Multi-Feature Fusion-Guided Multiscale Bidirectional Attention Networks for Logistics Pallet Segmentation

In the smart logistics industry,unmanned forklifts that intelligently identify logistics pallets can improve work efficiency in warehousing and transportation and are better than traditional manual forklifts driven by humans.Therefore,they play a critical role in smart warehousing,and semantics segmentation is an effective method to realize the intelligent identification of logistics pallets.However,most current recognition algorithms are ineffective due to the diverse types of pallets,their complex shapes,frequent blockades in production environments,and changing lighting conditions.This paper proposes a novel multi-feature fusion-guided multiscale bidirectional attention(MFMBA)neural network for logistics pallet segmentation.To better predict the foreground category(the pallet)and the background category(the cargo)of a pallet image,our approach extracts three types of features(grayscale,texture,and Hue,Saturation,Value features)and fuses them.The multiscale architecture deals with the problem that the size and shape of the pallet may appear different in the image in the actual,complex environment,which usually makes feature extraction difficult.Our study proposes a multiscale architecture that can extract additional semantic features.Also,since a traditional attention mechanism only assigns attention rights from a single direction,we designed a bidirectional attention mechanism that assigns cross-attention weights to each feature from two directions,horizontally and vertically,significantly improving segmentation.Finally,comparative experimental results show that the precision of the proposed algorithm is 0.53%–8.77%better than that of other methods we compared.

Sustainable activation of peroxymonosulfate by the Mo(Ⅳ)in MoS_(2) for the remediation of aromatic organic pollutants

Although MoS_(2) has been proved to be a very ideal cocatalyst in advanced oxidation process(AOPs),the activation process of peroxy mono sulfate(PMS)is still inseparable from metal ions which inevitably brings the risk of secondary pollution and it is not conducive to large-scale industrial application.In this study,the commercial MoS_(2),as a durable and efficient catalyst,was used for directly activating PMS to degrade aromatic organic pollutant.The commercial MoS_(2)/PMS catalytic system demonstrated excellent removal efficiency of phenol and the total organic carbon(TOC)residual rate reach to 25%.The degradation rate was significantly reduced if the used MoS_(2) was directly carried out the next cycle experiment without any post-treatment.Interestingly,the commercial MoS_(2) after post-treated with H2 O_(2) can exhibit good stability and recyclability for cyclic degradation of phenol.Furthermore,the mechanism for the activation of PMS had been investigated by density functional theory(DFT)calculation.The renewable Mo4+exposed on the surface of MoS_(2) was deduced as the primary active site,which realized the direct activation of PMS and avoided secondary pollution.Taking into account the reaction cost and efficient activity,the development of commercial MoS_(2) catalytic system is expected to be applied in industrial wastewater.