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The condensed matter Bose system may contain effective monopole quasiparticles in its excitation spectrum. In this paper, we first accomplish the mapping of the two-band Ginzburg–Landau theory to the extended CP1 model, and then perform the Monte Carlo simulations on the 50 × 50 × 50 cubic lattice with periodic boundary conditions. With the numerical data of monopole density and magnetic susceptibility, we indicate that there exists a monopole–antimonopole deconfinement transition for the two-band superconducting system with the critical temperature above 70 K. We also suggest the possible detection of this new monopole plasma phase in high- T c iron-based superconductors. [ABSTRACT FROM AUTHOR]

In order to increase agglomeration production and improve its quality while reducing energy consumption, a new intelligent blending method has been established at Ansteel to optimize its sinter blends. The statistical model of the ore blending results shows that (1) a blending ratio of 47.2% Ore A and 52.8% Ore C corresponded to the best sinter yield of 72.44%. (2) From the viewpoint of sinter reducibility, sinter basicity should not be less than 1.98 when the proportion of Ore A in the blend is more than 35%. Due to the low mixing efficiency of disc pelletizers, Ansteel has therefore gone through a massive technical transformation at Sinter Plant 3 to replace its 16 existing disc pelletizers with one drum granulator. Since the installation of the drum granulator, the standard deviation has decreased from 1.517 to 0.7332 for total Fe (T.Fe) and from 0.146 to 0.0956 for basicity. In the case of the drum granulator, the standard deviation for sinter T.Fe and basicity were 0.6926 and 0.05449, respectively, as compared to 0.8902 and 0.2033 for the disc pelletizers. In addition, a single lattice method is proposed to optimize the particle-size distribution of the coke breeze to further improve sinter quality and reduce fuel consumption. The lattice method indicated that the optimum coke breeze to achieve maximum sinter tumble strength should consist of approximately 57.20%: −1 mm, 25.63%: 1–3 mm, 11.17%: 3–5 mm, and 6.00%: >5 mm particles. Given the international trend of increasing bed depth, Ansteel has successfully achieved a bed height of 1050 mm or more under its blend conditions, which typically contain 75% concentrates. Finally, some new iron ore agglomerations research is discussed. [ABSTRACT FROM AUTHOR]

Since the development of 3D applications, the point cloud, as a spatial description easily acquired by sensors, has been widely used in multiple areas such as SLAM and 3D reconstruction. Point Cloud Compression (PCC) has also attracted more attention as a primary step before point cloud transferring and saving, where the geometry compression is an important component of PCC to compress the points geometrical structures. However, existing non-learning-based geometry compression methods are often limited by manually pre-defined compression rules. Though learning-based compression methods can significantly improve the algorithm performances by learning compression rules from data, they still have some defects. Voxel-based compression networks introduce precision errors due to the voxelized operations, while point-based methods may have relatively weak robustness and are mainly designed for sparse point clouds. In this work, we propose a novel learning-based point cloud compression framework named 3D Point Cloud Geometry Quantiation Compression Network (3QNet), which overcomes the robustness limitation of existing point-based methods and can handle dense points. By learning a codebook including common structural features from simple and sparse shapes, 3QNet can efficiently deal with multiple kinds of point clouds. According to experiments on object models, indoor scenes, and outdoor scans, 3QNet can achieve better compression performances than many representative methods. [ABSTRACT FROM AUTHOR]

To promote the application of almond expellers, sweet almond expeller globulin (amandin) was extracted for the preparation of bioactive peptides. After dual enzymatic hydrolysis, Sephadex G-15 gel isolation, reverse-phase high-performance liquid chromatography purification and ESI-MS/MS analysis, two novel peptides Val-Asp-Leu-Val-Ala-Glu-Val-Pro-Arg-Gly-Leu (1164.45 Da) and Leu-Asp-Arg-Leu-Glu (644.77 Da) were identified in sweet almond expeller amandin hydrolysates. Leu-Asp-Arg-Leu-Glu (LDRLE) of excellent zinc-chelating capacity (24.73 mg/g) was selected for preparation of peptide-zinc chelate. Structural analysis revealed that zinc ions were mainly bonded to amino group and carboxyl group of LDRLE. Potential toxicity and some physicochemical properties of LDRLE and Val-Asp-Leu-Val-Ala-Glu-Val-Pro-Arg-Gly-Leu (VDLVAEVPRGL) were predicted in silico. The results demonstrated that both LDRLE and VDLVAEVPRGL were not toxic. Additionally, zinc solubility of LDRLE-zinc chelate was much higher than that of zinc sulphate and zinc gluconate at pH 6.0–10.0 and against gastrointestinal digestion at 37 °C (p < 0.05). However, incubation at 100 °C for 20–60 min significantly reduced zinc-solubility of LDRLE-zinc chelate. Moreover, the chelate showed higher zinc transport ability in vitro than zinc sulphate and zinc gluconate (p < 0.05). Therefore, peptides isolated from sweet almond expeller amandin have potential applications as ingredient of zinc supplements. [ABSTRACT FROM AUTHOR]

