Cooperative network analysis of patent holders in the field of OLED technology

With the continuous development of science and technology, the number of patents continues to increase. At the same time, patent cooperation is more normal. It is particularly important to analyze the cooperation relationship among patent holders. The application of social network analysis methods solves this problem. OLED tends to gradually replace LCD. South Korea's Samsung and LG hold the majority of patents in the OLED field. How to break through has become a problem faced by Chinese companies. This paper uses the degree centrality, betweenness centrality and closeness centrality in the social network analysis method, and uses the data visualization tool Ucinet to systematically analyze the OLED technology patents from the Derwent Innovation Index. The results show that there is a clear trend of cooperation among patent holders in the OLED technology field. China's OLED enterprises should speed up the industrial chain layout, increase relevant R&D investment, and improve the R&D intensity of core technologies

A Novel Clustering Tree-based Video lookup Strategy for Supporting VCR-like Operations in MANETs

Mobile Peer-to-Peer (MP2P) network is a promising avenue for large-scale deployment of Video-on-Demand (VoD) applications over mobile ad-hoc networks (MANETs). In P2P VoD systems, fast search for resources is key determinants for improving the Quality of Service (QoS) due to the low delay of seeking resources caused by streaming interactivity. In this paper, we propose a novel Clustering Tree-based Video Lookup strategy for supporting VCR-like operations in MANETs (CTVL) CTVL selects the chunks with the high popularity as "overlay router" chunks to build the "virtual connection" with other chunks in terms of the popularities and external connection of video chunks. CTVL designs a new clustering strategy to group nodes in P2P networks and a maintenance mechanism of cluster structure, which achieves the high system scalability and fast resource search performance. Thorough simulation results also show how CTVL achieves higher average lookup success rate, lower maintenance cost, lower average end-to-end delay and lower packet loss ratio (PLR) in comparison with other state of the art solutions

Joint Projection Learning and Tensor Decomposition Based Incomplete Multi-view Clustering

Incomplete multi-view clustering (IMVC) has received increasing attention since it is often that some views of samples are incomplete in reality. Most existing methods learn similarity subgraphs from original incomplete multi-view data and seek complete graphs by exploring the incomplete subgraphs of each view for spectral clustering. However, the graphs constructed on the original high-dimensional data may be suboptimal due to feature redundancy and noise. Besides, previous methods generally ignored the graph noise caused by the inter-class and intra-class structure variation during the transformation of incomplete graphs and complete graphs. To address these problems, we propose a novel Joint Projection Learning and Tensor Decomposition Based method (JPLTD) for IMVC. Specifically, to alleviate the influence of redundant features and noise in high-dimensional data, JPLTD introduces an orthogonal projection matrix to project the high-dimensional features into a lower-dimensional space for compact feature learning.Meanwhile, based on the lower-dimensional space, the similarity graphs corresponding to instances of different views are learned, and JPLTD stacks these graphs into a third-order low-rank tensor to explore the high-order correlations across different views. We further consider the graph noise of projected data caused by missing samples and use a tensor-decomposition based graph filter for robust clustering.JPLTD decomposes the original tensor into an intrinsic tensor and a sparse tensor. The intrinsic tensor models the true data similarities. An effective optimization algorithm is adopted to solve the JPLTD model. Comprehensive experiments on several benchmark datasets demonstrate that JPLTD outperforms the state-of-the-art methods. The code of JPLTD is available at https://github.com/weilvNJU/JPLTD.Comment: IEEE Transactions on Neural Networks and Learning Systems, 202

Functional conservation and divergence of Miscanthus lutarioriparius GT43 gene family in xylan biosynthesis

Background: Xylan is the most abundant un-cellulosic polysaccharides of plant cell walls. Much progress in xylan biosynthesis has been gained in the model plant species Arabidopsis. Two homologous pairs Irregular Xylem 9 (IRX9)/9L and IRX14/14L from glycosyltransferase (GT) family 43 have been proved to play crucial roles in xylan backbone biosynthesis. However, xylan biosynthesis in grass such as Miscanthus remains poorly understood

Achieving of high-diet-fiber barley via managing fructan hydrolysis

High fructan content in the grain of cereals is an important trait in agriculture such as environmental resilience and dietary fiber food production. To understand the mechanism in determining final grain fructan content and achieve high fructan cereal, a cross breeding strategy based on fructan synthesis and hydrolysis activities was set up and have achieved barley lines with 11.8% storage fructan in the harvested grain. Our study discovered that high activity of fructan hydrolysis at later grain developmental stage leads to the low fructan content in mature seeds, simultaneously increasing fructan synthesis at early stage and decreasing fructan hydrolysis at later stage through crossing breeding is an efficient way to elevate grain diet-fiber content. A good correlation between fructan and beta glucans was also discovered with obvious interest. Field trials showed that the achieved high fructan barley produced over seven folds higher fructan content than control barley and pull carbon-flux to fructan through decreasing fructan hydrolysis without disruption starch synthesis will probably not bring yield deficiency

