133 resultados para Edda Saemundar.


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At Sleipner, CO2 is being separated from natural gas and injected into an underground saline aquifer for environmental purposes. Uncertainty in the aquifer temperature leads to uncertainty in the in situ density of CO2. In this study, gravity measurements were made over the injection site in 2002 and 2005 on top of 30 concrete benchmarks on the seafloor in order to constrain the in situ CO2 density. The gravity measurements have a repeatability of 4.3 µGal for 2003 and 3.5 µGal for 2005. The resulting time-lapse uncertainty is 5.3 µGal. Unexpected benchmark motions due to local sediment scouring contribute to the uncertainty. Forward gravity models are calculated based on both 3D seismic data and reservoir simulation models. The time-lapse gravity observations best fit a high temperature forward model based on the time-lapse 3D seismics, suggesting that the average in situ CO2 density is about to 530kg/m**3. Uncertainty in determining the average density is estimated to be ±65 kg/m**3 (95% confidence), however, this does not include uncertainties in the modeling. Additional seismic surveys and future gravity measurements will put better constraints on the CO2 density and continue to map out the CO2 flow.

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Mode of access: Internet.

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Bd. 4 includes text of Germania analyzed phrase by phrase.

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Mode of access: Internet.

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Mode of access: Internet.

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Vol. 2 has imprint: Leipzig, W. Nauck.

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Publisher's imprint varies.

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Beowulf.--The song of Roland.--The destruction of Dá Derga's hostel.--The story of the Volsungs and Niblungs.--Songs from the Elder Edda.

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Top Row: Jessica M. Adair, Casey Arnett, Amy Lynn Babchek, Mary E. Bartlett, Rhonda Bass, Nancy Bidlack, Heather Bjerke, Stacy Bodrie, Dana Boonstra, Kellu Bowers, Pamela Bowser, Rachel L. Bradley, Michele Brotherton, Stacie Buckler, Hope Bufkin

Row 2: Saran Burnley, Jennifer Caraan, Barbara Carpenter, Nutrena Helene Watts, Aimee Schuman, Debra Jameson, Jennifer Jennings, Mary Cassette, Nikki Burns, Lisa Multhaupt, Jeffrey M. Adams, Christine Hepner, Julie Chamberlain, Andy Chan, Jennifer Choike

Row 3: Heather Chrisman, Abbey C. Clark, Renita Cobb, Amy Cotton, Cattleya Crossen, Kimberly Curl, Christy Debolt, Patricia DeLamielleure, Jennifer Dyer, Lisa L. Eliasom, Patricia Fowler Faling, Rita Fallone

Row 4: Richard Fisher, Rebecca J. Forbes, Tiffany Fowler, Karen R. Fritz, Debbie M. Fulton, Michelle J. Gaskill, Ellen M. Gavin, Emily Golin, Umeika Makita Griffith, Lydia D. Hampton, Natalie Michele Hoffman, Julie Holbird

Row 5: Kathryn A. Huffman, Tara Lynn Humphrey, Nicole Jaccques, Michelle C. Johnson, Bryan Wayne Kerridge, Violet H. Barkauskas, Beverly Jones, Ada Sue Hinshaw, Nola Pender, Susan Boehm, Noelle Kirouac, Sarah Kohn, Sherri Krajenta, Brian Kubinski, Stephanie L. Kuczera

Row 6: Heather Lange, Sang Hee Lee, Soya Lee, Natalie Lehrer, Kimberly Lilley, Elizabeth A. Lundy, Darcey Lutz-Guenther, Michelle J. Malicsi, Dawn Marteeny, Sheila Mendiola, Sharon Mitchell, Caryl S. Molton, Colette Montilla, Celeste Montone-Horne, Emily T. Mooney, Naima Moore

Row 7: Kami Nobis, Thresa M. Nugent, Michelle Ober, Nisha Patel, Stephanie Perrett, Holly Powers, Julie L. Pryor, Elizabeth K. Rachubinski, Anne Rammelkamp, Kathy Rarog, Erin Richards, Amy Roehrig, Catherine Ann Rosloniec, Tansey Rosset, Kimberly Sanders, Marla Sands, James C. Sausser

Row 8: Juana Sebree, Erin J. Showers, Prabhjyot Singh, Lynn Sinkel, Nicole LaDon Smith, Nicole M. Speck, Mickie Speers, Krista Stapleton, Karon Starr, Elizabeth Studley, Janice Brenda Supena, Rashelle Talbert,Kimberly Tocco, Edda Toting, Lisa Uren, Lori VanBergen

