Change in sludge settling and filtration properties and membrane fouling trends in MBR activated sludge systems operated at different solids and hydraulic retention times

Membrane bioreactor (MBR) activated sludge process is increasingly used in wastewater treatment due to its excellence in solid-liquid separation and superior effluent quality, smaller bioreactor volume and foot print. However, operational issues such as membrane fouling and sludge bulking affect its broad applications. As solids retention time (SRT) and hydraulic retention time (HRT) are the most important operating parameters in activated sludge systems, this research determined the effect of different SRTs (180 d, 90 d and 45 d) and HRTs (24 h, 12 h, and 6 h) on the change in sludge settling and filtration properties and membrane fouling trends while keeping the SRT/HRT ratio constant throughout the study period. The biomass concentrations increased from about 8,000 to 10,000 mg COD/L as SRT and HRT decreased proportionally. As SRT decreased to 45 d and HTR decreased to 6 h, significant sludge bulking and poor filtration with high Time to Filter (TTF) values were observed, largely due to the operation at low DO concentrations under high organic loading conditions. However, the system recovered in about 50 d after correction of low DO concentrations in the MBR

Identification of Protein Pupylation Sites Using Bi-Profile Bayes Feature Extraction and Ensemble Learning

Pupylation, one of the most important posttranslational modifications of proteins, typically takes place when prokaryotic ubiquitin-like protein (Pup) is attached to specific lysine residues on a target protein. Identification of pupylation substrates and their corresponding sites will facilitate the understanding of the molecular mechanism of pupylation. Comparing with the labor-intensive and time-consuming experiment approaches, computational prediction of pupylation sites is much desirable for their convenience and fast speed. In this study, a new bioinformatics tool named EnsemblePup was developed that used an ensemble of support vector machine classifiers to predict pupylation sites. The highlight of EnsemblePup was to utilize the Bi-profile Bayes feature extraction as the encoding scheme. The performance of EnsemblePup was measured with a sensitivity of 79.49%, a specificity of 82.35%, an accuracy of 85.43%, and a Matthews correlation coefficient of 0.617 using the 5-fold cross validation on the training dataset. When compared with other existing methods on a benchmark dataset, the EnsemblePup provided better predictive performance, with a sensitivity of 80.00%, a specificity of 83.33%, an accuracy of 82.00%, and a Matthews correlation coefficient of 0.629. The experimental results suggested that EnsemblePup presented here might be useful to identify and annotate potential pupylation sites in proteins of interest. A web server for predicting pupylation sites was developed

Strength analysis of excavator bucket based on normal digging trajectory and limiting digging force

In view of the phenomenon that the excavator bucket is damaged before it reaches the theoretical life in the actual normal digging process. Based on the continuous trajectory theory, the three-segment continuous trajectory excavated alternately by bucket and rod is selected as the normal digging trajectory. The theoretical digging force (TDF) and limiting digging force (LDF) on the normal digging trajectory are calculated, compared, and analyzed. The influence of bucket structure strength and modal under two different digging force loads on normal excavation trajectory is analyzed. The constrained mode and free mode analysis of the bucket are carried out, and the modal analysis results are compared with the strength analysis results. The results show that on the selected normal digging trajectory, the LDF considering normal force and resistance moment is generally larger than the TDF, and the influence of the LDF load on the bucket structure strength is also larger. The results provide an explanation for the premature damage of the bucket in the process of normal digging

COVID-19 transmission inside a small passenger vessel: risks and mitigation

The global shipping industry has been severely influenced by the COVID-19 pandemic; in particular, a significant amount of passenger transportation has been suspended due to the concern of COVID-19 outbreak, as such voyages confine a dense crowd in a compact space. In order to accelerate the recovery of the maritime business and minimise passengers' risk of being infected, this work has developed a computational model to study the airborne transmission of COVID-19 viruses in the superstructure of a full-scale passenger vessel. Considering the vessel advancing in open water, simulations were conducted to study the particulate flow due to an infected person coughing and speaking, with the forward door open and closed. The results suggest that keeping the forward door closed will help prevent the external wind flow spreading the virus. When the forward door is closed, virus particles' coverage is shown to be limited to a radius of half a metre, less than a seat's width. Thus, an alternate seat arrangement is suggested. Furthermore, investigations were conducted on the influence of wall-mounted Air Conditioner (AC) on the virus transmission, and it was found that controlling the AC outlet direction at less than 15° downward can effectively limit the virus spread. Meanwhile, it was demonstrated that an AC's backflow tends to gather virus particles in a nearby area, thus sitting farther from an opening AC may reduce the risk of being infected. Overall, this work is expected to inform hygienic guidelines for operators to counter COVID-19 and potentially similar viruses in the future

UWB sensor based indoor LOS/NLOS localization with support vector machine learning

Ultra-wideband (UWB) sensor technology is known to achieve high-precision indoor localization accuracy in line-of-sight (LOS) environments, but its localization accuracy and stability suffer detrimentally in non-line-of-sight (NLOS) conditions. Current NLOS/LOS identification based on channel impulse response’s (CIR) characteristic parameters (CCP) improves location accuracy, but most CIR-based identification approaches did not sufficiently exploit the CIR information and are environment specific. This paper derives three new CCPs and proposes a novel two-step identification/classification methodology with dynamic threshold comparison (DTC) and the fuzzy credibility-based support vector machine (FC-SVM). The proposed SVM based classification methodology leverages on the derived CCPs obtained from the waveform and its channel analysis, which are more robust to environment and obstacles dynamic. This is achieved in two-step with a coarse-grained NLOS/LOS identification with the DTC strategy followed by FC-SVM to give the fine-grained result. Finally, based on the obtained identification results, a real-time ranging error mitigation strategy is then designed to improve the ranging and localization accuracy. Extensive experimental campaigns are conducted in different LOS/NLOS scenarios to evaluate the proposed methodology. The results show that the mean LOS/NLOS identification accuracy in various testing scenarios is 93.27 %, and the LOS and NLOS recalls are 94.27 % and 92.57 %, respectively. The ranging errors in LOS(NLOS) conditions are reduced from 0.106 m(1.442 m) to 0.065 m(0.739 m), demonstrating an improvement of 38.85 %(48.74 %) with 0.041 m(0.703 m) error reduction. On the other hand, the average positioning accuracy is also reduced from 0.250 m to 0.091 m with an improvement of 63.49 %(0.159 m), which outperforms the state-of-the-art approaches of the Least-squares support vector machine (LS-SVM) and K-Nearest Neighbor (KNN) algorithms