Selective removal of thiosulfate from coke oven gas desulfurization wastewater by catalytic wet air oxidation with manganese-based oxide from spent ternary lithium-ion batteries.

Selective and efficient removal of thiosulfates (S2O32-) to recover high-purity and value-added thiocyanate products by fractional crystallization process is a promising route for the resource treatment of coke oven gas desulfurization wastewater. Herein, catalytic wet air oxidation (CWAO), with manganese-based oxide synthesized from spent ternary lithium-ion batteries (MnOx-LIBs), was proposed to selectively remove S2O32- from desulfurization wastewater. 98.0 % of S2O32- is selectively removed by the MnOx-LIBs CWAO system, which was 4.1 times that of the MnOx CWAO system. The synergistic effect among multiple metals from spent LIBs induces the enlarged specific surface area, increased reactive sites and formation of oxygen vacancy, promoting the adsorption and activation of O2, thereby realizing high-efficiency removal of S2O32-. The satisfactory selective removal efficiency can be maintained in the proposed system under complex environmental conditions. Notably, the proposed system is cost-effective and applicable to actual wastewater, in which 81.2 % of S2O32- is selectively removed from coke oven gas desulfurization wastewater. More importantly, compared with the typical processes, the proposed process is simpler and more environmentally-friendly. This work provides an alternative route to selectively remove S2O32- from coke oven gas desulfurization wastewater, expecting to drive the development of resource utilization of coke oven gas desulfurization wastewater.

Mutual consistency learning for semi-supervised medical image segmentation.

In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively exploit the unlabeled data for semi-supervised medical image segmentation. The MC-Net+ model is motivated by the observation that deep models trained with limited annotations are prone to output highly uncertain and easily mis-classified predictions in the ambiguous regions (e.g., adhesive edges or thin branches) for medical image segmentation. Leveraging these challenging samples can make the semi-supervised segmentation model training more effective. Therefore, our proposed MC-Net+ model consists of two new designs. First, the model contains one shared encoder and multiple slightly different decoders (i.e., using different up-sampling strategies). The statistical discrepancy of multiple decoders' outputs is computed to denote the model's uncertainty, which indicates the unlabeled hard regions. Second, we apply a novel mutual consistency constraint between one decoder's probability output and other decoders' soft pseudo labels. In this way, we minimize the discrepancy of multiple outputs (i.e., the model uncertainty) during training and force the model to generate invariant results in such challenging regions, aiming at regularizing the model training. We compared the segmentation results of our MC-Net+ model with five state-of-the-art semi-supervised approaches on three public medical datasets. Extension experiments with two standard semi-supervised settings demonstrate the superior performance of our model over other methods, which sets a new state of the art for semi-supervised medical image segmentation. Our code is released publicly at https://github.com/ycwu1997/MC-Net.

Automatic coronary artery calcium scoring on routine chest computed tomography (CT): comparison of a deep learning algorithm and a dedicated calcium scoring CT.

Background: The aim of this study was to investigate the reliability and accuracy of automatic coronary artery calcium (CAC) scoring and risk classification in non-gated, non-contrast chest computed tomography (CT) of different slice thicknesses using a deep learning algorithm. Methods: This retrospective study was performed at 2 tertiary hospitals. Paired, dedicated calcium-scoring CT scans and non-gated, non-contrast chest CT scans taken within a month from the same patients were included. Chest CT images were grouped according to the slice thickness (group A: 1 mm; group B: 3 mm). For internal scans, the CAC score manually measured on dedicated calcium scoring CT images was used as the gold standard. The deep learning algorithm for group A was trained using 150 chest CT scans and tested using 144 scans, and that for group B was trained using 170 chest CT scans and tested using 144 scans. The intraclass correlation coefficient (ICC) was used to evaluate the correlation between the algorithm and the gold standard. Agreement between the deep learning algorithm, the manual results on chest CT, and the gold standard was determined by Bland-Altman analysis. Cardiac risk categories were compared. External validation was performed on 334 paired scans from a different organization. Results: A total of 608 internal paired scans (1 mm: 294; 3 mm: 314) of 406 individuals and 334 external paired scans (1 mm: 117; 3 mm: 117) of 117 individuals were included in the analysis. The ICCs between the deep learning algorithm and the gold standard were excellent in both group A (0.90; 95% CI: 0.85-0.93) and group B (0.94; 95% CI: 0.92-0.96). The Bland-Altman plots showed good agreement in both groups. For the cardiovascular risk category, the deep learning algorithm accurately classified 71% of cases in group A and 81% of cases in group B. The Kappa values for risk classification were 0.72 in group A and 0.82 in group B. External validation yielded equally good results. Conclusions: The automatic calculation of CAC score and cardiovascular risk stratification on non-gated chest CT using a deep learning algorithm was reliable and accurate on both 1 and 3 mm scans. Chest CT with a slice thickness of 3 mm was slightly more accurate in CAC detection and risk classification.

