Binary and ternary blends composed of poly(lactic acid) (PLA), starch, and poly(ethylene glycols) (PEGs) with different molecular weights (weight-average molecular weights 5 300, 2000, 4000, 6000, and 10, 000 g/mol) were prepared, and the plasticizing effect and miscibility of PEGs in poly(lactic acid)/starch (PTPS) or PLA were intensively studied. The results indicate that the PEGs were effective plasticizers for the PTPS blends. The small-molecule plasticizers of PEG300 (i.e., the M w of PEG was 300g/mol) and glycerol presented better plasticizing effects, whereas its migration and limited miscibility resulted in significant decreases in the water resistance and elongation at break. PEG2000, with a moderate molecular weight, was partially miscible in sample PTPS3; this led to better performance in water resistance and mechanical properties. For higher molecular weight PEG, its plasticization for both starch and PLA was depressed, and visible phase separation also occurred, especially for PTPS6. It was also found that the presence of PEG significantly decreased the glass-transition temperature and accelerated the crystallization of the PLA matrix, depending on the PEG molecular weight and concentration.
With the emergence of new vehicular applications, computation offloading based on mobile edge computing (MEC) has become a promising paradigm in resource-constrained vehicular networks. However, an unreasonable offloading strategy in offloading can cause serious energy consumption and latency. A real-time energy-aware offloading scheme for vehicle networks, based on MEC, is proposed to optimize communication and computation resource to decrease energy consumption and latency. Because the problem of computation offloading and resource allocation is the mixed-integer nonlinear problem (MINLP), this article uses a bi-level optimization method to transform the original MINLP into two subproblems. Furthermore, considering the mobility of vehicle users (V-UEs) and the availability of cloud resources, an offloading scheme based on deep reinforcement learning (DRL) is adopted to help users make the optimal offloading decisions. The simulation results show that the proposed bi-level optimization algorithm reduces the total overhead by nearly 40% to the compared algorithm.
Complex distributed systems are increasingly important in modern computer science, yet many undergraduate curricula do not give students the opportunity to develop the skill sets necessary to grapple with the complexity of such systems. We have developed and integrated into an undergraduate elective course on parallel and distributed computing a teaching tool that may help students develop these skill sets. The tool uses virtualization to ease the burden of resourcing and configuring complex systems for student study, and creates varied "firefighting" gaming scenarios in which students compete to keep the system up and running in the presence of multiple issues. Preliminary experience indicates that (1) students find the tool engaging and (2) it is a manageable way in which to give students a novel perspective on interaction with complex distributed systems.
In this paper, we propose one content-sharing system InnerLight based on IPFS and BlockChain, which is a creation and public discussion platform about mental health. InnerLight put copies of articles from creators on IPFS to achieve distributed storage of contents and complete the first step of returning the data to creators. At the same time, it also encourages creators and readers to maintain the sustainable development of the system through blockchain-based cryptocurrency. In addition to IPFS and Blockchain, ranking algoithms contribute to make Innerlight to be a decentralized autonomous ecosystem.
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