We compute upper limits on the nanohertz-frequency isotropic stochastic gravitational wave background (GWB) using the 9 year data set from the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) collaboration. Well-tested Bayesian techniques are used to set upper limits on the dimensionless strain amplitude (at a frequency of 1 yr −1 ) for a GWB from supermassive black hole binaries of <´-A 1.5 10 gw 15 . We also parameterize the GWB spectrum with a broken power-law model by placing priors on the strain amplitude derived from simulations of Sesana and McWilliams et al. Using Bayesian model selection we find that the data favor a broken power law to a pure power law with odds ratios of 2.2 and 22 to one for the Sesana and McWilliams prior models, respectively. Using the broken power-law analysis we construct posterior distributions on environmental factors that drive the binary to the GW-driven regime including the stellar mass density for stellar-scattering, mass accretion rate for circumbinary disk interaction, and orbital eccentricity for eccentric binaries, marking the first time that the shape of the GWB spectrum has been used to make astrophysical inferences. Returning to a power-law model, we place stringent limits on the energy density of relic GWs, W <´-f h 4.2 10 gw 2 1 0 ( ) . Our limit on the cosmic string GWB, W <´-f h 2.2 10 gw 2 1 0 ( ) , translates to a conservative limit on the cosmic string tension with m <´-G 3.3 10 8 , a factor of four better than the joint Planck and high-lcosmic microwave background data from other experiments.

We perform a search for continuous gravitational waves from individual supermassive black hole binaries using robust frequentist and Bayesian techniques. We augment standard pulsar timing models with the addition of timevariable dispersion measure and frequency variable pulse shape terms. We apply our techniques to the Five Year Data Release from the North American Nanohertz Observatory for Gravitational Waves. We find that there is no evidence for the presence of a detectable continuous gravitational wave; however, we can use these data to place the most constraining upper limits to date on the strength of such gravitational waves. Using the full 17 pulsar data set we place a 95% upper limit on the strain amplitude of h 0 3.0 × 10 −14 at a frequency of 10 nHz. Furthermore, we place 95% sky-averaged lower limits on the luminosity distance to such gravitational wave sources, finding that d L 425 Mpc for sources at a frequency of 10 nHz and chirp mass 10 10 M . We find that for gravitational wave sources near our best timed pulsars in the sky, the sensitivity of the pulsar timing array is increased by a factor of ∼four over the sky-averaged sensitivity. Finally we place limits on the coalescence rate of the most massive supermassive black hole binaries.

Among efforts to detect gravitational radiation, pulsar timing arrays are uniquely poised to detect "memory" signatures, permanent perturbations in spacetime from highly energetic astrophysical events such as mergers of supermassive black hole binaries. The North American Nanohertz Observatory for Gravitational Waves (NANOGrav) observes dozens of the most stable millisecond pulsars using the Arecibo and Green Bank radio telescopes in an effort to study, among other things, gravitational wave memory. We herein present the results of a search for gravitational wave bursts with memory (BWMs) using the first five years of NANOGrav observations. We develop original methods for dramatically speeding up searches for BWM signals. In the directions of the sky where our sensitivity to BWMs is best, we would detect mergers of binaries with reduced masses of 10 9 M out to distances of 30 Mpc; such massive mergers in the Virgo cluster would be marginally detectable. We find no evidence for BWMs. However, with our non-detection, we set upper limits on the rate at which BWMs of various amplitudes could have occurred during the time spanned by our data-e.g., BWMs with amplitudes greater than 10 −13 must occur at a rate less than 1.5 yr −1 .

An enterprise architecture is intended to be a comprehensive blueprint describing the key components and relationships for an enterprise from strategies to business processes to information systems and technologies. Enterprise architectures have become essential for managing change in complex organizations. While "motivation" has been recognized since Zachman 0 as an important element of enterprise architecture, yet to date, most enterprise architecture modeling only deals with structure, function, and behaviour, neglecting the intentional dimension of motivations, rationales, and goals. The contribution at hand explores this challenge and aims to illustrate the potentials of intentional modeling in the context of enterprise architecture. After introducing two intentional modeling languages and their potential relation to an enterprise architecture construction process, we report on an explorative case study that aimed to investigate the practical implications of intentional modeling and analysis for enterprise architectures. Finally, we present key observations from interviews that were conducted with practitioners to obtain feedback regarding the material developed in the case study.

ABSTRACT:There is an increasing focus on the usefulness of climate model-based seasonal precipitation forecasts as inputs for hydrological applications. This study reveals that most models from the North American Multi-Model Ensemble (NMME) have potential to forecast seasonal precipitation over 17 hydroclimatic regions in continental China. In this paper, we evaluated the NMME precipitation forecast against observations. The evaluation indices included the correlation coefficient (R), relative root-mean-square error (RRMSE), rank histogram (RH), and continuous ranked probability skill score (CRPSS). We presented the RRMSE-R diagram to distinguish differences between the performances of individual models. We find that the predictive skill is seasonally and regionally dependent, exhibiting higher values in autumn and spring and lower values in summer. Higher predictive skill is observed over most regions except the southeastern monsoon regions, which may be attributable to local climatology and variability. Among the 11 NMME models, CFS, especially CFSv2, exhibits the best predictive skill. The GFDL and NASA models, which are followed by CMC, perform worse than CFS. The performances of IRI and CCSM3 are relatively worse than that of the other models. The forecast skills are significantly improved in multi-model mean forecasts based on simple model averaging (SMA). The improvement is more obvious for Bayesian model averaging (BMA), which is employed to further improve the forecast skill and address model uncertainty using multiple model outputs, than individual model and SMA.