Lightweight, compact, and high-performance 3T MR system for imaging the brain and extremities.

PURPOSE: To build and evaluate a small-footprint, lightweight, high-performance 3T MRI scanner for advanced brain imaging with image quality that is equal to or better than conventional whole-body clinical 3T MRI scanners, while achieving substantial reductions in installation costs. METHODS: A conduction-cooled magnet was developed that uses less than 12 liters of liquid helium in a gas-charged sealed system, and standard NbTi wire, and weighs approximately 2000 kg. A 42-cm inner-diameter gradient coil with asymmetric transverse axes was developed to provide patient access for head and extremity exams, while minimizing magnet-gradient interactions that adversely affect image quality. The gradient coil was designed to achieve simultaneous operation of 80-mT/m peak gradient amplitude at a slew rate of 700 T/m/s on each gradient axis using readily available 1-MVA gradient drivers. RESULTS: In a comparison of anatomical imaging in 16 patients using T2 -weighted 3D fluid-attenuated inversion recovery (FLAIR) between the compact 3T and whole-body 3T, image quality was assessed as equivalent to or better across several metrics. The ability to fully use a high slew rate of 700 T/m/s simultaneously with 80-mT/m maximum gradient amplitude resulted in improvements in image quality across EPI, DWI, and anatomical imaging of the brain. CONCLUSIONS: The compact 3T MRI system has been in continuous operation at the Mayo Clinic since March 2016. To date, over 200 patient studies have been completed, including 96 comparison studies with a clinical 3T whole-body MRI. The increased gradient performance has reliably resulted in consistently improved image quality.

Magnetic field dependent stability and quench behavior and degradation limits in conduction-cooled MgB2 wires and coils.

Long lengths of metal/MgB2 composite conductors with high critical current density (Jc), fabricated by the power-in-tube (PIT) process, have recently become commercially available. Owing to its electromagnetic performance in the 20 K - 30 K range and relatively low cost, MgB2 may be attractive for a variety of applications. One of the key issues for magnet design is stability and quench protection, so the behavior of MgB2 wires and magnets must be understood before large systems can emerge. In this work, the stability and quench behavior of several conduction-cooled MgB2 wires are studied. Measurements of the minimum quench energy and normal zone propagation velocity are performed on short samples in a background magnetic field up to 3 T and on coils in self-field and the results are explained in terms of variations in the conductor architecture, electrical transport behavior, operating conditions (transport current and background magnetic field) and experimental setup (short sample vs small coil). Furthermore, one coil is quenched repeatedly with increasing hot-spot temperature until Jc is decreased. It is found that degradation during quenching correlates directly with temperature and not with peak voltage; a safe operating temperature limit of 260 K at the surface is identified.

Development of a 3 T-250 mm Bore MgB2 Magnet System.

The authors had reported components' development of 3 T-250 mm bore MgB2 magnet system. Pre-reacted MgB2 tape wire with copper lamination had n-value related problem due to raw Boron particle size inequality, but it had been corrected. Long MgB2 wires over 3 km had been supplied. All six component coils were made with a wet winding procedure. They were tested individually with the same cooling scheme of conduction cooling as the actual magnet assembly. Though all coils could be ramped to some extent, some coils showed fairly large remnant voltage. Since the voltage distribution over the coil was not even, the uniformity along the wire length may not be good enough. The stability of the coil was verified by its no training performance even with fast ramping. The magnet assembly and its test with conduction cooling were planned. I c of the superconducting joint with this pre-reacted wire was doubled during past one year's development.