Shanghai University

About: Shanghai University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Microstructure & Catalysis. The organization has 59583 authors who have published 56840 publications receiving 753549 citations. The organization is also known as: Shànghǎi Dàxué.

BeCome: Blockchain-Enabled Computation Offloading for IoT in Mobile Edge Computing

Abstract: Benefiting from the real-time processing ability of edge computing, computing tasks requested by smart devices in the Internet of Things are offloaded to edge computing devices (ECDs) for implementation. However, ECDs are often overloaded or underloaded with disproportionate resource requests. In addition, during the process of task offloading, the transmitted information is vulnerable, which can result in data incompleteness. In view of this challenge, a blockchain-enabled computation offloading method, named BeCome, is proposed in this article. Blockchain technology is employed in edge computing to ensure data integrity. Then, the nondominated sorting genetic algorithm III is adopted to generate strategies for balanced resource allocation. Furthermore, simple additive weighting and multicriteria decision making are utilized to identify the optimal offloading strategy. Finally, performance evaluations of BeCome are given through simulation experiments.

Akt: A Double-Edged Sword in Cell Proliferation and Genome Stability

Abstract: The Akt family of serine/threonine protein kinases are key regulators of multiple aspects of cell behaviour, including proliferation, survival, metabolism, and tumorigenesis. Growth-factor-activated Akt signalling promotes progression through normal, unperturbed cell cycles by acting on diverse downstream factors involved in controlling the G1/S and G2/M transitions. Remarkably, several recent studies have also implicated Akt in modulating DNA damage responses and genome stability. High Akt activity can suppress ATR/Chk1 signalling and homologous recombination repair (HRR) via direct phosphorylation of Chk1 or TopBP1 or, indirectly, by inhibiting recruitment of double-strand break (DSB) resection factors, such as RPA, Brca1, and Rad51, to sites of damage. Loss of checkpoint and/or HRR proficiency is therefore a potential cause of genomic instability in tumor cells with high Akt. Conversely, Akt is activated by DNA double-strand breaks (DSBs) in a DNA-PK- or ATM/ATR-dependent manner and in some circumstances can contribute to radioresistance by stimulating DNA repair by nonhomologous end joining (NHEJ). Akt therefore modifies both the response to and repair of genotoxic damage in complex ways that are likely to have important consequences for the therapy of tumors with deregulation of the PI3K-Akt-PTEN pathway.

Effective CU Size Decision for HEVC Intracoding

Abstract: In high efficiency video coding (HEVC), the tree structured coding unit (CU) is adopted to allow recursive splitting into four equally sized blocks. At each depth level (or CU size), it enables up to 35 intraprediction modes, including a planar mode, a dc mode, and 33 directional modes. The intraprediction via exhaustive mode search exploited in the test model of HEVC (HM) effectively improves coding efficiency, but results in a very high computational complexity. In this paper, a fast CU size decision algorithm for HEVC intracoding is proposed to speed up the process by reducing the number of candidate CU sizes required to be checked for each treeblock. The novelty of the proposed algorithm lies in the following two aspects: 1) an early determination of CU size decision with adaptive thresholds is developed based on the texture homogeneity and 2) a novel bypass strategy for intraprediction on large CU size is proposed based on the combination of texture property and coding information from neighboring coded CUs. Experimental results show that the proposed effective CU size decision algorithm achieves a computational complexity reduction up to 67%, while incurring only 0.06-dB loss on peak signal-to-noise ratio or 1.08% increase on bit rate compared with that of the original coding in HM.