In this paper, we develop a new adaptive hyperbolic-cross-space mapped Jacobi (AHMJ) method for solving multidimensional spatiotemporal integrodifferential equations in unbounded domains. By devising adaptive techniques for sparse mapped Jacobi spectral expansions defined in a hyperbolic cross space, our proposed AHMJ method can efficiently solve various spatiotemporal integrodifferential equations such as the anomalous diffusion model with reduced numbers of basis functions. Our analysis of the AHMJ method gives a uniform upper error bound for solving a class of spatiotemporal integrodifferential equations, leading to effective error control.
Quorum quenching (QQ)-based strategies are efficient for biofouling control. However, the feasibility of using QQ bacteria in antibiotic-stressed membrane bioreactors (MBRs) remains unknown. In this study, we isolated three novel QQ strains (Bacillus sp. QX01 and QX03, Delftia sp. QX14) from the activated sludge of an actual MBR. They can degrade 11 N-acyl-homoserine lactones (AHLs) with high efficiencies and rates through intracellular QQ pathways involving putative acylases and lactonases. Running two lab-scale MBRs, we found that introducing antibiotics (sulfamethoxazole, azithromycin, and ciprofloxacin, each at 100 μg/L) shortened the fouling cycle by 71.4 %. However, the immobilized inoculation of QX01 into one MBR extended the fouling cycle by 1.5-2.0 times. Quantitative detection revealed that QX01 significantly reduced the concentrations of two AHLs (C4-HSL and C8-HSL), which were positively correlated with the contents of extracellular polymeric substances (EPS) (Pearson's r = 0.62-0.83, P < 0.01). This suggests that QX01 could perform its QQ activity robustly under antibiotic stress, thereby inhibiting EPS production (proteins especially) and biofilm formation. Moreover, QX01 notably altered the succession patterns of both sludge and fouling communities, with more pronounced effects on abundant taxa. Genera associated with AHL synthesis and EPS production, such as Terrimonas and Rhodobacter, were significantly depleted, contributing to the mitigated biofouling. Additionally, QX01 increased the bacterial community diversity (evenness especially), which was inhibited by antibiotics. Overall, we demonstrate that the novel QQ bacteria could be effective for biofouling control in antibiotic-stressed MBRs, though future work is needed to develop practical approaches for prolonging QQ activity.
Wetlands are major microplastic sinks with a large atmospheric input. However, many details of such deposited atmospheric microplastics entering into wetlands remain unclear, including temporal patterns of input and ecological effects. We monitored the aerial microplastics during four seasons in eleven economically developed cities along the lower reaches of the Yangtze River Basin, China. The average microplastic deposition rate was 512.31 items m−2 d−1, equivalent to an annual contribution of 17.46 metric tons of plastic to the surveyed wetlands with a total area of 1652 km2. These microplastics were predominantly composed of polyamide and polyethylene terephthalate with 61.85 ± 92.29 µm sized pellets, and we obtained similar results for microplastics intercepted on moss in wetlands. Microplastic input varied between wet and dry periods, primarily influenced by wind, rainfall and ozone concentration. Civilian vehicle density and textile industry were the primary socioeconomic factors driving microplastic deposition. Further indoor microcosm experiments revealed that moss phyllosphere bacterial community structure and function were influenced by microplastic abundance and size, exemplifying the unique ecological risks of aerially deposited microplastics to wetlands. These results indicate that mosses and their phyllosphere microbiota could serve as bio-indicators of aerial microplastic characteristics and impacts.
As a judicious correspondence to the classical maxcut, the anti-Cheeger cut has more balanced structure, but few numerical results on it have been reported so far. In this paper, we propose a continuous iterative algorithm (CIA) for the anti-Cheeger cut problem through fully using an equivalent continuous formulation. It does not need rounding at all and has advantages that all subproblems have explicit analytic solutions, the objective function values are monotonically updated and the iteration points converge to a local optimum in finite steps via an appropriate subgradient selection. It can also be easily combined with the maxcut iterations for breaking out of local optima and improving the solution quality thanks to the similarity between the anti-Cheeger cut problem and the maxcut problem. The performance of CIAs is fully demonstrated through numerical experiments on G-set from two aspects: one is on the solution quality where we find that the approximate solutions obtained by CIAs are of comparable quality to those by the multiple search operator heuristic method; the other is on the computational cost where we show that CIAs always run faster than the often-used continuous iterative algorithm based on the rank-two relaxation.
Existing diffusion models for low-light image enhancement typically incrementally remove noise introduced during the forward diffusion process using a denoising loss, with the process being conditioned on input low-light images. While these models demonstrate remarkable abilities in generating realistic high-frequency details, they often struggle to restore fine details that are faithful to the input. To address this, we present a novel detail-preserving diffusion model for realistic and faithful low-light image enhancement. Our approach integrates a size-agnostic diffusion process with a reverse process reconstruction loss, significantly enhancing the fidelity of enhanced images to their low-light counterparts and enabling more accurate recovery of fine details. To ensure the preservation of region- and content-aware details, we employ an efficient noise estimation network with a simplified channel-spatial attention mechanism. Additionally, we propose a multiscale ensemble scheme to maintain detail fidelity across diverse illumination regions. Comprehensive experiments on eight benchmark datasets demonstrate that our method achieves state-of-the-art results compared to over twenty existing methods in terms of both perceptual quality (LPIPS) and distortion metrics (PSNR and SSIM). The code is available at: https://github.com/CSYanH/DePDiff.
