abstract Most work, to date, on analysis of effects of earthquakes on dams treats dam and reservoir as two uncoupled systems. The importance of interaction between the two systems in determining the water pressure on dams during earthquakes is investigated herein. The analysis presented is approximate in nature and is aimed at exploring the problem. The earthquake acceleration is first modeled as a stationary gaussian white noise process. For this excitation, certain qualitative conclusions concerning importance of interaction effects are derived. Hydrodynamic responses of typical systems subjected to El Centro (1940) earthquake are then determined to confirm these conclusions. On basis of these results, it appears that the interaction between reservoir and dam has significant influence on the hydrodynamic responses during earthquake excitations.
Up to about 1985, supernovae (SNe) generally were placed into the two Minkowski classes, type I and type II, defined by the absence or presence, respectively, of hydrogen in their optical spectra. Around that time it was acknowledged that several type I SNe were systematically peculiar, both spectroscopically and photometrically (Elias et al. 1985; Wheeler & Levreault 1985; Uomoto & Kirshner 1985; Branch 1986; Filippenko 1986), by missing the characteristic Si II spectral feature near 6150 å, having distinct infrared light curves, being optically redder and subluminous, and showing radio emission (Sramek et al. 1984). These SNe were designated as type Ib (Elias et al. 1985; Branch 1986) to distinguish them from the classical type Ia. Harkness et al. (1987) identified He I lines in spectra of the SN Ib 1984L, but some subsequent examples showed no He in their spectra and were further subclassified as Type Ic (Wheeler & Harkness 1990). The two subtypes, however, are nearly indistinguishable at late times. In this Symposium the entire class has been referred to as type Ib/c SNe. A recent bright example is SN 1994I in M51 (Filippenko et al. 1994).
Autonomous synthesis and characterization of inorganic materials requires the\nautomatic and accurate analysis of X-ray diffraction spectra. For this task, we\ndesigned a probabilistic deep learning algorithm to identify complex\nmulti-phase mixtures. At the core of this algorithm lies an ensemble\nconvolutional neural network trained on simulated diffraction spectra, which\nare systematically augmented with physics-informed perturbations to account for\nartifacts that can arise during experimental sample preparation and synthesis.\nLarger perturbations associated with off-stoichiometry are also captured by\nsupplementing the training set with hypothetical solid solutions. Spectra\ncontaining mixtures of materials are analyzed with a newly developed branching\nalgorithm that utilizes the probabilistic nature of the neural network to\nexplore suspected mixtures and identify the set of phases that maximize\nconfidence in the prediction. Our model is benchmarked on simulated and\nexperimentally measured diffraction spectra, showing exceptional performance\nwith accuracies exceeding those given by previously reported methods based on\nprofile matching and deep learning. We envision that the algorithm presented\nhere may be integrated in experimental workflows to facilitate the\nhigh-throughput and autonomous discovery of inorganic materials.\n
Uniform Ag nanowires have been synthesized within nanoscale channels of mesoporous silica SBA-15 by a simple chemical approach, which involves AgNO3 impregnation, followed by thermal decomposition.
Abstract This paper describes an alternative method for applying the Clausius-Clapeyron equation in a study of the effect of temperature on the superelastic stress in the shape memory alloy Nitinol—a candidate material for many medical devices. including, in particular, endovascular stents. This new analysis will provide some clarification on the controversy regarding estimation of the thermodynamic equilibrium temperature. The theoretical uniaxial transformation strain has been calculated by means of the Clausius–Clapeyron equation. The calculated value of strain, 5.0%, corresponded closely to the experimentally measured value of 4.7%.
No abstract is provided for this article.
The size of data and the complexity of analytics continue to grow along with the need for timely and cost-effective analysis. However, the growth of computation power cannot keep up with the growth of data. This calls for a paradigm shift from traditional batch OLAP processing model to an incremental OLAP processing model. In this paper, we propose iOLAP, an incremental OLAP query engine that provides a smooth trade-off between query accuracy and latency, and fulfills a full spectrum of user requirements from approximate but timely query execution to a more traditional accurate query execution. iOLAP enables interactive incremental query processing using a novel mini-batch execution model---given an OLAP query, iOLAP first randomly partitions the input dataset into smaller sets (mini-batches) and then incrementally processes through these mini-batches by executing a delta update query on each mini-batch, where each subsequent delta update query computes an update based on the output of the previous one. The key idea behind iOLAP is a novel delta update algorithm that models delta processing as an uncertainty propagation problem, and minimizes the recomputation during each subsequent delta update by minimizing the uncertainties in the partial (including intermediate) query results. We implement iOLAP on top of Apache Spark and have successfully demonstrated it at scale on over 100 machines. Extensive experiments on a multitude of queries and datasets demonstrate that iOLAP can deliver approximate query answers for complex OLAP queries orders of magnitude faster than traditional OLAP engines, while continuously delivering updates every few seconds.
