Genetic variation in eight Chinese cattle breeds based on the analysis of microsatellite markers

Genetic variability and genetic relationships were investigated among eight Chinese cattle breeds using 12 microsatellite markers. Three hundred and fifty-two alleles were detected and the average number of alleles per locus ranged from 8.33 ± 1.67 in the Jiaxian breed to 21.33 ± 5.60 in the Qinchuan breed with a mean value of 13.91. The total number of alleles per microsatellite ranged from 21 (INRA005, HEL1) to 40 (HEL13), with a mean of 29.33 per locus. The fixation indices at the 12 loci in the eight breeds were very low with a mean of 0.006. A principal components analysis and the construction of a neighborjoining tree showed that these eight Chinese cattle breeds cluster into three groups i.e. the Yanbian andChineseHolstein, theNanyang and Jiaxian, and the four remaining breeds.This clustering agrees with the origin and geographical distributions of these Chinese breeds

Rethinking GNN-based Entity Alignment on Heterogeneous Knowledge Graphs: New Datasets and A New Method

The development of knowledge graph (KG) applications has led to a rising need for entity alignment (EA) between heterogeneous KGs that are extracted from various sources. Recently, graph neural networks (GNNs) have been widely adopted in EA tasks due to GNNs' impressive ability to capture structure information. However, we have observed that the oversimplified settings of the existing common EA datasets are distant from real-world scenarios, which obstructs a full understanding of the advancements achieved by recent methods. This phenomenon makes us ponder: Do existing GNN-based EA methods really make great progress? In this paper, to study the performance of EA methods in realistic settings, we focus on the alignment of highly heterogeneous KGs (HHKGs) (e.g., event KGs and general KGs) which are different with regard to the scale and structure, and share fewer overlapping entities. First, we sweep the unreasonable settings, and propose two new HHKG datasets that closely mimic real-world EA scenarios. Then, based on the proposed datasets, we conduct extensive experiments to evaluate previous representative EA methods, and reveal interesting findings about the progress of GNN-based EA methods. We find that the structural information becomes difficult to exploit but still valuable in aligning HHKGs. This phenomenon leads to inferior performance of existing EA methods, especially GNN-based methods. Our findings shed light on the potential problems resulting from an impulsive application of GNN-based methods as a panacea for all EA datasets. Finally, we introduce a simple but effective method: Simple-HHEA, which comprehensively utilizes entity name, structure, and temporal information. Experiment results show Simple-HHEA outperforms previous models on HHKG datasets.Comment: 11 pages, 6 figure

HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation

In this paper, we focus on the problem of category-level object pose estimation, which is challenging due to the large intra-category shape variation. 3D graph convolution (3D-GC) based methods have been widely used to extract local geometric features, but they have limitations for complex shaped objects and are sensitive to noise. Moreover, the scale and translation invariant properties of 3D-GC restrict the perception of an object's size and translation information. In this paper, we propose a simple network structure, the HS-layer, which extends 3D-GC to extract hybrid scope latent features from point cloud data for category-level object pose estimation tasks. The proposed HS-layer: 1) is able to perceive local-global geometric structure and global information, 2) is robust to noise, and 3) can encode size and translation information. Our experiments show that the simple replacement of the 3D-GC layer with the proposed HS-layer on the baseline method (GPV-Pose) achieves a significant improvement, with the performance increased by 14.5% on 5d2cm metric and 10.3% on IoU75. Our method outperforms the state-of-the-art methods by a large margin (8.3% on 5d2cm, 6.9% on IoU75) on the REAL275 dataset and runs in real-time (50 FPS).Comment: Accepted by the 2023 IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR

On the Evolution of Knowledge Graphs: A Survey and Perspective

Knowledge graphs (KGs) are structured representations of diversified knowledge. They are widely used in various intelligent applications. In this article, we provide a comprehensive survey on the evolution of various types of knowledge graphs (i.e., static KGs, dynamic KGs, temporal KGs, and event KGs) and techniques for knowledge extraction and reasoning. Furthermore, we introduce the practical applications of different types of KGs, including a case study in financial analysis. Finally, we propose our perspective on the future directions of knowledge engineering, including the potential of combining the power of knowledge graphs and large language models (LLMs), and the evolution of knowledge extraction, reasoning, and representation

The development process and development trend of garabge classification in DanDong

In this thesis, lots of history about the development of waste classification in Dandong and the cur-rent situation of waste classification in Dandong were explained, and the discussion was carried out from the perspective of environmental history. Therefore, with the continuous enhancement of people environmental awareness and the continuous promotion of government participation in garbage classification work, Dandong has become one of the most inhabitable cities in China. In addition by introducing the garbage classification methods in Shanghai and comparing them with those in Dandong, the future development prospects of garbage classification in Dandong are proposed. However, a method has been discovered that can quickly convert household waste into waste oil and process it into biodiesel, which will be an important direction for the future development of waste classification in Dandong

Competitiveness Evaluation of Electric Bus Charging Services Based on Analytic Hierarchy Process

The premise of the large-scale operation of electric buses corresponds to efficient charging service guarantees. Recent research on charging stations mainly aims to obtain the construction location and construction sequence through optimization methods or decision-making methods. This research has considered the aspects of geography, charging efficiency, economic efficiency, and emergency response capacity. The increase of charging stations will lead to competition among charging stations, unbalanced use of charging facilities, and unnecessary loss of electricity to the power grid. In fact, few studies pay attention to the actual operation of existing charging stations. Therefore, it is necessary to establish a scientific, comprehensive, and efficient charging services evaluation framework to support the actual operation of charging stations. Based on the analytic hierarchy process (AHP), this paper designs a multi-level indicator evaluation framework, which includes 6 first-level indicators and 20 s-level indicators. The first-level indicators are cutting peak and filling valley (A1), location and scale (A2), intelligent technology (A3), equipment efficiency (A4), operating income (A5), and reliability (A6). Through the questionnaire survey of ten experts in related fields, we understood the importance and attention of these indicators. The results show that the weights of indicators of location and scale index (A2) and reliability (A6) are high, which are 0.2875 and 0.2957, respectively. The least concerned indicator is equipment utilization efficiency (A4), at a weight of 0.0531. According to the actual data of charging stations in Zhengzhou, China, the comprehensive competitiveness of several charging stations is evaluated by the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The result shows that station 1 has the highest comprehensive competitiveness, followed by station 2 and station 7. The evaluation framework proposed in this paper comprehensively considers a variety of factors. The combination of AHP and TOPSIS can reduce the uncertainty in experts’ evaluation of the service of the charging station

Genetic variation in eight Chinese cattle breeds based on the analysis of microsatellite markers

Genetic variability and genetic relationships were investigated among eight Chinese cattle breeds using 12 microsatellite markers. Three hundred and fifty-two alleles were detected and the average number of alleles per locus ranged from $8.33\pm 1.67$ in the Jiaxian breed to $21.33\pm 5.60$ in the Qinchuan breed with a mean value of 13.91. The total number of alleles per microsatellite ranged from 21 (INRA005, HEL1) to 40 (HEL13), with a mean of 29.33 per locus. The fixation indices at the 12 loci in the eight breeds were very low with a mean of 0.006. A principal components analysis and the construction of a neighborjoining tree showed that these eight Chinese cattle breeds cluster into three groups i.e. the Yanbian andChineseHolstein, theNanyang and Jiaxian, and the four remaining breeds.This clustering agrees with the origin and geographical distributions of these Chinese breeds

Effects of Water and Nitrogen on Grain Filling Characteristics, Canopy Microclimate with Chalkiness of Directly Seeded Rice

