188 research outputs found

    Foam front propagation in anisotropic oil reservoirs

    Get PDF
    The pressure-driven growth model is considered, describing the motion of a foam front through an oil reservoir during foam improved oil recovery, foam being formed as gas advances into an initially liquid-filled reservoir. In the model, the foam front is represented by a set of so called ‘material points’ that track the advance of gas into the liquid-filled region. According to the model, the shape of the foam front is prone to develop concave sharply-curved concavities, where the orientation of the front changes rapidly over a small spatial distance: these are referred to as 'concave corners'. These concave corners need to be propagated differently from the material points on the foam front itself. Typically the corner must move faster than those material points, otherwise spurious numerical artifacts develop in the comp uted shape of the front. A propagation rule or ‘speed up’ rule is derived for the concave corners, which is shown to be sensitive to the level of anisotropy in the permeability of the reservoir and also sensitive to the orientation of the corners themselves. In particular if a corner in an anisotropic reservoir were to be propagated according to an isotropic speed up rule, this might not be sufficient to suppress spurious numerical artifacts, at least for certain orientations of the corner. On the other hand, systems that are both heterogeneous and anisotropic tend to be well behaved numerically, regard less of whether one uses the isotropic or anisotropic speed up rule for corners. This comes about be cause, in the heterogeneous and anisotropic case, the orientation of the corner is such that the 'correct' anisotropic speed is just very slightly less than the ‘incorrect’ isotropic one. The anisotropic rule does however manage to keep the corner very slightly sharper than the isotropic rule does

    Analysis of a model for foam improved oil recovery

    Get PDF
    During improved oil recovery (IOR), gas may be introduced into a porous reservoir filled with surfactant solution in order to form foam. A model for the evolution of the resulting foam front known as ‘pressure-driven growth’ is analysed. An asymptotic solution of this model for long times is derived that shows that foam can propagate indefinitely into the reservoir without gravity override. Moreover, ‘pressure-driven growth’ is shown to correspond to a special case of the more general ‘viscous froth’ model. In particular, it is a singular limit of the viscous froth, corresponding to the elimination of a surface tension term, permitting sharp corners and kinks in the predicted shape of the front. Sharp corners tend to develop from concave regions of the front. The principal solution of interest has a convex front, however, so that although this solution itself has no sharp corners (except for some kinks that develop spuriously owing to errors in a numerical scheme), it is found nevertheless to exhibit milder singularities in front curvature, as the long-time asymptotic analytical solution makes clear. Numerical schemes for the evolving front shape which perform robustly (avoiding the development of spurious kinks) are also developed. Generalisations of this solution to geologically heterogeneous reservoirs should exhibit concavities and/or sharp corner singularities as an inherent part of their evolution: propagation of fronts containing such ‘inherent’ singularities can be readily incorporated into these numerical schemes

    Microhabitat preferences of fish assemblages in the Udzungwa Mountains (Eastern Africa)

    Full text link