Video object detection (VOD) focuses on detecting objects for each frame in a video, which is a challenging task due to appearance deterioration in certain video frames. Recent works usually distill crucial information from multiple support frames to improve the reference features, but they only perform at frame level or proposal level that cannot integrate spatial-temporal features sufficiently. To deal with this challenge, we treat VOD as a spatial-temporal hierarchical features interacting process and introduce a Multi-level Spatial-Temporal (MST) feature aggregation framework to fully exploit frame-level, proposal-level, and instance-level information in a unified framework. Specifically, MST first measures context similarity in pixel space to enhance all frame-level features rather than only update reference features. The proposal-level feature aggregation then models object relation to augment reference object proposals. Furthermore, to filter out irrelevant information from other classes and backgrounds, we introduce an instance ID constraint to boost instance-level features by leveraging support object proposal features that belong to the same object. Besides, we propose a Deformable Feature Alignment (DAlign) module before MST to achieve a more accurate pixel-level spatial alignment for better feature aggregation. Extensive experiments are conducted on ImageNet VID and UAVDT datasets that demonstrate the superiority of our method over state-of-the-art (SOTA) methods. Our method achieves 83.3% and 62.1% with ResNet-101 on two datasets, outperforming SOTA MEGA by 0.4% and 2.7%. [ABSTRACT FROM AUTHOR]

Audio-guided face reenactment aims to generate authentic target faces that have matched facial expression of the input audio, and many learning-based methods have successfully achieved this. However, most methods can only reenact a particular person once trained or suffer from the low-quality generation of the target images. Also, nearly none of the current reenactment works consider the model size and running speed that are important for practical use. To solve the above challenges, we propose an efficient Audio-guided Multi-face reenactment model named AMNet, which can reenact target faces among multiple persons with corresponding source faces and drive signals as inputs. Concretely, we design a Geometric Controller (GC) module to inject the drive signals so that the model can be optimized in an end-to-end manner and generate more authentic images. Also, we adopt a lightweight network for our face reenactor so that the model can run in real-time on both CPU and GPU devices. Abundant experiments prove our approach’s superiority over existing methods, e.g., averagely decreasing FID by 0.12 $\downarrow$ and increasing SSIM by 0.031 $\uparrow$ than APB2Face, while owning fewer parameters ($\times 4 \downarrow$) and faster CPU speed ($\times 4 \uparrow$). [ABSTRACT FROM AUTHOR]

A low‐cost recycling system for the production of pectinase was built to improve the yield and quality of Muzao date juice and to reduce production costs. An Aspergillus niger strain, Gyx086, was selected from 20 A. niger strains on potato agar plates containing Muzao date residues, and the strain was used to produce an enzyme cocktail with high pectinase activity using Muzao date residues as the medium. The high pectinase activity level was therefore used in the production of Muzao date juice. Under optimized enzymatic hydrolysis conditions, a date juice yield of 92.88% was obtained, which was 11% higher than the control, and was not significantly different from a commercial preparation. Further storage trials indicated that the clarity, viscosity, storage stability, and soluble solid content could also be greatly improved by enzymatic treatment. These results suggested that the cheap experimental preparation could effectively replace the commercial preparation used for the production of date juice. This is the first systematic report detailing the production of Muzao date juice using a cheap and effective pectinase cocktail, which could promote the development of the date juice industry. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd [ABSTRACT FROM AUTHOR]

Manual assembly is still an essential part in modern manufacturing. Understanding the actual state of the assembly process can not only improve quality control of products, but also collect comprehensive data for production planning and proficiency assessments. Addressing the rising complexity led by the uncertainty in manual assembly, this paper presents an efficient approach to automatically capture and analyze hand operations in the assembly process. In this paper, a detection-based tracking method is introduced to capture trajectories of hand movement from the camera installed in each workstation. Then, the actions in hand trajectories are identified with a novel temporal action localization model. The experimental results have proved that our method reached the application level with high accuracy and a low computational cost. The proposed system is lightweight enough to be quickly set up on an embedded computing device for real-time online inference and on a cloud server for offline analysis as well. [ABSTRACT FROM AUTHOR]