Predicting highly dynamic traffic noise using rotating mobile monitoring and machine learning method.

Traffic noise, characterized by its highly fluctuating nature, is the second biggest environmental problem in the world. Highly dynamic noise maps are indispensable for managing traffic noise pollution, but two key difficulties exist in generating these maps: the lack of large amounts of fine-scale noise monitoring data and the ability to predict noise levels in the absence of noise monitoring data. This study proposed a new noise monitoring method, the Rotating Mobile Monitoring method, that combines the advantages of stationary and mobile monitoring methods and expands the spatial extent and temporal resolution of noise data. A monitoring campaign was conducted in the Haidian District of Beijing, covering 54.79 km of roads and a total area of 22.15 km2, and gathered 18,213 A-weighted equivalent noise (LAeq) measurements at 1-s intervals from 152 stationary sampling sites. Additionally, street view images, meteorological data and built environment data were collected from all roads and stationary sites. Using computer vision and GIS analysis tools, 49 predictor variables were measured in four categories, including microscopic traffic composition, street form, land use and meteorology. Six machine learning models and linear regression models were trained to predict LAeq, with random forest performing the best (R2 = 0.72, RMSE = 3.28 dB), followed by K-nearest neighbors regression (R2 = 0.66, RMSE = 3.43 dB). The optimal random forest model identified distance to the major road, tree view index, and the maximum field of view index of cars in the last 3 s as the top three contributors. Finally, the model was applied to generate a 9-day traffic noise map of the study area at both the point and street levels. The study is easily replicable and can be extended to a larger spatial scale to obtain highly dynamic noise maps.

DCML: Deep contrastive mutual learning for COVID-19 recognition.

COVID-19 is a form of disease triggered by a new strain of coronavirus. Automatic COVID-19 recognition using computer-aided methods is beneficial for speeding up diagnosis efficiency. Current researches usually focus on a deeper or wider neural network for COVID-19 recognition. And the implicit contrastive relationship between different samples has not been fully explored. To address these problems, we propose a novel model, called deep contrastive mutual learning (DCML), to diagnose COVID-19 more effectively. A multi-way data augmentation strategy based on Fast AutoAugment (FAA) was employed to enrich the original training dataset, which helps reduce the risk of overfitting. Then, we incorporated the popular contrastive learning idea into the conventional deep mutual learning (DML) framework to mine the relationship between diverse samples and created more discriminative image features through a new adaptive model fusion method. Experimental results on three public datasets demonstrate that the DCML model outperforms other state-of-the-art baselines. More importantly, DCML is easier to reproduce and relatively efficient, strengthening its high practicality.

Whole-Cell Biotransformation of Penicillin G by a Three-Enzyme Co-expression System with Engineered Deacetoxycephalosporin†C Synthase.

Deacetoxycephalosporin C synthase (DAOCS) catalyzes the transformation of penicillin G to phenylacetyl-7-aminodeacetoxycephalosporanic acid (G-7-ADCA) for which it depends on 2-oxoglutarate (2OG) as co-substrate. However, the low activity of DAOCS and the expense of 2OG restricts its practical applications in the production of G-7-ADCA. Herein, a rational design campaign was performed on a DAOCS from Streptomyces clavuligerus (scDAOCS) in the quest to construct novel expandases. The resulting mutants showed 25∼58 % increase in activity compared to the template. The dominant DAOCS variants were then embedded into a three-enzyme co-expression system, consisting of a catalase and an L-glutamic oxidase for the generation of 2OG, to convert penicillin G to G-7-ADCA in E. coli. The engineered whole-cell enzyme cascade was applied to an up-scaled reaction, exhibiting a yield of G-7-ADCA up to 39.21 mM (14.6 g ⋅ L-1 ) with a conversion of 78.42 mol %. This work highlights the potential of the integrated whole-cell system that may inspire further research on green and efficient production of 7-ADCA.