Electrochemical oxidation (EO) is often used in the advanced treatment of refractory wastewater. However, in a conventional EO process of direct-current (DC) power supply, oxide layers often form on the anodes, which not only hinder the oxidation reaction on them but also cause higher energy consumption. In this paper, a biologically treated leachate (BTL) of municipal solid waste (MSW) was comparably treated by EO with DC (DC–EO), monopulse (MP–EO), and double pulse (DP–EO) power source models in a home-made multi-channel flow reactor. The effects of process parameters of current density (I A), superficial liquid velocity (U L), pulse frequency (f P), duty ratio (R D), and so forth on the removal efficiency of chemical oxygen demand (COD) (RECOD), total organic carbon (TOC) (RETOC), and total nitrogen (TN) (RETN) were investigated simultaneously. Average energy consumption ( ) and organic composition of the treated effluent of DC–EO and MP–EO were also compared comprehensively, and a new mechanism of MP–EO has been proposed accordingly. Under optimal conditions, 2 L of BTL was treated by MP–EO for 180 min, and the RECOD, RETOC, and RETN could reach as high as 80, 30, and 80%, respectively. Compared with DC–EO, the of MP–EO is reduced by 69.27%. Besides, the kinds of organic matter in the treated effluent of MP–EO are reduced from 53 in the BTL to 11, which is much less than in the DC–EO process of 29 kinds. Therefore, the MP–EO process exhibits excellent removal performance of organics and TN and economic prospects in the treatment of refractory organic wastewater.

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Dealkalization is the necessary step for the multipurpose use of red mud (RM), and acid leaching is a productive method to realize the dealkalization of RM. Most researches focus on recovering metals from the highly alkaline waste by pure acid leaching or stabilization by dealkalization. In this study, according to the strong alkalinity of RM and strong acidity of the waste acid from titanium dioxide production, the waste acid was used for the dealkalization of RM. The effects of leaching temperature, reaction time, the concentration of waste acid, liquid–solid ratio (L/S), and stirring rate on the dealkalization of RM were investigated, and the main metal ions in the dealkalization solution were analyzed. The results show that the leaching ratio of sodium can reach 92.3591% when the leaching temperature is 30 °C, the reaction time is 10 min, the concentration of waste acid is 0.6238 mol/L, the L/S is 4:1, and the stirring rate is 300 rpm. The residual alkali content in the treated RM is 0.2674%, which is a reduction to less than 1%. The phase analysis results show that the sodalite and cancrinite in RM are dissolved, decomposed, and transformed after acid leaching. Therefore, RM meets the requirements of building materials after dealkalization, which provides further development as building material products.

Entity re-identification is the foundation of tracking-and matching-based computer vision tasks, which are widely employed in a variety of applications. However, when trained exclusively on clear images, the models capacity to generalize is significantly affected by the presence of occlusion at referencing time, whereas data argumentation-based approaches are costly to construct without guaranteeing a testtime improvement. To tackle this problem, we propose a domain adaptation framework based on learning representations that generates occlusion-invariant feature representations by aligning the clean image embedding distribution with the occluded one, using a disparity discrepancy metric derived from the siamese network architecture. Without the need for additional processing modules during the inference stage or an expensive occlusion-augmentation-enlarged dataset during the training stage, we could obtain occlusion invariant embeddings that are free of the impact of occluders. Extensive experimental results for two tasks across three datasets indicate the proposed method's robustness and effectiveness to a variety of occlusions at all levels.