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As high-temperature-resistant inorganic fibers, Al 2 O 3 fibers possess high-temperature resistance, high-temperature oxidation resistance, and chemical stability, and thus are suitable for applications such as high temperature catalysis and filtration. However, their shortcomings, such as high brittleness and high fracturability, limit their applications. In this study, a flexible Al 2 O 3 /aramid composite felt was prepared by introducing aramid fibers into Al 2 O 3 fiber filaments, and the flexibility of the prepared composite felt was improved by using needle punching. The influence of the Al 2 O 3 /aramid doping ratio on the thermal insulation performance of the composite felt was explored. The findings showed that as the Al 2 O 3 /aramid doping ratio increased, the thermal insulation performance of the composite felt improved, whereas its thermal conductivity decreased. At room temperature of 25 °C and with an Al 2 O 3 /aramid doping ratio of 12:1, the thermal conductivity of the composite felt reached its minimum value of 0.0306 W m−1 K−1, while its compression and fracture moduli became 0.4 MPa and 21.5 GPa, respectively. The thermal-insulation mechanism of the composite felt was also explored in the study. [ABSTRACT FROM AUTHOR]

As high-temperature-resistant inorganic fibers, Al 2 O 3 fibers possess high-temperature resistance, high-temperature oxidation resistance, and chemical stability, and thus are suitable for applications such as high temperature catalysis and filtration. However, their shortcomings, such as high brittleness and high fracturability, limit their applications. In this study, a flexible Al 2 O 3 /aramid composite felt was prepared by introducing aramid fibers into Al 2 O 3 fiber filaments, and the flexibility of the prepared composite felt was improved by using needle punching. The influence of the Al 2 O 3 /aramid doping ratio on the thermal insulation performance of the composite felt was explored. The findings showed that as the Al 2 O 3 /aramid doping ratio increased, the thermal insulation performance of the composite felt improved, whereas its thermal conductivity decreased. At room temperature of 25 °C and with an Al 2 O 3 /aramid doping ratio of 12:1, the thermal conductivity of the composite felt reached its minimum value of 0.0306 W m−1 K−1, while its compression and fracture moduli became 0.4 MPa and 21.5 GPa, respectively. The thermal-insulation mechanism of the composite felt was also explored in the study. [ABSTRACT FROM AUTHOR]

[Display omitted] • A HN-AD bacterium, Alcaligenes faecalis TF-1 was isolated from landfill leachate. • NH 4 +-N tolerance of the strain TF-1 reached 2000 mg·L−1 with 65% of NH 4 +-N removal. • 52.87% of NH 4 +-N removal was realized under 70 g·L−1 salinity and 919.20 mg·L−1 NH 4 +-N. • The optimum C/N, pH and carbon source of the strain were 16, 7 and sodium citrate. • HN-AD pathway of TF-1 was revealed: NH 4 +-N → NH 2 OH → NO → N 2 O → N 2. A novel heterotrophic nitrifying bacterium with high salt and high ammonia nitrogen tolerance, Alcaligenes faecalis TF-1, was isolated from the leachate of a landfill. The verification of nitrogen removal efficiency of different nitrogen sources and PCR amplification electrophoresis results showed that the HN-AD pathway of the strain TF-1 was NH 4 + → NH 2 OH → NO → N 2 O → N 2. The results of parameter optimization showed that the optimal nitrogen removal conditions were as follows: sodium citrate as carbon source, C/N = 16, pH = 7, and NH 4 +-N loading of 808.21 mg/L. The strain TF-1 could remove about 94.60% of ammonia nitrogen (1963.94 mg/L). The salinity tolerance range of the strain TF-1 was 0–70 g/L, and the removal efficiency was 52.87% at salinity 70 g/L and NH 4 +-N concentration 919.20 mg/L and 55.67% at pH = 10 and NH 4 +-N concentration 994.82 mg/L. The extreme environmental adaptability and remarkable nitrogen removal performance make this strain a promising candidate in leachate treatment. [ABSTRACT FROM AUTHOR]

