This study presents a microbial activation strategy to repurpose coal gangue (CG) for enhancing soil quality in aeolian sandy soil. Phosphate-solubilizing bacteria (20−3) and nitrogen-fixing bacteria (SGD06) were employed to produce two microbially activated CGs (CG20-3 and CGSGD06). Adsorption experiments elucidated their colonization mechanisms on CG, while seed germination and pot trials with pakchoi validated remediation efficacy. Even after pakchoi harvest, both CG20-3 and CGSGD06 significantly increased soil bioavailable nutrients by 1.13–6.86-fold. CGSGD06 exhibited superior nitrogen delivery through biological fixation, providing 82.39% higher available nitrogen than CG20-3. Conversely, CG20-3 showed faster adsorption kinetics and stronger binding capacity. Both strains mediated nutrient liberation via acid-driven dissolution of CG clay minerals, including kaolinite, muscovite, and phosphates. Treated soils enhanced pakchoi germination (3.08%) and biomass (1.14–1.89-fold) through improved plant resistance and biochemical indicators, namely ascorbic acid, chlorophyll, and soluble sugar. The activated CGs reshaped the rhizosphere bacterial community and stimulated the secretion of organic acids, phytohormones, and key enzymes, collectively establishing a synergistic growth-promoting system. Additionally, rhizospheric micronutrient bioavailability increased 4.52–43.15-fold, and key soil enzyme activities were upregulated 1.42–267-fold. This work provides a preliminary basis for using microbially activated CG as a potential amendment for aeolian sandy soil, highlighting opportunities for solid waste valorization and soil restoration under controlled conditions.
The high cost of the power collection system (PCS) remains a notable challenge for constructing floating offshore wind farms (FOWFs). This letter introduces a new type of device called the current aggregator in the topology design of the PCS for FOWF, aiming to reduce the use of expensive dynamic cables and their additional components. Firstly, considering the cost components of the current aggregator from multiple perspectives, its life-cycle cost model is established. On this basis, the life-cycle cost model for the PCS system integrating the current aggregator is built. Then, a general solving scheme is presented. Finally, the proposed PCS design is implemented on an irregular 21 FOWF and a regular 6 & times; 6 FOWF, and its economic feasibility is preliminarily verified, achieving cost reduction for irregular and regular FOWFs. The letter may provide a practical new way of designing the PCS of FOWFs, which could reduce the high cost.
Capturing the rich, hierarchical information embedded in complex multivariate time series (MTS) necessitates modeling at multiple temporal scales. However, prevalent multi-scale forecasting methods suffer from critical limitations: a uniform scaling approach that overlooks individual variable characteristics, inconsistent modeling of temporal patterns and inter-variable dependency leading to modeling spurious correlations, and a neglect of global context. To overcome these challenges, we introduce LMSNet, a Lightweight Multi-Scale graph learning framework. Specifically, LMSNet introduces a novel variable-specific scaling mechanism, which leverages frequency-domain analysis to assign a tailored analytical window to each variable based on its intrinsic dynamics, establishing a robust foundation for accurately modeling multi-scale inter-variable dependencies. Furthermore, LMSNet synergizes global and local information and performs concurrent temporal and inter-variable dependency modeling at every network layer, aided by a momentum update mechanism that refines deep-layer graph node representations to mitigate the risk of modeling spurious correlations. LMSNet establishes a new state-of-the-art on eight benchmark datasets. Crucially, it achieves a 2.94% reduction in MAE with reducing GPU memory usage by 83% and accelerating training by 60% compared to strong baseline-TimeMixer. Our source code is provided at https://github.com/05Pikachu24/TSF-LMSNet.
A rapid prediction of track deterioration states under earthquakes for random complex structure scenarios is identified as a core task for post-earthquake emergency command and train speed limit scheme formulation. In this paper, the continuous prediction of seismic track irregularity is reduced to the discrete prediction of girder end misalignment, and the complex response prediction for random structures is reduced to the single response prediction combination of characteristic structures. Based on dual dimensionality reduction, the rapid construction of seismic track irregularity under random complex structure scenarios is realized using a bidirectional long short-term memory neural network. The conclusions indicate that the girder joint area of track alignment irregularity is the main factor causing wheel-rail impact, and both the girder joint and girder deck areas contribute to the lateral vibration of car body; the continuous seismic track alignment irregularity can be regarded as a spliced body of a series of mutually decoupled sub-segments, and the train response excited by continuous irregularity can be approximately decomposed into the superposition of the independent train response to each sub-segment; by aggregating seismic responses from multiple characteristic structures and applying an equivalent correction factor, the equivalent structural scenario can yield girder misalignments that envelope those of random structural scenarios at the 99 % confidence level.
Efficient activation of molecular oxygen into reactive oxygen species is central to photocatalytic antibiotic degradation. Here, we prepared an edge-oriented CeO2 at the edges of carbon nitride (10-CeO2/CN) through a straightforward precursor adsorption followed by a hydrothermal process, where Ce is preferentially anchored at the CN edges. The 10-CeO2/CN achieves 89.2% tetracycline (TC) removal and 63.9% mineralization under visible-light irradiation, markedly outperforming pristine CN and CeO2. Experimental results and finite-element simulations demonstrate that edge-oriented CeO2 functions as an optical antenna for photon harvesting and a dielectric mirror to confine light at the interface, leading to 2.58-fold enhancements in the local optical field relative to CN and extending the photon propagation path and residence time. Density functional theory calculations further reveal that edge-oriented CeO2 exposes a highly reactive (200) facet, substantially enhancing O2 adsorption for activation. The enhanced interfacial optical field, coupled with facet-dependent O2 activation, enables the generation of singlet oxygen, thereby enabling efficient TC removal. These findings reveal the pivotal role of crystallographic orientation in coupling interfacial optical-field modulation with facet-dependent O2 activation, providing mechanistic insight into the rational design of high-performance photocatalysts for antibiotic degradation.