Surface ozone (O3) pollution affects air quality, human health, and the ecosystem. Understanding the complex non-linear relationship between ozone formation and its precursors, nitrogen oxides (NOx), and volatile organic compounds (VOCs) is critical for policymakers to mitigate the pollution. The Empirical Kinetic Modeling Approach (EKMA) based on classical observation-constrained zero-dimension box model provides the sensitivity of ozone production to precursor concentrations instead of emissions. This makes the box-model EKMA hard to apply in a real emission reduction scenario. Here, we developed an alternative box model approach driven by localized emissions, which are derived from the field-observed concentrations. This model approach reproduced the O3 variations well by capturing the short-term changes of NOx and AVOCs emissions among different phases of pollution control during the 31st World University Games in Chengdu in 2023. The EKMA analysis based on this model approach showed a different O3 response to precursor reductions from the concentration-constrained approach, which overestimated the baseline of O3 concentration. The result from the EKMA analysis demonstrated that the O3 level was most sensitive to NOx due to stringent control strategies during the event and rapidly rebounded to almost VOC-limited regime after the event. The effects of VOCs reduction on O3 control examined by concentration-constrained model approach were less pronounced than those by emission-driven approach due to the lack of consideration of the emission-to-reaction process. Our findings suggest that the emission-driven box model is applicable for developing O3 control strategy in the local scale.
In the context of supply chain digitization and green development in full swing, it is crucial to clarify the impact of the former on green energy innovation. Using exogenous shocks deriving from supply chain innovation and application pilot events, this study examines the impact of supply chain digitization on green energy innovation based on the data of Chinese listed companies from 2012 to 2021. The findings show that supply chain digitization significantly enhances corporate green energy innovation and that receivable asset management is a path mechanism for supply chain digitization to drive green energy innovation. Moreover, there is a significant positive intra-city spillover. Supply chain digitization contributes significantly to corporate green energy innovation in state-controlled manufacturing firms with effective internal controls in the eastern region. This study has important policy implications for promoting green energy innovation and accelerating the development of modern supply chain systems.
Numerical resolution of moderately high-dimensional nonlinear PDEs remains a huge challenge due to the curse of dimensionality for the classical numerical methods including finite difference, finite element and spectral methods. Starting from the weak formulation of the Lawson-Euler scheme, this paper proposes a stochastic particle method (SPM) by tracking the deterministic motion, random jump, resampling and reweighting of particles. Real-valued weighted particles are adopted by SPM to approximate the high-dimensional solution, which automatically adjusts the point distribution to intimate the relevant feature of the solution. A piecewise constant reconstruction with virtual uniform grid is employed to evaluate the nonlinear terms, which fully exploits the intrinsic adaptive characteristic of SPM. Combining both, SPM can achieve the goal of adaptive sampling in time. Numerical experiments on the 6-D Allen-Cahn equation and the 7- D Hamiltonian-Jacobi-Bellman equation demonstrate the potential of SPM in solving moderately high-dimensional nonlinear PDEs efficiently while maintaining an acceptable accuracy
The proliferation and spread of antibiotic-resistant bacteria (ARB) significantly threaten human health and ecosystem. Periodate (PI) based advanced oxidation process has potentials for water purification but limited by complex activators or activation process. Herein, we demonstrated that H2O2 could be used to activate PI, achieving efficient ARB disinfection performance. Particularly, we found that the PI/H2O2 system (0.1 mM for both oxidants) could inactivate ARB (Escherichia coli) within 35 min. The intracellular defense system attacked by HO· radicals generated in the disinfection system, resulting in the inactivation of ARB. Antibiotic resistance genes (ARGs) released with the lysis of cell membrane could be further degraded by HO· radicals. Moreover, we found that the PI/H2O2 system was effective to inactivate ARB in a broad range of ionic strengths, with coexisting common ions and humic acid, as well as in four typical actual water bodies. The PI/H2O2 system could also efficiently disinfect other types of bacteria and degrade typical organic contaminants. In addition, under sunlight irradiation, the ARB inactivation performance of the PI/H2O2 system could be greatly improved. This study provided a practical and efficient way for decontaminating ARB/ARGs-polluted water.
Coastal sediment cores provide important records of land-based antibiotics' deposition. This study examined sediment cores from the Hangzhou Bay, East China Sea, dating back to 1980–2020 using 210Pbex. The 40-year analysis revealed a mismatch between sediment depth and age. Wastewater treatment facilities have significantly reduced antibiotics discharge into the sea. We identified 27 antibiotics, with enrofloxacin (ERFX) and nadifloxacin (NDFX) exhibiting the highest average concentrations of 84.9 and 83.4 ng/g, respectively. Quinolones (QNs) were prominent, displaying strong co-occurrence and similar distribution patterns shaped by comparable soil-water distribution coefficient (Kd). QNs correlated positively with total antibiotic concentration, serving as indicators. We proposed a multi-dimensional risk assessment of antibiotics, encompassing ecological and antimicrobial resistance (AMR) risks, complementing each other. The assessment revealed antibiotics with distinct risks: sulfacetamide (SCM) and clindamycin (CLIN) exhibited high ecological risks, while ERFX, ciprofloxacin (CFX), norfloxacin (NFX), gatifloxacin (GTFX), moxifloxacin (MXFX), and marbofloxacin (MBFX) presented high AMR risks.
