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With the increasing demands for the high efficiency of pulp-mixing, which is adapted to the fine particles entering into flotation section, an intensification pulp-mixing device with novel design has been developed. On the basis of numerical simulation, which indicated efficient circulation and shear effect of the “maleic-cutting” configuration equipped with static-guide-blade, the power dissipation amount PV, mixing time θm, power number NP, mixing time number Nθ, CS which reflects shear property, suspension percentage φ/Φ(%), and concentration variance σ2 were measured, analyzed and compared with the ordinary agitated tank. Combining with actual mineral separation tests, performance of the efficient pulp-mixing device characterized by “multi-stage and compulsory” has been validated: the “multi-stage and compulsory” mixing device corresponds to a different circulation/shear energy; under the same operating conditions, the absolute advantages in the aspect of mixing time are the root cause for the increase of mixing efficiency; the “maleic-cutting” configuration is less influenced by pulp-mixing time and has the characteristics of achieving fast mixing; the strong shear effect mechanism is the root cause for the realization of pulp-mixing intensification on fine-particle and microfine-particle materials. Therefore, the “multi-stage and compulsory” mixing device designed by this research successfully accomplished intensification of pulp-mixing effect.

Fully automated comprehensive analysis of carotid artery (localization of range of interest (ROI), direct quantitative measurement and segmentation of lumen diameter (CALD) and intima-media thickness (CIMT), and motion estimation of the carotid wall) is a reliable auxiliary diagnosis of cardiovascular diseases, which relieves physicians from laborious workloads. No work has achieved fully automated comprehensive analysis of carotid artery due to five intractable challenges: (1) The heavy reliance on experienced carotid physicians for the selection of ROI limits fully automated studies. (2) The weak structural information of intima-media thickness increases the difficulty of feature encoding. (3) The radial motion of the carotid wall results in the lack of discriminant features of boundaries. (4) Diseased carotid arteries lose many expression features. (5) Optimal weights of multitask regression are hard to tune manually. In this paper, we propose a novel uncertainty-guided multitask regression network aided by optical flow named OF-UMRN to solve the intractable challenges. The four modules and three innovations of the OF-UMRN take their responsibility. OF-UMRN takes localization and mapping of ROI as a pre-processing. It achieves direct quantitative measurement and segmentation by a multitask regression network. And we creatively model homoscedastic uncertainty to automated tune the weights of the two tasks optimally. The OF-UMRN adopts a bidirectional mechanism to encode the optical flow used to predict the carotid wall's motion fields. More importantly, we creatively propose a dual optimization module based on the co-promotion between segmentation and motion estimation to improve the performance of radially moving and diseased carotid arteries. Therefore, the OF-UMRN makes the most of the pathological relationship between multiple objects and co-promotion between various tasks. Extensive experiments on US sequences of 101 patients have demonstrated the superior performance of OF-UMRN on the fully automated comprehensive analysis of the carotid artery. Therefore the OF-UMRN has excellent potential in clinical disease diagnoses and assessments of the carotid artery.