The contribution of cloud computing and mobile computing technologies lead to the newly emerging mobile cloud computing paradigm. Three major approaches have been proposed for mobile cloud applications: 1) extending the access to cloud services to mobile devices; 2) enabling mobile devices to work collaboratively as cloud resource providers; 3) augmenting the execution of mobile applications on portable devices using cloud resources. In this paper, we focus on the third approach in supporting mobile data stream applications. More specifically, we study how to optimize the computation partitioning of a data stream application between mobile and cloud to achieve maximum speed/throughput in processing the streaming data. To the best of our knowledge, it is the first work to study the partitioning problem for mobile data stream applications, where the optimization is placed on achieving high throughput of processing the streaming data rather than minimizing the makespan of executions as in other applications. We first propose a framework to provide runtime support for the dynamic computation partitioning and execution of the application. Different from existing works, the framework not only allows the dynamic partitioning for a single user but also supports the sharing of computation instances among multiple users in the cloud to achieve efficient utilization of the underlying cloud resources. Meanwhile, the framework has better scalability because it is designed on the elastic cloud fabrics. Based on the framework, we design a genetic algorithm for optimal computation partition. Both numerical evaluation and real world experiment have been performed, and the results show that the partitioned application can achieve at least two times better performance in terms of throughput than the application without partitioning.
Forecasting complex dynamical phenomena in settings where only partial knowledge of their dynamics is available is a prevalent problem across various scientific fields. While purely data-driven approaches are arguably insufficient in this context, standard physical modeling-based approaches tend to be over-simplistic, inducing non-negligible errors. In this work, we introduce the APHYNITY framework, a principled approach for augmenting incomplete physical dynamics described by differential equations with deep data-driven models. It consists of decomposing the dynamics into two components: a physical component accounting for the dynamics for which we have some prior knowledge, and a data-driven component accounting for errors of the physical model. The learning problem is carefully formulated such that the physical model explains as much of the data as possible, while the data-driven component only describes information that cannot be captured by the physical model; no more, no less. This not only provides the existence and uniqueness for this decomposition, but also ensures interpretability and benefit generalization. Experiments made on three important use cases, each representative of a different family of phenomena, i.e. reaction–diffusion equations, wave equations and the non-linear damped pendulum, show that APHYNITY can efficiently leverage approximate physical models to accurately forecast the evolution of the system and correctly identify relevant physical parameters. The code is available at https://github.com/yuan-yin/APHYNITY.
In this article, polyamide (PA) 6 was dissolved in 98 wt % formic acids with a concentration of 13 wt %, and then the electrospun PA 6 nanofiber membranes were prepared. The filtration potential of the nanofiber membranes were firstly analyzed based on the scanning electron microscope images and pore size measurements, and then the effects of thermal treatment on the dimensional stability were studied. Subsequently, the continuous production process based on the above experiments and the deposition areas of the nanofibers on the collecting meshes was designed. Finally, the mechanical properties and the filtration performances of the electrospun PA 6 nanofiber membranes were assessed. The results showed the membranes with a thickness of 71 lm (electrospinning for 15 min) had good filtration potential for the microparticles with 0.3 lm diameter. After being treated by tension and relaxation heat setting, the membranes demonstrated excellent dimensional stability, the breaking tenacity, and the elongation reached 4.71 6 1.66 MPa and (69.97 6 6.56)%, respectively. The permeability decreased with the increase of the membranes thickness. However, the permeability of membranes with (72.9 6 1.04) lm thickness still maintained 516 L/m 2 s, and the actual filtration efficiency of 0.3 lm particles reached 99.98%. The above results showed that the membranes fabricated by the continuous production process could meet the demands of high efficiency filtration and provide a reference for the industrial use of electrospun nanofiber membranes.
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