Effect of influent nutrient ratios and hydraulic retention time (HRT) on simultaneous phosphorus and nitrogen removal in a two-sludge sequencing batch reactor process

A laboratory-scale anaerobic–anoxic/nitrification sequencing batch reactor (A2N- SBR) fed with domestic wastewater was operated to examine the effect of varying ratios of influent COD/P, COD/TN and TN/P on the nutrient removal. With the increased COD/P, the phosphorus removals exhibited an upward trend. The influent TN/P ratios had a positive linear correlation with the phosphorus removal efficiencies, mainly because nitrates act as electron acceptors for the phosphorus uptake in the A2N-SBR. Moreover, it was found that lower COD/TN ratio, e.g. 3.5, did not significantly weaken the phosphorus removal, though the nitrogen removal first decreased greatly. The optimal phosphorus and nitrogen removals of 94% and 91%, respectively were achieved with influent COD/P and COD/ TN ratios of 19.9 and 9.9, respectively. Additionally, a real-time control strategy for A2N-SBR can be undertaken based on some characteristic points of pH, redox potential (ORP) and dissolved oxygen (DO) profiles in order to obtain the optimum hydraulic retention time (HRT) and improve the operating reliabili

One-step hydrothermal synthesis of fluorescence carbon quantum dots with high product yield and quantum yield

A one-step hydrothermal synthesis of nitrogen and silicon co-doped fluorescence carbon quantum dots (N,Si-CQDs), from citric acid monohydrate and silane coupling agent KH-792 with a high product yield (PY) of 52.56% and high quantum yield (QY) of 97.32%, was developed. This greatly improves both the PY and QY of CQDs and provides a new approach for a large-scale production of high-quality CQDs. Furthermore, N,Si-CQDs were employed as phosphors without dispersants to fabricate white light-emitting diodes (WLEDs) with the color coordinates at (0.29, 0.32). It is suggested that N,Si-CQDs have great potential as promising fluorescent materials to be applied in WLEDs.Peer reviewe

Lyapunov-type inequalities for quasilinear systems with antiperiodic boundary conditions

We establish some new Lyapunov-type inequalities for one-dimensional p-Laplacian systems with antiperiodic boundary conditions. The lower bounds of eigenvalues are presented.Встановлено дєякі нові нєрівності типу Ляпунова для одновимірних p-лапласових систем з антиперіодичними граничними умовами. Наведено нижні межі для власних значень

Neural Related Work Summarization with a Joint Context-driven Attention Mechanism

Conventional solutions to automatic related work summarization rely heavily on human-engineered features. In this paper, we develop a neural data-driven summarizer by leveraging the seq2seq paradigm, in which a joint context-driven attention mechanism is proposed to measure the contextual relevance within full texts and a heterogeneous bibliography graph simultaneously. Our motivation is to maintain the topic coherency between a related work section and its target document, where both the textual and graphic contexts play a big role in characterizing the relationship among scientific publications accurately. Experimental results on a large dataset show that our approach achieves a considerable improvement over a typical seq2seq summarizer and five classical summarization baselines.Comment: 11 pages, 3 figures, in the Proceedings of EMNLP 201

Learning Efficient Convolutional Networks through Irregular Convolutional Kernels

As deep neural networks are increasingly used in applications suited for low-power devices, a fundamental dilemma becomes apparent: the trend is to grow models to absorb increasing data that gives rise to memory intensive; however low-power devices are designed with very limited memory that can not store large models. Parameters pruning is critical for deep model deployment on low-power devices. Existing efforts mainly focus on designing highly efficient structures or pruning redundant connections for networks. They are usually sensitive to the tasks or relay on dedicated and expensive hashing storage strategies. In this work, we introduce a novel approach for achieving a lightweight model from the views of reconstructing the structure of convolutional kernels and efficient storage. Our approach transforms a traditional square convolution kernel to line segments, and automatically learn a proper strategy for equipping these line segments to model diverse features. The experimental results indicate that our approach can massively reduce the number of parameters (pruned 69% on DenseNet-40) and calculations (pruned 59% on DenseNet-40) while maintaining acceptable performance (only lose less than 2% accuracy)

RainDiffusion:When Unsupervised Learning Meets Diffusion Models for Real-world Image Deraining

What will happen when unsupervised learning meets diffusion models for real-world image deraining? To answer it, we propose RainDiffusion, the first unsupervised image deraining paradigm based on diffusion models. Beyond the traditional unsupervised wisdom of image deraining, RainDiffusion introduces stable training of unpaired real-world data instead of weakly adversarial training. RainDiffusion consists of two cooperative branches: Non-diffusive Translation Branch (NTB) and Diffusive Translation Branch (DTB). NTB exploits a cycle-consistent architecture to bypass the difficulty in unpaired training of standard diffusion models by generating initial clean/rainy image pairs. DTB leverages two conditional diffusion modules to progressively refine the desired output with initial image pairs and diffusive generative prior, to obtain a better generalization ability of deraining and rain generation. Rain-Diffusion is a non adversarial training paradigm, serving as a new standard bar for real-world image deraining. Extensive experiments confirm the superiority of our RainDiffusion over un/semi-supervised methods and show its competitive advantages over fully-supervised ones.Comment: 9 page

Inactivation and adaptation of ammonia-oxidizing bacteria and nitrite-oxidizing bacteria when exposed to free nitrous acid

Inactivation and adaptation of ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) to free nitrous acid (FNA) was investigated. Batch test results showed that AOB and NOB were inactivated when treated with FNA. After an 85-day operating period, AOB in a continuous pre-denitrification reactor did not adapt to the FNA that was applied to treat some of the return activated sludge. In contrast, NOB did adapt to FNA. NOB activity in the seed sludge was only 11% of the original activity after FNA batch treatment, at 0.75 mg HNO2-N/L. NOB activity in the pre-denitrification reactor was not affected after being exposed to this FNA level. Nitrosomonas was the dominant AOB before and after long-term FNA treatment. However, dominant NOB changed from Nitrospira to Candidatus Nitrotoga, a novel NOB genus, after long-term FNA treatment. This adaptation of NOB to FNA may be due to the shift in NOB population makeup