Maternal deprivation (MD) is frequently used as an early life stress model in rodents to investigate behavioral and neurological responses under stressful conditions. However, the effect of MD on the early postnatal development of rodents, which is when multiple neural systems become established, is rarely investigated due to methodological limitations. Ultrasonic vocalizations (USVs) are one of the few responses produced by neonatal rodents that can be quantitatively analyzed, and the quantification of USVs is regarded as a novel approach to investigate possible alterations in the neurobehavioral and emotional development of infant rodents under stress. To investigate the effect of MD on pup mice, we subjected C57BL/6J mice to MD and recorded the USVs of pups on postnatal days 1, 3, 7, 8, and 14. To determine whether the effect of MD on USVs was acute or cumulative, pre- and post-separation USV groups were included; sex differences in pup USV emission were also investigated. Our results suggest that (i) USV activity was high on postnatal days 3–8; (ii) the MD effect on USVs was acute, and a cumulative effect was not found; (iii) the MD mice vocalized more and longer than the controls at a lower frequency, and the effect was closely related to age; and (iv) female pups were more susceptible than males to the effect of MD on USV number and duration between postnatal days 3–8.
To assess the level and nature of ground shaking in Hawaii for the purposes of earthquake hazard mitigation and seismic design, empirical groundmotion prediction models are desired. To develop such empirical relationships, knowledge of the subsurface site conditions beneath strong-motion stations is critical. Thus, as a first step to develop ground-motion prediction models for Hawaii, spectralanalysis-of-surface-waves (SASW) profiling was performed at the 22 free-field U.S. Geological Survey (USGS) strong-motion sites on the Big Island to obtain shear-wave velocity (V S ) data. Nineteen of these stations recorded the 2006 Kiholo Bay moment magnitude (M) 6.7 earthquake, and 17 stations recorded the triggered M 6.0 Mahukona earthquake. V S profiling was performed to reach depths of more than 100 ft. Most of the USGS stations are situated on sites underlain by basalt, based on surficial geologic maps. However, the sites have varying degrees of weathering and soil development. The remaining strong-motion stations are located on alluvium or volcanic ash. V S30 (average V S in the top 30 m) values for the stations on basalt ranged from 906 to 1908 ft=s [National Earthquake Hazards Reduction Program (NEHRP) site classes C and D], because most sites were covered with soil of variable thickness. Based on these data, an NEHRP site-class map was developed for the Big Island. These new V S data will be a significant input into an update of the USGS statewide hazard maps and to the operation of ShakeMap on the island of Hawaii.
Theoretical analysis shows that there are several values of the phase velocity to a certain frequency of Rayleigh wave on the surface of a layered medium model, which is called the multiple mode property of Rayleigh wave. The multiple mode property of surface wave results in the complication of measured dispersion curves, especially when a soft interlayer exists in strata. This brings more difficulties to the explanation of the dispersion curves measured on this kind of site. In order to facilitate engineering application, a new method, improved equivalent homogenous half‐space method, is put forward to compute the theoretical dispersion curve of a layered medium model based on the equivalent homogenous half‐space theory. This new method does not need to deal with the multiple value problem of the phase velocity, and the phase velocity resulting from this method can reflect the synthetic effect of all modes of Rayleigh waves. The inverse fitting program is compiled based on the improved equivalent homogenous half‐space method, which has gotten a very good effect in an engineering application.
Flexible pipelines are widely used in the offshore oil and gas projects. Comparing to steel pipes, flexible risers consist of multiple components layers, with each layer serving designated functions. For example, the helical tensile layers are used to work against axial tension from static pipe weight and amplification from the dynamic motions. As the risers are constantly experiencing stress variations due to different loadings, the fatigue performance of the tensile layers is essential to the design and operation of the flexible pipe system. Global and local fatigue analyses are usually performed in the design stage to estimate the fatigue life of tensile layers. Various parameters like vessel characteristic, pressure and temperature, environmental loadings affect the riser long-term performance. Also, multiple locations like top end fitting, bend stiffener region, touch-down zone are all potential fatigue hot-spots. To evaluate the fatigue damage at these locations, the industry standard practice is to run fatigue analysis in time domain with irregular wave. This approach is however time and resource consuming, especially when the designers have to consider thousands of load cases with long duration for numerous combinations of sea-states and operating conditions. Baker Hughes developed a frequency domain technique for fatigue assessment on tensile layers of flexible risers. The approach builds transfer functions based on chosen time domain simulation and predict the total stress spectrum for other cases. Dirlik’s approximation method is used to estimate the fatigue damage based on the stress spectrum. Comparing to time domain approach, this approach is considerably more efficient in implementation. Since frequency domain technique simplifies the riser response as a linear system, the discrepancy must be addressed by calibration factors. To improve accuracy, more transfer functions are required to be built prior to the damage calculation. In this paper, Baker Hughes introduces artificial neural network (ANN) technique to the developed frequency domain fatigue damage methodology. The ANN model is built between the wave spectrum and stress spectrum. Unlike the general AI technique, the type of neuron at each layer is carefully selected to represent the dynamic behavior of the riser system to improve the model efficiency. The ANN model is trained based on selected sea states. The trained model is used to predict riser damage and compared to that calculated from the time domain approach.
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