517 research outputs found
Mathew C, S. Klevar, T. Løken, R.H. Mdegela, G. Mwamengele, E. Skjerve, J. Godfroid and M. Stokstad
No full textJournal articleReproductive disorders have negative impact on performance in cattle worldwide. Studies on infections causing reproductive disorders
in Tanzania are few and fragmented, which complicates targeted disease prevention. To investigate the prevalence of selected infections and their
associations with reproductive disorders and risk factors in cattle under different management systems, a cross-sectional study was conducted in
two bordering regions in the southern highlands in Tanzania. Herd and individual animal level data were collected by direct observation and a semistructured
questionnaire interview of the farmer. Sera from 658 cattle from 202 herds were analyzed using a commercial ELISA kits for antibodies
to Bovine Viral Diarrhea Virus (BVDV), Brucella spp. and Neospora caninum. The logistic regression model identified herd size (odds ratio (OR):
14.5), location (OR: 23.1) and management system (grazing strategy) (OR: 22.7) as risk factors for Brucella spp. The same risk factors were also
identified for BVDV herd size (OR: 2.8), location (OR: 12.7) and management system (OR: 2.9). History of abortion was associated with seropositivity
for Brucella spp. (OR: 4.6). No risk factors, including location and presence of dogs, nor any association with reproductive disorders were identified
for N. caninum. In one region the herd level sero-prevalence was 66.7% for BVDV and 36.1% for Brucella spp., while in the other it was 6.5% for
BVDV and 0.6% for Brucella spp. In total, BVDV specific antibodies were found in 15.2% of the animals in 17.9% of the herds, and Brucella spp.
specific antibodies were detected in 5.4% of the animals in 7.4% of the herds. Anti- N. caninum antibodies were found in 4.5% of animals in 8.4% of
the herds. In conclusion, prevalence and impact of BVDV and Brucella spp. differed significantly between geographically closely related areas, most
probably due to differences in management system that affects the potential for survival of the agents in the population. This shows that all control
measures must be based on accurate epidemiological knowledge of the occurrence of the infection. Low-prevalence areas are highly susceptible for
introduction of infection, while in the high-prevalence areas control measures must be implemented to reduce the impact and the risk of transferring
Brucella spp. from livestock to humans.EPINAV project through Norwegian State Fund
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Heavy ion reactions: an experimental vista. [Review, angular momentum, compound-nucleus decay, reaction mechanism]
No full textExamples of recent experiments in the areas of fusion and deep-inelastic scattering are presented and discussed. Emphasis is placed on the importance of individual nucleons in the fusion process, the effects of high angular momentum, and the understanding of compound nuclear decay. Experiments on deep inelastic scattering are entering a new stage in which important parameters of the reaction mechanism are now open to investigation. Primarily through coincidence measurements, direct information on the angular momentum transferred in a collision and on the time scale of decay is being obtained
EXPAMOD: A methodological Tool for Linking Farm and Market Models by Means of Econometric Response Functions
