Optimizing Activators Regenerated by Electron Transfer for Atom Transfer Radical Polymerization of Methyl Methacrylate Initiated by Ethyl 2-bromopropionate

In this study, we used ethyl 2-bromopropionate (EBrP) as an initiator of activators regenerated by electron transfer for atom transfer radical polymerization (ARGET ATRP) of methyl methacrylate (MMA). We investigated in detail the effect on polymerization of different kinds of reducing agents and ligands, the amounts of the reducing agent and catalyst, and reaction temperature. We determined the molecular weight and dispersity of the polymers by gel permeation chromatography (GPC). The results reveal glucose to be the best reducing agent for this system. The monomer conversion increased with increases in the reaction temperature and in the feeding amounts of the reducing agent and catalyst. The optimum amount of the reducing agent and minimal amount of catalyst required depend on the particular system. For example, we polymerized MMA with 200 ppm of catalyst and 15-fold of glucose/CuCl2 resulting in a PMMA with high Mn (Mn,GPC = 48 700, Mn,theo = 48 500) and low dispersity (1.27). The first-order kinetics show that the molecular weights increased linearly with the monomer conversion and are consistent with the theoretical values, the chain extension reaction and end group analysis results also demonstrate that the characteristics of polymerization process belong to a typical "living"/controlled radical polymerization. Moreover, 1H-NMR analysis results indicate the stereoregularity of the polymer is given priority over syndiotactic architecture and the effect of the type of ligand on the stereoregularity is very slight

Mobile 5G Network Deployment Scheme on High-Speed Railway

The fifth-generation (5G) wireless communication has experienced an upsurge of interest for empowering vertical industries, due to its high data volume, extremely low latency, high reliability, and significant improvement in user experience. Specifically, deploying 5G on high-speed railway (HSR) is critical for the promotion of smart travelling such that passengers can connect to the Internet and utilize the on-board time to continue their usual activities. However, there remains a series of challenges in practical implementation, such as the serious Doppler shift caused by the high mobility, the carriage penetration loss especially in the high-frequency bands, frequent handovers, and economic issues. To address these challenges, we propose three schemes in this article to improve the coverage of 5G networks on the train. In particular, we provide a comprehensive description of each scheme in terms of their network architecture and service establishment procedures. Specifically, the mobile edge computing (MEC) is used as the key technology to provide low-latency services for on-board passengers. Moreover, these three schemes are compared among themselves regarding the quality-of-service, the scalability of service, and the related industry development status. Finally, we discuss various potential research directions and open issues in terms of deploying 5G networks on HSR

A C-terminal hydrophobic region is required for homo-oligomerization of the hepatitis E virus capsid (ORF2) protein

Hepatitis E virus (HEV) is the causative agent of hepatitis E, an acute form of viral hepatitis. The open reading frame 2 (ORF2) of HEV encodes the viral capsid protein, which can self-oligomerize into virus-like particles. To understand the domains within this protein important for capsid biogenesis, we have carried out in vitro analyses of association and folding patterns of wild type and mutant ORF2 proteins. When expressed in vitro or in transfected cells, the ORF2 protein assembled as dimers, trimers and higher order forms. While N-terminal deletions up to 111 amino acids had no effect, the deletion of amino acids 585–610 led to reduced homo-oligomerization. This deletion also resulted in aberrant folding of the protein, as determined by its sensitivity to trypsin. This study suggests that a C-terminal hydrophobic region encompassing amino acids 585–610 of the ORF2 protein might be critical for capsid biogenesis

Consensus-Based Group Task Assignment with Social Impact in Spatial Crowdsourcing

Abstract With the pervasiveness of GPS-enabled smart devices and increased wireless communication technologies, spatial crowdsourcing (SC) has drawn increasing attention in assigning location-sensitive tasks to moving workers. In real-world scenarios, for the complex tasks, SC is more likely to assign each task to more than one worker, called group task assignment (GTA), for the reason that an individual worker cannot complete the task well by herself. It is a challenging issue to assign worker groups the tasks that they are interested in and willing to perform. In this paper, we propose a novel framework for group task assignment based on worker groups’ preferences, which includes two components: social impact-based preference modeling (SIPM) and preference-aware group task assignment (PGTA). SIPM employs a bipartite graph embedding model and the attention mechanism to learn the social impact-based preferences of different worker groups on different task categories. PGTA utilizes an optimal task assignment algorithm based on the tree decomposition technique to maximize the overall task assignments, in which we give higher priorities to the worker groups showing more interests in the tasks. We further optimize the original framework by proposing strategies to improve the effectiveness of group task assignment, wherein a deep learning method and the group consensus are taken into consideration. Extensive empirical studies verify that the proposed techniques and optimization strategies can settle the problem nicely