Abstract-In this paper, we propose a timing model reduction algorithm for hierarchical timing analysis based on a bicliquestar replacement technique. In hierarchical timing analysis, each functional block is characterized into an abstract timing model. The complexity of analysis is linear to the number of edges in the abstract timing model for timing propagation. We propose a biclique-star replacement technique to minimize the number of edges in the timing model. The experiments on industry test cases show that by allowing acceptable errors, the proposed algorithm can largely reduce the number of edges in the timing model.
Main observation and conclusion
The morphology of polymeric nanoparticles prepared by polymerization‐induced self‐assembly (PISA) is depended on the degree of polymerization of the solvophilic and solvophobic blocks. Herein, a series of poly (N,N‐dimethylaminoethyl methacrylate)‐b‐poly (benzyl methacrylate) (PDMA‐b‐PBzMA) diblock copolymer spherical nanoparticles were synthesized via reversible addition‐fragmentation chain transfer (RAFT) mediated PISA. These diblock copolymer nanoparticles are with nearly the same hydrodynamic size and solvophobic chain length, but with different solvophilic chain length. We used these nanoparticles to stabilize the oil‐in‐water Pickering emulsion. We find that the stability of Pickering emulsion increases with the length of solvophilic chain of the nanoparticles. Moreover, the droplet size of the Pickering emulsion can be tailored by varying the oil/water ratio and concentration of nanoparticles.
With the development of reversible deactivated radical polymerization techniques, polymerization-induced self-assembly (PISA) is emerging as a facile method to prepare block copolymer nanoparticles in situ with high concentrations, providing wide potential applications in different fields, including nanomedicine, coatings, nanomanufacture, and Pickering emulsions. Polymeric emulsifiers synthesized by PISA have many advantages comparing with conventional nanoparticle emulsifiers. The morphologies, size, and amphiphilicity can be readily regulated via the synthetic process, post-modification, and external stimuli. By introducing stimulus responsiveness into PISA nanoparticles, Pickering emulsions stabilized with these nanoparticles can be endowed with "smart" behaviors. The emulsions can be regulated in reversible emulsification and demulsification. In this review, the authors focus on recent progress on Pickering emulsions stabilized by PISA nanoparticles with stimuli-responsiveness. The factors affecting the stability of emulsions during emulsification and demulsification are discussed in details. Furthermore, some viewpoints for preparing stimuli-responsive emulsions and their applications in antibacterial agents, diphase reaction platforms, and multi-emulsions are discussed as well. Finally, the future developments and applications of stimuli-responsive Pickering emulsions stabilized by PISA nanoparticles are highlighted.
Named data networking (NDN) is one representation and implementation of information-centric networking (ICN) and is considered to be among the most promising designs for the next generation of network architecture. The introduction of NDN into vehicular ad-hoc networks (VANETs) and utilization of its content-centric characteristic to improve data transmission and distribution in VANETs has become a research hotspot in recent years. However, research on mobility support of NDN-based VANETs still faces many challenges. To solve the issue of data transmission path breaking due to Consumer mobility in NDNbased VANETs, this article proposes a novel vehicle trackingbased Data packet forwarding scheme (VTDF) to improve the successful delivery rate of Data packets in mobile environments. In this approach, the urban road structure is divided into complex multi-junction and straight lane scenarios and Data packets are forwarded according to vehicle movement information. Simulations indicated that this vehicle tracking scheme provides a lower average data transmission delay, shorter handover delay between roadside units, and higher data delivery rate for Consumers compared to the standard methods.
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