No full textTechnical change at the farm level or changes in input prices often entail that the firm's supply function changes. These changes can take place in numerous ways. This paper presents a methodology that increases the consistency in supply responses across various sets of agricultural products and farm types with a market model based on a statistical response function approach. Since most farm simulation models are limited to a subset of regions and farm types, the linkage to an aggregated model requires a procedure for expanding these results to non sample regions, so that full regional coverage is achieved. This paper addresses theoretical aspects related to the consistency between micro and market level models. Next it deals with some empirical findings related to the selection of different functional forms for extrapolation. We conclude with a critical reflection on applicability of this method in addressing further needs on up-scaling of other economic as well as non-economic indicators.farm models, market models, extrapolation, Farm Management,
Photograph of the College of Law class of 1989
No full textStudent Names: Barbara J. Bagg, Mari L. Bailey, Patricia K. Bailie, David T. Bastian, Lori R. Baughman, Walter M. Beglau, Kenneth E. Bemis IV, Lori A. Bernard, Robert E. Bozgoz, Corey L. Bradley, Marlene L. Brookhart, Louis B. Byrd, Jr., Blake H. Call, Rebecca K. Cate, Kathleen M. Cegla, Charlie J. Cheek, Michael A. Code, Marie E. Colmey, David B. Connelly, Russell H. Conrow, James L. Contois, Robert L. Cook, John D. Curtis, Robert G. Deveny, Kevin P. Donnelly, Kent D. Durand, Brian Erb, Valerie Eves, L. Doug Federspiel, Celia M. Fitzwater, Louis T. Giaquinto, Christopher C. Gill, Lynda A. Golar, Troy D. Greenfield, Rawleigh H. Grove, David M. Hankins, Chad J. Hessel, Richard D. Himberger, John M. Hoadley, Karin L. Holma, Pamala R. Holsinger, Michael W. Horton, Susan M. Hough, Bruce M. Hull, Ian J. Imrich, Suzanne K. Ishii, Jeffery M. Jones, Kris A. Kaino, Paul G. Kalmansson, Lori A. Koseki, Kari C. Kristiansen, Janai P. Lane, Rob E. Lawrence-Berrey, Jr., Don W. Leach, Alexander D. Libmann, Steven M. Lippold, Stephanie Lommen, Deanna M. Loy, Scott D. MacArthur, Melanie E. Mansell, Richard G. Matson, Craig A. McCarthy, Kevin J. McCarty, Michelle L. McComb, Mingjin Mei, Anka Milikic, Loyal A. Miner, James I. Mitchell, Charles A. Murray, Thomas W. Nisbet-Lance, Edwin N. Norton, Kristjan P. Ochs, Randy L. Ommen, Teresa S. Ozias, Jacquelyn L. Parris, Lindsay R. Partridge, Steven L. Patterson, Joyce G. Pearson, Mark P. Pihl, Lori E. Pleshko, Ann C. Postlewaite, William H. Prentice III, John R. Putman, Leonardo M. Rapadas, Matthew D. Regan, Kristin V. Richardson, Benjamin N. Roehr, Kenneth E. Rossback, Mark S. Sabbah, Scott J. Schaub, Ronald E. Sergi, Brian S. Shaver, Kerry J. Shepherd, Anita C. Smith, Gerald C. Smith, Shannon E. Smith, William D. Stark, W. William Stein, Jim L. Stepovich, Peter J. Stokstad, Paige L. Sully, Connie R. Swofford, Suzanne K. Taylor, G. Victor Tiscornia II, Eric R. Toews, Boyd R. Twelves, J. Mike Unfred, David M. Vandenberg, Sheila M. Vierra, Steven C. Watson, Lynne J. Wehrlie, Jeffery B. Wilkinson, Billy J. Williams, Joan A. Beatty, John E. Bergin, Jody P. Brion, Lisa E. Creson, Ruth A. Crowley, John L. Daniels, John R. Eich, Steven C. Hathaway, Jordan E. Jacobsen, Kathy L. Lentz, Paula A. Mayfield, Joseph E. Monaco, Stephen M. Murrell, Michael J. Nagle, Thomas D. O'Neil IV, Nancy E. Ruiz, Anne C. Schultz, Daniel P. Stuenzi, Tracy E. Watson, William M. Weir, Mark A. Wisneski, Morgan M. WittBlack and Whit
The synthesis of the enantiomers of lipoic acid
No full textLipoic acid is a biologically important molecule.
Whilst the racemate has been available by a number of
syntheses for many years, no convenient preparation of the
pure enantiomers has so far been described. All the
evidence so far presented indicates that only the
dextrorotatory isomer is active in vivo, the absolute
configuration of which has not been established with
certainty. To further elucidate the biochemical role(s)
and biosynthesis of this compound, a convenient EPC
synthesis would be beneficial. This thesis describes
the development of a route to the (R)- and (S)- forms
of the target molecule from a member of the "chiral pool".