In order to determine how to reduce the chalkiness of rice grains through irrigation modes and nitrogen (N) fertilizer management. The experiment was designed using three irrigation modes (flooding (W1), dry–wet alternating (W2), and dry alternating (W3)), three N application strategies (under 150 kg ha−1, the application ratio of base:tiller:panicle fertilizer (30%:50%:20% (N1), 30%:30%:40% (N2), and 30%:10%:60% (N3)), and zero N as the control (N0) in 2019 and 2020. The results revealed that water–nitrogen interactions had a significant or extremely significant effect on the chalkiness characteristics of the superior and inferior grains. Compared with W1 and W3 treatments, W2 coupled with the N1 application strategy can further optimize grain filling characteristics and canopy microclimate parameters, thereby reducing grain chalkiness. Correlation analysis revealed that increasing grain filling parameters (Gmax or Gmean) and mean grain filling rates (MGRs) during the mid-filling stage in superior grains of the primary branches and inferior grains of the secondary branches, which were important factors in water–nitrogen interaction effects, could further reduce chalkiness. Improving the canopy microclimate (daily average temperature difference and daily average light intensity difference) during the early-filling stage for inferior grains and the mid-filling stage for superior grains could be another important method to reduce chalkiness

A Magnetic Abrasive Finishing Process with an Auxiliary Magnetic Machining Tool for the Internal Surface Finishing of a Thick-Walled Tube

This paper proposes a novel magnetic abrasive finishing (MAF) process that uses an auxiliary magnetic machining tool for the internal surface finishing of a thick-walled tube. The auxiliary magnetic machining tool and external poles form a closed magnetic field circuit. Thus, a stronger magnetic force can be generated during the process. In the current study, we focus on analyzing the distribution of the magnetic field and magnetic flux density and investigating the finishing characteristics of a mixed magnetic abrasive finishing process and speed of relative revolutions. Based on the finishing characteristics, we also conduct a stage-by-stage finishing process by changing the combinations of the mixed magnetic abrasive finishing process. The finishing quality of the internal surface was mainly evaluated by the measured roundness and surface roughness. The experimental results show that the roundness and surface roughness Ra are affected when the total amount of WA abrasive and iron powder is too much; a better surface roughness could be obtained when the difference in the speed of relative revolutions is considerable, but the roundness is the worst. Furthermore, the original roundness measurement of 270 µm can reach 10 µm, and the surface roughness Ra can increase from an original surface roughness of 4.1 µm to reach 10 nm after 105 min of the stage-by-stage finishing process

Carbon Dioxide Conversion with High-Performance Photocatalysis into Methanol on NiSe2/WSe2

Climate change has been recognized as a threatening environmental problem around the world. CO2 is considered to be the main component of greenhouse gas. By using solar energy (light energy) as the energy source, photocatalytic conversion is one of the most effective technologies to reveal the clean utilization of CO2. Herein, using sodium tungstate, nickel nitrate, and selenium powder as the main raw materials, the high absorption and utilization of WSe2 for light energy and the high intrinsic conductivity of NiSe2 were combined by a hydrothermal method to prepare NiSe2/WSe2 and hydrazine hydrate as the reductant. Then, high-performance NiSe2/WSe2 photocatalytic material was prepared. The characterization results of XRD, XPS, SEM, specific surface area, and UV-visible spectroscopy show that the main diffraction peak of synthesized NiSe2/WSe2 is sharp, which basically coincides with the standard card. After doping NiSe2, the morphology of WSe2 was changed from a flake shape to smaller and more trivial crystal flakes, which demonstrates richer exposed edges and more active sites; the specific surface area increased from 3.01 m2 g−1 to 8.52 m2 g−1, and the band gap becomes wider, increasing from 1.66 eV to 1.68 eV. The results of a photocatalytic experiment show that when the prepared NiSe2/WSe2 catalyst is used to conduct photocatalytic reduction of CO2, the yield of CH3OH is significantly increased. After reaction for 10 h, the maximum yield could reach 3.80 mmol g−1, which presents great photocatalytic activity