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A-Cu-BTC(A = Fe, Ni, Co, Mn) was obtained using preassembled method with direct ion-exchange treatment and then used as precursor to acquire AO x /CuO y /C at 600 °C under nitrogen atmosphere. The characterization of the catalysts were performed and evaluated as the catalysts for selective catalytic reduction of NO with CO (CO-SCR). A-Cu-BTC was successfully applied in CO-SCR and had similar performance for catalytic denitration, while Fe-Cu-BTC didn't have a good performance. It was found that AO x /CuO y /C all performed outstanding catalytic effect for NO-CO reaction with the addition of A atoms than single CuO y /C. Better lower temperature catalyst performance of AO x /CuO y /C indicates that future design of mixed-node MOFs as precursor will have a promising future for denitration. The possible reaction mechanism about CO-SCR is discussed. These results provide that interesting MOFs and MOFs precursor as dual-function catalysts. [ABSTRACT FROM AUTHOR]

The aim of this paper is to distinguish the vehicle detection and count the class number in each classification from the inputs. We proposed the use of Fuzzy Guided Scale Choice (FGSC)-based SSD deep neural network architecture for vehicle detection and class counting with parameter optimization. The 'FGSC' blocks are integrated into the convolutional layers of the model, which emphasize essential features while ignoring less important ones that are not significant for the operation. We created the passing detection lines and class counting windows and connected them with the proposed FGSC-SSD deep neural network model. The 'FGSC' blocks in the convolution layer emphasize essential features and find out unnecessary features by using the scale choice method at the training stage and eliminate that significant speedup of the model. In addition, FGSC blocks avoided many unusable parameters in the saturation interval and improved the performance efficiency. In addition, the Fuzzy Sigmoid Function (FSF) increases the activation interval through fuzzy logic. While performing operations, the FGSC-SSD model reduces the computational complexity of convolutional layers and their parameters. As a result, the model tested Frames Per Second (FPS) on edge artificial intelligence (AI) and reached a real-time processing speed of 38.4 and an accuracy rate of more than 94%. Therefore, this work might be considered an improvement to the traffic monitoring approach by using edge AI applications. [ABSTRACT FROM AUTHOR]