    [EN] Environmental flow assessment (EFA) involving microhabitat preference models is a common approach to set ecologically friendly flow regimes in territories with ongoing or planned projects to develop river basins, such as many rivers of Eastern Africa. However, habitat requirements of many African fish species are poorly studied, which may impair EFAs. This study investigated habitat preferences of fish assemblages, based on species presence-absence data from 300 microhabitats collected in two tributaries of the Kilombero River (Tanzania), aiming to disentangle differences in habitat preferences of African species at two levels: assemblage (i.e. between tributaries) and species (i.e. species-specific habitat preferences). Overall, flow velocity, which implies coarser substrates and shallower microhabitats, emerged as the most important driver responsible of the changes in stream-dwelling assemblages at the microhabitat scale. At the assemblage level, we identified two important groups of species according to habitat preferences: (a) cover-orientated and limnophilic species, including Barbus spp., Mormyridae and Chiloglanis deckenii, and (b) rheophilic species, including Labeo cylindricus, Amphilius uranoscopus and Parakneria spekii. Rheophilic species preferred boulders, fast flow velocity and deeper microhabitats. At the species level, we identified species-specific habitat preferences. For instance, Barbus spp. preferred low flow velocity shallow depth and fine-to-medium substratum, whereas L. cylindricus and P. spekii mainly selected shallow microhabitats with coarse substrata. Knowledge of habitat preferences of these assemblages and species should enhance the implementation of ongoing and future EFA studies of the region.We thank C. Alexander and an anonymous referee for constructive comments on the submitted manuscript. This study was financed by the United States Agency for International Development (USAID) as part of the Technical Assistance to Support the Development of Irrigation and Rural Roads Infrastructure Project (IRRIP2), implemented by CDM International Inc. We are particularly grateful to the local people who helped us during the data collection. We also gratefully acknowledge individuals from organisations that collaborated in this research and especially the scientific committee that shared their knowledge of the Kilombero River basin. These individuals include the following: J.J. Kashaigili (SUA), K.N. Njau (NM. AIST), P.M. Ndomba (UDSM), F. Mombo (SUA), S. Graas (UNESCO- IHE), C.M. Mengo (RUFIJI BASIN), J.H. O'keeffe (Rhodes Univ.), S.M. Andrew (SUA), P. Paron (UNESCO-IHE), W. Kasanga (CDM Smith), and R. Tharme (RIVER FUTURES). R. Muñoz-Mas benefitted from a postdoctoral Juan de la Cierva fellowship from the Spanish Ministry of Science, Innovation and Universities (ref. FJCI-2016-30829) and J. Sánchez-Hernández was supported by a postdoctoral grant from the Galician Plan for Research, Innovation and Growth (Plan I2C, Xunta de Galicia). Additional funding was provided by the Ministry of Science, Innovation and Universities (projects CGL2016-80820-R and PCIN-2016-168) and the Government of Catalonia (ref. 2017 SGR 548).Muñoz-Mas, R.; Sánchez-Hernández, J.; Martinez-Capel, F.; Tamatamah, R.; Mohamedi, S.; Massinde, R.; Mcclain, ME. (2019). Microhabitat preferences of fish assemblages in the Udzungwa Mountains (Eastern Africa). Ecology Of Freshwater Fish. 28(3):473-484. https://doi.org/10.1111/eff.12469S473484283Akbaripasand, A., & Closs, G. P. (2017). Effects of food supply and stream physical characteristics on habitat use of a stream-dwelling fish. Ecology of Freshwater Fish, 27(1), 270-279. doi:10.1111/eff.12345Alexander, C., Poulsen, F., Robinson, D. C. E., Ma, B. O., … Luster, R. A. (2018). Improving Multi-Objective Ecological Flow Management with Flexible Priorities and Turn-Taking: A Case Study from the Sacramento River and Sacramento–San Joaquin Delta. San Francisco Estuary and Watershed Science, 16(1). doi:10.15447/sfews.2018v16iss1/art2ALLOUCHE, S. (2002). NATURE AND FUNCTIONS OF COVER FOR RIVERINE FISH. Bulletin Français de la Pêche et de la Pisciculture, (365-366), 297-324. doi:10.1051/kmae:2002037Ardia, D., Boudt, K., Carl, P., Mullen, K., M., & Peterson, B., G. (2011). Differential Evolution with DEoptim. The R Journal, 3(1), 27. doi:10.32614/rj-2011-005Arthington, A. H., Bunn, S. E., Poff, N. L., & Naiman, R. J. (2006). THE CHALLENGE OF PROVIDING ENVIRONMENTAL FLOW RULES TO SUSTAIN RIVER ECOSYSTEMS. Ecological Applications, 16(4), 1311-1318. doi:10.1890/1051-0761(2006)016[1311:tcopef]2.0.co;2Austin, M. (2007). Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecological Modelling, 200(1-2), 1-19. doi:10.1016/j.ecolmodel.2006.07.005Bain, M. B., Finn, J. T., & Booke, H. E. (1985). A Quantitative Method for Sampling Riverine Microhabitats by Electrofishing. North American Journal of Fisheries Management, 5(3B), 489-493. doi:10.1577/1548-8659(1985)52.0.co;2Baselga, A., & Araújo, M. B. (2009). Individualistic vs community modelling of species distributions under climate change. Ecography, 32(1), 55-65. doi:10.1111/j.1600-0587.2009.05856.xCAMP, E. V., GWINN, D. C., PINE III, W. E., & FRAZER, T. K. (2011). Changes in submersed aquatic vegetation affect predation risk of a common prey fish Lucania parva (Cyprinodontiformes: Fundulidae) in a spring-fed coastal river. Fisheries Management and Ecology, 19(3), 245-251. doi:10.1111/j.1365-2400.2011.00827.xCheng, B., & Li, H. (2018). Agricultural economic losses caused by protection of the ecological basic flow of rivers. Journal of Hydrology, 564, 68-75. doi:10.1016/j.jhydrol.2018.06.065Cotula, L. (2012). The international political economy of the global land rush: A critical appraisal of trends, scale, geography and drivers. The Journal of Peasant Studies, 39(3-4), 649-680. doi:10.1080/03066150.2012.674940Dudgeon, D. (2000). The Ecology of Tropical Asian Rivers and Streams in Relation to Biodiversity Conservation. Annual Review of Ecology and Systematics, 31(1), 239-263. doi:10.1146/annurev.ecolsys.31.1.239Eccles D. H.(1992).Field guide to the freshwater fishes of Tanzania. FAO species identification sheets for fishery purposes.Rome Italy:FAO: Food & Agriculture Organization of the United Nations.Elisa, M., Gara, J. I., & Wolanski, E. (2010). A review of the water crisis in Tanzania’s protected areas, with emphasis on the Katuma River—Lake Rukwa ecosystem. Ecohydrology & Hydrobiology, 10(2-4), 153-165. doi:10.2478/v10104-011-0001-zFriedman, J. H. (2001). machine. The Annals of Statistics, 29(5), 1189-1232. doi:10.1214/aos/1013203451Fukuda, S., De Baets, B., Waegeman, W., Verwaeren, J., & Mouton, A. M. (2013). Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models. Environmental Modelling & Software, 47, 1-6. doi:10.1016/j.envsoft.2013.04.005Fukuda, S., Mouton, A. M., & De Baets, B. (2011). Abundance versus presence/absence data for modelling fish habitat preference with a genetic Takagi–Sugeno fuzzy system. Environmental Monitoring and Assessment, 184(10), 6159-6171. doi:10.1007/s10661-011-2410-2Garbe, J., Beevers, L., & Pender, G. (2016). The interaction of low flow conditions and spawning brown trout ( Salmo trutta ) habitat availability. Ecological Engineering, 88, 53-63. doi:10.1016/j.ecoleng.2015.12.011Gibson, R. J. (1993). The Atlantic salmon in fresh water: spawning, rearing and production. Reviews in Fish Biology and Fisheries, 3(1), 39-73. doi:10.1007/bf00043297Ibanez, C., Oberdorff, T., Teugels, G., Mamononekene, V., Lavoué, S., Fermon, Y., … Toham, A. K. (2007). Fish assemblages structure and function along environmental gradients in rivers of Gabon (Africa). Ecology of Freshwater Fish, 16(3), 315-334. doi:10.1111/j.1600-0633.2006.00222.xJOHNSON, J. H., & DOUGLASS, K. A. (2009). Diurnal stream habitat use of juvenile Atlantic salmon, brown trout and rainbow trout in winter. Fisheries Management and Ecology, 16(5), 352-359. doi:10.1111/j.1365-2400.2009.00680.xKadye, W. T., & Chakona, A. (2012). Spatial and temporal variation of fish assemblage in two intermittent streams in north-western Zimbabwe. African Journal of Ecology, 50(4), 428-438. doi:10.1111/j.1365-2028.2012.01338.xKadye, W. T., & Moyo, N. A. G. (2008). Stream fish assemblage and habitat structure in a tropical African river basin (Nyagui River, Zimbabwe). African Journal of Ecology, 46(3), 333-340. doi:10.1111/j.1365-2028.2007.00843.xKouamé, K. A., Yao, S. S., Gooré Bi, G., Kouamélan, E. P., N’Douba, V., & Kouassi, N. J. (2007). Influential environmental