Row 9: Lisa VanStratton, kathleen Veenstra, Kristen Venadam, Rhonda E. Walkowe, Ching-Ru Bonny Wang, Deborah Webb, Ruthann Clausen Weiss, Debra R. White, Rochelle Whiteman, Tara Wilson, Jessica Wise, Sheryl Woloskie, Denice Annette Zakalata, Rebecca S. Zeiler

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In this paper we explore the use of text-mining methods for the identification of the author of a text. We apply the support vector machine (SVM) to this problem, as it is able to cope with half a million of inputs it requires no feature selection and can process the frequency vector of all words of a text. We performed a number of experiments with texts from a German newspaper. With nearly perfect reliability the SVM was able to reject other authors and detected the target author in 60–80% of the cases. In a second experiment, we ignored nouns, verbs and adjectives and replaced them by grammatical tags and bigrams. This resulted in slightly reduced performance. Author detection with SVMs on full word forms was remarkably robust even if the author wrote about different topics.

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Ionic liquids based on 1-butyl-3-methylimidazolium tetrafluoroborate (BmimBF4) and 1-butyl-3-methylimidazolium hexafluorophosphate (BmimPF6) were used as reusable alternatives to volatile organic solvents (VOCs) for ethylenediammonium diacetate (EDDA) catalyzed Gewald synthesis of 2-aminothiophenes. Significant rate enhancement and improvement of the yield were observed. The ionic liquids containing catalyst EDDA were recycled several times with no decreases in yields and reaction rates.

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The moisture and air stable ionic liquids 1-butyl-3-methylimidazonium tetrafluoroborate [bmim]BF4 and 1-butyl-3-methylimidazonium hexafluorophosphate [bmim]PF6 were used as ‘green' recyclable alternatives to volatile organic solvents (VOCs) for ethylenediammonium diacetate (EDDA) catalyzed Knoevenagel condensation between aldehydes or ketones with active methylene compounds. Both aldehydes and ketones gave satisfactory results. The ionic liquids containing catalyst EDDA were recycled several times with no decreases in yields and reaction rates. In the case of 2-hydroxybenzaldehyde, the reactions led to the formation of 3-substituted coumarins under standard reaction conditions.

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El concurso de transformación mágica, esquema narrativo difundido en la tradición popular, se presenta en dos variantes principales: los hechiceros que compiten pueden metamorfosearse en varios seres o crear esos seres por medios mágicos. En cualquier caso el concursante ganador da a luz criaturas más fuertes que superan las de su oponente. La segunda variante fue preferida en el antiguo Cercano Oriente (Sumeria, Egipto, Israel). La primera se puede encontrar en algunos mitos griegos sobre cambiadores de forma (por ejemplo, Zeus y Némesis). El mismo esquema narrativo puede haber influido en un episodio de la Novela de Alejandro (1.36-38), en el que Darío envía regalos simbólicos a Alejandro y los dos monarcas enemigos ofrecen contrastantes explicaciones de ellos. Esta historia griega racionaliza el concurso de cuento de hadas, transfiriendo las fantásticas hazañas de creaciones milagrosas a un plano secundario pero realista de metáfora lingüística.

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Indigenous Arctic and Subarctic communities currently are facing a myriad of social and environmental changes. In response to these changes, studies concerning indigenous knowledge (IK) and climate change vulnerability, resiliency, and adaptation have increased dramatically in recent years. Risks to lives and livelihoods are often the focus of adaptation research; however, the cultural dimensions of climate change are equally important because cultural dimensions inform perceptions of risk. Furthermore, many Arctic and Subarctic IK climate change studies document observations of change and knowledge of the elders and older generations in a community, but few include the perspectives of the younger population. These observations by elders and older generations form a historical baseline record of weather and climate observations in these regions. However, many indigenous Arctic and Subarctic communities are composed of primarily younger residents. We focused on the differences in the cultural dimensions of climate change found between young adults and elders. We outlined the findings from interviews conducted in four indigenous communities in Subarctic Alaska. The findings revealed that (1) intergenerational observations of change were common among interview participants in all four communities, (2) older generations observed more overall change than younger generations interviewed by us, and (3) how change was perceived varied between generations. We defined “observations” as the specific examples of environmental and weather change that were described, whereas “perceptions” referred to the manner in which these observations of change were understood and contextualized by the interview participants. Understanding the differences in generational observations and perceptions of change are key issues in the development of climate change adaptation strategies.