The infamous numerical sign problem poses a fundamental obstacle to particle- based stochastic Wigner simulations in high-dimensional phase space. Although the existing particle annihilation (PA) via uniform mesh significantly alleviates the sign problem when dimensionality D <= 4, the mesh size grows dramatically when D >= 6 due to the curse of dimensionality and consequently makes the annihilation very inefficient. In this paper, we propose an adaptive PA algorithm, termed sequential-clustering particle annihilation via discrepancy estimation (SPADE), to overcome the sign problem. SPADE follows a divide-and-conquer strategy: adaptive clustering of particles via controlling their number-theoretic discrepancies and independent random matching in each cluster. The target is to alleviate the oversampling problem induced by the overpartitioning of phase space and to capture the nonclassicality of the Wigner function simultaneously. Combining SPADE with the variance reduction technique based on the stationary phase approximation, we attempt to simulate the proton-electron couplings in six- and 12-dimensional phase space. A thorough performance benchmark of SPADE is provided with the reference solutions in six-dimensional phase space produced by a characteristic-spectral-mixed scheme under a 733*803 uniform grid, which fully explores the limit of grid-based deterministic Wigner solvers.
Antibiotic-resistant bacteria (ARB) and antibiotic-resistant genes (ARGs) pose a significant threat to both ecosystems and human health. Owing to the excellent catalytic activity, eco-safety, and convenience for defect engineering, BiOBr with oxygen vacancies (OVs) of different density thus were fabricated and employed to activate H2O2 for ARB disinfection/ARGs degradation in present study. We found that BiOBr with OVs of appropriate density induced via ethanol reduction (BOB-E) could effectively activate H2O2, achieving excellent ARB disinfection and ARGs degradation efficiency. Moreover, this disinfection system exhibited remarkable tolerance to complex water environments and actual water conditions. In-situ characterization and theoretical calculations revealed that OVs in BOB-E could effectively capture and activate aqueous H2O2 into HO· and O2·−. The generated reactive oxygen species combined with electron transfer could damage the cell membrane system and degrade genetic materials of ARB, leading to effective disinfection. The impressive reusability, high performance achieved in two immobilized reaction systems (packed column and baffled ditch reactor), excellent degradation of emerging organic pollutants supported the feasibility of BOB-E/H2O2 system towards practical water decontamination. Overall, this study not only provides insights into fabrication of bismuth-based catalysts for efficient ARB disinfection/ARGs degradation via OVs regulation, but also paves the way for their practical applications.
In the era of a green economy, green innovation has become a way for enterprises to gain competitive advantage, and it is of great theoretical and practical significance to explore the driving force of enterprises' green innovation. This study explores the peer effect of an enterprise's green innovation and conducts an empirical test using data from 3338 Chinese listed companies in 2020. The results show a significant positive peer effect of enterprises' green innovation, and the green innovation of individual enterprises increases by 0.869 for each unit increase in industry-average green innovation. Further research shows that market power is the channel by which peer influence affects an enterprise's green innovation. Moreover, regional heterogeneity exists in the strength of the peer effect, which varies according to firm maturity and board size. These findings provide a reference for enterprises and governments to promote green transformation.
Sulphate-reducing microorganisms, or SRMs, are crucial to organic decomposition, the sulphur cycle, and the formation of pyrite. Despite their low energy-yielding metabolism and intense competition with other microorganisms, their ability to thrive in natural habitats often lacking sufficient substrates remains an enigma. This study delves into how Desulfovibrio desulfuricans G20, a representative SRM, utilizes photoelectrons from extracellular sphalerite (ZnS), a semiconducting mineral that often coexists with SRMs, for its metabolism and energy production. Batch experiments with sphalerite reveal that the initial rate and extent of sulphate reduction by G20 increased by 3.6 and 3.2 times respectively under light conditions compared to darkness, when lactate was not added. Analyses of microbial photoelectrochemical, transcriptomic, and metabolomic data suggest that in the absence of lactate, G20 extracts photoelectrons from extracellular sphalerite through cytochromes, nanowires, and electron shuttles. Genes encoding movement and biofilm formation are upregulated, suggesting that G20 might sense redox potential gradients and migrate towards sphalerite to acquire photoelectrons. This process enhances the intracellular electron transfer activity, sulphur metabolism, and ATP production of G20, which becomes dominant under conditions of carbon starvation and extends cell viability in such environments. This mechanism could be a vital strategy for SRMs to survive in energy-limited environments and contribute to sulphur cycling.