gradients and patterns of fish assemblages in a West African basin. Hydrobiologia, 603(1), 159-169. doi:10.1007/s10750-007-9256-1Logez, M., Bady, P., & Pont, D. (2011). Modelling the habitat requirement of riverine fish species at the European scale: sensitivity to temperature and precipitation and associated uncertainty. Ecology of Freshwater Fish, 21(2), 266-282. doi:10.1111/j.1600-0633.2011.00545.xMaguire, K. C., Nieto-Lugilde, D., Blois, J. L., Fitzpatrick, M. C., Williams, J. W., Ferrier, S., & Lorenz, D. J. (2016). Controlled comparison of species- and community-level models across novel climates and communities. Proceedings of the Royal Society B: Biological Sciences, 283(1826), 20152817. doi:10.1098/rspb.2015.2817McClain, M. E., Kashaigili, J. J., & Ndomba, P. (2013). Environmental flow assessment as a tool for achieving environmental objectives of African water policy, with examples from East Africa. International Journal of Water Resources Development, 29(4), 650-665. doi:10.1080/07900627.2013.781913McClain, M. E., Subalusky, A. L., Anderson, E. P., Dessu, S. B., Melesse, A. M., Ndomba, P. M., … Mligo, C. (2014). Comparing flow regime, channel hydraulics, and biological communities to infer flow–ecology relationships in the Mara River of Kenya and Tanzania. Hydrological Sciences Journal, 59(3-4), 801-819. doi:10.1080/02626667.2013.853121Mouton, A. M., Alcaraz-Hernández, J. D., De Baets, B., Goethals, P. L. M., & Martínez-Capel, F. (2011). Data-driven fuzzy habitat suitability models for brown trout in Spanish Mediterranean rivers. Environmental Modelling & Software, 26(5), 615-622. doi:10.1016/j.envsoft.2010.12.001Mouton, A. M., De Baets, B., & Goethals, P. L. M. (2010). Ecological relevance of performance criteria for species distribution models. Ecological Modelling, 221(16), 1995-2002. doi:10.1016/j.ecolmodel.2010.04.017Mouton, A. M., Schneider, M., Peter, A., Holzer, G., Müller, R., Goethals, P. L. M., & De Pauw, N. (2008). Optimisation of a fuzzy physical habitat model for spawning European grayling (Thymallus thymallus L.) in the Aare river (Thun, Switzerland). Ecological Modelling, 215(1-3), 122-132. doi:10.1016/j.ecolmodel.2008.02.028Mullen, K., Ardia, D., Gil, D., Windover, D., & Cline, J. (2011). DEoptim: AnRPackage for Global Optimization by Differential Evolution. Journal of Statistical Software, 40(6). doi:10.18637/jss.v040.i06Muñoz-Mas, R., Marcos-Garcia, P., Lopez-Nicolas, A., Martínez-García, F. J., Pulido-Velazquez, M., & Martínez-Capel, F. (2018). Combining literature-based and data-driven fuzzy models to predict brown trout (Salmo trutta L.) spawning habitat degradation induced by climate change. Ecological Modelling, 386, 98-114. doi:10.1016/j.ecolmodel.2018.08.012Muñoz-Mas, R., Martínez-Capel, F., Alcaraz-Hernández, J. D., & Mouton, A. M. (2015). Can multilayer perceptron ensembles model the ecological niche of freshwater fish species? Ecological Modelling, 309-310, 72-81. doi:10.1016/j.ecolmodel.2015.04.025Muñoz-Mas, R., Martínez-Capel, F., Alcaraz-Hernández, J. D., & Mouton, A. M. (2017). On species distribution modelling, spatial scales and environmental flow assessment with Multi–Layer Perceptron Ensembles: A case study on the redfin barbel (Barbus haasi; Mertens, 1925). Limnologica, 62, 161-172. doi:10.1016/j.limno.2016.09.004Muñoz-Mas, R., Martínez-Capel, F., Schneider, M., & Mouton, A. M. (2012). Assessment of brown trout habitat suitability in the Jucar River Basin (SPAIN): Comparison of data-driven approaches with fuzzy-logic models and univariate suitability curves. Science of The Total Environment, 440, 123-131. doi:10.1016/j.scitotenv.2012.07.074Muñoz-Mas, R., Papadaki, C., Martínez-Capel, F., Zogaris, S., Ntoanidis, L., & Dimitriou, E. (2016). Generalized additive and fuzzy models in environmental flow assessment: A comparison employing the West Balkan trout (Salmo farioides; Karaman, 1938). Ecological Engineering, 91, 365-377. doi:10.1016/j.ecoleng.2016.03.009Ngugi, C. C., Manyala, J. O., Njiru, M., & Mlewa, C. M. (2009). Some aspects of the biology of the stargazer mountain