Enhanced methane production from waste activated sludge by potassium ferrate combined with thermal hydrolysis pretreatment.

Anaerobic digestion of waste activated sludge (WAS) was one of the directions of sludge treatment, but how to effectively improve the production of methane as a resource product of anaerobic digestion of sludge still needs further research. The study examined how the combination of potassium ferrate (PF) and thermal hydrolysis (TH) pretreatment affected methane production from sludge. The results demonstrated a positive synergistic effect on methane production with PF-TH pretreatment. Specifically, by employing a 0.05 g/g TSS (total suspended solids) PF in conjunction with TH at 80 °C for 30 min, the methane yield increased from 170.66 ± 0.92 to 232.73 ± 2.21 mL/g VSS (volatile suspended solids). The co-pretreatment of PF and TH has been substantiated by mechanism studies to effectively enhance the disintegration and biodegradability of sludge. Additionally, the variation of microbial community revealed an enrichment of active microorganisms associated with anaerobic digestion after treated with PF + TH, resulting in a total abundance increase from 11.87 to 20.45% in the PF + TH group.

Continuous Refinement-based Digital Pathology Image Assistance Scheme in Medical Decision-Making Systems.

Digital pathology images' extensive cellular information provide a trustworthy foundation for tumor diagnosis. With the aid of computer-aided diagnostics, pathologists can locate crucial information more quickly. The cascade structure refines the segmentation results by utilizing its multi-task and multi-stage characteristics. However, cascade-based models require downsampling and cropping of patches during the inference process due to the ultra-high resolution and complex structure of pathology images. This not only increases the cost and computation time but also results in the loss of cellular details and corrupts the global contextual information. This study proposes a Digital Pathology Image Assistance Program (CRSDPI) for medical decision-making systems that is based on continuous improvement. After locating the region of interest using the maximum inter-class variance method, the pictures are preprocessed to account for the impacts of staining inconsistencies and sensitivity variations on the model's performance. Ultimately, we create a two-phase continuously refined segmentation network (TCRNet) by combining an enhanced continuous refinement model with a coarse segmentation network built on a pyramid scene parsing network. The coarse segmentation network introduces an auxiliary loss term to speed up convergence, and the refined model introduces an implicit function to reduce computational cost and reconstruct more details. The TCRNet model refines the target by successively aligning the features without the need to take cascading decoder operations after encoder. Experiments conducted on digital pathology images of breast cancer and osteosarcoma demonstrate the superior prediction accuracy and computational speed of our strategy.

Revitalizing urban lake cleanup: optimizing flocculation and dewatering of dredged sludge using cation polyacrylamide.

This study investigated the effect of cation polyacrylamide (CPAM) on the dewatering performance of dredged sludge by batch experiments and compared it with a novel organic agent (DRC-300) and a traditional inorganic agent (PAC). The results of batch experiments suggested that the CPAM could promote the dewatering performance of dredged sludge inland lake. And at the dosage of 0.07% g/g suspended solids (SS), the moisture content of 37% could be achieved with CAPM. CPAM could reduce the sludge resistance filtration (SRF) and capillary adsorption time (CST) by 73% and 62%, respectively. Mechanism experiments revealed that CPAM improved the dewatering performance of dredged mud by increasing the sedimentation rate, accelerating the dissolution of organic matter, neutralizing the surface charge of sludge, and improving the void structure. Furthermore, CPAM outperformed DRC-300 and PAC in above aspects.

A Medically Assisted Model for Precise Segmentation of Osteosarcoma Nuclei on Pathological Images.