Polyhydroxyalkanoates (PHAs) are a class of microbially synthesized polyesters with diverse structures with renewability, biodegradability, good biocompatibility, and broad application prospects. However, the level of commercialization of PHAs remains low. The high recovery cost is one of the main reasons preventing the widespread use of these "green polymers". For decades, efforts have been made to explore lower-cost, greener, and more economical PHAs recovery strategies, and significant progress has been made. This review presents cell lysis and yeast surface display (YSD)-based bio-recovery strategies for PHAs, and then proposes a model hypothesis for protein-mediated secretion of PHAs drawing on the lipid secretion model to provide essential information for further cost reduction and efficiency in the recovery of PHAs. In addition, this review also highlights the bio-recovery strategy of extracellular PHAs based on synthetic biology and exploring specific PHAs secretion mechanism is a promising strategy for reducing the cost of PHAs recovery in the future.
The plastisphere may act as reservoir of antibiotic resistome, accelerating global antimicrobial resistance dissemination. However, the environmental risks in the plastisphere of field microplastics (MPs) in farmland remain largely unknown. Here, antibiotic resistance genes (ARGs) and virulence factors (VFs) on polyethylene microplastics (PE-MPs) and polybutylene adipate terephthalate and polylactic acid microplastics (PBAT/PLA-MPs) from residues were investigated using metagenomic analysis. The results suggested that the profiles of ARG and VF in the plastisphere of PBAT/PLA-MPs had greater number of detected genes with statistically higher values of diversity and abundance than soil and PE-MP. Procrustes analysis indicated a good fitting correlation between ARG/VF profiles and bacterial community composition. Actinobacteria was the major host for tetracycline and glycopeptide resistance genes in the soil and PE-MP plastisphere, whereas the primary host for multidrug resistance genes changed to Proteobacteria in PBAT/PLA-MP plastisphere. Besides, three human pathogens, Sphingomonas paucimobilis, Lactobacillus plantarum and Pseudomonas aeruginosa were identified in the plastisphere. The PE-MP plastisphere exhibited a higher transfer potential of ARGs than PBAT/PLA-MP plastisphere. This work enhances our knowledge of potential environmental risks posed by microplastic in farmland and provides valuable insights for risk assessment and management of agricultural mulching applications.
The atmospheric aqueous-phase chemistry has received increasing attention in the last decades for its non-negligible environmental significance. Yet, the insufficient experimental data on oxidative reaction rate constants (kaq) obstructs the further analysis and modeling of this system. Predictive models based on machine learning (ML) algorithms have shown potential as an effective estimation tool, however, they are restricted to the lack of training data as well. To overcome this data limitation, we developed multi-task (MT) models that could exploit the common knowledge from reactions in gas- and aqueous-phases simultaneously. Toward kaq of organic compounds with hydroxyl radical (OH), nitrate radical (NO3), and ozone (O3), the MT models showed a notably better predictive ability compared to benchmark models, while obtaining wide applicability on compounds from different chemical classes. By interpreting the models using Shapley additive explanations (SHAP), we evidenced that the MT models utilized the common knowledge in both phases and correctly identified the reaction mechanisms. This study aims to provide new insight into the estimation of necessary kinetic parameters in atmospheric aqueous-phase chemistry, as well as a reference to ML research for other predictive tasks of atmospheric interest.
Mineral scale refers to the hard inorganic solids nucleated on substrates or deposited from the aqueous phase. The formation and deposition of barium sulfate and strontium sulfate in various industries, such as water treatment and oilfield operations, can significantly impact facility operations, posing serious threats. Machine learning (ML) approaches have been adopted recently in scale threat predictions to address the limitations of conventional scaling prediction models. However, there are few reports on collecting sulfate mineral scaling data, employing ML methods for data analysis, and evaluating the modeling results to gain deeper insights of sulfate mineral scaling process and to improve the accuracy of sulfate scaling threat prediction. Despite comprehensive experimental studies, the literature does not provide adequate guidance for identifying the influence on the solubility of barium sulfate and strontium sulfate under different aqueous environments and actual operating conditions. To this end, this study collected 1600 experimental datasets of barium/strontium sulfate from the literature to construct and evaluate the reliability and versatility of a ML-based model for sulfate solubility calculations. Single neural networks, hybrid neural networks, and optimization algorithms were employed to build solubility prediction models for barium sulfate and strontium sulfate across a wide range of temperatures, pressures, and different ions. The model's applicability in predicting sulfate scaling threats in various actual operating environments demonstrated its broad usability, consistent with its actual performance. This study marks the first stride towards constructing a reliable model for identifying the scaling trends of barium sulfate and strontium sulfate across various operating conditions, underscoring the importance of developing robust and accurate prediction models to address challenges in various industrial systems.