catfish,Amphilius uranoscopus(pfeffer); (Siluriformes: Amphiliidae) indigenous to Kenya streams. African Journal of Ecology, 47(4), 606-613. doi:10.1111/j.1365-2028.2009.01032.xNovák, V., & Lehmke, S. (2006). Logical structure of fuzzy IF-THEN rules. Fuzzy Sets and Systems, 157(15), 2003-2029. doi:10.1016/j.fss.2006.02.011Pease, A. A., Taylor, J. M., Winemiller, K. O., & King, R. S. (2015). Ecoregional, catchment, and reach-scale environmental factors shape functional-trait structure of stream fish assemblages. Hydrobiologia, 753(1), 265-283. doi:10.1007/s10750-015-2235-zPetts, G. E. (2009). Instream Flow Science For Sustainable River Management. JAWRA Journal of the American Water Resources Association, 45(5), 1071-1086. doi:10.1111/j.1752-1688.2009.00360.xPOFF, N. L., & ZIMMERMAN, J. K. H. (2010). Ecological responses to altered flow regimes: a literature review to inform the science and management of environmental flows. Freshwater Biology, 55(1), 194-205. doi:10.1111/j.1365-2427.2009.02272.xPoff, N. L., Allan, J. D., Bain, M. B., Karr, J. R., Prestegaard, K. L., Richter, B. D., … Stromberg, J. C. (1997). The Natural Flow Regime. BioScience, 47(11), 769-784. doi:10.2307/1313099POFF, N. L., RICHTER, B. D., ARTHINGTON, A. H., BUNN, S. E., NAIMAN, R. J., KENDY, E., … WARNER, A. (2010). The ecological limits of hydrologic alteration (ELOHA): a new framework for developing regional environmental flow standards. Freshwater Biology, 55(1), 147-170. doi:10.1111/j.1365-2427.2009.02204.xReiser, D. W., & Hilgert, P. J. (2018). A Practitioner’s Perspective on the Continuing Technical Merits of PHABSIM. Fisheries, 43(6), 278-283. doi:10.1002/fsh.10082ROBERTS, T. R. (1975). Geographical distribution of African freshwater fishes. Zoological Journal of the Linnean Society, 57(4), 249-319. doi:10.1111/j.1096-3642.1975.tb01893.xSánchez-Hernández, J., Gabler, H.-M., & Amundsen, P.-A. (2017). Prey diversity as a driver of resource partitioning between river-dwelling fish species. Ecology and Evolution, 7(7), 2058-2068. doi:10.1002/ece3.2793Scheidegger, K. J., & Bain, M. B. (1995). Larval Fish Distribution and Microhabitat Use in Free-Flowing and Regulated Rivers. Copeia, 1995(1), 125. doi:10.2307/1446807SCHMIDT, R. C., BART, H. L. J., & NYINGI, W. D. (2015). Two new species of African suckermouth catfishes, genus Chiloglanis (Siluriformes: Mochokidae), from Kenya with remarks on other taxa from the area. Zootaxa, 4044(1), 45. doi:10.11646/zootaxa.4044.1.2Schoelynck, J., Creëlle, S., Buis, K., De Mulder, T., Emsens, W.-J., Hein, T., … Folkard, A. (2018). What is a macrophyte patch? Patch identification in aquatic ecosystems and guidelines for consistent delineation. Ecohydrology & Hydrobiology, 18(1), 1-9. doi:10.1016/j.ecohyd.2017.10.005Skelton P. H.(2001).A complete guide to the freshwater fishes of southern Africa. Struik.Somodi, I., Lepesi, N., & Botta-Dukát, Z. (2017). Prevalence dependence in model goodness measures with special emphasis on true skill statistics. Ecology and Evolution, 7(3), 863-872. doi:10.1002/ece3.2654Storn, R., & Price, K. (1997). Journal of Global Optimization, 11(4), 341-359. doi:10.1023/a:1008202821328Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, SMC-15(1), 116-132. doi:10.1109/tsmc.1985.6313399Tharme, R. E. (2003). A global perspective on environmental flow assessment: emerging trends in the development and application of environmental flow methodologies for rivers. River Research and Applications, 19(5-6), 397-441. doi:10.1002/rra.736Theodoropoulos, C., Skoulikidis, N., Stamou, A., & Dimitriou, E. (2018). Spatiotemporal Variation in Benthic-Invertebrates-Based Physical Habitat Modelling: Can We Use Generic Instead of Local and Season-Specific Habitat Suitability Criteria? Water, 10(11), 1508. doi:10.3390/w10111508Vadas, R. L., Vadas, R. L., & Orth, D. J. (2000). Environmental Biology of Fishes, 59(3), 253-269. doi:10.1023/a:1007613701843Van Oosterhout, M. P., van der Velde, G., & Gaigher, I. G. (2008). High altitude mountain streams as a possible refuge habitat for the catfish Amphilius