Zhejiang Gongshang University

About: Zhejiang Gongshang University is a education organization based out in Hangzhou, China. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 8258 authors who have published 7670 publications receiving 90296 citations. The organization is also known as: Zhèjiāng Gōngshāng Dàxué.

A Power-Efficient and Social-Aware Relay Selection Method for Multi-Hop D2D Communications

Abstract: Due to the short communication range, users are selected as relays to support multi-hop transmission for content sharing/downloading in D2D communications. In this letter, we first exploit social relationships from interacts and contributions, and then formulate an optimal relay selection problem to enhance cooperative multi-hop D2D communications. A Power-efficient and Social-aware Relay Selection (PSRS) algorithm is provided to solve the above problem. Simulation results show that our method has the lowest average power consumption compared to the existing methods.

Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization

Abstract: Query-based moment localization is a new task that localizes the best matched segment in an untrimmed video according to a given sentence query. In this localization task, one should pay more attention to thoroughly mine visual and linguistic information. To this end, we propose a novel Cross- and Self-Modal Graph Attention Network (CSMGAN) that recasts this task as a process of iterative messages passing over a joint graph. Specifically, the joint graph consists of Cross-Modal interaction Graph (CMG) and Self-Modal relation Graph (SMG), where frames and words are represented as nodes, and the relations between cross- and self-modal node pairs are described by an attention mechanism. Through parametric message passing, CMG highlights relevant instances across video and sentence, and then SMG models the pairwise relation inside each modality for frame (word) correlating. With multiple layers of such a joint graph, our CSMGAN is able to effectively capture high-order interactions between two modalities, thus enabling a further precise localization. Besides, to better comprehend the contextual details in the query, we develop a hierarchical sentence encoder to enhance the query understanding. Extensive experiments on four public datasets demonstrate the effectiveness of our proposed model, and GCSMAN significantly outperforms the state-of-the-arts.

Biodegradation of 1,4-Dioxane by a Novel Strain and Its Biodegradation Pathway

Abstract: A Gram-negative strain DD1, which could use 1,4-dioxane as the sole carbon and energy source, was isolated from the mixture of activated sludge obtained from Qige urban sewage treatment plant. According to the Biolog GNIII detection and the 16S ribosomal DNA (rDNA) sequence, DD1 was identified as Acinetobacter baumannii. Cells of A. baumannii DD1 precultured in 1,4-dioxane could completely degrade 100 mg/L 1,4-dioxane in 42 h with a cell yield of 0.414 mg-protein (mg-1,4-dioxane)−1 and a generation time of 6.75 h, demonstrating that DD1 bears the highest 1,4-dioxane-degrading activity among the described strains. Moreover, DD1 tolerates higher 1,4-dioxane concentration almost up to 1,000 mg/L. The strain could also grow on several benzene homologues including benzene, toluene, ethylbenzene, o-xylene, m-xylene, and phenol. During the degradation process of 1,4-dioxane, the first oxidation was initiated by monooxygenase in DD1. However, the main second monooxygenation intermediate 2-hydroxyethoxyacetic acid was not detected. As replacer, 1,4-dioxene was identified, and other intermediates such as ethylene glycol and oxalic acid were also detected. Based on the analysis of degradation products, a partial degradation pathway was proposed.