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Finally, this review gives well-synchronized approaches to get more insights into these innovative methodologies and techniques and their use for various industrial applications.Small pore zeolites with chabazite structure have been commercialized for selective catalytic reduction (SCR) of nitrogen oxides (NOx) with ammonium (NH3) from diesel exhaust. However, conventional zeolite synthesis processes detrimental effects on the environment due to the consumption of large amount of water, organic templates. Thus, this study proposed a green synthesis process with addition of minimal amount of water, structure directing agent and shortened steps to prepare nano-sized SSZ-13 (0.12 μm) using trans-crystallization strategy and exhibited enhanced performance for NOx removal after copper ion-exchange. The operation temperature window (NOx conversion >90 %) as well as the SO2 and H2O resistance over the green-route prepared nano-sized SSZ-13 (178-480 °C) outperformed the conventional SSZ-13 (29.8 μm, 211-438 °C) mainly due to the much shorter diffusion path. This clearly implied that the mass transportation was key for NH3-SCR of NOx on such small pore zeolite catalysts, which was further confirmed via an in-depth mass transportation calculation process. These results demonstrate that the Cu-nano-sized SSZ-13 prepared by the environmental benign route has great potential to act as a new generation of deNOx catalyst for diesel exhaust and provided a guideline for researchers to develop new methods to synthesize nano-catalysts for air pollution control.Volatile organic compounds (VOCs) release from asphalt pavement construction is a potential emission source. The detailed emission behaviors were simulated in the laboratory, and the corresponding environmental impact was investigated as well. A set of dedicated devices were applied to mirror 3 representative scenarios namely mixture plant, transportation and paving processes with VOCs emission concentrations varied from 4.24 mg/m3 to 104.16 mg/m3. Ozone formation potential (OFP) and secondary organic aerosol (SOA) were built to evaluate the environmental impact, indicating that the reactive ability differed in the specified substances. The alkenes (n ≤ 4) and aldehydes, alkanes (n ≥ 6) and alkylbenzenes with relative lower concentration were the main sources for the OFP and SOA generation, and they contributed to more than 62% OFP and 97% SOA respectively. The top 10 contributors to concentration, OFP and SOA had been identified. For the complex species existed in VOCs emission and the lack of VOCs control standards, this study provided possible access to screen priority-controlled pollutants based on information entropy method, in terms of both environmental and human health impact. In addition, the first-class priority-controlled species had been determined, urgently needing more attention in the future VOCs management during asphalt pavement construction.Rivers are a significant reservoir of antibiotic resistance genes (ARGs), yet the biogeographic pattern of riverine ARGs and its underlying driving forces remain poorly understood. Here, we used metagenomic approach to investigate the spatio-temporal variation of ARGs in two adjacent sub-watersheds viz. North River (NR) and West River (WR), China. The results demonstrated that Bacitracin (22.8 % of the total ARGs), multidrug (20.7 %), sulfonamide (15.2 %) and tetracycline (10.9 %) were the dominant ARG types. SourceTracker analysis indicated that sewage treatment plants as the main source of ARGs, while animal feces mainly contributed in spreading the ARGs in the upstream of NR. Random forest and network analyses confirmed that NR was under the influence of fecal pollution. PCoA analysis demonstrated that the composition of ARGs changed along with the anthropogenic gradients, while the Raup-Crick null model showed that homogenizing selection mediated by class 1 integron intI1 resulted in stable ARG communities at whole watershed scale. Structural equation models revealed that microbial community, grassland and several non-antibiotic micropollutants may also play certain roles in influencing the distribution of ARGs. Overall, the observed deterministic formation of ARGs in riverine systems calls effective management strategies to mitigate the risks of antibiotic resistance on public health.Engineering drawings are commonly used in different industries such as Oil and Gas, construction, and other types of engineering. Digitising these drawings is becoming increasingly important. This is mainly due to the need to improve business practices such as inventory, assets management, risk analysis, and other types of applications. However, processing and analysing these drawings is a challenging task. A typical diagram often contains a large number of different types of symbols belonging to various classes and with very little variation among them. Another key challenge is the class-imbalance problem, where some types of symbols largely dominate the data while others are hardly represented in the dataset. In this paper, we propose methods to handle these two challenges. First, we propose an advanced bounding-box detection method for localising and recognising symbols in engineering diagrams. Our method is end-to-end with no user interaction. Thorough experiments on a large collection of diagrams from an industrial partner proved that our methods accurately recognise more than 94% of the symbols. Secondly, we present a method based on Deep Generative Adversarial Neural Network for handling class-imbalance. The proposed GAN model proved to be capable of learning from a small number of training examples. Experiment results showed that the proposed method greatly improved the classification of symbols in engineering drawings.Research explaining the behavior of convolutional neural networks (CNNs) has gained a lot of attention over the past few years. Although many visualization methods have been proposed to explain network predictions, most fail to provide clear correlations between the target output and the features extracted by convolutional layers. In this work, we define a concept, i.e., class-discriminative feature groups, to specify features that are extracted by groups of convolutional kernels correlated with a particular image class. We propose a detection method to detect class-discriminative feature groups and a visualization method to highlight image regions correlated with particular output and to interpret class-discriminative feature groups intuitively. Batimastat The experiments showed that the proposed method can disentangle features based on image classes and shed light on what feature groups are extracted from which regions of the image. We also applied this method to visualize "lost" features in adversarial samples and features in an image containing a non-class object to demonstrate its ability to debug why the network failed or succeeded.