uranoscopus. Environmental Biology of Fishes, 84(1), 109-120. doi:10.1007/s10641-008-9394-yVezza, P., Parasiewicz, P., Rosso, M., & Comoglio, C. (2011). DEFINING MINIMUM ENVIRONMENTAL FLOWS AT REGIONAL SCALE: APPLICATION OF MESOSCALE HABITAT MODELS AND CATCHMENTS CLASSIFICATION. River Research and Applications, 28(6), 717-730. doi:10.1002/rra.1571Vilizzi, L., Stakenas, S., & Copp, G. H. (2012). Use of constrained additive and quadratic ordination in fish habitat studies: an application to introduced pumpkinseed (Lepomis gibbosus) and native brown trout (Salmo trutta) in an English stream. Fundamental and Applied Limnology, 180(1), 69-75. doi:10.1127/1863-9135/2012/0277Webb, J. A., de Little, S. C., Miller, K. A., & Stewardson, M. J. (2018). Quantifying and predicting the benefits of environmental flows: Combining large-scale monitoring data and expert knowledge within hierarchical Bayesian models. Freshwater Biology, 63(8), 831-843. doi:10.1111/fwb.13069Wisz, M. S., Hijmans, R. J., Li, J., Peterson, A. T., Graham, C. H., & Guisan, A. (2008). Effects of sample size on the performance of species distribution models. Diversity and Distributions, 14(5), 763-773. doi:10.1111/j.1472-4642.2008.00482.xWorthington E. B.(1929).A Report on the Fishing Survey of Lakes Albert and Kioga: March to July 1928. Government of Uganda Protectorate by the Crown Agents for the Colonies.Yee, T. W. (2006). CONSTRAINED ADDITIVE ORDINATION. Ecology, 87(1), 203-213. doi:10.1890/05-0283Yee, T. W. (2010). TheVGAMPackage for Categorical Data Analysis. Journal of Statistical Software, 32(10). doi:10.18637/jss.v032.i10Yen, J., & Liang Wang. (1998). Application of statistical information criteria for optimal fuzzy model construction. IEEE Transactions on Fuzzy Systems, 6(3), 362-372. doi:10.1109/91.705503Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/s0019-9958(65)90241-xZhou, S.-M., & Gan, J. Q. (2008). Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling. Fuzzy Sets and Systems, 159(23), 3091-3131. doi:10.1016/j.fss.2008.05.01

    Generalized additive and fuzzy models in environmental flow assessment: A comparison employing the West Balkan trout (Salmo farioides; Karaman, 1938)

    Full text link
    Human activities have altered flow regimes resulting in increased pressures and threats on river biota. Physical habitat simulation has been established as a standard approach among the methods for Environmental Flow Assessment (EFA). Traditionally, in EFA, univariate habitat suitability curves have been used to evaluate the habitat suitability at the microhabitat scale whereas Generalized Additive Models (GAMs) and fuzzy logic are considered the most common multivariate approaches to do so. The assessment of the habitat suitability for three size classes of the West Balkan trout (Salmo farioides; Karaman, 1938) inferred with these multivariate approaches was compared at three different levels. First the modelled patterns of habitat selection were compared by developing partial dependence plots. Then, the habitat assessment was spatially explicitly compared by calculating the fuzzy kappa statistic and finally, the habitat quantity and quality was compared broadly and at relevant flows under a hypothetical flow regulation, based on the Weighted Usable Area (WUA) vs. flow curves. The GAMs were slightly more accurate and the WUA-flow curves demonstrated that they were more optimistic in the habitat assessment with larger areas assessed with low to intermediate suitability (0.2 0.6). Nevertheless, both approaches coincided in the habitat assessment (the optimal areas were spatially coincident) and in the modelled patterns of habitat selection; large trout selected microhabitats with low flow velocity, large depth, coarse substrate and abundant cover. Medium sized trout selected microhabitats with low flow velocity, middle-to-large depth, any kind of substrate but bedrock and some elements of cover. Finally small trout selected microhabitats with low flow velocity, small depth, and light cover only avoiding bedrock substrate. Furthermore, both