Isoprene hydroxy peroxy radicals (ISOPOO), derived from isoprene oxidation by hydroxy radicals (OH), are key intermediates for ozone and secondary organic aerosol (SOA) formation in the atmosphere. Although ISOPOO-water complexes are ubiquitous, their impacts on ISOPOO chemistry remain obscure. Here the previously overlooked water effect on the bimolecular reaction kinetics of ISOPOO was investigated in an oxidative flow reactor. The major first-generation products of ISOPOO, isoprene hydroxy hydroperoxides (ISOPOOH), methacrolein (MACR), and methyl vinyl ketone (MVK), were measured simultaneously at various relative humidity (RH) with the help of a cold trap to avoid potential losses in direct gas sampling. We found that ISOPOO reactions were accelerated significantly under wet conditions, with a greater enhancement on 1,2-ISOPOO than 4,3-ISOPOO. 1,2-ISOPOOH yield appeared faster growth with RH than 4,3-ISOPOOH. MVK yield showed an upward-downward trend with RH, while MACR yield plateaued from 30% RH. To explain the enhancement in the ISOPOOH yield from 3% to 80% RH, the overall rate constants of 1,2-ISOPOO + HO2 and 4,3-ISOPOO + HO2 reactions at 80% RH should be 13 times and twice those at 3% RH, respectively. The empirical formulas were proposed for the first time to parameterize the water effect on ISOPOO + HO2 reactions. The updated kinetics of ISOPOO reactions were incorporated in a box model to simulate the RH-dependent ISOPOOH and C4 carbonyl yields under typical atmospheric conditions. High RH can enhance the ISOPOOH yield in urban, rural, and forest areas, and promote SOA formation correspondingly. Our findings shed light on the critical role of humidity in the reactions of ISOPOO and benefit evaluating the fate of isoprene and its impacts on air quality more accurately in the ambient atmosphere.
What promotes female empowerment and gender equality? We investigate how internal population mobility and social interaction foster the advancement of female empowerment and gender equality across diverse subpopulations. Using the urban-to-rural youth resettlement program in China during the 1970s — the Send-down Movement — as our empirical context, we find that rural females with greater exposure to urban youths have achieved higher levels of education, increased labor force participation, greater financial independence, enhanced autonomy in marital and fertility decisions, increased political engagement, heightened self-confidence, reduced risk aversion, and a stronger belief in gender-equal ideologies and social values. Our findings underscore the role of population mobility in disseminating gender-equal ideologies and practices, both through human capital formation and social interactions, leading to lasting impacts on female empowerment in traditional societies.
Bacterial community is strongly associated with activated sludge performance, but there still remains a knowledge gap regarding the rare bacterial community assembly and their influence on the system performance in industrial wastewater treatment plants (IWWTPs). Here, we investigated bacterial communities in 11 full-scale IWWTPs with similar process designs, aiming to uncover ecological processes and functional traits regulating abundant and rare communities. Our findings indicated that abundant bacterial community assembly was governed by stochastic processes; thereby, abundant taxa are generally present in wastewater treatment compartments across different industrial types. On the contrary, rare bacterial taxa were primarily driven by deterministic processes (homogeneous selection 61.9%-79.7%), thus they only exited in specific IWWTPs compartments and wastewater types. The co-occurrence networks analysis showed that the majority of keystone taxa were rare bacterial taxa, with rare taxa contributing more to network stability. Furthermore, rare bacteria rather than abundant bacteria in the oxic compartment contributed more to the degradation of xenobiotics compounds, and they were main potential drivers of pollutant removal. This study demonstrated the irreplaceable roles of rare bacterial taxa in maintaining system performance of IWWTPs, and called for environmental engineers and microbial ecologists to increase their attention on rare biosphere.
