High-frequency variations in Earth rotation and the planetary momentum budget

The major focus of the subject contract was on helping to resolve one of the more notable discrepancies still existing in the axial momentum budget of the solid Earth-atmosphere system, namely the disappearance of coherence between length-of-day (l.o.d.) and atmospheric angular momentum (AAM) at periods shorter than about a fortnight. Recognizing the importance of identifying the source of the high-frequency momentum budget anomaly, the scientific community organized two special measurement campaigns (SEARCH '92 and CONT '94) to obtain the best possible determinations of l.o.d. and AAM. An additional goal was to analyze newly developed estimates of the torques that transfer momentum between the atmosphere and its underlying surface to determine whether the ocean might be a reservoir of momentum on short time scales. Discrepancies between AAM and l.o.d. at sub-fortnightly periods have been attributed to either measurement errors in these quantities or the need to incorporate oceanic angular momentum into the planetary budget. Results from the SEARCH '92 and CONT '94 campaigns suggest that when special attention is paid to the quality of the measurements, better agreement between l.o.d. and AAM at high frequencies can be obtained. The mechanism most responsible for the high-frequency changes observed in AAM during these campaigns involves a direct coupling to the solid Earth, i.e, the mountain torque, thereby obviating a significant oceanic role

Use of operational analyses to study the dynamics of troposphere-stratosphere interactions in polar regions

Operational analyses produced by large weather centers have been used in the past to monitor various aspects of the general circulation as well as address dynamical questions. For a number years researchers have been monitoring National Meteorological Center (NMC) analyses at 100 millibars because it is the level from which stratospheric analyses are built. In particular, they closely examined the pressure-work term at that level which is an important parameter related to the forcing of the stratosphere by the troposphere. Rapid fluctuations typically seen in this quanity during the months of July-November, and similarly noted by Randel et al., (1987) may raise some concern about the quality of the analyses. Researchers investigated the behavior of the term mainly responsible for these variations, namely the eddy flux of heat, and furthermore have corroborated the presence of these variations in contemporaneous analyses produced by the European Centre for Medium Range Forecasts (ECMWF). Researchers demonstrated that fluctuations in standing eddy heat fluxes, related to the forcing of the stratosphere by the troposphere, agree in two largely independent meteorological analyses. Researchers believe, that these fluctuations are mostly real

Activity-Dependent Modulation of Synaptic AMPA Receptor Accumulation

AbstractBoth theoretical and experimental work have suggested that central neurons compensate for changes in excitatory synaptic input in order to maintain a relatively constant output. We report here that inhibition of excitatory synaptic transmission in cultured spinal neurons leads to an increase in mEPSC amplitudes, accompanied by an equivalent increase in the accumulation of AMPA receptors at synapses. Conversely, increasing excitatory synaptic activity leads to a decrease in synaptic AMPA receptors and a decline in mEPSC amplitude. The time course of this synaptic remodeling is slow, similar to the metabolic half-life of neuronal AMPA receptors. Moreover, inhibiting excitatory synaptic transmission significantly prolongs the half-life of the AMPA receptor subunit GluR1, suggesting that synaptic activity modulates the size of the mEPSC by regulating the turnover of postsynaptic AMPA receptors

Report of the panel on earth rotation and reference frames, section 7

Objectives and requirements for Earth rotation and reference frame studies in the 1990s are discussed. The objectives are to observe and understand interactions of air and water with the rotational dynamics of the Earth, the effects of the Earth's crust and mantle on the dynamics and excitation of Earth rotation variations over time scales of hours to centuries, and the effects of the Earth's core on the rotational dynamics and the excitation of Earth rotation variations over time scales of a year or longer. Another objective is to establish, refine and maintain terrestrial and celestrial reference frames. Requirements include improvements in observations and analysis, improvements in celestial and terrestrial reference frames and reference frame connections, and improved observations of crustal motion and mass redistribution on the Earth

Deep Learning for Ultrasonic Crack Characterization in NDE

Machine learning for nondestructive evaluation (NDE) has the potential to bring significant improvements in defect characterization accuracy due to its effectiveness in pattern recognition problems. However, the application of modern machine learning methods to NDE has been obstructed by the scarcity of real defect data to train on. This article demonstrates how an efficient, hybrid finite element (FE) and ray-based simulation can be used to train a convolutional neural network (CNN) to characterize real defects. To demonstrate this methodology, an inline pipe inspection application is considered. This uses four plane wave images from two arrays and is applied to the characterization of cracks of length 1-5 mm and inclined at angles of up to 20° from the vertical. A standard image-based sizing technique, the 6-dB drop method, is used as a comparison point. For the 6-dB drop method, the average absolute error in length and angle prediction is ±1.1 mm and ±8.6°, respectively, while the CNN is almost four times more accurate at ±0.29 mm and ±2.9°. To demonstrate the adaptability of the deep learning approach, an error in sound speed estimation is included in the training and test set. With a maximum error of 10% in shear and longitudinal sound speed, the 6-dB drop method has an average error of ±1.5 mmm and ±12°, while the CNN has ±0.45 mm and ±3.0°. This demonstrates far superior crack characterization accuracy by using deep learning rather than traditional image-based sizing