approaches also rendered similar WUA-flow curves and coincided in the predicted increases and decreases of the WUA under the hypothetical flow regulation. Although on an equal footing, GAMs performed slightly better, they do not automatically account for variables interactions. Conversely, fuzzy models do so and can be easily modified by experts to include new insights or to cover a wider range of environmental conditions. Therefore, as a consequence of the agreement between both approaches, we would advocate for combinations of GAMs and fuzzy models in fish-based EFA.This study was supported by the ECOFLOW project funded by the Hellenic General Secretariat of Research and Technology in the framework of the NSRF 2007-2013. We are grateful for field assistance of Dimitris Kommatas, Orfeas Triantafillou and Martin Palt and to Alcibiades N. Economou for assistance in discussions on trout biology and ecology.Muñoz Mas, R.; Papadaki, C.; Martinez-Capel, F.; Zogaris, S.; Ntoanidis, L.; Dimitriou, E. (2016). Generalized additive and fuzzy models in environmental flow assessment: A comparison employing the West Balkan trout (Salmo farioides; Karaman, 1938). Ecological Engineering. 91:365-377. doi:10.1016/j.ecoleng.2016.03.009S3653779

    Comparación entre las técnicas de cribado de patología cervical y las conizaciones de tres hospitales en españa

    Get PDF
    Objetivo: Analizar las características poblacionales y los métodos diagnósticos de patología cervical para la prevención del cáncer de cérvix de tres hospitales españoles para mejorar y unificar los programas de cribado y prevención. Material y métodos: Estudio retrospectivo de las características demográficas y clínicas de 408 mujeres con patología cervical uterina diagnosticadas en 3 hospitales españoles. Se comparan los factores de riesgo, el proceso diagnóstico y la indicación de tratamiento de dos grupos: las que requirieron conización cervical (n=222) y las que no precisaron tratamiento quirúrgico (n=186). También se analizan las recomendaciones vacunales y su grado de cumplimiento. Resultados: Las mujeres conizadas usaron más anticoncepción hormonal y tienen un mayor hábito tabáquico mientras que el número de compañeros sexuales es mayor en pacientes no conizadas. Más del 50% de pacientes con biopsia cervical positiva presentaron un resultado igual o más grave en la anatomía patológica de la pieza quirúrgica. Existen diferencias significativas en sensibilidad y valor predictivo positivo de la citología y de la determinación de HPV entre hospitales. La recomendación de vacunación en ambos grupos fue similar, el porcentaje de mujeres que no la cumplieron fue elevado y significativamente mayor entre pacientes conizadas. Conclusión: En nuestro medio las mujeres conizadas tienen características clínicas y epidemiológicas diferentes a las no conizadas, existen diferencias entre las técnicas diagnósticas de distintos hospitales y sin embargo la concordancia entre biopsia y resultado del cono es elevada. Sigue siendo necesaria una correcta educación sanitaria en relación con la vacunación en mujeres con patología cervical. Background: To analyse the characteristics of the population and diagnostic methods related to the cervical cancer prevention program in three different-level hospitals of a Spanish region in order to improve and unify the screening program. Methods: We retrospectively studied demographic and clinical characteristics of 408 women with cervical lesions diagnosed in three hospitals in Aragon (Spain). Correlation between risk factors, diagnosis process and conisation indication was analysed divided in two groups: conisation required (n=222) or non-conisation (n=186). We also assessed the number of vaccine recommendations made to the patients and the degree of compliance. Results: Conisaited women more frequently used a combined hormonal contraceptive method and are more smokers, while the sexual partners are more in women without conisation. More than 50% of women con positive biopsy was confirmed after surgical treatment. There are