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A novel hyperbranched polymer was synthesized using acrylamide (AM), acrylic acid (AA),N-vinyl-2-pyrrolidone (NVP), and dendrite functional monomer as raw materials by redox initiation system in an aqueous medium. The hyperbranched polymer was characterized by infrared (IR) spectroscopy,1H NMR spectroscopy,13C NMR spectroscopy, elemental analysis, and scanning electron microscope (SEM). The viscosity retention rate of the hyperbranched polymer was 22.89% higher than that of the AM/AA copolymer (HPAM) at 95°C, and the viscosity retention rate was 8.17%, 12.49%, and 13.68% higher than that of HPAM in 18000 mg/L NaCl, 1800 mg/L CaCl2, and 1800 mg/L MgCl2·6H2O brine, respectively. The hyperbranched polymer exhibited higher apparent viscosity (25.2 mPa·s versus 8.1 mPa·s) under 500 s−1shear rate at 80°C. Furthermore, the enhanced oil recovery (EOR) of 1500 mg/L hyperbranched polymer solutions was up to 23.51% by the core flooding test at 80°C.

Software execution environment, interfaced with software through library functions and system calls, constitutes an important aspect of the software's semantics. Software analysis ought to take the execution environment into consideration. However, due to lack of source code and the inherent implementation complexity of these functions, it is quite difficult to co-analyze software and its environment. In this paper, we propose to extend program synthesis techniques to construct models for system and library functions. The technique samples the behavior of the original implementation of a function. The samples are used as the specification to synthesize the model, which is a C program. The generated model is iteratively refined. We have developed a prototype that can successfully construct models for a pool of system and library calls from their real world complex binary implementations. Moreover, our experiments have shown that the constructed models can improve dynamic test generation and failure tolerance.

From traditional clusters to cloud systems, job scheduling is one of the most critical factors for achieving high performance in any distributed environment. In this paper, we propose an adaptive algorithm for scheduling modular non-linear parallel jobs in meteorological Cloud, which has a unique parallelism that can only be configured at the very beginning of the execution. Different from existing work, our algorithm takes into account four characteristics of the jobs at the same time, including the average execution time, the deadlines of jobs, the number of assigned resources, and the overall system loads. We demonstrate the effectiveness and efficiency of our scheduling algorithm through simulations using WRF (Weather Research and Forecasting model) that which is widely used in scientific computing. Our evaluation results show that the proposed algorithm has multiple advantages compared with previous methods, including more than 10% reduction in terms of execution time, a higher completion ratio in terms of meeting soft deadlines, and a much smaller standard deviation of the average weighted execution time. Moreover, we show that the proposed algorithm can tolerate inaccuracy in system load estimation.

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