. C–S–H gels and calcite crystals were formed. The composite binder possesses lower energy consumption, high carbon reduction and low cost compared with Portland cement in favor of the low-carbon cementitious material production and effective utilization of SS. [ABSTRACT FROM AUTHOR]