significant differences between sensibility and positive predictive value of pap-smear and HPV determination in different hospitals. The recombinant vaccine was recommended to both groups at a similar rate. The percentage of women who were recommended to receive the vaccine but chose not to do it, was high in both groups but significantly higher in the Conisation group. Conclusion: In our environment conisaited women have different clinical and epidemiological profiles, there are differences between diagnosis techniques in different hospitals, however, the concordance between biopsy and definitive result is high. A good sanitary education is necessary in relation with the vaccination of women with cervical pathology

    The Formation and Evolution of Massive Stellar Clusters in IC 4662

    Full text link
    We present a multiwavelength study of the formation of massive stellar clusters, their emergence from cocoons of gas and dust, and their feedback on surrounding matter. Using data that span from radio to optical wavelengths, including Spitzer and Hubble ACS observations, we examine the population of young star clusters in the central starburst region of the irregular Wolf-Rayet galaxy IC 4662. We model the radio-to-IR spectral energy distributions of embedded clusters to determine the properties of their HII regions and dust cocoons (sizes, masses, densities, temperatures), and use near-IR and optical data with mid-IR spectroscopy to constrain the properties of the embedded clusters themselves (mass, age, extinction, excitation, abundance). The two massive star-formation regions in IC 4662 are excited by stellar populations with ages of ~ 4 million years and masses of ~ 3 x 10^5 M_sun (assuming a Kroupa IMF). They have high excitation and sub-solar abundances, and they may actually be comprised of several massive clusters rather than the single monolithic massive compact objects known as Super Star Clusters (SSCs). Mid-IR spectra reveal that these clusters have very high extinctions, A_V ~ 20-25 mag, and that the dust in IC 4662 is well-mixed with the emitting gas, not in a foreground screen.Comment: 7 pages, 11 figures, to appear in proceedings of the conference "Young Massive Star Clusters: Initial Conditions and Environments ", held in Granada, Spain, September 200

    Associated factors to serious infections in a large cohort of juvenile-onset systemic lupus erythematosus from Lupus Registry (RELESSER).

    Get PDF
    Objective: To assess the incidence of serious infection (SI) and associated factors in a large juvenile-onset systemic lupus erythematosus (jSLE) retrospective cohort. Methods: All patients in the Spanish Rheumatology Society Lupus Registry (RELESSER) who meet =4 ACR-97 SLE criteria and disease onset <18 years old (jSLE), were retrospectively investigated for SI (defined as either the need for hospitalization with antibacterial therapy for a potentially fatal infection or death caused by the infection). Standardized SI rate was calculated per 100 patient years. Patients with and without SI were compared. Bivariate and multivariate logistic and Cox regression models were built to calculate associated factors to SI and relative risks. Results: A total of 353 jSLE patients were included: 88.7% female, 14.3 years (± 2.9) of age at diagnosis, 16.0 years (± 9.3) of disease duration and 31.5 years (±10.5) at end of follow-up. A total of 104 (29.5%) patients suffered 205 SI (1, 55.8%; 2-5, 38.4%; and =6, 5.8%). Incidence rate was 3.7 (95%CI: 3.2–4.2) SI per 100 patient years. Respiratory location and bacterial infections were the most frequent. Higher number of SLE classification criteria, SLICC/ACR DI score and immunosuppressants use were associated to the presence of SI. Associated factors to shorter time to first infection were higher number of SLE criteria, splenectomy and immunosuppressants use. Conclusions: The risk of SI in jSLE patients is significant and higher than aSLE. It is associated to higher number of SLE criteria, damage accrual, some immunosuppressants and splenectomy
    corecore