Extraction and Recovery of Cerium from Rare Earth Ore by Solvent Extraction

Rare earth elements are widely found in many minerals, some of which, such as bastnaesite, monazite, and xenotime, are of great commercial value. Cerium (Ce) is the rare earth element with the highest content in light rare earth ore. Solvent extraction is the most effective and efficient method to recover and separate Ce from other light rare earth elements. After acid leaching of rare earth minerals, leaching solution was obtained, and cerium oxide of products of high purity was obtained by extraction and stripping. It is well known that Ce(IV) can be easily separated from the other RE(III) by adopting the traditional solvent extraction. Based on this principle, the clean process of oxidation roasting and Ce(IV) separation for Sichuan bastnaesite was developed. And then, a preliminary flow sheet of two-step oxidation and extraction of Ce(IV) for Bayan Obo mixed rare earth ores was further proposed

Towards Discriminative Representations with Contrastive Instances for Real-Time UAV Tracking

Maintaining high efficiency and high precision are two fundamental challenges in UAV tracking due to the constraints of computing resources, battery capacity, and UAV maximum load. Discriminative correlation filters (DCF)-based trackers can yield high efficiency on a single CPU but with inferior precision. Lightweight Deep learning (DL)-based trackers can achieve a good balance between efficiency and precision but performance gains are limited by the compression rate. High compression rate often leads to poor discriminative representations. To this end, this paper aims to enhance the discriminative power of feature representations from a new feature-learning perspective. Specifically, we attempt to learn more disciminative representations with contrastive instances for UAV tracking in a simple yet effective manner, which not only requires no manual annotations but also allows for developing and deploying a lightweight model. We are the first to explore contrastive learning for UAV tracking. Extensive experiments on four UAV benchmarks, including UAV123@10fps, DTB70, UAVDT and VisDrone2018, show that the proposed DRCI tracker significantly outperforms state-of-the-art UAV tracking methods.Comment: arXiv admin note: substantial text overlap with arXiv:2308.1026

miRNAs as Regulators of Antidiabetic Effects of Fucoidans

open access articleDiabetes mellitus is a metabolic disease with a high mortality rate worldwide. MicroRNAs (miRNAs), and other small noncoding RNAs, serve as endogenous gene regulators through binding to specific sequences in RNA and modifying gene expression toward up- or down-regulation. miRNAs have become compelling therapeutic targets and play crucial roles in regulating the process of insulin resistance. Fucoidan has shown potential function as an a-amylase inhibitor, which may be beneficial in the management of type 2 diabetes mellitus. In recent years, many studies on fucoidan focused on the decrease in blood glucose levels caused by ingesting low-glucose food or glucose-lowering components. However, the importance of miRNAs as regulators of antidiabetic effects was rarely recognized. Hence, this review emphasizes the antidiabetic mechanisms of fucoidan through regulation of miRNAs. Fucoidan exerts a vital antidiabetic effect by regulation of miRNA expression and thus provides a novel biological target for future research

Near-Infrared (NIR) Luminescent Homoleptic Lanthanide Salen Complexes Ln(4)(Salen)(4) (Ln = Nd, Yb Or Er)

The series of homoleptic tetranuclear [Ln(4)(L)(2)(HL)(2)(NO3)(2)(OH)(2)]center dot 2(NO3) (Ln = Nd, 1; Ln = Yb, 2; Ln = Er, 3; Ln = Gd, 4) have been self-assembled from the reaction of the Salen-type Schiff-base ligand H2L with Ln(NO3)(3)center dot 6H(2)O (Ln = Nd, Yb, Er or Gd), respectively (H2L: N, N'-bis(salicylidene) cyclohexane-1,2-diamine). The result of their photophysical properties shows that the strong and characteristic NIR luminescence for complexes 1 and 2 with emissive lifetimes in microsecond ranges are observed and the sensitization arises from the excited state (both (LC)-L-1 and (LC)-L-3) of the Salen-type Schiff-base ligand with the flexible linker.National Natural Science Foundation 21173165, 20871098Ministry of Education of China NCET-10-0936Higher Education of China 20116101110003State Key Laboratory of Structure Chemistry 20100014Education Committee Foundation of Shaanxi Province 11JK0588Hong Kong Research Grants Council, P. R. of China HKBU 202407, FRG/06-07/II-16)Hong Kong Research Grants Council, Robert A. Welch Foundation F-816Texas Higher Education Coordinating Board ARP 003658-0010-2006Petroleum Research Fund 47014-AC5Chemistr

When and why vision-language models behave like bags-of-words, and what to do about it?

Despite the success of large vision and language models (VLMs) in many downstream applications, it is unclear how well they encode compositional information. Here, we create the Attribution, Relation, and Order (ARO) benchmark to systematically evaluate the ability of VLMs to understand different types of relationships, attributes, and order. ARO consists of Visual Genome Attribution, to test the understanding of objects' properties; Visual Genome Relation, to test for relational understanding; and COCO & Flickr30k-Order, to test for order sensitivity. ARO is orders of magnitude larger than previous benchmarks of compositionality, with more than 50,000 test cases. We show where state-of-the-art VLMs have poor relational understanding, can blunder when linking objects to their attributes, and demonstrate a severe lack of order sensitivity. VLMs are predominantly trained and evaluated on large datasets with rich compositional structure in the images and captions. Yet, training on these datasets has not been enough to address the lack of compositional understanding, and evaluating on these datasets has failed to surface this deficiency. To understand why these limitations emerge and are not represented in the standard tests, we zoom into the evaluation and training procedures. We demonstrate that it is possible to perform well on retrieval over existing datasets without using the composition and order information. Given that contrastive pretraining optimizes for retrieval on datasets with similar shortcuts, we hypothesize that this can explain why the models do not need to learn to represent compositional information. This finding suggests a natural solution: composition-aware hard negative mining. We show that a simple-to-implement modification of contrastive learning significantly improves the performance on tasks requiring understanding of order and compositionality.Comment: ICLR 2023 Oral (notable-top-5%