This paper presents a convolutional neural network (CNN) based filter bank multicarrier (FBMC) system for underwater acoustic (UWA) communications. Different from traditional FBMC receivers that only detect the transmitted symbols after expliciting channel estimation and equalization, the proposed system takes a pre-trained CNN model as a receiver to recover the transmitted symbols directly and avoid the inherent imaginary interference. At the offline training stage, the CNN model takes the known transmitted data as the labels and the received data as input for iterative learning. At the testing stage, unlabeled received data are directly output to CNN to estimate the online transmitted symbols. The CNN based UWA FBMC system is analysed under various system parameters such as input permutations, convolution kernels and prototype filters, and sufficient communication data are generated according to the measured UWA channel impulse responses. Simulation results show that the proposed system achieves admirable performance for signal detection compared to the previous UWA FBMC system. [ABSTRACT FROM AUTHOR]

[Display omitted] • Performance of MBBR inoculated with bacteria was improved by Acinetobacter sp. TAC-1. • TN removal rate was increased from 16 mg/L/h to 24 mg/L/h by the bioaugmentation. • 86.40% of TN in real swine wastewater was removed by bioaugmented MBBR within 24 h. • Bioaugmentation mechanism was revealed by the functional bacteria, enzyme and gene. To enhance the performance of moving bed biofilm reactor (MBBR) inoculated with heterotrophic nitrification-aerobic denitrification (HN-AD) bacteria, bioaugmentation with Acinetobacter sp. TAC-1 was firstly employed and then the treatment performance for real swine wastewater was presented in this study. Results indicated that NH 4 +-N and TN removal rates of bioaugmented reactor were significantly improved from 16.53 mg/L/h and 16.15 mg/L/h to 24.58 mg/L/h and 24.45 mg/L/h, respectively. The efficient removal performance (NH 4 +-N 95.01%, TN 86.40%) for real swine wastewater was achieved within 24 h. Microbial analysis indicated that the composition of functional bacteria varied with the introduction of Acinetobacter sp. TAC-1, especially the abundance of Acinetobacter , Paracoccus and Rhodococcus related to the nitrogen removal. Furthermore, bioaugmentation with Acinetobacter sp. TAC-1 increased abundance of enzymes and functional genes (nirS, nirK and norZ) corresponding to denitrification that may be responsible for the enhanced nitrogen removal performance. [ABSTRACT FROM AUTHOR]

To improve the low pollutant removal efficiency of traditional biological methods for treating livestock and poultry breeding wastewater under a relatively low temperature, a rotating biological contactor (RBC) inoculated with heterotrophic nitrification-aerobic denitrification (HN-AD) bacteria was designed. A quick start-up process and a well removal performance had been achieved in the novel RBC. To elucidate the anti-load shock ability of the novel RBC, the effects of C/N ratio on nitrogen removal and microbial assemblage were focused under a low temperature (12 ± 2 °C). Results showed that the highest NH 4 +-N and TN removal efficiency were 99.57 ± 0.31% and 68.41 ± 0.52%. Microbial diversity analysis based on high throughput sequencing technique showed that Arcobacter and Flavobacterium with an increasing relative abundance were the key to ensure high nitrogen removal efficiently at a low C/N ratio and temperature. Moreover, nitrogen transferring pathways of the novel RBC was revealed and dissimilatory nitrate reduction and denitrification were the main pathways. The excellent pollutant removal performance demonstrates that the novel RBC is a promising process to effectively treat wastewater with low C/N ratio and low temperature. [Display omitted] • Rotating biological contactor (RBC) started up by HN-AD bacteria has been proposed. • A short start-up time and a high nitrogen removal efficiency were achieved. • Impact of C/N ratio on pollutant removal and microbial assemblage were evaluated. • Arcobacter and Flavobacterium played a key role at low C/N ratio and temperature. • DNRA and denitrification were the main nitrogen transferring pathways in the H-RBC. [ABSTRACT FROM AUTHOR]

