Underwater wireless optical communication has attracted widespread attention due to its advantages of high bandwidth and low delay. Seawater environment contains different substances, which will affect the received intensity and time delay of communication. This paper proposes an adaptive Monte-Carlo method to analyze the impact of zooplankton on the received intensity, receiving time, spatial distribution of energy, transmission distance and misalignment. According to the simulation results, when seawater contains more zooplankton, the received intensity is weaker, the receiving time is longer, and the energy is more dispersed.
Fenton reaction is an effective method to remove refractory organics such as carbamazepine (CBZ) from water streams. Nevertheless, its application is greatly compromised by extra hydrogen peroxide (H2O2) addition and iron mud accumulation. Herein, Fenton-like process with in situ produced H2O2 by biosynthesized palladium nanoparticles (bioPd-NPs) and natural iron-bearing clay minerals is proposed for CBZ degradation. The bioPd-NPs prepared by Shewanella loihica PV-4 were in the size range of 5–20 nm, which catalyzed the in situ production of H2O2 from formic acid (FA) and oxygen. Then the in situ generated H2O2 underwent Fenton-like reactions with nontronite for CBZ degradation. With bioPd-NPs and nontronite dosage of 1 g/L and FA concentration of 20 mM, the complete CBZ (10 mg/L) degradation was achieved within 60 min. Oxidative radicals such as HO· and H2O2 generated in our constructed system played key roles in CBZ degradation. Intermediates/products identification and theoretical calculation revealed that hydroxylation was the main CBZ degradation pathway. This work provides a promising Fenton-like technology for elimination of CBZ from environment with prevention of additional H2O2 supplementation and excessive iron mud production.
Spiking Neural Networks (SNNs) have attracted enormous research interest due to temporal information processing capability, low power consumption, and high biological plausibility. However, the formulation of efficient and high-performance learning algorithms for SNNs is still challenging. Most existing learning methods learn weights only, and require manual tuning of the membrane-related parameters that determine the dynamics of a single spiking neuron. These parameters are typically chosen to be the same for all neurons, which limits the diversity of neurons and thus the expressiveness of the resulting SNNs. In this paper, we take inspiration from the observation that membrane-related parameters are different across brain regions, and propose a training algorithm that is capable of learning not only the synaptic weights but also the membrane time constants of SNNs. We show that incorporating learnable membrane time constants can make the network less sensitive to initial values and can speed up learning. In addition, we reevaluate the pooling methods in SNNs and find that max-pooling will not lead to significant information loss and have the advantage of low computation cost and binary compatibility. We evaluate the proposed method for image classification tasks on both traditional static MNIST, Fashion-MNIST, CIFAR-10 datasets, and neuromorphic N-MNIST, CIFAR10-DVS, DVS128 Gesture datasets. The experiment results show that the proposed method outperforms the state-of-the-art accuracy on nearly all datasets, using fewer time-steps. Our codes are available at https://github.com/fangwei123456/Parametric-Leaky-Integrate-and-Fire-Spiking-Neuron.
Despite growing attention to living conditions as a social determinant of health, few studies have focused on its diverse impacts on self-rated health. Using data from the China Family Panel Study in 2018, this study used logistic regression analysis to examine how living conditions affect self-rated health in China, finding that people cooking with sanitary water and clean fuel were more likely to report good health, and that homeownership was associated with higher self-rated health. The self-rated health of people living in high-quality housing was lower than that of people living in ordinary housing, and people living in tidy homes were more likely to report good health. The findings suggest that the link between multiple living conditions and self-rated health is dynamic. Public health policies and housing subsidy programs should therefore be designed based on a comprehensive account of not only housing grade or income status, but also whole dwelling conditions.
We utilize an unprecedented liberalization episode in China, namely its World Trade Organization accession, to estimate the impact of trade liberalization on firm markup and markup distribution. Using a panel data quantile regression, we show that the impact of tariff reduction on markup can be heterogeneous to different firms, resulting in an unevenly distributed markup change across firms. In particular, reduction in output tariff reduces markup and markup dispersion, while reduction in input tariff increases markup and markup dispersion.