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No full textIceCube-Gen2: A Vision for the Future of Neutrino Astronomy in Antarctica
No full text20 pages, 12 figures. Address correspondence to: E. Blaufuss, F. Halzen, C. Kopper (Changed to add one missing author, no other changes from initial version.)20 pages, 12 figures. Address correspondence to: E. Blaufuss, F. Halzen, C. Kopper (Changed to add one missing author, no other changes from initial version.)20 pages, 12 figures. Address correspondence to: E. Blaufuss, F. Halzen, C. Kopper (Changed to add one missing author, no other changes from initial version.)The recent observation by the IceCube neutrino observatory of an astrophysical flux of neutrinos represents the "first light" in the nascent field of neutrino astronomy. The observed diffuse neutrino flux seems to suggest a much larger level of hadronic activity in the non-thermal universe than previously thought and suggests a rich discovery potential for a larger neutrino observatory. This document presents a vision for an substantial expansion of the current IceCube detector, IceCube-Gen2, including the aim of instrumenting a volume of clear glacial ice at the South Pole to deliver substantial increases in the astrophysical neutrino sample for all flavors. A detector of this size would have a rich physics program with the goal to resolve the sources of these astrophysical neutrinos, discover GZK neutrinos, and be a leading observatory in future multi-messenger astronomy programs
Fishing for data in the Ross Sea
No full textWe are among the scientists objecting to the eco-certification of Ross Sea Antarctic toothfish (Dissostichus mawsoni), as described by E. Stokstad in his News Focus story “Behind the eco-label, a debate over Antarctic toothfish” (24 September, p. 1596). The public perceives a certification by the Marine Stewardship Council (MSC) to mean an environmentally friendly fishery, not one characterized by the “dearth of key data” as indicated in the article.
Significant data deficiencies lead us to conclude that an eco-friendly label for this fishery is scientifically indefensible. Credible life history data are missing: Spawning areas, eggs, and larvae have never been found, spawning intervals are unknown, and most density-dependent aspects of ecological relationships are undetermined (1, 2). Stock assessment is problematic because severe Antarctic pack ice conditions for more than 9 months a year prevent scientists from effectively using standard models, which require random tagging over time, space, and age classes (3). The number of fish harvested by illegal, unregulated, and unreported fisheries is likely substantial (4, 5). Finally, ecosystem effects of removing 50% of spawning biomass [the fishery's stated management goal (6, 7)] of this slow-to-mature species are unlikely to be neutral: The large, adult toothfish targeted by the fishery are a key structural link in the food web of the Ross Sea (8–11), currently the most pristine marine area on Earth. As with MSC-certified fisheries elsewhere, toothfish certification requires that industry eventually provide missing biological data (13, 14). However, the harsh Antarctic environment makes data collection painstaking and often prohibitively expensive. Thus, such expectations are unrealistic for a commercially viable fishery. Instead of a certification that lacks proper data, a moratorium should be placed on further Ross Sea fishing until the quality of science at least equals that of certified fisheries elsewhere
Multiwavelength follow-up of a rare IceCube neutrino multiplet