Osteosarcoma is the most common malignant bone tumor with a high degree of malignancy and misdiagnosis rates. Pathological images are crucial for its diagnosis. However, underdeveloped regions currently lack sufficient high-level pathologists, leading to uncertain diagnostic accuracy and efficiency. Existing research on pathological image segmentation often neglects the differences in staining styles and lack of data, without considering medical backgrounds. To alleviate the difficulty in diagnosing osteosarcoma in underdeveloped areas, an intelligent assisted diagnosis and treatment scheme for osteosarcoma pathological images, ENMViT, is proposed. ENMViT utilizes KIN to achieve normalization of mismatched images with limited GPU resources and uses traditional data enhancement methods, such as cleaning, cropping, mosaic, Laplacian sharpening, and other techniques to alleviate the issue of insufficient data. A multi-path semantic segmentation network combining Transformer and CNN is used to segment images, and the degree of edge offset in the spatial domain is introduced into the loss function. Finally, noise is filtered according to the size of the connecting domain. This article experimented on more than 2000 osteosarcoma pathological images from Central South University. The experimental results demonstrate that this scheme performs well in each stage of the osteosarcoma pathological image processing, and the segmentation results' IoU index is 9.4% higher than the comparative models, demonstrating its significant value in the medical industry.

Material Extrusion Additive Manufacturing with Polyethylene Vitrimers.

Polyethylene (PE) is one of the most widely used polymers in conventional polymer manufacturing processes. However, it remains a challenge to use PE in extrusion-based additive manufacturing (AM). Some of the challenges that this material presents include low self-adhesion and shrinkage during the printing process. These two issues lead to higher mechanical anisotropy when compared to other materials, along with poor dimensional accuracy and warpage. Vitrimers are a new class of polymers that have a dynamic crosslinked network, allowing the material to be healed and reprocessed. Prior studies on polyolefin vitrimers suggest that the crosslinks reduce the degree of crystallinity and increase the dimensional stability at elevated temperatures. In this study, high-density polyethylene (HDPE) and HDPE vitrimers (HDPE-V) were successfully processed using a screw-assisted 3D printer. It was demonstrated that HDPE-V were able to reduce shrinkage during the printing process. This shows that 3D printing with HDPE-V will provide better dimensional stability when compared to regular HDPE. Furthermore, after an annealing process, 3D-printed HDPE-V samples showed a decrease in mechanical anisotropy. This annealing process was only possible in HDPE-V due to their superior dimensional stability at elevated temperatures, with minimal deformation above melting temperature.

A plasma thermal slag-derived from hazardous waste has a born hydrothermal stability.

Hydrothermal instability restricts performances of silica-based catalysts, which have wide applications in both industry and environment. For the first time, plasma-thermal slag was revealed to be a catalyst with a born hydrothermal stability in selective catalytic reduction of nitric oxide. The slag catalyst removed 98.5 % of NO with a high N2 selectivity (> 95 %) at 200 °C. After a hydrothermal treatment at 900 °C, the activity of the slag only decreased to 84.0 %. According to characterizations of XRD, HTREM, XPS, and EPR, active metals existed in coordination states in the slag at first. Under hydrothermal conditions, these species transformed to short-range single crystals, which were hindered from sintering by surrounded Si-O bands. At the same time, in-situ DRIFT indicated that more Brønsted and Lewis acid sites were formed. Hence, enough active sties were reserved for effective catalytic reduction of nitric oxide. The main result of this work helps us to understand hydrothermal stability of a catalyst. What's more, the high-value-added utilization of plasma-thermal slag is in favor of the development of hazardous-waste treatment.

Understanding of the high hydrothermal stability of a catalyst prepared from Mn slag for low-temperature selective catalytic reduction of NO.

Manganese slag is a hazardous waste, which lacks proper treatment. For the first time, an effective catalyst for selective catalytic reduction of nitric oxide was synthesized from manganese slag by a sol-gel method. The obtained catalyst had an excellent low-temperature activity and high hydrothermal stability. It removed 46.3% of nitric oxide (990 ppm) at a temperature as low as 100 °C; its removal increased to 100% at 220 °C, which lasted for more than 2000 min. Moreover, hydrothermal treatment at 400 °C showed little influence on its activity. Even after hydrothermal treatment at 900 °C, the catalyst still removed 39.7% of NO at 220 °C, 22.7% higher than another catalyst synthesized from pure reagents. The hydrothermal stability was attributed to an amorphous layer of MnSixOy. This layer covered the catalyst surface, protected active metal species, pore size and pore volume from steam attacking. Manganese slag thus realized the high-value-added utilization by synthesizing a catalyst with the high hydrothermal stability.