Enhanced phosphate flotation using novel depressants

Froth flotation is the most efficient method for phosphate separation, which is a physic-chemical separation process based on the difference of surface properties between the valuable minerals and unwanted gangue minerals. However, the presence of clay slimes in the slurry after grinding consumes a large amount of reagents, decreases the collision probability between bubbles and minerals, prevents phosphate particle attachment to air bubbles, and thus considerably reduces flotation recovery and concentrate grade. Georgia Pacific Chemical, LLC has recently developed novel depressants, i.e., clay binders, which are a series of low molecular weight specialty polymers to help improve phosphate flotation performance by selectively agglomerating and depressing clay particles, thus lowering their surface area and reducing the adsorption of surfactants. This thesis addresses the effects of clay binders on phosphate flotation performance and their adsorption behavior on different minerals in a sedimentary phosphate ore. Quartz Crystal Microbalance with Dissipation technique (QCM-D) was used to study adsorption characteristics of clay binders and batch flotation tests were performed under different conditions to investigate phosphate flotation performance. The experimental results have shown that clay binders significantly improved phosphate flotation selectivity and reduced the dosages of collector and sodium silicate used as dispersant in the industry

Unsupervised Gaze-aware Contrastive Learning with Subject-specific Condition

Appearance-based gaze estimation has shown great promise in many applications by using a single general-purpose camera as the input device. However, its success is highly depending on the availability of large-scale well-annotated gaze datasets, which are sparse and expensive to collect. To alleviate this challenge we propose ConGaze, a contrastive learning-based framework that leverages unlabeled facial images to learn generic gaze-aware representations across subjects in an unsupervised way. Specifically, we introduce the gaze-specific data augmentation to preserve the gaze-semantic features and maintain the gaze consistency, which are proven to be crucial for effective contrastive gaze representation learning. Moreover, we devise a novel subject-conditional projection module that encourages a share feature extractor to learn gaze-aware and generic representations. Our experiments on three public gaze estimation datasets show that ConGaze outperforms existing unsupervised learning solutions by 6.7% to 22.5%; and achieves 15.1% to 24.6% improvement over its supervised learning-based counterpart in cross-dataset evaluations

Dynamics of a stochastic fractional nonlocal reaction-diffusion model driven by additive noise

In this paper, we are concerned with the long-time behavior of stochastic fractional nonlocal reaction-diffusion equations driven by additive noise. We use the techniques of random dynamical systems to transform the stochastic model into a random one. To deal with the new nonlocal term appeared in the transformed equation, we first use a generalization of Peano’s theorem to prove the existence of local solutions, and then adopt the Galerkin method to prove existence and uniqueness of weak solutions. Next, the existence of pullback attractors for the equation and its associated Wong-Zakai approximation equation driven by colored noise are shown, respectively. Furthermore, we establish the upper semi-continuity of random attractors of the Wong-Zakai approximation equation as δ → 0 +

Parameter Estimation of Induction Machine at Standstill Using Two-Stage Recursive Least Squares Method

This paper presents a two-stage recursive least squares (TSRLS) algorithm for the electric parameter estimation of the induction machine (IM) at standstill. The basic idea of this novel algorithm is to decouple an identifying system into two subsystems by using decomposition technique and identify the parameters of each subsystem, respectively. The TSRLS is an effective implementation of the recursive least squares (RLS). Compared with the conventional (RLS) algorithm, the TSRLS reduces the number of arithmetic operations. Experimental results verify the effectiveness of the proposed TSRLS algorithm for parameter estimation of IMs

Prediction of the anti-inflammatory effects of bioactive components of a Hippocampus species-based TCM formulation on chronic kidney disease using network pharmacology

Purpose: To systematically study and predict the therapeutic targets and signaling pathways of Hippocampus (HPC) against chronic kidney disease (CKD) using network pharmacology.Methods: By combining database mining, literature searching, screening of disease targets, and network construction, the effects of various components of HPC on several proteins related to CKD were predicted and the active compounds were screened. Genes related to the selected compounds were linked using the SEA database. The correlation between CKD and genes was determined using OMIM, DisGenNet, and GeneCards databases. Pathway-enrichment analyses of overlapping genes were undertaken using online databases.Results: A total of 144 compounds in HPC were identified. Analyses of clusters suggest that the active components of HPC and the target genes against the inflammation caused by CKD were due to 10 compounds and 25 genes. Metascape results showed that these HPC targets are related to CKD inflammation.Conclusion: The active components of HPC and the target genes against CKD inflammation are involved in multiple signaling pathways, such as AGE-RAGE, TLR, TNF, and NF-κB. This work provides scientific evidence to support the clinical use of HPC against CKD