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Metal-organic polyhedra and frameworks (MOPs and MOFs) were prepared by linking square units M2(CO2)4 (M = Cu and Zn) with a variety of organic linkers designed to control the dimensionality (periodicity) and topology of the resulting structures. We describe the preparation, characterization, and crystal structures of 5 new MOPs and 11 new MOFs (termed MOP-14, -15, -17, -23, -24 and MOF-114, -115, -116, -117, -118, -119, -222, -601, -602, -603, -604) and show how their structures are related to the shape and functionality of the building blocks. The gas uptake behaviors of MOP-23 and MOF-601 to -603 are also presented as evidence that these structures have permanent porosity and rigid architectures.
Summary The Legendre–Hadamard necessary condition for energy minimizers is derived in the framework of Cosserat elasticity theory.
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As cyclic fatigue is considered to be a major cause of clinical tooth fractures, achieving a comprehensive understanding of the fatigue behavior of dentin is of importance. In this note, the fatigue behavior of human dentin is examined in the context of the Kitagawa‐Takahashi diagram to define the limiting conditions for fatigue failure. Specifically, this approach incorporates two limiting threshold criteria for fatigue: (i) a threshold stress for fatigue failure, specifically the smooth‐bar (unnotched) fatigue endurance strength, at small crack sizes and (ii) a threshold stress‐intensity range for fatigue‐crack growth at larger crack sizes. The approach provides a “bridge” between the traditional fatigue life and fracture mechanics based damage‐tolerant approaches to fatigue‐life estimation, and as such defines a “failure envelope” of applied stresses and flaw sizes where fatigue failure is likely in dentin This approach may also be applied to fatigue failure in human cortical bone (i.e. clinical “stress fractures”), which exhibits similar fatigue behavior characteristics, and in principle may aid clinicians in making quantitative evaluations of the risk of fractures in mineralized tissues. © 2006 Wiley Periodicals, Inc. J Biomed Mater Res, 2006
A growing number of observations reveal a subset of Type Ia supernovae undergoing circumstellar interaction (SNe Ia-CSM). We present unpublished archival Spitzer Space Telescope data on SNe Ia-CSM 2002ic and 2005gj obtained > 1300 and 500 days post-discovery, respectively. Both SNe show evidence for late-time mid-infrared (mid-IR) emission from warm dust. The dust parameters are most consistent with a pre-existing dust shell that lies beyond the forward-shock radius, most likely radiatively heated by optical and X-ray emission continuously generated by late-time CSM interaction. In the case of SN 2005gj, the mid-IR luminosity more than doubles after 1 year post-discovery. While we are not aware of any late-time optical-wavelength observations at these epochs, we attribute this rebrightening to renewed shock interaction with a dense circumstellar shell.
We have examined ROSAT soft X-ray observations of a complete, distance-limited sample of Seyfert and LINER galaxies. X-ray data are available for 46 out of 60 such objects which lie within a hemisphere of radius 18 Mpc. We have constructed radial profiles of the nuclear sources in order to characterize their spatial extent and, in some cases, to help constrain the amount of flux associated with a nuclear point source. PSPC data from ROSAT have been used to explore the spectral characteristics of the objects with sufficient numbers of detected counts. Based on the typical spectral parameters of these sources, we have estimated the luminosities of the weaker sources in the sample. We then explore the relationship between the soft X-ray and H alpha luminosities of the observed objects; these quantities are correlated for higher-luminosity AGNs. We find a weak correlation at low luminosities as well, and have used this relationship to predict L_X for the 14 objects in our sample that lack X-ray data. Using the results of the spatial and spectral analyses, we have compared the X-ray properties of Seyferts and LINERs, finding no striking differences between the two classes of objects. However, both types of objects often exhibit significant amounts of extended emission, which could minimize the appearance of differences in their nuclear properties. The soft X-ray characteristics of the type 1 and type 2 active galaxies in the sample are also discussed. We then compute the local X-ray volume emissivity of low-luminosity Seyferts and LINERs and investigate their contribution to the cosmic X-ray background. The 0.5-2.0 keV volume emissivity of 2.2e38 ergs/s/Mpc^3 we obtain for our sample suggests that low-luminosity AGNs produce at least 9% of the soft X-ray background.
Today, big and small organizations alike collect huge amounts of data, and they do so with one goal in mind: extract "value" through sophisticated exploratory analysis, and use it as the basis to make decisions as varied as personalized treatment and ad targeting. Unfortunately, existing data analytics tools are slow in answering queries, as they typically require to sift through huge amounts of data stored on disk, and are even less suitable for complex computations, such as machine learning algorithms. These limitations leave the potential of extracting value of big data unfulfilled. To address this challenge, we are developing Berkeley Data Analytics Stack (BDAS), an open source data analytics stack that provides interactive response times for complex computations on massive data. To achieve this goal, BDAS supports efficient, large-scale in-memory data processing, and allows users and applications to trade between query accuracy, time, and cost. In this talk, I'll present the architecture, challenges, results, and our experience with developing BDAS, with a focus on Apache Spark, an in-memory cluster computing engine that provides support for a variety of workloads, including batch, streaming, and iterative computations. In a relatively short time, Spark has become the most active big data project in the open source community, and is already being used by over one hundred of companies and research institutions.