Water use strategies of Nitraria tangutorum in the lake-basin region of the Badain Jaran Desert

Information regarding plant water-use strategies is essential for understanding the hydrological processes and plant survival adaptation mechanisms in desert lake basin regions. To examine the water use strategies of plants in desert lake basin areas, water uptake patterns, water use efficiency, and water potential of Nitraria tangutorum were investigated at different distances from the lake duringhe growing seasons in the lake basin regions of the Badain Jaran Desert. The results indicate that N. tangutorum primarily absorbed groundwater in May (63.8%) and August (53.5%), relied on deep soil water in June (75.1%), and uniformly absorbed soil water from different layers in July. These observations could be explained by periodic fluctuations in the groundwater level and the consequent decrease in soil water availability, as well as plant root adjustments. As soil water availability decreases, N. tangutorum adapts to water variation by increasing its water use efficiency (WUE) and reducing its leaf water potential (Ψ). With intensified water stress, N. tangutorum gradually shifted from adventurous anisohydric regulation to conservative isohydric regulation. Thus, N. tangutorum responds to diverse degrees of environmental changes by altering its water-use strategy. A better understanding of the adaptive water use strategies developed by desert plants under varying water availability conditions provides insight into the diversity of species’ reactions to long-term drought and quantifies the hydrological cycle of desert ecosystems against the background of worldwide climate warming

Improved bioenergy value of residual rice straw by increased lipid levels from upregulation of fatty acid biosynthesis

BackgroundRice (Oryza sativa) straw is a common waste product that represents a considerable amount of bound energy. This energy can be used for biogas production, but the rate and level of methane produced from rice straw is still low. To investigate the potential for an increased biogas production from rice straw, we have here utilized WRINKLED1 (WRI1), a plant AP2/ERF transcription factor, to increase triacylglycerol (TAG) biosynthesis in rice plants. Two forms of Arabidopsis thaliana WRI1 were evaluated by transient expression and stable transformation of rice plants, and transgenic plants were analyzed both for TAG levels and biogas production from straw.ResultsBoth full-length AtWRI1, and a truncated form lacking the initial 141 amino acids (including the N-terminal AP2 domain), increased fatty acid and TAG levels in vegetative and reproductive tissues of Indica rice. The stimulatory effect of the truncated AtWRI1 was significantly lower than that of the full-length protein, suggesting a role for the deleted AP2 domain in WRI1 activity. Full-length AtWRI1 increased TAG levels also in Japonica rice, indicating a conserved effect of WRI1 in rice lipid biosynthesis. The bio-methane production from rice straw was 20% higher in transformants than in the wild type. Moreover, a higher producing rate and final yield of methane was obtained for rice straw compared with rice husks, suggesting positive links between methane production and a high amount of fatty acids.ConclusionsOur results suggest that heterologous WRI1 expression in transgenic plants can be used to improve the metabolic potential for bioenergy purposes, in particular methane production

Accurate Monitoring of Algal Blooms in Key Nearshore Zones of Lakes and Reservoirs Using Binocular Video Surveillance System

In recent years, algal blooms break out frequently and often accumulate in nearshore zones of eutrophic lakes and reservoirs, which seriously threaten regional water supply security. It is of great significance to grasp the status of algal blooms in key nearshore zones timely for the emergency prevention and control of algal blooms. A video surveillance system provides a new method for achieving this goal. The results of algal-bloom monitoring in current research, however, are usually interfered by onshore vegetation for their similar textural features. Accordingly, there are great limitations in current works in terms of decision support for emergency prevention and control of algal blooms. To solve this problem, a binocular video surveillance system based an accurate monitoring method of algal blooms is proposed in this paper. Binocular images of monitoring areas are obtained periodically by exploiting the binocular video surveillance system, which is performed by a stereoscopic 3D reconstruction method to obtain the 3D point cloud data of monitoring areas. Afterward, water regions and non-water regions are intelligently discriminated according to the elevation characteristics of point clouds, and only the image data of the water regions are finally adopted for algal-bloom extraction. Thus, the influence of onshore vegetation on the extraction of algal blooms can be eliminated. The system was implemented and applied, and the experimental results show that the proposed method can eliminate effectively the interference of onshore vegetation on the extraction of algal blooms and improve significantly the accuracy of existing methods for algal-bloom monitoring based on video surveillance system