Phycoremediation and valorization of hypersaline pickled mustard wastewater via Chaetoceros muelleri and indigenous bacteria.

Hypersaline pickled mustard wastewater (PMW), a typical food wastewater with high nutrient content, was successfully bioremediated via the co-treatment of Chaetoceros muelleri and indigenous bacteria in this study. Chemical oxygen demand, ammonia nitrogen, total nitrogen and total phosphorus in 10 % PMW could be effectively reduced by 82 %, 90 %, 94 % and 96 %, respectively, after 12 days treatment. Oxygen species activities, malondialdehyde content, microalgal biomass, photosynthesis and extracellular polymeric substances were characterized during the treatment to determine the responses of the consortium when exposed to different concentration of PMW. Microbial community analysis demonstrated a significant increase in the relative abundance of Halomonas and Marinobacter in the 10 % PMW after 12 days treatment, which was beneficial for nutrients recycling by the diatoms. Meanwhile, C. muelleri was effective in reducing the relative abundance of potentially pathogenic bacteria Malaciobacter. In conclusion, the work here offers a promising and environmentally friendly approach for hypersaline wastewater treatment.

Enhanced photo-Fenton-like performance of biotemplated manganese-doped cobalt silicate catalysts.

Cobalt-based catalysts are one of the preferred materials for effective activation of hydrogen peroxide, and metal element doping and active site dispersion are effective methods to enhance their catalytic activity. In this work, manganese-doped cobalt silicate@diatomite composites with enhanced photo-Fenton-like oxidation performance were prepared and used for degradation of methyl orange (MO) dyes. Experiments showed that manganese doping increased the specific surface area of the samples and decreased the band gap energy of the materials. Moreover, the samples doped with manganese elements had better photo-Fenton-like properties. The degradation of methyl orange by Co0.25MnSi@DE/H2O2-UV reached more than 95%. In addition, density-functional theory (DFT) calculations showed that the Mn-doped samples were more prone to activate H2O2 than non-manganese-doped samples, and the synergistic effect from using a bimetallic catalyst increased the photo-Fenton oxidation activity in the system. ESR spectroscopy and bursting tests indicated that the possible degradation mechanism consisted of hydroxyl radicals and superoxide radicals generated by the synergistic effect of cobalt ions and manganese under UV radiation. This study thus presents a feasible idea for the preparation of cobalt-based photo-Fenton catalysts that also provides a basis for understanding the catalytic mechanism analysis of other types of bimetallic catalysts.

Tomato Maturity Detection and Counting Model Based on MHSA-YOLOv8.

The online automated maturity grading and counting of tomato fruits has a certain promoting effect on digital supervision of fruit growth status and unmanned precision operations during the planting process. The traditional grading and counting of tomato fruit maturity is mostly done manually, which is time-consuming and laborious work, and its precision depends on the accuracy of human eye observation. The combination of artificial intelligence and machine vision has to some extent solved this problem. In this work, firstly, a digital camera is used to obtain tomato fruit image datasets, taking into account factors such as occlusion and external light interference. Secondly, based on the tomato maturity grading task requirements, the MHSA attention mechanism is adopted to improve YOLOv8's backbone to enhance the network's ability to extract diverse features. The Precision, Recall, F1-score, and mAP50 of the tomato fruit maturity grading model constructed based on MHSA-YOLOv8 were 0.806, 0.807, 0.806, and 0.864, respectively, which improved the performance of the model with a slight increase in model size. Finally, thanks to the excellent performance of MHSA-YOLOv8, the Precision, Recall, F1-score, and mAP50 of the constructed counting models were 0.990, 0.960, 0.975, and 0.916, respectively. The tomato maturity grading and counting model constructed in this study is not only suitable for online detection but also for offline detection, which greatly helps to improve the harvesting and grading efficiency of tomato growers. The main innovations of this study are summarized as follows: (1) a tomato maturity grading and counting dataset collected from actual production scenarios was constructed; (2) considering the complexity of the environment, this study proposes a new object detection method, MHSA-YOLOv8, and constructs tomato maturity grading models and counting models, respectively; (3) the models constructed in this study are not only suitable for online grading and counting but also for offline grading and counting.