In this study, batch experiments were conducted to investigate the immobilization of HMs (Cr and Pb) by DOM derived from biochar in the presence and absence of zero-valent iron (Fe) in nitrate and HMs co-contaminated groundwater. Both Cr and Pb were removed effectively in biochar-Fe aqueous systems, while only Pb could be mitigated in biochar systems. Excitation-emission spectrophotometry combined with parallel factor analysis (EEM-PARAFAC) revealed that DOM released from biochar mainly contained human-like and tryptophan-like substances. Moreover, the fluorescence of hemic-like components could be quenched differently by the complexation of HMs, which proved the different removal efficiencies of Cr and Pb in biochar aqueous phase. In biocharFe aqueous systems, Fe-C micro-electrolysis was formed in prior to the complexation of DOM-Fe hydroxides. Thus, the chemical reduction was the primary way to removal HMs in batch-Fe systems, which was corresponding with the less variation of DOM components when adding Cr and Pb into aqueous systems. Besides, the observed DOM components with higher aromaticity and humification after adding Cr and Pb, further indicated the complexation of DOM-HMs through the analysis of adsorption and fluorescence indices. These results will provide new insights into the HMs retention on biochar, particularly for the role of Fe on the complexation process. (c) 2021 Elsevier B.V. All rights reserved.
Developing efficient pharmaceuticals and personal care products (PPCPs) degradation technologies is of scientifical and practical importance to restrain their discharge into natural water environment. This study fabricated and applied a composite material of amorphous MnO2 nanoparticles in-situ anchored titanate nanotubes (AMnTi) to activate peroxymonosulfate (PMS) for efficient degradation and mineralization of carbamazepine (CBZ). The degradation pathway and toxicity evolution of CBZ during elimination were deeply evaluated through produced intermediates identification and theoretical calculations. AMnTi with a composition of (0.3MnO2)•(Na1.22H0.78Ti3O7) offered high activation efficiency of PMS, which exhibited 21- and 3-times degradation rate of CBZ compared with the pristine TNTs and MnO2, respectively. The high catalytic activity can be attributed to its unique structure, leading to a lattice shrinkage and small pores to confine the PMS molecule onto the interface. Therefore, efficient charge transfer and catalytic activation through MnOTi linkage occurred, and a MnTi cycle mediating catalytic PMS activation was found. Both hydroxyl and sulfate radicals played key roles in CBZ degradation. Theoretical calculations, i.e., density functional theory (DFT) and computational toxicity calculations, combined with intermediates identification revealed that CBZ degradation pathway was hydroxyl addition and NC cleavage. CBZ degradation in this system was also a toxicity-attenuation process.
In order to obtain in-depth insight of the behavioral fate and ecological risks of antibiotics in coastal environment, this study investigated the distribution, partitioning and primary influencing factors of antibiotics in water and sediment in the East China Sea. After quantification of 77 target antibiotics in 6 categories, ten antibiotics were detected simultaneously with a detection frequency >50.0% in water and sediment; the concentrations of these ten antibiotics were 0.1–1508.0 ng L−1 and 0.01–9.4 ng g−1 in water and dry sediment, respectively. Sulfadiazine and Azithromycin (Pseudo partitioning coefficient were 28–3814 L kg−1 and 21–2405 L kg−1, respectively.) had the largest partitioning coefficient between sediment and water. In addition, pseudo partitioning coefficient of Sulfadiazine and Clindamycin were higher than the values of corresponding equilibrium partitioning constant (Kd), which would likely cause them to re-release from sediment to water. Compared to the physiochemical properties of the sediment, water quality has a greater impact on antibiotic partitioning. We found that the partitioning of antibiotics was significantly positively correlated with salinity, suspended solids, pH, NH4+-N and Zn; and negatively correlated with temperature, dissolved oxygen, PO43−, chemical oxygen demand, NO3−-N, oil, Cu and Cd. The ecological risks of antibiotics in water and sediment were also evaluated for revealing their relationship with the concentration partitioning of antibiotics. Results showed that the target antibiotics mainly pose ecological risks to Daphnia with low and median chronic toxicity risk rather than fish and green algae. The antibiotics in sediment were more chronically toxic to Daphnia than that in water. The risk quotient ratio of sediment and water (RQs/RQw) ranged from 0 to 1154.0, which were exactly opposite of the values of organic carbon normalized partition coefficient (Koc), suggesting that the physical properties of antibiotics drove the ecological risk allocation of antibiotics in sediment and water.