No full textFull list of authors: Aartsen, M. G.; Ackermann, M.; Adams, J.; Aguilar, J. A.; Ahlers, M.; Ahrens, M.; Al Samarai, I.; Altmann, D.; Andeen, K.; Anderson, T.; Ansseau, I.; Anton, G.; Archinger, M.; Argüelles, C.; Auffenberg, J.; Axani, S.; Bai, X.; Barwick, S. W.; Baum, V. Bay, R.; Beatty, J. J.; Tjus, J. Becker; Becker, K. -H.; Benzvi, S.; Berley, D.; Bernardini, E.; Bernhard, A.; Besson, D. Z.; Binder, G.; Bindig, D.; Blaufuss, E.; Blot, S.; Bohm, C.; Börner, M.; Bos, F.; Bose, D.; Böser, S.; Botner, O.; Braun, J.; Brayeur, L.; Bretz, H. -P.; Bron, S.; Burgman, A.; Carver, T.; Casier, M.; Cheung, E.; Chirkin, D.; Christov, A.; Clark, K.; Classen, L.; Coenders, S.; Collin, G. H.; Conrad, J. M.; Cowen, D. F.; Cross, R.; Day, M.; de André, J. P. A. M.; de Clercq, C.; Del Pino Rosendo, E.; Dembinski, H.; De Ridder, S.; Desiati, P.; de Vries, K. D.; de Wasseige, G.; de With, M.; Deyoung, T.; di Lorenzo, V.; Dujmovic, H.; Dumm, J. P.; Dunkman, M.; Eberhardt, B.; Ehrhardt, T.; Eichmann, B.; Eller, P.; Euler, S.; Evenson, P. A.; Fahey, S.; Fazely, A. R.; Feintzeig, J.; Felde, J.; Filimonov, K.; Finley, C.; Flis, S.; Fösig, C. -C.; Franckowiak, A.; Friedman, E.; Fuchs, T.; Gaisser, T. K.; Gallagher, J.; Gerhardt, L.; Ghorbani, K.; Giang, W.; Gladstone, L.; Glauch, T.; Glüsenkamp, T.; Goldschmidt, A.; Gonzalez, J. G.; Grant, D.; Griffith, Z.; Haack, C.; Hallgren, A.; Halzen, F.; Hansen, E.; Hansmann, T.; Hanson, K.; Hebecker, D.; Heereman, D.; Helbing, K.; Hellauer, R.; Hickford, S.; Hignight, J.; Hill, G. C.; Hoffman, K. D.; Hoffmann, R.; Hoshina, K.; Huang, F.; Huber, M.; Hultqvist, K.; in, S.; Ishihara, A.; Jacobi, E.; Japaridze, G. S.; Jeong, M.; Jero, K.; Jones, B. J. P.; Kang, W.; Kappes, A.; Karg, T.; Karle, A.; Katz, U.; Kauer, M.; Keivani, A.; Kelley, J. L.; Kheirandish, A.; Kim, J.; Kim, M.; Kintscher, T.; Kiryluk, J.; Kittler, T.; Klein, S. R.; Kohnen, G.; Koirala, R.; Kolanoski, H.; Konietz, R.; Köpke, L.; Kopper, C.; Kopper, S.; Koskinen, D. J.; Kowalski, M.; Krings, K.; Kroll, M.; Krückl, G.; Krüger, C.; Kunnen, J.; Kunwar, S.; Kurahashi, N.; Kuwabara, T.; Kyriacou, A.; Labare, M.; Lanfranchi, J. L.; Larson, M. J.; Lauber, F.; Lesiak-Bzdak, M.; Leuermann, M.; Lu, L.; Lünemann, J.; Madsen, J.; Maggi, G.; Mahn, K. B. M.; Mancina, S.; Mandelartz, M.; Maruyama, R.; Mase, K.; Maunu, R.; McNally, F.; Meagher, K.; Medici, M.; Meier, M.; Menne, T.; Merino, G.; Meures, T.; Miarecki, S.; Micallef, J.; Momenté, G.; Montaruli, T.; Moulai, M.; Nahnhauer, R.; Naumann, U.; Neer, G.; Niederhausen, H.; Nowicki, S. C.; Nygren, D. R.; Obertacke Pollmann, A.; Olivas, A.; O'Murchadha, A.; Palczewski, T.; Pandya, H.; Pankova, D. V.; Peiffer, P.; Penek, Ö.; Pepper, J. A.; Pérez de Los Heros, C.; Pieloth, D.; Pinat, E.; Price, P. B.; Przybylski, G. T.; Quinnan, M.; Raab, C.; Rädel, L.; Rameez, M.; Rawlins, K.; Reimann, R.; Relethford, B.; Relich, M.; Resconi, E.; Rhode, W.; Richman, M.; Riedel, B.; Robertson, S.; Rongen, M.; Rott, C.; Ruhe, T.; Ryckbosch, D.; Rysewyk, D.; Sabbatini, L.; Sanchez Herrera, S. E.; Sandrock, A.; Sandroos, J.; Sarkar, S.; Satalecka, K.; Schlunder, P.; Schmidt, T.; Schoenen, S.; Schöneberg, S.; Schumacher, L.; Seckel, D.; Seunarine, S.; Soldin, D.; Song, M.; Spiczak, G. M.; Spiering, C.; Stachurska, J.; Stanev, T.; Stasik, A.; Stettner, J.; Steuer, A.; Stezelberger, T.; Stokstad, R. G.; Stößl, A.; Ström, R.; Strotjohann, N. L.; Sullivan, G. W.; Sutherland, M.; Taavola, H.; Taboada, I.; Tatar, J.; Tenholt, F.; Ter-Antonyan, S.; Terliuk, A.; Tešić, G.; Tilav, S.; Toale, P. A.; Tobin, M. N.; Toscano, S.; Tosi, D.; Tselengidou, M.; Tung, C. F.; Turcati, A.; Unger, E.; Usner, M.; Vandenbroucke, J.; van Eijndhoven, N.; Vanheule, S.; van Rossem, M.; van Santen, J.; Vehring, M.; Voge, M.; Vogel, E.; Vraeghe, M.; Walck, C.; Wallace, A.; Wallraff, M.; Wandkowsky, N.; Waza, A.; Weaver, Ch.; Weiss, M. J.; Wendt, C.; Westerhoff, S.; Whelan, B. J.; Wickmann, S.; Wiebe, K.; Wiebusch, C. H.; Wille, L.; Williams, D. R.; Wills, L.; Wolf, M.; Wood, T. R.; Woolsey, E.; Woschnagg, K.; Xu, D. L.; Xu, X. W.; Xu, Y.; Yanez, J. P.; Yodh, G.; Yoshida, S.; Zoll, M.; Asas-Sn