• HN-AD bacteria and microalgae firstly combined to construct a novel symbiotic system. • The optimal volume ratio was 1/3 and TN and TP removal increased by 28.9% and 67.6%. • Ammonia tolerance of algae increased to 500 mg/L and gas–water ratio decreased to 50. • Halomonas and Rhodocyclaceae play key roles in nitrogen and phosphorus removal. • NarG, narH and narL genes are the main nitrogen removal genes of key HN-AD bacteria. A novel symbiotic system combined by heterotrophic nitrification-aerobic denitrification (HN-AD) mixed bacteria and Chlorella pyrenoidosa was firstly proposed to resolve the poor tolerance and nitrogen removal performance of traditional symbiotic system for treating high ammonia biogas slurry. Results showed that the volume ratio of bacteria to algae had significant effects on nitrogen removal efficiency, microbial community structure, functional bacteria and genes. The optimal ratio was 1/3, and the average removal efficiency of TN and TP increased by 28.9% and 67.6% respectively, compared to those of HN-AD bacteria. High-throughput sequencing indicated nitrogen removal was jointly completed by HN-AD and heterotrophic denitrification. HN-AD bacteria Halomonas and Pseudomonas played a key role in nitrogen removal, and Rhodocyclaceae and Paracoccus took an important part in phosphorus removal. According to the functional gene prediction, the total relative abundance of nitrogen removal genes (0.0127%) and narG, narH and narL genes (0.0054%) were highest in 1/3 system. [ABSTRACT FROM AUTHOR]

[Display omitted] • Operation parameter for MBBR firstly inoculated with HN-AD bacteria was optimized. • Nitrogen removal efficiency of PVA gel was double that of K1 at low C/N ratios. • PVA gel was beneficial to the growth of oligotrophic and HN-AD bacteria. • HN-AD bacteria Paracoccus and Acinetobacter played a key role at low C/N ratios. • DNRA and denitrification were the main nitrogen transforming pathway. To operate the moving bed biofilm reactor inoculated with HN-AD bacteria (B-MBBR) instead of activated sludge for livestock and poultry breeding wastewater (LPBW) disposal in most efficient manner, nitrogen removal (NR) efficiency and microbial composition of two MBBRs with different carrier types under various C/N ratios were explored. Results indicated that the performance on NR greatly various in different carrier types under various C/N ratios. Attributing to the bacterial protection provided by the porous structure of polyvinyl alcohol (PVA) gel, MBBR using PVA gel as the carrier exhibited a more stable NR performance (range from 78.05% to 83.76%) versus that using Kaldnes (K1) as the carrier (range from 78.05% to 83.76%). Besides, microbial analysis indicated that MBBR with PVA gel as the carrier is conducive to the growth of oligotrophic and HN-AD bacteria (Paracoccus and Acinetobacter), and the highest relative abundance was 16.37% at C/N ratio of 6. [ABSTRACT FROM AUTHOR]

Predictive analytics aims to build machine learning models to predict behavior patterns and use predictions to guide decision-making. Predictive analytics is human involved, thus the machine learning model is preferred to be interpretable. In literature, Generalized Additive Model (GAM) is a standard for interpretability. However, due to the one-to-many and many-to-one phenomena which appear commonly in real-world scenarios, existing GAMs have limitations to serve predictive analytics in terms of both accuracy and training efficiency. In this paper, we propose FXAM (Fast and eXplainable Additive Model), a unified and fast interpretable model for predictive analytics. FXAM extends GAM's modeling capability with a unified additive model for numerical, categorical, and temporal features. FXAM conducts a novel training procedure called Three-Stage Iteration (TSI). TSI corresponds to learning over numerical, categorical, and temporal features respectively. Each stage learns a local optimum by fixing the parameters of other stages. We design joint learning over categorical features and partial learning over temporal features to achieve high accuracy and training efficiency. We prove that TSI is guaranteed to converge to the global optimum. We further propose a set of optimization techniques to speed up FXAM's training algorithm to meet the needs of interactive analysis. Thorough evaluations conducted on diverse data sets verify that FXAM significantly outperforms existing GAMs in terms of training speed, and modeling categorical and temporal features. In terms of interpretability, we compare FXAM with the typical post-hoc approach XGBoost+SHAP on two real-world scenarios, which shows the superiority of FXAM's inherent interpretability for predictive analytics. • A unified and fast interpretable model (FXAM) for predictive analytics is proposed. • FXAM extends modeling capability and efficiency of Generalized Additive Model. • FXAM conducts a novel training procedure called Three-Stage Iteration. • Joint learning and partial learning help achieve high accuracy and efficiency. • FXAM performs much better on synthetic data sets and 13 real datasets. [ABSTRACT FROM AUTHOR]