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In Cope's gray treefrog (Hyla chrysoscelis), thresholds for recognizing conspecific calls are lower in temporally modulated noise backgrounds compared with unmodulated noise. The effect of modulated noise on discrimination among different conspecific calls is unknown. In quiet, females prefer calls with relatively more pulses. This study tested the hypotheses that noise impairs selectivity for longer calls and that processes akin to dip listening in modulated noise can ameliorate this impairment. In two-stimulus choice tests, female subjects were allowed to choose between an average-length call and a shorter or longer alternative. Tests were replicated at two signal levels in quiet and in the presence of chorus-shaped noise that was unmodulated, modulated by a sinusoid, or modulated by envelopes resembling natural choruses. When subjects showed a preference, it was always for the relatively longer call. Noise reduced preferences for longer calls, but the magnitude of this reduction was unrelated to whether the noise envelope was modulated or unmodulated. Together, the results are inconsistent with the hypothesis that dip listening improves a female gray treefrog's ability to select longer calls in modulated compared with unmodulated noise.

In a manufacturing system, production control-related decision-making activities occur at different levels. At the process level, one of the main control activities is to tune the parameters of individual manufacturing equipment. At the system level, the main activity is to coordinate production resources and to route parts to appropriate workstations based on their processing requirement, priority indices, and control policy. At the factory level, the goal is to plan and schedule the processing of parts at different operations for the entire system in order to optimize certain objectives. Note that the results of such activities at different levels are closely coupled and affect the overall performance of the manufacturing system as a whole. Therefore, it is important to systematically integrate these control and optimization activities into one unified platform to ensure the goal of each individual activity is aligned with the overall performance of the system. In this paper, we develop a simulation-based virtual testbed that implements dynamic optimization, automatic information exchange, and decision-making from the process-level, system-level, and factory-level of a manufacturing system into an integrated computation environment. This is demonstrated by connecting a Python-based numerical computation program, discrete-event simulation software (Simul8), and an optimization solver (CPLEX) via a third-party master program. The application of this simulation-based virtual testbed is illustrated by a case study in a machining shop.

Corrosion and sustained casing pressure have serious threats to the integrity of tubing of gas well. Researching the residual strength of corroded tubing has great significance to ensure the safety of gas well. The finite element method was used to study the relationships between residual strength and corrosion defects size, internal pressure, external pressure, axial load. The results show that, for tubing with uniform corrosion, the defect depth, internal pressure and external pressure have greater impacts on the von Mises equivalent stress of tubing, and the defect width and defect length have little effects on it. For tubing with pitting corrosion, the defect depth, internal pressure and external pressure have greater impacts on the von Mises equivalent stress of tubing, while the defect radius has little effect on it. These simulation data were fitted into the functions of residual strength of corroded tubing according to different corrosion morphology types. Both of the verifications of the fitting results show that most of the error between the original calculation data and the fitting calculation data is less than 4%. The fitting formulas can be used conveniently to evaluate the safety of the tubing of gas well with sustained casing pressure.

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