Collaboration; Stanek, K. Z.; Shappee, B. J.; Kochanek, C. S.; Holoien, T. W. -S.; Prieto, J. L.; Astrophysical Multimessenger Observatory Network; Fox, D. B.; Delaunay, J. J.; Turley, C. F.; Barthelmy, S. D.; Lien, A. Y.; Mészáros, P.; Murase, K.; Fermi Collaboration; Kocevski, D.; Buehler, R.; Giomi, M.; Racusin, J. L.; Hawc Collaboration; Albert, A.; Alfaro, R.; Alvarez, C.; Álvarez, J. D.; Arceo, R.; Arteaga-Velázquez, J. C.; Ayala Solares, H. A.; Barber, A. S.; Baustista-Elivar, N.; Becerril, A.; Belmont-Moreno, E.; Bernal, A.; Brisbois, C.; Caballero-Mora, K. S.; Capistrán, T.; Carramiñana, A.; Casanova, S.; Castillo, M.; Cotti, U.; Coutiño de León, S.; de La Fuente, E.; de León, C.; Diaz Hernandez, R.; Díaz-Vélez, J. C.; Dingus, B. L.; Duvernois, M. A.; Ellsworth, R. W.; Engel, K.; Fiorino, D. W.; Fraija, N.; García-González, J. A.; Gerhardt, M.; González Muñoz, A.; González, M. M.; Goodman, J. A.; Hampel-Arias, Z.; Harding, J. P.; Hernandez, S.; Hui, C. M.; Hüntemeyer, P.; Iriarte, A.; Jardin-Blicq, A.; Joshi, V.; Kaufmann, S.; Lara, A.; Lauer, R. J.; Lee, W. H.; Lennarz, D.; León Vargas, H.; Linnemann, J. T.; Luis Raya, G.; Luna-García, R.; López-Coto, R.; Malone, K.; Marinelli, S. S.; Martinez, O.; Martinez-Castellanos, I.; Martínez-Castro, J.; Martínez-Huerta, H.; Matthews, J. A.; Miranda-Romagnoli, P.; Moreno, E.; Mostafá, M.; Nellen, L.; Newbold, M.; Nisa, M. U.; Noriega-Papaqui, R.; Pelayo, R.; Pretz, J.; Pérez-Pérez, E. G.; Ren, Z.; Rho, C. D.; Rivière, C.; Rosa-González, D.; Rosenberg, M.; Salesa Greus, F.; Sandoval, A.; Schneider, M.; Schoorlemmer, H.; Sinnis, G.; Smith, A. J.; Springer, R. W.; Surajbali, P.; Tibolla, O.; Tollefson, K.; Torres, I.; Ukwatta, T. N.; Villaseñor, L.; Weisgarber, T.; Wisher, I. G.; Wood, J.; Yapici, T.; Zepeda, A.; Zhou, H.; Lco Collaboration; Arcavi, I.; Hosseinzadeh, G.; Howell, D. A.; Valenti, S.; McCully, C.; Master Collaboration; Lipunov, V. M.; Gorbovskoy, E. S.; Tiurina, N. V.; Balanutsa, P. V.; Kuznetsov, A. S.; Kornilov, V. G.; Chazov, V.; Budnev, N. M.; Gress, O. A.; Ivanov, K. I.; Tlatov, A. G.; Rebolo Lopez, R.; Serra-Ricart, M.; Swift Collaboration; Evans, P. A.; Kennea, J. A.; Gehrels, N.; Osborne, J. P.; Page, K. L.; VERITAS Collaboration; Abeysekara, A. U.; Archer, A.; Benbow, W.; Bird, R.; Brantseg, T.; Bugaev, V.; v Cardenzana, J.; Connolly, M. P.; Cui, W.; Falcone, A.; Feng, Q.; Finley, J. P.; Fleischhack, H.; Fortson, L.; Furniss, A.; Griffin, S.; Grube, J.; Hütten, M.; Hervet, O.; Holder, J.; Hughes, G.; Humensky, T. B.; Johnson, C. A.; Kaaret, P.; Kar, P.; Kelley-Hoskins, N.; Kertzman, M.; Krause, M.; Kumar, S.; Lang, M. J.; Lin, T. T. Y.; McArthur, S.; Moriarty, P.; Mukherjee, R.; Nieto, D.; Ong, R. A.; Otte, A. N.; Pohl, M.; Popkow, A.; Pueschel, E.; Quinn, J.; Ragan, K.; Reynolds, P. T.; Richards, G. T.; Roache, E.; Rulten, C.; Sadeh, I.; Santander, M.; Sembroski, G. H.; Staszak, D.; Trépanier, S.; Tyler, J.; Wakely, S. P.; Weinstein, A.; Wilcox, P.; Wilhelm, A.; Williams, D. A.; Zitzer, B.; Bellm, E.; Cano, Z.; Gal-Yam, A.; Kann, D. A.; Ofek, E. O.; Rigault, M.; Soumagnac, M.On February 17, 2016, the IceCube real-time neutrino search identified, for the first time, three muon neutrino candidates arriving within 100 s of one another, consistent with coming from the same point in the sky. Such a triplet is expected once every 13.7 years as a random coincidence of background events. However, considering the lifetime of the follow-up program the probability of detecting at least one triplet from atmospheric background is 32%. Follow-up observatories were notified in order to search for an electromagnetic counterpart. Observations were obtained by Swift's X-ray telescope, by ASAS-SN, LCO and MASTER at optical wavelengths, and by VERITAS in the very-high-energy gamma-ray regime. Moreover, the Swift BAT serendipitously observed the location 100 s after the first neutrino