Renmin University of China

About: Renmin University of China is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: China & Population. The organization has 11325 authors who have published 15498 publications receiving 238419 citations. The organization is also known as: Renmin University & People's University of China.

A one-step deep eutectic solvent assisted synthesis of carbon nitride/metal oxide composites for photocatalytic nitrogen fixation

Abstract: Nitrogen fixation remains a challenge from both academic and industrial perspectives. Photocatalysis based on versatile semiconductors is a promising route for nitrogen fixation under mild conditions. In this study, for the first time, a ternary deep eutectic solvent (DES) strategy was developed for scalable synthesis of g-C3N4/metal oxide composites. A series of g-C3N4/metal oxide composites were prepared by a facile one-step calcination process using a ternary DES containing urea, melamine and metal chlorides. Moreover, since a DES is a homogeneous solution, the metal oxide after pyrolysis can be uniformly supported on the g-C3N4 nanosheet. This greatly increases the number of sites for photocatalysis and increases the catalytic efficiency. These composites show extraordinary photocatalytic effects for the reduction of nitrogen in atmospheric environment. With g-C3N4/40 wt% Fe2O3 as the photocatalyst, the maximum efficiency for ammonia generation can reach 4380 μmol L−1 h−1, which is the best yield to the best of our knowledge. The highly photocatalytic activity may be attributed to the efficient interfacial interaction between the 2D g-C3N4 nanosheets and α-Fe2O3 nanoparticles, which promotes the transfer and separation efficiency of charge carriers.

On the sequencing of tree structures for XML indexing

Abstract: Sequence-based XML indexing aims at avoiding expensive join operations in query processing. It transforms structured XML data into sequences so that a structured query can be answered holistically through subsequence matching. In this paper, we address the problem of query equivalence with respect to this transformation, and we introduce a performance-oriented principle for sequencing tree structures. With query equivalence, XML queries can be performed through subsequence matching without join operations, post-processing, or other special handling for problems such as false alarms. We identify a class of sequencing methods for this purpose, and we present a novel subsequence matching algorithm that observe query equivalence. Still, query equivalence is just a prerequisite for sequence-based XML indexing. Our goal is to find the best sequencing strategy with regard to the time and space complexity in indexing and querying XML data. To this end, we introduce a performance-oriented principle to guide the sequencing of tree structures. For any given XML data set, the principle finds an optimal sequencing strategy according to its schema and its data distribution. We present a novel method that realizes this principle. In our experiments, we show the advantages of sequence-based indexing over traditional XML indexing methods, and we compare several sequencing strategies and demonstrate the benefit of the performance-oriented sequencing principle.

Searching Trajectories by Regions of Interest

Abstract: With the increasing availability of moving-object tracking data, trajectory search is increasingly important. We propose and investigate a novel query type named trajectory search by regions of interest (TSR query). Given an argument set of trajectories, a TSR query takes a set of regions of interest as a parameter and returns the trajectory in the argument set with the highest spatial-density correlation to the query regions. This type of query is useful in many popular applications such as trip planning and recommendation, and location based services in general. TSR query processing faces three challenges: how to model the spatial-density correlation between query regions and data trajectories, how to effectively prune the search space, and how to effectively schedule multiple so-called query sources. To tackle these challenges, a series of new metrics are defined to model spatial-density correlations. An efficient trajectory search algorithm is developed that exploits upper and lower bounds to prune the search space and that adopts a query-source selection strategy, as well as integrates a heuristic search strategy based on priority ranking to schedule multiple query sources. The performance of TSR query processing is studied in extensive experiments based on real and synthetic spatial data.