The effect of copper, zinc, chromium, and lead on the anaerobic co-digestion of waste activated sludge and septic tank sludge in Hanoi was studied in the fermentation tests by investigating the substrate degradation, biogas production, and process stability at the mesophilic fermentation. The tested heavy metals were in a range of concentrations between 19 and 80 ppm. After the anaerobic tests, the TS, VS, and COD removal efficiency was 4.12%, 9.01%, and 23.78% for the Cu(II) added sample. Similarly, the efficiencies of the Zn(II) sample were 1.71%, 13.87%, and 16.1% and Cr(VI) efficiencies were 15.28%, 6.6%, and 18.65%, while the TS, VS, and COD removal efficiency of the Pb(II) added sample was recorded at 16.1%, 17.66%, and 16.03% at the concentration of 80 ppm, respectively. Therefore, the biogas yield also decreased by 36.33%, 31.64%, 31.64%, and 30.60% for Cu(II), Zn(II), Cr(VI), and Pb(II) at the concentration of 80 ppm, compared to the raw sample, respectively. These results indicated that Cu(II) had more inhibiting effect on the anaerobic digestion of the sludge mixture than Zn(II), Cr(VI), and Pb(II). The relative toxicity of these heavy metals to the co-digestion process was as follows: Cu (the most toxic) > Zn > Cr > Pb (the least toxic). The anaerobic co-digestion process was inhibited at high heavy metal concentration, which resulted in decreased removal of organic substances and produced biogas.
Background and Objective: Context-aware computer-assisted surgical systems require real time automatic and accurate surgical workflow recognition. Since several years, video has been the most common modality used to develop such methods. With the democratization of robotic-assisted surgery and segmentation method, new modalities are now accessible, such as kinematics. Some previous works used these new modalities as input of their models, but the added value of these modalities is rarely studied. This paper presents the design and results of the "PEg TRAnsfert Workflow recognition" (PETRAW) challenge whose objective was to develop surgical workflow recognition methods based on one or several modalities in order to study their added value. Methods: The PETRAW challenge provided a data set of 150 peg transfer sequences performed on a virtual simulator. This data set was composed of videos, kinematics, semantic segmentation, and workflow annotations which described the sequences at three different granularity levels: phase, step, and activity. Five tasks were proposed to the participants: three of them were related to the recognition of all granularities with one of the available modalities, while the others addressed the recognition with a combination of modalities. Average application-dependent balanced accuracy (AD-Accuracy) was used as evaluation metric to take unbalanced classes into account and because it is more clinically relevant than a frame-by-frame score. Results: Seven teams participated in at least one task and four of them in all tasks. Best results are obtained with the use of the video and the kinematics data with an AD-Accuracy between 93% and 90% for the four teams who participated in all tasks. Conclusion:The improvement between video/kinematic-based methods and the uni-modality ones was significant for all of the teams. However, the difference in testing execution time between the video/kinematic-based and the kinematic-based methods has to be taken into consideration. Is it relevant to spend 20 to 200 times more computing time for less than 3% of improvement? The PETRAW data set is publicly available at www.synapse.org/PETRAW to encourage further research in surgical workflow recognition.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.