was detected, and data from the Fermi LAT and HAWC observatory were analyzed. We present details of the neutrino triplet and the follow-up observations. No likely electromagnetic counterpart was detected, and we discuss the implications of these constraints on candidate neutrino sources such as gamma-ray bursts, core-collapse supernovae and active galactic nucleus flares. This study illustrates the potential of and challenges for future follow-up campaigns. © ESO, 2017. © ESO, 2017.The IceCube Collaboration acknowledges the support from the following agencies: US National Science Foundation-Office of Polar Programs, US National Science Foundation-Physics Division, University of Wisconsin Alumni Research Foundation, the Grid Laboratory of Wisconsin (GLOW) grid infrastructure at the University of Wisconsin – Madison, the Open Science Grid (OSG) grid infrastructure; US Department of Energy, and National Energy Research Scientific Computing Center, the Louisiana Optical Network Initiative (LONI) grid computing resources; Natural Sciences and Engineering Research Council of Canada, WestGrid and Compute/Calcul Canada; Swedish Research Council, Swedish Polar Research Secretariat, Swedish National Infrastructure for Computing (SNIC), and Knut and Alice Wallenberg Foundation, Sweden; German Ministry for Education and Research (BMBF), Deutsche Forschungsgemeinschaft (DFG), Helmholtz Alliance for Astroparticle Physics (HAP), Research Department of Plasmas with Complex Interactions (Bochum), Germany; Fund for Scientific Research (FNRS-FWO), FWO Odysseus programme, Flanders Institute to encourage scientific and technological research in industry (IWT), Belgian Federal Science Policy Office (Belspo); University of Oxford, United Kingdom; Marsden Fund, New Zealand; Australian Research Council; Japan Society for Promotion of Science (JSPS); the Swiss National Science Foundation (SNSF), Switzerland; National Research Foundation of Korea (NRF); Villum Fonden, Danish National Research Foundation (DNRF), Denmark This work made use of data supplied by the UK Swift Science Data Centre at the University of Leicester. Funding for the Swift project in the UK is provided by the UK Space Agency. Part of this work was facilitated by the GROWTH project, a partnership in international research and education, NSF PIRE Grant No. 1545949. ASAS-SN is supported by NSF grant AST-1515927. Development of ASAS-SN has been supported by NSF grant AST-0908816, the Center for Cosmology and AstroParticle Physics at the Ohio State University, the Mt. Cuba Astronomical Foundation, and by George Skestos. The Fermi LAT Collaboration acknowledges generous ongoing support from a number of agencies and institutes that have supported both the development and the operation of the LAT as well as scientific data analysis. These include the National Aeronautics and Space Administration and the Department of Energy in the United States, the Commissariat à l’Energie Atomique and the Centre National de la Recherche Scientifique/Institut National de Physique Nucléaire et de Physique des Particules in France, the Agenzia Spaziale Italiana and the Istituto Nazionale di Fisica Nucleare in Italy, the Ministry of Education, Culture, Sports, Science and Technology (MEXT), high-energy Accelerator Research Organization (KEK) and Japan Aerospace Exploration Agency (JAXA) in Japan, and the K. A. Wallenberg Foundation, the Swedish Research Council and the Swedish National Space Board in Sweden. Additional support for science analysis during the operations phase is gratefully acknowledged from the Istituto Nazionale di Astrofisica in Italy and the Centre National d’Études Spatiales in France. The HAWC Collaboration acknowledges the support from: the US National Science Foundation (NSF); the US Department of Energy Office of High-Energy Physics; the Laboratory Directed Research and Development (LDRD) program of Los Alamos National Laboratory; Consejo Nacional de Ciencia y Tecnología (CONACyT), México (grants 271051, 232656, 260378, 179588, 239762, 254964, 271737, 258865, 243290, 132197), Laboratorio Nacional HAWC de rayos gamma; L’OREAL Fellowship for Women in Science 2014; Red HAWC, México; DGAPA-UNAM (grants IG100317, IN111315, IN111716-3, IA102715, 109916, IA102917); VIEP-BUAP; PIFI 2012, 2013, PROFOCIE 2014, 2015;the University of Wisconsin Alumni Research Foundation; the Institute of Geophysics, Planetary Physics, andSignatures at Los Alamos National Laboratory; Polish Science Centre grant DEC-2014/13/B/ST9/945; Coordinación de la Investigación Científica de la Universidad Michoacana. We thank Luciano Díaz and Eduardo Murrieta for technical support of the HAWC detector. Support for I. Arcavi was provided by NASA through the Einstein Fellowship Program, grant PF6-170148. D. A. Howell, C. McCully, and G. Hosseinzadeh are supported by NSF-1313484. This work makes use of observations from the LCO network. VERITAS research is supported by grants from the U.S. Department of Energy Office of Science, the U.S. National Science Foundation and the Smithsonian Institution, and by NSERC in Canada. VERITAS acknowledges the excellent work of the technical support staff at the Fred Lawrence Whipple Observatory and at the collaborating institutions in the construction and operation of the instrument. E. O. Ofek and A. Gal-Yam acknowledge a Minerva grant
Integration of all FSSIM components within SEAMLESS-IF and a stand alone Graphical User Interface for FSSIM
No full textSEAMLESS (System for Environmental and Agricultural Modelling; Linking European Science and Society) integrated project, EU 6th Framework Programme, contract no. 010036-2International audiencePolicy makers and farmers have an interest in making ex-ante assessments of the outcomes of their choices in terms of policy and farm plan. This interest mainly concerns the assessment of socio-economic and environmental performance of farms as a result of innovations and policies. Mathematical models based on systems analysis are suited to explore and assess uncertain future states of systems. A Bio-Economic Farm Model (BEFM) is defined as a model that links formulations describing farmers’ resource management decisions to formulations that describe current and alternative production possibilities in terms of required inputs to achieve certain outputs and associated externalities. Currently many descriptions and applications of BEFMs are being published. A BEFM that is easy to transfer between locations or farm types is called generic, which we define as ‘being able to deal with different scales, locations, functionalities, and levels of detail. Although some model studies claim that their model is easily transferable to other regions and farm types, there is little evidence from literature supporting these claims. A generic bio-economic farm model should be able to adequately represent arable, livestock and perennial activities, current agricultural activities and future alternative activities, different objective functions, different resource and policy constraints, future policies and technological innovations as scenarios and should include good calibration procedures. In SEAMLESS the Farming Systems SIMulator (FSSIM) has been developed as a generic and transferable model that can easily be extended with new features and re-used across data-sets, farm types and locations. In this deliverable we (i) describe FSSIM and its design as an integrated generic bio-economic farm model, (ii) briefly describe each of the components of FSSIM and provide references to relevant deliverables and publications for more detail and (iii) evaluating FSSIM on criteria for generic models by describing applications of FSSIM
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