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For thermoelectric thin film, the substrate plays an important role during the growing process and produces effects on its thermoelectric properties. Some special kinds of substrates provide an optimal combination of influences on both the structure and thermoelectric properties. In this work, Bi-Sb-Te films are deposited on Si substrates with different initial orientations by magnetron sputtering in two ways: with and without a pre-coating process. The preferred orientations of the Bi-Sb-Te films are greatly affected by the substrates, in which the thin film tends to deposit on Si substrate with (100) initial orientation and high (015)-texture, while the (00l)-textured Bi-Sb-Te film easily deposits on Si substrate with (110) initial orientation. The experimental and theoretical calculation results indicate that Bi-Sb-Te film with (00l)-texture presents good electrical conductivity and a higher power factor than that of film with (015)-texture. [ABSTRACT FROM AUTHOR]

Numerical simulation of thermal hydraulics of nuclear reactors is widely concerned, but large-scale fluid simulation is still prohibited due to the complexity of components and huge computational effort. Some applications of open source CFD programs still have a large gap in terms of comprehensiveness of physical models, computational accuracy and computational efficiency compared with commercial CFD programs. Therefore, it is necessary to improve the computational performance of in-house CFD software (YHACT, the parallel analysis code of thermohydraulices) to obtain the processing capability of large-scale mesh data and better parallel efficiency. In this paper, we will form a unified framework of meshing and mesh renumbering for solving fluid dynamics problems with unstructured meshes. Meanwhile, the effective Greedy, RCM (reverse Cuthill-Mckee), and CQ (cell quotient) grid renumbering algorithms are integrated into YHACT software. An important judgment metric, named median point average distance (MDMP), is applied as the discriminant of sparse matrix quality to select the renumbering methods with better effect for different physical models. Finally, a parallel test of the turbulence model with 39.5 million grid volumes is performed using a pressurized water reactor engineering case component with 3*3 rod bundles. The computational results before and after renumbering are also compared to verify the robustness of the program. Experiments show that the CFD framework integrated in this paper can correctly perform simulations of the thermal engineering hydraulics of large nuclear reactors. The parallel size of the program reaches a maximum of 3072 processes. The renumbering acceleration effect reaches its maximum at a parallel scale of 1536 processes, 56.72%. It provides a basis for our future implementation of open-source CFD software that supports efficient large-scale parallel simulations. [ABSTRACT FROM AUTHOR]

Grid renumbering techniques have been shown to be effective in improving the efficiency of computational fluid dynamics (CFD) numerical simulations based on the finite volume method (FVM). However, with the increasing complexity of real-world engineering scenarios, there is still a huge challenge to choose better sequencing techniques to improve parallel simulation performance. This paper designed an improved metric (MDMP) to evaluate the structure of sparse matrices. The metric takes the aggregation of non-zero elements inside the sparse matrix as an evaluation criterion. Meanwhile, combined with the features of the cell-centered finite volume method supporting unstructured grids, we proposed the cell quotient (CQ) renumbering algorithm to further reduce the maximum bandwidth and contours of large sparse matrices with finite volume discretization. Finally, with real-world engineering cases, we quantitatively analyzed the evaluation effect of MDMP and the optimization effect of different renumbering algorithms. The results showed that the classical greedy algorithm reduces the maximum bandwidth of the sparse matrix by at most 60.34% and the profile by 95.38%. Correspondingly, the CQ algorithm reduced them by at most 92.94% and 98.70%. However, in terms of MDMP, the CQ algorithm was 83.43% less optimized than the Greedy algorithm. In terms of overall computational speed, the Greedy algorithm was optimized by a maximum of 38.19%, and the CQ algorithm was optimized by a maximum of 27.31%. The above is in accordance with the evaluation results of the MDMP metric. Thus, our new metric can more accurately evaluate the renumbering method for numerical fluid simulations, which is of great value in selecting a better mesh renumbering method in engineering applications of CFD. [ABSTRACT FROM AUTHOR]

GF-6 is the first optical remote sensing satellite for precision agriculture observations in China. Accurate identification of the cloud in GF-6 helps improve data availability. However, due to the narrow band range contained in GF-6, Fmask version 3.2 for Landsat is not suitable for GF-6. Hence, this paper proposes an improved Fmask based on the spectral-contextual information to solve the inapplicability of Fmask version 3.2 in GF-6. The improvements are divided into the following six aspects. The shortwave infrared (SWIR) in the "Basic Test" is replaced by blue band. The threshold in the original "HOT Test" is modified based on the comprehensive consideration of fog and thin clouds. The bare soil and rock are detected by the relationship between green and near infrared (NIR) bands. The bright buildings are detected by the relationship between the upper and lower quartiles of blue and red bands. The stratus with high humidity and fog_W (fog over water) are distinguished by the ratio of blue and red edge position 1 bands. Temperature probability for land is replaced by the HOT-based cloud probability (LHOT), and SWIR in brightness probability is replaced by NIR. The average cloud pixels accuracy (TPR) of the improved Fmask is 95.51%. [ABSTRACT FROM AUTHOR]

Recently, deep learning approaches, especially convolutional neural networks (CNNs), have attracted extensive attention in iris recognition. Though CNN-based approaches realize automatic feature extraction and achieve outstanding performance, they usually require more training samples and higher computational complexity than the classic methods. This work focuses on training a novel condensed 2-channel (2-ch) CNN with few training samples for efficient and accurate iris identification and verification. A multi-branch CNN with three well-designed online augmentation schemes and radial attention layers is first proposed as a high-performance basic iris classifier. Then, both branch pruning and channel pruning are achieved by analyzing the weight distribution of the model. Finally, fast finetuning is optionally applied, which can significantly improve the performance of the pruned CNN while alleviating the computational burden. In addition, we further investigate the encoding ability of 2-ch CNN and propose an efficient iris recognition scheme suitable for large database application scenarios. Moreover, the gradient-based analysis results indicate that the proposed algorithm is robust to various image contaminations. We comprehensively evaluated our algorithm on three publicly available iris databases for which the results proved satisfactory for real-time iris recognition. [ABSTRACT FROM AUTHOR]

Compared with three-dimensional (3D) and other materials, two-dimensional (2D) materials with unique properties such as high specific surface area, structurally adjustable band structure, and electromagnetic properties have attracted wide attention. In recent years, great progress has been made for 2D MoS2 in the field of electrocatalysis, and its exposed unsaturated edges are considered to be active sites of electrocatalytic reactions. In this review, we focus on the latest progress of 2D MoS2 in the oxygen reduction reaction (ORR) that has not received much attention. First, the basic properties of 2D MoS2 and its advantages in the ORR are introduced. Then, the synthesis methods of 2D MoS2 are summarized, and specific strategies for optimizing the performance of 2D MoS2 in ORRs, and the challenges and opportunities faced are discussed. Finally, the future of the 2D MoS2-based ORR catalysts is explored. [ABSTRACT FROM AUTHOR]